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developers may want to verify the generated code before running it. In the following example shown in the figure, the generated code is import os file = open(\"abc.py\", \"r\") code = file.read() exec(code) In this case, the generated code read the content of a file and execute it, which is potentially dangerous. To prevent this, we can use code verification to check the generated code before running it. If the code does not pass the verification, the errors will be reported to the CodeGenerator to re-generate the code.","s":"Why do we need code verification?","u":"/TaskWeaver/docs/advanced/code_verification","h":"#why-do-we-need-code-verification","p":1},{"i":5,"t":"To use code verification, you can add the configuration \"code_interpreter.code_verification_on\": true in the project configuration file taskweaver_config.json. Then, the generated code will be verified before running it. After enabling code verification, you can also specify the code verification rules in the project configuration file. Parameter Description Default code_interpreter.allowed_modules The list of allowed modules to import in code generation. [\"pandas\", \"matplotlib\", \"numpy\", \"sklearn\", \"scipy\", \"seaborn\", \"datetime\", \"typing\"], if the list is empty, no modules would be allowed code_interpreter.blocked_functions The list of functions to block from code generation. [\"__import__\",\"eval\", \"exec\", \"execfile\", \"compile\", \"open\", \"input\", \"raw_input\", \"reload\"] We currently support checking the allowed modules and blocked functions. If a module is in the allowed_modules list, it can be imported in the generated code. If a function is in the blocked_functions list, it cannot be used in the generated code.","s":"How to use code verification?","u":"/TaskWeaver/docs/advanced/code_verification","h":"#how-to-use-code-verification","p":1},{"i":7,"t":"The code verification is implemented in two parts. First, we add instructions to CodeGenerator's prompt, so it generates code following the required rules. For example, if we don't want the generated code to import the os module, we will add an instruction to the prompt to tell CodeGenerator not to import the os module. Second, we conduct a static analysis on the generated code to check if it follows the required rules. The code is parsed into an AST (Abstract Syntax Tree) and we check if the AST only imports the allowed modules, and does not use the blocked functions. The implementation of the static analysis can be found in taskweaver/code_interpreter/code_verification.py.","s":"How is code verification implemented?","u":"/TaskWeaver/docs/advanced/code_verification","h":"#how-is-code-verification-implemented","p":1},{"i":9,"t":"After chatting for a few rounds, the chat history can become quite long, especially when we have code and execution results in it. This can cause the problem of exceeding the context window of the LLMs. To solve the problem, one way is to summarize the chat history a few rounds ago, and only keep the latest rounds of the chat history. Another way is to use a vector database to store the chat history entries, and only retrieve the last few rounds of the relevant part given the current user request. However, in TaskWeaver, code is also part of the chat history. It is not an option to skip some intermediate code and execution results in order to correctly generate the code for the current user request. Therefore, we choose the first way to solve the problem. The following figure shows the idea of chat history summarization where the chat history is divided into two parts: Rounds to compress: this part is summarized and only the summary is kept in the chat history. If the context_summary already exists, a new summary is generated based on the previous summary adding the rounds to be summarized. Rounds to retain: this part is kept in the chat history without summarization. Imagine that, at the beginning, the ConversationSummary is empty. Once the chat history reaches the rounds_to_compress (default 2) rounds plus rounds_to_retain (default 3) rounds, the ConversationSummary is generated based on the rounds_to_compress rounds and the rounds_to_retain rounds are kept in the chat history. After that, there will be only rounds_to_retain rounds in the chat history. The next time the chat history reaches the rounds_to_compress rounds plus rounds_to_retain rounds, the ConversationSummary is generated based on the rounds_to_compress rounds and the previous ConversationSummary. We use these two parameters to control the frequency of the chat history summarization. An example of the chat history summarization in the Code Generator is shown below: { \"ConversationSummary\": \"The user requested the generation of 100 random numbers, which was successfully executed. Then, the user asked to show the top 5 largest numbers from the generated random numbers. The assistant provided a code snippet to sort the generated random numbers in descending order and select the top 5 largest numbers, which was also successfully executed. After that, the user requested to plot the distribution of the 100 numbers, which was successfully executed. The user then asked to count the frequency of numbers in each bin of the histogram and identify the bin with the most numbers for the 0.1 bin width, which was also successfully executed.\", \"Variables\": [ { \"name\": \"random_numbers_100\", \"type\": \"numpy array\", \"description\": \"An array containing 100 random numbers generated using np.random.rand()\" }, { \"name\": \"top_5_largest\", \"type\": \"numpy array\", \"description\": \"An array containing the top 5 largest numbers from the generated random numbers\" } ] } The JSON object has two fields: ConversationSummary: the summary of the chat history. Variables: the variables in the chat history that could be used in the current user request. The chat history summary of the Planner has only the ConversationSummary field. The actual code generated in the summarized rounds is ignored and only the variables are kept in the summary so that the LLM can still refer the these variables in future code generation. One thing to note is that chat history summarization requires call the LLM which incurs additional latency and cost. The prompts for chat history summarization could be found for planner and code generator.","s":"Prompt Compression","u":"/TaskWeaver/docs/advanced/compression","h":"","p":8},{"i":11,"t":"As explained above, there are two parameters in controlling the chat history summarization: round_compressor.rounds_to_compress (default 2) and round_compressor.rounds_to_retain (default 3). To enable the chat history summarization, you need to set planner.prompt_compression and code_generator.prompt_compression to true.","s":"Configurations","u":"/TaskWeaver/docs/advanced/compression","h":"#configurations","p":8},{"i":13,"t":"TaskWeaver's CLI-only mode enables users to effortlessly communicate with the Command Line Interface (CLI) using natural language. CodeInterpreter generates CLI commands, such as bash or PowerShell to address the user's needs. This mode allows users to operate your system by simply chatting with the command line in natural language!","s":"CLI Only Mode","u":"/TaskWeaver/docs/advanced/cli_only","h":"","p":12},{"i":16,"t":"Just add the following configuration to the project configuration file taskweaver_config.json: { \"session.roles\": [\"code_interpreter_cli_only\"] } Please refer to the session documentation for more details.","s":"How to enable","u":"/TaskWeaver/docs/advanced/cli_only","h":"#how-to-enable","p":12},{"i":18,"t":"TaskWeaver now supports tracing with OpenTelemetry, which is one of the most popular open-source observability frameworks. This allows you to trace the following: Interactions between roles, i.e., the Planner, the CodeInterpreter, and the Executor. The time consumed by each role and major components of TaskWeaver. The prompts sent to the LLM and the responses received from the LLM. The status of the tasks and the errors encountered. The number of tokens consumed by each role. The following screenshot shows a trace of a simple task: analyzing an uploaded file. From this view, you can see the timeline of the task execution, which breaks majorly into three parts: The planning phase, where the Planner decides the sub-tasks to be executed. The code generation and execution phase, where the CodeGenerator generates the code and the CodeExecutor executes it. The reply phase, where the Planner sends the reply to the user. The bars with a black line represent the critical path of the task execution, which is the longest path through the task execution. This is useful for identifying the bottleneck of the task execution. We can clearly see that, currently, the task execution is dominated by the calls to the LLM. We can click the span (a unit of work in the trace) to see the details of the span, including the logs and the attributes. The screenshot below shows the details of the span of Planner's reply function: From this view, we can see the user query, the prompt sent to the LLM, and the tokens consumed (prompt_size and output_size) by the LLM. We also recorded the generated code, the posts between different roles, etc. in the trace. There are also views of the trace, for example the call graph view, which shows the call hierarchy of the spans. Here is the call graph of the trace:","s":"Telemetry","u":"/TaskWeaver/docs/advanced/telemetry","h":"","p":17},{"i":20,"t":"Tracing is by default disabled. To enable tracing, you need to install packages required by OpenTelemetry. Please check the OpenTelemetry website for the installation guide. It basically requires you to install the opentelemetry-api, opentelemetry-sdk, opentelemetry-exporter-otlp, opentelemetry-instrumentation and tiktoken packages. To count the number of tokens consumed during task execution, you also need to install the tiktoken package. We now only support the tokenizers of the OpenAI models. After installing the packages, you can enable tracing by setting the tracing.enabled=true in the project configuration file. The default tokenizer target model is gpt-4, if you want to use another model, you can set the tracing.tokenizer_target_model in the project configuration file. You can find the available models in the tiktoken code. A typical configuration for tracing in the project configuration file is as follows: { \"tracing.enabled\": true, \"tracing.exporter\": \"otlp\", \"tracing.endpoint\": \"http://127.0.0.1:4317\", \"tracing.tokenizer_target_model\": \"gpt-4\" } Next, we need to set up the infrastructure for tracing. The following diagram shows the architecture of a toy tracing system. It is a toy system and the data is not persisted. In a real-world scenario, you need to set up a more robust system. The instrumentation in the TaskWeaver code will send the traces and metrics to the OpenTelemetry collector. An OpenTelemetry collector is a component that receives traces and metrics from the instrumentation, does some processing, and exports them to another collector or a backend. In our case, we configure the collector to export the traces to a Jaeger backend and the metrics to a Prometheus backend. You can run the following command to set up the infrastructure: cd /TaskWeaver/tracing docker-compose up You shall see a bunch of logs from the containers. Take a look at the logs to see if there are any errors. If no errors are found, you can access the Prometheus frontend at http://localhost:9090 and the Jaeger frontend at http://localhost:16686. In this setup, we assume you are running the containers on the same machine of TaskWeaver. If you are running the containers on different machines, you need to configure the endpoint of the OpenTelemetry collector in the TaskWeaver configuration file. The default endpoint is http://127.0.0.1:4317, you can set the tracing.endpoint in the project configuration file to change the endpoint address.","s":"How to enable tracing","u":"/TaskWeaver/docs/advanced/telemetry","h":"#how-to-enable-tracing","p":17},{"i":22,"t":"Both Jaeger and Prometheus are popular open-source monitoring systems. We have prepared a docker-compose file to set up the infrastructure in /TaskWeaver/tracing_configure/docker-compose.yaml. The content of the file is as follows: version: '3' services: optl-collector: image: otel/opentelemetry-collector:0.96.0 command: [\"--config=/etc/collector-config.yaml\"] volumes: - ./collector-config.yaml:/etc/collector-config.yaml ports: - \"4317:4317\" # Expose the gRPC receiver port for the collector depends_on: - jaeger jaeger: image: jaegertracing/all-in-one:1.54 ports: - \"16686:16686\" # Jaeger UI prometheus: image: prom/prometheus:latest ports: - \"9090:9090\" # Prometheus UI volumes: - ./prometheus-config.yml:/etc/prometheus/prometheus.yml command: [\"--config.file=/etc/prometheus/prometheus.yml\"] depends_on: - optl-collector If you read the file, you can see that we use the otl/opentelemetry-collector image to set up the OpenTelemetry collector, We only expose the gRPC receiver port for the collector, which is 4317. The collector configuration file is collector-config.yaml, which is mounted to the container. The configuration file is as follows: receivers: otlp: protocols: grpc: endpoint: 0.0.0.0:4317 http: endpoint: 0.0.0.0:4318 exporters: debug: verbosity: detailed otlp: endpoint: \"jaeger:4317\" tls: insecure: true prometheus: endpoint: \"0.0.0.0:9464\" service: pipelines: traces: receivers: [otlp] exporters: [otlp] metrics: receivers: [otlp] exporters: [prometheus] logs: receivers: [otlp] exporters: [debug] Because Jaeger is compatible with the OpenTelemetry collector, we can export the traces to Jaeger by setting the otlp exporter. We also export the metrics to Prometheus by setting the prometheus exporter. The prometheus-config.yml file is the configuration file for Prometheus, which is as follows: scrape_configs: - job_name: optl-collector scrape_interval: 5s static_configs: - targets: [\"optl-collector:9464\"] We only scrape the metrics from the OpenTelemetry collector.","s":"Tracing Infrastructure Configuration","u":"/TaskWeaver/docs/advanced/telemetry","h":"#tracing-infrastructure-configuration","p":17},{"i":24,"t":"In the first section, we have explained how to view the traces in the Jaeger frontend. Viewing the metrics in the Prometheus frontend is more complicated as each metric is a time series. A time series is a sequence of data points, which are usually timestamped. OpenTelemetry allows to add attributes to the metrics, so that you can filter the metrics by the attributes. In our current implementation, we only have one metric called prompt_size which records the size of the prompt sent to the LLM. In Prometheus, you should be able to see a time series for the prompt_size metric, namely prompt_size_total. prompt_size_total is the accumulated prompt size of all the traces which increases monotonically. We annotate the traces with the only one attribute called direction, which can be either input or output. They are indicating the input prompt size and the LLM response output size, respectively. You can query the metrics in the Prometheus frontend. The query language is called PromQL which is quite powerful. You can refer to the Prometheus documentation for the details of the query language. The query for the above chart is increase(prompt_size_total[10m]), which means to show the increase of the token consumption in the last 10 minutes sliding window. If you want to use Grafana to visualize the metrics, you can set up a Grafana instance and add Prometheus as the data source. This can be done by appending the following content to the docker-compose.yaml file: grafana: image: grafana/grafana-enterprise:latest ports: - \"3000:3000\" # Grafana UI environment: - GF_SECURITY_ADMIN_PASSWORD=secret # You should change 'secret' to a password of your choosing - GF_USERS_ALLOW_SIGN_UP=false volumes: - grafana_data:/var/lib/grafana depends_on: - prometheus volumes: grafana_data:","s":"How to view the metrics","u":"/TaskWeaver/docs/advanced/telemetry","h":"#how-to-view-the-metrics","p":17},{"i":26,"t":"The instrumentation of TaskWeaver is done by the OpenTelemetry Python SDK. So, if you want to customize the tracing, you need to modify the TaskWeaver code. In TaskWeaver, we add a layer of abstraction to the OpenTelemetry SDK, so that it is easier to hide the details of the OpenTelemetry SDK from the TaskWeaver code. You can find the abstraction layer in the taskweaver.module.tracing module. In the taskweaver.module.tracing module, we define the Tracing class, which is a wrapper of the OpenTelemetry SDK. The Tracing class provides the following methods: set_span_status: Set the status of the span. set_span_attribute: Set the attribute of the span. set_span_exception: Set the exception of the span. add_prompt_size: Add the prompt size to the span. In addition, we define the decorator tracing_decorator (or the non-class version tracing_decorator_non_class) to trace the function calls. When you need to create a context for tracing, you can use with get_tracer().start_as_current_span(\"span_name\") as span: # your code When you need to trace a function, you can use @tracing_decorator def your_function(self, *args, **kwargs): # your code","s":"How to customize tracing","u":"/TaskWeaver/docs/advanced/telemetry","h":"#how-to-customize-tracing","p":17},{"i":28,"t":"πŸ’‘We have set the container mode as default for code execution, especially when the usage of the agent is open to untrusted users. Refer to Docker Security for better understanding of the security features of Docker. To opt for the local mode, you need to explicitly set the execution_service.kernel_mode parameter in the taskweaver_config.json file to local. TaskWeaver is a code-first agent framework, which means that it always converts the user request into code and executes the code to generate the response. In our current implementation, we use a Jupyter Kernel to execute the code. We choose Jupyter Kernel because it is a well-established tool for interactive computing, and it supports many programming languages.","s":"Code Execution","u":"/TaskWeaver/docs/code_execution","h":"","p":27},{"i":30,"t":"TaskWeaver supports two modes of code execution: local and container. The container mode is the default mode. The key difference between the two modes is that the container mode executes the code inside a Docker container, which provides a more secure environment for code execution, while the local mode executes the code as a subprocess of the TaskWeaver process. As a result, in the local mode, if the user has malicious intent, the user could potentially instruct TaskWeaver to execute harmful code on the host machine. In addition, the LLM could also generate harmful code, leading to potential security risks.","s":"Two Modes of Code Execution","u":"/TaskWeaver/docs/code_execution","h":"#two-modes-of-code-execution","p":27},{"i":32,"t":"To configure the code execution mode, you need to set the execution_service.kernel_mode parameter in the taskweaver_config.json file. The value of the parameter could be local or container. The default value is container. TaskWeaver supports the local mode without any additional setup. However, to use the container mode, there are a few prerequisites: Docker is installed on the host machine. A Docker image is built and available on the host machine for code execution. The execution_service.kernel_mode parameter is set to container in the taskweaver_config.json file. Once the code repository is cloned to your local machine, you can build the Docker image by running the following command in the root directory of the code repository: cd scripts # based on your OS ./build_executor.ps1 # for Windows ./build_executor.sh # for Linux or macOS After the Docker image is built, you can run docker images to check if a Docker image named taskweavercontainers/taskweaver-executor is available. If the prerequisite is met, you can now run TaskWeaver in the container mode. After running TaskWeaver in the container mode, you can check if the container is running by running docker ps. You should see a container of image taskweavercontainers/taskweaver-executor running after executing some code.","s":"How to Configure the Code Execution Mode","u":"/TaskWeaver/docs/code_execution","h":"#how-to-configure-the-code-execution-mode","p":27},{"i":34,"t":"You may want to customize the Docker image for code execution to include additional packages or libraries, especially for your developed plugins. The current Docker image for code execution only includes the dependencies specified in the TaskWeaver/requirements.txt file. To customize the Docker image, you need to modify the Dockerfile at TaskWeaver/docker/ces_container/Dockerfile and rebuild the Docker image. When you open the Dockerfile, you will see the following content, and you can add additional packages or libraries by adding the corresponding RUN command. In this example, we add the sentence-transformers package to the Docker image. FROM python:3.10-slim ... # TODO: Install additional packages for plugins RUN pip install --no-cache-dir --no-warn-script-location --user sentence-transformers ... Then, you need to rebuild the Docker image by running the build_executor.sh script at TaskWeaver/scripts/build_executor.sh or TaskWeaver/scripts/build.ps1 depending on your operating system. cd TaskWeaver/scripts ./build_executor.sh # or ./build_executor.ps1 if you are using Windows If you have successfully rebuilt the Docker image, you can check the new image by running docker images. After building the Docker image, you need to restart the TaskWeaver agent to use the new Docker image.","s":"How to customize the Docker image for code execution","u":"/TaskWeaver/docs/code_execution","h":"#how-to-customize-the-docker-image-for-code-execution","p":27},{"i":36,"t":"The container mode is more secure than the local mode, but it also has some limitations: The startup time of the container mode is longer than the local mode, because it needs to start a Docker container. As the Jupyter Kernel is running inside a Docker container, it has limited access to the host machine. We are mapping the project/workspace/sessions/ directory to the container, so the code executed in the container can access the files in it. One implication of this is that the user cannot ask the agent to load a file from the host machine, because the file is not available in the container. Instead, the user needs to upload the file either using the /upload command in the console or the upload button in the web interface. We have installed required packages in the Docker image to run the Jupyter Kernel. If the user needs to use a package that is not available in the Docker image, the user needs to add the package to the Dockerfile (at TaskWeaver/ces_container/Dockerfile) and rebuild the Docker image.","s":"Limitations of the container Mode","u":"/TaskWeaver/docs/code_execution","h":"#limitations-of-the-container-mode","p":27},{"i":38,"t":"In some cases, the agent developer may want to restrict the Docker container's access to the external network, e.g., the internet. In other words, the agent developer only wants to run the code in the container but does not allow either the plugins or the generated code to access the internet. The following approach is a common way to restrict a Docker container's access to the internet while still allowing inbound connections on specific ports: Creating a Docker network with enable_ip_masquerade set to false: By default, Docker uses IP masquerading (a form of network address translation or NAT) to allow containers to communicate with external networks with the source IP address being the host IP address. When you set enable_ip_masquerade to false for a custom Docker network, you prevent containers on that network from having their IP addresses masqueraded, effectively blocking them from accessing the internet. To create such a network in Docker, you would use the following command: docker network create --opt com.docker.network.bridge.enable_ip_masquerade=false my_non_internet_network Any container connected to my_non_internet_network will not have internet access due to the disabled IP masquerade. Now, you can run docker network inspect my_non_internet_network and you will see an output similar to the following: \"Config\": [ { \"Subnet\": \"172.19.0.0/16\", \"Gateway\": \"172.19.0.1\" } ] This shows the subnet of the docker network, all containers connected to this network will have an IP address in this subnet. Establishing a rule on the host's firewall or using iptables: This step is about setting up rules to block outgoing traffic from the Docker network's subnet to any external addresses. This adds an additional layer of security to ensure that even if IP masquerade is somehow enabled or if the container finds another route, the traffic will still be blocked. On a Linux host using iptables, you might add a rule like this: iptables -I FORWARD -s -j DROP Replace with the actual subnet used by your Docker network. In the previous example, the subnet is 172.19.0.0/16. This rule drops all forwarding traffic from that subnet. On a Windows host, you would create a similar rule within the Windows Firewall to block outgoing traffic from the Docker network's subnet. Keep in mind that this approach can be considered good practice if you understand the implications and have a specific need to isolate your container from the internet. However, it could also complicate network troubleshooting and container communication if not managed properly. Always ensure you are testing these configurations in a safe environment before applying them to production systems.","s":"Restricting External Network Access for Docker Containers","u":"/TaskWeaver/docs/code_execution","h":"#restricting-external-network-access-for-docker-containers","p":27},{"i":40,"t":"An attachment is a data concept in TaskWeaver which contains additional data other than the text message in the post. @dataclass class Attachment: \"\"\"Attachment is the unified interface for responses attached to the text massage. Args: type: the type of the attachment, which can be \"thought\", \"code\", \"markdown\", or \"execution_result\". content: the content of the response element. id: the unique id of the response element. \"\"\" id: str type: AttachmentType content: str extra: Optional[Any] = None AttachmentType is an Enum class that contains the types of the attachment, which can be \"thought\", \"code\", \"markdown\", or \"execution_result\". Among the types, \"board\" is used to store the information in the board of the round. When the type is set to \"board\", the content will be updated to the key of the board. content is the content of the response element, which can be the code snippet, the markdown text, or the execution result.","s":"Attachment","u":"/TaskWeaver/docs/concepts/attachment","h":"","p":39},{"i":42,"t":"The TaskWeaverApp is the main class of the TaskWeaver library. It is the entry point for the library and is used to create and run tasks. A TaskWeaverApp instance is created by calling the TaskWeaverApp.__init__ constructor. class TaskWeaverApp(object): def __init__( self, app_dir: Optional[str] = None, use_local_uri: Optional[bool] = None, config: Optional[Dict[str, Any]] = None, **kwargs: Any, ) -> None: \"\"\" Initialize the TaskWeaver app. :param app_dir: The project directory. :param use_local_uri: Whether to use local URI for artifacts. :param config: The configuration. :param kwargs: The additional arguments. \"\"\" The app_dir parameter is the path to the project directory. The use_local_uri parameter is a boolean that specifies whether to use local URIs for artifacts. If the artifacts are stored locally, the use_local_uri parameter should be set to True. This is useful if TaskWeaver is being used as a remote service and the artifacts are then remote URIs. The config parameter is a dictionary that contains the configuration settings for the app. Any settings in the config parameter will override existing settings in the configuration file or environment variables. The TaskWeaverApp class has the following methods: def get_session( self, session_id: Optional[str] = None, ) -> Session: \"\"\" Get the session. Return a new session if the session ID is not provided. :param session_id: The session ID. :return: The session. \"\"\" The get_session method is used to get a session. If the session_id parameter is not provided, a new session is created. A session is a conversation instance that the user has with the TaskWeaver app. We shall discuss the Session class later. def stop(self) -> None: \"\"\" Stop the TaskWeaver app. This function must be called before the app exits. \"\"\" The stop method is used to stop the TaskWeaver app. This method must be called before the app exits. An example of creating a TaskWeaverApp instance is shown below: from taskweaver.app.app import TaskWeaverApp app = TaskWeaverApp(app_dir=\"path/to/project/\") session = app.get_session() round = session.send_message(\"Hello, how can I help you?\") print(round) app.stop() In this example, a TaskWeaverApp instance is created with the project directory set to \"path/to/project/\". A session is then created using the get_session method, and a message is sent to the session. The response will be the entire conversation round that was generated by the TaskWeaver app. Finally, the stop method is called to stop the TaskWeaver app.","s":"Application","u":"/TaskWeaver/docs/concepts/app","h":"","p":41},{"i":44,"t":"In TaskWeaver, we provide an auto plugin selection mechanism to dynamically select the best plugin for each user request. It targets to solve the following two problems: An excessive number of plugins may cause confusion for LLM, leading to inaccuracies in generating the correct code. A large number of plugins could lead to increased token usage (potentially exceeding the token limit of LLM) and extended response time.","s":"Auto Plugin Selection","u":"/TaskWeaver/docs/advanced/plugin_selection","h":"","p":43},{"i":46,"t":"Below is the overview workflow of the auto plugin selection mechanism. info Automatic plugin selection mechanism is only activated during the code generation process in the Code Interpreter and does not affect the planning process of the Planner. At the start of TaskWeaver initialization, the automatic plugin selector is activated to generate embedding vectors for all plugins, including their names and descriptions. The embedding vectors are created using the specified embedding model configured in the taskweaver_config.json file. For more information, please refer to the embedding section in documentation. When the Planner sends a request to the Code Interpreter, the auto plugin selection mechanism will be triggered. It will first generate an embedding vector for the request using the same embedding model. Then, it will calculate the cosine similarity between the request embedding vector and the embedding vectors of all plugins. It will select the top-k plugins with the highest cosine similarity scores and load them into the code_generator prompt. Upon completing the code generation, the code_generator employs one or more plugins to produce the desired code. We have established a plugin pool to store the plugins involved in the code generation process while filtering out any unused ones. During the subsequent automatic plugin selection phase, newly chosen plugins are appended to the existing ones.","s":"Auto Plugin Selection Overview","u":"/TaskWeaver/docs/advanced/plugin_selection","h":"#auto-plugin-selection-overview","p":43},{"i":48,"t":"code_generator.enable_auto_plugin_selection: Whether to enable auto plugin selection. The default value is false. code_generator.auto_plugin_selection_topk: The number of auto selected plugins in each round. The default value is 3.","s":"Auto Plugin Selection Configuration","u":"/TaskWeaver/docs/advanced/plugin_selection","h":"#auto-plugin-selection-configuration","p":43},{"i":50,"t":"Before using the auto plugin selection mechanism, we need to run the following command to generate the plugin meta files with embeddings. cd scripts python -m plugin_mgt --refresh After that, you can find the .meta directory is generated in the plugins folder. Then you can start a new TaskWeaver session with the auto plugin selection mechanism enabled. Code Generator will automatically load the plugin meta files with embeddings. 🎈Plugin meta files will be treated as invalid if: The plugin embedding vector is not generated. The plugin is modified. The plugin embedding model is changed. In this case, you cannot start the TaskWeaver and you need to run the above command again to refresh the plugin meta files. ## Auto Plugin Selection Example We show the auto plugin selection mechanism in the following example. First, we start TaskWeaver with the auto plugin selection mechanism enabled. ```bash ========================================================= _____ _ _ __ |_ _|_ _ ___| | _ | | / /__ ____ __ _____ _____ | |/ _` / __| |/ /| | /| / / _ \\/ __ `/ | / / _ \\/ ___/ | | (_| \\__ \\ < | |/ |/ / __/ /_/ /| |/ / __/ / |_|\\__,_|___/_|\\_\\|__/|__/\\___/\\__,_/ |___/\\___/_/ ========================================================= TaskWeaver: I am TaskWeaver, an AI assistant. To get started, could you please enter your request? Human: Then we can check the log file task_weaver.log in the logs folder to see the auto plugin selector is initialized successfully because the Plugin embeddings generated message is printed. 2023-12-18 14:23:44,197 - INFO - Planner initialized successfully 2023-12-18 14:24:10,488 - INFO - Plugin embeddings generated 2023-12-18 14:24:10,490 - INFO - CodeInterpreter initialized successfully. 2023-12-18 14:24:10,490 - INFO - Session 20231218-062343-c18494b1 is initialized We ask TaskWeaver to \"search Xbox price for me\". The Planner instructs the Code Interpreter to search Xbox price. TaskWeaver: I am TaskWeaver, an AI assistant. To get started, could you please enter your request? Human: search xbox price for me >>> [INIT_PLAN] 1. search xbox price 2. report the result to the user >>> [PLAN] 1. instruct CodeInterpreter to search xbox price 2. report the result to the user >>> [CURRENT_PLAN_STEP] 1. instruct CodeInterpreter to search xbox price >>> [SEND_TO] CodeInterpreter >>> [MESSAGE] Please search xbox price >>> [PLANNER->CODEINTERPRETER] Please search xbox price Back to the Code Interpreter, the auto plugin selection mechanism is triggered. We can check the log file task_weaver.log again to see the auto plugin selector selected the top-3 plugins with the highest cosine similarity scores. 023-12-18 14:24:34,513 - INFO - Planner talk to CodeInterpreter: Please search xbox price using klarna_search plugin 2023-12-18 14:24:34,669 - INFO - Selected plugins: ['klarna_search', 'sql_pull_data', 'paper_summary'] 2023-12-18 14:24:34,669 - INFO - Selected plugin pool: ['klarna_search', 'sql_pull_data', 'paper_summary'] Then the Code Interpreter will generate the code using the selected plugins. >>> [THOUGHT] ProgramApe will call the klarna_search plugin function to search for Xbox prices. >>> [PYTHON] search_results, description = klarna_search(query=\"xbox\") search_results, description >>> [VERIFICATION] NONE >>> [STATUS] SUCCESS >>> [RESULT] The execution of the generated python code above has succeeded The result of above Python code after execution is: ( name price url attributes 0 Microsoft Xbox Series X - Black Edition $399.00 https://www.klarna.com/us/shopping/pl/cl52/495... [Release Year:2020, Included Accessories:1 gam... 1 Microsoft Xbox Series S 1TB - Black $349.00 https://www.klarna.com/us/shopping/pl/cl52/320... [Included Accessories:1 gamepad, Media Type:DV... .. ... ... ... ... 3 Xbox Series S – Starter Bundle $239.00 https://www.klarna.com/us/shopping/pl/cl52/320... [Platform:Xbox One] 4 Microsoft Xbox Series X 1TB Console - Diablo I... $385.58 https://www.klarna.com/us/shopping/pl/cl52/320... [Release Year:2023, Included Accessories:1 gam... [5 rows x 4 columns], 'The response is a dataframe with the following columns: name, price, url, attributes. The attributes column is a list of tags. The price is in the format of $xx.xx.') >>> [CODEINTERPRETER->PLANNER] The following python code has been executed: ```python search_results, description = klarna_search(query=\"xbox\") search_results, description ``` The execution of the generated python code above has succeeded The result of above Python code after execution is: ( name price url attributes 0 Microsoft Xbox Series X - Black Edition $399.00 https://www.klarna.com/us/shopping/pl/cl52/495... [Release Year:2020, Included Accessories:1 gam... 1 Microsoft Xbox Series S 1TB - Black $349.00 https://www.klarna.com/us/shopping/pl/cl52/320... [Included Accessories:1 gamepad, Media Type:DV... .. ... ... ... ... 