diff --git a/search-index-docs.json b/search-index-docs.json
index 52d83f50..490e5180 100644
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+++ b/search-index-docs.json
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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":1},{"i":4,"t":"TaskWeaver-CLI","s":"Demo","u":"/TaskWeaver/docs/advanced/cli_only","h":"#demo","p":1},{"i":6,"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":1},{"i":8,"t":"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/advanced/code_execution","h":"","p":7},{"i":10,"t":"TaskWeaver supports two modes of code execution: local and container. The local 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. 💡We recommend using the container mode for code execution, especially when the usage of the agent is open to untrusted users. In the container mode, the code is executed in a Docker container, which is isolated from the host machine.","s":"Two Modes of Code Execution","u":"/TaskWeaver/docs/advanced/code_execution","h":"#two-modes-of-code-execution","p":7},{"i":12,"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 local. 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.ps1 # for Windows ./build.sh # for Linux or macOS After the Docker image is built, you can run docker images to check if a Docker image named executor_container 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 taskweaver/executor running after executing some code.","s":"How to Configure the Code Execution Mode","u":"/TaskWeaver/docs/advanced/code_execution","h":"#how-to-configure-the-code-execution-mode","p":7},{"i":14,"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/<session_id> 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/advanced/code_execution","h":"#limitations-of-the-container-mode","p":7},{"i":16,"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 <docker_network_subnet> -j DROP Replace <docker_network_subnet> 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/advanced/code_execution","h":"#restricting-external-network-access-for-docker-containers","p":7},{"i":18,"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":17},{"i":20,"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":17},{"i":23,"t":"Agent 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":21},{"i":25,"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":21},{"i":27,"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":21},{"i":29,"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":28},{"i":31,"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_configure 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":28},{"i":33,"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":28},{"i":35,"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":28},{"i":37,"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":28},{"i":39,"t":"More details about some important configurations","s":"More about Configurations","u":"/TaskWeaver/docs/configurations/configurations_in_detail","h":"","p":38},{"i":41,"t":"In TaskWeaver, the Planner is responsible for generating a plan to accomplish the user's task. The plan is a sequence of steps, where each step will be executed by the Code Interpreter. Taken the response from the Code Interpreter or new requests from the user as input, the Planner will update the plan and move on to the next step. 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.","s":"Planner Configuration","u":"/TaskWeaver/docs/configurations/configurations_in_detail","h":"#planner-configuration","p":38},{"i":43,"t":"session is the entrance of TaskWeaver. It is responsible for the communication between the user and TaskWeaver. You can refer to taskweaver_as_a_lib to see how to setup a TaskWeaver session and start chatting with TaskWeaver. 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. roles: the roles included for the conversation. The default value is [\"planner\", \"code_interpreter\"]. Roles can be divided into two parts: planner roles and worker roles. In TaskWeaver, the Planner is fixed. Planner roles are responsible for generating a plan to accomplish the user's task, while worker roles are responsible for executing the plan generated by the Planner. TaskWeaver supports 3 worker roles: code_interpreter, code_interpreter_cli_only, and code_interpreter_plugin_only. code_interpreter: it will generate Python code to fulfill the user's request. 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. CodeInterpreter will generate CLI commands (e.g., bash/powershell), instead of Python code, to fulfill the user's request. 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 Configration","u":"/TaskWeaver/docs/configurations/configurations_in_detail","h":"#session-configration","p":38},{"i":45,"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 Configration","u":"/TaskWeaver/docs/configurations/configurations_in_detail","h":"#embedding-configration","p":38},{"i":47,"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. 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 💡 ${AppBaseDir} is the project directory. 💡 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. 💡 Read this for more information for planner.prompt_compression and code_generator.prompt_compression.","s":"Configuration File","u":"/TaskWeaver/docs/configurations/overview","h":"","p":46},{"i":50,"t":"5 items","s":"🗃️ Plugin","u":"/TaskWeaver/docs/customization/","h":"","p":48},{"i":52,"t":"1 items","s":"🗃️ Example","u":"/TaskWeaver/docs/customization/","h":"","p":48},{"i":54,"t":"1 items","s":"🗃️ Experience","u":"/TaskWeaver/docs/customization/","h":"","p":48},{"i":56,"t":"There are two types of examples: (1) planning examples and (2) code interpreter examples. Planning examples are used to demonstrate how to use TaskWeaver to plan for a specific task. Code generation examples are used to demonstrate how to generate code or orchestrate plugins to perform a specific task.","s":"Customizing Examples","u":"/TaskWeaver/docs/customization/example/","h":"","p":55},{"i":58,"t":"A planning example tells LLMs how to plan for a specific query from the user; talk to the code interpreter; receive the execution result from the code interpreter; and summarize the execution result. Before constructing the planning example, we strongly encourage you to go through the planner prompt. The following is an example of a planning example which contains 4 posts. Each post contains a message, a sender, a receiver, and a list of attachments. The first post is sent from the user to the planner. The message is \"count the rows of /home/data.csv\", which is the same as the user query. The second post is sent from the planner to the code interpreter. 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, which is a markdown string. The second attachment is the plan, which is a markdown string. The third attachment is the current plan step, which is a markdown string. The third post is sent from the code interpreter 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: The first attachment is the initial plan, which is the same as the second attachment of the second post. The second attachment is the plan, which is the same as the third attachment of the second post. The third attachment is the current plan step, which is a markdown string. 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 <sequentially depends on 1> 3. report the result to the user <interactively depends on 2> - 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 <sequentially depends on 1> 3. report the result to the user <interactively depends on 2> - 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","s":"Planning Examples","u":"/TaskWeaver/docs/customization/example/","h":"#planning-examples","p":55},{"i":60,"t":"A code interpreter example tells LLMs how to generate code or orchestrate plugins to perform a specific task. The task is from the planner. Before constructing the code interpreter example, we strongly encourage you to read the code generator prompt. The following is an example of a code interpreter example which contains 2 posts. Each post contains a message, a sender, a receiver, and a list of attachments. The first post is sent from the planner to the code interpreter. The message is \"Please read file /abc/def.txt\". The second post is sent from the code interpreter 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 code interpreter, which is a markdown string. 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, which is a markdown string. 