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chore(cli): remove ruff exclusion of templates (#32864)

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This commit is contained in:
Christophe Bornet
2025-09-09 20:56:47 +02:00
committed by GitHub
parent 256a0b5f2f
commit 352ff363ca
19 changed files with 1317 additions and 1302 deletions
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516
libs/cli/langchain_cli/integration_template/docs/chat.ipynb
View File

@@ -1,260 +1,262 @@
{
"cells": [
{
"cell_type": "raw",
"id": "afaf8039",
"metadata": {},
"source": [
"---\n",
"sidebar_label: __ModuleName__\n",
"---"
]
},
{
"cell_type": "markdown",
"id": "e49f1e0d",
"metadata": {},
"source": [
"# Chat__ModuleName__\n",
"\n",
"- TODO: Make sure API reference link is correct.\n",
"\n",
"This will help you get 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://python.langchain.com/api_reference/__package_name_short_snake__/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/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/docs/integrations/chat/__package_name_short_snake__) | Package downloads | Package latest |\n",
"| :--- | :--- | :---: | :---: | :---: | :---: | :---: |\n",
"| [Chat__ModuleName__](https://python.langchain.com/api_reference/__package_name_short_snake__/chat_models/__module_name__.chat_models.Chat__ModuleName__.html) | [__package_name__](https://python.langchain.com/api_reference/__package_name_short_snake__/) | ✅/❌ | 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 | [Token-level 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": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
"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": "72ee0c4b-9764-423a-9dbf-95129e185210",
"metadata": {},
"source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
},
{
"cell_type": "code",
"execution_count": null,
"id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
"metadata": {},
"outputs": [],
"source": [
"# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
"# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
]
},
{
"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",
")"
]
},
{
"cell_type": "markdown",
"id": "2b4f3e15",
"metadata": {},
"source": [
"## Invocation\n",
"\n",
"- TODO: Run cells so output can be seen."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "62e0dbc3",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"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",
"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(\n",
" [\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 | 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://python.langchain.com/api_reference/__package_name_short_snake__/chat_models/__module_name__.chat_models.Chat__ModuleName__.html)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.9"
}
"cells": [
{
"cell_type": "raw",
"id": "afaf8039",
"metadata": {},
"source": [
"---\n",
"sidebar_label: __ModuleName__\n",
"---"
]
},
"nbformat": 4,
"nbformat_minor": 5
{
"cell_type": "markdown",
"id": "e49f1e0d",
"metadata": {},
"source": [
"# Chat__ModuleName__\n",
"\n",
"- TODO: Make sure API reference link is correct.\n",
"\n",
"This will help you get 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://python.langchain.com/api_reference/__package_name_short_snake__/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/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/docs/integrations/chat/__package_name_short_snake__) | Package downloads | Package latest |\n",
"| :--- | :--- | :---: | :---: | :---: | :---: | :---: |\n",
"| [Chat__ModuleName__](https://python.langchain.com/api_reference/__package_name_short_snake__/chat_models/__module_name__.chat_models.Chat__ModuleName__.html) | [__package_name__](https://python.langchain.com/api_reference/__package_name_short_snake__/) | ✅/❌ | 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 | [Token-level 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": "433e8d2b-9519-4b49-b2c4-7ab65b046c94",
"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(\n",
" \"Enter your __ModuleName__ API key: \"\n",
" )"
]
},
{
"cell_type": "markdown",
"id": "72ee0c4b-9764-423a-9dbf-95129e185210",
"metadata": {},
"source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
},
{
"cell_type": "code",
"execution_count": null,
"id": "a15d341e-3e26-4ca3-830b-5aab30ed66de",
"metadata": {},
"outputs": [],
"source": [
"# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
"# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
]
},
{
"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",
")"
]
},
{
"cell_type": "markdown",
"id": "2b4f3e15",
"metadata": {},
"source": [
"## Invocation\n",
"\n",
"- TODO: Run cells so output can be seen."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "62e0dbc3",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"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",
"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(\n",
" [\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 | 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://python.langchain.com/api_reference/__package_name_short_snake__/chat_models/__module_name__.chat_models.Chat__ModuleName__.html)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

430
libs/cli/langchain_cli/integration_template/docs/document_loaders.ipynb
View File