3 Xbox Series S – Starter Bundle $239.00 https://www.klarna.com/us/shopping/pl/cl52/320... [Platform:Xbox One] 4 Microsoft Xbox Series X 1TB Console - Diablo I... $385.58 https://www.klarna.com/us/shopping/pl/cl52/320... [Release Year:2023, Included Accessories:1 gam... [5 rows x 4 columns], 'The response is a dataframe with the following columns: name, price, url, attributes. The attributes column is a list of tags. The price is in the format of $xx.xx.') Finally, the Planner will report the result to the user. >>> [INIT_PLAN] 1. search xbox price using klarna_search plugin 2. report the result to the user >>> [PLAN] 1. instruct CodeInterpreter to search xbox price using klarna_search plugin 2. report the result to the user >>> [CURRENT_PLAN_STEP] 2. report the result to the user >>> [SEND_TO] User >>> [MESSAGE] Here are some Xbox prices I found: 1. Microsoft Xbox Series X - Black Edition: $399.00 2. Microsoft Xbox Series S 1TB - Black: $349.00 3. Xbox Series S – Starter Bundle: $239.00 4. Microsoft Xbox Series X 1TB Console - Diablo Immortal Edition: $385.58 Please note that these prices are subject to change and availability. >>> [PLANNER->USER] Here are some Xbox prices I found: 1. Microsoft Xbox Series X - Black Edition: $399.00 2. Microsoft Xbox Series S 1TB - Black: $349.00 3. Xbox Series S – Starter Bundle: $239.00 4. Microsoft Xbox Series X 1TB Console - Diablo Immortal Edition: $385.58 Please note that these prices are subject to change and availability. TaskWeaver: Here are some Xbox prices I found: 1. Microsoft Xbox Series X - Black Edition: $399.00 2. Microsoft Xbox Series S 1TB - Black: $349.00 3. Xbox Series S – Starter Bundle: $239.00 4. Microsoft Xbox Series X 1TB Console - Diablo Immortal Edition: $385.58 Please note that these prices are subject to change and availability.","s":"Auto Plugin Selection Preparation","u":"/TaskWeaver/docs/advanced/plugin_selection","h":"#auto-plugin-selection-preparation","p":43},{"i":52,"t":"A conversation is a data concept in TaskWeaver which contains a dialog between the user and the TaskWeaver app. Each session has a corresponding conversation. @dataclass class Conversation: \"\"\"A conversation denotes the interaction with the user, which is a collection of rounds. The conversation is also used to construct the Examples. Args: id: the unique id of the conversation. rounds: a list of rounds. plugins: a list of plugins that are used in the conversation. enabled: whether the conversation is enabled, used for Example only. \"\"\" id: str = \"\" rounds: List[Round] = field(default_factory=list) plugins: List[PluginEntry] = field(default_factory=list) roles: List[str] = field(default_factory=list) enabled: bool = True A conversation is a collection of rounds, where each round starts with the user's input and ends with the TaskWeaver app's response to the user. The plugins are the plugins available in the conversation, and the roles are the roles that the conversation is associated with. In TaskWeaver, the conversation is also used to store the Examples. The examples in the project folder are parsed into Conversations in the memory, and then composed into the prompt of the Planner or the CodeInterpreter. The enabled flag is used to control if this conversation is presented in the prompt.","s":"Conversation","u":"/TaskWeaver/docs/concepts/conversation","h":"","p":51},{"i":54,"t":"Plugins are the user-defined functions that extend TaskWeaver CodeInterpreter's capabilities. More details about plugins can be found in the Plugin Introduction. In TaskWeaver, the collection of available plugins are attached to the Conversation object.","s":"Plugin","u":"/TaskWeaver/docs/concepts/plugin","h":"","p":53},{"i":56,"t":"The post is a data concept in TaskWeaver which contains a single message in the conversation. @dataclass class Post: \"\"\" A post is the message used to communicate between two roles. It should always have a text_message to denote the string message, while other data formats should be put in the attachment. The role can be either a User, a Planner, or others. Args: id: the unique id of the post. send_from: the role who sends the post. send_to: the role who receives the post. message: the text message in the post. attachment_list: a list of attachments in the post. \"\"\" id: str send_from: RoleName send_to: RoleName message: str attachment_list: List[Attachment] A post is the message used to communicate between two roles. It should always have a text message to denote the string message. In addition, a post has send_from and send_to roles, which are the roles who send and receive the post, respectively. In some cases, the send_from and send_to roles are the same, to denote the self-communication of the role. The attachment_list is a list of attachments in the post. The attachment is used to store various data other than the text message, such as the code snippet or an artifact file path. An attachment may be used only by the role who sends the post, or it may be used by the role who receives the post. In usual cases, the message will present in the prompt as the past chat rounds. However, the message can sometimes be too long and should only be kept in the current round. In the next round, the message will be deleted from the prompt to keep the prompt short. As an example, the CodeInterpreter may generate a long execution result, which only needs to be kept in the current round. In this case, we provide a way of annotating the message (or part of the message) to be kept in the current round only. message = PromptUtil.wrap_text_with_delimiter(message, delimiter=PromptUtil.DELIMITER_TEMPORAL) In this way, the message will be kept in the current round only, and will not be presented in the prompt since the next round.","s":"Post","u":"/TaskWeaver/docs/concepts/post","h":"","p":55},{"i":58,"t":"A project folder is a directory that stores the configuration files, plugins, examples, logs, and workspace data. A TaskWeaverApp instance is associated with a project folder. The project folder is created by the user and contains all the necessary files and folders for the TaskWeaverApp to run. The following is a typical project directory structure: πŸ“¦project ┣ πŸ“œtaskweaver_config.json # the project configuration file for TaskWeaver ┣ πŸ“‚plugins # the folder to store plugins ┣ πŸ“‚planner_examples # the folder to store planner examples ┣ πŸ“‚codeinterpreter_examples # the folder to store code interpreter examples ┣ πŸ“‚logs # the folder to store logs, will be generated after program starts β”— πŸ“‚workspace # the directory stores session data， will be generated after program starts β”— πŸ“‚ session_id ┣ πŸ“‚ces # the folder used by the code execution service ┣ πŸ“‚cwd # the current working directory to run the generated code β”— other session data The workspace folder stores the session data, which includes the code execution service (CES) folder and the current working directory (CWD) folder. Therefore, if the code execution results in any files, they will be stored in the CWD folder. If you are running in local mode and want to load files from your local file system, the CWD is the base directory to load the files from.","s":"Project","u":"/TaskWeaver/docs/concepts/project","h":"","p":57},{"i":60,"t":"A round is a data concept in TaskWeaver which contains a single round of chat between the user and the TaskWeaver app. @dataclass class Round: \"\"\"A round is the basic unit of conversation in the project, which is a collection of posts. Args: id: the unique id of the round. post_list: a list of posts in the round. user_query: the query of the user. state: the state of the round. board: a dict to store the bulletin of the round. \"\"\" id: str user_query: str state: RoundState post_list: List[Post] board: Dict[str, str] = field(default_factory=dict) user_query is the query of the user, and post_list is a list of posts in the round. The state is among \"finished\", \"failed\", \"created\". When the round is created, the state is \"created\". When the round is finished successfully, the state is \"finished\". When the round is failed, the state is \"failed\". The board is a dictionary to store the bulletin of the round, which can be used to store the information of the round. This may sound confusing, given that different roles already have their messages in the posts. However, the board is used to store critical information that must be aware of in the round context. A very simple example would be the original user query. When the user issues a request to the Planner, the Planner will decompose the task and send a subtask to the CodeInterpreter. However, the CodeInterpreter needs to know the original user query and the full plan of the Planner to provide a more accurate response. In this case, the Planner can store the original user query in the board, and the CodeInterpreter can access it when needed. We provide two methods to access the board: write_board and read_board. tip The board is a dictionary, and you can store any information you want in the board for the current round. This is useful to eliminate the issue of information loss between different roles.","s":"Round","u":"/TaskWeaver/docs/concepts/round","h":"","p":59},{"i":62,"t":"The role is a concept in TaskWeaver which represents the different roles in the conversation system. The Planner and CodeInterpreter are two examples of roles in TaskWeaver. class Role: @inject def __init__( self, config: ModuleConfig, logger: TelemetryLogger, tracing: Tracing, event_emitter: SessionEventEmitter, role_entry: Optional[RoleEntry] = None, ): \"\"\" The base class for all roles. \"\"\" info We use the inject decorator from the injector package to inject the dependencies into the role class. We allow adding extra roles into the system by inheriting the Role class and implementing the role in the taskweaver.ext_role module. We have provided examples on how to implement a new role. A very simple example is the Echo role which echoes the user's message back to the user. A role should have at least two files: role_name.py and role_name.role.yaml. The files of the role should be put in the taskweaver/ext_role/role_name folder. We need to follow the convention of the role name, which is exactly the same as the folder name, otherwise an exception will be raised. We typically use the style of snake_case for the role name. In the role_name.role.yaml file, we define the role's configuration. This following is echo.role.yaml of the Echo role configuration. alias: Echo module: taskweaver.ext_role.echo.echo.Echo intro : |- - Echo is responsible for echoing the user input. The configuration file contains the following fields: alias: the alias of the role, which is the name of role shown in the prompt and the conversation. module: the module path of the role class. TaskWeaver will import the module and instantiate the role class. intro: the introduction of the role, which will be shown in Planner's prompt for choosing the role for certain tasks. In the role_name.py file, we define the role class. The following is echo.py of the Echo role class. class EchoConfig(RoleConfig): def _configure(self): # configuration for the Echo role # can be configured in the project configuration file with `echo.decorator` self.decorator = self._get_str(\"decorator\", \"\") class Echo(Role): @inject def __init__( self, config: EchoConfig, logger: TelemetryLogger, tracing: Tracing, event_emitter: SessionEventEmitter, role_entry: RoleEntry, ): # configuration for the parent class; this is required super().__init__(config, logger, tracing, event_emitter, role_entry) def reply(self, memory: Memory, **kwargs) -> Post: rounds = memory.get_role_rounds( role=self.alias, include_failure_rounds=False, ) # obtain the query from the last round last_post = rounds[-1].post_list[-1] post_proxy = self.event_emitter.create_post_proxy(self.alias) post_proxy.update_send_to(last_post.send_from) post_proxy.update_message( self.config.decorator + last_post.message + self.config.decorator ) return post_proxy.end() The role implementation should inherit the Role class and implement the reply method. The above example demonstrates how to get the query from the last round. The reply function of the Echo role is simply echoing the user's message back to the user with optional decoration. The reply function should return a Post object, which is the response of the role to the user. We provide facilities to help the role to interact with the memory, the event emitter, and the logger. For example, the event_emitter.create_post_proxy function is used to create a PostProxy object, which is a helper class to create a Post object. The PostProxy object is used to update the Post object with the new message, send_to, and other attributes. Once the PostProxy object is updated, the event emitter will send this event to a handler to display the event to a frontend. To enable the role in TaskWeaver, we need to add the role configuration to the taskweaver_config.json file. The following is an example of the taskweaver_config.json file with the Echo role configuration in addition to the Planner and CodeInterpreter roles. Note that the name of the role should be the same as the folder name of the role, not the alias. { \"session.roles\": [\"planner\", \"echo\", \"code_interpreter\"] } tip How to determine if I should create a new role? or implement a new plugin for the CodeInterpreter? The answer depends on the functionality you want to implement. If the functionality is to reply in text message given a user query, and you don't envision the need to process the reply text in code, you should create a new role. In contrast, if the functionality is to process the user query and return the result in a structured format, or if both input and output are in a structured format, you should implement a new plugin for the CodeInterpreter.","s":"Role","u":"/TaskWeaver/docs/concepts/role","h":"","p":61},{"i":64,"t":"A session is a conversation instance that the user has with the TaskWeaver app. A new session is created when the user interacts with the app. When the user finishes interacting with the app, the session should be closed. TaskWeaver allows multiple sessions to be created and managed by the app. Therefore, multiple users can interact with the app at the same time in different sessions. A new session is created by calling the get_session method of the TaskWeaverApp class. In the background, the get_session method creates a new Session instance. class Session: @inject def __init__( self, session_id: str, workspace: Workspace, app_injector: Injector, logger: TelemetryLogger, tracing: Tracing, config: AppSessionConfig, role_registry: RoleRegistry, ) -> None: \"\"\" Initialize the session. :param session_id: The session ID. :param workspace: The workspace. :param app_injector: The app injector. :param logger: The logger. :param tracing: The tracing. :param config: The configuration. :param role_registry: The role registry. \"\"\" info In TaskWeaver, we use an injector to take care of most dependency injection. The Session class has the following methods: def send_message( self, message: str, event_handler: Optional[SessionEventHandler] = None, files: Optional[List[Dict[Literal[\"name\", \"path\", \"content\"], Any]]] = None, ) -> Round: \"\"\" Send a message. :param message: The message. :param event_handler: The event handler. :param files: The files. :return: The chat round. \"\"\" send_message is used to send a message to the app. The message parameter is the text message that the user sends to the app. The event_handler parameter is a function that handles events during the conversation. We have defined a variety of events that can be handled by the event handler. Each event has a specific type and a message. By implementing the event handler, you can customize the display of events during the conversation. A very simple example of an event handler is shown below: class ConsoleEventHandler(SessionEventHandler): def handle(self, event: TaskWeaverEvent): print(event.t, event.msg) session.send_message(\"Hello, how can I help you?\", ConsoleEventHandler()) The ConsoleEventHandler class is a simple event handler that prints the event type and message to the console. The files parameter is used to upload files to the app for processing. def stop(self) -> None: \"\"\" Stop the session. This function must be called before the session exits. \"\"\" The stop method is used to stop the session. def update_session_var( self, variables: Dict[str, str] ): \"\"\" Update the session variables. :param variables: The variables to update. \"\"\" The update_session_var method is used to update the session variables. A session variable is a key-value pair that is only available in a specific session. Session variables can be used in the plugins to store information that is specific to the session. For example, you can store different user names in the session variables of different sessions. Then, in the plugin, you can access the user name by using the session variable. @register_plugin class PluginClass(Plugin): def __call__(self, argument1: str): ... # this line of code in the plugin implementation self.ctx.get_session_var(\"user_name\", \"anonymous\") ... The update_session_var method can be called multiple times to update multiple session variables.","s":"Session","u":"/TaskWeaver/docs/concepts/session","h":"","p":63},{"i":66,"t":"An overview of all configurations available in the config file, which is located at project/taskweaver_config.json. You can edit this file to configure TaskWeaver. tip The configuration file is in JSON format. So for boolean values, use true or false instead of True or False. For null values, use null instead of None or \"null\". All other values should be strings in double quotes. The following table lists the parameters in the configuration file: Parameter Description Default Value llm.model The model name used by the language model. gpt-4 llm.api_base The base URL of the OpenAI API. https://api.openai.com/v1 llm.api_key The API key of the OpenAI API. null llm.api_type The type of the OpenAI API, could be openai or azure. openai llm.api_version The version of the OpenAI API. 2023-07-01-preview llm.response_format The response format of the OpenAI API, could be json_object, text or null. json_object llm.embedding_api_type The type of the embedding API sentence_transformers llm.embedding_model The name of the embedding model all-mpnet-base-v2 code_interpreter.code_verification_on Whether to enable code verification. false code_interpreter.allowed_modules The list of allowed modules to import in code generation. [\"pandas\", \"matplotlib\", \"numpy\", \"sklearn\", \"scipy\", \"seaborn\", \"datetime\", \"typing\"], if the list is empty, no modules would be allowed code_interpreter.blocked_functions The list of functions to block from code generation. [\"__import__\", \"eval\", \"exec\", \"execfile\", \"compile\", \"open\", \"input\", \"raw_input\", \"reload\"] logging.log_file The name of the log file. taskweaver.log logging.log_folder The folder to store the log file. logs plugin.base_path The folder to store plugins. ${AppBaseDir}/plugins planner.example_base_path The folder to store planner examples. ${AppBaseDir}/planner_examples planner.prompt_compression Whether to compress the chat history for planner. false planner.use_experience Whether to use experience summarized from the previous chat history in planner. false code_generator.example_base_path The folder to store code interpreter examples. ${AppBaseDir}/codeinterpreter_examples code_generator.prompt_compression Whether to compress the chat history for code interpreter. false code_generator.enable_auto_plugin_selection Whether to enable auto plugin selection. false code_generator.use_experience Whether to use experience summarized from the previous chat history in code generator. false code_generator.auto_plugin_selection_topk The number of auto selected plugins in each round. 3 session.max_internal_chat_round_num The maximum number of internal chat rounds between Planner and Code Interpreter. 10 session.roles The roles included for the conversation. [\"planner\", \"code_interpreter\"] round_compressor.rounds_to_compress The number of rounds to compress. 2 round_compressor.rounds_to_retain The number of rounds to retain. 3 execution_service.kernel_mode The mode of the code executor, could be local or container. local tip ${AppBaseDir} is the project directory. tip Up to 11/30/2023, the json_object and text options of llm.response_format is only supported by the OpenAI models later than 1106. If you are using an older version of OpenAI model, you need to set the llm.response_format to null. tip Read this for more information for planner.prompt_compression and code_generator.prompt_compression. tip We support to set configurations via environment variables. You need to transform the configuration key to uppercase and replace the dot with underscore. For example, llm.model should be set as LLM_MODEL, llm.api_base should be set as LLM_API_BASE, etc.","s":"Configuration File","u":"/TaskWeaver/docs/configurations/overview","h":"","p":65},{"i":68,"t":"More details about important configurations.","s":"More about Configurations","u":"/TaskWeaver/docs/configurations/configurations_in_detail","h":"","p":67},{"i":70,"t":"planner.example_base_path: The folder to store planner examples. The default value is ${AppBaseDir}/planner_examples. If you want to create your own planner examples, you can add them to this folder. More details about example can referred to example. planner.prompt_compression: At times, lengthy conversations with the Planner may exceed the input limitations of the LLM model. To address this issue, we can compress the chat history and send it to the LLM model. The default value for this setting is false. More details about prompt_compression can be referred to prompt_compression. planner.use_experience: Whether to use experience summarized from the previous chat history in planner. The default value is false. planner.llm_alias: The alias of the LLM used by the Planner. If you do not specify the LLM for the Planner, the primary LLM will be used by default.","s":"Planner Configuration","u":"/TaskWeaver/docs/configurations/configurations_in_detail","h":"#planner-configuration","p":67},{"i":72,"t":"session.max_internal_chat_round_num: the maximum number of internal chat rounds between Planner and Code Interpreter. If the number of internal chat rounds exceeds this number, the session will be terminated. The default value is 10. session.roles: the roles included for the conversation. The default value is [\"planner\", \"code_interpreter\"]. TaskWeaver has 3 code interpreters: code_interpreter: it will generate Python code to fulfill the user's request. This is the default code interpreter. code_interpreter_plugin_only: please refer to plugin_only_mode for more details. code_interpreter_cli_only: allow users to directly communicate with the Command Line Interface (CLI) in natural language. Please refer to cli_only_mode for more details. If you do not specify planner in the roles, you will enter the \"no-planner\" mode. It allows users to directly communicate with the worker role, such as code_interpreter. In this mode, users can only send messages to the CodeInterpreter and receive messages from the CodeInterpreter. Note that only single worker role is allowed in the \"no-planner\" mode because all user requests will be sent to the worker role directly. Here is an example: ========================================================= _____ _ _ __ |_ _|_ _ ___| | _ | | / /__ ____ __ _____ _____ | |/ _` / __| |/ /| | /| / / _ \\/ __ `/ | / / _ \\/ ___/ | | (_| \\__ \\ < | |/ |/ / __/ /_/ /| |/ / __/ / |_|\\__,_|___/_|\\_\\|__/|__/\\___/\\__,_/ |___/\\___/_/ ========================================================= TaskWeaver: I am TaskWeaver, an AI assistant. To get started, could you please enter your request? Human: generate 10 random numbers >>> [PYTHON]Starting... import numpy as np random_numbers = np.random.rand(10) random_numbers >>> [VERIFICATION] NONE >>> [STATUS]Starting... SUCCESS >>> [RESULT] The execution of the generated python code above has succeeded The result of above Python code after execution is: array([0.09918602, 0.68732778, 0.44413814, 0.4756623 , 0.48302334, 0.8286594 , 0.80994359, 0.35677263, 0.45719317, 0.68240194]) >>> [CODEINTERPRETER->PLANNER] The following python code has been executed: ```python import numpy as np random_numbers = np.random.rand(10) random_numbers ``` The execution of the generated python code above has succeeded The result of above Python code after execution is: array([0.09918602, 0.68732778, 0.44413814, 0.4756623 , 0.48302334, 0.8286594 , 0.80994359, 0.35677263, 0.45719317, 0.68240194]) TaskWeaver: The following python code has been executed: ```python import numpy as np random_numbers = np.random.rand(10) random_numbers ``` The execution of the generated python code above has succeeded The result of above Python code after execution is: array([0.09918602, 0.68732778, 0.44413814, 0.4756623 , 0.48302334, 0.8286594 , 0.80994359, 0.35677263, 0.45719317, 0.68240194])","s":"Session Configuration","u":"/TaskWeaver/docs/configurations/configurations_in_detail","h":"#session-configuration","p":67},{"i":74,"t":"In TaskWeaver, we support various embedding models to generate embeddings for auto plugin selection. llm.embedding_api_type: The type of the embedding API. We support the following types: openai qwen ollama sentence_transformers glm llm.embedding_model: The embedding model name. The model name should be aligned with llm.embedding_api_type. We only list some embedding models we have tested below: openai text-embedding-ada-002 qwen text-embedding-v1 ollama llama2 sentence_transformers all-mpnet-base-v2 multi-qa-mpnet-base-dot-v1 all-distilroberta-v1 all-MiniLM-L12-v2 multi-qa-MiniLM-L6-cos-v1 zhipuai embedding-2 You also can use other embedding models supported by the above embedding APIs.","s":"Embedding Configuration","u":"/TaskWeaver/docs/configurations/configurations_in_detail","h":"#embedding-configuration","p":67},{"i":77,"t":"A: In the default setting, TaskWeaver typically goes through the following steps to generate the output: The User sends a query to the Planner. The Planner generates a plan based on the query and sends the plan to the CodeInterpreter. The CodeInterpreter executes the plan and sends the execution result back to the Planner. The Planner summarizes the execution result and sends the summary to the User. The User receives the summary from the Planner. In some cases, the Planer and CodeInterpreter can go back and forth multiple times to generate the output. This process can take a long time, mostly due to the latency of calling the LLMs. To speed up the process, you can try the following: Use a smaller LLM model, such as GPT-3.5 instead of GPT-4. However, you need to experiment with your use case to see if the smaller model can still generate the output correctly. Use the CodeInterpreter only mode, which skips the Planner and generates the code directly from the User query. This mode is faster because it skips the planning step. However, you should check if your use case needs the planning step.","s":"Q: Why it takes so long to generate the output?","u":"/TaskWeaver/docs/FAQ","h":"#q-why-it-takes-so-long-to-generate-the-output","p":75},{"i":79,"t":"A: This error typically happens when TaskWeaver is running in the container mode and cannot connect to the Docker daemon. We have switched to the container mode by default to provide a more secure environment for code execution. To opt out of the container mode, you can set the execution_service.kernel_mode parameter to local in the taskweaver_config.json file. However, you should be aware that TaskWeaver can interact with the host machine directly in the local mode, which may have security risks.","s":"Q: Why TaskWeaver fails and the logs say \"Failed to connect to docker.daemon\"?","u":"/TaskWeaver/docs/FAQ","h":"#q-why-taskweaver-fails-and-the-logs-say-failed-to-connect-to-dockerdaemon","p":75},{"i":81,"t":"A: This is typically due to that the LLM failed to generate its output follow our schema. In Planner's prompt, we asked the LLM to generate a JSON object that contains send_to, message, init_plan, plan, and current_plan_step. Missing any of these fields will cause the parsing error. The most effective way to mitigate this issue is to use switch to more powerful LLM model, such as GPT-3.5 or GPT-4.","s":"Q: Why I see errors saying the Planner failed to generate the send_to, message or other fields?","u":"/TaskWeaver/docs/FAQ","h":"#q-why-i-see-errors-saying-the-planner-failed-to-generate-the-send_to-message-or-other-fields","p":75},{"i":83,"t":"A: A simple way to check if TaskWeaver can see your plugin is to ask \"What can you do?\" to TaskWeaver. The typical response is to list all the available plugins like the following: I can assist you with various tasks, including: - Detecting anomalies in time series data. - Rendering text into ASCII art. - Searching and comparing prices from thousands of online shops (US only). - Telling a joke. If you have a specific task in mind, please let me know, and I'll do my best to assist you. If you see your plugin in the list, it means TaskWeaver can see your plugin. But this is not a reliable way to check if TaskWeaver can see your plugin because the response is generated by the LLM. A more reliable way is to check the prompt of the Planner. You can find the prompts from project/workspace/sessions//planner_prompt_log_xxxx.yaml. Then, search for this section as follows: CodeInterpreter has the following plugin functions and their required parameters need to be provided before the execution: - anomaly_detection: anomaly_detection function identifies anomalies from an input DataFrame of time series. It will add a new column \\\"Is_Anomaly\\\", where each entry will be marked with \\\"True\\\" if the value is an anomaly or \\\"False\\\" otherwise. Arguments required: df: DataFrame, time_col_name: str, value_col_name: str - ascii_render: This plugin renders the input text into ASCII art form. Arguments required: text: str - klarna_search: Search and compare prices from thousands of online shops. Only available in the US. This plugin only takes user requests when searching for merchandise. If not clear, confirm with the user if they want to search for merchandise from Klarna. Arguments required: query: str - tell_joke: Call this plugin to tell a joke. Check if your plugin is in the list. If it is, it means TaskWeaver can see your plugin.","s":"Q: How do I know if TaskWeaver can see my plugin?","u":"/TaskWeaver/docs/FAQ","h":"#q-how-do-i-know-if-taskweaver-can-see-my-plugin","p":75},{"i":85,"t":"First, make sure you have read our Plugin Introduction and this tutorial carefully. You should have two files in the plugins folder, e.g., ascii_render.py and ascii_render.yaml. Now, if TaskWeaver cannot see your plugin, the root cause is typically syntax errors in the yaml file. Check the console output if you are using the command line interface, or the console logs if you are using the web interface. You may see the following error message: failed to loading component from .yaml, skipping: Yaml loading failed due to: The error message will tell you the reason why the yaml file cannot be loaded. It is typically easy to fix the syntax errors by using a yaml linter (e.g., in Visual Studio Code) or an online yaml linter. If you have checked the syntax of the yaml file and TaskWeaver still cannot see your plugin, please check if the yaml file has included all the required fields such as the parameters and returns fields.","s":"Q: Why TaskWeaver cannot see my plugin?","u":"/TaskWeaver/docs/FAQ","h":"#q-why-taskweaver-cannot-see-my-plugin","p":75},{"i":87,"t":"A: In this case, you may see the generated code has called the plugin function, but the execution result is an error message saying that the plugin function is undefined. If this happens, please check the console output if you are using the command line interface, or the console logs if you are using the web interface. You may see the following error message: Plugin failed to load: Plugin failed to register: failed to load plugin This error message will tell you the reason why the plugin function cannot be loaded. It is typically easy to fix the errors by checking the console output or logs. The root cause is typically errors in the python file that causes the plugin function cannot be loaded. Typical errors include syntax errors, missing imports, or missing packages. Note that this sort of error is not caused by the implementation \"inside\" the plugin function. Otherwise, the errors would be caught during the execution of the plugin function, not during the loading of the plugin function.","s":"Q: Why TaskWeaver can see my plugin but cannot call it?","u":"/TaskWeaver/docs/FAQ","h":"#q-why-taskweaver-can-see-my-plugin-but-cannot-call-it","p":75},{"i":89,"t":"A: We are working on a debugging tool to help you debug your plugin. For now, a simple way to debug your plugin is to define a main function in the python file and run it in your local environment. For example, you can define a main function in ascii_render.py as follows: if __name__ == \"__main__\": from taskweaver.plugin.context import temp_context with temp_context() as temp_ctx: render = AsciiRenderPlugin(name=\"ascii_render\", ctx=temp_ctx, config={}) print(render(text=\"hello world!\")) In this main function, we create a temporary context and call the plugin function with some input. You need not change the plugin implementation. Just add the main function to the end of the python file. Then, run the python file in your local environment. If there are any errors, you can see them in the console output. If you have the configurations section in the yaml file, you can manually set the configurations in the config parameter of the plugin constructor. We currently do not read the yaml file, so you need to make sure that the configurations are set correctly in the config parameter. For example, if an integer configuration max_length is defined in the yaml file, you can set it in the config parameter as follows: config = { \"max_length\": 100 } Then, pass the config to the plugin constructor. As yaml is type sensitive, you need to make sure that the type of the configuration is correct.","s":"Q: How to debug my plugin?","u":"/TaskWeaver/docs/FAQ","h":"#q-how-to-debug-my-plugin","p":75},{"i":91,"t":"A: We use a Jupyter Kernel to execute the code in TaskWeaver. The Jupyter Kernel uses an event loop to manage the execution of the code. If you see the error message \"RuntimeError: This event loop is already running.