diff --git a/docs/docs/expression_language/get_started.ipynb b/docs/docs/expression_language/get_started.ipynb index d6db4200383..f3f55a36fe5 100644 --- a/docs/docs/expression_language/get_started.ipynb +++ b/docs/docs/expression_language/get_started.ipynb @@ -17,12 +17,6 @@ "id": "befa7fd1", "metadata": {}, "source": [ - "```{=mdx}\n", - "\n", - " \n", - "\n", - "```\n", - "\n", "LCEL makes it easy to build complex chains from basic components, and supports out of the box functionality such as streaming, parallelism, and logging." ] }, diff --git a/docs/docs/expression_language/index.mdx b/docs/docs/expression_language/index.mdx index 4aff0314d3f..8bc1b52ff86 100644 --- a/docs/docs/expression_language/index.mdx +++ b/docs/docs/expression_language/index.mdx @@ -4,9 +4,6 @@ sidebar_class_name: hidden # LangChain Expression Language (LCEL) - - - LangChain Expression Language, or LCEL, is a declarative way to easily compose chains together. LCEL was designed from day 1 to **support putting prototypes in production, with no code changes**, from the simplest “prompt + LLM” chain to the most complex chains (we’ve seen folks successfully run LCEL chains with 100s of steps in production). To highlight a few of the reasons you might want to use LCEL: diff --git a/docs/docs/expression_language/interface.ipynb b/docs/docs/expression_language/interface.ipynb index 91197e28d25..e5798575158 100644 --- a/docs/docs/expression_language/interface.ipynb +++ b/docs/docs/expression_language/interface.ipynb @@ -16,12 +16,7 @@ "id": "9a9acd2e", "metadata": {}, "source": [ - "```{=mdx}\n", - "\n", - " \n", - "\n", - "```\n", - "\n", + "To make it as easy as possible to create custom chains, we've implemented a [\"Runnable\"](https://api.python.langchain.com/en/stable/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable) protocol. Many LangChain components implement the `Runnable` protocol, including chat models, LLMs, output parsers, retrievers, prompt templates, and more. There are also several useful primitives for working with runnables, which you can read about [in this section](/docs/expression_language/primitives).\n", "\n", "This is a standard interface, which makes it easy to define custom chains as well as invoke them in a standard way. \n", diff --git a/docs/docs/get_started/installation.mdx b/docs/docs/get_started/installation.mdx index 41e9b399f8f..b1d712e21a5 100644 --- a/docs/docs/get_started/installation.mdx +++ b/docs/docs/get_started/installation.mdx @@ -4,9 +4,6 @@ sidebar_position: 2 # Installation - - - ## Official release diff --git a/docs/docs/get_started/introduction.mdx b/docs/docs/get_started/introduction.mdx index 0dbd2b27d3a..f26214cd6c5 100644 --- a/docs/docs/get_started/introduction.mdx +++ b/docs/docs/get_started/introduction.mdx @@ -5,9 +5,6 @@ sidebar_class_name: hidden # Introduction - - - **LangChain** is a framework for developing applications powered by large language models (LLMs). diff --git a/docs/docs/get_started/quickstart.mdx b/docs/docs/get_started/quickstart.mdx index 9ba099db689..2af16e54ae0 100644 --- a/docs/docs/get_started/quickstart.mdx +++ b/docs/docs/get_started/quickstart.mdx @@ -4,9 +4,6 @@ sidebar_position: 1 # Quickstart - - - In this quickstart we'll show you how to: - Get setup with LangChain, LangSmith and LangServe diff --git a/docs/docs/integrations/chat/openai.ipynb b/docs/docs/integrations/chat/openai.ipynb index a68ea4a2f0d..75bc039bf0d 100644 --- a/docs/docs/integrations/chat/openai.ipynb +++ b/docs/docs/integrations/chat/openai.ipynb @@ -17,12 +17,7 @@ "source": [ "# ChatOpenAI\n", "\n", - "```{=mdx}\n", - "\n", - " \n", - "\n", - "```\n", - "\n", + "This notebook covers how to get started with OpenAI chat models." ] }, diff --git a/docs/docs/integrations/llms/llamacpp.ipynb b/docs/docs/integrations/llms/llamacpp.ipynb index e9bd62e7e4f..f63ceb735ab 100644 --- a/docs/docs/integrations/llms/llamacpp.ipynb +++ b/docs/docs/integrations/llms/llamacpp.ipynb @@ -6,12 +6,7 @@ "source": [ "# Llama.cpp\n", "\n", - "```{=mdx}\n", - "\n", - " \n", - "\n", - "```\n", - "\n", + "[llama-cpp-python](https://github.com/abetlen/llama-cpp-python) is a Python binding for [llama.cpp](https://github.com/ggerganov/llama.cpp).\n", "\n", "It supports inference for [many LLMs](https://github.com/ggerganov/llama.cpp#description) models, which can be accessed on [Hugging Face](https://huggingface.co/TheBloke).\n", diff --git a/docs/docs/integrations/llms/ollama.ipynb b/docs/docs/integrations/llms/ollama.ipynb index f6cebb31fe9..797e1e4e418 100644 --- a/docs/docs/integrations/llms/ollama.ipynb +++ b/docs/docs/integrations/llms/ollama.ipynb @@ -6,12 +6,7 @@ "source": [ "# Ollama\n", "\n", - "```{=mdx}\n", - "\n", - " \n", - "\n", - "```\n", - "\n", + "[Ollama](https://ollama.ai/) allows you to run open-source large language models, such as Llama 2, locally.\n", "\n", "Ollama bundles model weights, configuration, and data into a single package, defined by a Modelfile. \n", diff --git a/docs/docs/integrations/llms/openai.ipynb b/docs/docs/integrations/llms/openai.ipynb index 6af90fb1401..4b7cab19633 100644 --- a/docs/docs/integrations/llms/openai.ipynb +++ b/docs/docs/integrations/llms/openai.ipynb @@ -7,12 +7,7 @@ "source": [ "# OpenAI\n", "\n", - "```{=mdx}\n", - "\n", - " \n", - "\n", - "```\n", - "\n", + "[OpenAI](https://platform.openai.com/docs/introduction) offers a spectrum of models with different levels of power suitable for different tasks.\n", "\n", "This example goes over how to use LangChain to interact with `OpenAI` [models](https://platform.openai.com/docs/models)" diff --git a/docs/docs/integrations/platforms/index.mdx b/docs/docs/integrations/platforms/index.mdx index 9ad7a0c5008..c10ab942cff 100644 --- a/docs/docs/integrations/platforms/index.mdx +++ b/docs/docs/integrations/platforms/index.mdx @@ -5,9 +5,6 @@ sidebar_class_name: hidden # Providers - - - :::info diff --git a/docs/docs/integrations/vectorstores/chroma.ipynb b/docs/docs/integrations/vectorstores/chroma.ipynb index 33f5707dcf5..e427788da3f 100644 --- a/docs/docs/integrations/vectorstores/chroma.ipynb +++ b/docs/docs/integrations/vectorstores/chroma.ipynb @@ -7,12 +7,7 @@ "source": [ "# Chroma\n", "\n", - "```{=mdx}\n", - "\n", - " \n", - "\n", - "```\n", - "\n", + ">[Chroma](https://docs.trychroma.com/getting-started) is a AI-native open-source vector database focused on developer productivity and happiness. Chroma is licensed under Apache 2.0.