@@ -1,217 +1,219 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"sidebar_label: __ModuleName__\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# __ModuleName__Loader\n",
"\n",
"- TODO: Make sure API reference link is correct.\n",
"\n",
"This notebook provides a quick overview for getting started with __ModuleName__ [document loader](https://python.langchain.com/docs/concepts/document_loaders). For detailed documentation of all __ModuleName__Loader features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.__module_name___loader.__ModuleName__Loader.html).\n",
"\n",
"- TODO: Add any other relevant links, like information about underlying API, etc.\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/docs/integrations/document_loaders/web_loaders/__module_name___loader)|\n",
"| :--- | :--- | :---: | :---: | :---: |\n",
"| [__ModuleName__Loader](https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.__module_name__loader.__ModuleName__Loader.html) | [langchain_community](https://api.python.langchain.com/en/latest/community_api_reference.html) | ✅/❌ | beta/❌ | ✅/❌ | \n",
"### Loader features\n",
"| Source | Document Lazy Loading | Native Async Support\n",
"| :---: | :---: | :---: | \n",
"| __ModuleName__Loader | ✅/❌ | ✅/❌ | \n",
"\n",
"## Setup\n",
"\n",
"- TODO: Update with relevant info.\n",
"\n",
"To access __ModuleName__ document loader you'll need to install the `__package_name__` integration package, and create a **ModuleName** account and get an API key.\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,
"metadata": {},
"outputs": [],
"source": [
"import getpass\n",
"import os\n",
"\n",
"os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\"Enter your __ModuleName__ API key: \")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
"# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Installation\n",
"\n",
"Install **langchain_community**.\n",
"\n",
"- TODO: Add any other required packages"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%pip install -qU langchain_community"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initialization\n",
"\n",
"Now we can instantiate our model object and load documents:\n",
"\n",
"- TODO: Update model instantiation with relevant params."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from langchain_community.document_loaders import __ModuleName__Loader\n",
"\n",
"loader = __ModuleName__Loader(\n",
" # required params = ...\n",
" # optional params = ...\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load\n",
"\n",
"- TODO: Run cells to show loading capabilities"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"docs = loader.load()\n",
"docs[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(docs[0].metadata)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Lazy Load\n",
"\n",
"- TODO: Run cells to show lazy loading capabilities. Delete if lazy loading is not implemented."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"page = []\n",
"for doc in loader.lazy_load():\n",
" page.append(doc)\n",
" if len(page) >= 10:\n",
" # do some paged operation, e.g.\n",
" # index.upsert(page)\n",
"\n",
" page = []"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## TODO: Any functionality specific to this document loader\n",
"\n",
"E.g. using specific configs for different loading behavior. Delete if not relevant."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## API reference\n",
"\n",
"For detailed documentation of all __ModuleName__Loader features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.__module_name___loader.__ModuleName__Loader.html"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.9"
}
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"sidebar_label: __ModuleName__\n",
"---"
]
},
"nbformat": 4,
"nbformat_minor": 4
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# __ModuleName__Loader\n",
"\n",
"- TODO: Make sure API reference link is correct.\n",
"\n",
"This notebook provides a quick overview for getting started with __ModuleName__ [document loader](https://python.langchain.com/docs/concepts/document_loaders). For detailed documentation of all __ModuleName__Loader features and configurations head to the [API reference](https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.__module_name___loader.__ModuleName__Loader.html).\n",
"\n",
"- TODO: Add any other relevant links, like information about underlying API, etc.\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/docs/integrations/document_loaders/web_loaders/__module_name___loader)|\n",
"| :--- | :--- | :---: | :---: | :---: |\n",
"| [__ModuleName__Loader](https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.__module_name__loader.__ModuleName__Loader.html) | [langchain_community](https://api.python.langchain.com/en/latest/community_api_reference.html) | ✅/❌ | beta/❌ | ✅/❌ | \n",
"### Loader features\n",
"| Source | Document Lazy Loading | Native Async Support\n",
"| :---: | :---: | :---: | \n",
"| __ModuleName__Loader | ✅/❌ | ✅/❌ | \n",
"\n",
"## Setup\n",
"\n",
"- TODO: Update with relevant info.\n",
"\n",
"To access __ModuleName__ document loader you'll need to install the `__package_name__` integration package, and create a **ModuleName** account and get an API key.\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,
"metadata": {},
"outputs": [],
"source": [
"import getpass\n",
"import os\n",
"\n",
"os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\n",
" \"Enter your __ModuleName__ API key: \"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")\n",
"# os.environ[\"LANGSMITH_TRACING\"] = \"true\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Installation\n",
"\n",
"Install **langchain_community**.\n",
"\n",
"- TODO: Add any other required packages"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%pip install -qU langchain_community"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initialization\n",
"\n",
"Now we can instantiate our model object and load documents:\n",
"\n",
"- TODO: Update model instantiation with relevant params."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from langchain_community.document_loaders import __ModuleName__Loader\n",
"\n",
"loader = __ModuleName__Loader(\n",
" # required params = ...\n",
" # optional params = ...\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load\n",
"\n",
"- TODO: Run cells to show loading capabilities"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"docs = loader.load()\n",
"docs[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(docs[0].metadata)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Lazy Load\n",
"\n",
"- TODO: Run cells to show lazy loading capabilities. Delete if lazy loading is not implemented."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"page = []\n",
"for doc in loader.lazy_load():\n",
" page.append(doc)\n",
" if len(page) >= 10:\n",
" # do some paged operation, e.g.\n",
" # index.upsert(page)\n",
"\n",
" page = []"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## TODO: Any functionality specific to this document loader\n",
"\n",
"E.g. using specific configs for different loading behavior. Delete if not relevant."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## API reference\n",
"\n",
"For detailed documentation of all __ModuleName__Loader features and configurations head to the API reference: https://python.langchain.com/v0.2/api_reference/community/document_loaders/langchain_community.document_loaders.__module_name___loader.__ModuleName__Loader.html"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.9"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

