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cli/docs: llm integration template standardization (#24795)

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Erick Friis 2024-07-29 17:47:13 -07:00 committed by GitHub
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9
libs/cli/langchain_cli/integration_template/docs/chat.ipynb
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@ -62,7 +62,8 @@
"import getpass\n",
"import os\n",
"\n",
"os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\"Enter your __ModuleName__ API key: \")"
"if not os.getenv(\"__MODULE_NAME___API_KEY\"):\n",
" os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\"Enter your __ModuleName__ API key: \")"
]
},
{
@ -80,8 +81,8 @@
"metadata": {},
"outputs": [],
"source": [
"# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
"# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
"# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
"# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
]
},
{
@ -196,7 +197,7 @@
"source": [
"from langchain_core.prompts import ChatPromptTemplate\n",
"\n",
"prompt = ChatPromptTemplate.from_messages(\n",
"prompt = ChatPromptTemplate(\n",
" [\n",
" (\n",
" \"system\",\n",

172
libs/cli/langchain_cli/integration_template/docs/llms.ipynb
View File

@ -17,9 +17,75 @@
"source": [
"# __ModuleName__LLM\n",
"\n",
"This example goes over how to use LangChain to interact with `__ModuleName__` models.\n",
"- [ ] TODO: Make sure API reference link is correct\n",
"\n",
"## Installation"
"This will help you get started with __ModuleName__ completion models (LLMs) using LangChain. For detailed documentation on `__ModuleName__LLM` features and configuration options, please refer to the [API reference](https://api.python.langchain.com/en/latest/llms/__module_name__.llms.__ModuleName__LLM.html).\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",
"| [__ModuleName__LLM](https://api.python.langchain.com/en/latest/llms/__module_name__.llms.__ModuleName__LLM.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",
"## 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": "bc51e756",
"metadata": {},
"outputs": [],
"source": [
"import getpass\n",
"import os\n",
"\n",
"if not os.getenv(\"__MODULE_NAME___API_KEY\"):\n",
" os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\"Enter your __ModuleName__ API key: \")"
]
},
{
"cell_type": "markdown",
"id": "4b6e1ca6",
"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": "196c2b41",
"metadata": {},
"outputs": [],
"source": [
"# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
"# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
]
},
{
"cell_type": "markdown",
"id": "809c6577",
"metadata": {},
"source": [
"### Installation\n",
"\n",
"The LangChain __ModuleName__ integration lives in the `__package_name__` package:"
]
},
{
@ -29,8 +95,38 @@
"metadata": {},
"outputs": [],
"source": [
"# install package\n",
"!pip install -U __package_name__"
"%pip install -qU __package_name__"
]
},
{
"cell_type": "markdown",
"id": "0a760037",
"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": "a0562a13",
"metadata": {},
"outputs": [],
"source": [
"from __module_name__ import __ModuleName__LLM\n",
"\n",
"llm = __ModuleName__LLM(\n",
" model=\"model-name\",\n",
" temperature=0,\n",
" max_tokens=None,\n",
" timeout=None,\n",
" max_retries=2,\n",
" # other params...\n",
")"
]
},
{
@ -38,13 +134,9 @@
"id": "0ee90032",
"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 +148,64 @@
},
"outputs": [],
"source": [
"from langchain_core.prompts import PromptTemplate\n",
"from __module_name__.llms import __ModuleName__LLM\n",
"input_text = \"__ModuleName__ is an AI company that \"\n",
"\n",
"template = \"\"\"Question: {question}\n",
"completion = llm.invoke(input_text)\n",
"completion"
]
},
{
"cell_type": "markdown",
"id": "add38532",
"metadata": {},
"source": [
"## Chaining\n",
"\n",
"Answer: Let's think step by step.\"\"\"\n",
"We can [chain](/docs/how_to/sequence/) our completion model with a prompt template like so:\n",
"\n",
"prompt = PromptTemplate.from_string(template)\n",
"- TODO: Run cells so output can be seen."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "078e9db2",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.prompts import ChatPromptTemplate\n",
"\n",
"model = __ModuleName__LLM()\n",
"prompt = PromptTemplate(\n",
" \"How to say {input} in {output_language}:\\n\"\n",
")\n",
"\n",
"chain = prompt | model\n",
"chain = prompt | llm\n",
"chain.invoke(\n",
" {\n",
" \"output_language\": \"German\",\n",
" \"input\": \"I love programming.\",\n",
" }\n",
")"
]
},
{
"cell_type": "markdown",
"id": "e99eef30",
"metadata": {},
"source": [
"## TODO: Any functionality specific to this model provider\n",
"\n",
"chain.invoke({\"question\": \"What is LangChain?\"})"
"E.g. creating/using finetuned models via this provider. Delete if not relevant"
]
},
{
"cell_type": "markdown",
"id": "e9bdfcef",
"metadata": {},
"source": [
"## API reference\n",
"\n",
"For detailed documentation of all `__ModuleName__LLM` features and configurations head to the API reference: https://api.python.langchain.com/en/latest/llms/__module_name__.llms.__ModuleName__LLM.html"
]
}
],