\n", "\n", "\n", diff --git a/docs/docs/integrations/vectorstores/faiss.ipynb b/docs/docs/integrations/vectorstores/faiss.ipynb index f4a3fb2e8aa..b8193477b49 100644 --- a/docs/docs/integrations/vectorstores/faiss.ipynb +++ b/docs/docs/integrations/vectorstores/faiss.ipynb @@ -7,12 +7,7 @@ "source": [ "# Faiss\n", "\n", - "```{=mdx}\n", - "\n", - " \n", - "\n", - "```\n", - "\n", + ">[Facebook AI Similarity Search (Faiss)](https://engineering.fb.com/2017/03/29/data-infrastructure/faiss-a-library-for-efficient-similarity-search/) is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning.\n", "\n", "[Faiss documentation](https://faiss.ai/).\n", diff --git a/docs/docs/modules/agents/agent_types/index.mdx b/docs/docs/modules/agents/agent_types/index.mdx index 7f9a28e463b..0b8bb3c5310 100644 --- a/docs/docs/modules/agents/agent_types/index.mdx +++ b/docs/docs/modules/agents/agent_types/index.mdx @@ -5,9 +5,6 @@ title: Types # Agent Types - - - This categorizes all the available agents along a few dimensions. diff --git a/docs/docs/modules/agents/agent_types/react.ipynb b/docs/docs/modules/agents/agent_types/react.ipynb index d94cde17412..a793e962165 100644 --- a/docs/docs/modules/agents/agent_types/react.ipynb +++ b/docs/docs/modules/agents/agent_types/react.ipynb @@ -17,12 +17,7 @@ "source": [ "# ReAct\n", "\n", - "```{=mdx}\n", - "\n", - " \n", - "\n", - "```\n", - "\n", + "This walkthrough showcases using an agent to implement the [ReAct](https://react-lm.github.io/) logic." ] }, diff --git a/docs/docs/modules/agents/agent_types/tool_calling.ipynb b/docs/docs/modules/agents/agent_types/tool_calling.ipynb index a250a03adbf..3332544d5c7 100644 --- a/docs/docs/modules/agents/agent_types/tool_calling.ipynb +++ b/docs/docs/modules/agents/agent_types/tool_calling.ipynb @@ -16,12 +16,7 @@ "source": [ "# Tool calling agent\n", "\n", - "```{=mdx}\n", - "\n", - " \n", - "\n", - "```\n", - "\n", + "[Tool calling](/docs/modules/model_io/chat/function_calling) allows a model to detect when one or more tools should be called and respond with the inputs that should be passed to those tools. In an API call, you can describe tools and have the model intelligently choose to output a structured object like JSON containing arguments to call these tools. The goal of tools APIs is to more reliably return valid and useful tool calls than what can be done using a generic text completion or chat API.\n", "\n", "We can take advantage of this structured output, combined with the fact that you can bind multiple tools to a [tool calling chat model](/docs/integrations/chat/) and\n", diff --git a/docs/docs/modules/agents/how_to/custom_agent.ipynb b/docs/docs/modules/agents/how_to/custom_agent.ipynb index 5ec9a77ec66..a08ccbcd161 100644 --- a/docs/docs/modules/agents/how_to/custom_agent.ipynb +++ b/docs/docs/modules/agents/how_to/custom_agent.ipynb @@ -17,12 +17,7 @@ "source": [ "# Custom agent\n", "\n", - "```{=mdx}\n", - "\n", - " \n", - "\n", - "```\n", - "\n", + "This notebook goes through how to create your own custom agent.\n", "\n", "In this example, we will use OpenAI Tool Calling to create this agent.\n", diff --git a/docs/docs/modules/agents/index.ipynb b/docs/docs/modules/agents/index.ipynb index 9552a51f62d..9ef505927d5 100644 --- a/docs/docs/modules/agents/index.ipynb +++ b/docs/docs/modules/agents/index.ipynb @@ -17,12 +17,7 @@ "id": "f4c03f40-1328-412d-8a48-1db0cd481b77", "metadata": {}, "source": [ - "```{=mdx}\n", - "\n", - " \n", - "\n", - "```\n", - "\n", + "The core idea of agents is to use a language model to choose a sequence of actions to take.\n", "In chains, a sequence of actions is hardcoded (in code).\n", "In agents, a language model is used as a reasoning engine to determine which actions to take and in which order.\n", diff --git a/docs/docs/modules/agents/quick_start.ipynb b/docs/docs/modules/agents/quick_start.ipynb index 93db0ab765b..0cd22f85205 100644 --- a/docs/docs/modules/agents/quick_start.ipynb +++ b/docs/docs/modules/agents/quick_start.ipynb @@ -18,12 +18,7 @@ "source": [ "# Quickstart\n", "\n", - "```{=mdx}\n", - "\n", - " \n", - "\n", - "```\n", - "\n", + "To best understand the agent framework, let's build an agent that has two tools: one to look things up online, and one to look up specific data that we've loaded into a index.\n", "\n", "This will assume knowledge of [LLMs](/docs/modules/model_io/) and [retrieval](/docs/modules/data_connection/) so if you haven't already explored those sections, it is recommended you do so.\n", diff --git a/docs/docs/modules/chains.ipynb b/docs/docs/modules/chains.ipynb index 78949e56e1e..85554fd1d13 100644 --- a/docs/docs/modules/chains.ipynb +++ b/docs/docs/modules/chains.ipynb @@ -18,12 +18,7 @@ "id": "b872d874-ad6e-49b5-9435-66063a64d1a8", "metadata": {}, "source": [ - "```{=mdx}\n", - "\n", - " \n", - "\n", - "```\n", - "\n", + "Chains refer to sequences of calls - whether to an LLM, a tool, or a data preprocessing step. The primary supported way to do this is with [LCEL](/docs/expression_language). \n", "\n", "LCEL is great for constructing your chains, but it's also nice to have chains used off the shelf. There are two types of off-the-shelf chains that LangChain supports:\n", diff --git a/docs/docs/modules/data_connection/document_loaders/index.mdx b/docs/docs/modules/data_connection/document_loaders/index.mdx index 56e88570dd1..6920a0a1c04 100644 --- a/docs/docs/modules/data_connection/document_loaders/index.mdx +++ b/docs/docs/modules/data_connection/document_loaders/index.mdx @@ -4,9 +4,6 @@ sidebar_class_name: hidden --- # Document loaders - - - :::info Head to [Integrations](/docs/integrations/document_loaders/) for documentation on built-in document loader integrations with 3rd-party tools. diff --git a/docs/docs/modules/data_connection/document_loaders/pdf.mdx b/docs/docs/modules/data_connection/document_loaders/pdf.mdx index 695a6d3761b..e674265cbd1 100644 --- a/docs/docs/modules/data_connection/document_loaders/pdf.mdx +++ b/docs/docs/modules/data_connection/document_loaders/pdf.mdx @@ -4,9 +4,6 @@ keywords: [PyPDFDirectoryLoader, PyMuPDFLoader] # PDF - - - >[Portable Document Format (PDF)](https://en.wikipedia.org/wiki/PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. diff --git a/docs/docs/modules/data_connection/document_transformers/index.mdx b/docs/docs/modules/data_connection/document_transformers/index.mdx index 899da93de33..566ec7982b7 100644 --- a/docs/docs/modules/data_connection/document_transformers/index.mdx +++ b/docs/docs/modules/data_connection/document_transformers/index.mdx @@ -4,9 +4,6 @@ sidebar_class_name: hidden --- # Text Splitters - - - Once you've loaded documents, you'll often want to transform them to better suit your application. The simplest example is you may want to split a long document into smaller chunks that can fit into your model's context window. LangChain diff --git a/docs/docs/modules/data_connection/document_transformers/recursive_text_splitter.ipynb b/docs/docs/modules/data_connection/document_transformers/recursive_text_splitter.ipynb index b6c840e17e9..c36e0f323a3 100644 --- a/docs/docs/modules/data_connection/document_transformers/recursive_text_splitter.ipynb +++ b/docs/docs/modules/data_connection/document_transformers/recursive_text_splitter.ipynb @@ -7,12 +7,7 @@ "source": [ "# Recursively split by character\n", "\n", - "```{=mdx}\n", - "\n", - " \n", - "\n", - "```\n", - "\n", + "This text splitter is the recommended one for generic text. It is parameterized by a list of characters. It tries to split on them in order until the chunks are small enough. The default list is `[\"\\n\\n\", \"\\n\", \" \", \"\"]`. This has the effect of trying to keep all paragraphs (and then sentences, and then words) together as long as possible, as those would generically seem to be the strongest semantically related pieces of text.\n", "\n", "1. How the text is split: by list of characters.\n", diff --git a/docs/docs/modules/data_connection/document_transformers/semantic-chunker.ipynb b/docs/docs/modules/data_connection/document_transformers/semantic-chunker.ipynb index ed0b5a33152..8c005c93321 100644 --- a/docs/docs/modules/data_connection/document_transformers/semantic-chunker.ipynb +++ b/docs/docs/modules/data_connection/document_transformers/semantic-chunker.ipynb @@ -7,12 +7,7 @@ "source": [ "# Semantic Chunking\n", "\n", - "```{=mdx}\n", - "\n", - " \n", - "\n", - "```\n", - "\n", + "Splits the text based on semantic similarity.\n", "\n", "Taken from Greg Kamradt's wonderful notebook:\n", diff --git a/docs/docs/modules/data_connection/index.mdx b/docs/docs/modules/data_connection/index.mdx index 138121b9e7b..d0c7d402a69 100644 --- a/docs/docs/modules/data_connection/index.mdx +++ b/docs/docs/modules/data_connection/index.mdx @@ -5,9 +5,6 @@ sidebar_class_name: hidden # Retrieval - - - Many LLM applications require user-specific data that is not part of the model's training set. The primary way of accomplishing this is through Retrieval Augmented Generation (RAG). diff --git a/docs/docs/modules/data_connection/retrievers/index.mdx b/docs/docs/modules/data_connection/retrievers/index.mdx index 413ce68e7fd..ff95f5b7f72 100644 --- a/docs/docs/modules/data_connection/retrievers/index.mdx +++ b/docs/docs/modules/data_connection/retrievers/index.mdx @@ -6,9 +6,6 @@ sidebar_class_name: hidden # Retrievers - - - A retriever is an interface that returns documents given an unstructured query. It is more general than a vector store. A retriever does not need to be able to store documents, only to return (or retrieve) them. Vector stores can be used diff --git a/docs/docs/modules/data_connection/retrievers/vectorstore.ipynb b/docs/docs/modules/data_connection/retrievers/vectorstore.ipynb index 6442a6b5bd6..ef49f412972 100644 --- a/docs/docs/modules/data_connection/retrievers/vectorstore.ipynb +++ b/docs/docs/modules/data_connection/retrievers/vectorstore.ipynb @@ -17,12 +17,7 @@ "source": [ "# Vector store-backed retriever\n", "\n", - "```{=mdx}\n", - "\n", - " \n", - "\n", - "```\n", - "\n", + "A vector store retriever is a retriever that uses a vector store to retrieve documents. It is a lightweight wrapper around the vector store class to make it conform to the retriever interface.\n", "It uses the search methods implemented by a vector store, like similarity search and MMR, to query the texts in the vector store.\n", "\n", diff --git a/docs/docs/modules/data_connection/text_embedding/index.mdx b/docs/docs/modules/data_connection/text_embedding/index.mdx index cd77a9b4322..c51d0e87872 100644 --- a/docs/docs/modules/data_connection/text_embedding/index.mdx +++ b/docs/docs/modules/data_connection/text_embedding/index.mdx @@ -4,9 +4,6 @@ sidebar_class_name: hidden --- # Text embedding models - - - :::info Head to [Integrations](/docs/integrations/text_embedding/) for documentation on built-in integrations with text embedding model providers. diff --git a/docs/docs/modules/data_connection/vectorstores/index.mdx b/docs/docs/modules/data_connection/vectorstores/index.mdx index 16ef9b146c3..f5acd28fcc1 100644 --- a/docs/docs/modules/data_connection/vectorstores/index.mdx +++ b/docs/docs/modules/data_connection/vectorstores/index.mdx @@ -4,9 +4,6 @@ sidebar_class_name: hidden --- # Vector stores - - - :::info Head to [Integrations](/docs/integrations/vectorstores/) for documentation on built-in integrations with 3rd-party vector stores. diff --git a/docs/docs/modules/index.mdx b/docs/docs/modules/index.mdx index baeb3dddebb..897279473d7 100644 --- a/docs/docs/modules/index.mdx +++ b/docs/docs/modules/index.mdx @@ -4,9 +4,6 @@ sidebar_class_name: hidden # Components - - - LangChain provides standard, extendable interfaces and external integrations for the following main components: diff --git a/docs/docs/modules/model_io/chat/function_calling.ipynb b/docs/docs/modules/model_io/chat/function_calling.ipynb index 252b4a8010a..4ccd01af3f6 100644 --- a/docs/docs/modules/model_io/chat/function_calling.ipynb +++ b/docs/docs/modules/model_io/chat/function_calling.ipynb @@ -18,12 +18,7 @@ "source": [ "# Tool calling\n", "\n", - "```{=mdx}\n", - "\n", - " \n", - "\n", - "```\n", - "\n", + "```{=mdx}\n", ":::info\n", "We use the term \"tool calling\" interchangeably with \"function calling\". Although\n", diff --git a/docs/docs/modules/model_io/chat/index.mdx b/docs/docs/modules/model_io/chat/index.mdx index 84ed334e518..2f12a5dd307 100644 --- a/docs/docs/modules/model_io/chat/index.mdx +++ b/docs/docs/modules/model_io/chat/index.mdx @@ -5,9 +5,6 @@ sidebar_class_name: hidden # Chat Models - - - Chat Models are a core component of LangChain. diff --git a/docs/docs/modules/model_io/llms/index.mdx b/docs/docs/modules/model_io/llms/index.mdx index 8d1fa4cb817..9d94ea4ac34 100644 --- a/docs/docs/modules/model_io/llms/index.mdx +++ b/docs/docs/modules/model_io/llms/index.mdx @@ -5,9 +5,6 @@ sidebar_class_name: hidden # LLMs - - - Large Language Models (LLMs) are a core component of LangChain. LangChain does not serve its own LLMs, but rather provides a standard interface for interacting with many different LLMs. To be