466
libs/cli/langchain_cli/integration_template/docs/llms.ipynb
View File

@@ -1,236 +1,236 @@
{
"cells": [
{
"cell_type": "raw",
"id": "67db2992",
"metadata": {},
"source": [
"---\n",
"sidebar_label: __ModuleName__\n",
"---"
]
},
{
"cell_type": "markdown",
"id": "9597802c",
"metadata": {},
"source": [
"# __ModuleName__LLM\n",
"\n",
"- [ ] TODO: Make sure API reference link is correct\n",
"\n",
"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/docs/integrations/llms/__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": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
},
{
"cell_type": "code",
"execution_count": null,
"id": "196c2b41",
"metadata": {},
"outputs": [],
"source": [
"# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
"# os.environ[\"LANGSMITH_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:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "59c710c4",
"metadata": {},
"outputs": [],
"source": [
"%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",
")"
]
},
{
"cell_type": "markdown",
"id": "0ee90032",
"metadata": {},
"source": [
"## Invocation\n",
"\n",
"- [ ] TODO: Run cells so output can be seen."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "035dea0f",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"input_text = \"__ModuleName__ is an AI company that \"\n",
"\n",
"completion = llm.invoke(input_text)\n",
"completion"
]
},
{
"cell_type": "markdown",
"id": "add38532",
"metadata": {},
"source": [
"## Chaining\n",
"\n",
"We can [chain](/docs/how_to/sequence/) our completion model with a prompt template like so:\n",
"\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 PromptTemplate\n",
"\n",
"prompt = PromptTemplate(\n",
" \"How to say {input} in {output_language}:\\n\"\n",
")\n",
"\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",
"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"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.11.1 64-bit",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
},
"vscode": {
"interpreter": {
"hash": "e971737741ff4ec9aff7dc6155a1060a59a8a6d52c757dbbe66bf8ee389494b1"
}
}
"cells": [
{
"cell_type": "raw",
"id": "67db2992",
"metadata": {},
"source": [
"---\n",
"sidebar_label: __ModuleName__\n",
"---"
]
},
"nbformat": 4,
"nbformat_minor": 5
{
"cell_type": "markdown",
"id": "9597802c",
"metadata": {},
"source": [
"# __ModuleName__LLM\n",
"\n",
"- [ ] TODO: Make sure API reference link is correct\n",
"\n",
"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/docs/integrations/llms/__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(\n",
" \"Enter your __ModuleName__ API key: \"\n",
" )"
]
},
{
"cell_type": "markdown",
"id": "4b6e1ca6",
"metadata": {},
"source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
},
{
"cell_type": "code",
"execution_count": null,
"id": "196c2b41",
"metadata": {},
"outputs": [],
"source": [
"# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
"# os.environ[\"LANGSMITH_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:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "59c710c4",
"metadata": {},
"outputs": [],
"source": [
"%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",
")"
]
},
{
"cell_type": "markdown",
"id": "0ee90032",
"metadata": {},
"source": [
"## Invocation\n",
"\n",
"- [ ] TODO: Run cells so output can be seen."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "035dea0f",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"input_text = \"__ModuleName__ is an AI company that \"\n",
"\n",
"completion = llm.invoke(input_text)\n",
"completion"
]
},
{
"cell_type": "markdown",
"id": "add38532",
"metadata": {},
"source": [
"## Chaining\n",
"\n",
"We can [chain](/docs/how_to/sequence/) our completion model with a prompt template like so:\n",
"\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 PromptTemplate\n",
"\n",
"prompt = PromptTemplate(\"How to say {input} in {output_language}:\\n\")\n",
"\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",
"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"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.11.1 64-bit",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
},
"vscode": {
"interpreter": {
"hash": "e971737741ff4ec9aff7dc6155a1060a59a8a6d52c757dbbe66bf8ee389494b1"
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}

4
libs/cli/langchain_cli/integration_template/docs/stores.ipynb
View File

@@ -62,7 +62,9 @@
"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: \")"
" os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\n",
" \"Enter your __ModuleName__ API key: \"\n",
" )"
]
},
{

484
libs/cli/langchain_cli/integration_template/docs/text_embedding.ipynb
View File

