diff --git a/libs/cli/langchain_cli/integration_template/docs/chat.ipynb b/libs/cli/langchain_cli/integration_template/docs/chat.ipynb index e19681176f..2b838e3b09 100644 --- a/libs/cli/langchain_cli/integration_template/docs/chat.ipynb +++ b/libs/cli/langchain_cli/integration_template/docs/chat.ipynb @@ -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", diff --git a/libs/cli/langchain_cli/integration_template/docs/llms.ipynb b/libs/cli/langchain_cli/integration_template/docs/llms.ipynb index 69c34d1af0..2629c7718d 100644 --- a/libs/cli/langchain_cli/integration_template/docs/llms.ipynb +++ b/libs/cli/langchain_cli/integration_template/docs/llms.ipynb @@ -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:" ] }, { @@ -29,22 +95,48 @@ "metadata": {}, "outputs": [], "source": [ - "# install package\n", - "!pip install -U __package_name__" + "%pip install -qU __package_name__" ] }, { "cell_type": "markdown", - "id": "0ee90032", + "id": "0a760037", "metadata": {}, "source": [ - "## Environment Setup\n", + "## Instantiation\n", "\n", - "Make sure to set the following environment variables:\n", + "Now we can instantiate our model object and generate chat completions:\n", "\n", - "- TODO: fill out relevant environment variables or secrets\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", - "## Usage" + "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", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "0ee90032", + "metadata": {}, + "source": [ + "## Invocation\n", + "\n", + "- [ ] 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", + "prompt = PromptTemplate(\n", + " \"How to say {input} in {output_language}:\\n\"\n", + ")\n", "\n", - "model = __ModuleName__LLM()\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 = prompt | model\n", + "E.g. creating/using finetuned models via this provider. Delete if not relevant" + ] + }, + { + "cell_type": "markdown", + "id": "e9bdfcef", + "metadata": {}, + "source": [ + "## API reference\n", "\n", - "chain.invoke({\"question\": \"What is LangChain?\"})" + "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" ] } ], diff --git a/libs/cli/langchain_cli/integration_template/integration_template/llms.py b/libs/cli/langchain_cli/integration_template/integration_template/llms.py index 675e95f158..2dbe4ac918 100644 --- a/libs/cli/langchain_cli/integration_template/integration_template/llms.py +++ b/libs/cli/langchain_cli/integration_template/integration_template/llms.py @@ -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"