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

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[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
-    "# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
+    "# 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",

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:"
    ]
   },
   {
@@ -29,8 +95,38 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# install package\n",
+    "%pip install -qU __package_name__"
-    "!pip install -U __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",
+    "- [ ] TODO: Run cells so output can be seen."
-    "\n",
-    "- TODO: fill out relevant environment variables or secrets\n",
-    "\n",
-    "## Usage"
    ]
   },
   {
@@ -56,20 +148,64 @@
    },
    "outputs": [],
    "source": [
-    "from langchain_core.prompts import PromptTemplate\n",
+    "input_text = \"__ModuleName__ is an AI company that \"\n",
-    "from __module_name__.llms import __ModuleName__LLM\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"
    ]
   }
  ],

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()
+            llm = __ModuleName__LLM(
-            model.invoke("Come up with 10 names for a song about parrots")
+                model="...",
-    """
+                temperature=0,
+                max_tokens=None,
+                timeout=None,
+                max_retries=2,
+                # api_key="...",
+                # other params...
+            )
 
-    @property
+    Invoke:
-    def _llm_type(self) -> str:
+        .. code-block:: python
-        """Return type of LLM."""
+
-        return "__package_name_short__-llm"
+            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(
+    # async def _agenerate(
-        self,
+    #     self,
-        prompts: List[str],
+    #     prompts: List[str],
-        stop: Optional[List[str]] = None,
+    #     stop: Optional[List[str]] = None,
-        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+    #     run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
-        **kwargs: Any,
+    #     **kwargs: Any,
-    ) -> LLMResult:
+    # ) -> LLMResult:
-        raise NotImplementedError
+    #     raise NotImplementedError
 
     # TODO: Implement if __ModuleName__LLM supports streaming. Otherwise delete method.
-    def _stream(
+    # def _stream(
-        self,
+    #     self,
-        prompt: str,
+    #     prompt: str,
-        stop: Optional[List[str]] = None,
+    #     stop: Optional[List[str]] = None,
-        run_manager: Optional[CallbackManagerForLLMRun] = None,
+    #     run_manager: Optional[CallbackManagerForLLMRun] = None,
-        **kwargs: Any,
+    #     **kwargs: Any,
-    ) -> Iterator[GenerationChunk]:
+    # ) -> Iterator[GenerationChunk]:
-        raise NotImplementedError
+    #     raise NotImplementedError
 
     # TODO: Implement if __ModuleName__LLM supports async streaming. Otherwise delete
     # method.
-    async def _astream(
+    # async def _astream(
-        self,
+    #     self,
-        prompt: str,
+    #     prompt: str,
-        stop: Optional[List[str]] = None,
+    #     stop: Optional[List[str]] = None,
-        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
+    #     run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
-        **kwargs: Any,
+    #     **kwargs: Any,
-    ) -> AsyncIterator[GenerationChunk]:
+    # ) -> AsyncIterator[GenerationChunk]:
-        raise NotImplementedError
+    #     raise NotImplementedError
+
+    @property
+    def _llm_type(self) -> str:
+        """Return type of LLM."""
+        return "__package_name_short__-llm"