\", it typically means that the event loop is already running. This is typically caused by the enviroment where TaskWeaver is running. For example, if you are running TaskWeaver in a Jupyter Notebook.","s":"Q: Why I see the error message \"RuntimeError: This event loop is already running\"?","u":"/TaskWeaver/docs/FAQ","h":"#q-why-i-see-the-error-message-runtimeerror-this-event-loop-is-already-running","p":75},{"i":93,"t":"There are currently two types of examples: (1) Planner examples and (2) CodeInterpreter examples. Planning examples are used to demonstrate how the Planner should plan for a specific query from the User. Code generation examples are used to help the CodeInterpreter generate code or orchestrate plugins to perform a specific task. Both types of examples are actually the conversation between a collection of roles, such as the User, the Planner, and the CodeInterpreter. Each conversation consists of one or more rounds, and each round has a User query and contains one or more posts. A post is a message sent from one role to another role, and it may contain some attachments. One example looks like this: enabled: True rounds: - User_query: ping state: created post_list: - message: ping send_from: User send_to: Planner attachment_list: [] - message: pong send_from: Planner send_to: User attachment_list: [] We explicitly break all examples into the two types only to make it easier to understand and maintain. When the roles are initialized, each role will only read the examples of its type. tip All the text field in the examples such as the messages are in Markdown format.","s":"Customizing Examples","u":"/TaskWeaver/docs/customization/example/","h":"","p":92},{"i":95,"t":"A planning example tells LLMs how to plan for a specific query from the User, talk to the CodeInterpreter, receive the execution result from the CodeInterpreter, and summarize the execution result. The following is an example of a conversation between the User, the Planner, and the CodeInterpreter. One of the purposes of this example is to teach the Planner on the annotation of the dependencies between the steps in the plan. Another purpose is to teach the Planner to follow the right output format. enabled: True rounds: - User_query: count the rows of /home/data.csv state: created post_list: - message: count the rows of /home/data.csv send_from: User send_to: Planner attachment_list: - message: Please load the data file /home/data.csv and count the rows of the loaded data send_from: Planner send_to: CodeInterpreter attachment_list: - type: init_plan content: |- 1. load the data file 2. count the rows of the loaded data 3. report the result to the User - type: plan content: |- 1. instruct CodeInterpreter to load the data file and count the rows of the loaded data 2. report the result to the User - type: current_plan_step content: 1. instruct CodeInterpreter to load the data file and count the rows of the loaded data - message: Load the data file /home/data.csv successfully and there are 100 rows in the data file send_from: CodeInterpreter send_to: Planner attachment_list: - message: The data file /home/data.csv is loaded and there are 100 rows in the data file send_from: Planner send_to: User attachment_list: - type: init_plan content: |- 1. load the data file 2. count the rows of the loaded data 3. report the result to the User - type: plan content: |- 1. instruct CodeInterpreter to load the data file and count the rows of the loaded data 2. report the result to the User - type: current_plan_step content: 2. report the result to the User tip To better understand the attachments including init_plan, plan, and current_plan_step, please refer to the Planner's prompt. In this example, there are 4 posts: The first post is sent from the User to the Planner. The message is \"count the rows of /home/data.csv\", which must be the same with the User query. The second post is sent from the Planner to the CodeInterpreter. The message is \"Please load the data file /home/data.csv and count the rows of the loaded data\". The attachment list contains 3 attachments: The first attachment is the initial plan. The second attachment is the final plan. The third attachment is the current plan step. The third post is sent from the CodeInterpreter to the Planner. The message is \"Load the data file /home/data.csv successfully and there are 100 rows in the data file\". The fourth post is sent from the Planner to the User. The message is \"The data file /home/data.csv is loaded and there are 100 rows in the data file\". The attachment list contains 3 attachments, which are the same as the second post.","s":"Planner Examples","u":"/TaskWeaver/docs/customization/example/","h":"#planner-examples","p":92},{"i":97,"t":"A CodeInterpreter example tells CodeInterpreter how to generate code or orchestrate plugins to perform a specific task. The task is always from the Planner. The purpose of this example is to teach CodeInterpreter how to handle errors in execution. enabled: True rounds: - User_query: read file /abc/def.txt state: finished post_list: - message: read file /abc/def.txt send_from: Planner send_to: CodeInterpreter attachment_list: [] - message: I'm sorry, I cannot find the file /abc/def.txt. An FileNotFoundException has been raised. send_from: CodeInterpreter send_to: Planner attachment_list: - type: thought content: \"{ROLE_NAME} will generate a code snippet to read the file /abc/def.txt and present the content to the User.\" - type: python content: |- file_path = \"/abc/def.txt\" with open(file_path, \"r\") as file: file_contents = file.read() print(file_contents) - type: verification content: CORRECT - type: code_error content: No code error. - type: execution_status content: FAILURE - type: execution_result content: FileNotFoundException, the file /abc/def.txt does not exist. tip Read the prompt of the code generation to better understand the attachments in the example code generator prompt. This conversation has two posts: The first post is sent from the Planner to the CodeInterpreter. The message is \"Please read file /abc/def.txt\". The second post is sent from the CodeInterpreter to the Planner. The message is \"read file /abc/def.txt\". The attachment list contains 6 attachments: The first attachment is the thought of the CodeInterpreter. The second attachment is the generated code, which is in python. The third attachment is the verification status, which is CORRECT, INCORRECT, or NONE. The fourth attachment is the verification error message. The fifth attachment is the execution status, which is SUCCESS, FAILURE, or NONE. The sixth attachment is the execution result, which is a markdown string. In this example, verification is about whether the generated code is correct or not. We implemented a module to verify the generated code. If the code is syntactically incorrect, or the code violates the constraints, the verification status will be INCORRECT and some error messages will be returned. A verification of NONE means that the code has not been verified, which means verification has been turned off. In this example, execution_status is about whether the generated code can be executed successfully or not. If the execution is successful, the execution status will be SUCCESS and the execution result will be returned. Otherwise, the execution status will be FAILURE and some error messages will be returned. A execution_status of NONE means that the code has not been executed.","s":"CodeInterpreter Examples","u":"/TaskWeaver/docs/customization/example/","h":"#codeinterpreter-examples","p":92},{"i":100,"t":"The agent developers can add examples to guide the planning and code generation. Alternatively, we also provide another way of saving user experiences to long-term memory. In practice, if the user asks TaskWeaver to solve a hard problem. TaskWeaver can first go wrong. But after several attempts or the user gives more instructions, the agent can finally solve the problem. However, next time, if the user asks a similar problem or even exactly the same problem. The agent is still hard to come up with the right solution at first because it does not memorize the experiences. Therefore, we proposed a mechanism called experience memory. Here is the basic idea. A user can issue a command to TaskWeaver to save chat history and then extract experience tips from it and save them into the experience pool. Later, when the agent sees similar requests, it will retrieve the experience from the memory to guide its planning and code generation. An experience tip is about what should do or should not do when seeing a request like this. We will add the retrieved experiences to the prompt when received a similar request afterward.","s":"Motivation","u":"/TaskWeaver/docs/customization/experience","h":"#motivation","p":98},{"i":102,"t":"To enable the experience feature, you only need to set the planner.use_experience and code_generator.use_experience parameter in the configuration file to true. Start a new conversation with TaskWeaver. You will find experience directory is created in your project directory. Note that there is no experience now because we have not saved any chat history yet. If you think the current chat history is worth saving, you can save it by typing command /save and you will find a new file named raw_exp_{session_id}.yaml is created in the experience directory. Restart TaskWeaver and start a new conversation. In the initialization stage, TaskWeaver will read the raw_exp_{session_id}.yaml file and make a summarization in a new file named All_exp_{session_id}.yaml. This process may take a while. All_ denotes that this experience will be loaded for Planner and CodeInterpreter. When user send a similar query to TaskWeaver, it will retrieve the relevant experience and load it into the system prompt (for Planner and CodeInterpreter). In this way, the experience can be used to guide the future conversation.","s":"How to use experience memory","u":"/TaskWeaver/docs/customization/experience","h":"#how-to-use-experience-memory","p":98},{"i":104,"t":"Start a new conversation with TaskWeaver. Send a request \"calculate the mean value of ./project/sample_data/data_demo.csv\" to TaskWeaver. You will find TaskWeaver made a mistake, calculating the mean value of the non-numerical column. After several attempts, TaskWeaver fixed this issue. ========================================================= _____ _ _ __ |_ _|_ _ ___| | _ | | / /__ ____ __ _____ _____ | |/ _` / __| |/ /| | /| / / _ \\/ __ `/ | / / _ \\/ ___/ | | (_| \\__ \\ < | |/ |/ / __/ /_/ /| |/ / __/ / |_|\\__,_|___/_|\\_\\|__/|__/\\___/\\__,_/ |___/\\___/_/ ========================================================= TaskWeaver β–Ά I am TaskWeaver, an AI assistant. To get started, could you please enter your request? Human β–Ά calculate the mean value of d:/demo_data.csv ╭───< Planner > β”œβ”€β–Ί [init_plan] β”‚ 1. load the data file β”‚ 2. calculate the mean value of the loaded data β”‚ 3. report the result to the user β”œβ”€β–Ί [plan] β”‚ 1. instruct CodeInterpreter to load the data file and calculate the mean value of the loaded data β”‚ 2. report the result to the user β”œβ”€β–Ί [current_plan_step] 1. instruct CodeInterpreter to load the data file and calculate the mean value of the loaded data β”œβ”€β”€β— Please load the data file d:/demo_data.csv and calculate the mean value of the loaded data ╰──● sending message to CodeInterpreter ╭───< CodeInterpreter > β”œβ”€β–Ί [thought] ProgramApe will generate a code snippet to load the data file d:/demo_data.csv and calculate the mean value of the loaded data using pandas. β”œβ”€β–Ί [python] β”‚ import pandas as pd β”‚ β”‚ data_file_path = \"d:/demo_data.csv\" β”‚ data_df = pd.read_csv(data_file_path) β”‚ mean_value = data_df.mean() β”‚ mean_value β”œβ”€β–Ί [verification] NONE β”œβ”€β–Ί [code_error] No code verification is performed. β”œβ”€β–Ί [execution_status] FAILURE β”œβ”€β–Ί [execution_result] β”‚ The execution of the generated python code above has failed β”‚ β”‚ During execution, the following messages were logged: β”‚ Traceback (most recent call last): β”‚ β”‚ Cell In[1], line 5 β”‚ mean_value = data_df.mean() β”‚ β”‚ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\frame.py:11335 in mean β”‚ result = super().mean(axis, skipna, numeric_only, **kwargs) β”‚ β”‚ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\generic.py:11984 in mean β”‚ return self._stat_function( β”‚ β”‚ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\generic.py:11941 in _stat_function β”‚ return self._reduce( β”‚ β”‚ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\frame.py:11204 in _reduce β”‚ res = df._mgr.reduce(blk_func) β”‚ β”‚ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:1459 in reduce β”‚ nbs = blk.reduce(func) β”‚ β”‚ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\internals\\blocks.py:377 in reduce β”‚ result = func(self.values) β”‚ β”‚ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\frame.py:11136 in blk_func β”‚ ret β”‚ β”œβ”€β–Ί [artifact_paths] [] β”œβ”€β”€β— The following python code has been executed: β”‚ ```python β”‚ import pandas as pd β”‚ β”‚ data_file_path = \"d:/demo_data.csv\" β”‚ data_df = pd.read_csv(data_file_path) β”‚ mean_value = data_df.mean() β”‚ mean_value β”‚ ``` β”‚ β”‚ β”‚ The execution of the generated python code above has failed β”‚ β”‚ During execution, the following messages were logged: β”‚ Traceback (most recent call last): β”‚ β”‚ Cell In[1], line 5 β”‚ mean_value = data_df.mean() β”‚ β”‚ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\frame.py:11335 in mean β”‚ result = super().mean(axis, skipna, numeric_only, **kwargs) β”‚ β”‚ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\generic.py:11984 in mean β”‚ return self._stat_function( β”‚ β”‚ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\generic.py:11941 in _stat_function β”‚ return self._reduce( β”‚ β”‚ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\frame.py:11204 in _reduce β”‚ res = df._mgr.reduce(blk_func) β”‚ β”‚ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:1459 in reduce β”‚ nbs = blk.reduce(func) β”‚ β”‚ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\internals\\blocks.py:377 in reduce β”‚ result = func(self.values) β”‚ β”‚ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\frame.py:11136 in blk_func β”‚ ret β”‚ β”œβ”€β–Ί [revise_message] β”‚ The execution of the previous generated code has failed. If you think you can fix the problem by rewriting the code, please generate code and run it again. β”‚ Otherwise, please explain the problem to me. ╰──● sending message to CodeInterpreter ╭───< CodeInterpreter > β”œβ”€β–Ί [thought] ProgramApe will generate a code snippet to load the data file d:/demo_data.csv and calculate the mean value of the loaded data using pandas, considering the execution β”‚ error. β”œβ”€β–Ί [python] β”‚ import pandas as pd β”‚ β”‚ data_file_path = \"d:/demo_data.csv\" β”‚ data_df = pd.read_csv(data_file_path) β”‚ mean_value = data_df.mean(axis=0, numeric_only=True) β”‚ mean_value β”œβ”€β–Ί [verification] NONE β”œβ”€β–Ί [code_error] No code verification is performed. β”œβ”€β–Ί [execution_status] SUCCESS β”œβ”€β–Ί [execution_result] β”‚ The execution of the generated python code above has succeeded β”‚ β”‚ The result of above Python code after execution is: β”‚ Count 78172.75 β”‚ dtype: float64 β”œβ”€β–Ί [artifact_paths] [] β”œβ”€β”€β— The following python code has been executed: β”‚ ```python β”‚ import pandas as pd β”‚ β”‚ data_file_path = \"d:/demo_data.csv\" β”‚ data_df = pd.read_csv(data_file_path) β”‚ mean_value = data_df.mean(axis=0, numeric_only=True) β”‚ mean_value β”‚ ``` β”‚ β”‚ β”‚ The execution of the generated python code above has succeeded β”‚ β”‚ The result of above Python code after execution is: β”‚ Count 78172.75 β”‚ dtype: float64 ╰──● sending message to Planner ╭───< Planner > β”œβ”€β–Ί [init_plan] β”‚ 1. load the data file β”‚ 2. calculate the mean value of the loaded data β”‚ 3. report the result to the user β”œβ”€β–Ί [plan] β”‚ 1. instruct CodeInterpreter to load the data file and calculate the mean value of the loaded data β”‚ 2. report the result to the user β”œβ”€β–Ί [current_plan_step] 2. report the result to the user β”œβ”€β”€β— The mean value of the data in d:/demo_data.csv is 78172.75 ╰──● sending message to User TaskWeaver β–Ά The mean value of the data in d:/demo_data.csv is 78172.75 We can issue the /save command from the console chat interface to save the chat history. Then, after we restart TaskWeaver, we can see the experience summarized from the last chat history is below: exp_id: 20231228-073922-9b411afd experience_text: \"User Query: get the mean value of d:/demo_data.csv Error Resolution: - Error 1: The initial code failed to execute because it tried to calculate the mean value of all columns, including non-numeric columns. - Resolution 1: The code was revised to check for non-numeric columns and only calculate the mean value for numeric columns. Preferences: - Preference 1: The user prefers to calculate the mean value of the \"Count\" column in the data.\" raw_experience_path: D:\\project\\experience\\raw_exp_20231228-073922-9b411afd.yaml embedding_model: text-embedding-ada-002 embedding: ... Send a similar request \"calculate the variance value of ./project/sample_data/data_demo.csv\" to TaskWeaver. You will find TaskWeaver will not make the same mistake again. It will ask User to confirm the column name to calculate the variance value. ========================================================= _____ _ _ __ |_ _|_ _ ___| | _ | | / /__ ____ __ _____ _____ | |/ _` / __| |/ /| | /| / / _ \\/ __ `/ | / / _ \\/ ___/ | | (_| \\__ \\ < | |/ |/ / __/ /_/ /| |/ / __/ / |_|\\__,_|___/_|\\_\\|__/|__/\\___/\\__,_/ |___/\\___/_/ ========================================================= TaskWeaver β–Ά I am TaskWeaver, an AI assistant. To get started, could you please enter your request? Human β–Ά calculate the variance value of d:/demo_data.csv ╭───< Planner >TaskWeaver] preparing <=οΏ½=> β”œβ”€β–Ί [init_plan] β”‚ 1. load the data file β”‚ 2. confirm the columns to calculate variance β”‚ 3. calculate the variance value for the selected columns β”‚ 4. report the result to the user β”œβ”€β–Ί [plan] β–Ά [Planner][plan] 1. instruct CodeInterpreter to load the data file 2. confirm the columns to calcu <=οΏ½=> β”‚ 1. instruct CodeInterpreter to load the data file β”‚ 2. confirm the columns to calculate variance β”œβ”€β–Ί [current_plan_step] 1. instruct CodeInterpreter to load the data file load the data file <=οΏ½=> β”œβ”€β”€β— Please load the data file d:/demo_data.csva file d:/demo_dat <=οΏ½=> ╰──● sending message to CodeInterpreter ╭───< CodeInterpreter > β”œβ”€β–Ί [thought] ProgramApe will generate a code snippet to load the data file d:/demo_data.csv using pandas.demo_data.csv using panda <=οΏ½=> β”œβ”€β–Ί [python]β–Ά [CodeInterpreter][python] import pandas as pd data_file_path = \"d:/demo_data.csv\" df = pd.read_csv(data_fil <=οΏ½=> β”‚ import pandas as pd β”‚ β”‚ data_file_path = \"d:/demo_data.csv\" β”‚ df = pd.read_csv(data_file_path) β”‚ df β”œβ”€β–Ί [verification] NONE β”œβ”€β–Ί [code_error] No code verification is performed. β”œβ”€β–Ί [execution_status] SUCCESS] executing code <=οΏ½=> β”œβ”€β–Ί [execution_result] β”‚ The execution of the generated python code above has succeeded β”‚ β”‚ The result of above Python code after execution is: β”‚ TimeBucket Count β”‚ 0 2023-02-01T00:00:00Z 67814 β”‚ 1 2023-02-01T04:00:00Z 84569 β”‚ .. ... ... β”‚ 718 2023-05-31T16:00:00Z 89912 β”‚ 719 2023-05-31T20:00:00Z 87249 β”‚ β”‚ [720 rows x 2 columns] β”œβ”€β–Ί [artifact_paths] [] β”œβ”€β”€β— The following python code has been executed: β”‚ ```python β”‚ import pandas as pd β”‚ β”‚ data_file_path = \"d:/demo_data.csv\" β”‚ df = pd.read_csv(data_file_path) β”‚ df β”‚ ``` β”‚ β”‚ β”‚ The execution of the generated python code above has succeeded β”‚ β”‚ The result of above Python code after execution is: β”‚ TimeBucket Count β”‚ 0 2023-02-01T00:00:00Z 67814 β”‚ 1 2023-02-01T04:00:00Z 84569 β”‚ .. ... ... β”‚ 718 2023-05-31T16:00:00Z 89912 β”‚ 719 2023-05-31T20:00:00Z 87249 β”‚ β”‚ [720 rows x 2 columns] ╰──● sending message to Planner ╭───< Planner >CodeInterpreter] finished <=οΏ½=> β”œβ”€β–Ί [init_plan] β”‚ 1. load the data file β”‚ 2. confirm the columns to calculate variance β”‚ 3. calculate the variance value for the selected columns β”‚ 4. report the result to the user β”œβ”€β–Ί [plan] β–Ά [Planner][plan] 1. instruct CodeInterpreter to load the data file 2. confirm the columns to calculate var <=οΏ½=> β”‚ 1. instruct CodeInterpreter to load the data file β”‚ 2. confirm the columns to calculate variance β”œβ”€β–Ί [current_plan_step] 2. confirm the columns to calculate variance calculate varia <=οΏ½=> β”œβ”€β”€β— The data file d:/demo_data.csv is loaded. The columns in the data are: β”‚ β”‚ 1. TimeBucket β”‚ 2. Count β”‚ β”‚ Please specify which column(s) you would like to calculate the variance for. ╰──● sending message to User TaskWeaver β–Ά The data file d:/demo_data.csv is loaded. The columns in the data are: 1. TimeBucket 2. Count Please specify which column(s) you would like to calculate the variance for. Human β–Ά Count ╭───< Planner >TaskWeaver] preparing <=οΏ½=> β”œβ”€β–Ί [init_plan] β”‚ 1. load the data file β”‚ 2. confirm the columns to calculate variance β”‚ 3. calculate the variance value for the selected columns β”‚ 4. report the result to the user β”œβ”€β–Ί [plan] β”‚ 1. instruct CodeInterpreter to load the data file β”‚ 2. confirm the columns to calculate variance β”‚ 3. instruct CodeInterpreter to calculate the variance value for the Count column β”‚ 4. report the result to the user β”œβ”€β–Ί [current_plan_step] 3. instruct CodeInterpreter to calculate the variance value for the Count columnor the Count col <=οΏ½=> β”œβ”€β”€β— Please calculate the variance value for the Count column in the loaded datalumn in the loaded <=οΏ½=> ╰──● sending message to CodeInterpreter ╭───< CodeInterpreter > β”œβ”€β–Ί [thought] ProgramApe will generate a code snippet to calculate the variance value for the Count column in the loaded data. column in the loaded dat <=οΏ½=> β”œβ”€β–Ί [python]β–Ά [CodeInterpreter][python] variance_count = df['Count'].var() varia <=οΏ½=> β”‚ variance_count = df['Count'].var() β”‚ variance_count β”œβ”€β–Ί [verification] NONE β”œβ”€β–Ί [code_error] No code verification is performed. β”œβ”€β–Ί [execution_status] SUCCESS] executing code <=οΏ½=> β”œβ”€β–Ί [execution_result] β”‚ The execution of the generated python code above has succeeded β”‚ β”‚ The result of above Python code after execution is: β”‚ 263514550.45479834 β”œβ”€β–Ί [artifact_paths] [] β”œβ”€β”€β— The following python code has been executed: β”‚ ```python β”‚ variance_count = df['Count'].var() β”‚ variance_count β”‚ ``` β”‚ β”‚ β”‚ The execution of the generated python code above has succeeded β”‚ β”‚ The result of above Python code after execution is: β”‚ 263514550.45479834 ╰──● sending message to Planner ╭───< Planner >CodeInterpreter] finished <=οΏ½=> β”œβ”€β–Ί [init_plan] β”‚ 1. load the data file β”‚ 2. confirm the columns to calculate variance β”‚ 3. calculate the variance value for the selected columns β”‚ 4. report the result to the user β”œβ”€β–Ί [plan] β”‚ 1. instruct CodeInterpreter to load the data file β”‚ 2. confirm the columns to calculate variance β”‚ 3. instruct CodeInterpreter to calculate the variance value for the Count column β”‚ 4. report the result to the user β”œβ”€β–Ί [current_plan_step] 4. report the result to the user result to the u <=οΏ½=> β”œβ”€β”€β— The variance value for the Count column in the data file d:/demo_data.csv is 263,514,550.45a.csv is 263,51 <=οΏ½=> ╰──● sending message to User TaskWeaver β–Ά The variance value for the Count column in the data file d:/demo_data.csv is 263,514,550.45","s":"A walk-through example","u":"/TaskWeaver/docs/customization/experience","h":"#a-walk-through-example","p":98},{"i":107,"t":"3 items","s":"πŸ—ƒοΈ Plugin","u":"/TaskWeaver/docs/customization/","h":"","p":105},{"i":109,"t":"1 items","s":"πŸ—ƒοΈ Example","u":"/TaskWeaver/docs/customization/","h":"","p":105},{"i":111,"t":"1 items","s":"πŸ—ƒοΈ Experience","u":"/TaskWeaver/docs/customization/","h":"","p":105},{"i":114,"t":"Using LLMs from OpenAI","s":"πŸ“„οΈ OpenAI","u":"/TaskWeaver/docs/llms/","h":"","p":112},{"i":116,"t":"Using LLMs from OpenAI/AOAI","s":"πŸ“„οΈ Azure OpenAI","u":"/TaskWeaver/docs/llms/","h":"","p":112},{"i":118,"t":"Using LLMs from LiteLLM","s":"πŸ“„οΈ LiteLLM","u":"/TaskWeaver/docs/llms/","h":"","p":112},{"i":120,"t":"1. Go to Ollama and follow the instructions to serve a LLM model on your local environment.","s":"πŸ“„οΈ Ollama","u":"/TaskWeaver/docs/llms/","h":"","p":112},{"i":122,"t":"1. Create an account on Google AI and get your API key.","s":"πŸ“„οΈ Gemini","u":"/TaskWeaver/docs/llms/","h":"","p":112},{"i":124,"t":"1. QWen (Tongyi Qianwen) is a LLM developed by Alibaba. Go to QWen and register an account and get the API key. More details can be found here (in Chinese).","s":"πŸ“„οΈ QWen","u":"/TaskWeaver/docs/llms/","h":"","p":112},{"i":126,"t":"1. GLM (ChatGLM) is a LLM developed by Zhipu AI and Tsinghua KEG. Go to ZhipuAI and register an account and get the API key. More details can be found here.","s":"πŸ“„οΈ GLM","u":"/TaskWeaver/docs/llms/","h":"","p":112},{"i":128,"t":"We welcome developers to use your customized LLM API in TaskWeaver.","s":"πŸ“„οΈ Customized LLM API","u":"/TaskWeaver/docs/llms/","h":"","p":112},{"i":130,"t":"In some cases, you may want to use different LLMs for different components.","s":"πŸ“„οΈ Multi LLM APIs","u":"/TaskWeaver/docs/llms/","h":"","p":112},{"i":132,"t":"Create an account on Azure OpenAI and get your API key. Add the following to your taskweaver_config.json file: { \"llm.api_base\":\"YOUR_AOAI_ENDPOINT\", \"llm.api_key\":\"YOUR_API_KEY\", \"llm.api_type\":\"azure\", \"llm.auth_mode\":\"api-key\", \"llm.model\":\"gpt-4-1106-preview\", # this is known as deployment_name in Azure OpenAI \"llm.response_format\": \"json_object\" } info For model versions or after 1106, llm.response_format can be set to json_object. However, for the earlier models, which do not support JSON response explicitly, llm.response_format should be set to null. Start TaskWeaver and chat with TaskWeaver. You can refer to the Quick Start for more details.","s":"Azure OpenAI","u":"/TaskWeaver/docs/llms/aoai","h":"","p":131},{"i":134,"t":"We welcome developers to use your customized LLM API in TaskWeaver. In this tutorial, we will show you how to contribute your LLM API to TaskWeaver. Create a new Python script .py in the taskweaver/llm folder. Import the CompletionService, LLMServiceConfig and EmbeddingService from taskweaver.llm.base and other necessary libraries. from injector import inject from taskweaver.llm.base import CompletionService, EmbeddingService, LLMServiceConfig from taskweaver.llm.util import ChatMessageType ... Create a new class YourLLMServiceConfig that inherits from LLMServiceConfig and implements the _configure method. In this method, you can set the name, API key, model name, backup model name, and embedding model name of your LLM. class YourLLMServiceConfig(LLMServiceConfig): def _configure(self) -> None: self._set_name(\"your_llm_name\") shared_api_key = self.llm_module_config.api_key self.api_key = self._get_str( \"api_key\", shared_api_key, ) shared_model = self.llm_module_config.model self.model = self._get_str( \"model\", shared_model if shared_model is not None else \"your_llm_model_name\", ) shared_backup_model = self.llm_module_config.backup_model self.backup_model = self._get_str( \"backup_model\", shared_backup_model if shared_backup_model is not None else self.model, ) shared_embedding_model = self.llm_module_config.embedding_model self.embedding_model = self._get_str( \"embedding_model\", shared_embedding_model if shared_embedding_model is not None else self.model, ) Create a new class YourLLMService that inherits from CompletionService and EmbeddingService and implements the chat_completion and get_embeddings methods. class YourLLMService(CompletionService, EmbeddingService): @inject def __init__(self, config: YourLLMServiceConfig): self.config = config pass def chat_completion( self, messages: List[ChatMessageType], stream: bool = True, temperature: Optional[float] = None, max_tokens: Optional[int] = None, top_p: Optional[float] = None, stop: Optional[List[str]] = None, **kwargs: Any, ) -> Generator[ChatMessageType, None, None]: pass def get_embeddings(self, strings: List[str]) -> List[List[float]]: pass Note: We set stream mode by default in chat_completion. You need to use self.config to get the configuration variables of your LLM API (e.g., api key/model name) in YourLLMService class. The get_embeddings method is optional. If you need to import other libraries for your LLM API, please import them in __init__ function of YourLLMService class. You can refer to QWen dashscope library import for an example. Register your LLM service in taskweaver/llm/__init__.py by adding your LLM service to the LLMApi __init__ function . ...... from .your_llm_name import YourLLMService # import your LLM service here class LLMApi(object): @inject def __init__(self, config: LLMModuleConfig, injector: Injector) -> None: self.config = config self.injector = injector if self.config.api_type in [\"openai\", \"azure\", \"azure_ad\"]: self._set_completion_service(OpenAIService) ...... elif self.config.api_type == \"your_llm_name\": self._set_completion_service(YourLLMService) # register your LLM service here else: raise ValueError(f\"API type {self.config.api_type} is not supported\") if self.config.embedding_api_type in [\"openai\", \"azure\", \"azure_ad\"]: self._set_embedding_service(OpenAIService) ...... elif self.config.embedding_api_type == \"azure_ml\": self.embedding_service = PlaceholderEmbeddingService( \"Azure ML does not support embeddings yet. Please configure a different embedding API.\", ) # register your embedding service here, if do not have embedding service, please use `PlaceholderEmbeddingService` referring to the above line elif self.config.embedding_api_type == \"your_llm_name\": self._set_embedding_service(YourLLMService) else: raise ValueError( f\"Embedding API type {self.config.embedding_api_type} is not supported\", ) Configurate taskweaver_config.json file in the project dir based on your implemented LLM API. Run the following command to test your LLM API. If the LLM API is successfully set up, you will see the response from your LLM API. cd ./scripts python llm_api_test.py You also can specify the project dir and query to be sent to your LLM API by using the following command: python llm_api_test.py --project --query \"hello, what can you do?\"","s":"Customized LLM API","u":"/TaskWeaver/docs/llms/customized_llm_api","h":"","p":133},{"i":136,"t":"GLM (ChatGLM) is a LLM developed by Zhipu AI and Tsinghua KEG. Go to ZhipuAI and register an account and get the API key. More details can be found here. Install the required packages dashscope. pip install zhipuai Add the following configuration to taskweaver_config.json: { \"llm.api_type\": \"zhipuai\", \"llm.model\": \"glm-4\", \"llm.embedding_model\": \"embedding-2\", \"llm.embedding_api_type\": \"zhipuai\", \"llm.api_key\": \"YOUR_API_KEY\" } NOTE: llm.model is the model name of zhipuai API. You can find the model name in the GLM model list. Start TaskWeaver and chat with TaskWeaver. You can refer to the Quick Start for more details.","s":"GLM","u":"/TaskWeaver/docs/llms/glm","h":"","p":135},{"i":138,"t":"Create an account on Google AI and get your API key. Add the following content to your taskweaver_config.json file: { \"llm.api_type\": \"google_genai\", \"llm.google_genai.api_key\": \"YOUR_API_KEY\", \"llm.google_genai.model\": \"gemini-pro\" } Start TaskWeaver and chat with TaskWeaver. You can refer to the Quick Start for more details.","s":"Gemini","u":"/TaskWeaver/docs/llms/gemini","h":"","p":137},{"i":140,"t":"info LiteLLM provides a unified interface to call 100+ LLMs using the same Input/Output format, including OpenAI, Huggingface, Anthropic, vLLM, Cohere, and even custom LLM API server. Taking LiteLLM as the bridge, many LLMs can be onboarded to TaskWeaver. Here we use the OpenAI Proxy Server provided by LiteLLM to make configuration. Install LiteLLM Proxy and configure the LLM server by following the instruction here. In general, there are a few steps: Install the package pip install litellm[proxy] Setup the API key and other necessary environment variables which vary by LLM. Taking Cohere as an example, it is required to setup export COHERE_API_KEY=my-api-key. Run LiteLLM proxy server by litellm --model MODEL_NAME --drop_params, for example, in Cohere, the model name can be command-nightly. The drop-params argument is used to ensure the API compatibility. Then, a server will be automatically started on http://0.0.0.0:8000. tip The full list of supported models by LiteLLM can be found in the page. Add the following content to your taskweaver_config.json file: { \"llm.api_base\": \"http://0.0.0.0:8000\", \"llm.api_key\": \"anything\", \"llm.model\": \"gpt-3.5-turbo\" } info llm.api_key and llm.model are mainly used as placeholder for API call, whose actual values are not used. If the configuration does not work, please refer to LiteLLM documents to locally test whether you can send requests to the LLM. Open a new terminal, start TaskWeaver and chat. You can refer to the Quick Start for more details.","s":"LiteLLM","u":"/TaskWeaver/docs/llms/liteLLM","h":"","p":139},{"i":142,"t":"In some cases, you may want to use different LLMs for different components. For example, you may want to use OpenAI GPT-4 for the Planner but use Google gemini-pro for the CodeInterpreter. In this part, we show you how to use different LLMs for different components. If you need only one LLM, you can have only the primary LLM settings in the taskweaver_config.json file. If you need multiple LLMs, you need to have ext_llms.llm_configs in the taskweaver_config.json file to specify the extra LLMs for different components. In the following, we show you how to configure the taskweaver_config.json file to use multiple LLMs. \"llm.api_type\":\"openai\", \"llm.api_base\": \"https://api.openai.com/v1\", \"llm.api_key\": \"YOUR_API_KEY\", \"llm.model\": \"gpt-3.5-turbo-1106\", \"llm.response_format\": \"json_object\" \"ext_llms.llm_configs\": { \"llm_A\": { \"llm.api_type\": \"openai\", \"llm.api_base\": \"https://api.openai.com/v1\", \"llm.api_key\": \"YOUR_API_KEY\", \"llm.model\": \"gpt-4-1106-preview\", \"llm.response_format\": \"json_object\", }, \"llm_B\": { \"llm.api_type\": \"google_genai\", \"llm.api_key\": \"YOUR_API_KEY\", \"llm.model\": \"gemini-pro\", }, }, The primary LLM settings are specified in the llm. fields and it is mandatory. ext_llms.llm_configs is optional and is a dict of extra LLMs for different components. If you do not specify it, only the primary LLM will be used. Specify the LLMs for different components in the taskweaver_config.json. For example, we want to use OpenAI GPT-4 for the Planner and use Google gemini-pro for the CodeInterpreter. \"planner.llm_alias\": \"llm_A\", \"code_generator.llm_alias\": \"llm_B\" tip If you do not specify the LLM for a component, the primary LLM will be used by default. In the above example, GPT-3.5-turbo-1106 will be used for both the Planner and the CodeInterpreter.","s":"Multi LLM APIs","u":"/TaskWeaver/docs/llms/multi-llm","h":"","p":141},{"i":144,"t":"Create an account on OpenAI and get your API key. Add the following to your taskweaver_config.json file: { \"llm.api_type\":\"openai\", \"llm.api_base\": \"https://api.openai.com/v1\", \"llm.api_key\": \"YOUR_API_KEY\", \"llm.model\": \"gpt-4\", \"llm.response_format\": \"json_object\" } tip llm.model is the model name you want to use. You can find the list of models here. info For model versions equal or after 1106, llm.response_format can be set to json_object. However, for the earlier models which do not support JSON response explicitly, llm.response_format should be set to null. Start TaskWeaver and chat with TaskWeaver. You can refer to the Quick Start for more details.","s":"OpenAI","u":"/TaskWeaver/docs/llms/openai","h":"","p":143},{"i":146,"t":"QWen (Tongyi Qianwen) is a LLM developed by Alibaba. Go to QWen and register an account and get the API key. More details can be found here (in Chinese). Install the required packages dashscope. pip install dashscope Add the following configuration to taskweaver_config.json: { \"llm.api_type\": \"qwen\", \"llm.model\": \"qwen-max\", \"llm.api_key\": \"YOUR_API_KEY\" } NOTE: llm.model is the model name of QWen LLM API. You can find the model name in the QWen LLM model list. Start TaskWeaver and chat with TaskWeaver. You can refer to the Quick Start for more details.","s":"QWen","u":"/TaskWeaver/docs/llms/qwen","h":"","p":145},{"i":148,"t":"Go to Ollama and follow the instructions to serve a LLM model on your local environment. We provide a short example to show how to configure the ollama in the following, which might change if ollama makes updates. install ollama and serve LLMs in local ## Install ollama on Linux & WSL2 curl https://ollama.ai/install.sh | sh ## Run the serving ollama serve Open another terminal and run: ollama run llama2:13b tip We recommend deploying the LLM with a parameter scale exceeding 13B for enhanced performance (such as Llama 2 13B). info When serving LLMs via Ollama, it will by default start a server at http://localhost:11434, which will later be used as the API base in taskweaver_config.json. Add following configuration to taskweaver_config.json: { \"llm.api_base\": \"http://localhost:11434\", \"llm.api_key\": \"ARBITRARY_STRING\", \"llm.api_type\": \"ollama\", \"llm.model\": \"llama2:13b\" } NOTE: llm.api_base is the URL started in the Ollama LLM server and llm.model is the model name of Ollama LLM, it should be same as the one you served before. Start TaskWeaver and chat with TaskWeaver. You can refer to the Quick Start for more details.","s":"Ollama","u":"/TaskWeaver/docs/llms/ollama","h":"","p":147},{"i":150,"t":"TaskWeaver is a code-first agent framework for seamlessly planning and executing data analytics tasks. This innovative framework interprets user requests through coded snippets and efficiently coordinates a variety of plugins in the form of functions to execute data analytics or workflow automation tasks. Highlighted Features Rich data structure - TaskWeaver allows you to work with rich data structures in Python, such as Lists, Dictionaries, or Pandas DataFrames, instead of having to restrict to text strings. Customized algorithms - TaskWeaver allows you to encapsulate your own algorithms or functions into plugins (in the form of Python functions), and orchestrate them to achieve complex tasks. Incorporating domain-specific knowledge - TaskWeaver is designed to be easily incorporating