@@ -1,244 +1,246 @@
{
"cells": [
{
"cell_type": "raw",
"id": "afaf8039",
"metadata": {},
"source": [
"---\n",
"sidebar_label: __ModuleName__\n",
"---"
]
},
{
"cell_type": "markdown",
"id": "9a3d6f34",
"metadata": {},
"source": [
"# __ModuleName__Embeddings\n",
"\n",
"- [ ] TODO: Make sure API reference link is correct\n",
"\n",
"This will help you get started with __ModuleName__ embedding models using LangChain. For detailed documentation on `__ModuleName__Embeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/v0.2/api_reference/__package_name_short__/embeddings/__module_name__.embeddings__ModuleName__Embeddings.html).\n",
"\n",
"## Overview\n",
"### Integration details\n",
"\n",
"| Provider | Package |\n",
"|:--------:|:-------:|\n",
"| [__ModuleName__](/docs/integrations/providers/__package_name_short__/) | [__package_name__](https://python.langchain.com/v0.2/api_reference/__module_name__/embeddings/__module_name__.embeddings__ModuleName__Embeddings.html) |\n",
"\n",
"## Setup\n",
"\n",
"- [ ] TODO: Update with relevant info.\n",
"\n",
"To access __ModuleName__ embedding 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": "36521c2a",
"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": "c84fb993",
"metadata": {},
"source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
},
{
"cell_type": "code",
"execution_count": null,
"id": "39a4953b",
"metadata": {},
"outputs": [],
"source": [
"# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
"# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
]
},
{
"cell_type": "markdown",
"id": "d9664366",
"metadata": {},
"source": [
"### Installation\n",
"\n",
"The LangChain __ModuleName__ integration lives in the `__package_name__` package:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "64853226",
"metadata": {},
"outputs": [],
"source": [
"%pip install -qU __package_name__"
]
},
{
"cell_type": "markdown",
"id": "45dd1724",
"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": "9ea7a09b",
"metadata": {},
"outputs": [],
"source": [
"from __module_name__ import __ModuleName__Embeddings\n",
"\n",
"embeddings = __ModuleName__Embeddings(\n",
" model=\"model-name\",\n",
")"
]
},
{
"cell_type": "markdown",
"id": "77d271b6",
"metadata": {},
"source": [
"## Indexing and Retrieval\n",
"\n",
"Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/docs/tutorials/).\n",
"\n",
"Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d817716b",
"metadata": {},
"outputs": [],
"source": [
"# Create a vector store with a sample text\n",
"from langchain_core.vectorstores import InMemoryVectorStore\n",
"\n",
"text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
"\n",
"vectorstore = InMemoryVectorStore.from_texts(\n",
" [text],\n",
" embedding=embeddings,\n",
")\n",
"\n",
"# Use the vectorstore as a retriever\n",
"retriever = vectorstore.as_retriever()\n",
"\n",
"# Retrieve the most similar text\n",
"retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
"\n",
"# show the retrieved document's content\n",
"retrieved_documents[0].page_content"
]
},
{
"cell_type": "markdown",
"id": "e02b9855",
"metadata": {},
"source": [
"## Direct Usage\n",
"\n",
"Under the hood, the vectorstore and retriever implementations are calling `embeddings.embed_documents(...)` and `embeddings.embed_query(...)` to create embeddings for the text(s) used in `from_texts` and retrieval `invoke` operations, respectively.\n",
"\n",
"You can directly call these methods to get embeddings for your own use cases.\n",
"\n",
"### Embed single texts\n",
"\n",
"You can embed single texts or documents with `embed_query`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0d2befcd",
"metadata": {},
"outputs": [],
"source": [
"single_vector = embeddings.embed_query(text)\n",
"print(str(single_vector)[:100]) # Show the first 100 characters of the vector"
]
},
{
"cell_type": "markdown",
"id": "1b5a7d03",
"metadata": {},
"source": [
"### Embed multiple texts\n",
"\n",
"You can embed multiple texts with `embed_documents`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2f4d6e97",
"metadata": {},
"outputs": [],
"source": [
"text2 = (\n",
" \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
")\n",
"two_vectors = embeddings.embed_documents([text, text2])\n",
"for vector in two_vectors:\n",
" print(str(vector)[:100]) # Show the first 100 characters of the vector"
]
},
{
"cell_type": "markdown",
"id": "98785c12",
"metadata": {},
"source": [
"## API Reference\n",
"\n",
"For detailed documentation on `__ModuleName__Embeddings` features and configuration options, please refer to the [API reference](https://api.python.langchain.com/en/latest/embeddings/__module_name__.embeddings.__ModuleName__Embeddings.html).\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.5"
}
"cells": [
{
"cell_type": "raw",
"id": "afaf8039",
"metadata": {},