specific, this interface is one that takes as input a string and returns a string. diff --git a/docs/docs/modules/model_io/output_parsers/index.mdx b/docs/docs/modules/model_io/output_parsers/index.mdx index ce2a7377223..ec377f16979 100644 --- a/docs/docs/modules/model_io/output_parsers/index.mdx +++ b/docs/docs/modules/model_io/output_parsers/index.mdx @@ -5,9 +5,6 @@ sidebar_class_name: hidden --- # Output Parsers - - - Output parsers are responsible for taking the output of an LLM and transforming it to a more suitable format. This is very useful when you are using LLMs to generate any form of structured data. diff --git a/docs/docs/modules/model_io/prompts/index.mdx b/docs/docs/modules/model_io/prompts/index.mdx index bf4298a86d8..4e98d465c88 100644 --- a/docs/docs/modules/model_io/prompts/index.mdx +++ b/docs/docs/modules/model_io/prompts/index.mdx @@ -4,9 +4,6 @@ sidebar_class_name: hidden --- # Prompts - - - A prompt for a language model is a set of instructions or input provided by a user to guide the model's response, helping it understand the context and generate relevant diff --git a/docs/docs/modules/model_io/prompts/quick_start.ipynb b/docs/docs/modules/model_io/prompts/quick_start.ipynb index 57af391a80c..3b31c700f0c 100644 --- a/docs/docs/modules/model_io/prompts/quick_start.ipynb +++ b/docs/docs/modules/model_io/prompts/quick_start.ipynb @@ -18,12 +18,7 @@ "source": [ "# Quick reference\n", "\n", - "```{=mdx}\n", - "\n", - " \n", - "\n", - "```\n", - "\n", + "Prompt templates are predefined recipes for generating prompts for language models.\n", "\n", "A template may include instructions, few-shot examples, and specific context and\n", diff --git a/docs/docs/modules/tools/custom_tools.ipynb b/docs/docs/modules/tools/custom_tools.ipynb index 9a02d7aafd6..a5cf94a0e97 100644 --- a/docs/docs/modules/tools/custom_tools.ipynb +++ b/docs/docs/modules/tools/custom_tools.ipynb @@ -7,12 +7,7 @@ "source": [ "# Defining Custom Tools\n", "\n", - "```{=mdx}\n", - "\n", - " \n", - "\n", - "```\n", - "\n", + "When constructing your own agent, you will need to provide it with a list of Tools that it can use. Besides the actual function that is called, the Tool consists of several components:\n", "\n", "- `name` (str), is required and must be unique within a set of tools provided to an agent\n", diff --git a/docs/docs/modules/tools/index.ipynb b/docs/docs/modules/tools/index.ipynb index 35c6ab3b2e3..c275a543a68 100644 --- a/docs/docs/modules/tools/index.ipynb +++ b/docs/docs/modules/tools/index.ipynb @@ -18,12 +18,7 @@ "source": [ "# Tools\n", "\n", - "```{=mdx}\n", - "\n", - " \n", - "\n", - "```\n", - "\n", + "Tools are interfaces that an agent, chain, or LLM can use to interact with the world.\n", "They combine a few things:\n", "\n", diff --git a/docs/docs/use_cases/chatbots/index.ipynb b/docs/docs/use_cases/chatbots/index.ipynb index 1832938e780..7afbfe872e8 100644 --- a/docs/docs/use_cases/chatbots/index.ipynb +++ b/docs/docs/use_cases/chatbots/index.ipynb @@ -15,12 +15,7 @@ "source": [ "# Chatbots\n", "\n", - "```{=mdx}\n", - "\n", - " \n", - "\n", - "```\n", - "\n", + "## Overview\n", "\n", "Chatbots are one of the most popular use-cases for LLMs. The core features of chatbots are that they can have long-running, stateful conversations and can answer user questions using relevant information.\n", diff --git a/docs/docs/use_cases/chatbots/quickstart.ipynb b/docs/docs/use_cases/chatbots/quickstart.ipynb index c6a0b9e17fd..a82d4feb820 100644 --- a/docs/docs/use_cases/chatbots/quickstart.ipynb +++ b/docs/docs/use_cases/chatbots/quickstart.ipynb @@ -15,12 +15,7 @@ "source": [ "# Quickstart\n", "\n", - "```{=mdx}\n", - "\n", - " \n", - "\n", - "```\n", - "\n", + "[![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/use_cases/chatbots.ipynb)" ] }, diff --git a/docs/docs/use_cases/chatbots/tool_usage.ipynb b/docs/docs/use_cases/chatbots/tool_usage.ipynb index 2e8c12cd492..08f7283f07e 100644 --- a/docs/docs/use_cases/chatbots/tool_usage.ipynb +++ b/docs/docs/use_cases/chatbots/tool_usage.ipynb @@ -15,12 +15,7 @@ "source": [ "# Tool usage\n", "\n", - "```{=mdx}\n", - "\n", - " \n", - "\n", - "```\n", - "\n", + "This section will cover how to create conversational agents: chatbots that can interact with other systems and APIs using tools.\n", "\n", "Before reading this guide, we recommend you read both [the chatbot quickstart](/docs/use_cases/chatbots/quickstart) in this section and be familiar with [the documentation on agents](/docs/modules/agents/).\n", diff --git a/docs/docs/use_cases/extraction/index.ipynb b/docs/docs/use_cases/extraction/index.ipynb index 75ef2926737..353feeeee5b 100644 --- a/docs/docs/use_cases/extraction/index.ipynb +++ b/docs/docs/use_cases/extraction/index.ipynb @@ -18,12 +18,7 @@ "source": [ "## Overview\n", "\n", - "```{=mdx}\n", - "\n", - " \n", - "\n", - "```\n", - "\n", + "Large Language Models (LLMs) are emerging as an extremely capable technology for powering information extraction applications.\n", "\n", "Classical solutions to information extraction rely on a combination of people, (many) hand-crafted rules (e.g., regular expressions), and custom fine-tuned ML models.\n", diff --git a/docs/docs/use_cases/question_answering/chat_history.ipynb b/docs/docs/use_cases/question_answering/chat_history.ipynb index 1a039224541..878eda4e979 100644 --- a/docs/docs/use_cases/question_answering/chat_history.ipynb +++ b/docs/docs/use_cases/question_answering/chat_history.ipynb @@ -17,12 +17,7 @@ "source": [ "# Add chat history\n", "\n", - "```{=mdx}\n", - "\n", - " \n", - "\n", - "```\n", - "\n", + "In many Q&A applications we want to allow the user to have a back-and-forth conversation, meaning the application needs some sort of \"memory\" of past questions and answers, and some logic for incorporating those into its current thinking.\n", "\n", "In this guide we focus on **adding logic for incorporating historical messages.** Further details on chat history management is [covered here](/docs/expression_language/how_to/message_history).