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docs, cli[patch]: chat model doc template (#22290)

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Update ChatModel integration doc template, integration docstring, and
adds langchain-cli command to easily create just doc (for updating
existing integrations):

```bash
langchain-cli integration create-doc --name "foo-bar"
```
This commit is contained in:
Bagatur
2024-05-29 13:34:58 -07:00
committed by GitHub
parent f40e341a03
commit 627a337887
5 changed files with 549 additions and 85 deletions
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202
libs/cli/langchain_cli/integration_template/docs/chat.ipynb
View File

@@ -17,20 +17,122 @@
"source": [
"# Chat__ModuleName__\n",
"\n",
"This notebook covers how to get started with __ModuleName__ chat models.\n",
"- TODO: Make sure API reference link is correct.\n",
"\n",
"## Installation"
"This notebook provides a quick overview for getting started with __ModuleName__ [chat models](/docs/concepts/#chat-models). For detailed documentation of all Chat__ModuleName__ features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/chat_models/__module_name__.chat_models.Chat__ModuleName__.html).\n",
"\n",
"- TODO: Add any other relevant links, like information about models, prices, context windows, etc. See https://python.langchain.com/v0.2/docs/integrations/chat/openai/ for an example.\n",
"\n",
"## Overview\n",
"### Integration details\n",
"\n",
"- TODO: Fill in table features.\n",
"- TODO: Remove JS support link if not relevant, otherwise ensure link is correct.\n",
"- TODO: Make sure API reference links are correct.\n",
"\n",
"| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/chat/__package_name_short_snake__) | Package downloads | Package latest |\n",
"| :--- | :--- | :---: | :---: | :---: | :---: | :---: |\n",
"| [Chat__ModuleName__](https://api.python.langchain.com/en/latest/chat_models/__module_name__.chat_models.Chat__ModuleName__.html) | [__package__name__](https://api.python.langchain.com/en/latest/__package_name_short_snake___api_reference.html) | ✅/❌ | beta/❌ | ✅/❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/__package_name__?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/__package_name__?style=flat-square&label=%20) |\n",
"\n",
"### Model features\n",
"| [Tool calling](/docs/how_to/tool_calling/) | [Structured output](/docs/how_to/structured_output/) | JSON mode | [Image input](/docs/how_to/multimodal_inputs/) | Audio input | Video input | [Native streaming](/docs/how_to/chat_streaming/) | Native async | [Token usage](/docs/how_to/chat_token_usage_tracking/) | [Logprobs](/docs/how_to/logprobs/) |\n",
"| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n",
"| ✅/❌ | ✅/❌ | ✅/❌ | ✅/❌ | ✅/❌ | ✅/❌ | ✅/❌ | ✅/❌ | ✅/❌ | ✅/❌ | \n",
"\n",
"## Setup\n",
"\n",
"- TODO: Update with relevant info.\n",
"\n",
"To access __ModuleName__ models you'll need to create a/an __ModuleName__ account, get an API key, and install the `__package_name__` integration package.\n",
"\n",
"### Credentials\n",
"\n",
"- TODO: Update with relevant info.\n",
"\n",
"Head to (TODO: link) to sign up to __ModuleName__ and generate an API key. Once you've done this set the __MODULE_NAME___API_KEY environment variable:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4c3bef91",
"id": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
"metadata": {},
"outputs": [],
"source": [
"# install package\n",
"!pip install -U __package_name__"
"import getpass\n",
"import os\n",
"\n",
"os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\"Enter your __ModuleName__ API key: \")"
]
},
{
"cell_type": "markdown",
"id": "72ee0c4b-9764-423a-9dbf-95129e185210",
"metadata": {},
"source": [
"If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
"metadata": {},
"outputs": [],
"source": [
"# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
"# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
]
},
{
"cell_type": "markdown",
"id": "0730d6a1-c893-4840-9817-5e5251676d5d",
"metadata": {},
"source": [
"### Installation\n",
"\n",
"The LangChain __ModuleName__ integration lives in the `__package_name__` package:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "652d6238-1f87-422a-b135-f5abbb8652fc",
"metadata": {},
"outputs": [],
"source": [
"%pip install -qU __package_name__"
]
},
{
"cell_type": "markdown",
"id": "a38cde65-254d-4219-a441-068766c0d4b5",
"metadata": {},
"source": [
"## Instantiation\n",
"\n",
"Now we can instantiate our model object and generate chat completions:\n",
"\n",
"- TODO: Update model instantiation with relevant params."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
"metadata": {},
"outputs": [],
"source": [
"from __module_name__ import Chat__ModuleName__\n",
"\n",
"llm = Chat__ModuleName__(\n",
" model=\"model-name\",\n",
" temperature=0,\n",
" max_tokens=None,\n",
" timeout=None,\n",
" max_retries=2,\n",
" # other params...\n",
")"
]
},
{
@@ -38,13 +140,9 @@
"id": "2b4f3e15",
"metadata": {},
"source": [
"## Environment Setup\n",
"## Invocation\n",
"\n",
"Make sure to set the following environment variables:\n",
"\n",
"- TODO: fill out relevant environment variables or secrets\n",
"\n",
"## Usage"
"- TODO: Run cells so output can be seen."