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. 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. 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":"Code Interpreter Examples","u":"/TaskWeaver/docs/customization/example/","h":"#code-interpreter-examples","p":55},{"i":62,"t":"Q: How do I know if TaskWeaver can see my plugin? 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/<session_id>/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. Q: Why TaskWeaver cannot see my plugin? 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 <name>.yaml, skipping: Yaml loading failed due to: <reason> 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. Q: Why TaskWeaver can see my plugin but cannot call it? 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 <name> failed to load: Plugin <name> failed to register: failed to load plugin <name> <reason> 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. Q: How to debug my plugin? 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":"FAQ","u":"/TaskWeaver/docs/FAQ","h":"","p":61},{"i":65,"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":63},{"i":67,"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":63},{"i":69,"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 ▶ I am TaskWeaver, an AI assistant. To get started, could you please enter your request? Human ▶ calculate the mean value of d:/demo_data.csv ╭───< Planner > ├─► [init_plan] │ 1. load the data file │ 2. calculate the mean value of the loaded data <sequentially depends on 1> │ 3. report the result to the user <interactively depends on 2> ├─► [plan] │ 1. instruct CodeInterpreter to load the data file and calculate the mean value of the loaded data │ 2. report the result to the user ├─► [current_plan_step] 1. instruct CodeInterpreter to load the data file and calculate the mean value of the loaded data ├──● Please load the data file d:/demo_data.csv and calculate the mean value of the loaded data ╰──● sending message to CodeInterpreter ╭───< CodeInterpreter > ├─► [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. ├─► [python] │ import pandas as pd │ │ data_file_path = \"d:/demo_data.csv\" │ data_df = pd.read_csv(data_file_path) │ mean_value = data_df.mean() │ mean_value ├─► [verification] NONE ├─► [code_error] No code verification is performed. ├─► [execution_status] FAILURE ├─► [execution_result] │ The execution of the generated python code above has failed │ │ During execution, the following messages were logged: │ Traceback (most recent call last): │ │ Cell In[1], line 5 │ mean_value = data_df.mean() │ │ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\frame.py:11335 in mean │ result = super().mean(axis, skipna, numeric_only, **kwargs) │ │ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\generic.py:11984 in mean │ return self._stat_function( │ │ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\generic.py:11941 in _stat_function │ return self._reduce( │ │ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\frame.py:11204 in _reduce │ res = df._mgr.reduce(blk_func) │ │ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:1459 in reduce │ nbs = blk.reduce(func) │ │ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\internals\\blocks.py:377 in reduce │ result = func(self.values) │ │ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\frame.py:11136 in blk_func │ ret │ ├─► [artifact_paths] [] ├──● The following python code has been executed: │ ```python │ import pandas as pd │ │ data_file_path = \"d:/demo_data.csv\" │ data_df = pd.read_csv(data_file_path) │ mean_value = data_df.mean() │ mean_value │ ``` │ │ │ The execution of the generated python code above has failed │ │ During execution, the following messages were logged: │ Traceback (most recent call last): │ │ Cell In[1], line 5 │ mean_value = data_df.mean() │ │ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\frame.py:11335 in mean │ result = super().mean(axis, skipna, numeric_only, **kwargs) │ │ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\generic.py:11984 in mean │ return self._stat_function( │ │ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\generic.py:11941 in _stat_function │ return self._reduce( │ │ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\frame.py:11204 in _reduce │ res = df._mgr.reduce(blk_func) │ │ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:1459 in reduce │ nbs = blk.reduce(func) │ │ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\internals\\blocks.py:377 in reduce │ result = func(self.values) │ │ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\frame.py:11136 in blk_func │ ret │ ├─► [revise_message] │ 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. │ Otherwise, please explain the problem to me. ╰──● sending message to CodeInterpreter ╭───< CodeInterpreter > ├─► [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 │ error. ├─► [python] │ import pandas as pd │ │ data_file_path = \"d:/demo_data.csv\" │ data_df = pd.read_csv(data_file_path) │ mean_value = data_df.mean(axis=0, numeric_only=True) │ mean_value ├─► [verification] NONE ├─► [code_error] No code verification is performed. ├─► [execution_status] SUCCESS ├─► [execution_result] │ The execution of the generated python code above has succeeded │ │ The result of above Python code after execution is: │ Count 78172.75 │ dtype: float64 ├─► [artifact_paths] [] ├──● The following python code has been executed: │ ```python │ import pandas as pd │ │ data_file_path = \"d:/demo_data.csv\" │ data_df = pd.read_csv(data_file_path) │ mean_value = data_df.mean(axis=0, numeric_only=True) │ mean_value │ ``` │ │ │ The execution of the generated python code above has succeeded │ │ The result of above Python code after execution is: │ Count 78172.75 │ dtype: float64 ╰──● sending message to Planner ╭───< Planner > ├─► [init_plan] │ 1. load the data file │ 2. calculate the mean value of the loaded data <sequentially depends on 1> │ 3. report the result to the user <interactively depends on 2> ├─► [plan] │ 1. instruct CodeInterpreter to load the data file and calculate the mean value of the loaded data │ 2. report the result to the user ├─► [current_plan_step] 2. report the result to the user ├──● The mean value of the data in d:/demo_data.csv is 78172.75 ╰──● sending message to User TaskWeaver ▶ 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 ▶ I am TaskWeaver, an AI assistant. To get started, could you please enter your request? Human ▶ calculate the variance value of d:/demo_data.csv ╭───< Planner >TaskWeaver] preparing <=�=> ├─► [init_plan] │ 1. load the data file │ 2. confirm the columns to calculate variance <interactively depends on 1> │ 3. calculate the variance value for the selected columns <sequentially depends on 2> │ 4. report the result to the user <interactively depends on 3> ├─► [plan] ▶ [Planner][plan] 1. instruct CodeInterpreter to load the data file 2. confirm the columns to calcu <=�=> │ 1. instruct CodeInterpreter to load the data file │ 2. confirm the columns to calculate variance ├─► [current_plan_step] 1. instruct CodeInterpreter to load the data file load the data file <=�=> ├──● Please load the data file d:/demo_data.csva file d:/demo_dat <=�=> ╰──● sending message to CodeInterpreter ╭───< CodeInterpreter > ├─► [thought] ProgramApe will generate a code snippet to load the data file d:/demo_data.csv using pandas.demo_data.csv using panda <=�=> ├─► [python]▶ [CodeInterpreter][python] import pandas as pd data_file_path = \"d:/demo_data.csv\" df = pd.read_csv(data_fil <=�=> │ import pandas as pd │ │ data_file_path = \"d:/demo_data.csv\" │ df = pd.read_csv(data_file_path) │ df ├─► [verification] NONE ├─► [code_error] No code verification is performed. ├─► [execution_status] SUCCESS] executing code <=�=> ├─► [execution_result] │ The execution of the generated python code above has succeeded │ │ The result of above Python code after execution is: │ TimeBucket Count │ 0 2023-02-01T00:00:00Z 67814 │ 1 2023-02-01T04:00:00Z 84569 │ .. ... ... │ 718 2023-05-31T16:00:00Z 89912 │ 719 2023-05-31T20:00:00Z 87249 │ │ [720 rows x 2 columns] ├─► [artifact_paths] [] ├──● The following python code has been executed: │ ```python │ import pandas as pd │ │ data_file_path = \"d:/demo_data.csv\" │ df = pd.read_csv(data_file_path) │ df │ ``` │ │ │ The execution of the generated python code above has succeeded │ │ The result of above Python code after execution is: │ TimeBucket Count │ 0 