"source": [
"---\n",
"sidebar_label: __ModuleName__\n",
"---"
]
},
"nbformat": 4,
"nbformat_minor": 5
{
"cell_type": "markdown",
"id": "9a3d6f34",
"metadata": {},
"source": [
"# __ModuleName__Embeddings\n",
"\n",
"- [ ] TODO: Make sure API reference link is correct\n",
"\n",
"This will help you get started with __ModuleName__ embedding models using LangChain. For detailed documentation on `__ModuleName__Embeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/v0.2/api_reference/__package_name_short__/embeddings/__module_name__.embeddings__ModuleName__Embeddings.html).\n",
"\n",
"## Overview\n",
"### Integration details\n",
"\n",
"| Provider | Package |\n",
"|:--------:|:-------:|\n",
"| [__ModuleName__](/docs/integrations/providers/__package_name_short__/) | [__package_name__](https://python.langchain.com/v0.2/api_reference/__module_name__/embeddings/__module_name__.embeddings__ModuleName__Embeddings.html) |\n",
"\n",
"## Setup\n",
"\n",
"- [ ] TODO: Update with relevant info.\n",
"\n",
"To access __ModuleName__ embedding 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": "36521c2a",
"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(\n",
" \"Enter your __ModuleName__ API key: \"\n",
" )"
]
},
{
"cell_type": "markdown",
"id": "c84fb993",
"metadata": {},
"source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
},
{
"cell_type": "code",
"execution_count": null,
"id": "39a4953b",
"metadata": {},
"outputs": [],
"source": [
"# os.environ[\"LANGSMITH_TRACING\"] = \"true\"\n",
"# os.environ[\"LANGSMITH_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
]
},
{
"cell_type": "markdown",
"id": "d9664366",
"metadata": {},
"source": [
"### Installation\n",
"\n",
"The LangChain __ModuleName__ integration lives in the `__package_name__` package:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "64853226",
"metadata": {},
"outputs": [],
"source": [
"%pip install -qU __package_name__"
]
},
{
"cell_type": "markdown",
"id": "45dd1724",
"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": "9ea7a09b",
"metadata": {},
"outputs": [],
"source": [
"from __module_name__ import __ModuleName__Embeddings\n",
"\n",
"embeddings = __ModuleName__Embeddings(\n",
" model=\"model-name\",\n",
")"
]
},
{
"cell_type": "markdown",
"id": "77d271b6",
"metadata": {},
"source": [
"## Indexing and Retrieval\n",
"\n",
"Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/docs/tutorials/).\n",
"\n",
"Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d817716b",
"metadata": {},
"outputs": [],
"source": [
"# Create a vector store with a sample text\n",
"from langchain_core.vectorstores import InMemoryVectorStore\n",
"\n",
"text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
"\n",
"vectorstore = InMemoryVectorStore.from_texts(\n",
" [text],\n",
" embedding=embeddings,\n",
")\n",
"\n",
"# Use the vectorstore as a retriever\n",
"retriever = vectorstore.as_retriever()\n",
"\n",
"# Retrieve the most similar text\n",
"retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
"\n",
"# show the retrieved document's content\n",
"retrieved_documents[0].page_content"
]
},
{
"cell_type": "markdown",
"id": "e02b9855",
"metadata": {},
"source": [
"## Direct Usage\n",
"\n",
"Under the hood, the vectorstore and retriever implementations are calling `embeddings.embed_documents(...)` and `embeddings.embed_query(...)` to create embeddings for the text(s) used in `from_texts` and retrieval `invoke` operations, respectively.\n",
"\n",
"You can directly call these methods to get embeddings for your own use cases.\n",
"\n",
"### Embed single texts\n",
"\n",
"You can embed single texts or documents with `embed_query`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0d2befcd",
"metadata": {},
"outputs": [],
"source": [
"single_vector = embeddings.embed_query(text)\n",
"print(str(single_vector)[:100]) # Show the first 100 characters of the vector"
]
},
{
"cell_type": "markdown",
"id": "1b5a7d03",
"metadata": {},
"source": [
"### Embed multiple texts\n",
"\n",
"You can embed multiple texts with `embed_documents`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2f4d6e97",
"metadata": {},
"outputs": [],
"source": [
"text2 = (\n",
" \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
")\n",
"two_vectors = embeddings.embed_documents([text, text2])\n",
"for vector in two_vectors:\n",
" print(str(vector)[:100]) # Show the first 100 characters of the vector"
]
},
{
"cell_type": "markdown",
"id": "98785c12",
"metadata": {},
"source": [
"## API Reference\n",
"\n",
"For detailed documentation on `__ModuleName__Embeddings` features and configuration options, please refer to the [API reference](https://api.python.langchain.com/en/latest/embeddings/__module_name__.embeddings.__ModuleName__Embeddings.html).\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