domain-specific knowledge, such as the knowledge or experience in task planning and code generation, to improve the reliability of task execution. Stateful conversation - TaskWeaver is designed to support stateful conversation which means you can interact with your data in memory across multiple chat rounds. Code verification - TaskWeaver is designed to verify the generated code before execution. It can detect potential issues in the generated code and automatically fix them. Security consideration - TaskWeaver supports running code inside a sandbox environment to prevent malicious code execution. It also provides a basic session management to keep different users' data separate. Easy to debug - TaskWeaver is designed to be easy to debug. We have detailed logs, metrics, and traces, instrumenting using OpenTelemetry, to help you understand what is going on under the simple chat interface. Easy to extend - TaskWeaver is designed to be easily extended to accomplish more complex tasks. Beyond the plugins, you can create extra roles, define how they interact with the Planner and orchestrate them to achieve more complex tasks. Easy to use - TaskWeaver is designed to be easy to use. We provide a set of sample plugins and a tutorial to help you get started. We also provide a all-in-one Docker image to help you quickly play with TaskWeaver.","s":"Overview","u":"/TaskWeaver/docs/overview","h":"","p":149},{"i":152,"t":"In this tutorial, we will introduce how to develop a strawman plugin in TaskWeaver. This plugin can render a input text in ascii art.","s":"An Example of Developing a New Plugin","u":"/TaskWeaver/docs/plugin/how_to_develop_a_new_plugin","h":"","p":151},{"i":154,"t":"Create a python file named ascii_render.py in the plugins folder. The file name should be the same as the plugin name. The plugin name is defined in the plugin schema. In this example, the plugin name is ascii_render. The following code is the template of the plugin implementation. from taskweaver.plugin import Plugin, register_plugin @register_plugin class PluginTemplate(Plugin): def __call__(self, *args, **kwargs): \"\"\"Implementation Starts\"\"\" result, description = YourImplementation() \"\"\"Implementation Ends\"\"\" # if your want to add artifact from the execution result, uncomment the following code # self.ctx.add_artifact( # name=\"artifact\", # file_name=\"artifact.csv\", # type=\"df\", # val=result, # ) return result, description The typical way of implementing the plugin is to change the code between Implementation Starts and Implementation Ends. Note that the return are two variables result and description. The result stores whatever output required for follow-up processing (e.g., a DataFrame). The description is a string to describe the result. Let's make some changes to the code and the result is as follows: from taskweaver.plugin import Plugin, register_plugin @register_plugin class AsciiRenderPlugin(Plugin): def __call__(self, text: str): import pyfiglet ascii_art_str = pyfiglet.figlet_format(text, font='isometric1') return ascii_art_str Note that this function depends on the package pyfiglet, so we need to install it with pip install pyfiglet.","s":"Implement the python code","u":"/TaskWeaver/docs/plugin/how_to_develop_a_new_plugin","h":"#implement-the-python-code","p":151},{"i":156,"t":"Next, we need to configure the schema so that the LLM can understand the function of the plugin. In the schema, there are several fields that should be filled, including name, enabled, required, description, parameters and returns. Please check Plugin Introduction for more details. Create a yaml file named ascii_render.yaml and copy the following content into it. name: ascii_render enabled: true required: true description: >- This plugin renders the input text into ASCII art form. The input should be a string and the output is also a string in ASCII art. For example, result = ascii_render(text='Hello World'). parameters: - name: text type: str required: true description: >- This is the input text to be rendered into ASCII art form. returns: - name: result type: str description: >- The rendered text in ASCII art.","s":"Configure the schema","u":"/TaskWeaver/docs/plugin/how_to_develop_a_new_plugin","h":"#configure-the-schema","p":151},{"i":158,"t":"After the plugin is implemented and configured, we can call the plugin in the conversation. The full conversation is as follows: ========================================================= _____ _ _ __ |_ _|_ _ ___| | _ | | / /__ ____ __ _____ _____ | |/ _` / __| |/ /| | /| / / _ \\/ __ `/ | / / _ \\/ ___/ | | (_| \\__ \\ < | |/ |/ / __/ /_/ /| |/ / __/ / |_|\\__,_|___/_|\\_\\|__/|__/\\___/\\__,_/ |___/\\___/_/ ========================================================= TaskWeaver: I am TaskWeaver, an AI assistant. To get started, could you please enter your request? Human: render ABC in ascii art >>> [INIT_PLAN] 1. Render the text 'ABC' into ASCII art >>> [PLAN] 1. Instruct CodeInterpreter to render the text 'ABC' into ASCII art >>> [CURRENT_PLAN_STEP] 1. Instruct CodeInterpreter to render the text 'ABC' into ASCII art >>> [SEND_TO] CodeInterpreter >>> [MESSAGE] Please render the text 'ABC' into ASCII art >>> [PLANNER->CODEINTERPRETER] Please render the text 'ABC' into ASCII art >>> [PYTHON] # Call the plugin function to render the text 'ABC' into ASCII art ascii_art_result = ascii_render(\"ABC\") ascii_art_result >>> [VERIFICATION] NONE >>> [STATUS] SUCCESS >>> [RESULT] The execution of the generated python code above has succeeded The result of above Python code after execution is: ___ ___ ___ /\\ \\ /\\ \\ /\\ \\ /::\\ \\ /::\\ \\ /::\\ \\ /:/\\:\\ \\ /:/\\:\\ \\ /:/\\:\\ \\ /::\\~\\:\\ \\ /::\\~\\:\\__\\ /:/ \\:\\ \\ /:/\\:\\ \\:\\__\\ /:/\\:\\ \\:|__| /:/__/ \\:\\__\\ \\/__\\:\\/:/ / \\:\\~\\:\\/:/ / \\:\\ \\ \\/__/ \\::/ / \\:\\ \\::/ / \\:\\ \\ /:/ / \\:\\/:/ / \\:\\ \\ /:/ / \\::/__/ \\:\\__\\ \\/__/ ~~ \\/__/ >>> [CODEINTERPRETER->PLANNER] The following python code has been executed: # Call the plugin function to render the text 'ABC' into ASCII art ascii_art_result = ascii_render(\"ABC\") ascii_art_result The execution of the generated python code above has succeeded The result of above Python code after execution is: ___ ___ ___ /\\ \\ /\\ \\ /\\ \\ /::\\ \\ /::\\ \\ /::\\ \\ /:/\\:\\ \\ /:/\\:\\ \\ /:/\\:\\ \\ /::\\~\\:\\ \\ /::\\~\\:\\__\\ /:/ \\:\\ \\ /:/\\:\\ \\:\\__\\ /:/\\:\\ \\:|__| /:/__/ \\:\\__\\ \\/__\\:\\/:/ / \\:\\~\\:\\/:/ / \\:\\ \\ \\/__/ \\::/ / \\:\\ \\::/ / \\:\\ \\ /:/ / \\:\\/:/ / \\:\\ \\ /:/ / \\::/__/ \\:\\__\\ \\/__/ ~~ \\/__/ >>> [INIT_PLAN] 1. Render the text 'ABC' into ASCII art >>> [PLAN] 1. Instruct CodeInterpreter to render the text 'ABC' into ASCII art >>> [CURRENT_PLAN_STEP] Report the ASCII art to the user >>> [SEND_TO] User >>> [MESSAGE] Here is the text 'ABC' rendered in ASCII art: ___ ___ ___ /\\ \\ /\\ \\ /\\ \\ /::\\ \\ /::\\ \\ /::\\ \\ /:/\\:\\ \\ /:/\\:\\ \\ /:/\\:\\ \\ /::\\~\\:\\ \\ /::\\~\\:\\__\\ /:/ \\:\\ \\ /:/\\:\\ \\:\\__\\ /:/\\:\\ \\:|__| /:/__/ \\:\\__\\ \\/__\\:\\/:/ / \\:\\~\\:\\/:/ / \\:\\ \\ \\/__/ \\::/ / \\:\\ \\::/ / \\:\\ \\ /:/ / \\:\\/:/ / \\:\\ \\ /:/ / \\::/__/ \\:\\__\\ \\/__/ ~~ \\/__/ >>> [PLANNER->USER] Here is the text 'ABC' rendered in ASCII art: ___ ___ ___ /\\ \\ /\\ \\ /\\ \\ /::\\ \\ /::\\ \\ /::\\ \\ /:/\\:\\ \\ /:/\\:\\ \\ /:/\\:\\ \\ /::\\~\\:\\ \\ /::\\~\\:\\__\\ /:/ \\:\\ \\ /:/\\:\\ \\:\\__\\ /:/\\:\\ \\:|__| /:/__/ \\:\\__\\ \\/__\\:\\/:/ / \\:\\~\\:\\/:/ / \\:\\ \\ \\/__/ \\::/ / \\:\\ \\::/ / \\:\\ \\ /:/ / \\:\\/:/ / \\:\\ \\ /:/ / \\::/__/ \\:\\__\\ \\/__/ ~~ \\/__/ TaskWeaver: Here is the text 'ABC' rendered in ASCII art: ___ ___ ___ /\\ \\ /\\ \\ /\\ \\ /::\\ \\ /::\\ \\ /::\\ \\ /:/\\:\\ \\ /:/\\:\\ \\ /:/\\:\\ \\ /::\\~\\:\\ \\ /::\\~\\:\\__\\ /:/ \\:\\ \\ /:/\\:\\ \\:\\__\\ /:/\\:\\ \\:|__| /:/__/ \\:\\__\\ \\/__\\:\\/:/ / \\:\\~\\:\\/:/ / \\:\\ \\ \\/__/ \\::/ / \\:\\ \\::/ / \\:\\ \\ /:/ / \\:\\/:/ / \\:\\ \\ /:/ / \\::/__/ \\:\\__\\ \\/__/ ~~ \\/__/ info Check the FAQs if you have any issues in developing a plugin before submitting an issue on GitHub.","s":"Call the plugin","u":"/TaskWeaver/docs/plugin/how_to_develop_a_new_plugin","h":"#call-the-plugin","p":151},{"i":161,"t":"The plugin-only mode is a restricted mode of TaskWeaver that only allows you to use plugins. Compared to the full mode, the plugin-only mode has the following restrictions: The generated code only contains the calls to the plugins. For example, the following code only calls the ascii_render plugin and does not contain any \"free-form\" code. r1=ascii_render(**{\"text\":\"Hello\"}) r1 Only the plugins with plugin_only: true in the yaml file will be loaded. For example, the following plugin will be loaded in the plugin-only mode: name: ascii_render plugin_only: true ... If this field is not specified, the default value is false. For plugins in the plugin-only mode, the argument type can only be str, int, boolean, or float. Other types such as DataFrame are not allowed. Essentially, we consider these plugins only produce \"text-like\" output that can be directly consumed by the LLM. To support the plugin-only mode, we developed a new role called PluginOrchestrator. To enable the plugin-only mode, you can add the configuration \"session.roles\": [\"planner\", \"code_interpreter_plugin_only\"] in the project configuration file taskweaver_config.json. info Although the plugin-only mode is restricted, it is still useful in some scenarios. For example, you may want to use TaskWeaver to only generate the code to call a certain plugin, and you want to get the response from the plugin directly, without generating any other code for safety reasons.","s":"What is the plugin-only mode?","u":"/TaskWeaver/docs/plugin/plugin_only","h":"#what-is-the-plugin-only-mode","p":159},{"i":163,"t":"The plugin-only mode is implemented based on the function calling mode of LLMs. In this mode, the LLM is trained to generate a JSON object that contains the function name and the arguments. For example, the following JSON object is generated by the LLM: { \"function\": \"ascii_render\", \"arguments\": { \"text\": \"Hello\" } } With this JSON object, we assemble the code to call the plugin: r1=ascii_render(**{\"text\":\"Hello\"}) r1 Then, we execute the code and get the response from the plugin. Therefore, the code is not directly generated by the LLM in the plugin-only mode.","s":"How is the plugin-only mode implemented?","u":"/TaskWeaver/docs/plugin/plugin_only","h":"#how-is-the-plugin-only-mode-implemented","p":159},{"i":165,"t":"Currently, the plugin-only mode is only supported by OpenAI models ZhipuAI models Likely other models that are compatible with the OpenAI models will also support the plugin-only mode.","s":"Which models support the plugin-only mode?","u":"/TaskWeaver/docs/plugin/plugin_only","h":"#which-models-support-the-plugin-only-mode","p":159},{"i":167,"t":"In practice, you may want to have multiple plugins that share the same implementation. For example, you may want to have two plugins that both pull data from a database, but one pulls data from database A and the other pulls data from database B. In this case, the plugin implementation code is the same, but the plugin configuration is different. It would be cumbersome to copy and paste the same implementation code to two different files. To solve this problem, TaskWeaver allows you to have multiple plugin configurations that share the same implementation. Here is an example of the plugin configuration for the two plugins that pull data from database A and B respectively: The configuration for the plugin that pulls data from database A: name: sql_pull_data_from_A code: sql_pull_data ... description: >- Pull data from a SQL database A. The database A contains information about merchandise sales. examples: |- df, description = sql_pull_data_from_A(\"pull data from time_series table\") parameters: ... returns: ... configurations: ... sqlite_db_path: /path/to/sqlite_A.db The configuration for the plugin that pulls data from database B: name: sql_pull_data_from_B code: sql_pull_data ... description: >- Pull data from a SQL database B. The database B contains information about customer information. examples: |- df, description = sql_pull_data_from_B(\"pull data from time_series table\") parameters: ... returns: ... configurations: ... sqlite_db_path: /path/to/sqlite_B.db Let's discuss the differences between the two configurations. First, you can see that the name field is different, and the names are different from the python file name (without extension) which is sql_pull_data.py. This name is used in CodeInterpreter for code generation. So, you can see that in the examples field, the function name is sql_pull_data_from_A and sql_pull_data_from_B, respectively. Second, you can see that the code field is the same, and the code file name is sql_pull_data.py. This means that the two plugins share the same implementation code. The code field is optional, and if you don't specify it, the plugin name will be used as the code file name without the extension. Third, you can see that the configurations field is different, and the sqlite_db_path is different. This means that the two plugins have different configurations. This is typically the key reason why you want to have multiple plugin configurations that share the same implementation. Finally, you can see that the description field is different, and the descriptions are different. This is important because the Planner and the CodeInterpreter will use the description to make decisions and generate code. The two descriptions should be explicit enough to distinguish the two plugins. Otherwise, the Planner and the CodeInterpreter may not be able to make the right decisions.","s":"Multiple YAML files to one Python implementation","u":"/TaskWeaver/docs/plugin/multi_yaml_single_impl","h":"","p":166},{"i":169,"t":"When you meet the situation where you want to have multiple plugin configurations that share the same implementation, you can use the code field to specify the code file name, and use the configurations field to specify the configurations. The name field is used to distinguish the plugins, and the description field is used to help the Planner and the CodeInterpreter make right decisions.","s":"Conclusion","u":"/TaskWeaver/docs/plugin/multi_yaml_single_impl","h":"#conclusion","p":166},{"i":171,"t":"Plugins are the units that could be orchestrated by TaskWeaver's CodeInterpreter. One could view the plugins as tools that the LLM can utilize to accomplish certain tasks. In TaskWeaver, each plugin is represented as a Python function that can be called within the generated code snippet. One concrete example would be pulling data from database and apply anomaly detection. The generated code (simplified) looks like follows: df, data_description = sql_pull_data(query=\"pull data from time_series table\") anomaly_df, anomaly_description = anomaly_detection(df, time_col_name=\"ts\", value_col_name=\"val\") The generated code snippet above calls two plugins: sql_pull_data and anomaly_detection. The sql_pull_data plugin pulls data from a database, and the anomaly_detection plugin detects anomalies in the data.","s":"Plugin Introduction","u":"/TaskWeaver/docs/plugin/plugin_intro","h":"","p":170},{"i":173,"t":"A plugin involves two files: Plugin Implementation: a Python file that defines the plugin Plugin Schema: a file in yaml that defines the schema of the plugin","s":"Plugin Structure","u":"/TaskWeaver/docs/plugin/plugin_intro","h":"#plugin-structure","p":170},{"i":175,"t":"The plugin function needs to be implemented in Python. To be coordinated with the orchestration by TaskWeaver, a plugin python file consists of two parts: Plugin function implementation code TaskWeaver plugin decorator Here we exhibit an example of the anomaly detection plugin as the following code: import pandas as pd from pandas.api.types import is_numeric_dtype from taskweaver.plugin import Plugin, register_plugin @register_plugin class AnomalyDetectionPlugin(Plugin): def __call__(self, df: pd.DataFrame, time_col_name: str, value_col_name: str): \"\"\" anomaly_detection function identifies anomalies from an input dataframe of time series. It will add a new column \"Is_Anomaly\", where each entry will be marked with \"True\" if the value is an anomaly or \"False\" otherwise. :param df: the input data, must be a dataframe :param time_col_name: name of the column that contains the datetime :param value_col_name: name of the column that contains the numeric values. :return df: a new df that adds an additional \"Is_Anomaly\" column based on the input df. :return description: the description about the anomaly detection results. \"\"\" try: df[time_col_name] = pd.to_datetime(df[time_col_name]) except Exception: print(\"Time column is not datetime\") return if not is_numeric_dtype(df[value_col_name]): try: df[value_col_name] = df[value_col_name].astype(float) except ValueError: print(\"Value column is not numeric\") return mean, std = df[value_col_name].mean(), df[value_col_name].std() cutoff = std * 3 lower, upper = mean - cutoff, mean + cutoff df[\"Is_Anomaly\"] = df[value_col_name].apply(lambda x: x < lower or x > upper) anomaly_count = df[\"Is_Anomaly\"].sum() description = \"There are {} anomalies in the time series data\".format(anomaly_count) self.ctx.add_artifact( name=\"anomaly_detection_results\", # a brief description of the artifact file_name=\"anomaly_detection_results.csv\", # artifact file name type=\"df\", # artifact data type, support chart/df/file/txt/svg val=df, # variable to be dumped ) return df, description You need to go through the following steps to register a plugin: import the TaskWeaver plugin decorator from taskWeaver.plugin import Plugin, register_plugin create your plugin class inherited from Plugin parent class (e.g., AnomalyDetectionPlugin(Plugin)), which is decorated by @register_plugin implement your plugin function in __call__ method of the plugin class. We provide an example process of developing a new plugin in this tutorial. tip A good practice in a plugin implementation is to return a description of the result in natural language. As LLMs only understand natural language, it is important to let the model understand what the execution result is. In the example implementation above, the description says how many anomalies are detected. In other cases such as loading a csv file, a good description could be showing the schema of the loaded data. This description can be used by the LLM to plan the next steps.","s":"Plugin Implementation","u":"/TaskWeaver/docs/plugin/plugin_intro","h":"#plugin-implementation","p":170},{"i":177,"t":"If the functionality of your plugin depends on additional libraries or packages, it is essential to ensure that they are installed before proceeding. If you wish to persist intermediate results, such as data, figures, or prompts, in your plugin implementation, TaskWeaver provides an add_artifact API that allows you to store these results in the workspace. In the example we provide, if you have performed anomaly detection and obtained results in the form of a CSV file, you can utilize the add_artifact API to save this file as an artifact. The artifacts are stored in the project/workspace/session_id/cwd folder in the project directory. self.ctx.add_artifact( name=\"anomaly_detection_results\", # a brief description of the artifact file_name=\"anomaly_detection_results.csv\", # artifact file name type=\"df\", # artifact data type, support chart/df/file/txt/svg val=df, # variable to be dumped )","s":"Important Notes","u":"/TaskWeaver/docs/plugin/plugin_intro","h":"#important-notes","p":170},{"i":179,"t":"The plugin schema is composed of several parts: *name: The main function name of the Python code. enabled: determine whether the plugin is enabled for selection during conversations. The default value is true. plugin_only: determine if this plugin is enabled under the plugin-only mode. The default value is false. code: the code file name of the plugin. The default value is the same as the plugin name. *descriptions: A brief description that introduces the plugin function. *parameters: This section lists all the input parameter information. It includes the parameter's name, type, whether it is required or optional, and a description providing more details about the parameter. *returns: This section lists all the return value information. It includes the return value's name, type, and description that provides information about the value that is returned by the function. configurations: the configuration parameters for the plugin. The default value is an empty dictionary. tip The addition of any extra fields or missing of mandatory fields (marked by * above) would result in a validation failure within the plugin schema. The plugin schema is required to be written in YAML format. Here is the plugin schema example of the above anomaly detection plugin: name: anomaly_detection enabled: true plugin_only: false required: false description: >- anomaly_detection function identifies anomalies from an input DataFrame of time series. It will add a new column \"Is_Anomaly\", where each entry will be marked with \"True\" if the value is an anomaly or \"False\" otherwise. parameters: - name: df type: DataFrame required: true description: >- the input data from which we can identify the anomalies with the 3-sigma algorithm. - name: time_col_name type: str required: true description: name of the column that contains the datetime - name: value_col_name type: str required: true description: name of the column that contains the numeric values. returns: - name: df type: DataFrame description: >- This DataFrame extends the input DataFrame with a newly-added column \"Is_Anomaly\" containing the anomaly detection result. - name: description type: str description: This is a string describing the anomaly detection results. info Without specifying the code field, the plugin schema will use the plugin name as the code file name. For example, the plugin name is anomaly_detection and the code file name is anomaly_detection.py. In cases where the plugin name is not the same as the code file name, you can specify the code name (code file) in the plugin schema to ensure clarity and accuracy. For example, the plugin name is anomaly_detection and the code file name is anomaly_detection_code.py. Then, you can specify the code name in the plugin schema as follows: code: anomaly_detection_code Note that the code file name should be the same as the code name without the .py extension. Refer to Multiple YAML files to one Python implementation for more information on how to use this feature. info When using common code that requires some configuration parameter modifications for different plugins, it is important to specify these configuration parameters in the plugin schema. The configuration parameters are specified in the plugin schema as follows: configurations: key1: value1 key2: value2 These configuration parameters can be accessed in the plugin implementation as follows: self.config.get(\"key1\") self.config.get(\"key2\") info When this plugin is enabled for the plugin-only mode, set the plugin_only field to true. The default value is false. Note that all plugins will be loaded in the non-plugin-only mode which is the default mode. But in the plugin-only mode, only the plugins with plugin_only: true will be loaded.","s":"Plugin Schema","u":"/TaskWeaver/docs/plugin/plugin_intro","h":"#plugin-schema","p":170},{"i":182,"t":"You can install TaskWeaver by running the following command: # [optional] create a conda environment to isolate the dependencies # conda create -n taskweaver python=3.10 # conda activate taskweaver # clone the repository git clone https://github.com/microsoft/TaskWeaver.git cd TaskWeaver # install the requirements pip install -r requirements.txt","s":"Installation","u":"/TaskWeaver/docs/quickstart","h":"#installation","p":180},{"i":184,"t":"TaskWeaver runs as a process, you need to create a project directory to store plugins and configuration files. We provided a sample project directory in the project folder. You can copy the project folder to your workspace. A project directory typically contains the following files and folders: πŸ“¦project ┣ πŸ“œtaskweaver_config.json # the project configuration file for TaskWeaver ┣ πŸ“‚plugins # the folder to store plugins ┣ πŸ“‚planner_examples # the folder to store planner examples ┣ πŸ“‚codeinterpreter_examples # the folder to store code interpreter examples ┣ πŸ“‚logs # the folder to store logs, will be generated after program starts β”— πŸ“‚workspace # the directory stores session data， will be generated after program starts β”— πŸ“‚ session_id ┣ πŸ“‚ces # the folder used by the code execution service ┣ πŸ“‚cwd # the current working directory to run the generated code β”— other session data","s":"Project Directory","u":"/TaskWeaver/docs/quickstart","h":"#project-directory","p":180},{"i":186,"t":"Before running TaskWeaver, you need to provide your OpenAI API key and other necessary information. You can do this by editing the taskweaver_config.json file. If you are using Azure OpenAI, you need to set the following parameters in the taskweaver_config.json file:","s":"OpenAI Configuration","u":"/TaskWeaver/docs/quickstart","h":"#openai-configuration","p":180},{"i":188,"t":"{ \"llm.api_base\": \"https://xxx.openai.azure.com/\", \"llm.api_key\": \"your_api_key\", \"llm.api_type\": \"azure\", \"llm.api_version\": \"the api version\", \"llm.model\": \"the model name, e.g., gpt-4\" # In Azure OpenAI, the model name is the deployment_name }","s":"Azure OpenAI","u":"/TaskWeaver/docs/quickstart","h":"#azure-openai","p":180},{"i":190,"t":"{ \"llm.api_key\": \"the api key\", \"llm.model\": \"the model name, e.g., gpt-4\" } πŸ’‘ Only the latest OpenAI API supports the json_object response format. If you are using an older version of OpenAI API, you need to set the \"llm.response_format\"=null in the taskweaver_config.json file. More configuration options can be found in the configuration documentation.","s":"OpenAI","u":"/TaskWeaver/docs/quickstart","h":"#openai","p":180},{"i":192,"t":"# assume you are in the TaskWeaver folder cloned from the repository python -m taskweaver -p ./project/ # -p is the path to the project directory This will start the TaskWeaver process and you can interact with it through the command line (CLI) interface. If everything goes well, you will see the following prompt: ========================================================= _____ _ _ __ |_ _|_ _ ___| | _ | | / /__ ____ __ _____ _____ | |/ _` / __| |/ /| | /| / / _ \\/ __ `/ | / / _ \\/ ___/ | | (_| \\__ \\ < | |/ |/ / __/ /_/ /| |/ / __/ / |_|\\__,_|___/_|\\_\\|__/|__/\\___/\\__,_/ |___/\\___/_/ ========================================================= TaskWeaver: I am TaskWeaver, an AI assistant. To get started, could you please enter your request? Human: ___ There are other ways to start TaskWeaver: A Chainlit UI interface: TaskWeaver provides an experimental web-based interface to interact with the system. A Library: You can also use TaskWeaver as a library in your Python code. The all-in-one Docker image: We provide a Docker image that contains all the dependencies to run TaskWeaver.","s":"Start TaskWeaver","u":"/TaskWeaver/docs/quickstart","h":"#start-taskweaver","p":180},{"i":194,"t":"In this document, we will show you how to run TaskWeaver using the All-in-One Docker Image. Please note that the All-in-One Docker Image is for development and testing purposes only.","s":"All-in-One Docker Image","u":"/TaskWeaver/docs/usage/docker","h":"","p":193},{"i":196,"t":"You need to have Docker installed on your machine. For Windows and macOS users, you can use Docker Desktop. You can download it from Docker's official website. For Linux users, you can install following the instructions in the Docker's official website. Please find the installation guide for your specific Linux distribution.","s":"Prerequisites","u":"/TaskWeaver/docs/usage/docker","h":"#prerequisites","p":193},{"i":198,"t":"There are two versions of the TaskWeaver All-in-One Docker Image: taskweavercontainers/taskweaver-all-in-one:latest: This version includes the Planner and CodeInterpreter roles only. You can use this container for code generation and execution tasks. taskweavercontainers/taskweaver-all-in-one:latest-ws: This version includes an additional WebSearch role which can search the web for information. As it requires dependencies to the sentence-transformers library, it is larger. Open a terminal and run the following command to obtain the TaskWeaver image: docker pull taskweavercontainers/taskweaver-all-in-one:latest # if you want to use the version with the WebSearch role # docker pull taskweavercontainers/taskweaver-all-in-one:latest-ws Once the image is pulled, you can run the TaskWeaver container using the following command: docker run -it -e LLM_API_BASE= \\ -e LLM_API_KEY= \\ -e LLM_API_TYPE= \\ -e LLM_MODEL= \\ taskweavercontainers/taskweaver-all-in-one:latest If you want to run TaskWeaver in UI mode, you can use the following command: docker run -it -e LLM_API_BASE= \\ -e LLM_API_KEY= \\ -e LLM_API_TYPE= \\ -e LLM_MODEL= \\ -p 8000:8000 \\ taskweavercontainers/taskweaver-all-in-one:latest /bin/sh -c \"cd playground/UI/ && python -m chainlit run --host 0.0.0.0 --port 8000 app.py\" Then you can access the TaskWeaver Web UI by visiting http://localhost:8000 in your web browser.","s":"Run TaskWeaver using the All-in-One Docker Image","u":"/TaskWeaver/docs/usage/docker","h":"#run-taskweaver-using-the-all-in-one-docker-image","p":193},{"i":200,"t":"You can mount your local project directory to the container. For example, you can use the following command: docker run -it -e LLM_API_BASE= \\ -e LLM_API_KEY= \\ -e LLM_API_TYPE= \\ -e LLM_MODEL= \\ --mount type=bind,source=,target=/app/TaskWeaver/project/ \\ taskweavercontainers/taskweaver-all-in-one:latest Then you can edit the taskweaver_config.json file in your local project directory to configure TaskWeaver. In addition, you also can customize the plugins and examples in your local project directory. The structure of the project directory can be referred to the taskweaver/project directory.","s":"How to run TaskWeaver on your own project directory","u":"/TaskWeaver/docs/usage/docker","h":"#how-to-run-taskweaver-on-your-own-project-directory","p":193},{"i":202,"t":"You can mount your local directory to the container. For example, you can use the following command: docker run -it -e LLM_API_BASE= \\ -e LLM_API_KEY= \\ -e LLM_API_TYPE= \\ -e LLM_MODEL= \\ --mount type=bind,source=,target=/app/TaskWeaver/local/ \\ taskweavercontainers/taskweaver-all-in-one:latest Then you can access your local files in the container by visiting the /app/TaskWeaver/local/ directory. You can load a file under the /app/TaskWeaver/local/ directory in the TaskWeaver CLI with the /load command. For example, you can load a file named example.csv by running the following command: TaskWeaver β–Ά I am TaskWeaver, an AI assistant. To get started, could you please enter your request? Human β–Ά /load /app/TaskWeaver/local/example.csv Human β–Ά display the column names of the loaded file","s":"How to access your local files in the container","u":"/TaskWeaver/docs/usage/docker","h":"#how-to-access-your-local-files-in-the-container","p":193},{"i":204,"t":"If you want to use TaskWeaver as a library, you can refer to the following code example: from taskweaver.app.app import TaskWeaverApp # This is the folder that contains the taskweaver_config.json file and not the repo root. Defaults to \"./project/\" app_dir = \"./project/\" app = TaskWeaverApp(app_dir=app_dir) session = app.get_session() user_query = \"hello, what can you do?\" response_round = session.send_message(user_query) print(response_round.to_dict()) Essentially, you need to create a TaskWeaverApp object and then get a session object from it. Each time, you can send a message to TaskWeaver by calling session.send_message(user_query). The return value of session.send_message(user_query) is a Round object, which contains the response from TaskWeaver. A round is a conversation round between the user and TaskWeaver, which contains a list of posts. An example of the Round object is shown below. To better understand the structure, you can refer to the Concepts section. { \"id\": \"round-20231201-043134-218a2681\", \"user_query\": \"hello, what can you do?\", \"state\": \"finished\", \"post_list\": [ { \"id\": \"post-20231201-043134-10eedcca\", \"message\": \"hello, what can you do?\", \"send_from\": \"User\", \"send_to\": \"Planner\", \"attachment_list\": [] }, { \"id\": \"post-20231201-043141-86a2aaff\", \"message\": \"I can help you with various tasks, such as counting rows in a data file, detecting anomalies in a dataset, searching for products on Klarna, summarizing research papers, and pulling data from a SQL database. Please provide more information about the task you want to accomplish, and I'll guide you through the process.