2023-02-01T00:00:00Z 67814 │ 1 2023-02-01T04:00:00Z 84569 │ .. ... ... │ 718 2023-05-31T16:00:00Z 89912 │ 719 2023-05-31T20:00:00Z 87249 │ │ [720 rows x 2 columns] ╰──● sending message to Planner ╭───< Planner >CodeInterpreter] finished <=�=> ├─► [init_plan] │ 1. load the data file │ 2. confirm the columns to calculate variance <interactively depends on 1> │ 3. calculate the variance value for the selected columns <sequentially depends on 2> │ 4. report the result to the user <interactively depends on 3> ├─► [plan] ▶ [Planner][plan] 1. instruct CodeInterpreter to load the data file 2. confirm the columns to calculate var <=�=> │ 1. instruct CodeInterpreter to load the data file │ 2. confirm the columns to calculate variance ├─► [current_plan_step] 2. confirm the columns to calculate variance calculate varia <=�=> ├──● 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. ╰──● sending message to User TaskWeaver ▶ 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 ▶ Count ╭───< Planner >TaskWeaver] preparing <=�=> ├─► [init_plan] │ 1. load the data file │ 2. confirm the columns to calculate variance <interactively depends on 1> │ 3. calculate the variance value for the selected columns <sequentially depends on 2> │ 4. report the result to the user <interactively depends on 3> ├─► [plan] │ 1. instruct CodeInterpreter to load the data file │ 2. confirm the columns to calculate variance │ 3. instruct CodeInterpreter to calculate the variance value for the Count column │ 4. report the result to the user ├─► [current_plan_step] 3. instruct CodeInterpreter to calculate the variance value for the Count columnor the Count col <=�=> ├──● Please calculate the variance value for the Count column in the loaded datalumn in the loaded <=�=> ╰──● sending message to CodeInterpreter ╭───< CodeInterpreter > ├─► [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 <=�=> ├─► [python]▶ [CodeInterpreter][python] variance_count = df['Count'].var() varia <=�=> │ variance_count = df['Count'].var() │ variance_count ├─► [verification] NONE ├─► [code_error] No code verification is performed. ├─► [execution_status] SUCCESS] executing code <=�=> ├─► [execution_result] │ The execution of the generated python code above has succeeded │ │ The result of above Python code after execution is: │ 263514550.45479834 ├─► [artifact_paths] [] ├──● The following python code has been executed: │ ```python │ variance_count = df['Count'].var() │ variance_count │ ``` │ │ │ The execution of the generated python code above has succeeded │ │ The result of above Python code after execution is: │ 263514550.45479834 ╰──● sending message to Planner ╭───< Planner >CodeInterpreter] finished <=�=> ├─► [init_plan] │ 1. load the data file │ 2. confirm the columns to calculate variance <interactively depends on 1> │ 3. calculate the variance value for the selected columns <sequentially depends on 2> │ 4. report the result to the user <interactively depends on 3> ├─► [plan] │ 1. instruct CodeInterpreter to load the data file │ 2. confirm the columns to calculate variance │ 3. instruct CodeInterpreter to calculate the variance value for the Count column │ 4. report the result to the user ├─► [current_plan_step] 4. report the result to the user result to the u <=�=> ├──● The variance value for the Count column in the data file d:/demo_data.csv is 263,514,550.45a.csv is 263,51 <=�=> ╰──● sending message to User TaskWeaver ▶ 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":63},{"i":71,"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/customization/plugin/plugin_selection","h":"","p":70},{"i":73,"t":"Below is the overview workflow of the auto plugin selection mechanism. NOTE: the 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/customization/plugin/plugin_selection","h":"#auto-plugin-selection-overview","p":70},{"i":75,"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/customization/plugin/plugin_selection","h":"#auto-plugin-selection-configuration","p":70},{"i":77,"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 <interactively depends on 1> >>> [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 <interactively depends on 1> >>> [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/customization/plugin/plugin_selection","h":"#auto-plugin-selection-preparation","p":70},{"i":80,"t":"Using LLMs from OpenAI","s":"📄️ OpenAI","u":"/TaskWeaver/docs/llms/","h":"","p":78},{"i":82,"t":"Using LLMs from OpenAI/AOAI","s":"📄️ Azure OpenAI","u":"/TaskWeaver/docs/llms/","h":"","p":78},{"i":84,"t":"Using LLMs from LiteLLM","s":"📄️ LiteLLM","u":"/TaskWeaver/docs/llms/","h":"","p":78},{"i":86,"t":"1. Go to Ollama and follow the instructions to serve a LLM model on your local environment.","s":"📄️ Ollama","u":"/TaskWeaver/docs/llms/","h":"","p":78},{"i":88,"t":"1. Create an account on Google AI and get your API key.","s":"📄️ Gemini","u":"/TaskWeaver/docs/llms/","h":"","p":78},{"i":90,"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":"📄️ QWen","u":"/TaskWeaver/docs/llms/","h":"","p":78},{"i":92,"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":"📄️ GLM","u":"/TaskWeaver/docs/llms/","h":"","p":78},{"i":94,"t":"We welcome developers to use your customized LLM API in TaskWeaver.","s":"📄️ Customized LLM API","u":"/TaskWeaver/docs/llms/","h":"","p":78},{"i":96,"t":"In some cases, you may want to use different LLMs for different components.","s":"📄️ Multi LLM APIs","u":"/TaskWeaver/docs/llms/","h":"","p":78},{"i":98,"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\", \"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 gpt-4-1106-preview and gpt-3.5-turbo-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":97},{"i":100,"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":99},{"i":102,"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":101},{"i":104,"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 section, we will show you how to use different LLMs for different components.","s":"Multi LLM APIs","u":"/TaskWeaver/docs/llms/multi-llm","h":"","p":103},{"i":106,"t":"Configure the primary LLM and extra LLMs settings in the taskweaver_config.json file under project directory, just as follows: \"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\", }, }, Notes: ext_llms.llm_configs is a dict of extra LLMs for different components. It is an optional field. If you do not specify it, only the primary LLM will be used. For each LLM setting, please should follow the configuration method described in each LLM configuration page. Besides the extra LLMs, you should also configure the primary LLM settings in the taskweaver_config.json, just as shown in the top 5 lines in the above code snippet. 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\" Notes: If you do not specify the LLM for a component, the primary LLM will be used by default. In this case, GPT-3.5-turbo-1106 will be used for both the Planner and the CodeInterpreter, if you do not specify the LLM for them.","s":"Usage","u":"/TaskWeaver/docs/llms/multi-llm","h":"#usage","p":103},{"i":108,"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":107},{"i":110,"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 <your_LLM_name>.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 <your_project_path> --query \"hello, what can you do?\"","s":"Customized LLM API","u":"/TaskWeaver/docs/llms/customized_llm_api","h":"","p":109},{"i":112,"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-4-1106-preview\", \"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 gpt-4-1106-preview and gpt-3.5-turbo-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":111},{"i":114,"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":113},{"i":116,"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 tasks Highlighted Features Rich data structure - TaskWeaver allows you to work with rich data structures in Python, such as DataFrames, instead of having to work with text strings. Customized algorithms - TaskWeaver allows you to encapsulate your own algorithms 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 of execution flow, to improve the reliability of the AI copilot. Stateful conversation - TaskWeaver is designed to support stateful conversation. It can remember the context of the conversation and leverage it to improve the user experience. Code verification - TaskWeaver is designed to verify the generated code before execution. It can detect potential issues in the generated code and provide suggestions to fix them. 