]
},
{
@@ -56,20 +154,86 @@
},
"outputs": [],
"source": [
"from __module_name__.chat_models import Chat__ModuleName__\n",
"from langchain_core.prompts import ChatPromptTemplate\n",
"messages = [\n",
" (\n",
" \"system\",\n",
" \"You are a helpful assistant that translates English to French. Translate the user sentence.\",\n",
" ),\n",
" (\"human\", \"I love programming.\"),\n",
"]\n",
"ai_msg = llm.invoke(messages)\n",
"ai_msg"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d86145b3-bfef-46e8-b227-4dda5c9c2705",
"metadata": {},
"outputs": [],
"source": [
"print(ai_msg.content)"
]
},
{
"cell_type": "markdown",
"id": "18e2bfc0-7e78-4528-a73f-499ac150dca8",
"metadata": {},
"source": [
"## Chaining\n",
"\n",
"chat = Chat__ModuleName__()\n",
"We can [chain](/docs/how_to/sequence/) our model with a prompt template like so:\n",
"\n",
"- TODO: Run cells so output can be seen."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e197d1d7-a070-4c96-9f8a-a0e86d046e0b",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.prompts import ChatPromptTemplate\n",
"\n",
"prompt = ChatPromptTemplate.from_messages(\n",
" [\n",
" (\"system\", \"You are a helpful assistant that translates English to French.\"),\n",
" (\"human\", \"Translate this sentence from English to French. {english_text}.\"),\n",
" (\n",
" \"system\",\n",
" \"You are a helpful assistant that translates {input_language} to {output_language}.\",\n",
" ),\n",
" (\"human\", \"{input}\"),\n",
" ]\n",
")\n",
"\n",
"chain = prompt | chat\n",
"chain.invoke({\"english_text\": \"Hello, how are you?\"})"
"chain = prompt | llm\n",
"chain.invoke(\n",
" {\n",
" \"input_language\": \"English\",\n",
" \"output_language\": \"German\",\n",
" \"input\": \"I love programming.\",\n",
" }\n",
")"
]
},
{
"cell_type": "markdown",
"id": "d1ee55bc-ffc8-4cfa-801c-993953a08cfd",
"metadata": {},
"source": [
"## TODO: Any functionality specific to this model provider\n",
"\n",
"E.g. creating/using finetuned models via this provider. Delete if not relevant."
]
},
{
"cell_type": "markdown",
"id": "3a5bb5ca-c3ae-4a58-be67-2cd18574b9a3",
"metadata": {},
"source": [
"## API reference\n",
"\n",
"For detailed documentation of all Chat__ModuleName__ features and configurations head to the API reference: https://api.python.langchain.com/en/latest/chat_models/__module_name__.chat_models.Chat__ModuleName__.html"
]
}
],
@@ -89,7 +253,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.5"
"version": "3.9.1"
}
},
"nbformat": 4,

314
libs/cli/langchain_cli/integration_template/integration_template/chat_models.py
View File

@@ -1,34 +1,262 @@
"""__ModuleName__ chat models."""
from typing import Any, AsyncIterator, Iterator, List, Optional
from typing import Any, List, Optional
from langchain_core.callbacks import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain_core.language_models.chat_models import BaseChatModel
from langchain_core.messages import BaseMessage
from langchain_core.outputs import ChatGenerationChunk, ChatResult
from langchain_core.outputs import ChatResult
class Chat__ModuleName__(BaseChatModel):
"""Chat__ModuleName__ chat model.