\", \"send_from\": \"Planner\", \"send_to\": \"User\", \"attachment_list\": [ { \"id\": \"atta-20231201-043141-6bc4da86\", \"type\": \"init_plan\", \"content\": \"1. list the available functions\" }, { \"id\": \"atta-20231201-043141-6f29f6c9\", \"type\": \"plan\", \"content\": \"1. list the available functions\" }, { \"id\": \"atta-20231201-043141-76186c7a\", \"type\": \"current_plan_step\", \"content\": \"1. list the available functions\" } ] } ] } tip If you need to see the intermediate states of the conversation, you need to implement a SessionEventHandler class and pass it at calling session.send_message(user_query, event_handler=your_event_handler). Find more information about the event handler in this section.","s":"Library","u":"/TaskWeaver/docs/usage/library","h":"","p":203},{"i":206,"t":"Please note that this Web UI is for development and testing purposes only. Follow the instruction in Quick Start to clone the repository and fill in the necessary configurations. Install the chainlit package by pip install -U chainlit if you don't have it in your environment. Start the service by running the following command. # assume you are in the TaskWeaver folder cd playground/UI/ # make sure you are in playground/UI/ folder chainlit run app.py Open the browser with http://localhost:8000 if it doesn't open automatically. info We now support uploading files using the Web UI. Below are some screenshots of the Web UI:","s":"Web UI","u":"/TaskWeaver/docs/usage/webui","h":"","p":205},{"i":208,"t":"This is the command line interface for TaskWeaver. You can interact with TaskWeaver through this interface. Follow the instruction in Quick Start to clone the repository and fill in the necessary configurations. Run the following command in terminal. # assume you are in the TaskWeaver folder python -m taskweaver -p ./project/ # -p is the path to the project directory This will start the TaskWeaver process, and you can interact with it through the command line interface. If everything goes well, you will see the following prompt: ========================================================= _____ _ _ __ |_ _|_ _ ___| | _ | | / /__ ____ __ _____ _____ | |/ _` / __| |/ /| | /| / / _ \\/ __ `/ | / / _ \\/ ___/ | | (_| \\__ \\ < | |/ |/ / __/ /_/ /| |/ / __/ / |_|\\__,_|___/_|\\_\\|__/|__/\\___/\\__,_/ |___/\\___/_/ ========================================================= TaskWeaver: I am TaskWeaver, an AI assistant. To get started, could you please enter your request? Human: ___ We have provided a set of commands to interact with TaskWeaver. You can type help to see the list of available commands. All commands start with a / character. For example, you can type /help to see the list of available commands. TaskWeaver β–Ά I am TaskWeaver, an AI assistant. To get started, could you please enter your request? Human β–Ά /help TaskWeaver Chat Console ----------------------- /load : load a file /info: print the information of the current session /reset: reset the session /clear: clear the console /exit: exit the chat console /help: print this help message /save: save the chat history of the current session for experience extraction The table of commands supported by TaskWeaver is as follows: Command Description /load Load a file by its absolute path, e.g., /load /home/taskweaver/sample.csv /info Print the session id and the active roles of the current session /reset Reset the current session and start a new session /clear Clear the console content /exit Exit the chat console /help Print the help message /save Save the chat history of the current session for experience extraction tip When TaskWeaver runs generated code, the CWD (current working directory) is set to the project/workspace/session_id/cwd directory. If you need to use relative paths in the generated code, the cwd directory should be the base 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developers may want to verify the generated code before running it. In the following example shown in the figure, the generated code is import os file = open(\"abc.py\", \"r\") code = file.read() exec(code) In this case, the generated code read the content of a file and execute it, which is potentially dangerous. To prevent this, we can use code verification to check the generated code before running it. If the code does not pass the verification, the errors will be reported to the CodeGenerator to re-generate the code.","s":"Why do we need code verification?","u":"/TaskWeaver/docs/advanced/code_verification","h":"#why-do-we-need-code-verification","p":1},{"i":5,"t":"To use code verification, you can add the configuration \"code_interpreter.code_verification_on\": true in the project configuration file taskweaver_config.json. Then, the generated code will be verified before running it. After enabling code verification, you can also specify the code verification rules in the project configuration file. Parameter Description Default code_interpreter.allowed_modules The list of allowed modules to import in code generation. [\"pandas\", \"matplotlib\", \"numpy\", \"sklearn\", \"scipy\", \"seaborn\", \"datetime\", \"typing\"], if the list is empty, no modules would be allowed code_interpreter.blocked_functions The list of functions to block from code generation. [\"__import__\",\"eval\", \"exec\", \"execfile\", \"compile\", \"open\", \"input\", \"raw_input\", \"reload\"] We currently support checking the allowed modules and blocked functions. If a module is in the allowed_modules list, it can be imported in the generated code. If a function is in the blocked_functions list, it cannot be used in the generated code.","s":"How to use code verification?","u":"/TaskWeaver/docs/advanced/code_verification","h":"#how-to-use-code-verification","p":1},{"i":7,"t":"The code verification is implemented in two parts. First, we add instructions to CodeGenerator's prompt, so it generates code following the required rules. For example, if we don't want the generated code to import the os module, we will add an instruction to the prompt to tell CodeGenerator not to import the os module. Second, we conduct a static analysis on the generated code to check if it follows the required rules. The code is parsed into an AST (Abstract Syntax Tree) and we check if the AST only imports the allowed modules, and does not use the blocked functions. The implementation of the static analysis can be found in taskweaver/code_interpreter/code_verification.py.","s":"How is code verification implemented?","u":"/TaskWeaver/docs/advanced/code_verification","h":"#how-is-code-verification-implemented","p":1},{"i":9,"t":"TaskWeaver's CLI-only mode enables users to effortlessly communicate with the Command Line Interface (CLI) using natural language. CodeInterpreter generates CLI commands, such as bash or PowerShell to address the user's needs. This mode allows users to operate your system by simply chatting with the command line in natural language!","s":"CLI Only Mode","u":"/TaskWeaver/docs/advanced/cli_only","h":"","p":8},{"i":12,"t":"Just add the following configuration to the project configuration file taskweaver_config.json: { \"session.roles\": [\"code_interpreter_cli_only\"] } Please refer to the session documentation for more details.","s":"How to enable","u":"/TaskWeaver/docs/advanced/cli_only","h":"#how-to-enable","p":8},{"i":14,"t":"After chatting for a few rounds, the chat history can become quite long, especially when we have code and execution results in it. This can cause the problem of exceeding the context window of the LLMs. To solve the problem, one way is to summarize the chat history a few rounds ago, and only keep the latest rounds of the chat history. Another way is to use a vector database to store the chat history entries, and only retrieve the last few rounds of the relevant part given the current user request. However, in TaskWeaver, code is also part of the chat history. It is not an option to skip some intermediate code and execution results in order to correctly generate the code for the current user request. Therefore, we choose the first way to solve the problem. The following figure shows the idea of chat history summarization where the chat history is divided into two parts: Rounds to compress: this part is summarized and only the summary is kept in the chat history. If the context_summary already exists, a new summary is generated based on the previous summary adding the rounds to be summarized. Rounds to retain: this part is kept in the chat history without summarization. Imagine that, at the beginning, the ConversationSummary is empty. Once the chat history reaches the rounds_to_compress (default 2) rounds plus rounds_to_retain (default 3) rounds, the ConversationSummary is generated based on the rounds_to_compress rounds and the rounds_to_retain rounds are kept in the chat history. After that, there will be only rounds_to_retain rounds in the chat history. The next time the chat history reaches the rounds_to_compress rounds plus rounds_to_retain rounds, the ConversationSummary is generated based on the rounds_to_compress rounds and the previous ConversationSummary. We use these two parameters to control the frequency of the chat history summarization. An example of the chat history summarization in the Code Generator is shown below: { \"ConversationSummary\": \"The user requested the generation of 100 random numbers, which was successfully executed. Then, the user asked to show the top 5 largest numbers from the generated random numbers. The assistant provided a code snippet to sort the generated random numbers in descending order and select the top 5 largest numbers, which was also successfully executed. After that, the user requested to plot the distribution of the 100 numbers, which was successfully executed. The user then asked to count the frequency of numbers in each bin of the histogram and identify the bin with the most numbers for the 0.1 bin width, which was also successfully executed.\", \"Variables\": [ { \"name\": \"random_numbers_100\", \"type\": \"numpy array\", \"description\": \"An array containing 100 random numbers generated using np.random.rand()\" }, { \"name\": \"top_5_largest\", \"type\": \"numpy array\", \"description\": \"An array containing the top 5 largest numbers from the generated random numbers\" } ] } The JSON object has two fields: ConversationSummary: the summary of the chat history. Variables: the variables in the chat history that could be used in the current user request. The chat history summary of the Planner has only the ConversationSummary field. The actual code generated in the summarized rounds is ignored and only the variables are kept in the summary so that the LLM can still refer the these variables in future code generation. One thing to note is that chat history summarization requires call the LLM which incurs additional latency and cost. The prompts for chat history summarization could be found for planner and code generator.","s":"Prompt Compression","u":"/TaskWeaver/docs/advanced/compression","h":"","p":13},{"i":16,"t":"As explained above, there are two parameters in controlling the chat history summarization: round_compressor.rounds_to_compress (default 2) and round_compressor.rounds_to_retain (default 3). To enable the chat history summarization, you need to set planner.prompt_compression and code_generator.prompt_compression to true.","s":"Configurations","u":"/TaskWeaver/docs/advanced/compression","h":"#configurations","p":13},{"i":18,"t":"In TaskWeaver, we provide an auto plugin selection mechanism to dynamically select the best plugin for each user request. It targets to solve the following two problems: An excessive number of plugins may cause confusion for LLM, leading to inaccuracies in generating the correct code. A large number of plugins could lead to increased token usage (potentially exceeding the token limit of LLM) and extended response time.","s":"Auto Plugin Selection","u":"/TaskWeaver/docs/advanced/plugin_selection","h":"","p":17},{"i":20,"t":"Below is the overview workflow of the auto plugin selection mechanism. info Automatic plugin selection mechanism is only activated during the code generation process in the Code Interpreter and does not affect the planning process of the Planner. At the start of TaskWeaver initialization, the automatic plugin selector is activated to generate embedding vectors for all plugins, including their names and descriptions. The embedding vectors are created using the specified embedding model configured in the taskweaver_config.json file. For more information, please refer to the embedding section in documentation. When the Planner sends a request to the Code Interpreter, the auto plugin selection mechanism will be triggered. It will first generate an embedding vector for the request using the same embedding model. Then, it will calculate the cosine similarity between the request embedding vector and the embedding vectors of all plugins. It will select the top-k plugins with the highest cosine similarity scores and load them into the code_generator prompt. Upon completing the code generation, the code_generator employs one or more plugins to produce the desired code. We have established a plugin pool to store the plugins involved in the code generation process while filtering out any unused ones. During the subsequent automatic plugin selection phase, newly chosen plugins are appended to the existing ones.","s":"Auto Plugin Selection Overview","u":"/TaskWeaver/docs/advanced/plugin_selection","h":"#auto-plugin-selection-overview","p":17},{"i":22,"t":"code_generator.enable_auto_plugin_selection: Whether to enable auto plugin selection. The default value is false. code_generator.auto_plugin_selection_topk: The number of auto selected plugins in each round. The default value is 3.","s":"Auto Plugin Selection Configuration","u":"/TaskWeaver/docs/advanced/plugin_selection","h":"#auto-plugin-selection-configuration","p":17},{"i":24,"t":"Before using the auto plugin selection mechanism, we need to run the following command to generate the plugin meta files with embeddings. cd scripts python -m plugin_mgt --refresh After that, you can find the .meta directory is generated in the plugins folder. Then you can start a new TaskWeaver session with the auto plugin selection mechanism enabled. Code Generator will automatically load the plugin meta files with embeddings. 🎈Plugin meta files will be treated as invalid if: The plugin embedding vector is not generated. The plugin is modified. The plugin embedding model is changed. In this case, you cannot start the TaskWeaver and you need to run the above command again to refresh the plugin meta files. ## Auto Plugin Selection Example We show the auto plugin selection mechanism in the following example. First, we start TaskWeaver with the auto plugin selection mechanism enabled. ```bash ========================================================= _____ _ _ __ |_ _|_ _ ___| | _ | | / /__ ____ __ _____ _____ | |/ _` / __| |/ /| | /| / / _ \\/ __ `/ | / / _ \\/ ___/ | | (_| \\__ \\ < | |/ |/ / __/ /_/ /| |/ / __/ / |_|\\__,_|___/_|\\_\\|__/|__/\\___/\\__,_/ |___/\\___/_/ ========================================================= TaskWeaver: I am TaskWeaver, an AI assistant. To get started, could you please enter your request? Human: Then we can check the log file task_weaver.log in the logs folder to see the auto plugin selector is initialized successfully because the Plugin embeddings generated message is printed. 2023-12-18 14:23:44,197 - INFO - Planner initialized successfully 2023-12-18 14:24:10,488 - INFO - Plugin embeddings generated 2023-12-18 14:24:10,490 - INFO - CodeInterpreter initialized successfully. 2023-12-18 14:24:10,490 - INFO - Session 20231218-062343-c18494b1 is initialized We ask TaskWeaver to \"search Xbox price for me\". The Planner instructs the Code Interpreter to search Xbox price. TaskWeaver: I am TaskWeaver, an AI assistant. To get started, could you please enter your request? Human: search xbox price for me >>> [INIT_PLAN] 1. search xbox price 2. report the result to the user >>> [PLAN] 1. instruct CodeInterpreter to search xbox price 2. report the result to the user >>> [CURRENT_PLAN_STEP] 1. instruct CodeInterpreter to search xbox price >>> [SEND_TO] CodeInterpreter >>> [MESSAGE] Please search xbox price >>> [PLANNER->CODEINTERPRETER] Please search xbox price Back to the Code Interpreter, the auto plugin selection mechanism is triggered. We can check the log file task_weaver.log again to see the auto plugin selector selected the top-3 plugins with the highest cosine similarity scores. 023-12-18 14:24:34,513 - INFO - Planner talk to CodeInterpreter: Please search xbox price using klarna_search plugin 2023-12-18 14:24:34,669 - INFO - Selected plugins: ['klarna_search', 'sql_pull_data', 'paper_summary'] 2023-12-18 14:24:34,669 - INFO - Selected plugin pool: ['klarna_search', 'sql_pull_data', 'paper_summary'] Then the Code Interpreter will generate the code using the selected plugins. >>> [THOUGHT] ProgramApe will call the klarna_search plugin function to search for Xbox prices. >>> [PYTHON] search_results, description = klarna_search(query=\"xbox\") search_results, description >>> [VERIFICATION] NONE >>> [STATUS] SUCCESS >>> [RESULT] The execution of the generated python code above has succeeded The result of above Python code after execution is: ( name price url attributes 0 Microsoft Xbox Series X - Black Edition $399.00 https://www.klarna.com/us/shopping/pl/cl52/495... [Release Year:2020, Included Accessories:1 gam... 1 Microsoft Xbox Series S 1TB - Black $349.00 https://www.klarna.com/us/shopping/pl/cl52/320... [Included Accessories:1 gamepad, Media Type:DV... .. ... ... ... ... 3 Xbox Series S – Starter Bundle $239.00 https://www.klarna.com/us/shopping/pl/cl52/320... [Platform:Xbox One] 4 Microsoft Xbox Series X 1TB Console - Diablo I... $385.58 https://www.klarna.com/us/shopping/pl/cl52/320... [Release Year:2023, Included Accessories:1 gam... [5 rows x 4 columns], 'The response is a dataframe with the following columns: name, price, url, attributes. The attributes column is a list of tags. The price is in the format of $xx.xx.') >>> [CODEINTERPRETER->PLANNER] The following python code has been executed: ```python search_results, description = klarna_search(query=\"xbox\") search_results, description ``` The execution of the generated python code above has succeeded The result of above Python code after execution is: ( name price url attributes 0 Microsoft Xbox Series X - Black Edition $399.00 https://www.klarna.com/us/shopping/pl/cl52/495... [Release Year:2020, Included Accessories:1 gam... 1 Microsoft Xbox Series S 1TB - Black $349.00 https://www.klarna.com/us/shopping/pl/cl52/320... [Included Accessories:1 gamepad, Media Type:DV... .. ... ... ... ... 3 Xbox Series S – Starter Bundle $239.00 https://www.klarna.com/us/shopping/pl/cl52/320... [Platform:Xbox One] 4 Microsoft Xbox Series X 1TB Console - Diablo I... $385.58 https://www.klarna.com/us/shopping/pl/cl52/320... [Release Year:2023, Included Accessories:1 gam... [5 rows x 4 columns], 'The response is a dataframe with the following columns: name, price, url, attributes. The attributes column is a list of tags. The price is in the format of $xx.xx.') Finally, the Planner will report the result to the user. >>> [INIT_PLAN] 1. search xbox price using klarna_search plugin 2. report the result to the user >>> [PLAN] 1. instruct CodeInterpreter to search xbox price using klarna_search plugin 2. report the result to the user >>> [CURRENT_PLAN_STEP] 2. report the result to the user >>> [SEND_TO] User >>> [MESSAGE] Here are some Xbox prices I found: 1. Microsoft Xbox Series X - Black Edition: $399.00 2. Microsoft Xbox Series S 1TB - Black: $349.00 3. Xbox Series S – Starter Bundle: $239.00 4. Microsoft Xbox Series X 1TB Console - Diablo Immortal Edition: $385.58 Please note that these prices are subject to change and availability. >>> [PLANNER->USER] Here are some Xbox prices I found: 1. Microsoft Xbox Series X - Black Edition: $399.00 2. Microsoft Xbox Series S 1TB - Black: $349.00 3. Xbox Series S – Starter Bundle: $239.00 4. Microsoft Xbox Series X 1TB Console - Diablo Immortal Edition: $385.58 Please note that these prices are subject to change and availability. TaskWeaver: Here are some Xbox prices I found: 1. Microsoft Xbox Series X - Black Edition: $399.00 2. Microsoft Xbox Series S 1TB - Black: $349.00 3. Xbox Series S – Starter Bundle: $239.00 4. Microsoft Xbox Series X 1TB Console - Diablo Immortal Edition: $385.58 Please note that these prices are subject to change and availability.","s":"Auto Plugin Selection Preparation","u":"/TaskWeaver/docs/advanced/plugin_selection","h":"#auto-plugin-selection-preparation","p":17},{"i":26,"t":"πŸ’‘We have set the container mode as default for code execution, especially when the usage of the agent is open to untrusted users. Refer to Docker Security for better understanding of the security features of Docker. To opt for the local mode, you need to explicitly set the execution_service.kernel_mode parameter in the taskweaver_config.json file to local. TaskWeaver is a code-first agent framework, which means that it always converts the user request into code and executes the code to generate the response. In our current implementation, we use a Jupyter Kernel to execute the code. We choose Jupyter Kernel because it is a well-established tool for interactive computing, and it supports many programming languages.","s":"Code Execution","u":"/TaskWeaver/docs/code_execution","h":"","p":25},{"i":28,"t":"TaskWeaver supports two modes of code execution: local and container. The container mode is the default mode. The key difference between the two modes is that the container mode executes the code inside a Docker container, which provides a more secure environment for code execution, while the local mode executes the code as a subprocess of the TaskWeaver process. As a result, in the local mode, if the user has malicious intent, the user could potentially instruct TaskWeaver to execute harmful code on the host machine. In addition, the LLM could also generate harmful code, leading to potential security risks.","s":"Two Modes of Code Execution","u":"/TaskWeaver/docs/code_execution","h":"#two-modes-of-code-execution","p":25},{"i":30,"t":"To configure the code execution mode, you need to set the execution_service.kernel_mode parameter in the taskweaver_config.json file. The value of the parameter could be local or container. The default value is container. TaskWeaver supports the local mode without any additional setup. However, to use the container mode, there are a few prerequisites: Docker is installed on the host machine. A Docker image is built and available on the host machine for code execution. The execution_service.kernel_mode parameter is set to container in the taskweaver_config.json file. Once the code repository is cloned to your local machine, you can build the Docker image by running the following command in the root directory of the code repository: cd scripts # based on your OS ./build_executor.ps1 # for Windows ./build_executor.sh # for Linux or macOS After the Docker image is built, you can run docker images to check if a Docker image named taskweavercontainers/taskweaver-executor is available. If the prerequisite is met, you can now run TaskWeaver in the container mode. After running TaskWeaver in the container mode, you can check if the container is running by running docker ps. You should see a container of image taskweavercontainers/taskweaver-executor running after executing some code.","s":"How to Configure the Code Execution Mode","u":"/TaskWeaver/docs/code_execution","h":"#how-to-configure-the-code-execution-mode","p":25},{"i":32,"t":"You may want to customize the Docker image for code execution to include additional packages or libraries, especially for your developed plugins. The current Docker image for code execution only includes the dependencies specified in the TaskWeaver/requirements.txt file. To customize the Docker image, you need to modify the Dockerfile at TaskWeaver/docker/ces_container/Dockerfile and rebuild the Docker image. When you open the Dockerfile, you will see the following content, and you can add additional packages or libraries by adding the corresponding RUN command. In this example, we add the sentence-transformers package to the Docker image. FROM python:3.10-slim ... # TODO: Install additional packages for plugins RUN pip install --no-cache-dir --no-warn-script-location --user sentence-transformers ... Then, you need to rebuild the Docker image by running the build_executor.sh script at TaskWeaver/scripts/build_executor.sh or TaskWeaver/scripts/build.ps1 depending on your operating system. cd TaskWeaver/scripts ./build_executor.sh # or ./build_executor.ps1 if you are using Windows If you have successfully rebuilt the Docker image, you can check the new image by running docker images. After building the Docker image, you need to restart the TaskWeaver agent to use the new Docker image.","s":"How to customize the Docker image for code execution","u":"/TaskWeaver/docs/code_execution","h":"#how-to-customize-the-docker-image-for-code-execution","p":25},{"i":34,"t":"The container mode is more secure than the local mode, but it also has some limitations: The startup time of the container mode is longer than the local mode, because it needs to start a Docker container. As the Jupyter Kernel is running inside a Docker container, it has limited access to the host machine. We are mapping the project/workspace/sessions/ directory to the container, so the code executed in the container can access the files in it. One implication of this is that the user cannot ask the agent to load a file from the host machine, because the file is not available in the container. Instead, the user needs to upload the file either using the /upload command in the console or the upload button in the web interface. We have installed required packages in the Docker image to run the Jupyter Kernel. If the user needs to use a package that is not available in the Docker image, the user needs to add the package to the Dockerfile (at TaskWeaver/ces_container/Dockerfile) and rebuild the Docker image.","s":"Limitations of the container Mode","u":"/TaskWeaver/docs/code_execution","h":"#limitations-of-the-container-mode","p":25},{"i":36,"t":"In some cases, the agent developer may want to restrict the Docker container's access to the external network, e.g., the internet. In other words, the agent developer only wants to run the code in the container but does not allow either the plugins or the generated code to access the internet. The following approach is a common way to restrict a Docker container's access to the internet while still allowing inbound connections on specific ports: Creating a Docker network with enable_ip_masquerade set to false: By default, Docker uses IP masquerading (a form of network address translation or NAT) to allow containers to communicate with external networks with the source IP address being the host IP address. When you set enable_ip_masquerade to false for a custom Docker network, you prevent containers on that network from having their IP addresses masqueraded, effectively blocking them from accessing the internet. To create such a network in Docker, you would use the following command: docker network create --opt com.docker.network.bridge.enable_ip_masquerade=false my_non_internet_network Any container connected to my_non_internet_network will not have internet access due to the disabled IP masquerade. Now, you can run docker network inspect my_non_internet_network and you will see an output similar to the following: \"Config\": [ { \"Subnet\": \"172.19.0.0/16\", \"Gateway\": \"172.19.0.1\" } ] This shows the subnet of the docker network, all containers connected to this network will have an IP address in this subnet. Establishing a rule on the host's firewall or using iptables: This step is about setting up rules to block outgoing traffic from the Docker network's subnet to any external addresses. This adds an additional layer of security to ensure that even if IP masquerade is somehow enabled or if the container finds another route, the traffic will still be blocked. On a Linux host using iptables, you might add a rule like this: iptables -I FORWARD -s -j DROP Replace with the actual subnet used by your Docker network. In the previous example, the subnet is 172.19.0.0/16. This rule drops all forwarding traffic from that subnet. On a Windows host, you would create a similar rule within the Windows Firewall to block outgoing traffic from the Docker network's subnet. Keep in mind that this approach can be considered good practice if you understand the implications and have a specific need to isolate your container from the internet. However, it could also complicate network troubleshooting and container communication if not managed properly. Always ensure you are testing these configurations in a safe environment before applying them to production systems.","s":"Restricting External Network Access for Docker Containers","u":"/TaskWeaver/docs/code_execution","h":"#restricting-external-network-access-for-docker-containers","p":25},{"i":38,"t":"The TaskWeaverApp is the main class of the TaskWeaver library. It is the entry point for the library and is used to create and run tasks. A TaskWeaverApp instance is created by calling the TaskWeaverApp.__init__ constructor. class TaskWeaverApp(object): def __init__( self, app_dir: Optional[str] = None, use_local_uri: Optional[bool] = None, config: Optional[Dict[str, Any]] = None, **kwargs: Any, ) -> None: \"\"\" Initialize the TaskWeaver app. :param app_dir: The project directory. :param use_local_uri: Whether to use local URI for artifacts. :param config: The configuration. :param kwargs: The additional arguments. \"\"\" The app_dir parameter is the path to the project directory. The use_local_uri parameter is a boolean that specifies whether to use local URIs for artifacts. If the artifacts are stored locally, the use_local_uri parameter should be set to True. This is useful if TaskWeaver is being used as a remote service and the artifacts are then remote URIs. The config parameter is a dictionary that contains the configuration settings for the app. Any settings in the config parameter will override existing settings in the configuration file or environment variables. The TaskWeaverApp class has the following methods: def get_session( self, session_id: Optional[str] = None, ) -> Session: \"\"\" Get the session. Return a new session if the session ID is not provided. :param session_id: The session ID. :return: The session. \"\"\" The get_session method is used to get a session. If the session_id parameter is not provided, a new session is created. A session is a conversation instance that the user has with the TaskWeaver app. We shall discuss the Session class later. def stop(self) -> None: \"\"\" Stop the TaskWeaver app. This function must be called before the app exits. \"\"\" The stop method is used to stop the TaskWeaver app. This method must be called before the app exits. An example of creating a TaskWeaverApp instance is shown below: from taskweaver.app.app import TaskWeaverApp app = TaskWeaverApp(app_dir=\"path/to/project/\") session = app.get_session() round = session.send_message(\"Hello, how can I help you?\") print(round) app.stop() In this example, a TaskWeaverApp instance is created with the project directory set to \"path/to/project/\". A session is then created using the get_session method, and a message is sent to the session. The response will be the entire conversation round that was generated by the TaskWeaver app. Finally, the stop method is called to stop the TaskWeaver app.","s":"Application","u":"/TaskWeaver/docs/concepts/app","h":"","p":37},{"i":40,"t":"An attachment is a data concept in TaskWeaver which contains additional data other than the text message in the post. @dataclass class Attachment: \"\"\"Attachment is the unified interface for responses attached to the text massage. Args: type: the type of the attachment, which can be \"thought\", \"code\", \"markdown\", or \"execution_result\". content: the content of the response element. id: the unique id of the response element. \"\"\" id: str type: AttachmentType content: str extra: Optional[Any] = None AttachmentType is an Enum class that contains the types of the attachment, which can be \"thought\", \"code\", \"markdown\", or \"execution_result\". Among the types, \"board\" is used to store the information in the board of the round. When the type is set to \"board\", the content will be updated to the key of the board. content is the content of the response element, which can be the code snippet, the markdown text, or the execution result.","s":"Attachment","u":"/TaskWeaver/docs/concepts/attachment","h":"","p":39},{"i":42,"t":"A conversation is a data concept in TaskWeaver which contains a dialog between the user and the TaskWeaver app. Each session has a corresponding conversation. @dataclass class Conversation: \"\"\"A conversation denotes the interaction with the user, which is a collection of rounds. The conversation is also used to construct the Examples. Args: id: the unique id of the conversation. rounds: a list of rounds. plugins: a list of plugins that are used in the conversation. enabled: whether the conversation is enabled, used for Example only. \"\"\" id: str = \"\" rounds: List[Round] = field(default_factory=list) plugins: List[PluginEntry] = field(default_factory=list) roles: List[str] = field(default_factory=list) enabled: bool = True A conversation is a collection of rounds, where each round starts with the user's input and ends with the TaskWeaver app's response to the user. The plugins are the plugins available in the conversation, and the roles are the roles that the conversation is associated with. In TaskWeaver, the conversation is also used to store the Examples. The examples in the project folder are parsed into Conversations in the memory, and then composed into the prompt of the Planner or the CodeInterpreter. The enabled flag is used to control if this conversation is presented in the prompt.","s":"Conversation","u":"/TaskWeaver/docs/concepts/conversation","h":"","p":41},{"i":44,"t":"Plugins are the user-defined functions that extend TaskWeaver CodeInterpreter's capabilities. More details about plugins can be found in the Plugin Introduction. In TaskWeaver, the collection of available plugins are attached to the Conversation object.","s":"Plugin","u":"/TaskWeaver/docs/concepts/plugin","h":"","p":43},{"i":46,"t":"TaskWeaver now supports tracing with OpenTelemetry, which is one of the most popular open-source observability frameworks. This allows you to trace the following: Interactions between roles, i.e., the Planner, the CodeInterpreter, and the Executor. The time consumed by each role and major components of TaskWeaver. The prompts sent to the LLM and the responses received from the LLM. The status of the tasks and the errors encountered. The number of tokens consumed by each role. The following screenshot shows a trace of a simple task: analyzing an uploaded file. From this view, you can see the timeline of the task execution, which breaks majorly into three parts: The planning phase, where the Planner decides the sub-tasks to be executed. The code generation and execution phase, where the CodeGenerator generates the code and the CodeExecutor executes it. The reply phase, where the Planner sends the reply to the user. The bars with a black line represent the critical path of the task execution, which is the longest path through the task execution. This is useful for identifying the bottleneck of the task execution. We can clearly see that, currently, the task execution is dominated by the calls to the LLM. We can click the span (a unit of work in the trace) to see the details of the span, including the logs and the attributes. The screenshot below shows the details of the span of Planner's reply function: From this view, we can see the user query, the prompt sent to the LLM, and the tokens consumed (prompt_size and output_size) by the LLM. We also recorded the generated code, the posts between different roles, etc. in the trace. There are also views of the trace, for example the call graph view, which shows the call hierarchy of the spans. Here is the call graph of the trace:","s":"Telemetry","u":"/TaskWeaver/docs/advanced/telemetry","h":"","p":45},{"i":48,"t":"Tracing is by default disabled. To enable tracing, you need to install packages required by OpenTelemetry. Please check the OpenTelemetry website for the installation guide. It basically requires you to install the opentelemetry-api, opentelemetry-sdk, opentelemetry-exporter-otlp, opentelemetry-instrumentation and tiktoken packages. To count the number of tokens consumed during task execution, you also need to install the tiktoken package. We now only support the tokenizers of the OpenAI models. After installing the packages, you can enable tracing by setting the tracing.enabled=true in the project configuration file. The default tokenizer target model is gpt-4, if you want to use another model, you can set the tracing.tokenizer_target_model in the project configuration file. You can find the available models in the tiktoken code. A typical configuration for tracing in the project configuration file is as follows: { \"tracing.enabled\": true, \"tracing.exporter\": \"otlp\", \"tracing.endpoint\": \"http://127.0.0.1:4317\", \"tracing.tokenizer_target_model\": \"gpt-4\" } Next, we need to set up the infrastructure for tracing. The following diagram shows the architecture of a toy tracing system. It is a toy system and the data is not persisted. In a real-world scenario, you need to set up a more robust system. The instrumentation in the TaskWeaver code will send the traces and metrics to the OpenTelemetry collector. An OpenTelemetry collector is a component that receives traces and metrics from the instrumentation, does some processing, and exports them to another collector or a backend. In our case, we configure the collector to export the traces to a Jaeger backend and the metrics to a Prometheus backend. You can run the following command to set up the infrastructure: cd /TaskWeaver/tracing docker-compose up You shall see a bunch of logs from the containers. Take a look at the logs to see if there are any errors. If no errors are found, you can access the Prometheus frontend at http://localhost:9090 and the Jaeger frontend at http://localhost:16686. In this setup, we assume you are running the containers on the same machine of TaskWeaver. If you are running the containers on different machines, you need to configure the endpoint of the OpenTelemetry collector in the TaskWeaver configuration file. The default endpoint is http://127.0.0.1:4317, you can set the tracing.endpoint in the project configuration file to change the endpoint address.","s":"How to enable tracing","u":"/TaskWeaver/docs/advanced/telemetry","h":"#how-to-enable-tracing","p":45},{"i":50,"t":"Both Jaeger and Prometheus are popular open-source monitoring systems. We have prepared a docker-compose file to set up the infrastructure in /TaskWeaver/tracing_configure/docker-compose.yaml. The content of the file is as follows: version: '3' services: optl-collector: image: otel/opentelemetry-collector:0.96.0 command: [\"--config=/etc/collector-config.yaml\"] volumes: - ./collector-config.yaml:/etc/collector-config.yaml ports: - \"4317:4317\" # Expose the gRPC receiver port for the collector depends_on: - jaeger jaeger: image: jaegertracing/all-in-one:1.54 ports: - \"16686:16686\" # Jaeger UI prometheus: image: prom/prometheus:latest ports: - \"9090:9090\" # Prometheus UI volumes: - ./prometheus-config.yml:/etc/prometheus/prometheus.yml command: [\"--config.file=/etc/prometheus/prometheus.yml\"] depends_on: - optl-collector If you read the file, you can see that we use the otl/opentelemetry-collector image to set up the OpenTelemetry collector, We only expose the gRPC receiver port for the collector, which is 4317. The collector configuration file is collector-config.yaml, which is mounted to the container. The configuration file is as follows: receivers: otlp: protocols: grpc: endpoint: 0.0.0.0:4317 http: endpoint: 0.0.0.0:4318 exporters: debug: verbosity: detailed otlp: endpoint: \"jaeger:4317\" tls: insecure: true prometheus: endpoint: \"0.0.0.0:9464\" service: pipelines: traces: receivers: [otlp] exporters: [otlp] metrics: receivers: [otlp] exporters: [prometheus] logs: receivers: [otlp] exporters: [debug] Because Jaeger is compatible