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. Users can easily create their own plugins based on the sample plugins. TaskWeaver offers an open-box experience, allowing users to run a service immediately after installation. Easy to debug - TaskWeaver is designed to be easy to debug. We have detailed logs to help you understand what is going on during calling the LLM, the code generation, and execution process. Security consideration - TaskWeaver supports a basic session management to keep different users' data separate. The code execution is separated into different processes in order not to interfere with each other. Easy extension - TaskWeaver is designed to be easily extended to accomplish more complex tasks. You can create multiple AI copilots to act in different roles, and orchestrate them to achieve complex tasks.","s":"Overview","u":"/TaskWeaver/docs/overview","h":"","p":115},{"i":118,"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":117},{"i":120,"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":"How to develop a new plugin","u":"/TaskWeaver/docs/plugin/how_to_develop_a_new_plugin","h":"","p":119},{"i":122,"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":119},{"i":124,"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":119},{"i":126,"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: ___ ___ ___ /\\ \\ /\\ \\ /\\ \\ /::\\ \\ /::\\ \\ /::\\ \\ /:/\\:\\ \\ /:/\\:\\ \\ /:/\\:\\ \\ /::\\~\\:\\ \\ /::\\~\\:\\__\\ /:/ \\:\\ \\ /:/\\:\\ \\:\\__\\ /:/\\:\\ \\:|__| /:/__/ \\:\\__\\ \\/__\\:\\/:/ / \\:\\~\\:\\/:/ / \\:\\ \\ \\/__/ \\::/ / \\:\\ \\::/ / \\:\\ \\ /:/ / \\:\\/:/ / \\:\\ \\ /:/ / \\::/__/ \\:\\__\\ \\/__/ ~~ \\/__/","s":"Call the plugin","u":"/TaskWeaver/docs/plugin/how_to_develop_a_new_plugin","h":"#call-the-plugin","p":119},{"i":128,"t":"Q: How do I know if TaskWeaver can see my plugin? 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/<session_id>/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. Q: Why TaskWeaver cannot see my plugin? 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 <name>.yaml, skipping: Yaml loading failed due to: <reason> 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. Q: Why TaskWeaver can see my plugin but cannot call it? 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 <name> failed to load: Plugin <name> failed to register: failed to load plugin <name> <reason> 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. Q: How to debug my plugin? 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":"FAQ","u":"/TaskWeaver/docs/plugin/how_to_develop_a_new_plugin","h":"#faq","p":119},{"i":130,"t":"Plugins are the units that could be orchestrated by TaskWeaver. 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 a code snippet. The orchestration is essentially the process of generating Python code snippets consisting of a certain number of plugins. 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\")","s":"Plugin Introduction","u":"/TaskWeaver/docs/plugin/plugin_intro","h":"","p":129},{"i":132,"t":"A plugin has 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":129},{"i":134,"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 implement your own 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. Most importantly, it is mandatory to include descriptions of your execution results in the return values of your plugin function. These descriptions can be utilized by the LLM to effectively summarize your execution results. 💡A key difference in a plugin implementation and a normal python function is that it always 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. Behind the scene, only the description will be passed to the LLM model. In contrast, the execution result (e.g., df in the above example) is not handled by the LLM.","s":"Plugin Implementation","u":"/TaskWeaver/docs/plugin/plugin_intro","h":"#plugin-implementation","p":129},{"i":136,"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":129},{"i":138,"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. 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. Note: The addition of any extra fields 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 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. Besides, we also set two optional fields as below: code: Without specifying the code file name, 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. configurations: 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\") plugin_only: When this plugin is only used for the plugin-only mode, set this 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":129},{"i":140,"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":139},{"i":142,"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":139},{"i":145,"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 code: 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 enable the plugin-only mode, you can add the configuration \"session.code_gen_mode\": \"plugin_only\" in the project configuration file taskweaver_config.json.","s":"What is the plugin-only mode?","u":"/TaskWeaver/docs/plugin/plugin_only","h":"#what-is-the-plugin-only-mode","p":143},{"i":147,"t":"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":"Why do we need the plugin-only mode?","u":"/TaskWeaver/docs/plugin/plugin_only","h":"#why-do-we-need-the-plugin-only-mode","p":143},{"i":149,"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":143},{"i":151,"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":143},{"i":153,"t":"Follow the instruction in Quick Start to clone the repo and make configurations git clone https://github.com/microsoft/TaskWeaver.git cd TaskWeaver # install the requirements pip install -r requirements.txt { \"llm.api_key\": \"the api key\", \"llm.model\": \"the model name, e.g., gpt-4\" } Run the following command in terminal. # assume you are in the taskweaver folder # -p is the path to the project directory python -m taskweaver -p ./project/ 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: ___","s":"Terminal","u":"/TaskWeaver/docs/usage/cmd","h":"","p":152},{"i":156,"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":154},{"i":158,"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: 📦project ┣ 📜taskweaver_config.json # the configuration file for TaskWeaver ┣ 📂plugins # the folder to store plugins ┣ 📂planner_examples # the folder to store planner examples ┣ 📂codeinterpreter_examples # the folder to store code interpreter examples ┣ 📂sample_data # the folder to store sample data used for evaluations ┣ 📂logs # the folder to store logs, will be generated after program starts ┗ 📂workspace # the directory stores session data， will be generated after program starts ┗ 📂 session_id ┣ 📂ces # the folder used by the code execution service ┗ 📂cwd # the current working directory to run the generated code","s":"Project Directory","u":"/TaskWeaver/docs/quickstart","h":"#project-directory","p":154},{"i":160,"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":154},{"i":162,"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\" }","s":"Azure OpenAI","u":"/TaskWeaver/docs/quickstart","h":"#azure-openai","p":154},{"i":164,"t":"{ \"llm.api_key\": \"the api key\", \"llm.model\": \"the model name, e.g., gpt-4\" } 💡 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 to null. More configuration options can be found in the configuration documentation.","s":"OpenAI","u":"/TaskWeaver/docs/quickstart","h":"#openai","p":154},{"i":166,"t":"# assume you are in the taskweaver folder # -p is the path to the project directory python -m taskweaver -p ./project/ 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: ___","s":"Start TaskWeaver","u":"/TaskWeaver/docs/quickstart","h":"#start-taskweaver","p":154},{"i":168,"t":"Follow the instruction in Quick Start to clone the repo and make configurations git clone https://github.com/microsoft/TaskWeaver.git cd TaskWeaver # install the requirements pip install -r requirements.txt { \"llm.api_key\": \"the api key\", \"llm.model\": \"the model name, e.g., gpt-4\" } Install the chainlit package by pip install chainlit==1.0.100 if you don't have it in your env. Start the service by running the following command. cd playground/UI/ chainlit run app.py Open the browser with http://localhost:8000 and you can start the trial. Below are some screenshots:","s":"Web UI","u":"/TaskWeaver/docs/usage/webui","h":"","p":167},{"i":170,"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()) Note: response_round: the response from TaskWeaver. which is an object of the Round class. An example of the Round object is shown below: { \"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\" } ] } ] 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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":1},{"i":4,"t":"TaskWeaver-CLI","s":"Demo","u":"/TaskWeaver/docs/advanced/cli_only","h":"#demo","p":1},{"i":6,"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":1},{"i":8,"t":"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/advanced/code_execution","h":"","p":7},{"i":10,"t":"TaskWeaver supports two modes of code execution: local and container. The local 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. 