4
libs/cli/langchain_cli/integration_template/docs/tools.ipynb
View File

@@ -120,9 +120,7 @@
"from langchain_community.tools import __ModuleName__\n",
"\n",
"\n",
"tool = __ModuleName__(\n",
" ...\n",
")"
"tool = __ModuleName__(...)"
]
},
{

667
libs/cli/langchain_cli/integration_template/docs/vectorstores.ipynb
View File

@@ -1,340 +1,333 @@
{
"cells": [
{
"cell_type": "raw",
"id": "1957f5cb",
"metadata": {},
"source": [
"---\n",
"sidebar_label: __ModuleName__\n",
"---"
]
},
{
"cell_type": "markdown",
"id": "ef1f0986",
"metadata": {},
"source": [
"# __ModuleName__VectorStore\n",
"\n",
"This notebook covers how to get started with the __ModuleName__ vector store."
]
},
{
"cell_type": "markdown",
"id": "36fdc060",
"metadata": {},
"source": [
"## Setup\n",
"\n",
"- TODO: Update with relevant info.\n",
"- TODO: Update minimum version to be correct.\n",
"\n",
"To access __ModuleName__ vector stores you'll need to create a/an __ModuleName__ account, get an API key, and install the `__package_name__` integration package."
]
},
{
"cell_type": "raw",
"id": "64e28aa6",
"metadata": {
"vscode": {
"languageId": "raw"
}
},
"source": [
"%pip install -qU \"__package_name__>=MINIMUM_VERSION\""
]
},
{
"cell_type": "markdown",
"id": "9695dee7",
"metadata": {},
"source": [
"### 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": "894c30e4",
"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": "7f98392b",
"metadata": {},
"source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
},
{
"cell_type": "code",
"execution_count": null,
"id": "e7b6a6e0",
"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": "93df377e",
"metadata": {},
"source": [
"## Initialization\n",
"\n",
"- TODO: Fill out with relevant init params\n",
"\n",
"\n",
"```{=mdx}\n",
"import EmbeddingTabs from \"@theme/EmbeddingTabs\";\n",
"\n",
"<EmbeddingTabs/>\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dc37144c-208d-4ab3-9f3a-0407a69fe052",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from __module_name__.vectorstores import __ModuleName__VectorStore\n",
"\n",
"vector_store = __ModuleName__VectorStore(embeddings=embeddings)"
]
},
{
"cell_type": "markdown",
"id": "ac6071d4",
"metadata": {},
"source": [
"## Manage vector store\n",
"\n",
"### Add items to vector store\n",
"\n",
"- TODO: Edit and then run code cell to generate output"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "17f5efc0",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.documents import Document\n",
"\n",
"document_1 = Document(\n",
" page_content=\"foo\",\n",
" metadata={\"source\": \"https://example.com\"}\n",
")\n",
"\n",
"document_2 = Document(\n",
" page_content=\"bar\",\n",
" metadata={\"source\": \"https://example.com\"}\n",
")\n",
"\n",
"document_3 = Document(\n",
" page_content=\"baz\",\n",
" metadata={\"source\": \"https://example.com\"}\n",
")\n",
"\n",
"documents = [document_1, document_2, document_3]\n",
"\n",
"vector_store.add_documents(documents=documents,ids=[\"1\",\"2\",\"3\"])"
]
},
{
"cell_type": "markdown",
"id": "c738c3e0",
"metadata": {},
"source": [
"### Update items in vector store\n",
"\n",
"- TODO: Edit and then run code cell to generate output"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f0aa8b71",
"metadata": {},
"outputs": [],
"source": [
"updated_document = Document(\n",
" page_content=\"qux\",\n",
" metadata={\"source\": \"https://another-example.com\"}\n",
")\n",
"\n",
"vector_store.update_documents(document_id=\"1\",document=updated_document)"
]
},
{
"cell_type": "markdown",
"id": "dcf1b905",
"metadata": {},
"source": [
"### Delete items from vector store\n",
"\n",
"- TODO: Edit and then run code cell to generate output"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ef61e188",
"metadata": {},
"outputs": [],
"source": [
"vector_store.delete(ids=[\"3\"])"
]
},
{
"cell_type": "markdown",
"id": "c3620501",
"metadata": {},
"source": [
"## Query vector store\n",
"\n",
"Once your vector store has been created and the relevant documents have been added you will most likely wish to query it during the running of your chain or agent.\n",
"\n",
"### Query directly\n",
"\n",
"Performing a simple similarity search can be done as follows:\n",
"\n",
"- TODO: Edit and then run code cell to generate output"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "aa0a16fa",
"metadata": {},
"outputs": [],
"source": [
"results = vector_store.similarity_search(query=\"thud\",k=1,filter={\"source\":\"https://another-example.com\"})\n",
"for doc in results:\n",
" print(f\"* {doc.page_content} [{doc.metadata}]\")"
]
},
{
"cell_type": "markdown",
"id": "3ed9d733",
"metadata": {},
"source": [
"If you want to execute a similarity search and receive the corresponding scores you can run:\n",
"\n",
"- TODO: Edit and then run code cell to generate output"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5efd2eaa",
"metadata": {},
"outputs": [],
"source": [
"results = vector_store.similarity_search_with_score(query=\"thud\",k=1,filter={\"source\":\"https://example.com\"})\n",
"for doc, score in results:\n",
" print(f\"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]\")"
]
},
{
"cell_type": "markdown",
"id": "0c235cdc",
"metadata": {},
"source": [
"### Query by turning into retriever\n",
"\n",
"You can also transform the vector store into a retriever for easier usage in your chains.\n",
"\n",
"- TODO: Edit and then run code cell to generate output"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f3460093",
"metadata": {},
"outputs": [],
"source": [
"retriever = vector_store.as_retriever(\n",
" search_type=\"mmr\",\n",
" search_kwargs={\"k\": 1}\n",
")\n",
"retriever.invoke(\"thud\")"
]
},
{
"cell_type": "markdown",
"id": "901c75dc",
"metadata": {},
"source": [
"## Usage for retrieval-augmented generation\n",
"\n",
"For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
"\n",
"- [Tutorials](/docs/tutorials/)\n",
"- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n",
"- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/retrieval/)"
]
},
{
"cell_type": "markdown",
"id": "069f1b5f",
"metadata": {},
"source": [
"## TODO: Any functionality specific to this vector store\n",
"\n",
"E.g. creating a persisten database to save to your disk, etc."
]
},
{
"cell_type": "markdown",
"id": "8a27244f",
"metadata": {},
"source": [
"## API reference\n",
"\n",
"For detailed documentation of all __ModuleName__VectorStore features and configurations head to the API reference: https://api.python.langchain.com/en/latest/vectorstores/__module_name__.vectorstores.__ModuleName__VectorStore.html"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.12"