# TODO: Replace all TODOs in docstring. See example docstring:
# https://github.com/langchain-ai/langchain/blob/7ff05357bac6eaedf5058a2af88f23a1817d40fe/libs/partners/openai/langchain_openai/chat_models/base.py#L1120
"""__ModuleName__ chat model integration.
Example:
# TODO: Replace with relevant packages, env vars.
Setup:
Install ``__package_name__`` and set environment variable ``__MODULE_NAME___API_KEY``.
.. code-block:: bash
pip install -U __package_name__
export __MODULE_NAME___API_KEY="your-api-key"
# TODO: Populate with relevant params.
Key init args — completion params:
model: str
Name of __ModuleName__ model to use.
temperature: float
Sampling temperature.
max_tokens: Optional[int]
Max number of tokens to generate.
# TODO: Populate with relevant params.
Key init args — client params:
timeout:
Timeout for requests.
max_retries:
Max number of retries.
api_key:
__ModuleName__ API key. If not passed in will be read from env var __MODULE_NAME___API_KEY.
See full list of supported init args and their descriptions in the params section.
# TODO: Replace with relevant init params.
Instantiate:
.. code-block:: python
from langchain_core.messages import HumanMessage
from __module_name__ import Chat__ModuleName__
model = Chat__ModuleName__()
model.invoke([HumanMessage(content="Come up with 10 names for a song about parrots.")])
""" # noqa: E501
llm = Chat__ModuleName__(
model="...",
temperature=0,
max_tokens=None,
timeout=None,
max_retries=2,
# api_key="...",
# other params...
)
@property
def _llm_type(self) -> str:
"""Return type of chat model."""
return "chat-__package_name_short__"
Invoke:
.. code-block:: python
messages = [
("system", "You are a helpful translator. Translate the user sentence to French."),
("human", "I love programming."),
]
llm.invoke(messages)
.. code-block:: python
# TODO: Example output.
# TODO: Delete if token-level streaming isn't supported.
Stream:
.. code-block:: python
for chunk in llm.stream(messages):
print(chunk)
.. code-block:: python
# TODO: Example output.
.. code-block:: python
stream = llm.stream(messages)
full = next(stream)
for chunk in stream:
full += chunk
full
.. code-block:: python
# TODO: Example output.
# TODO: Delete if native async isn't supported.
Async:
.. code-block:: python
await llm.ainvoke(messages)
# stream:
# async for chunk in (await llm.astream(messages))
# batch:
# await llm.abatch([messages])
.. code-block:: python
# TODO: Example output.
# TODO: Delete if .bind_tools() isn't supported.
Tool calling:
.. code-block:: python
from langchain_core.pydantic_v1 import BaseModel, Field
class GetWeather(BaseModel):
'''Get the current weather in a given location'''
location: str = Field(..., description="The city and state, e.g. San Francisco, CA")
class GetPopulation(BaseModel):
'''Get the current population in a given location'''
location: str = Field(..., description="The city and state, e.g. San Francisco, CA")
llm_with_tools = llm.bind_tools([GetWeather, GetPopulation])
ai_msg = llm_with_tools.invoke("Which city is hotter today and which is bigger: LA or NY?")
ai_msg.tool_calls
.. code-block:: python
# TODO: Example output.
See ``Chat__ModuleName__.bind_tools()`` method for more.