💡We recommend using the container mode for code execution, especially when the usage of the agent is open to untrusted users. In the container mode, the code is executed in a Docker container, which is isolated from the host machine.","s":"Two Modes of Code Execution","u":"/TaskWeaver/docs/advanced/code_execution","h":"#two-modes-of-code-execution","p":7},{"i":12,"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 local. 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.ps1 # for Windows ./build.sh # for Linux or macOS After the Docker image is built, you can run docker images to check if a Docker image named executor_container 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 taskweaver/executor running after executing some code.","s":"How to Configure the Code Execution Mode","u":"/TaskWeaver/docs/advanced/code_execution","h":"#how-to-configure-the-code-execution-mode","p":7},{"i":14,"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/<session_id> 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/advanced/code_execution","h":"#limitations-of-the-container-mode","p":7},{"i":16,"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 <docker_network_subnet> -j DROP Replace <docker_network_subnet> 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/advanced/code_execution","h":"#restricting-external-network-access-for-docker-containers","p":7},{"i":19,"t":"Agent 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":17},{"i":21,"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":17},{"i":23,"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":17},{"i":25,"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":24},{"i":27,"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":24},{"i":29,"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. 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 💡 ${AppBaseDir} is the project directory. 💡 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. 💡 Read this for more information for planner.prompt_compression and code_generator.prompt_compression.","s":"Configuration File","u":"/TaskWeaver/docs/configurations/overview","h":"","p":28},{"i":32,"t":"5 items","s":"🗃️ Plugin","u":"/TaskWeaver/docs/customization/","h":"","p":30},{"i":34,"t":"1 items","s":"🗃️ Example","u":"/TaskWeaver/docs/customization/","h":"","p":30},{"i":36,"t":"1 items","s":"🗃️ Experience","u":"/TaskWeaver/docs/customization/","h":"","p":30},{"i":38,"t":"More details about some important configurations","s":"More about Configurations","u":"/TaskWeaver/docs/configurations/configurations_in_detail","h":"","p":37},{"i":40,"t":"In TaskWeaver, the Planner is responsible for generating a plan to accomplish the user's task. The plan is a sequence of steps, where each step will be executed by the Code Interpreter. Taken the response from the Code Interpreter or new requests from the user as input, the Planner will update the plan and move on to the next step. 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.","s":"Planner Configuration","u":"/TaskWeaver/docs/configurations/configurations_in_detail","h":"#planner-configuration","p":37},{"i":42,"t":"session is the entrance of TaskWeaver. It is responsible for the communication between the user and TaskWeaver. You can refer to taskweaver_as_a_lib to see how to setup a TaskWeaver session and start chatting with TaskWeaver. 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. roles: the roles included for the conversation. The default value is [\"planner\", \"code_interpreter\"]. Roles can be divided into two parts: planner roles and worker roles. In TaskWeaver, the Planner is fixed. Planner roles are responsible for generating a plan to accomplish the user's task, while worker roles are responsible for executing the plan generated by the Planner. TaskWeaver supports 3 worker roles: code_interpreter, code_interpreter_cli_only, and code_interpreter_plugin_only. code_interpreter: it will generate Python code to fulfill the user's request. 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. CodeInterpreter will generate CLI commands (e.g., bash/powershell), instead of Python code, to fulfill the user's request. 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 Configration","u":"/TaskWeaver/docs/configurations/configurations_in_detail","h":"#session-configration","p":37},{"i":44,"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 Configration","u":"/TaskWeaver/docs/configurations/configurations_in_detail","h":"#embedding-configration","p":37},{"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_configure 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":"There are two types of examples: (1) planning examples and (2) code interpreter examples. Planning examples are used to demonstrate how to use TaskWeaver to plan for a specific task. Code generation examples are used to demonstrate how to generate code or orchestrate plugins to perform a specific task.","s":"Customizing Examples","u":"/TaskWeaver/docs/customization/example/","h":"","p":55},{"i":58,"t":"A planning example tells LLMs how to plan for a specific query from the user; talk to the code interpreter; receive the execution result from the code interpreter; and summarize the execution result. Before constructing the planning example, we strongly encourage you to go through the planner prompt. The following is an example of a planning example which contains 4 posts. Each post contains a message, a sender, a receiver, and a list of attachments. The first post is sent from the user to the planner. The message is \"count the rows of /home/data.csv\", which is the same as the user query. The second post is sent from the planner to the code interpreter. 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, which is a markdown string. The second attachment is the plan, which is a markdown string. The third attachment is the current plan step, which is a markdown string. The third post is sent from the code interpreter 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: The first attachment is the initial plan, which is the same as the second attachment of the second post. The second attachment is the plan, which is the same as the third attachment of the second post. The third attachment is the current plan step, which is a markdown string. 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 <sequentially depends on 1> 3. report the result to the user <interactively depends on 2> - 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 <sequentially depends on 1> 3. report the result to the user <interactively depends on 2> - 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","s":"Planning Examples","u":"/TaskWeaver/docs/customization/example/","h":"#planning-examples","p":55},{"i":60,"t":"A code interpreter example tells LLMs how to generate code or orchestrate plugins to perform a specific task. The task is from the planner. Before constructing the code interpreter example, we strongly encourage you to read the code generator prompt. The following is an example of a code interpreter example which contains 2 posts. Each post contains a message, a sender, a receiver, and a list of attachments. The first post is sent from the planner to the code interpreter. The message is \"Please read file /abc/def.txt\". The second post is sent from the code interpreter 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 code interpreter, which is a markdown string. 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, which is a markdown string. 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. 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. 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":"Code Interpreter Examples","u":"/TaskWeaver/docs/customization/example/","h":"#code-interpreter-examples","p":55},{"i":62,"t":"Q: How do I know if TaskWeaver can see my plugin? 