}
"cells": [
{
"cell_type": "raw",
"id": "1957f5cb",
"metadata": {},
"source": [
"---\n",
"sidebar_label: __ModuleName__\n",
"---"
]
},
"nbformat": 4,
"nbformat_minor": 5
{
"cell_type": "markdown",
"id": "ef1f0986",
"metadata": {},
"source": [
"# __ModuleName__VectorStore\n",
"\n",
"This notebook covers how to get started with the __ModuleName__ vector store."
]
},
{
"cell_type": "markdown",
"id": "36fdc060",
"metadata": {},
"source": [
"## Setup\n",
"\n",
"- TODO: Update with relevant info.\n",
"- TODO: Update minimum version to be correct.\n",
"\n",
"To access __ModuleName__ vector stores you'll need to create a/an __ModuleName__ account, get an API key, and install the `__package_name__` integration package."
]
},
{
"cell_type": "raw",
"id": "64e28aa6",
"metadata": {
"vscode": {
"languageId": "raw"
}
},
"source": [
"%pip install -qU \"__package_name__>=MINIMUM_VERSION\""
]
},
{
"cell_type": "markdown",
"id": "9695dee7",
"metadata": {},
"source": [
"### 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": "894c30e4",
"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(\n",
" \"Enter your __ModuleName__ API key: \"\n",
" )"
]
},
{
"cell_type": "markdown",
"id": "7f98392b",
"metadata": {},
"source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
},
{
"cell_type": "code",
"execution_count": null,
"id": "e7b6a6e0",
"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": "93df377e",
"metadata": {},
"source": [
"## Initialization\n",
"\n",
"- TODO: Fill out with relevant init params\n",
"\n",
"\n",
"```{=mdx}\n",
"import EmbeddingTabs from \"@theme/EmbeddingTabs\";\n",
"\n",
"<EmbeddingTabs/>\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dc37144c-208d-4ab3-9f3a-0407a69fe052",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from __module_name__.vectorstores import __ModuleName__VectorStore\n",
"\n",
"vector_store = __ModuleName__VectorStore(embeddings=embeddings)"
]
},
{
"cell_type": "markdown",
"id": "ac6071d4",
"metadata": {},
"source": [
"## Manage vector store\n",
"\n",
"### Add items to vector store\n",
"\n",
"- TODO: Edit and then run code cell to generate output"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "17f5efc0",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.documents import Document\n",
"\n",
"document_1 = Document(page_content=\"foo\", metadata={\"source\": \"https://example.com\"})\n",
"\n",
"document_2 = Document(page_content=\"bar\", metadata={\"source\": \"https://example.com\"})\n",
"\n",
"document_3 = Document(page_content=\"baz\", metadata={\"source\": \"https://example.com\"})\n",
"\n",
"documents = [document_1, document_2, document_3]\n",
"\n",
"vector_store.add_documents(documents=documents, ids=[\"1\", \"2\", \"3\"])"
]
},
{
"cell_type": "markdown",
"id": "c738c3e0",
"metadata": {},
"source": [
"### Update items in vector store\n",
"\n",
"- TODO: Edit and then run code cell to generate output"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f0aa8b71",
"metadata": {},
"outputs": [],
"source": [
"updated_document = Document(\n",
" page_content=\"qux\", metadata={\"source\": \"https://another-example.com\"}\n",
")\n",
"\n",
"vector_store.update_documents(document_id=\"1\", document=updated_document)"
]
},
{
"cell_type": "markdown",
"id": "dcf1b905",
"metadata": {},
"source": [
"### Delete items from vector store\n",
"\n",
"- TODO: Edit and then run code cell to generate output"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ef61e188",
"metadata": {},
"outputs": [],
"source": [
"vector_store.delete(ids=[\"3\"])"
]
},
{
"cell_type": "markdown",
"id": "c3620501",
"metadata": {},
"source": [
"## Query vector store\n",
"\n",
"Once your vector store has been created and the relevant documents have been added you will most likely wish to query it during the running of your chain or agent.\n",
"\n",
"### Query directly\n",
"\n",
"Performing a simple similarity search can be done as follows:\n",
"\n",
"- TODO: Edit and then run code cell to generate output"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "aa0a16fa",
"metadata": {},
"outputs": [],
"source": [
"results = vector_store.similarity_search(\n",
" query=\"thud\", k=1, filter={\"source\": \"https://another-example.com\"}\n",
")\n",
"for doc in results:\n",
" print(f\"* {doc.page_content} [{doc.metadata}]\")"
]
},
{
"cell_type": "markdown",
"id": "3ed9d733",
"metadata": {},
"source": [
"If you want to execute a similarity search and receive the corresponding scores you can run:\n",
"\n",
"- TODO: Edit and then run code cell to generate output"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5efd2eaa",
"metadata": {},
"outputs": [],
"source": [
"results = vector_store.similarity_search_with_score(\n",
" query=\"thud\", k=1, filter={\"source\": \"https://example.com\"}\n",
")\n",
"for doc, score in results:\n",
" print(f\"* [SIM={score:3f}] {doc.page_content} [{doc.metadata}]\")"
]
},
{
"cell_type": "markdown",
"id": "0c235cdc",
"metadata": {},
"source": [
"### Query by turning into retriever\n",
"\n",
"You can also transform the vector store into a retriever for easier usage in your chains.\n",
"\n",
"- TODO: Edit and then run code cell to generate output"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f3460093",
"metadata": {},
"outputs": [],
"source": [
"retriever = vector_store.as_retriever(search_type=\"mmr\", search_kwargs={\"k\": 1})\n",
"retriever.invoke(\"thud\")"
]
},
{
"cell_type": "markdown",
"id": "901c75dc",
"metadata": {},
"source": [
"## Usage for retrieval-augmented generation\n",
"\n",
"For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
"\n",
"- [Tutorials](/docs/tutorials/)\n",
"- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n",
"- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/retrieval/)"
]
},
{
"cell_type": "markdown",
"id": "069f1b5f",
"metadata": {},
"source": [
"## TODO: Any functionality specific to this vector store\n",
"\n",
"E.g. creating a persisten database to save to your disk, etc."
]
},
{
"cell_type": "markdown",
"id": "8a27244f",
"metadata": {},
"source": [
"## API reference\n",
"\n",
"For detailed documentation of all __ModuleName__VectorStore features and configurations head to the API reference: https://api.python.langchain.com/en/latest/vectorstores/__module_name__.vectorstores.__ModuleName__VectorStore.html"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