with the OpenTelemetry collector, we can export the traces to Jaeger by setting the otlp exporter. We also export the metrics to Prometheus by setting the prometheus exporter. The prometheus-config.yml file is the configuration file for Prometheus, which is as follows: scrape_configs: - job_name: optl-collector scrape_interval: 5s static_configs: - targets: [\"optl-collector:9464\"] We only scrape the metrics from the OpenTelemetry collector.","s":"Tracing Infrastructure Configuration","u":"/TaskWeaver/docs/advanced/telemetry","h":"#tracing-infrastructure-configuration","p":45},{"i":52,"t":"In the first section, we have explained how to view the traces in the Jaeger frontend. Viewing the metrics in the Prometheus frontend is more complicated as each metric is a time series. A time series is a sequence of data points, which are usually timestamped. OpenTelemetry allows to add attributes to the metrics, so that you can filter the metrics by the attributes. In our current implementation, we only have one metric called prompt_size which records the size of the prompt sent to the LLM. In Prometheus, you should be able to see a time series for the prompt_size metric, namely prompt_size_total. prompt_size_total is the accumulated prompt size of all the traces which increases monotonically. We annotate the traces with the only one attribute called direction, which can be either input or output. They are indicating the input prompt size and the LLM response output size, respectively. You can query the metrics in the Prometheus frontend. The query language is called PromQL which is quite powerful. You can refer to the Prometheus documentation for the details of the query language. The query for the above chart is increase(prompt_size_total[10m]), which means to show the increase of the token consumption in the last 10 minutes sliding window. If you want to use Grafana to visualize the metrics, you can set up a Grafana instance and add Prometheus as the data source. This can be done by appending the following content to the docker-compose.yaml file: grafana: image: grafana/grafana-enterprise:latest ports: - \"3000:3000\" # Grafana UI environment: - GF_SECURITY_ADMIN_PASSWORD=secret # You should change 'secret' to a password of your choosing - GF_USERS_ALLOW_SIGN_UP=false volumes: - grafana_data:/var/lib/grafana depends_on: - prometheus volumes: grafana_data:","s":"How to view the metrics","u":"/TaskWeaver/docs/advanced/telemetry","h":"#how-to-view-the-metrics","p":45},{"i":54,"t":"The instrumentation of TaskWeaver is done by the OpenTelemetry Python SDK. So, if you want to customize the tracing, you need to modify the TaskWeaver code. In TaskWeaver, we add a layer of abstraction to the OpenTelemetry SDK, so that it is easier to hide the details of the OpenTelemetry SDK from the TaskWeaver code. You can find the abstraction layer in the taskweaver.module.tracing module. In the taskweaver.module.tracing module, we define the Tracing class, which is a wrapper of the OpenTelemetry SDK. The Tracing class provides the following methods: set_span_status: Set the status of the span. set_span_attribute: Set the attribute of the span. set_span_exception: Set the exception of the span. add_prompt_size: Add the prompt size to the span. In addition, we define the decorator tracing_decorator (or the non-class version tracing_decorator_non_class) to trace the function calls. When you need to create a context for tracing, you can use with get_tracer().start_as_current_span(\"span_name\") as span: # your code When you need to trace a function, you can use @tracing_decorator def your_function(self, *args, **kwargs): # your code","s":"How to customize tracing","u":"/TaskWeaver/docs/advanced/telemetry","h":"#how-to-customize-tracing","p":45},{"i":56,"t":"The post is a data concept in TaskWeaver which contains a single message in the conversation. @dataclass class Post: \"\"\" A post is the message used to communicate between two roles. It should always have a text_message to denote the string message, while other data formats should be put in the attachment. The role can be either a User, a Planner, or others. Args: id: the unique id of the post. send_from: the role who sends the post. send_to: the role who receives the post. message: the text message in the post. attachment_list: a list of attachments in the post. \"\"\" id: str send_from: RoleName send_to: RoleName message: str attachment_list: List[Attachment] A post is the message used to communicate between two roles. It should always have a text message to denote the string message. In addition, a post has send_from and send_to roles, which are the roles who send and receive the post, respectively. In some cases, the send_from and send_to roles are the same, to denote the self-communication of the role. The attachment_list is a list of attachments in the post. The attachment is used to store various data other than the text message, such as the code snippet or an artifact file path. An attachment may be used only by the role who sends the post, or it may be used by the role who receives the post. In usual cases, the message will present in the prompt as the past chat rounds. However, the message can sometimes be too long and should only be kept in the current round. In the next round, the message will be deleted from the prompt to keep the prompt short. As an example, the CodeInterpreter may generate a long execution result, which only needs to be kept in the current round. In this case, we provide a way of annotating the message (or part of the message) to be kept in the current round only. message = PromptUtil.wrap_text_with_delimiter(message, delimiter=PromptUtil.DELIMITER_TEMPORAL) In this way, the message will be kept in the current round only, and will not be presented in the prompt since the next round.","s":"Post","u":"/TaskWeaver/docs/concepts/post","h":"","p":55},{"i":58,"t":"A project folder is a directory that stores the configuration files, plugins, examples, logs, and workspace data. A TaskWeaverApp instance is associated with a project folder. The project folder is created by the user and contains all the necessary files and folders for the TaskWeaverApp to run. The following is a typical project directory structure: πŸ“¦project ┣ πŸ“œtaskweaver_config.json # the project configuration file for TaskWeaver ┣ πŸ“‚plugins # the folder to store plugins ┣ πŸ“‚planner_examples # the folder to store planner examples ┣ πŸ“‚codeinterpreter_examples # the folder to store code interpreter examples ┣ πŸ“‚logs # the folder to store logs, will be generated after program starts β”— πŸ“‚workspace # the directory stores session data， will be generated after program starts β”— πŸ“‚ session_id ┣ πŸ“‚ces # the folder used by the code execution service ┣ πŸ“‚cwd # the current working directory to run the generated code β”— other session data The workspace folder stores the session data, which includes the code execution service (CES) folder and the current working directory (CWD) folder. Therefore, if the code execution results in any files, they will be stored in the CWD folder. If you are running in local mode and want to load files from your local file system, the CWD is the base directory to load the files from.","s":"Project","u":"/TaskWeaver/docs/concepts/project","h":"","p":57},{"i":60,"t":"A round is a data concept in TaskWeaver which contains a single round of chat between the user and the TaskWeaver app. @dataclass class Round: \"\"\"A round is the basic unit of conversation in the project, which is a collection of posts. Args: id: the unique id of the round. post_list: a list of posts in the round. user_query: the query of the user. state: the state of the round. board: a dict to store the bulletin of the round. \"\"\" id: str user_query: str state: RoundState post_list: List[Post] board: Dict[str, str] = field(default_factory=dict) user_query is the query of the user, and post_list is a list of posts in the round. The state is among \"finished\", \"failed\", \"created\". When the round is created, the state is \"created\". When the round is finished successfully, the state is \"finished\". When the round is failed, the state is \"failed\". The board is a dictionary to store the bulletin of the round, which can be used to store the information of the round. This may sound confusing, given that different roles already have their messages in the posts. However, the board is used to store critical information that must be aware of in the round context. A very simple example would be the original user query. When the user issues a request to the Planner, the Planner will decompose the task and send a subtask to the CodeInterpreter. However, the CodeInterpreter needs to know the original user query and the full plan of the Planner to provide a more accurate response. In this case, the Planner can store the original user query in the board, and the CodeInterpreter can access it when needed. We provide two methods to access the board: write_board and read_board. tip The board is a dictionary, and you can store any information you want in the board for the current round. This is useful to eliminate the issue of information loss between different roles.","s":"Round","u":"/TaskWeaver/docs/concepts/round","h":"","p":59},{"i":62,"t":"The role is a concept in TaskWeaver which represents the different roles in the conversation system. The Planner and CodeInterpreter are two examples of roles in TaskWeaver. class Role: @inject def __init__( self, config: ModuleConfig, logger: TelemetryLogger, tracing: Tracing, event_emitter: SessionEventEmitter, role_entry: Optional[RoleEntry] = None, ): \"\"\" The base class for all roles. \"\"\" info We use the inject decorator from the injector package to inject the dependencies into the role class. We allow adding extra roles into the system by inheriting the Role class and implementing the role in the taskweaver.ext_role module. We have provided examples on how to implement a new role. A very simple example is the Echo role which echoes the user's message back to the user. A role should have at least two files: role_name.py and role_name.role.yaml. The files of the role should be put in the taskweaver/ext_role/role_name folder. We need to follow the convention of the role name, which is exactly the same as the folder name, otherwise an exception will be raised. We typically use the style of snake_case for the role name. In the role_name.role.yaml file, we define the role's configuration. This following is echo.role.yaml of the Echo role configuration. alias: Echo module: taskweaver.ext_role.echo.echo.Echo intro : |- - Echo is responsible for echoing the user input. The configuration file contains the following fields: alias: the alias of the role, which is the name of role shown in the prompt and the conversation. module: the module path of the role class. TaskWeaver will import the module and instantiate the role class. intro: the introduction of the role, which will be shown in Planner's prompt for choosing the role for certain tasks. In the role_name.py file, we define the role class. The following is echo.py of the Echo role class. class EchoConfig(RoleConfig): def _configure(self): # configuration for the Echo role # can be configured in the project configuration file with `echo.decorator` self.decorator = self._get_str(\"decorator\", \"\") class Echo(Role): @inject def __init__( self, config: EchoConfig, logger: TelemetryLogger, tracing: Tracing, event_emitter: SessionEventEmitter, role_entry: RoleEntry, ): # configuration for the parent class; this is required super().__init__(config, logger, tracing, event_emitter, role_entry) def reply(self, memory: Memory, **kwargs) -> Post: rounds = memory.get_role_rounds( role=self.alias, include_failure_rounds=False, ) # obtain the query from the last round last_post = rounds[-1].post_list[-1] post_proxy = self.event_emitter.create_post_proxy(self.alias) post_proxy.update_send_to(last_post.send_from) post_proxy.update_message( self.config.decorator + last_post.message + self.config.decorator ) return post_proxy.end() The role implementation should inherit the Role class and implement the reply method. The above example demonstrates how to get the query from the last round. The reply function of the Echo role is simply echoing the user's message back to the user with optional decoration. The reply function should return a Post object, which is the response of the role to the user. We provide facilities to help the role to interact with the memory, the event emitter, and the logger. For example, the event_emitter.create_post_proxy function is used to create a PostProxy object, which is a helper class to create a Post object. The PostProxy object is used to update the Post object with the new message, send_to, and other attributes. Once the PostProxy object is updated, the event emitter will send this event to a handler to display the event to a frontend. To enable the role in TaskWeaver, we need to add the role configuration to the taskweaver_config.json file. The following is an example of the taskweaver_config.json file with the Echo role configuration in addition to the Planner and CodeInterpreter roles. Note that the name of the role should be the same as the folder name of the role, not the alias. { \"session.roles\": [\"planner\", \"echo\", \"code_interpreter\"] } tip How to determine if I should create a new role? or implement a new plugin for the CodeInterpreter? The answer depends on the functionality you want to implement. If the functionality is to reply in text message given a user query, and you don't envision the need to process the reply text in code, you should create a new role. In contrast, if the functionality is to process the user query and return the result in a structured format, or if both input and output are in a structured format, you should implement a new plugin for the CodeInterpreter.","s":"Role","u":"/TaskWeaver/docs/concepts/role","h":"","p":61},{"i":64,"t":"A session is a conversation instance that the user has with the TaskWeaver app. A new session is created when the user interacts with the app. When the user finishes interacting with the app, the session should be closed. TaskWeaver allows multiple sessions to be created and managed by the app. Therefore, multiple users can interact with the app at the same time in different sessions. A new session is created by calling the get_session method of the TaskWeaverApp class. In the background, the get_session method creates a new Session instance. class Session: @inject def __init__( self, session_id: str, workspace: Workspace, app_injector: Injector, logger: TelemetryLogger, tracing: Tracing, config: AppSessionConfig, role_registry: RoleRegistry, ) -> None: \"\"\" Initialize the session. :param session_id: The session ID. :param workspace: The workspace. :param app_injector: The app injector. :param logger: The logger. :param tracing: The tracing. :param config: The configuration. :param role_registry: The role registry. \"\"\" info In TaskWeaver, we use an injector to take care of most dependency injection. The Session class has the following methods: def send_message( self, message: str, event_handler: Optional[SessionEventHandler] = None, files: Optional[List[Dict[Literal[\"name\", \"path\", \"content\"], Any]]] = None, ) -> Round: \"\"\" Send a message. :param message: The message. :param event_handler: The event handler. :param files: The files. :return: The chat round. \"\"\" send_message is used to send a message to the app. The message parameter is the text message that the user sends to the app. The event_handler parameter is a function that handles events during the conversation. We have defined a variety of events that can be handled by the event handler. Each event has a specific type and a message. By implementing the event handler, you can customize the display of events during the conversation. A very simple example of an event handler is shown below: class ConsoleEventHandler(SessionEventHandler): def handle(self, event: TaskWeaverEvent): print(event.t, event.msg) session.send_message(\"Hello, how can I help you?\", ConsoleEventHandler()) The ConsoleEventHandler class is a simple event handler that prints the event type and message to the console. The files parameter is used to upload files to the app for processing. def stop(self) -> None: \"\"\" Stop the session. This function must be called before the session exits. \"\"\" The stop method is used to stop the session. def update_session_var( self, variables: Dict[str, str] ): \"\"\" Update the session variables. :param variables: The variables to update. \"\"\" The update_session_var method is used to update the session variables. A session variable is a key-value pair that is only available in a specific session. Session variables can be used in the plugins to store information that is specific to the session. For example, you can store different user names in the session variables of different sessions. Then, in the plugin, you can access the user name by using the session variable. @register_plugin class PluginClass(Plugin): def __call__(self, argument1: str): ... # this line of code in the plugin implementation self.ctx.get_session_var(\"user_name\", \"anonymous\") ... The update_session_var method can be called multiple times to update multiple session variables.","s":"Session","u":"/TaskWeaver/docs/concepts/session","h":"","p":63},{"i":66,"t":"An overview of all configurations available in the config file, which is located at project/taskweaver_config.json. You can edit this file to configure TaskWeaver. tip The configuration file is in JSON format. So for boolean values, use true or false instead of True or False. For null values, use null instead of None or \"null\". All other values should be strings in double quotes. The following table lists the parameters in the configuration file: Parameter Description Default Value llm.model The model name used by the language model. gpt-4 llm.api_base The base URL of the OpenAI API. https://api.openai.com/v1 llm.api_key The API key of the OpenAI API. null llm.api_type The type of the OpenAI API, could be openai or azure. openai llm.api_version The version of the OpenAI API. 2023-07-01-preview llm.response_format The response format of the OpenAI API, could be json_object, text or null. json_object llm.embedding_api_type The type of the embedding API sentence_transformers llm.embedding_model The name of the embedding model all-mpnet-base-v2 code_interpreter.code_verification_on Whether to enable code verification. false code_interpreter.allowed_modules The list of allowed modules to import in code generation. [\"pandas\", \"matplotlib\", \"numpy\", \"sklearn\", \"scipy\", \"seaborn\", \"datetime\", \"typing\"], if the list is empty, no modules would be allowed code_interpreter.blocked_functions The list of functions to block from code generation. [\"__import__\", \"eval\", \"exec\", \"execfile\", \"compile\", \"open\", \"input\", \"raw_input\", \"reload\"] logging.log_file The name of the log file. taskweaver.log logging.log_folder The folder to store the log file. logs plugin.base_path The folder to store plugins. ${AppBaseDir}/plugins planner.example_base_path The folder to store planner examples. ${AppBaseDir}/planner_examples planner.prompt_compression Whether to compress the chat history for planner. false planner.use_experience Whether to use experience summarized from the previous chat history in planner. false code_generator.example_base_path The folder to store code interpreter examples. ${AppBaseDir}/codeinterpreter_examples code_generator.prompt_compression Whether to compress the chat history for code interpreter. false code_generator.enable_auto_plugin_selection Whether to enable auto plugin selection. false code_generator.use_experience Whether to use experience summarized from the previous chat history in code generator. false code_generator.auto_plugin_selection_topk The number of auto selected plugins in each round. 3 session.max_internal_chat_round_num The maximum number of internal chat rounds between Planner and Code Interpreter. 10 session.roles The roles included for the conversation. [\"planner\", \"code_interpreter\"] round_compressor.rounds_to_compress The number of rounds to compress. 2 round_compressor.rounds_to_retain The number of rounds to retain. 3 execution_service.kernel_mode The mode of the code executor, could be local or container. local tip ${AppBaseDir} is the project directory. tip Up to 11/30/2023, the json_object and text options of llm.response_format is only supported by the OpenAI models later than 1106. If you are using an older version of OpenAI model, you need to set the llm.response_format to null. tip Read this for more information for planner.prompt_compression and code_generator.prompt_compression. tip We support to set configurations via environment variables. You need to transform the configuration key to uppercase and replace the dot with underscore. For example, llm.model should be set as LLM_MODEL, llm.api_base should be set as LLM_API_BASE, etc.","s":"Configuration File","u":"/TaskWeaver/docs/configurations/overview","h":"","p":65},{"i":68,"t":"More details about important configurations.","s":"More about Configurations","u":"/TaskWeaver/docs/configurations/configurations_in_detail","h":"","p":67},{"i":70,"t":"planner.example_base_path: The folder to store planner examples. The default value is ${AppBaseDir}/planner_examples. If you want to create your own planner examples, you can add them to this folder. More details about example can referred to example. planner.prompt_compression: At times, lengthy conversations with the Planner may exceed the input limitations of the LLM model. To address this issue, we can compress the chat history and send it to the LLM model. The default value for this setting is false. More details about prompt_compression can be referred to prompt_compression. planner.use_experience: Whether to use experience summarized from the previous chat history in planner. The default value is false. planner.llm_alias: The alias of the LLM used by the Planner. If you do not specify the LLM for the Planner, the primary LLM will be used by default.","s":"Planner Configuration","u":"/TaskWeaver/docs/configurations/configurations_in_detail","h":"#planner-configuration","p":67},{"i":72,"t":"session.max_internal_chat_round_num: the maximum number of internal chat rounds between Planner and Code Interpreter. If the number of internal chat rounds exceeds this number, the session will be terminated. The default value is 10. session.roles: the roles included for the conversation. The default value is [\"planner\", \"code_interpreter\"]. TaskWeaver has 3 code interpreters: code_interpreter: it will generate Python code to fulfill the user's request. This is the default code interpreter. code_interpreter_plugin_only: please refer to plugin_only_mode for more details. code_interpreter_cli_only: allow users to directly communicate with the Command Line Interface (CLI) in natural language. Please refer to cli_only_mode for more details. If you do not specify planner in the roles, you will enter the \"no-planner\" mode. It allows users to directly communicate with the worker role, such as code_interpreter. In this mode, users can only send messages to the CodeInterpreter and receive messages from the CodeInterpreter. Note that only single worker role is allowed in the \"no-planner\" mode because all user requests will be sent to the worker role directly. Here is an example: ========================================================= _____ _ _ __ |_ _|_ _ ___| | _ | | / /__ ____ __ _____ _____ | |/ _` / __| |/ /| | /| / / _ \\/ __ `/ | / / _ \\/ ___/ | | (_| \\__ \\ < | |/ |/ / __/ /_/ /| |/ / __/ / |_|\\__,_|___/_|\\_\\|__/|__/\\___/\\__,_/ |___/\\___/_/ ========================================================= TaskWeaver: I am TaskWeaver, an AI assistant. To get started, could you please enter your request? Human: generate 10 random numbers >>> [PYTHON]Starting... import numpy as np random_numbers = np.random.rand(10) random_numbers >>> [VERIFICATION] NONE >>> [STATUS]Starting... SUCCESS >>> [RESULT] The execution of the generated python code above has succeeded The result of above Python code after execution is: array([0.09918602, 0.68732778, 0.44413814, 0.4756623 , 0.48302334, 0.8286594 , 0.80994359, 0.35677263, 0.45719317, 0.68240194]) >>> [CODEINTERPRETER->PLANNER] The following python code has been executed: ```python import numpy as np random_numbers = np.random.rand(10) random_numbers ``` The execution of the generated python code above has succeeded The result of above Python code after execution is: array([0.09918602, 0.68732778, 0.44413814, 0.4756623 , 0.48302334, 0.8286594 , 0.80994359, 0.35677263, 0.45719317, 0.68240194]) TaskWeaver: The following python code has been executed: ```python import numpy as np random_numbers = np.random.rand(10) random_numbers ``` The execution of the generated python code above has succeeded The result of above Python code after execution is: array([0.09918602, 0.68732778, 0.44413814, 0.4756623 , 0.48302334, 0.8286594 , 0.80994359, 0.35677263, 0.45719317, 0.68240194])","s":"Session Configuration","u":"/TaskWeaver/docs/configurations/configurations_in_detail","h":"#session-configuration","p":67},{"i":74,"t":"In TaskWeaver, we support various embedding models to generate embeddings for auto plugin selection. llm.embedding_api_type: The type of the embedding API. We support the following types: openai qwen ollama sentence_transformers glm llm.embedding_model: The embedding model name. The model name should be aligned with llm.embedding_api_type. We only list some embedding models we have tested below: openai text-embedding-ada-002 qwen text-embedding-v1 ollama llama2 sentence_transformers all-mpnet-base-v2 multi-qa-mpnet-base-dot-v1 all-distilroberta-v1 all-MiniLM-L12-v2 multi-qa-MiniLM-L6-cos-v1 zhipuai embedding-2 You also can use other embedding models supported by the above embedding APIs.","s":"Embedding Configuration","u":"/TaskWeaver/docs/configurations/configurations_in_detail","h":"#embedding-configuration","p":67},{"i":76,"t":"There are currently two types of examples: (1) Planner examples and (2) CodeInterpreter examples. Planning examples are used to demonstrate how the Planner should plan for a specific query from the User. Code generation examples are used to help the CodeInterpreter generate code or orchestrate plugins to perform a specific task. Both types of examples are actually the conversation between a collection of roles, such as the User, the Planner, and the CodeInterpreter. Each conversation consists of one or more rounds, and each round has a User query and contains one or more posts. A post is a message sent from one role to another role, and it may contain some attachments. One example looks like this: enabled: True rounds: - User_query: ping state: created post_list: - message: ping send_from: User send_to: Planner attachment_list: [] - message: pong send_from: Planner send_to: User attachment_list: [] We explicitly break all examples into the two types only to make it easier to understand and maintain. When the roles are initialized, each role will only read the examples of its type. tip All the text field in the examples such as the messages are in Markdown format.","s":"Customizing Examples","u":"/TaskWeaver/docs/customization/example/","h":"","p":75},{"i":78,"t":"A planning example tells LLMs how to plan for a specific query from the User, talk to the CodeInterpreter, receive the execution result from the CodeInterpreter, and summarize the execution result. The following is an example of a conversation between the User, the Planner, and the CodeInterpreter. One of the purposes of this example is to teach the Planner on the annotation of the dependencies between the steps in the plan. Another purpose is to teach the Planner to follow the right output format. enabled: True rounds: - User_query: count the rows of /home/data.csv state: created post_list: - message: count the rows of /home/data.csv send_from: User send_to: Planner attachment_list: - message: Please load the data file /home/data.csv and count the rows of the loaded data send_from: Planner send_to: CodeInterpreter attachment_list: - type: init_plan content: |- 1. load the data file 2. count the rows of the loaded data 3. report the result to the User - type: plan content: |- 1. instruct CodeInterpreter to load the data file and count the rows of the loaded data 2. report the result to the User - type: current_plan_step content: 1. instruct CodeInterpreter to load the data file and count the rows of the loaded data - message: Load the data file /home/data.csv successfully and there are 100 rows in the data file send_from: CodeInterpreter send_to: Planner attachment_list: - message: The data file /home/data.csv is loaded and there are 100 rows in the data file send_from: Planner send_to: User attachment_list: - type: init_plan content: |- 1. load the data file 2. count the rows of the loaded data 3. report the result to the User - type: plan content: |- 1. instruct CodeInterpreter to load the data file and count the rows of the loaded data 2. report the result to the User - type: current_plan_step content: 2. report the result to the User tip To better understand the attachments including init_plan, plan, and current_plan_step, please refer to the Planner's prompt. In this example, there are 4 posts: The first post is sent from the User to the Planner. The message is \"count the rows of /home/data.csv\", which must be the same with the User query. The second post is sent from the Planner to the CodeInterpreter. The message is \"Please load the data file /home/data.csv and count the rows of the loaded data\". The attachment list contains 3 attachments: The first attachment is the initial plan. The second attachment is the final plan. The third attachment is the current plan step. The third post is sent from the CodeInterpreter to the Planner. The message is \"Load the data file /home/data.csv successfully and there are 100 rows in the data file\". The fourth post is sent from the Planner to the User. The message is \"The data file /home/data.csv is loaded and there are 100 rows in the data file\". The attachment list contains 3 attachments, which are the same as the second post.","s":"Planner Examples","u":"/TaskWeaver/docs/customization/example/","h":"#planner-examples","p":75},{"i":80,"t":"A CodeInterpreter example tells CodeInterpreter how to generate code or orchestrate plugins to perform a specific task. The task is always from the Planner. The purpose of this example is to teach CodeInterpreter how to handle errors in execution. enabled: True rounds: - User_query: read file /abc/def.txt state: finished post_list: - message: read file /abc/def.txt send_from: Planner send_to: CodeInterpreter attachment_list: [] - message: I'm sorry, I cannot find the file /abc/def.txt. An FileNotFoundException has been raised. send_from: CodeInterpreter send_to: Planner attachment_list: - type: thought content: \"{ROLE_NAME} will generate a code snippet to read the file /abc/def.txt and present the content to the User.\" - type: python content: |- file_path = \"/abc/def.txt\" with open(file_path, \"r\") as file: file_contents = file.read() print(file_contents) - type: verification content: CORRECT - type: code_error content: No code error. - type: execution_status content: FAILURE - type: execution_result content: FileNotFoundException, the file /abc/def.txt does not exist. tip Read the prompt of the code generation to better understand the attachments in the example code generator prompt. This conversation has two posts: The first post is sent from the Planner to the CodeInterpreter. The message is \"Please read file /abc/def.txt\". The second post is sent from the CodeInterpreter to the Planner. The message is \"read file /abc/def.txt\". The attachment list contains 6 attachments: The first attachment is the thought of the CodeInterpreter. The second attachment is the generated code, which is in python. The third attachment is the verification status, which is CORRECT, INCORRECT, or NONE. The fourth attachment is the verification error message. The fifth attachment is the execution status, which is SUCCESS, FAILURE, or NONE. The sixth attachment is the execution result, which is a markdown string. In this example, verification is about whether the generated code is correct or not. We implemented a module to verify the generated code. If the code is syntactically incorrect, or the code violates the constraints, the verification status will be INCORRECT and some error messages will be returned. A verification of NONE means that the code has not been verified, which means verification has been turned off. In this example, execution_status is about whether the generated code can be executed successfully or not. If the execution is successful, the execution status will be SUCCESS and the execution result will be returned. Otherwise, the execution status will be FAILURE and some error messages will be returned. A execution_status of NONE means that the code has not been executed.","s":"CodeInterpreter Examples","u":"/TaskWeaver/docs/customization/example/","h":"#codeinterpreter-examples","p":75},{"i":83,"t":"The agent developers can add examples to guide the planning and code generation. Alternatively, we also provide another way of saving user experiences to long-term memory. In practice, if the user asks TaskWeaver to solve a hard problem. TaskWeaver can first go wrong. But after several attempts or the user gives more instructions, the agent can finally solve the problem. However, next time, if the user asks a similar problem or even exactly the same problem. The agent is still hard to come up with the right solution at first because it does not memorize the experiences. Therefore, we proposed a mechanism called experience memory. Here is the basic idea. A user can issue a command to TaskWeaver to save chat history and then extract experience tips from it and save them into the experience pool. Later, when the agent sees similar requests, it will retrieve the experience from the memory to guide its planning and code generation. An experience tip is about what should do or should not do when seeing a request like this. We will add the retrieved experiences to the prompt when received a similar request afterward.","s":"Motivation","u":"/TaskWeaver/docs/customization/experience","h":"#motivation","p":81},{"i":85,"t":"To enable the experience feature, you only need to set the planner.use_experience and code_generator.use_experience parameter in the configuration file to true. Start a new conversation with TaskWeaver. You will find experience directory is created in your project directory. Note that there is no experience now because we have not saved any chat history yet. If you think the current chat history is worth saving, you can save it by typing command /save and you will find a new file named raw_exp_{session_id}.yaml is created in the experience directory. Restart TaskWeaver and start a new conversation. In the initialization stage, TaskWeaver will read the raw_exp_{session_id}.yaml file and make a summarization in a new file named All_exp_{session_id}.yaml. This process may take a while. All_ denotes that this experience will be loaded for Planner and CodeInterpreter. When user send a similar query to TaskWeaver, it will retrieve the relevant experience and load it into the system prompt (for Planner and CodeInterpreter). In this way, the experience can be used to guide the future conversation.","s":"How to use experience memory","u":"/TaskWeaver/docs/customization/experience","h":"#how-to-use-experience-memory","p":81},{"i":87,"t":"Start a new conversation with TaskWeaver. Send a request \"calculate the mean value of ./project/sample_data/data_demo.csv\" to TaskWeaver. You will find TaskWeaver made a mistake, calculating the mean value of the non-numerical column. After several attempts, TaskWeaver fixed this issue. ========================================================= _____ _ _ __ |_ _|_ _ ___| | _ | | / /__ ____ __ _____ _____ | |/ _` / __| |/ /| | /| / / _ \\/ __ `/ | / / _ \\/ ___/ | | (_| \\__ \\ < | |/ |/ / __/ /_/ /| |/ / __/ / |_|\\__,_|___/_|\\_\\|__/|__/\\___/\\__,_/ |___/\\___/_/ ========================================================= TaskWeaver β–Ά I am TaskWeaver, an AI assistant. To get started, could you please enter your request? Human β–Ά calculate the mean value of d:/demo_data.csv ╭───< Planner > β”œβ”€β–Ί [init_plan] β”‚ 1. load the data file β”‚ 2. calculate the mean value of the loaded data β”‚ 3. report the result to the user β”œβ”€β–Ί [plan] β”‚ 1. instruct CodeInterpreter to load the data file and calculate the