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/<session_id>/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. Q: Why TaskWeaver cannot see my plugin? 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 <name>.yaml, skipping: Yaml loading failed due to: <reason> 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. Q: Why TaskWeaver can see my plugin but cannot call it? 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 <name> failed to load: Plugin <name> failed to register: failed to load plugin <name> <reason> 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. Q: How to debug my plugin? 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":"FAQ","u":"/TaskWeaver/docs/FAQ","h":"","p":61},{"i":65,"t":"Using LLMs from OpenAI","s":"📄️ OpenAI","u":"/TaskWeaver/docs/llms/","h":"","p":63},{"i":67,"t":"Using LLMs from OpenAI/AOAI","s":"📄️ Azure OpenAI","u":"/TaskWeaver/docs/llms/","h":"","p":63},{"i":69,"t":"Using LLMs from LiteLLM","s":"📄️ LiteLLM","u":"/TaskWeaver/docs/llms/","h":"","p":63},{"i":71,"t":"1. Go to Ollama and follow the instructions to serve a LLM model on your local environment.","s":"📄️ Ollama","u":"/TaskWeaver/docs/llms/","h":"","p":63},{"i":73,"t":"1. Create an account on Google AI and get your API key.","s":"📄️ Gemini","u":"/TaskWeaver/docs/llms/","h":"","p":63},{"i":75,"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":"📄️ QWen","u":"/TaskWeaver/docs/llms/","h":"","p":63},{"i":77,"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":"📄️ GLM","u":"/TaskWeaver/docs/llms/","h":"","p":63},{"i":79,"t":"We welcome developers to use your customized LLM API in TaskWeaver.","s":"📄️ Customized LLM API","u":"/TaskWeaver/docs/llms/","h":"","p":63},{"i":81,"t":"In some cases, you may want to use different LLMs for different components.","s":"📄️ Multi LLM APIs","u":"/TaskWeaver/docs/llms/","h":"","p":63},{"i":83,"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/customization/plugin/plugin_selection","h":"","p":82},{"i":85,"t":"Below is the overview workflow of the auto plugin selection mechanism. NOTE: the 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/customization/plugin/plugin_selection","h":"#auto-plugin-selection-overview","p":82},{"i":87,"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/customization/plugin/plugin_selection","h":"#auto-plugin-selection-configuration","p":82},{"i":89,"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 <interactively depends on 1> >>> [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 <interactively depends on 1> >>> [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/customization/plugin/plugin_selection","h":"#auto-plugin-selection-preparation","p":82},{"i":91,"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\", \"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 gpt-4-1106-preview and gpt-3.5-turbo-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":90},{"i":93,"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":92},{"i":96,"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":94},{"i":98,"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":94},{"i":100,"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 ▶ I am TaskWeaver, an AI assistant. To get started, could you please enter your request? Human ▶ calculate the mean value of d:/demo_data.csv ╭───< Planner > ├─► [init_plan] │ 1. load the data file │ 2. calculate the mean value of the loaded data <sequentially depends on 1> │ 3. report the result to the user <interactively depends on 2> ├─► [plan] │ 1. instruct CodeInterpreter to load the data file and calculate the mean value of the loaded data │ 2. report the result to the user ├─► [current_plan_step] 1. instruct CodeInterpreter to load the data file and calculate the mean value of the loaded data ├──● Please load the data file d:/demo_data.csv and calculate the mean value of the loaded data ╰──● sending message to CodeInterpreter ╭───< CodeInterpreter > ├─► [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. ├─► [python] │ import pandas as pd │ │ data_file_path = \"d:/demo_data.csv\" │ data_df = pd.read_csv(data_file_path) │ mean_value = data_df.mean() │ mean_value ├─► [verification] NONE ├─► [code_error] No code verification is performed. ├─► [execution_status] FAILURE ├─► [execution_result] │ The execution of the generated python code above has failed │ │ During execution, the following messages were logged: │ Traceback (most recent call last): │ │ Cell In[1], line 5 │ mean_value = data_df.mean() │ │ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\frame.py:11335 in mean │ result = super().mean(axis, skipna, numeric_only, **kwargs) │ │ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\generic.py:11984 in mean │ return self._stat_function( │ │ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\generic.py:11941 in _stat_function │ return self._reduce( │ │ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\frame.py:11204 in _reduce │ res = df._mgr.reduce(blk_func) │ │ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:1459 in reduce │ nbs = blk.reduce(func) │ │ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\internals\\blocks.py:377 in reduce │ result = func(self.values) │ │ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\frame.py:11136 in blk_func │ ret │ ├─► [artifact_paths] [] ├──● The following python code has been executed: │ ```python │ import pandas as pd │ │ data_file_path = \"d:/demo_data.csv\" │ data_df = pd.read_csv(data_file_path) │ mean_value = data_df.mean() │ mean_value │ ``` │ │ │ The execution of the generated python code above has failed │ │ During execution, the following messages were logged: │ Traceback (most recent call last): │ │ Cell In[1], line 5 │ mean_value = data_df.mean() │ │ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\frame.py:11335 in mean │ result = super().mean(axis, skipna, numeric_only, **kwargs) │ │ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\generic.py:11984 in mean │ return self._stat_function( │ │ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\generic.py:11941 in _stat_function │ return self._reduce( │ │ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\frame.py:11204 in _reduce │ res = df._mgr.reduce(blk_func) │ │ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:1459 in reduce │ nbs = blk.reduce(func) │ │ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\internals\\blocks.py:377 in reduce │ result = func(self.values) │ │ File D:\\python_project\\Octopus\\venv\\Lib\\site-packages\\pandas\\core\\frame.py:11136 in blk_func │ ret │ ├─► [revise_message] │ 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. │ Otherwise, please explain the problem to me. ╰──● sending message to CodeInterpreter ╭───< CodeInterpreter > ├─► [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 │ error. ├─► [python] │ import pandas as pd │ │ data_file_path = \"d:/demo_data.csv\" │ data_df = pd.read_csv(data_file_path) │ mean_value = data_df.mean(axis=0, numeric_only=True) │ mean_value ├─► [verification] NONE ├─► [code_error] No code verification is performed. ├─► [execution_status] SUCCESS ├─► [execution_result] │ The execution of the generated python code above has succeeded │ │ The result of above Python code after execution is: │ Count 78172.75 │ dtype: float64 ├─► [artifact_paths] [] ├──● The following python code has been executed: │ ```python │ import pandas as pd │ │ data_file_path = \"d:/demo_data.csv\" │ data_df = pd.read_csv(data_file_path) │ mean_value = data_df.mean(axis=0, numeric_only=True) │ mean_value │ ``` │ │ │ The execution of the generated python code above has succeeded │ │ The result of above Python code after execution is: │ Count 78172.75 │ dtype: float64 ╰──● sending message to Planner ╭───< Planner > ├─► [init_plan] │ 1. load the data file │ 2. calculate the mean value of the loaded data <sequentially depends on 1> │ 3. report the result to the user <interactively depends on 2> ├─► [plan] │ 1. instruct CodeInterpreter to load the data file and calculate the mean value of the loaded data │ 2. report the result to the user ├─► [current_plan_step] 2. report the result to the user ├──● The mean value of the data in d:/demo_data.csv is 78172.75 ╰──● sending message to User TaskWeaver ▶ 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 ▶ I am TaskWeaver, an AI assistant. To get started, could you please enter your