9
libs/cli/langchain_cli/integration_template/integration_template/chat_models.py
View File

@@ -21,11 +21,13 @@ class Chat__ModuleName__(BaseChatModel):
# https://github.com/langchain-ai/langchain/blob/7ff05357bac6eaedf5058a2af88f23a1817d40fe/libs/partners/openai/langchain_openai/chat_models/base.py#L1120
"""__ModuleName__ chat model integration.
The default implementation echoes the first `parrot_buffer_length` characters of the input.
The default implementation echoes the first `parrot_buffer_length` characters of
the input.
# TODO: Replace with relevant packages, env vars.
Setup:
Install ``__package_name__`` and set environment variable ``__MODULE_NAME___API_KEY``.
Install ``__package_name__`` and set environment variable
``__MODULE_NAME___API_KEY``.
.. code-block:: bash
@@ -48,7 +50,8 @@ class Chat__ModuleName__(BaseChatModel):
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.
__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.

5
libs/cli/langchain_cli/integration_template/integration_template/document_loaders.py
View File

@@ -14,7 +14,8 @@ class __ModuleName__Loader(BaseLoader):
# TODO: Replace with relevant packages, env vars.
Setup:
Install ``__package_name__`` and set environment variable ``__MODULE_NAME___API_KEY``.
Install ``__package_name__`` and set environment variable
``__MODULE_NAME___API_KEY``.
.. code-block:: bash
@@ -62,7 +63,7 @@ class __ModuleName__Loader(BaseLoader):
TODO: Example output
""" # noqa: E501
"""
# TODO: This method must be implemented to load documents.
# Do not implement load(), a default implementation is already available.

5
libs/cli/langchain_cli/integration_template/integration_template/toolkits.py
View File

@@ -12,7 +12,8 @@ class __ModuleName__Toolkit(BaseToolkit):
# TODO: Replace with relevant packages, env vars, etc.
Setup:
Install ``__package_name__`` and set environment variable ``__MODULE_NAME___API_KEY``.
Install ``__package_name__`` and set environment variable
``__MODULE_NAME___API_KEY``.
.. code-block:: bash
@@ -65,7 +66,7 @@ class __ModuleName__Toolkit(BaseToolkit):
# TODO: Example output.
""" # noqa: E501
"""
# TODO: This method must be implemented to list tools.
def get_tools(self) -> List[BaseTool]:

3
libs/cli/langchain_cli/integration_template/integration_template/tools.py
View File

@@ -27,7 +27,8 @@ class __ModuleName__Tool(BaseTool): # type: ignore[override]
Setup:
# TODO: Replace with relevant packages, env vars.
Install ``__package_name__`` and set environment variable ``__MODULE_NAME___API_KEY``.
Install ``__package_name__`` and set environment variable
``__MODULE_NAME___API_KEY``.
.. code-block:: bash

3
libs/cli/langchain_cli/integration_template/pyproject.toml
View File

@@ -25,6 +25,9 @@ langchain-core = "^0.3.15"
[tool.ruff.lint]
select = ["E", "F", "I", "T201"]
[tool.ruff.lint.per-file-ignores]
"docs/**" = [ "ALL",]
[tool.coverage.run]
omit = ["tests/*"]