\n", diff --git a/docs/docs/use_cases/question_answering/index.ipynb b/docs/docs/use_cases/question_answering/index.ipynb index b3b8fad7ece..0bcca9487a9 100644 --- a/docs/docs/use_cases/question_answering/index.ipynb +++ b/docs/docs/use_cases/question_answering/index.ipynb @@ -15,13 +15,7 @@ "id": "86fc5bb2-017f-434e-8cd6-53ab214a5604", "metadata": {}, "source": [ - "# Q&A with RAG\n", - "\n", - "```{=mdx}\n", - "\n", - " \n", - "\n", - "```" + "# Q&A with RAG" ] }, { diff --git a/docs/docs/use_cases/question_answering/quickstart.mdx b/docs/docs/use_cases/question_answering/quickstart.mdx index 12319bd724b..bad553926fd 100644 --- a/docs/docs/use_cases/question_answering/quickstart.mdx +++ b/docs/docs/use_cases/question_answering/quickstart.mdx @@ -5,9 +5,6 @@ title: Quickstart # Quickstart - - - LangChain has a number of components designed to help build question-answering applications, and RAG applications more generally. To diff --git a/docs/docs/use_cases/sql/agents.ipynb b/docs/docs/use_cases/sql/agents.ipynb index b3b73dafa10..77bbcdfcb54 100644 --- a/docs/docs/use_cases/sql/agents.ipynb +++ b/docs/docs/use_cases/sql/agents.ipynb @@ -15,12 +15,7 @@ "source": [ "# Agents\n", "\n", - "```{=mdx}\n", - "\n", - " \n", - "\n", - "```\n", - "\n", + "LangChain has a SQL Agent which provides a more flexible way of interacting with SQL Databases than a chain. The main advantages of using the SQL Agent are:\n", "\n", "- It can answer questions based on the databases' schema as well as on the databases' content (like describing a specific table).\n", diff --git a/docs/docs/use_cases/sql/index.ipynb b/docs/docs/use_cases/sql/index.ipynb index 355ba95d43a..9f50cf6b8a2 100644 --- a/docs/docs/use_cases/sql/index.ipynb +++ b/docs/docs/use_cases/sql/index.ipynb @@ -15,12 +15,7 @@ "source": [ "# SQL\n", "\n", - "```{=mdx}\n", - "\n", - " \n", - "\n", - "```\n", - "\n", + "One of the most common types of databases that we can build Q&A systems for are SQL databases. LangChain comes with a number of built-in chains and agents that are compatible with any SQL dialect supported by SQLAlchemy (e.g., MySQL, PostgreSQL, Oracle SQL, Databricks, SQLite). They enable use cases such as:\n", "\n", "* Generating queries that will be run based on natural language questions,\n", diff --git a/docs/docs/use_cases/sql/quickstart.ipynb b/docs/docs/use_cases/sql/quickstart.ipynb index 1794b6c1d4d..7006f935a85 100644 --- a/docs/docs/use_cases/sql/quickstart.ipynb +++ b/docs/docs/use_cases/sql/quickstart.ipynb @@ -15,12 +15,7 @@ "source": [ "# Quickstart\n", "\n", - "```{=mdx}\n", - "\n", - " \n", - "\n", - "```\n", - "\n", + "In this guide we'll go over the basic ways to create a Q&A chain and agent over a SQL database. These systems will allow us to ask a question about the data in a SQL database and get back a natural language answer. The main difference between the two is that our agent can query the database in a loop as many time as it needs to answer the question.\n", "\n", "## ⚠️ Security note ⚠️\n", diff --git a/docs/docs/use_cases/summarization.ipynb b/docs/docs/use_cases/summarization.ipynb index f975108261b..386e56fc36e 100644 --- a/docs/docs/use_cases/summarization.ipynb +++ b/docs/docs/use_cases/summarization.ipynb @@ -16,12 +16,7 @@ "id": "cf13f702", "metadata": {}, "source": [ - "```{=mdx}\n", - "\n", - " \n", - "\n", - "```\n", - "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/v0.1/docs/docs/use_cases/summarization.ipynb)\n", "\n", "## Use case\n", diff --git a/docs/src/theme/SiteMetadata/index.js b/docs/src/theme/SiteMetadata/index.js new file mode 100644 index 00000000000..38e7d8b2670 --- /dev/null +++ b/docs/src/theme/SiteMetadata/index.js @@ -0,0 +1,2981 @@ +import React from 'react'; +import Head from '@docusaurus/Head'; +import useDocusaurusContext from '@docusaurus/useDocusaurusContext'; +import useBaseUrl from '@docusaurus/useBaseUrl'; +import {PageMetadata, useThemeConfig} from '@docusaurus/theme-common'; +import { + DEFAULT_SEARCH_TAG, + useAlternatePageUtils, + keyboardFocusedClassName, +} from '@docusaurus/theme-common/internal'; +import {useLocation} from '@docusaurus/router'; +import {applyTrailingSlash} from '@docusaurus/utils-common'; +import SearchMetadata from '@theme/SearchMetadata'; +// TODO move to SiteMetadataDefaults or theme-common ? +// Useful for i18n/SEO +// See https://developers.google.com/search/docs/advanced/crawling/localized-versions +// See https://github.com/facebook/docusaurus/issues/3317 +function AlternateLangHeaders() { + const { + i18n: {defaultLocale, localeConfigs}, + } = useDocusaurusContext(); + const alternatePageUtils = useAlternatePageUtils(); + // Note: it is fine to use both "x-default" and "en" to target the same url + // See https://www.searchviu.com/en/multiple-hreflang-tags-one-url/ + return ( + + {Object.entries(localeConfigs).map(([locale, {htmlLang}]) => ( + + ))} + + + ); +} +// Default canonical url inferred from current page location pathname +function useDefaultCanonicalUrl() { + const { + siteConfig: {url: siteUrl, baseUrl, trailingSlash}, + } = useDocusaurusContext(); + // TODO using useLocation().pathname is not a super idea + // See https://github.com/facebook/docusaurus/issues/9170 + const {pathname} = useLocation(); + const baseUrlPathname = useBaseUrl(pathname); + const canonicalPathname = applyTrailingSlash(baseUrlPathname, { + trailingSlash, + baseUrl, + }); + const canonicalPathnameNoVersion = canonicalPathname.startsWith('/v0.') ? "/"+canonicalPathname.split('/').slice(2).join('/') : canonicalPathname; + const suggestedLookup = suggestedLinks[canonicalPathnameNoVersion]; + const finalPathname = suggestedLookup?.canonical || canonicalPathname; + return siteUrl + finalPathname; +} + +// TODO move to SiteMetadataDefaults or theme-common ? +function CanonicalUrlHeaders({permalink}) { + const { + siteConfig: {url: siteUrl}, + } = useDocusaurusContext(); + const defaultCanonicalUrl = useDefaultCanonicalUrl(); + const canonicalUrl = permalink + ? `${siteUrl}${permalink}` + : defaultCanonicalUrl; + return ( + + + + + ); +} +export default function SiteMetadata() { + const { + i18n: {currentLocale}, + } = useDocusaurusContext(); + // TODO maybe move these 2 themeConfig to siteConfig? + // These seems useful for other themes as well + const {metadata, image: defaultImage} = useThemeConfig(); + return ( + <> + + + {/* The keyboard focus class name need to be applied when SSR so links + are outlined when JS is disabled */} + + + + {defaultImage && } + + + + + + + + {/* + It's important to have an additional element here, as it allows + react-helmet to override default metadata values set in previous + like "twitter:card". In same Head, the same meta would appear twice + instead of overriding. + */} + + {/* Yes, "metadatum" is the grammatically correct term */} + {metadata.map((metadatum, i) => ( + + ))} + + + ); +} + +const suggestedLinks = { + "/docs/changelog/core/": { + "canonical": "https://github.com/langchain-ai/langchain/releases?q=tag:%22langchain-core%3D%3D0%22&expanded=true", + "alternative": [ + "/v0.1/docs/changelog/core/" + ] + }, + "/docs/changelog/langchain/": { + "canonical": "https://github.com/langchain-ai/langchain/releases?q=tag:%22langchain%3D%3D0%22&expanded=true", + "alternative": [ + "/v0.1/docs/changelog/langchain/" + ] + }, + "/docs/contributing/documentation/technical_logistics/": { + "canonical": "/docs/contributing/documentation/", + "alternative": [ + "/v0.1/docs/contributing/documentation/technical_logistics/" + ] + }, + "/docs/cookbook/": { + "canonical": "/docs/tutorials/", + "alternative": [ + "/v0.1/docs/cookbook/" + ] + }, + "/docs/expression_language/": { + 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"/docs/how_to/migrate_agent/", "alternative": ["/v0.1/docs/modules/agents/how_to/streaming/"]}, + "/docs/modules/model_io/prompts/prompts_pipelining/": {"canonical": "/docs/how_to/prompts_composition/", "alternative": ["/v0.1/docs/modules/model_io/prompts/composition/"]}, + "/docs/modules/model_io/output_parsers/enum/": {"canonical": "/docs/how_to/output_parser_structured/", "alternative": ["/v0.1/docs/modules/model_io/output_parsers/types/enum/"]}, + "/docs/modules/model_io/output_parsers/pandas_dataframe/": {"canonical": "/docs/how_to/output_parser_structured/", "alternative": ["/v0.1/docs/modules/model_io/output_parsers/types/pandas_dataframe/"]}, + "/docs/modules/model_io/output_parsers/structured/": {"canonical": "/docs/how_to/output_parser_structured/", "alternative": ["/v0.1/docs/modules/model_io/output_parsers/types/structured/"]}, + "/docs/modules/model_io/output_parsers/xml/": {"canonical": "/docs/how_to/output_parser_xml/", "alternative": ["/v0.1/docs/modules/model_io/output_parsers/types/xml/"]}, + "/docs/use_cases/question_answering/code_understanding/": {"canonical": "https://langchain-ai.github.io/langgraph/tutorials/code_assistant/langgraph_code_assistant/", "alternative": ["/v0.1/docs/use_cases/code_understanding/"]}, + "/docs/use_cases/question_answering/document-context-aware-QA/": {"canonical": "/docs/how_to/#text-splitters", "alternative": ["/v0.1/docs/modules/data_connection/document_transformers/"]}, + "/docs/integrations/providers/alibabacloud_opensearch/": {"canonical": "/docs/integrations/providers/alibaba_cloud/"}, + "/docs/integrations/chat/pai_eas_chat_endpoint/": {"canonical": "/docs/integrations/chat/alibaba_cloud_pai_eas/"}, + "/docs/integrations/providers/tencentvectordb/": {"canonical": "/docs/integrations/providers/tencent/"}, + "/docs/integrations/chat/hunyuan/": {"canonical": "/docs/integrations/chat/tencent_hunyuan/"}, + "/docs/integrations/document_loaders/excel/": {"canonical": "/docs/integrations/document_loaders/microsoft_excel/"}, + "/docs/integrations/document_loaders/onenote/": {"canonical": "/docs/integrations/document_loaders/microsoft_onenote/"}, + "/docs/integrations/providers/aws_dynamodb/": {"canonical": "/docs/integrations/platforms/aws/"}, + "/docs/integrations/providers/scann/": {"canonical": "/docs/integrations/platforms/google/"}, + "/docs/integrations/toolkits/google_drive/": {"canonical": "/docs/integrations/tools/google_drive/"}, + "/docs/use_cases/question_answering/chat_vector_db/": {"canonical": "/docs/tutorials/rag/", "alternative": ["/v0.1/docs/use_cases/question_answering/"]}, + "/docs/use_cases/question_answering/in_memory_question_answering/": {"canonical": "/docs/tutorials/rag/", "alternative": ["/v0.1/docs/use_cases/question_answering/"]}, + "/docs/use_cases/question_answering/multi_retrieval_qa_router/": {"canonical": "/docs/tutorials/rag/", "alternative": ["/v0.1/docs/use_cases/question_answering/"]}, + "/docs/use_cases/question_answering/multiple_retrieval/": {"canonical": "/docs/tutorials/rag/", "alternative": ["/v0.1/docs/use_cases/question_answering/"]}, + "/docs/use_cases/question_answering/vector_db_qa/": {"canonical": "/docs/tutorials/rag/", "alternative": ["/v0.1/docs/use_cases/question_answering/"]}, + "/docs/use_cases/question_answering/vector_db_text_generation/": {"canonical": "/docs/tutorials/rag/", "alternative": ["/v0.1/docs/use_cases/question_answering/"]}, + "/docs/guides/langsmith/": {"canonical": "https://docs.smith.langchain.com/", "alternative": ["/v0.1/docs/langsmith/"]}, + "/docs/guides/langsmith/walkthrough/": {"canonical": "https://docs.smith.langchain.com/", "alternative": ["/v0.1/docs/langsmith/walkthrough/"]}, + "/docs/use_cases/qa_structured/integrations/sqlite/": {"canonical": "/docs/tutorials/sql_qa/", "alternative": ["/v0.1/docs/use_cases/sql/"]}, + "/docs/use_cases/more/data_generation/": {"canonical": "/docs/tutorials/data_generation/", "alternative": ["/v0.1/docs/use_cases/data_generation/"]}, + "/docs/use_cases/question_answering/how_to/chat_vector_db/": {"canonical": "/docs/tutorials/rag/", "alternative": ["/v0.1/docs/use_cases/question_answering/"]}, + "/docs/use_cases/question_answering/how_to/conversational_retrieval_agents/": {"canonical": "/docs/tutorials/qa_chat_history/", "alternative": ["/v0.1/docs/use_cases/question_answering/conversational_retrieval_agents/"]}, + "/docs/use_cases/question_answering/question_answering/": {"canonical": "/docs/tutorials/rag/", "alternative": ["/v0.1/docs/use_cases/question_answering/"]}, + "/docs/use_cases/question_answering/how_to/local_retrieval_qa/": {"canonical": "/docs/tutorials/rag/", "alternative": ["/v0.1/docs/use_cases/question_answering/local_retrieval_qa/"]}, + "/docs/use_cases/question_answering/how_to/question_answering/": {"canonical": "/docs/tutorials/rag/", "alternative": ["/v0.1/docs/use_cases/question_answering/"]}, + "/docs/modules/agents/agents/examples/mrkl_chat(.html?)