request? Human ▶ calculate the variance value of d:/demo_data.csv ╭───< Planner >TaskWeaver] preparing <=�=> ├─► [init_plan] │ 1. load the data file │ 2. confirm the columns to calculate variance <interactively depends on 1> │ 3. calculate the variance value for the selected columns <sequentially depends on 2> │ 4. report the result to the user <interactively depends on 3> ├─► [plan] ▶ [Planner][plan] 1. instruct CodeInterpreter to load the data file 2. confirm the columns to calcu <=�=> │ 1. instruct CodeInterpreter to load the data file │ 2. confirm the columns to calculate variance ├─► [current_plan_step] 1. instruct CodeInterpreter to load the data file load the data file <=�=> ├──● Please load the data file d:/demo_data.csva file d:/demo_dat <=�=> ╰──● sending message to CodeInterpreter ╭───< CodeInterpreter > ├─► [thought] ProgramApe will generate a code snippet to load the data file d:/demo_data.csv using pandas.demo_data.csv using panda <=�=> ├─► [python]▶ [CodeInterpreter][python] import pandas as pd data_file_path = \"d:/demo_data.csv\" df = pd.read_csv(data_fil <=�=> │ import pandas as pd │ │ data_file_path = \"d:/demo_data.csv\" │ df = pd.read_csv(data_file_path) │ df ├─► [verification] NONE ├─► [code_error] No code verification is performed. ├─► [execution_status] SUCCESS] executing code <=�=> ├─► [execution_result] │ The execution of the generated python code above has succeeded │ │ The result of above Python code after execution is: │ TimeBucket Count │ 0 2023-02-01T00:00:00Z 67814 │ 1 2023-02-01T04:00:00Z 84569 │ .. ... ... │ 718 2023-05-31T16:00:00Z 89912 │ 719 2023-05-31T20:00:00Z 87249 │ │ [720 rows x 2 columns] ├─► [artifact_paths] [] ├──● The following python code has been executed: │ ```python │ import pandas as pd │ │ data_file_path = \"d:/demo_data.csv\" │ df = pd.read_csv(data_file_path) │ df │ ``` │ │ │ The execution of the generated python code above has succeeded │ │ The result of above Python code after execution is: │ TimeBucket Count │ 0 2023-02-01T00:00:00Z 67814 │ 1 2023-02-01T04:00:00Z 84569 │ .. ... ... │ 718 2023-05-31T16:00:00Z 89912 │ 719 2023-05-31T20:00:00Z 87249 │ │ [720 rows x 2 columns] ╰──● sending message to Planner ╭───< Planner >CodeInterpreter] finished <=�=> ├─► [init_plan] │ 1. load the data file │ 2. confirm the columns to calculate variance <interactively depends on 1> │ 3. calculate the variance value for the selected columns <sequentially depends on 2> │ 4. report the result to the user <interactively depends on 3> ├─► [plan] ▶ [Planner][plan] 1. instruct CodeInterpreter to load the data file 2. confirm the columns to calculate var <=�=> │ 1. instruct CodeInterpreter to load the data file │ 2. confirm the columns to calculate variance ├─► [current_plan_step] 2. confirm the columns to calculate variance calculate varia <=�=> ├──● 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. ╰──● sending message to User TaskWeaver ▶ 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 ▶ Count ╭───< Planner >TaskWeaver] preparing <=�=> ├─► [init_plan] │ 1. load the data file │ 2. confirm the columns to calculate variance <interactively depends on 1> │ 3. calculate the variance value for the selected columns <sequentially depends on 2> │ 4. report the result to the user <interactively depends on 3> ├─► [plan] │ 1. instruct CodeInterpreter to load the data file │ 2. confirm the columns to calculate variance │ 3. instruct CodeInterpreter to calculate the variance value for the Count column │ 4. report the result to the user ├─► [current_plan_step] 3. instruct CodeInterpreter to calculate the variance value for the Count columnor the Count col <=�=> ├──● Please calculate the variance value for the Count column in the loaded datalumn in the loaded <=�=> ╰──● sending message to CodeInterpreter ╭───< CodeInterpreter > ├─► [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 <=�=> ├─► [python]▶ [CodeInterpreter][python] variance_count = df['Count'].var() varia <=�=> │ variance_count = df['Count'].var() │ variance_count ├─► [verification] NONE ├─► [code_error] No code verification is performed. ├─► [execution_status] SUCCESS] executing code <=�=> ├─► [execution_result] │ The execution of the generated python code above has succeeded │ │ The result of above Python code after execution is: │ 263514550.45479834 ├─► [artifact_paths] [] ├──● The following python code has been executed: │ ```python │ variance_count = df['Count'].var() │ variance_count │ ``` │ │ │ The execution of the generated python code above has succeeded │ │ The result of above Python code after execution is: │ 263514550.45479834 ╰──● sending message to Planner ╭───< Planner >CodeInterpreter] finished <=�=> ├─► [init_plan] │ 1. load the data file │ 2. confirm the columns to calculate variance <interactively depends on 1> │ 3. calculate the variance value for the selected columns <sequentially depends on 2> │ 4. report the result to the user <interactively depends on 3> ├─► [plan] │ 1. instruct CodeInterpreter to load the data file │ 2. confirm the columns to calculate variance │ 3. instruct CodeInterpreter to calculate the variance value for the Count column │ 4. report the result to the user ├─► [current_plan_step] 4. report the result to the user result to the u <=�=> ├──● The variance value for the Count column in the data file d:/demo_data.csv is 263,514,550.45a.csv is 263,51 <=�=> ╰──● sending message to User TaskWeaver ▶ 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":94},{"i":102,"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 <your_LLM_name>.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 <your_project_path> --query \"hello, what can you do?\"","s":"Customized LLM API","u":"/TaskWeaver/docs/llms/customized_llm_api","h":"","p":101},{"i":104,"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":103},{"i":106,"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":105},{"i":108,"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":107},{"i":110,"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 section, we will show you how to use different LLMs for different components.","s":"Multi LLM APIs","u":"/TaskWeaver/docs/llms/multi-llm","h":"","p":109},{"i":112,"t":"Configure the primary LLM and extra LLMs settings in the taskweaver_config.json file under project directory, just as follows: \"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\", }, }, Notes: ext_llms.llm_configs is a dict of extra LLMs for different components. It is an optional field. If you do not specify it, only the primary LLM will be used. For each LLM setting, please should follow the configuration method described in each LLM configuration page. Besides the extra LLMs, you should also configure the primary LLM settings in the taskweaver_config.json, just as shown in the top 5 lines in the above code snippet. 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\" Notes: If you do not specify the LLM for a component, the primary LLM will be used by default. In this case, GPT-3.5-turbo-1106 will be used for both the Planner and the CodeInterpreter, if you do not specify the LLM for them.","s":"Usage","u":"/TaskWeaver/docs/llms/multi-llm","h":"#usage","p":109},{"i":114,"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 tasks Highlighted Features Rich data structure - TaskWeaver allows you to work with rich data structures in Python, such as DataFrames, instead of having to work with text strings. Customized algorithms - TaskWeaver allows you to encapsulate your own algorithms 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 of execution flow, to improve the reliability of the AI copilot. Stateful conversation - TaskWeaver is designed to support stateful conversation. It can remember the context of the conversation and leverage it to improve the user experience. Code verification - TaskWeaver is designed to verify the generated code before execution. It can detect potential issues in the generated code and provide suggestions to fix them. 