# TODO: Delete if .with_structured_output() isn't supported.
Structured output:
.. code-block:: python
from typing import Optional
from langchain_core.pydantic_v1 import BaseModel, Field
class Joke(BaseModel):
'''Joke to tell user.'''
setup: str = Field(description="The setup of the joke")
punchline: str = Field(description="The punchline to the joke")
rating: Optional[int] = Field(description="How funny the joke is, from 1 to 10")
structured_llm = llm.with_structured_output(Joke)
structured_llm.invoke("Tell me a joke about cats")
.. code-block:: python
# TODO: Example output.
See ``Chat__ModuleName__.with_structured_output()`` for more.
# TODO: Delete if JSON mode response format isn't supported.
JSON mode:
.. code-block:: python
# TODO: Replace with appropriate bind arg.
json_llm = llm.bind(response_format={"type": "json_object"})
ai_msg = json_llm.invoke("Return a JSON object with key 'random_ints' and a value of 10 random ints in [0-99]")
ai_msg.content
.. code-block:: python
# TODO: Example output.
# TODO: Delete if image inputs aren't supported.
Image input:
.. code-block:: python
import base64
import httpx
from langchain_core.messages import HumanMessage
image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
image_data = base64.b64encode(httpx.get(image_url).content).decode("utf-8")
# TODO: Replace with appropriate message content format.
message = HumanMessage(
content=[
{"type": "text", "text": "describe the weather in this image"},
{
"type": "image_url",
"image_url": {"url": f"data:image/jpeg;base64,{image_data}"},
},
],
)
ai_msg = llm.invoke([message])
ai_msg.content
.. code-block:: python
# TODO: Example output.
# TODO: Delete if audio inputs aren't supported.
Audio input:
.. code-block:: python
# TODO: Example input
.. code-block:: python
# TODO: Example output
# TODO: Delete if video inputs aren't supported.
Video input:
.. code-block:: python
# TODO: Example input
.. code-block:: python
# TODO: Example output
# TODO: Delete if token usage metadata isn't supported.
Token usage:
.. code-block:: python
ai_msg = llm.invoke(messages)
ai_msg.usage_metadata
.. code-block:: python
{'input_tokens': 28, 'output_tokens': 5, 'total_tokens': 33}
# TODO: Delete if logprobs aren't supported.
Logprobs:
.. code-block:: python
# TODO: Replace with appropriate bind arg.
logprobs_llm = llm.bind(logprobs=True)
ai_msg = logprobs_llm.invoke(messages)
ai_msg.response_metadata["logprobs"]
.. code-block:: python
# TODO: Example output.
Response metadata
.. code-block:: python
ai_msg = llm.invoke(messages)
ai_msg.response_metadata
.. code-block:: python
# TODO: Example output.
""" # noqa: E501
# TODO: This method must be implemented to generate chat responses.
def _generate(
@@ -38,36 +266,36 @@ class Chat__ModuleName__(BaseChatModel):
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
raise NotImplementedError
raise NotImplementedError()
# TODO: Implement if Chat__ModuleName__ supports streaming. Otherwise delete method.
def _stream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
raise NotImplementedError
# def _stream(
# self,
# messages: List[BaseMessage],
# stop: Optional[List[str]] = None,
# run_manager: Optional[CallbackManagerForLLMRun] = None,
# **kwargs: Any,
# ) -> Iterator[ChatGenerationChunk]:
# TODO: Implement if Chat__ModuleName__ supports async streaming. Otherwise delete
# method.
async def _astream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> AsyncIterator[ChatGenerationChunk]:
raise NotImplementedError
# TODO: Implement if Chat__ModuleName__ supports async streaming. Otherwise delete.
# async def _astream(
# self,
# messages: List[BaseMessage],
# stop: Optional[List[str]] = None,
# run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
# **kwargs: Any,
# ) -> AsyncIterator[ChatGenerationChunk]:
# TODO: Implement if Chat__ModuleName__ supports async generation. Otherwise delete
# method.
async def _agenerate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
raise NotImplementedError
# TODO: Implement if Chat__ModuleName__ supports async generation. Otherwise delete.
# async def _agenerate(
# self,
# messages: List[BaseMessage],
# stop: Optional[List[str]] = None,
# run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
# **kwargs: Any,
# ) -> ChatResult:
@property
def _llm_type(self) -> str:
"""Return type of chat model."""
return "chat-__package_name_short__"