153
libs/cli/langchain_cli/integration_template/integration_template/llms.py
View File

@ -2,36 +2,110 @@
from typing import (
Any,
AsyncIterator,
Iterator,
List,
Optional,
)
from langchain_core.callbacks import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain_core.language_models import BaseLLM
from langchain_core.outputs import GenerationChunk, LLMResult
from langchain_core.outputs import LLMResult
class __ModuleName__LLM(BaseLLM):
"""__ModuleName__LLM large language models.
"""__ModuleName__ completion 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: Optional[float]
Timeout for requests.
max_retries: int
Max number of retries.
api_key: Optional[str]
__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 __module_name__ import __ModuleName__LLM
model = __ModuleName__LLM()
model.invoke("Come up with 10 names for a song about parrots")
"""
llm = __ModuleName__LLM(
model="...",
temperature=0,
max_tokens=None,
timeout=None,
max_retries=2,
# api_key="...",
# other params...
)
@property
def _llm_type(self) -> str:
"""Return type of LLM."""
return "__package_name_short__-llm"
Invoke:
.. code-block:: python
input_text = "The meaning of life is "
llm.invoke(input_text)
.. code-block:: python
# TODO: Example output.
# TODO: Delete if token-level streaming isn't supported.
Stream:
.. code-block:: python
for chunk in llm.stream(input_text):
print(chunk)
.. code-block:: python
# TODO: Example output.
.. code-block:: python
''.join(llm.stream(input_text))
.. code-block:: python
# TODO: Example output.
# TODO: Delete if native async isn't supported.
Async:
.. code-block:: python
await llm.ainvoke(input_text)
# stream:
# async for chunk in (await llm.astream(input_text))
# batch:
# await llm.abatch([input_text])
.. code-block:: python
# TODO: Example output.
"""
# TODO: This method must be implemented to generate text completions.
def _generate(
@ -45,32 +119,37 @@ class __ModuleName__LLM(BaseLLM):
# TODO: Implement if __ModuleName__LLM supports async generation. Otherwise
# delete method.
async def _agenerate(
self,
prompts: List[str],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> LLMResult:
raise NotImplementedError
# async def _agenerate(
# self,
# prompts: List[str],
# stop: Optional[List[str]] = None,
# run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
# **kwargs: Any,
# ) -> LLMResult:
# raise NotImplementedError
# TODO: Implement if __ModuleName__LLM supports streaming. Otherwise delete method.
def _stream(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[GenerationChunk]:
raise NotImplementedError
# def _stream(
# self,
# prompt: str,
# stop: Optional[List[str]] = None,
# run_manager: Optional[CallbackManagerForLLMRun] = None,
# **kwargs: Any,
# ) -> Iterator[GenerationChunk]:
# raise NotImplementedError
# TODO: Implement if __ModuleName__LLM supports async streaming. Otherwise delete
# method.
async def _astream(
self,
prompt: str,
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> AsyncIterator[GenerationChunk]:
raise NotImplementedError
# async def _astream(
# self,
# prompt: str,
# stop: Optional[List[str]] = None,
# run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
# **kwargs: Any,
# ) -> AsyncIterator[GenerationChunk]:
# raise NotImplementedError
@property
def _llm_type(self) -> str:
"""Return type of LLM."""
return "__package_name_short__-llm"