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. Users can easily create their own plugins based on the sample plugins. TaskWeaver offers an open-box experience, allowing users to run a service immediately after installation. Easy to debug - TaskWeaver is designed to be easy to debug. We have detailed logs to help you understand what is going on during calling the LLM, the code generation, and execution process. Security consideration - TaskWeaver supports a basic session management to keep different users' data separate. The code execution is separated into different processes in order not to interfere with each other. Easy extension - TaskWeaver is designed to be easily extended to accomplish more complex tasks. You can create multiple AI copilots to act in different roles, and orchestrate them to achieve complex tasks.","s":"Overview","u":"/TaskWeaver/docs/overview","h":"","p":113},{"i":116,"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-4-1106-preview\", \"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 gpt-4-1106-preview and gpt-3.5-turbo-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":115},{"i":118,"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":117},{"i":120,"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":117},{"i":122,"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":121},{"i":124,"t":"Plugins are the units that could be orchestrated by TaskWeaver. 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 a code snippet. The orchestration is essentially the process of generating Python code snippets consisting of a certain number of plugins. 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\")","s":"Plugin Introduction","u":"/TaskWeaver/docs/plugin/plugin_intro","h":"","p":123},{"i":126,"t":"A plugin has 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":123},{"i":128,"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 implement your own 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. Most importantly, it is mandatory to include descriptions of your execution results in the return values of your plugin function. These descriptions can be utilized by the LLM to effectively summarize your execution results. 💡A key difference in a plugin implementation and a normal python function is that it always 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. Behind the scene, only the description will be passed to the LLM model. In contrast, the execution result (e.g., df in the above example) is not handled by the LLM.","s":"Plugin Implementation","u":"/TaskWeaver/docs/plugin/plugin_intro","h":"#plugin-implementation","p":123},{"i":130,"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":123},{"i":132,"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. 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. Note: The addition of any extra fields 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 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. Besides, we also set two optional fields as below: code: Without specifying the code file name, 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. configurations: 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\") plugin_only: When this plugin is only used for the plugin-only mode, set this 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":123},{"i":134,"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":"How to develop a new plugin","u":"/TaskWeaver/docs/plugin/how_to_develop_a_new_plugin","h":"","p":133},{"i":136,"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":133},{"i":138,"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":133},{"i":140,"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: ___ ___ ___ /\\ \\ /\\ \\ /\\ \\ /::\\ \\ /::\\ \\ /::\\ \\ /:/\\:\\ \\ /:/\\:\\ \\ /:/\\:\\ \\ /::\\~\\:\\ \\ /::\\~\\:\\__\\ /:/ \\:\\ \\ /:/\\:\\ \\:\\__\\ /:/\\:\\ \\:|__| /:/__/ \\:\\__\\ \\/__\\:\\/:/ / \\:\\~\\:\\/:/ / \\:\\ \\ \\/__/ \\::/ / \\:\\ \\::/ / \\:\\ \\ /:/ / \\:\\/:/ / \\:\\ \\ /:/ / \\::/__/ \\:\\__\\ \\/__/ ~~ \\/__/","s":"Call the plugin","u":"/TaskWeaver/docs/plugin/how_to_develop_a_new_plugin","h":"#call-the-plugin","p":133},{"i":142,"t":"Q: How do I know if TaskWeaver can see my plugin? 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/<session_id>/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. Q: Why TaskWeaver cannot see my plugin? 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 <name>.yaml, skipping: Yaml loading failed due to: <reason> 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. Q: Why TaskWeaver can see my plugin but cannot call it? 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 <name> failed to load: Plugin <name> failed to register: failed to load plugin <name> <reason> 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. Q: How to debug my plugin? 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":"FAQ","u":"/TaskWeaver/docs/plugin/how_to_develop_a_new_plugin","h":"#faq","p":133},{"i":145,"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":143},{"i":147,"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: 📦project ┣ 📜taskweaver_config.json # the configuration file for TaskWeaver ┣ 📂plugins # the folder to store plugins ┣ 📂planner_examples # the folder to store planner examples ┣ 📂codeinterpreter_examples # the folder to store code interpreter examples ┣ 📂sample_data # the folder to store sample data used for evaluations ┣ 📂logs # the folder to store logs, will be generated after program starts ┗ 📂workspace # the directory stores session data， will be generated after program starts ┗ 📂 session_id ┣ 📂ces # the folder used by the code execution service ┗ 📂cwd # the current working directory to run the generated code","s":"Project Directory","u":"/TaskWeaver/docs/quickstart","h":"#project-directory","p":143},{"i":149,"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":143},{"i":151,"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\" }","s":"Azure OpenAI","u":"/TaskWeaver/docs/quickstart","h":"#azure-openai","p":143},{"i":153,"t":"{ \"llm.api_key\": \"the api key\", \"llm.model\": \"the model name, e.g., gpt-4\" } 💡 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 to null. More configuration options can be found in the configuration documentation.","s":"OpenAI","u":"/TaskWeaver/docs/quickstart","h":"#openai","p":143},{"i":155,"t":"# assume you are in the taskweaver folder # -p is the path to the project directory python -m taskweaver -p ./project/ 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: ___","s":"Start TaskWeaver","u":"/TaskWeaver/docs/quickstart","h":"#start-taskweaver","p":143},{"i":157,"t":"Follow the instruction in Quick Start to clone the repo and make configurations git clone https://github.com/microsoft/TaskWeaver.git cd TaskWeaver # install the requirements pip install -r requirements.txt { \"llm.api_key\": \"the api key\", \"llm.model\": \"the model name, e.g., gpt-4\" } Run the following command in terminal. # assume you are in the taskweaver folder # -p is the path to the project directory python -m taskweaver -p ./project/ 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: ___","s":"Terminal","u":"/TaskWeaver/docs/usage/cmd","h":"","p":156},{"i":160,"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 code: 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 enable the plugin-only mode, you can add the configuration \"session.code_gen_mode\": \"plugin_only\" in the project configuration file taskweaver_config.json.","s":"What is the plugin-only mode?","u":"/TaskWeaver/docs/plugin/plugin_only","h":"#what-is-the-plugin-only-mode","p":158},{"i":162,"t":"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":"Why do we need the plugin-only mode?","u":"/TaskWeaver/docs/plugin/plugin_only","h":"#why-do-we-need-the-plugin-only-mode","p":158},{"i":164,"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":158},{"i":166,"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":158},{"i":168,"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()) Note: response_round: the response from TaskWeaver. which is an object of the Round class. An example of the Round object is shown below: { \"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\" } ] } ] }","s":"Library","u":"/TaskWeaver/docs/usage/library","h":"","p":167},{"i":170,"t":"Follow the instruction in Quick Start to clone the repo and make configurations git clone https://github.com/microsoft/TaskWeaver.git cd TaskWeaver # install the requirements pip install -r requirements.txt { \"llm.api_key\": \"the api key\", \"llm.model\": \"the model name, e.g., gpt-4\" } Install the chainlit package by pip install chainlit==1.0.100 if you don't have it in your env. Start the service by running the following command. cd playground/UI/ chainlit run app.py Open the browser with http://localhost:8000 and you can start the trial. Below are some screenshots:","s":"Web 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