/": {"canonical": "/docs/how_to/#agents", "alternative": ["/v0.1/docs/modules/agents/"]}, + "/docs/integrations/": {"canonical": "/docs/integrations/providers/"}, + "/docs/expression_language/cookbook/routing/": {"canonical": "/docs/how_to/routing/", "alternative": ["/v0.1/docs/expression_language/how_to/routing/"]}, + "/docs/guides/expression_language/": {"canonical": "/docs/how_to/#langchain-expression-language-lcel", "alternative": ["/v0.1/docs/expression_language/"]}, + "/docs/integrations/providers/amazon_api_gateway/": {"canonical": "/docs/integrations/platforms/aws/"}, + "/docs/integrations/providers/huggingface/": {"canonical": "/docs/integrations/platforms/huggingface/"}, + "/docs/integrations/providers/azure_blob_storage/": {"canonical": "/docs/integrations/platforms/microsoft/"}, + "/docs/integrations/providers/google_vertexai_matchingengine/": {"canonical": "/docs/integrations/platforms/google/"}, + "/docs/integrations/providers/aws_s3/": {"canonical": "/docs/integrations/platforms/aws/"}, + "/docs/integrations/providers/azure_openai/": {"canonical": "/docs/integrations/platforms/microsoft/"}, + "/docs/integrations/providers/azure_cognitive_search_/": {"canonical": "/docs/integrations/platforms/microsoft/"}, + "/docs/integrations/providers/bedrock/": {"canonical": "/docs/integrations/platforms/aws/"}, + "/docs/integrations/providers/google_bigquery/": {"canonical": "/docs/integrations/platforms/google/"}, + "/docs/integrations/providers/google_cloud_storage/": {"canonical": "/docs/integrations/platforms/google/"}, + "/docs/integrations/providers/google_drive/": {"canonical": "/docs/integrations/platforms/google/"}, + "/docs/integrations/providers/google_search/": {"canonical": "/docs/integrations/platforms/google/"}, + "/docs/integrations/providers/microsoft_onedrive/": {"canonical": "/docs/integrations/platforms/microsoft/"}, + "/docs/integrations/providers/microsoft_powerpoint/": {"canonical": "/docs/integrations/platforms/microsoft/"}, + "/docs/integrations/providers/microsoft_word/": {"canonical": "/docs/integrations/platforms/microsoft/"}, + "/docs/integrations/providers/sagemaker_endpoint/": {"canonical": "/docs/integrations/platforms/aws/"}, + "/docs/integrations/providers/sagemaker_tracking/": {"canonical": "/docs/integrations/callbacks/sagemaker_tracking/"}, + "/docs/integrations/providers/openai/": {"canonical": "/docs/integrations/platforms/openai/"}, + "/docs/integrations/cassandra/": {"canonical": "/docs/integrations/providers/cassandra/"}, + "/docs/integrations/providers/providers/semadb/": {"canonical": "/docs/integrations/providers/semadb/"}, + "/docs/integrations/vectorstores/vectorstores/semadb/": {"canonical": "/docs/integrations/vectorstores/semadb/"}, + "/docs/integrations/vectorstores/async_faiss/": {"canonical": "/docs/integrations/vectorstores/faiss_async/"}, + "/docs/integrations/vectorstores/matchingengine/": {"canonical": "/docs/integrations/vectorstores/google_vertex_ai_vector_search/"}, + "/docs/integrations/tools/sqlite/": {"canonical": "/docs/tutorials/sql_qa/", "alternative": ["/v0.1/docs/use_cases/sql/"]}, + "/docs/integrations/document_loaders/pdf-amazonTextractPDFLoader/": {"canonical": "/docs/integrations/document_loaders/amazon_textract/"}, + "/docs/integrations/document_loaders/Etherscan/": {"canonical": "/docs/integrations/document_loaders/etherscan/"}, + "/docs/integrations/document_loaders/merge_doc_loader/": {"canonical": "/docs/integrations/document_loaders/merge_doc/"}, + "/docs/integrations/document_loaders/recursive_url_loader/": {"canonical": "/docs/integrations/document_loaders/recursive_url/"}, + "/docs/integrations/providers/google_document_ai/": {"canonical": "/docs/integrations/platforms/google/"}, + "/docs/integrations/memory/motorhead_memory_managed/": {"canonical": "/docs/integrations/memory/motorhead_memory/"}, + "/docs/integrations/memory/dynamodb_chat_message_history/": {"canonical": "/docs/integrations/memory/aws_dynamodb/"}, + "/docs/integrations/memory/entity_memory_with_sqlite/": {"canonical": "/docs/integrations/memory/sqlite/"}, + "/docs/modules/model_io/chat/integrations/anthropic/": {"canonical": "/docs/integrations/chat/anthropic/"}, + "/docs/modules/model_io/chat/integrations/azure_chat_openai/": {"canonical": "/docs/integrations/chat/azure_chat_openai/"}, + "/docs/modules/model_io/chat/integrations/google_vertex_ai_palm/": {"canonical": "/docs/integrations/chat/google_vertex_ai_palm/"}, + "/docs/modules/model_io/chat/integrations/openai/": {"canonical": "/docs/integrations/chat/openai/"}, + "/docs/modules/model_io/chat/integrations/promptlayer_chatopenai/": {"canonical": "/docs/integrations/chat/promptlayer_chatopenai/"}, + "/docs/modules/model_io/llms/integrations/ai21/": {"canonical": "/docs/integrations/llms/ai21/"}, + "/docs/modules/model_io/llms/integrations/aleph_alpha/": {"canonical": "/docs/integrations/llms/aleph_alpha/"}, + "/docs/modules/model_io/llms/integrations/anyscale/": {"canonical": "/docs/integrations/llms/anyscale/"}, + "/docs/modules/model_io/llms/integrations/banana/": {"canonical": "/docs/integrations/llms/banana/"}, + "/docs/modules/model_io/llms/integrations/baseten/": {"canonical": "/docs/integrations/llms/baseten/"}, + "/docs/modules/model_io/llms/integrations/beam/": {"canonical": "/docs/integrations/llms/beam/"}, + "/docs/modules/model_io/llms/integrations/bedrock/": {"canonical": "/docs/integrations/llms/bedrock/"}, + "/docs/modules/model_io/llms/integrations/cohere/": {"canonical": "/docs/integrations/llms/cohere/"}, + "/docs/modules/model_io/llms/integrations/ctransformers/": {"canonical": "/docs/integrations/llms/ctransformers/"}, + "/docs/modules/model_io/llms/integrations/databricks/": {"canonical": "/docs/integrations/llms/databricks/"}, + "/docs/modules/model_io/llms/integrations/google_vertex_ai_palm/": {"canonical": "/docs/integrations/llms/google_vertex_ai_palm/"}, + "/docs/modules/model_io/llms/integrations/huggingface_pipelines/": {"canonical": "/docs/integrations/llms/huggingface_pipelines/"}, + "/docs/modules/model_io/llms/integrations/jsonformer_experimental/": {"canonical": "/docs/integrations/llms/jsonformer_experimental/"}, + "/docs/modules/model_io/llms/integrations/llamacpp/": {"canonical": "/docs/integrations/llms/llamacpp/"}, + "/docs/modules/model_io/llms/integrations/manifest/": {"canonical": "/docs/integrations/llms/manifest/"}, + "/docs/modules/model_io/llms/integrations/modal/": {"canonical": "/docs/integrations/llms/modal/"}, + "/docs/modules/model_io/llms/integrations/mosaicml/": {"canonical": "/docs/integrations/llms/mosaicml/"}, + "/docs/modules/model_io/llms/integrations/nlpcloud/": {"canonical": "/docs/integrations/llms/nlpcloud/"}, + "/docs/modules/model_io/llms/integrations/openai/": {"canonical": "/docs/integrations/llms/openai/"}, + "/docs/modules/model_io/llms/integrations/openlm/": {"canonical": "/docs/integrations/llms/openlm/"}, + "/docs/modules/model_io/llms/integrations/predictionguard/": {"canonical": "/docs/integrations/llms/predictionguard/"}, + "/docs/modules/model_io/llms/integrations/promptlayer_openai/": {"canonical": "/docs/integrations/llms/promptlayer_openai/"}, + "/docs/modules/model_io/llms/integrations/rellm_experimental/": {"canonical": "/docs/integrations/llms/rellm_experimental/"}, + "/docs/modules/model_io/llms/integrations/replicate/": {"canonical": "/docs/integrations/llms/replicate/"}, + "/docs/modules/model_io/llms/integrations/runhouse/": {"canonical": "/docs/integrations/llms/runhouse/"}, + "/docs/modules/model_io/llms/integrations/sagemaker/": {"canonical": "/docs/integrations/llms/sagemaker/"}, + "/docs/modules/model_io/llms/integrations/stochasticai/": {"canonical": "/docs/integrations/llms/stochasticai/"}, + "/docs/modules/model_io/llms/integrations/writer/": {"canonical": "/docs/integrations/llms/writer/"}, + "/en/latest/use_cases/apis.html/": {"canonical": null, "alternative": ["/v0.1/docs/use_cases/apis/"]}, + "/en/latest/use_cases/extraction.html/": {"canonical": "/docs/tutorials/extraction/", "alternative": ["/v0.1/docs/use_cases/extraction/"]}, + "/en/latest/use_cases/summarization.html/": {"canonical": "/docs/tutorials/summarization/", "alternative": ["/v0.1/docs/use_cases/summarization/"]}, + "/en/latest/use_cases/tabular.html/": {"canonical": "/docs/tutorials/sql_qa/", "alternative": ["/v0.1/docs/use_cases/sql/"]}, + "/en/latest/youtube.html/": {"canonical": "/docs/additional_resources/youtube/"}, + "/docs/": {"canonical": "/"}, + "/en/latest/": {"canonical": "/"}, + "/en/latest/index.html/": {"canonical": "/"}, + "/en/latest/modules/models.html/": {"canonical": "/docs/how_to/#chat-models", "alternative": ["/v0.1/docs/modules/model_io/"]}, + "/docs/integrations/retrievers/google_cloud_enterprise_search/": {"canonical": "/docs/integrations/retrievers/google_vertex_ai_search/"}, + "/docs/integrations/tools/metaphor_search/": {"canonical": "/docs/integrations/tools/exa_search/"}, + "/docs/expression_language/how_to/fallbacks/": {"canonical": "https://docs.smith.langchain.com/", "alternative": ["/v0.1/docs/guides/productionization/fallbacks/"]}, + "/docs/expression_language/cookbook/retrieval/": {"canonical": "/docs/tutorials/rag/", "alternative": ["/v0.1/docs/use_cases/question_answering/"]}, + "/docs/expression_language/cookbook/agent/": {"canonical": "/docs/how_to/migrate_agent/", "alternative": ["/v0.1/docs/modules/agents/agent_types/xml_agent/"]}, + "/docs/modules/model_io/prompts/message_prompts/": {"canonical": "/docs/how_to/#prompt-templates", "alternative": ["/v0.1/docs/modules/model_io/prompts/quick_start/"]}, + "/docs/modules/model_io/prompts/pipeline/": {"canonical": "/docs/how_to/prompts_composition/", "alternative": ["/v0.1/docs/modules/model_io/prompts/composition/"]}, + "/docs/expression_language/cookbook/memory/": {"canonical": "/docs/how_to/chatbots_memory/", "alternative": ["/v0.1/docs/modules/memory/"]}, + "/docs/expression_language/cookbook/tools/": {"canonical": "/docs/tutorials/agents/", "alternative": ["/v0.1/docs/use_cases/tool_use/quickstart/"]}, + "/docs/expression_language/cookbook/sql_db/": {"canonical": "/docs/tutorials/sql_qa/", "alternative": ["/v0.1/docs/use_cases/sql/quickstart/"]}, + "/docs/expression_language/cookbook/moderation/": {"canonical": "https://docs.smith.langchain.com/", "alternative": ["/v0.1/docs/guides/productionization/safety/moderation/"]}, + "/docs/expression_language/cookbook/embedding_router/": {"canonical": "/docs/how_to/routing/", "alternative": ["/v0.1/docs/expression_language/how_to/routing/"]}, + "/docs/guides/structured_output/": {"canonical": "/docs/how_to/structured_output/", "alternative": ["/v0.1/docs/modules/model_io/chat/structured_output/"]}, + "/docs/modules/agents/how_to/structured_tools/": {"canonical": "/docs/how_to/#tools", "alternative": ["/v0.1/docs/modules/tools/"]}, + "/docs/use_cases/csv/": {"canonical": "/docs/tutorials/sql_qa/", "alternative": ["/v0.1/docs/use_cases/sql/csv/"]}, + "/docs/guides/debugging/": {"canonical": "/docs/how_to/debugging/", "alternative": ["/v0.1/docs/guides/development/debugging/"]}, + "/docs/guides/extending_langchain/": {"canonical": "/docs/how_to/#custom", "alternative": ["/v0.1/docs/guides/development/extending_langchain/"]}, + "/docs/guides/fallbacks/": {"canonical": "https://docs.smith.langchain.com/", "alternative": ["/v0.1/docs/guides/productionization/fallbacks/"]}, + "/docs/guides/model_laboratory/": {"canonical": "https://docs.smith.langchain.com/", "alternative": ["/v0.1/docs/guides/productionization/evaluation/"]}, + "/docs/guides/pydantic_compatibility/": {"canonical": "/docs/how_to/pydantic_compatibility/", "alternative": ["/v0.1/docs/guides/development/pydantic_compatibility/"]}, + "/docs/guides/local_llms/": {"canonical": "/docs/how_to/local_llms/", "alternative": ["/v0.1/docs/guides/development/local_llms/"]}, + "/docs/modules/model_io/quick_start/": {"canonical": "/docs/how_to/#chat-models", "alternative": ["/v0.1/docs/modules/model_io/"]}, + "/docs/expression_language/how_to/generators/": {"canonical": "/docs/how_to/functions/", "alternative": ["/v0.1/docs/expression_language/primitives/functions/"]}, + "/docs/expression_language/how_to/functions/": {"canonical": "/docs/how_to/functions/", "alternative": ["/v0.1/docs/expression_language/primitives/functions/"]}, + "/docs/expression_language/how_to/passthrough/": {"canonical": "/docs/how_to/passthrough/", "alternative": ["/v0.1/docs/expression_language/primitives/passthrough/"]}, + "/docs/expression_language/how_to/map/": {"canonical": "/docs/how_to/parallel/", "alternative": ["/v0.1/docs/expression_language/primitives/parallel/"]}, + "/docs/expression_language/how_to/binding/": {"canonical": "/docs/how_to/binding/", "alternative": ["/v0.1/docs/expression_language/primitives/binding/"]}, + "/docs/expression_language/how_to/configure/": {"canonical": "/docs/how_to/configure/", "alternative": ["/v0.1/docs/expression_language/primitives/configure/"]}, + "/docs/expression_language/cookbook/prompt_llm_parser/": {"canonical": "/docs/how_to/sequence/", "alternative": ["/v0.1/docs/expression_language/get_started/"]}, + "/docs/contributing/documentation/": {"canonical": "/docs/contributing/documentation/", "alternative": ["/v0.1/docs/contributing/documentation/technical_logistics/"]}, + "/docs/expression_language/cookbook/": {"canonical": "/docs/how_to/#langchain-expression-language-lcel", "alternative": ["/v0.1/docs/expression_language/"]}, + "/docs/integrations/text_embedding/solar/": {"canonical": "/docs/integrations/text_embedding/upstage/"}, + "/docs/integrations/chat/solar/": {"canonical": "/docs/integrations/chat/upstage/"}, + // custom ones + + "/docs/modules/model_io/chat/llm_chain/": { + "canonical": "/docs/tutorials/llm_chain/" + }, + + "/docs/modules/agents/toolkits/": { + "canonical": "/docs/integrations/tools/", + "alternative": [ + "/v0.1/docs/integrations/toolkits/" + ] + } +} \ No newline at end of file