mean value of the loaded data β”‚ 2. report the result to the user β”œβ”€β–Ί [current_plan_step] 1. instruct CodeInterpreter to load the data file and calculate the mean value of the loaded data β”œβ”€β”€β— Please load the data file d:/demo_data.csv and calculate the mean value of the loaded data ╰──● sending message to CodeInterpreter ╭───< CodeInterpreter > β”œβ”€β–Ί [thought] ProgramApe will generate a code snippet to load the data file d:/demo_data.csv and calculate the mean value of the loaded data using pandas. β”œβ”€β–Ί [python] β”‚ import pandas as pd β”‚ β”‚ data_file_path = \"d:/demo_data.csv\" β”‚ data_df = pd.read_csv(data_file_path) β”‚ mean_value = data_df.mean() β”‚ mean_value β”œβ”€β–Ί [verification] NONE β”œβ”€β–Ί [code_error] No code verification is performed. β”œβ”€β–Ί [execution_status] FAILURE β”œβ”€β–Ί [execution_result] β”‚ The execution of the generated python code above has failed β”‚ β”‚ During execution, the following messages were logged: β”‚ Traceback (most recent call last): β”‚ β”‚ Cell In[1], line 5 β”‚ mean_value = data_df.mean() β”‚ β”‚ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\frame.py:11335 in mean β”‚ result = super().mean(axis, skipna, numeric_only, **kwargs) β”‚ β”‚ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\generic.py:11984 in mean β”‚ return self._stat_function( β”‚ β”‚ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\generic.py:11941 in _stat_function β”‚ return self._reduce( β”‚ β”‚ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\frame.py:11204 in _reduce β”‚ res = df._mgr.reduce(blk_func) β”‚ β”‚ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:1459 in reduce β”‚ nbs = blk.reduce(func) β”‚ β”‚ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\internals\\blocks.py:377 in reduce β”‚ result = func(self.values) β”‚ β”‚ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\frame.py:11136 in blk_func β”‚ ret β”‚ β”œβ”€β–Ί [artifact_paths] [] β”œβ”€β”€β— The following python code has been executed: β”‚ ```python β”‚ import pandas as pd β”‚ β”‚ data_file_path = \"d:/demo_data.csv\" β”‚ data_df = pd.read_csv(data_file_path) β”‚ mean_value = data_df.mean() β”‚ mean_value β”‚ ``` β”‚ β”‚ β”‚ The execution of the generated python code above has failed β”‚ β”‚ During execution, the following messages were logged: β”‚ Traceback (most recent call last): β”‚ β”‚ Cell In[1], line 5 β”‚ mean_value = data_df.mean() β”‚ β”‚ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\frame.py:11335 in mean β”‚ result = super().mean(axis, skipna, numeric_only, **kwargs) β”‚ β”‚ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\generic.py:11984 in mean β”‚ return self._stat_function( β”‚ β”‚ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\generic.py:11941 in _stat_function β”‚ return self._reduce( β”‚ β”‚ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\frame.py:11204 in _reduce β”‚ res = df._mgr.reduce(blk_func) β”‚ β”‚ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:1459 in reduce β”‚ nbs = blk.reduce(func) β”‚ β”‚ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\internals\\blocks.py:377 in reduce β”‚ result = func(self.values) β”‚ β”‚ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\frame.py:11136 in blk_func β”‚ ret β”‚ β”œβ”€β–Ί [revise_message] β”‚ The execution of the previous generated code has failed. If you think you can fix the problem by rewriting the code, please generate code and run it again. β”‚ Otherwise, please explain the problem to me. ╰──● sending message to CodeInterpreter ╭───< CodeInterpreter > β”œβ”€β–Ί [thought] ProgramApe will generate a code snippet to load the data file d:/demo_data.csv and calculate the mean value of the loaded data using pandas, considering the execution β”‚ error. β”œβ”€β–Ί [python] β”‚ import pandas as pd β”‚ β”‚ data_file_path = \"d:/demo_data.csv\" β”‚ data_df = pd.read_csv(data_file_path) β”‚ mean_value = data_df.mean(axis=0, numeric_only=True) β”‚ mean_value β”œβ”€β–Ί [verification] NONE β”œβ”€β–Ί [code_error] No code verification is performed. β”œβ”€β–Ί [execution_status] SUCCESS β”œβ”€β–Ί [execution_result] β”‚ The execution of the generated python code above has succeeded β”‚ β”‚ The result of above Python code after execution is: β”‚ Count 78172.75 β”‚ dtype: float64 β”œβ”€β–Ί [artifact_paths] [] β”œβ”€β”€β— The following python code has been executed: β”‚ ```python β”‚ import pandas as pd β”‚ β”‚ data_file_path = \"d:/demo_data.csv\" β”‚ data_df = pd.read_csv(data_file_path) β”‚ mean_value = data_df.mean(axis=0, numeric_only=True) β”‚ mean_value β”‚ ``` β”‚ β”‚ β”‚ The execution of the generated python code above has succeeded β”‚ β”‚ The result of above Python code after execution is: β”‚ Count 78172.75 β”‚ dtype: float64 ╰──● sending message to Planner ╭───< Planner > β”œβ”€β–Ί [init_plan] β”‚ 1. load the data file β”‚ 2. calculate the mean value of the loaded data β”‚ 3. report the result to the user β”œβ”€β–Ί [plan] β”‚ 1. instruct CodeInterpreter to load the data file and calculate the mean value of the loaded data β”‚ 2. report the result to the user β”œβ”€β–Ί [current_plan_step] 2. report the result to the user β”œβ”€β”€β— The mean value of the data in d:/demo_data.csv is 78172.75 ╰──● sending message to User TaskWeaver β–Ά The mean value of the data in d:/demo_data.csv is 78172.75 We can issue the /save command from the console chat interface to save the chat history. Then, after we restart TaskWeaver, we can see the experience summarized from the last chat history is below: exp_id: 20231228-073922-9b411afd experience_text: \"User Query: get the mean value of d:/demo_data.csv Error Resolution: - Error 1: The initial code failed to execute because it tried to calculate the mean value of all columns, including non-numeric columns. - Resolution 1: The code was revised to check for non-numeric columns and only calculate the mean value for numeric columns. Preferences: - Preference 1: The user prefers to calculate the mean value of the \"Count\" column in the data.\" raw_experience_path: D:\\project\\experience\\raw_exp_20231228-073922-9b411afd.yaml embedding_model: text-embedding-ada-002 embedding: ... Send a similar request \"calculate the variance value of ./project/sample_data/data_demo.csv\" to TaskWeaver. You will find TaskWeaver will not make the same mistake again. It will ask User to confirm the column name to calculate the variance value. ========================================================= _____ _ _ __ |_ _|_ _ ___| | _ | | / /__ ____ __ _____ _____ | |/ _` / __| |/ /| | /| / / _ \\/ __ `/ | / / _ \\/ ___/ | | (_| \\__ \\ < | |/ |/ / __/ /_/ /| |/ / __/ / |_|\\__,_|___/_|\\_\\|__/|__/\\___/\\__,_/ |___/\\___/_/ ========================================================= TaskWeaver β–Ά I am TaskWeaver, an AI assistant. To get started, could you please enter your request? Human β–Ά calculate the variance value of d:/demo_data.csv ╭───< Planner >TaskWeaver] preparing <=οΏ½=> β”œβ”€β–Ί [init_plan] β”‚ 1. load the data file β”‚ 2. confirm the columns to calculate variance β”‚ 3. calculate the variance value for the selected columns β”‚ 4. report the result to the user β”œβ”€β–Ί [plan] β–Ά [Planner][plan] 1. instruct CodeInterpreter to load the data file 2. confirm the columns to calcu <=οΏ½=> β”‚ 1. instruct CodeInterpreter to load the data file β”‚ 2. confirm the columns to calculate variance β”œβ”€β–Ί [current_plan_step] 1. instruct CodeInterpreter to load the data file load the data file <=οΏ½=> β”œβ”€β”€β— Please load the data file d:/demo_data.csva file d:/demo_dat <=οΏ½=> ╰──● sending message to CodeInterpreter ╭───< CodeInterpreter > β”œβ”€β–Ί [thought] ProgramApe will generate a code snippet to load the data file d:/demo_data.csv using pandas.demo_data.csv using panda <=οΏ½=> β”œβ”€β–Ί [python]β–Ά [CodeInterpreter][python] import pandas as pd data_file_path = \"d:/demo_data.csv\" df = pd.read_csv(data_fil <=οΏ½=> β”‚ import pandas as pd β”‚ β”‚ data_file_path = \"d:/demo_data.csv\" β”‚ df = pd.read_csv(data_file_path) β”‚ df β”œβ”€β–Ί [verification] NONE β”œβ”€β–Ί [code_error] No code verification is performed. β”œβ”€β–Ί [execution_status] SUCCESS] executing code <=οΏ½=> β”œβ”€β–Ί [execution_result] β”‚ The execution of the generated python code above has succeeded β”‚ β”‚ The result of above Python code after execution is: β”‚ TimeBucket Count β”‚ 0 2023-02-01T00:00:00Z 67814 β”‚ 1 2023-02-01T04:00:00Z 84569 β”‚ .. ... ... β”‚ 718 2023-05-31T16:00:00Z 89912 β”‚ 719 2023-05-31T20:00:00Z 87249 β”‚ β”‚ [720 rows x 2 columns] β”œβ”€β–Ί [artifact_paths] [] β”œβ”€β”€β— The following python code has been executed: β”‚ ```python β”‚ import pandas as pd β”‚ β”‚ data_file_path = \"d:/demo_data.csv\" β”‚ df = pd.read_csv(data_file_path) β”‚ df β”‚ ``` β”‚ β”‚ β”‚ The execution of the generated python code above has succeeded β”‚ β”‚ The result of above Python code after execution is: β”‚ TimeBucket Count β”‚ 0 2023-02-01T00:00:00Z 67814 β”‚ 1 2023-02-01T04:00:00Z 84569 β”‚ .. ... ... β”‚ 718 2023-05-31T16:00:00Z 89912 β”‚ 719 2023-05-31T20:00:00Z 87249 β”‚ β”‚ [720 rows x 2 columns] ╰──● sending message to Planner ╭───< Planner >CodeInterpreter] finished <=οΏ½=> β”œβ”€β–Ί [init_plan] β”‚ 1. load the data file β”‚ 2. confirm the columns to calculate variance β”‚ 3. calculate the variance value for the selected columns β”‚ 4. report the result to the user β”œβ”€β–Ί [plan] β–Ά [Planner][plan] 1. instruct CodeInterpreter to load the data file 2. confirm the columns to calculate var <=οΏ½=> β”‚ 1. instruct CodeInterpreter to load the data file β”‚ 2. confirm the columns to calculate variance β”œβ”€β–Ί [current_plan_step] 2. confirm the columns to calculate variance calculate varia <=οΏ½=> β”œβ”€β”€β— The data file d:/demo_data.csv is loaded. The columns in the data are: β”‚ β”‚ 1. TimeBucket β”‚ 2. Count β”‚ β”‚ Please specify which column(s) you would like to calculate the variance for. ╰──● sending message to User TaskWeaver β–Ά The data file d:/demo_data.csv is loaded. The columns in the data are: 1. TimeBucket 2. Count Please specify which column(s) you would like to calculate the variance for. Human β–Ά Count ╭───< Planner >TaskWeaver] preparing <=οΏ½=> β”œβ”€β–Ί [init_plan] β”‚ 1. load the data file β”‚ 2. confirm the columns to calculate variance β”‚ 3. calculate the variance value for the selected columns β”‚ 4. report the result to the user β”œβ”€β–Ί [plan] β”‚ 1. instruct CodeInterpreter to load the data file β”‚ 2. confirm the columns to calculate variance β”‚ 3. instruct CodeInterpreter to calculate the variance value for the Count column β”‚ 4. report the result to the user β”œβ”€β–Ί [current_plan_step] 3. instruct CodeInterpreter to calculate the variance value for the Count columnor the Count col <=οΏ½=> β”œβ”€β”€β— Please calculate the variance value for the Count column in the loaded datalumn in the loaded <=οΏ½=> ╰──● sending message to CodeInterpreter ╭───< CodeInterpreter > β”œβ”€β–Ί [thought] ProgramApe will generate a code snippet to calculate the variance value for the Count column in the loaded data. column in the loaded dat <=οΏ½=> β”œβ”€β–Ί [python]β–Ά [CodeInterpreter][python] variance_count = df['Count'].var() varia <=οΏ½=> β”‚ variance_count = df['Count'].var() β”‚ variance_count β”œβ”€β–Ί [verification] NONE β”œβ”€β–Ί [code_error] No code verification is performed. β”œβ”€β–Ί [execution_status] SUCCESS] executing code <=οΏ½=> β”œβ”€β–Ί [execution_result] β”‚ The execution of the generated python code above has succeeded β”‚ β”‚ The result of above Python code after execution is: β”‚ 263514550.45479834 β”œβ”€β–Ί [artifact_paths] [] β”œβ”€β”€β— The following python code has been executed: β”‚ ```python β”‚ variance_count = df['Count'].var() β”‚ variance_count β”‚ ``` β”‚ β”‚ β”‚ The execution of the generated python code above has succeeded β”‚ β”‚ The result of above Python code after execution is: β”‚ 263514550.45479834 ╰──● sending message to Planner ╭───< Planner >CodeInterpreter] finished <=οΏ½=> β”œβ”€β–Ί [init_plan] β”‚ 1. load the data file β”‚ 2. confirm the columns to calculate variance β”‚ 3. calculate the variance value for the selected columns β”‚ 4. report the result to the user β”œβ”€β–Ί [plan] β”‚ 1. instruct CodeInterpreter to load the data file β”‚ 2. confirm the columns to calculate variance β”‚ 3. instruct CodeInterpreter to calculate the variance value for the Count column β”‚ 4. report the result to the user β”œβ”€β–Ί [current_plan_step] 4. report the result to the user result to the u <=οΏ½=> β”œβ”€β”€β— The variance value for the Count column in the data file d:/demo_data.csv is 263,514,550.45a.csv is 263,51 <=οΏ½=> ╰──● sending message to User TaskWeaver β–Ά The variance value for the Count column in the data file d:/demo_data.csv is 263,514,550.45","s":"A walk-through example","u":"/TaskWeaver/docs/customization/experience","h":"#a-walk-through-example","p":81},{"i":90,"t":"A: In the default setting, TaskWeaver typically goes through the following steps to generate the output: The User sends a query to the Planner. The Planner generates a plan based on the query and sends the plan to the CodeInterpreter. The CodeInterpreter executes the plan and sends the execution result back to the Planner. The Planner summarizes the execution result and sends the summary to the User. The User receives the summary from the Planner. In some cases, the Planer and CodeInterpreter can go back and forth multiple times to generate the output. This process can take a long time, mostly due to the latency of calling the LLMs. To speed up the process, you can try the following: Use a smaller LLM model, such as GPT-3.5 instead of GPT-4. However, you need to experiment with your use case to see if the smaller model can still generate the output correctly. Use the CodeInterpreter only mode, which skips the Planner and generates the code directly from the User query. This mode is faster because it skips the planning step. However, you should check if your use case needs the planning step.","s":"Q: Why it takes so long to generate the output?","u":"/TaskWeaver/docs/FAQ","h":"#q-why-it-takes-so-long-to-generate-the-output","p":88},{"i":92,"t":"A: This error typically happens when TaskWeaver is running in the container mode and cannot connect to the Docker daemon. We have switched to the container mode by default to provide a more secure environment for code execution. To opt out of the container mode, you can set the execution_service.kernel_mode parameter to local in the taskweaver_config.json file. However, you should be aware that TaskWeaver can interact with the host machine directly in the local mode, which may have security risks.","s":"Q: Why TaskWeaver fails and the logs say \"Failed to connect to docker.daemon\"?","u":"/TaskWeaver/docs/FAQ","h":"#q-why-taskweaver-fails-and-the-logs-say-failed-to-connect-to-dockerdaemon","p":88},{"i":94,"t":"A: This is typically due to that the LLM failed to generate its output follow our schema. In Planner's prompt, we asked the LLM to generate a JSON object that contains send_to, message, init_plan, plan, and current_plan_step. Missing any of these fields will cause the parsing error. The most effective way to mitigate this issue is to use switch to more powerful LLM model, such as GPT-3.5 or GPT-4.","s":"Q: Why I see errors saying the Planner failed to generate the send_to, message or other fields?","u":"/TaskWeaver/docs/FAQ","h":"#q-why-i-see-errors-saying-the-planner-failed-to-generate-the-send_to-message-or-other-fields","p":88},{"i":96,"t":"A: A simple way to check if TaskWeaver can see your plugin is to ask \"What can you do?\" to TaskWeaver. The typical response is to list all the available plugins like the following: I can assist you with various tasks, including: - Detecting anomalies in time series data. - Rendering text into ASCII art. - Searching and comparing prices from thousands of online shops (US only). - Telling a joke. If you have a specific task in mind, please let me know, and I'll do my best to assist you. If you see your plugin in the list, it means TaskWeaver can see your plugin. But this is not a reliable way to check if TaskWeaver can see your plugin because the response is generated by the LLM. A more reliable way is to check the prompt of the Planner. You can find the prompts from project/workspace/sessions//planner_prompt_log_xxxx.yaml. Then, search for this section as follows: CodeInterpreter has the following plugin functions and their required parameters need to be provided before the execution: - anomaly_detection: anomaly_detection function identifies anomalies from an input DataFrame of time series. It will add a new column \\\"Is_Anomaly\\\", where each entry will be marked with \\\"True\\\" if the value is an anomaly or \\\"False\\\" otherwise. Arguments required: df: DataFrame, time_col_name: str, value_col_name: str - ascii_render: This plugin renders the input text into ASCII art form. Arguments required: text: str - klarna_search: Search and compare prices from thousands of online shops. Only available in the US. This plugin only takes user requests when searching for merchandise. If not clear, confirm with the user if they want to search for merchandise from Klarna. Arguments required: query: str - tell_joke: Call this plugin to tell a joke. Check if your plugin is in the list. If it is, it means TaskWeaver can see your plugin.","s":"Q: How do I know if TaskWeaver can see my plugin?","u":"/TaskWeaver/docs/FAQ","h":"#q-how-do-i-know-if-taskweaver-can-see-my-plugin","p":88},{"i":98,"t":"First, make sure you have read our Plugin Introduction and this tutorial carefully. You should have two files in the plugins folder, e.g., ascii_render.py and ascii_render.yaml. Now, if TaskWeaver cannot see your plugin, the root cause is typically syntax errors in the yaml file. Check the console output if you are using the command line interface, or the console logs if you are using the web interface. You may see the following error message: failed to loading component from .yaml, skipping: Yaml loading failed due to: The error message will tell you the reason why the yaml file cannot be loaded. It is typically easy to fix the syntax errors by using a yaml linter (e.g., in Visual Studio Code) or an online yaml linter. If you have checked the syntax of the yaml file and TaskWeaver still cannot see your plugin, please check if the yaml file has included all the required fields such as the parameters and returns fields.","s":"Q: Why TaskWeaver cannot see my plugin?","u":"/TaskWeaver/docs/FAQ","h":"#q-why-taskweaver-cannot-see-my-plugin","p":88},{"i":100,"t":"A: In this case, you may see the generated code has called the plugin function, but the execution result is an error message saying that the plugin function is undefined. If this happens, please check the console output if you are using the command line interface, or the console logs if you are using the web interface. You may see the following error message: Plugin failed to load: Plugin failed to register: failed to load plugin This error message will tell you the reason why the plugin function cannot be loaded. It is typically easy to fix the errors by checking the console output or logs. The root cause is typically errors in the python file that causes the plugin function cannot be loaded. Typical errors include syntax errors, missing imports, or missing packages. Note that this sort of error is not caused by the implementation \"inside\" the plugin function. Otherwise, the errors would be caught during the execution of the plugin function, not during the loading of the plugin function.","s":"Q: Why TaskWeaver can see my plugin but cannot call it?","u":"/TaskWeaver/docs/FAQ","h":"#q-why-taskweaver-can-see-my-plugin-but-cannot-call-it","p":88},{"i":102,"t":"A: We are working on a debugging tool to help you debug your plugin. For now, a simple way to debug your plugin is to define a main function in the python file and run it in your local environment. For example, you can define a main function in ascii_render.py as follows: if __name__ == \"__main__\": from taskweaver.plugin.context import temp_context with temp_context() as temp_ctx: render = AsciiRenderPlugin(name=\"ascii_render\", ctx=temp_ctx, config={}) print(render(text=\"hello world!\")) In this main function, we create a temporary context and call the plugin function with some input. You need not change the plugin implementation. Just add the main function to the end of the python file. Then, run the python file in your local environment. If there are any errors, you can see them in the console output. If you have the configurations section in the yaml file, you can manually set the configurations in the config parameter of the plugin constructor. We currently do not read the yaml file, so you need to make sure that the configurations are set correctly in the config parameter. For example, if an integer configuration max_length is defined in the yaml file, you can set it in the config parameter as follows: config = { \"max_length\": 100 } Then, pass the config to the plugin constructor. As yaml is type sensitive, you need to make sure that the type of the configuration is correct.","s":"Q: How to debug my plugin?","u":"/TaskWeaver/docs/FAQ","h":"#q-how-to-debug-my-plugin","p":88},{"i":104,"t":"A: We use a Jupyter Kernel to execute the code in TaskWeaver. The Jupyter Kernel uses an event loop to manage the execution of the code. If you see the error message \"RuntimeError: This event loop is already running.\", it typically means that the event loop is already running. This is typically caused by the enviroment where TaskWeaver is running. For example, if you are running TaskWeaver in a Jupyter Notebook.","s":"Q: Why I see the error message \"RuntimeError: This event loop is already running\"?","u":"/TaskWeaver/docs/FAQ","h":"#q-why-i-see-the-error-message-runtimeerror-this-event-loop-is-already-running","p":88},{"i":107,"t":"Using LLMs from OpenAI","s":"πŸ“„οΈ OpenAI","u":"/TaskWeaver/docs/llms/","h":"","p":105},{"i":109,"t":"Using LLMs from OpenAI/AOAI","s":"πŸ“„οΈ Azure OpenAI","u":"/TaskWeaver/docs/llms/","h":"","p":105},{"i":111,"t":"Using LLMs from LiteLLM","s":"πŸ“„οΈ LiteLLM","u":"/TaskWeaver/docs/llms/","h":"","p":105},{"i":113,"t":"1. Go to Ollama and follow the instructions to serve a LLM model on your local environment.","s":"πŸ“„οΈ Ollama","u":"/TaskWeaver/docs/llms/","h":"","p":105},{"i":115,"t":"1. Create an account on Google AI and get your API key.","s":"πŸ“„οΈ Gemini","u":"/TaskWeaver/docs/llms/","h":"","p":105},{"i":117,"t":"1. QWen (Tongyi Qianwen) is a LLM developed by Alibaba. Go to QWen and register an account and get the API key. More details can be found here (in Chinese).","s":"πŸ“„οΈ QWen","u":"/TaskWeaver/docs/llms/","h":"","p":105},{"i":119,"t":"1. GLM (ChatGLM) is a LLM developed by Zhipu AI and Tsinghua KEG. Go to ZhipuAI and register an account and get the API key. More details can be found here.","s":"πŸ“„οΈ GLM","u":"/TaskWeaver/docs/llms/","h":"","p":105},{"i":121,"t":"We welcome developers to use your customized LLM API in TaskWeaver.","s":"πŸ“„οΈ Customized LLM API","u":"/TaskWeaver/docs/llms/","h":"","p":105},{"i":123,"t":"In some cases, you may want to use different LLMs for different components.","s":"πŸ“„οΈ Multi LLM APIs","u":"/TaskWeaver/docs/llms/","h":"","p":105},{"i":125,"t":"Create an account on Azure OpenAI and get your API key. Add the following to your taskweaver_config.json file: { \"llm.api_base\":\"YOUR_AOAI_ENDPOINT\", \"llm.api_key\":\"YOUR_API_KEY\", \"llm.api_type\":\"azure\", \"llm.auth_mode\":\"api-key\", \"llm.model\":\"gpt-4-1106-preview\", # this is known as deployment_name in Azure OpenAI \"llm.response_format\": \"json_object\" } info For model versions or after 1106, llm.response_format can be set to json_object. However, for the earlier models, which do not support JSON response explicitly, llm.response_format should be set to null. Start TaskWeaver and chat with TaskWeaver. You can refer to the Quick Start for more details.","s":"Azure OpenAI","u":"/TaskWeaver/docs/llms/aoai","h":"","p":124},{"i":127,"t":"Create an account on Google AI and get your API key. Add the following content to your taskweaver_config.json file: { \"llm.api_type\": \"google_genai\", \"llm.google_genai.api_key\": \"YOUR_API_KEY\", \"llm.google_genai.model\": \"gemini-pro\" } Start TaskWeaver and chat with TaskWeaver. You can refer to the Quick Start for more details.","s":"Gemini","u":"/TaskWeaver/docs/llms/gemini","h":"","p":126},{"i":129,"t":"We welcome developers to use your customized LLM API in TaskWeaver. In this tutorial, we will show you how to contribute your LLM API to TaskWeaver. Create a new Python script .py in the taskweaver/llm folder. Import the CompletionService, LLMServiceConfig and EmbeddingService from taskweaver.llm.base and other necessary libraries. from injector import inject from taskweaver.llm.base import CompletionService, EmbeddingService, LLMServiceConfig from taskweaver.llm.util import ChatMessageType ... Create a new class YourLLMServiceConfig that inherits from LLMServiceConfig and implements the _configure method. In this method, you can set the name, API key, model name, backup model name, and embedding model name of your LLM. class YourLLMServiceConfig(LLMServiceConfig): def _configure(self) -> None: self._set_name(\"your_llm_name\") shared_api_key = self.llm_module_config.api_key self.api_key = self._get_str( \"api_key\", shared_api_key, ) shared_model = self.llm_module_config.model self.model = self._get_str( \"model\", shared_model if shared_model is not None else \"your_llm_model_name\", ) shared_backup_model = self.llm_module_config.backup_model self.backup_model = self._get_str( \"backup_model\", shared_backup_model if shared_backup_model is not None else self.model, ) shared_embedding_model = self.llm_module_config.embedding_model self.embedding_model = self._get_str( \"embedding_model\", shared_embedding_model if shared_embedding_model is not None else self.model, ) Create a new class YourLLMService that inherits from CompletionService and EmbeddingService and implements the chat_completion and get_embeddings methods. class YourLLMService(CompletionService, EmbeddingService): @inject def __init__(self, config: YourLLMServiceConfig): self.config = config pass def chat_completion( self, messages: List[ChatMessageType], stream: bool = True, temperature: Optional[float] = None, max_tokens: Optional[int] = None, top_p: Optional[float] = None, stop: Optional[List[str]] = None, **kwargs: Any, ) -> Generator[ChatMessageType, None, None]: pass def get_embeddings(self, strings: List[str]) -> List[List[float]]: pass Note: We set stream mode by default in chat_completion. You need to use self.config to get the configuration variables of your LLM API (e.g., api key/model name) in YourLLMService class. The get_embeddings method is optional. If you need to import other libraries for your LLM API, please import them in __init__ function of YourLLMService class. You can refer to QWen dashscope library import for an example. Register your LLM service in taskweaver/llm/__init__.py by adding your LLM service to the LLMApi __init__ function . ...... from .your_llm_name import YourLLMService # import your LLM service here class LLMApi(object): @inject def __init__(self, config: LLMModuleConfig, injector: Injector) -> None: self.config = config self.injector = injector if self.config.api_type in [\"openai\", \"azure\", \"azure_ad\"]: self._set_completion_service(OpenAIService) ...... elif self.config.api_type == \"your_llm_name\": self._set_completion_service(YourLLMService) # register your LLM service here else: raise ValueError(f\"API type {self.config.api_type} is not supported\") if self.config.embedding_api_type in [\"openai\", \"azure\", \"azure_ad\"]: self._set_embedding_service(OpenAIService) ...... elif self.config.embedding_api_type == \"azure_ml\": self.embedding_service = PlaceholderEmbeddingService( \"Azure ML does not support embeddings yet. Please configure a different embedding API.\", ) # register your embedding service here, if do not have embedding service, please use `PlaceholderEmbeddingService` referring to the above line elif self.config.embedding_api_type == \"your_llm_name\": self._set_embedding_service(YourLLMService) else: raise ValueError( f\"Embedding API type {self.config.embedding_api_type} is not supported\", ) Configurate taskweaver_config.json file in the project dir based on your implemented LLM API. Run the following command to test your LLM API. If the LLM API is successfully set up, you will see the response from your LLM API. cd ./scripts python llm_api_test.py You also can specify the project dir and query to be sent to your LLM API by using the following command: python llm_api_test.py --project --query \"hello, what can you do?\"","s":"Customized LLM API","u":"/TaskWeaver/docs/llms/customized_llm_api","h":"","p":128},{"i":131,"t":"GLM (ChatGLM) is a LLM developed by Zhipu AI and Tsinghua KEG. Go to ZhipuAI and register an account and get the API key. More details can be found here. Install the required packages dashscope. pip install zhipuai Add the following configuration to taskweaver_config.json: { \"llm.api_type\": \"zhipuai\", \"llm.model\": \"glm-4\", \"llm.embedding_model\": \"embedding-2\", \"llm.embedding_api_type\": \"zhipuai\", \"llm.api_key\": \"YOUR_API_KEY\" } NOTE: llm.model is the model name of zhipuai API. You can find the model name in the GLM model list. Start TaskWeaver and chat with TaskWeaver. You can refer to the Quick Start for more details.","s":"GLM","u":"/TaskWeaver/docs/llms/glm","h":"","p":130},{"i":133,"t":"info LiteLLM provides a unified interface to call 100+ LLMs using the same Input/Output format, including OpenAI, Huggingface, Anthropic, vLLM, Cohere, and even custom LLM API server. Taking LiteLLM as the bridge, many LLMs can be onboarded to TaskWeaver. Here we use the OpenAI Proxy Server provided by LiteLLM to make configuration. Install LiteLLM Proxy and configure the LLM server by following the instruction here. In general, there are a few steps: Install the package pip install litellm[proxy] Setup the API key and other necessary environment variables which vary by LLM. Taking Cohere as an example, it is required to setup export COHERE_API_KEY=my-api-key. Run LiteLLM proxy server by litellm --model MODEL_NAME --drop_params, for example, in Cohere, the model name can be command-nightly. The drop-params argument is used to ensure the API compatibility. Then, a server will be automatically started on http://0.0.0.0:8000. tip The full list of supported models by LiteLLM can be found in the page. Add the following content to your taskweaver_config.json file: { \"llm.api_base\": \"http://0.0.0.0:8000\", \"llm.api_key\": \"anything\", \"llm.model\": \"gpt-3.5-turbo\" } info llm.api_key and llm.model are mainly used as placeholder for API call, whose actual values are not used. If the configuration does not work, please refer to LiteLLM documents to locally test whether you can send requests to the LLM. Open a new terminal, start TaskWeaver and chat. You can refer to the Quick Start for more details.","s":"LiteLLM","u":"/TaskWeaver/docs/llms/liteLLM","h":"","p":132},{"i":135,"t":"Go to Ollama and follow the instructions to serve a LLM model on your local environment. We provide a short example to show how to configure the ollama in the following, which might change if ollama makes updates. install ollama and serve LLMs in local ## Install ollama on Linux & WSL2 curl https://ollama.ai/install.sh | sh ## Run the serving ollama serve Open another terminal and run: ollama run llama2:13b tip We recommend deploying the LLM with a parameter scale exceeding 13B for enhanced performance (such as Llama 2 13B). info When serving LLMs via Ollama, it will by default start a server at http://localhost:11434, which will later be used as the API base in taskweaver_config.json. Add following configuration to taskweaver_config.json: { \"llm.api_base\": \"http://localhost:11434\", \"llm.api_key\": \"ARBITRARY_STRING\", \"llm.api_type\": \"ollama\", \"llm.model\": \"llama2:13b\" } NOTE: llm.api_base is the URL started in the Ollama LLM server and llm.model is the model name of Ollama LLM, it should be same as the one you served before. Start TaskWeaver and chat with TaskWeaver. You can refer to the Quick Start for more details.","s":"Ollama","u":"/TaskWeaver/docs/llms/ollama","h":"","p":134},{"i":137,"t":"Create an account on OpenAI and get your API key. Add the following to your taskweaver_config.json file: { \"llm.api_type\":\"openai\", \"llm.api_base\": \"https://api.openai.com/v1\", \"llm.api_key\": \"YOUR_API_KEY\", \"llm.model\": \"gpt-4\", \"llm.response_format\": \"json_object\" } tip llm.model is the model name you want to use. You can find the list of models here. info For model versions equal or after 1106, llm.response_format can be set to json_object. However, for the earlier models which do not support JSON response explicitly, llm.response_format should be set to null. Start TaskWeaver and chat with TaskWeaver. You can refer to the Quick Start for more details.","s":"OpenAI","u":"/TaskWeaver/docs/llms/openai","h":"","p":136},{"i":139,"t":"In some cases, you may want to use different LLMs for different components. For example, you may want to use OpenAI GPT-4 for the Planner but use Google gemini-pro for the CodeInterpreter. In this part, we show you how to use different LLMs for different components. If you need only one LLM, you can have only the primary LLM settings in the taskweaver_config.json file. If you need multiple LLMs, you need to have ext_llms.llm_configs in the taskweaver_config.json file to specify the extra LLMs for different components. In the following, we show you how to configure the taskweaver_config.json file to use multiple LLMs. \"llm.api_type\":\"openai\", \"llm.api_base\": \"https://api.openai.com/v1\", \"llm.api_key\": \"YOUR_API_KEY\", \"llm.model\": \"gpt-3.5-turbo-1106\", \"llm.response_format\": \"json_object\" \"ext_llms.llm_configs\": { \"llm_A\": { \"llm.api_type\": \"openai\", \"llm.api_base\": \"https://api.openai.com/v1\", \"llm.api_key\": \"YOUR_API_KEY\", \"llm.model\": \"gpt-4-1106-preview\", \"llm.response_format\": \"json_object\", }, \"llm_B\": { \"llm.api_type\": \"google_genai\", \"llm.api_key\": \"YOUR_API_KEY\", \"llm.model\": \"gemini-pro\", }, }, The primary LLM settings are specified in the llm. fields and it is mandatory. ext_llms.llm_configs is optional and is a dict of extra LLMs for different components. If you do not specify it, only the primary LLM will be used. Specify the LLMs for different components in the taskweaver_config.json. For example, we want to use OpenAI GPT-4 for the Planner and use Google gemini-pro for the CodeInterpreter. \"planner.llm_alias\": \"llm_A\", \"code_generator.llm_alias\": \"llm_B\" tip If you do not specify the LLM for a component, the primary LLM will be used by default. In the above example, GPT-3.5-turbo-1106 will be used for both the Planner and the CodeInterpreter.","s":"Multi LLM APIs","u":"/TaskWeaver/docs/llms/multi-llm","h":"","p":138},{"i":141,"t":"QWen (Tongyi Qianwen) is a LLM developed by Alibaba. Go to QWen and register an account and get the API key. More details can be found here (in Chinese). Install the required packages dashscope. pip install dashscope Add the following configuration to taskweaver_config.json: { \"llm.api_type\": \"qwen\", \"llm.model\": \"qwen-max\", \"llm.api_key\": \"YOUR_API_KEY\" } NOTE: llm.model is the model name of QWen LLM API. You can find the model name in the QWen LLM model list. Start TaskWeaver and chat with TaskWeaver. You can refer to the Quick Start for more details.","s":"QWen","u":"/TaskWeaver/docs/llms/qwen","h":"","p":140},{"i":143,"t":"TaskWeaver is a code-first agent framework for seamlessly planning and executing data analytics tasks. This innovative framework interprets user requests through coded snippets and efficiently coordinates a variety of plugins in the form of functions to execute data analytics or workflow automation tasks. Highlighted Features Rich data structure - TaskWeaver allows you to work with rich data structures in Python, such as Lists, Dictionaries, or Pandas DataFrames, instead of having to restrict to text strings. Customized algorithms - TaskWeaver allows you to encapsulate your own algorithms or functions into plugins (in the form of Python functions), and orchestrate them to achieve complex tasks. Incorporating domain-specific knowledge - TaskWeaver is designed to be easily incorporating domain-specific knowledge, such as the knowledge or experience in task planning and code generation, to improve the reliability of task execution. Stateful conversation - TaskWeaver is designed to support stateful conversation which means you can interact with your data in memory across multiple chat