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fix doc mismatching #422

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Oct 9, 2024
Merged

fix doc mismatching #422

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15bead1
fix doc mismatching
liqul Sep 27, 2024
770fe0f
fix aoai llm doc
Jack-Q Oct 9, 2024
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4 changes: 2 additions & 2 deletions auto_eval/ds1000_scripts/README.md
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Expand Up @@ -15,8 +15,8 @@ This directory contains the scripts used to evaluate the performance of the [DS-
- metadata.json: the metadata of the test case.
- prompt.txt: the composed prompt of the test case.
- reference_code.py: the ground truth code.
4. Copy the example files from `ds1000_scritps/planner_examples` to `project/planner_examples` directory;
and the example files from `ds1000_scritps/codeinterpreter_examples` to `project/codeinterpreter_examples` directory.
4. Copy the example files from `ds1000_scritps/planner_examples` to `project/examples/planner_examples` directory;
and the example files from `ds1000_scritps/codeinterpreter_examples` to `project/examples/code_generator_examples` directory.
Disable (or discard) the original example files from the project directory. See the notes below for understanding why.
5. Once the test cases are generated, follow the instructions in `auto_eval/README.md` to evaluate the performance of the benchmark.

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5 changes: 0 additions & 5 deletions taskweaver/llm/openai.py
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Expand Up @@ -51,11 +51,6 @@ def _configure(self) -> None:

# openai specific config
self.api_version = self._get_str("api_version", "2024-06-01")
self.api_auth_type = self._get_enum(
"api_auth_type",
["openai", "azure", "azure_ad"],
"openai",
)
is_azure_ad_login = self.api_type == "azure_ad"
self.aad_auth_mode = self._get_enum(
"aad_auth_mode",
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28 changes: 19 additions & 9 deletions website/blog/experience.md
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Expand Up @@ -98,15 +98,25 @@ def reply(self, memory: Memory, **kwargs: ...) -> Post:
In a role that needs to set the experience subdirectory, we can get the experience subdirectory from the shared memory.

```python
exp_sub_paths = memory.get_shared_memory_entries(
entry_type="experience_sub_path",
)

if exp_sub_paths:
exp_sub_path = exp_sub_paths[0].content
else:
exp_sub_path = ""
selected_experiences = self.role_load_experience(query=query, sub_path=exp_sub_path)
def reply(
self,
memory: Memory,
post_proxy: Optional[PostEventProxy] = None,
prompt_log_path: Optional[str] = None,
**kwargs: ...,
) -> Post:
...
rounds = memory.get_role_rounds(
role=self.alias,
include_failure_rounds=False,
)

# obtain the query from the last round
query = rounds[-1].post_list[-1].message

# retrieve the experience based on the query
self.role_load_experience(query=query, memory=memory)
...
```

:::tip
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15 changes: 10 additions & 5 deletions website/docs/configurations/overview.md
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Expand Up @@ -24,22 +24,27 @@ The following table lists the parameters in the configuration file:
| `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` |
| `{RoleName}.use_example` | Whether to use the example for the role. | `true` |
| `{RoleName}.example_base_path` | The folder to store the examples for the role. | `${AppBaseDir}/examples/{RoleName}_examples` |
| `{RoleName}.dynamic_example_sub_path` | Whether to enable dynamic example loading based on sub-path. | `false` |
| `{RoleName}.use_experience` | Whether to use experience summarized from the previous chat history for the role. | `false` |
| `{RoleName}.experience_dir` | The folder to store the experience for the role. | `${AppBaseDir}/experience/` |
| `{RoleName}.dynamic_experience_sub_path` | Whether to enable dynamic experience loading based on sub-path. | `false` |
| `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` |
| `execution_service.kernel_mode` | The mode of the code executor, could be `local` or `container`. | `container` |

:::tip
$\{AppBaseDir\} is the project directory.

$\{RoleName\} is the name of the role, such as `planner` or `code_generator`. In the current implementation, the `code_interpreter` role has all code generation functions
in a "sub-role" named `code_generator`. So, the configuration for the code generation part should be set to `code_generator`.
:::

:::tip
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50 changes: 26 additions & 24 deletions website/docs/llms/aoai.md
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Expand Up @@ -8,40 +8,42 @@ description: Using LLMs from OpenAI/AOAI
1. Create an account on [Azure OpenAI](https://azure.microsoft.com/en-us/products/ai-services/openai-service) and get your API key.
2. Create a new deployment of the model and get the deployment name.
3. Add the following to your `taskweaver_config.json` file:
```jsonc showLineNumbers
{
"llm.api_base":"YOUR_AOAI_ENDPOINT", // in the format of https://<my-resource>.openai.azure.com"
"llm.api_key":"YOUR_API_KEY",
"llm.api_type":"azure",
"llm.auth_mode":"api-key",
"llm.model":"gpt-4-1106-preview", // this is known as deployment_name in Azure OpenAI
"llm.response_format": "json_object"
}
```
```jsonc showLineNumbers
{
"llm.api_base":"YOUR_AOAI_ENDPOINT", // in the format of https://<my-resource>.openai.azure.com"
"llm.api_key":"YOUR_API_KEY",
"llm.api_type":"azure",
"llm.model":"gpt-4-1106-preview", // this is known as deployment_name in Azure OpenAI
"llm.response_format": "json_object",
"llm.azure.api_version": "2024-06-01"
}
```

:::info
For model versions or after `1106`, `llm.response_format` can be set to `json_object`.
However, for the earlier models, which do not support JSON response explicitly, `llm.response_format` should be set to `null`.
:::
:::info
For model versions or after `1106`, `llm.response_format` can be set to `json_object`.
However, for the earlier models, which do not support JSON response explicitly, `llm.response_format` should be set to `null`.
:::

4. Start TaskWeaver and chat with TaskWeaver.
You can refer to the [Quick Start](../quickstart.md) for more details.

You can refer to the [Quick Start](../quickstart.md) for more details.

## Using Entra Authentication

1. Create an account on [Azure OpenAI](https://azure.microsoft.com/en-us/products/ai-services/openai-service) and
[assign the proper Azure RBAC Role](https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/role-based-access-control) to your account (or service principal).
2. Create a new deployment of the model and get the deployment name.
3. Add the following to your `taskweaver_config.json` file:
```jsonc showLineNumbers
{
"llm.api_base":"YOUR_AOAI_ENDPOINT", // in the format of https://<my-resource>.openai.azure.com"
"llm.api_type":"azure_ad",
"llm.auth_mode":"default_azure_credential",
"llm.model":"gpt-4-1106-preview", // this is known as deployment_name in Azure OpenAI
"llm.response_format": "json_object"
}
```
```jsonc showLineNumbers
{
"llm.api_base":"YOUR_AOAI_ENDPOINT", // in the format of https://<my-resource>.openai.azure.com"
"llm.api_type":"azure_ad",
"llm.model":"gpt-4-1106-preview", // this is known as deployment_name in Azure OpenAI
"llm.response_format": "json_object",
"llm.azure_ad.api_version": "2024-06-01",
"llm.azure_ad.aad_auth_mode": "default_azure_credential"
}
```
4. Install extra dependencies:
```bash
pip install azure-identity
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