rounds. Code verification - TaskWeaver is designed to verify the generated code before execution. It can detect potential issues in the generated code and automatically fix them. Security consideration - TaskWeaver supports running code inside a sandbox environment to prevent malicious code execution. It also provides a basic session management to keep different users' data separate. Easy to debug - TaskWeaver is designed to be easy to debug. We have detailed logs, metrics, and traces, instrumenting using OpenTelemetry, to help you understand what is going on under the simple chat interface. Easy to extend - TaskWeaver is designed to be easily extended to accomplish more complex tasks. Beyond the plugins, you can create extra roles, define how they interact with the Planner and orchestrate them to achieve more complex tasks. Easy to use - TaskWeaver is designed to be easy to use. We provide a set of sample plugins and a tutorial to help you get started. We also provide a all-in-one Docker image to help you quickly play with TaskWeaver.","s":"Overview","u":"/TaskWeaver/docs/overview","h":"","p":142},{"i":145,"t":"In practice, you may want to have multiple plugins that share the same implementation. For example, you may want to have two plugins that both pull data from a database, but one pulls data from database A and the other pulls data from database B. In this case, the plugin implementation code is the same, but the plugin configuration is different. It would be cumbersome to copy and paste the same implementation code to two different files. To solve this problem, TaskWeaver allows you to have multiple plugin configurations that share the same implementation. Here is an example of the plugin configuration for the two plugins that pull data from database A and B respectively: The configuration for the plugin that pulls data from database A: name: sql_pull_data_from_A code: sql_pull_data ... description: >- Pull data from a SQL database A. The database A contains information about merchandise sales. examples: |- df, description = sql_pull_data_from_A(\"pull data from time_series table\") parameters: ... returns: ... configurations: ... sqlite_db_path: /path/to/sqlite_A.db The configuration for the plugin that pulls data from database B: name: sql_pull_data_from_B code: sql_pull_data ... description: >- Pull data from a SQL database B. The database B contains information about customer information. examples: |- df, description = sql_pull_data_from_B(\"pull data from time_series table\") parameters: ... returns: ... configurations: ... sqlite_db_path: /path/to/sqlite_B.db Let's discuss the differences between the two configurations. First, you can see that the name field is different, and the names are different from the python file name (without extension) which is sql_pull_data.py. This name is used in CodeInterpreter for code generation. So, you can see that in the examples field, the function name is sql_pull_data_from_A and sql_pull_data_from_B, respectively. Second, you can see that the code field is the same, and the code file name is sql_pull_data.py. This means that the two plugins share the same implementation code. The code field is optional, and if you don't specify it, the plugin name will be used as the code file name without the extension. Third, you can see that the configurations field is different, and the sqlite_db_path is different. This means that the two plugins have different configurations. This is typically the key reason why you want to have multiple plugin configurations that share the same implementation. Finally, you can see that the description field is different, and the descriptions are different. This is important because the Planner and the CodeInterpreter will use the description to make decisions and generate code. The two descriptions should be explicit enough to distinguish the two plugins. Otherwise, the Planner and the CodeInterpreter may not be able to make the right decisions.","s":"Multiple YAML files to one Python implementation","u":"/TaskWeaver/docs/plugin/multi_yaml_single_impl","h":"","p":144},{"i":147,"t":"When you meet the situation where you want to have multiple plugin configurations that share the same implementation, you can use the code field to specify the code file name, and use the configurations field to specify the configurations. The name field is used to distinguish the plugins, and the description field is used to help the Planner and the CodeInterpreter make right decisions.","s":"Conclusion","u":"/TaskWeaver/docs/plugin/multi_yaml_single_impl","h":"#conclusion","p":144},{"i":149,"t":"In this tutorial, we will introduce how to develop a strawman plugin in TaskWeaver. This plugin can render a input text in ascii art.","s":"An Example of Developing a New Plugin","u":"/TaskWeaver/docs/plugin/how_to_develop_a_new_plugin","h":"","p":148},{"i":151,"t":"Create a python file named ascii_render.py in the plugins folder. The file name should be the same as the plugin name. The plugin name is defined in the plugin schema. In this example, the plugin name is ascii_render. The following code is the template of the plugin implementation. from taskweaver.plugin import Plugin, register_plugin @register_plugin class PluginTemplate(Plugin): def __call__(self, *args, **kwargs): \"\"\"Implementation Starts\"\"\" result, description = YourImplementation() \"\"\"Implementation Ends\"\"\" # if your want to add artifact from the execution result, uncomment the following code # self.ctx.add_artifact( # name=\"artifact\", # file_name=\"artifact.csv\", # type=\"df\", # val=result, # ) return result, description The typical way of implementing the plugin is to change the code between Implementation Starts and Implementation Ends. Note that the return are two variables result and description. The result stores whatever output required for follow-up processing (e.g., a DataFrame). The description is a string to describe the result. Let's make some changes to the code and the result is as follows: from taskweaver.plugin import Plugin, register_plugin @register_plugin class AsciiRenderPlugin(Plugin): def __call__(self, text: str): import pyfiglet ascii_art_str = pyfiglet.figlet_format(text, font='isometric1') return ascii_art_str Note that this function depends on the package pyfiglet, so we need to install it with pip install pyfiglet.","s":"Implement the python code","u":"/TaskWeaver/docs/plugin/how_to_develop_a_new_plugin","h":"#implement-the-python-code","p":148},{"i":153,"t":"Next, we need to configure the schema so that the LLM can understand the function of the plugin. In the schema, there are several fields that should be filled, including name, enabled, required, description, parameters and returns. Please check Plugin Introduction for more details. Create a yaml file named ascii_render.yaml and copy the following content into it. name: ascii_render enabled: true required: true description: >- This plugin renders the input text into ASCII art form. The input should be a string and the output is also a string in ASCII art. For example, result = ascii_render(text='Hello World'). parameters: - name: text type: str required: true description: >- This is the input text to be rendered into ASCII art form. returns: - name: result type: str description: >- The rendered text in ASCII art.","s":"Configure the schema","u":"/TaskWeaver/docs/plugin/how_to_develop_a_new_plugin","h":"#configure-the-schema","p":148},{"i":155,"t":"After the plugin is implemented and configured, we can call the plugin in the conversation. The full conversation is as follows: ========================================================= _____ _ _ __ |_ _|_ _ ___| | _ | | / /__ ____ __ _____ _____ | |/ _` / __| |/ /| | /| / / _ \\/ __ `/ | / / _ \\/ ___/ | | (_| \\__ \\ < | |/ |/ / __/ /_/ /| |/ / __/ / |_|\\__,_|___/_|\\_\\|__/|__/\\___/\\__,_/ |___/\\___/_/ ========================================================= TaskWeaver: I am TaskWeaver, an AI assistant. To get started, could you please enter your request? Human: render ABC in ascii art >>> [INIT_PLAN] 1. Render the text 'ABC' into ASCII art >>> [PLAN] 1. Instruct CodeInterpreter to render the text 'ABC' into ASCII art >>> [CURRENT_PLAN_STEP] 1. Instruct CodeInterpreter to render the text 'ABC' into ASCII art >>> [SEND_TO] CodeInterpreter >>> [MESSAGE] Please render the text 'ABC' into ASCII art >>> [PLANNER->CODEINTERPRETER] Please render the text 'ABC' into ASCII art >>> [PYTHON] # Call the plugin function to render the text 'ABC' into ASCII art ascii_art_result = ascii_render(\"ABC\") ascii_art_result >>> [VERIFICATION] NONE >>> [STATUS] SUCCESS >>> [RESULT] The execution of the generated python code above has succeeded The result of above Python code after execution is: ___ ___ ___ /\\ \\ /\\ \\ /\\ \\ /::\\ \\ /::\\ \\ /::\\ \\ /:/\\:\\ \\ /:/\\:\\ \\ /:/\\:\\ \\ /::\\~\\:\\ \\ /::\\~\\:\\__\\ /:/ \\:\\ \\ /:/\\:\\ \\:\\__\\ /:/\\:\\ \\:|__| /:/__/ \\:\\__\\ \\/__\\:\\/:/ / \\:\\~\\:\\/:/ / \\:\\ \\ \\/__/ \\::/ / \\:\\ \\::/ / \\:\\ \\ /:/ / \\:\\/:/ / \\:\\ \\ /:/ / \\::/__/ \\:\\__\\ \\/__/ ~~ \\/__/ >>> [CODEINTERPRETER->PLANNER] The following python code has been executed: # Call the plugin function to render the text 'ABC' into ASCII art ascii_art_result = ascii_render(\"ABC\") ascii_art_result The execution of the generated python code above has succeeded The result of above Python code after execution is: ___ ___ ___ /\\ \\ /\\ \\ /\\ \\ /::\\ \\ /::\\ \\ /::\\ \\ /:/\\:\\ \\ /:/\\:\\ \\ /:/\\:\\ \\ /::\\~\\:\\ \\ /::\\~\\:\\__\\ /:/ \\:\\ \\ /:/\\:\\ \\:\\__\\ /:/\\:\\ \\:|__| /:/__/ \\:\\__\\ \\/__\\:\\/:/ / \\:\\~\\:\\/:/ / \\:\\ \\ \\/__/ \\::/ / \\:\\ \\::/ / \\:\\ \\ /:/ / \\:\\/:/ / \\:\\ \\ /:/ / \\::/__/ \\:\\__\\ \\/__/ ~~ \\/__/ >>> [INIT_PLAN] 1. Render the text 'ABC' into ASCII art >>> [PLAN] 1. Instruct CodeInterpreter to render the text 'ABC' into ASCII art >>> [CURRENT_PLAN_STEP] Report the ASCII art to the user >>> [SEND_TO] User >>> [MESSAGE] Here is the text 'ABC' rendered in ASCII art: ___ ___ ___ /\\ \\ /\\ \\ /\\ \\ /::\\ \\ /::\\ \\ /::\\ \\ /:/\\:\\ \\ /:/\\:\\ \\ /:/\\:\\ \\ /::\\~\\:\\ \\ /::\\~\\:\\__\\ /:/ \\:\\ \\ /:/\\:\\ \\:\\__\\ /:/\\:\\ \\:|__| /:/__/ \\:\\__\\ \\/__\\:\\/:/ / \\:\\~\\:\\/:/ / \\:\\ \\ \\/__/ \\::/ / \\:\\ \\::/ / \\:\\ \\ /:/ / \\:\\/:/ / \\:\\ \\ /:/ / \\::/__/ \\:\\__\\ \\/__/ ~~ \\/__/ >>> [PLANNER->USER] Here is the text 'ABC' rendered in ASCII art: ___ ___ ___ /\\ \\ /\\ \\ /\\ \\ /::\\ \\ /::\\ \\ /::\\ \\ /:/\\:\\ \\ /:/\\:\\ \\ /:/\\:\\ \\ /::\\~\\:\\ \\ /::\\~\\:\\__\\ /:/ \\:\\ \\ /:/\\:\\ \\:\\__\\ /:/\\:\\ \\:|__| /:/__/ \\:\\__\\ \\/__\\:\\/:/ / \\:\\~\\:\\/:/ / \\:\\ \\ \\/__/ \\::/ / \\:\\ \\::/ / \\:\\ \\ /:/ / \\:\\/:/ / \\:\\ \\ /:/ / \\::/__/ \\:\\__\\ \\/__/ ~~ \\/__/ TaskWeaver: Here is the text 'ABC' rendered in ASCII art: ___ ___ ___ /\\ \\ /\\ \\ /\\ \\ /::\\ \\ /::\\ \\ /::\\ \\ /:/\\:\\ \\ /:/\\:\\ \\ /:/\\:\\ \\ /::\\~\\:\\ \\ /::\\~\\:\\__\\ /:/ \\:\\ \\ /:/\\:\\ \\:\\__\\ /:/\\:\\ \\:|__| /:/__/ \\:\\__\\ \\/__\\:\\/:/ / \\:\\~\\:\\/:/ / \\:\\ \\ \\/__/ \\::/ / \\:\\ \\::/ / \\:\\ \\ /:/ / \\:\\/:/ / \\:\\ \\ /:/ / \\::/__/ \\:\\__\\ \\/__/ ~~ \\/__/ info Check the FAQs if you have any issues in developing a plugin before submitting an issue on GitHub.","s":"Call the plugin","u":"/TaskWeaver/docs/plugin/how_to_develop_a_new_plugin","h":"#call-the-plugin","p":148},{"i":158,"t":"You can install TaskWeaver by running the following command: # [optional] create a conda environment to isolate the dependencies # conda create -n taskweaver python=3.10 # conda activate taskweaver # clone the repository git clone https://github.com/microsoft/TaskWeaver.git cd TaskWeaver # install the requirements pip install -r requirements.txt","s":"Installation","u":"/TaskWeaver/docs/quickstart","h":"#installation","p":156},{"i":160,"t":"TaskWeaver runs as a process, you need to create a project directory to store plugins and configuration files. We provided a sample project directory in the project folder. You can copy the project folder to your workspace. A project directory typically contains the following files and folders: πŸ“¦project ┣ πŸ“œtaskweaver_config.json # the project configuration file for TaskWeaver ┣ πŸ“‚plugins # the folder to store plugins ┣ πŸ“‚planner_examples # the folder to store planner examples ┣ πŸ“‚codeinterpreter_examples # the folder to store code interpreter examples ┣ πŸ“‚logs # the folder to store logs, will be generated after program starts β”— πŸ“‚workspace # the directory stores session data， will be generated after program starts β”— πŸ“‚ session_id ┣ πŸ“‚ces # the folder used by the code execution service ┣ πŸ“‚cwd # the current working directory to run the generated code β”— other session data","s":"Project Directory","u":"/TaskWeaver/docs/quickstart","h":"#project-directory","p":156},{"i":162,"t":"Before running TaskWeaver, you need to provide your OpenAI API key and other necessary information. You can do this by editing the taskweaver_config.json file. If you are using Azure OpenAI, you need to set the following parameters in the taskweaver_config.json file:","s":"OpenAI Configuration","u":"/TaskWeaver/docs/quickstart","h":"#openai-configuration","p":156},{"i":164,"t":"{ \"llm.api_base\": \"https://xxx.openai.azure.com/\", \"llm.api_key\": \"your_api_key\", \"llm.api_type\": \"azure\", \"llm.api_version\": \"the api version\", \"llm.model\": \"the model name, e.g., gpt-4\" # In Azure OpenAI, the model name is the deployment_name }","s":"Azure OpenAI","u":"/TaskWeaver/docs/quickstart","h":"#azure-openai","p":156},{"i":166,"t":"{ \"llm.api_key\": \"the api key\", \"llm.model\": \"the model name, e.g., gpt-4\" } πŸ’‘ Only the latest OpenAI API supports the json_object response format. If you are using an older version of OpenAI API, you need to set the \"llm.response_format\"=null in the taskweaver_config.json file. More configuration options can be found in the configuration documentation.","s":"OpenAI","u":"/TaskWeaver/docs/quickstart","h":"#openai","p":156},{"i":168,"t":"# assume you are in the TaskWeaver folder cloned from the repository python -m taskweaver -p ./project/ # -p is the path to the project directory This will start the TaskWeaver process and you can interact with it through the command line (CLI) interface. If everything goes well, you will see the following prompt: ========================================================= _____ _ _ __ |_ _|_ _ ___| | _ | | / /__ ____ __ _____ _____ | |/ _` / __| |/ /| | /| / / _ \\/ __ `/ | / / _ \\/ ___/ | | (_| \\__ \\ < | |/ |/ / __/ /_/ /| |/ / __/ / |_|\\__,_|___/_|\\_\\|__/|__/\\___/\\__,_/ |___/\\___/_/ ========================================================= TaskWeaver: I am TaskWeaver, an AI assistant. To get started, could you please enter your request? Human: ___ There are other ways to start TaskWeaver: A Chainlit UI interface: TaskWeaver provides an experimental web-based interface to interact with the system. A Library: You can also use TaskWeaver as a library in your Python code. The all-in-one Docker image: We provide a Docker image that contains all the dependencies to run TaskWeaver.","s":"Start TaskWeaver","u":"/TaskWeaver/docs/quickstart","h":"#start-taskweaver","p":156},{"i":170,"t":"Plugins are the units that could be orchestrated by TaskWeaver's CodeInterpreter. One could view the plugins as tools that the LLM can utilize to accomplish certain tasks. In TaskWeaver, each plugin is represented as a Python function that can be called within the generated code snippet. One concrete example would be pulling data from database and apply anomaly detection. The generated code (simplified) looks like follows: df, data_description = sql_pull_data(query=\"pull data from time_series table\") anomaly_df, anomaly_description = anomaly_detection(df, time_col_name=\"ts\", value_col_name=\"val\") The generated code snippet above calls two plugins: sql_pull_data and anomaly_detection. The sql_pull_data plugin pulls data from a database, and the anomaly_detection plugin detects anomalies in the data.","s":"Plugin Introduction","u":"/TaskWeaver/docs/plugin/plugin_intro","h":"","p":169},{"i":172,"t":"A plugin involves two files: Plugin Implementation: a Python file that defines the plugin Plugin Schema: a file in yaml that defines the schema of the plugin","s":"Plugin Structure","u":"/TaskWeaver/docs/plugin/plugin_intro","h":"#plugin-structure","p":169},{"i":174,"t":"The plugin function needs to be implemented in Python. To be coordinated with the orchestration by TaskWeaver, a plugin python file consists of two parts: Plugin function implementation code TaskWeaver plugin decorator Here we exhibit an example of the anomaly detection plugin as the following code: import pandas as pd from pandas.api.types import is_numeric_dtype from taskweaver.plugin import Plugin, register_plugin @register_plugin class AnomalyDetectionPlugin(Plugin): def __call__(self, df: pd.DataFrame, time_col_name: str, value_col_name: str): \"\"\" anomaly_detection function identifies anomalies from an input dataframe of time series. It will add a new column \"Is_Anomaly\", where each entry will be marked with \"True\" if the value is an anomaly or \"False\" otherwise. :param df: the input data, must be a dataframe :param time_col_name: name of the column that contains the datetime :param value_col_name: name of the column that contains the numeric values. :return df: a new df that adds an additional \"Is_Anomaly\" column based on the input df. :return description: the description about the anomaly detection results. \"\"\" try: df[time_col_name] = pd.to_datetime(df[time_col_name]) except Exception: print(\"Time column is not datetime\") return if not is_numeric_dtype(df[value_col_name]): try: df[value_col_name] = df[value_col_name].astype(float) except ValueError: print(\"Value column is not numeric\") return mean, std = df[value_col_name].mean(), df[value_col_name].std() cutoff = std * 3 lower, upper = mean - cutoff, mean + cutoff df[\"Is_Anomaly\"] = df[value_col_name].apply(lambda x: x < lower or x > upper) anomaly_count = df[\"Is_Anomaly\"].sum() description = \"There are {} anomalies in the time series data\".format(anomaly_count) self.ctx.add_artifact( name=\"anomaly_detection_results\", # a brief description of the artifact file_name=\"anomaly_detection_results.csv\", # artifact file name type=\"df\", # artifact data type, support chart/df/file/txt/svg val=df, # variable to be dumped ) return df, description You need to go through the following steps to register a plugin: import the TaskWeaver plugin decorator from taskWeaver.plugin import Plugin, register_plugin create your plugin class inherited from Plugin parent class (e.g., AnomalyDetectionPlugin(Plugin)), which is decorated by @register_plugin implement your plugin function in __call__ method of the plugin class. We provide an example process of developing a new plugin in this tutorial. tip A good practice in a plugin implementation is to return a description of the result in natural language. As LLMs only understand natural language, it is important to let the model understand what the execution result is. In the example implementation above, the description says how many anomalies are detected. In other cases such as loading a csv file, a good description could be showing the schema of the loaded data. This description can be used by the LLM to plan the next steps.","s":"Plugin Implementation","u":"/TaskWeaver/docs/plugin/plugin_intro","h":"#plugin-implementation","p":169},{"i":176,"t":"If the functionality of your plugin depends on additional libraries or packages, it is essential to ensure that they are installed before proceeding. If you wish to persist intermediate results, such as data, figures, or prompts, in your plugin implementation, TaskWeaver provides an add_artifact API that allows you to store these results in the workspace. In the example we provide, if you have performed anomaly detection and obtained results in the form of a CSV file, you can utilize the add_artifact API to save this file as an artifact. The artifacts are stored in the project/workspace/session_id/cwd folder in the project directory. self.ctx.add_artifact( name=\"anomaly_detection_results\", # a brief description of the artifact file_name=\"anomaly_detection_results.csv\", # artifact file name type=\"df\", # artifact data type, support chart/df/file/txt/svg val=df, # variable to be dumped )","s":"Important Notes","u":"/TaskWeaver/docs/plugin/plugin_intro","h":"#important-notes","p":169},{"i":178,"t":"The plugin schema is composed of several parts: *name: The main function name of the Python code. enabled: determine whether the plugin is enabled for selection during conversations. The default value is true. plugin_only: determine if this plugin is enabled under the plugin-only mode. The default value is false. code: the code file name of the plugin. The default value is the same as the plugin name. *descriptions: A brief description that introduces the plugin function. *parameters: This section lists all the input parameter information. It includes the parameter's name, type, whether it is required or optional, and a description providing more details about the parameter. *returns: This section lists all the return value information. It includes the return value's name, type, and description that provides information about the value that is returned by the function. configurations: the configuration parameters for the plugin. The default value is an empty dictionary. tip The addition of any extra fields or missing of mandatory fields (marked by * above) would result in a validation failure within the plugin schema. The plugin schema is required to be written in YAML format. Here is the plugin schema example of the above anomaly detection plugin: name: anomaly_detection enabled: true plugin_only: false required: false description: >- anomaly_detection function identifies anomalies from an input DataFrame of time series. It will add a new column \"Is_Anomaly\", where each entry will be marked with \"True\" if the value is an anomaly or \"False\" otherwise. parameters: - name: df type: DataFrame required: true description: >- the input data from which we can identify the anomalies with the 3-sigma algorithm. - name: time_col_name type: str required: true description: name of the column that contains the datetime - name: value_col_name type: str required: true description: name of the column that contains the numeric values. returns: - name: df type: DataFrame description: >- This DataFrame extends the input DataFrame with a newly-added column \"Is_Anomaly\" containing the anomaly detection result. - name: description type: str description: This is a string describing the anomaly detection results. info Without specifying the code field, the plugin schema will use the plugin name as the code file name. For example, the plugin name is anomaly_detection and the code file name is anomaly_detection.py. In cases where the plugin name is not the same as the code file name, you can specify the code name (code file) in the plugin schema to ensure clarity and accuracy. For example, the plugin name is anomaly_detection and the code file name is anomaly_detection_code.py. Then, you can specify the code name in the plugin schema as follows: code: anomaly_detection_code Note that the code file name should be the same as the code name without the .py extension. Refer to Multiple YAML files to one Python implementation for more information on how to use this feature. info When using common code that requires some configuration parameter modifications for different plugins, it is important to specify these configuration parameters in the plugin schema. The configuration parameters are specified in the plugin schema as follows: configurations: key1: value1 key2: value2 These configuration parameters can be accessed in the plugin implementation as follows: self.config.get(\"key1\") self.config.get(\"key2\") info When this plugin is enabled for the plugin-only mode, set the plugin_only field to true. The default value is false. Note that all plugins will be loaded in the non-plugin-only mode which is the default mode. But in the plugin-only mode, only the plugins with plugin_only: true will be loaded.","s":"Plugin Schema","u":"/TaskWeaver/docs/plugin/plugin_intro","h":"#plugin-schema","p":169},{"i":180,"t":"In this document, we will show you how to run TaskWeaver using the All-in-One Docker Image. Please note that the All-in-One Docker Image is for development and testing purposes only.","s":"All-in-One Docker Image","u":"/TaskWeaver/docs/usage/docker","h":"","p":179},{"i":182,"t":"You need to have Docker installed on your machine. For Windows and macOS users, you can use Docker Desktop. You can download it from Docker's official website. For Linux users, you can install following the instructions in the Docker's official website. Please find the installation guide for your specific Linux distribution.","s":"Prerequisites","u":"/TaskWeaver/docs/usage/docker","h":"#prerequisites","p":179},{"i":184,"t":"There are two versions of the TaskWeaver All-in-One Docker Image: taskweavercontainers/taskweaver-all-in-one:latest: This version includes the Planner and CodeInterpreter roles only. You can use this container for code generation and execution tasks. taskweavercontainers/taskweaver-all-in-one:latest-ws: This version includes an additional WebSearch role which can search the web for information. As it requires dependencies to the sentence-transformers library, it is larger. Open a terminal and run the following command to obtain the TaskWeaver image: docker pull taskweavercontainers/taskweaver-all-in-one:latest # if you want to use the version with the WebSearch role # docker pull taskweavercontainers/taskweaver-all-in-one:latest-ws Once the image is pulled, you can run the TaskWeaver container using the following command: docker run -it -e LLM_API_BASE= \\ -e LLM_API_KEY= \\ -e LLM_API_TYPE= \\ -e LLM_MODEL= \\ taskweavercontainers/taskweaver-all-in-one:latest If you want to run TaskWeaver in UI mode, you can use the following command: docker run -it -e LLM_API_BASE= \\ -e LLM_API_KEY= \\ -e LLM_API_TYPE= \\ -e LLM_MODEL= \\ -p 8000:8000 \\ taskweavercontainers/taskweaver-all-in-one:latest /bin/sh -c \"cd playground/UI/ && python -m chainlit run --host 0.0.0.0 --port 8000 app.py\" Then you can access the TaskWeaver Web UI by visiting http://localhost:8000 in your web browser.","s":"Run TaskWeaver using the All-in-One Docker Image","u":"/TaskWeaver/docs/usage/docker","h":"#run-taskweaver-using-the-all-in-one-docker-image","p":179},{"i":186,"t":"You can mount your local project directory to the container. For example, you can use the following command: docker run -it -e LLM_API_BASE= \\ -e LLM_API_KEY= \\ -e LLM_API_TYPE= \\ -e LLM_MODEL= \\ --mount type=bind,source=,target=/app/TaskWeaver/project/ \\ taskweavercontainers/taskweaver-all-in-one:latest Then you can edit the taskweaver_config.json file in your local project directory to configure TaskWeaver. In addition, you also can customize the plugins and examples in your local project directory. The structure of the project directory can be referred to the taskweaver/project directory.","s":"How to run TaskWeaver on your own project directory","u":"/TaskWeaver/docs/usage/docker","h":"#how-to-run-taskweaver-on-your-own-project-directory","p":179},{"i":188,"t":"You can mount your local directory to the container. For example, you can use the following command: docker run -it -e LLM_API_BASE= \\ -e LLM_API_KEY= \\ -e LLM_API_TYPE= \\ -e LLM_MODEL= \\ --mount type=bind,source=,target=/app/TaskWeaver/local/ \\ taskweavercontainers/taskweaver-all-in-one:latest Then you can access your local files in the container by visiting the /app/TaskWeaver/local/ directory. You can load a file under the /app/TaskWeaver/local/ directory in the TaskWeaver CLI with the /load command. For example, you can load a file named example.csv by running the following command: TaskWeaver β–Ά I am TaskWeaver, an AI assistant. To get started, could you please enter your request? Human β–Ά /load /app/TaskWeaver/local/example.csv Human β–Ά display the column names of the loaded file","s":"How to access your local files in the container","u":"/TaskWeaver/docs/usage/docker","h":"#how-to-access-your-local-files-in-the-container","p":179},{"i":190,"t":"This is the command line interface for TaskWeaver. You can interact with TaskWeaver through this interface. Follow the instruction in Quick Start to clone the repository and fill in the necessary configurations. Run the following command in terminal. # assume you are in the TaskWeaver folder python -m taskweaver -p ./project/ # -p is the path to the project directory This will start the TaskWeaver process, and you can interact with it through the command line interface. If everything goes well, you will see the following prompt: ========================================================= _____ _ _ __ |_ _|_ _ ___| | _ | | / /__ ____ __ _____ _____ | |/ _` / __| |/ /| | /| / / _ \\/ __ `/ | / / _ \\/ ___/ | | (_| \\__ \\ < | |/ |/ / __/ /_/ /| |/ / __/ / |_|\\__,_|___/_|\\_\\|__/|__/\\___/\\__,_/ |___/\\___/_/ ========================================================= TaskWeaver: I am TaskWeaver, an AI assistant. To get started, could you please enter your request? Human: ___ We have provided a set of commands to interact with TaskWeaver. You can type help to see the list of available commands. All commands start with a / character. For example, you can type /help to see the list of available commands. TaskWeaver β–Ά I am TaskWeaver, an AI assistant. To get started, could you please enter your request? Human β–Ά /help TaskWeaver Chat Console ----------------------- /load : load a file /info: print the information of the current session /reset: reset the session /clear: clear the console /exit: exit the chat console /help: print this help message /save: save the chat history of the current session for experience extraction The table of commands supported by TaskWeaver is as follows: Command Description /load Load a file by its absolute path, e.g., /load /home/taskweaver/sample.csv /info Print the session id and the active roles of the current session /reset Reset the current session and start a new session /clear Clear the console content /exit Exit the chat console /help Print the help message /save Save the chat history of the current session for experience extraction tip When TaskWeaver runs generated code, the CWD (current working directory) is set to the project/workspace/session_id/cwd directory. If you need to use relative paths in the generated code, the cwd directory should be the base path.","s":"Terminal","u":"/TaskWeaver/docs/usage/cmd","h":"","p":189},{"i":192,"t":"If you want to use TaskWeaver as a library, you can refer to the following code example: from taskweaver.app.app import TaskWeaverApp # This is the folder that contains the taskweaver_config.json file and not the repo root. Defaults to \"./project/\" app_dir = \"./project/\" app = TaskWeaverApp(app_dir=app_dir) session = app.get_session() user_query = \"hello, what can you do?\" response_round = session.send_message(user_query) print(response_round.to_dict()) Essentially, you need to create a TaskWeaverApp object and then get a session object from it. Each time, you can send a message to TaskWeaver by calling session.send_message(user_query). The return value of session.send_message(user_query) is a Round object, which contains the response from TaskWeaver. A round is a conversation round between the user and TaskWeaver, which contains a list of posts. An example of the Round object is shown below. To better understand the structure, you can refer to the Concepts section. { \"id\": \"round-20231201-043134-218a2681\", \"user_query\": \"hello, what can you do?\", \"state\": \"finished\", \"post_list\": [ { \"id\": \"post-20231201-043134-10eedcca\", \"message\": \"hello, what can you do?\", \"send_from\": \"User\", \"send_to\": \"Planner\", \"attachment_list\": [] }, { \"id\": \"post-20231201-043141-86a2aaff\", \"message\": \"I can help you with various tasks, such as counting rows in a data file, detecting anomalies in a dataset, searching for products on Klarna, summarizing research papers, and pulling data from a SQL database. Please provide more information about the task you want to accomplish, and I'll guide you through the process.\", \"send_from\": \"Planner\", \"send_to\": \"User\", \"attachment_list\": [ { \"id\": \"atta-20231201-043141-6bc4da86\", \"type\": \"init_plan\", \"content\": \"1. list the available functions\" }, { \"id\": \"atta-20231201-043141-6f29f6c9\", \"type\": \"plan\", \"content\": \"1. list the available functions\" }, { \"id\": \"atta-20231201-043141-76186c7a\", \"type\": \"current_plan_step\", \"content\": \"1. list the available functions\" } ] } ] } tip If you need to see the intermediate states of the conversation, you need to implement a SessionEventHandler class and pass it at calling session.send_message(user_query, event_handler=your_event_handler). Find more information about the event handler in this section.","s":"Library","u":"/TaskWeaver/docs/usage/library","h":"","p":191},{"i":195,"t":"3 items","s":"πŸ—ƒοΈ Plugin","u":"/TaskWeaver/docs/customization/","h":"","p":193},{"i":197,"t":"1 items","s":"πŸ—ƒοΈ Example","u":"/TaskWeaver/docs/customization/","h":"","p":193},{"i":199,"t":"1 items","s":"πŸ—ƒοΈ Experience","u":"/TaskWeaver/docs/customization/","h":"","p":193},{"i":201,"t":"Please note that this Web UI is for development and testing purposes only. Follow the instruction in Quick Start to clone the repository and fill in the necessary configurations. Install the chainlit package by pip install -U chainlit if you don't have it in your environment. Start the service by running the following command. # assume you are in the TaskWeaver folder cd playground/UI/ # make sure you are in playground/UI/ folder chainlit run app.py Open the browser with http://localhost:8000 if it doesn't open automatically. info We now support uploading files using the Web UI. Below are some screenshots of the Web UI:","s":"Web UI","u":"/TaskWeaver/docs/usage/webui","h":"","p":200},{"i":204,"t":"The plugin-only mode is a restricted mode of TaskWeaver that only allows you to use plugins. Compared to the full mode, the plugin-only mode has the following restrictions: The generated code only contains the calls to the plugins. For example, the following code only calls the ascii_render plugin and does not contain any \"free-form\" code. r1=ascii_render(**{\"text\":\"Hello\"}) r1 Only the plugins with plugin_only: true in the yaml file will be loaded. For example, the following plugin will be loaded in the plugin-only mode: name: ascii_render plugin_only: true ... If this field is not specified, the default value is false. For plugins in the plugin-only mode, the argument type can only be str, int, boolean, or float. Other types such as DataFrame are not allowed. Essentially, we consider these plugins only produce \"text-like\" output that can be directly consumed by the LLM. To support the plugin-only mode, we developed a new role called PluginOrchestrator. To enable the plugin-only mode, you can add the configuration \"session.roles\": [\"planner\", \"code_interpreter_plugin_only\"] in the project configuration file taskweaver_config.json. info Although the plugin-only mode is restricted, it is still useful in some scenarios. For example, you may want to use TaskWeaver to only generate the code to call a certain plugin, and you want to get the response from the plugin directly, without generating any other code for safety reasons.","s":"What is the plugin-only mode?","u":"/TaskWeaver/docs/plugin/plugin_only","h":"#what-is-the-plugin-only-mode","p":202},{"i":206,"t":"The plugin-only mode is implemented based on the function calling mode of LLMs. In this mode, the LLM is trained to generate a JSON object that contains the function name and the arguments. For example, the following JSON object is generated by the LLM: { \"function\": \"ascii_render\", \"arguments\": { \"text\": \"Hello\" } } With this JSON object, we assemble the code to call the plugin: r1=ascii_render(**{\"text\":\"Hello\"}) r1 Then, we execute the code and get the response from the plugin. Therefore, the code is not directly generated by the LLM in the plugin-only mode.","s":"How is the plugin-only mode implemented?","u":"/TaskWeaver/docs/plugin/plugin_only","h":"#how-is-the-plugin-only-mode-implemented","p":202},{"i":208,"t":"Currently, the plugin-only mode is only supported by OpenAI models ZhipuAI models Likely other models that are compatible with the OpenAI models will also support the plugin-only mode.","s":"Which models support the plugin-only 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