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*   [Introduction](/v0.2/docs/introduction)
*   [Tutorials](/v0.2/docs/tutorials/)
    
    *   [Build a Question Answering application over a Graph Database](/v0.2/docs/tutorials/graph)
    *   [Tutorials](/v0.2/docs/tutorials/)
    *   [Build a Simple LLM Application](/v0.2/docs/tutorials/llm_chain)
    *   [Build a Query Analysis System](/v0.2/docs/tutorials/query_analysis)
    *   [Build a Chatbot](/v0.2/docs/tutorials/chatbot)
    *   [Build an Agent](/v0.2/docs/tutorials/agents)
    *   [Build an Extraction Chain](/v0.2/docs/tutorials/extraction)
    *   [Summarize Text](/v0.2/docs/tutorials/summarization)
    *   [Tagging](/v0.2/docs/tutorials/classification)
    *   [Build a Local RAG Application](/v0.2/docs/tutorials/local_rag)
    *   [Conversational RAG](/v0.2/docs/tutorials/qa_chat_history)
    *   [Build a Retrieval Augmented Generation (RAG) App](/v0.2/docs/tutorials/rag)
    *   [Build a Question/Answering system over SQL data](/v0.2/docs/tutorials/sql_qa)
*   [How-to guides](/v0.2/docs/how_to/)
    
    *   [How-to guides](/v0.2/docs/how_to/)
    *   [How to use example selectors](/v0.2/docs/how_to/example_selectors)
    *   [Installation](/v0.2/docs/how_to/installation)
    *   [How to stream responses from an LLM](/v0.2/docs/how_to/streaming_llm)
    *   [How to stream chat model responses](/v0.2/docs/how_to/chat_streaming)
    *   [How to embed text data](/v0.2/docs/how_to/embed_text)
    *   [How to use few shot examples in chat models](/v0.2/docs/how_to/few_shot_examples_chat)
    *   [How to cache model responses](/v0.2/docs/how_to/llm_caching)
    *   [How to cache chat model responses](/v0.2/docs/how_to/chat_model_caching)
    *   [How to create a custom LLM class](/v0.2/docs/how_to/custom_llm)
    *   [How to use few shot examples](/v0.2/docs/how_to/few_shot_examples)
    *   [How to use output parsers to parse an LLM response into structured format](/v0.2/docs/how_to/output_parser_structured)
    *   [How to return structured data from a model](/v0.2/docs/how_to/structured_output)
    *   [How to add ad-hoc tool calling capability to LLMs and Chat Models](/v0.2/docs/how_to/tools_prompting)
    *   [How to create a custom chat model class](/v0.2/docs/how_to/custom_chat)
    *   [How to do per-user retrieval](/v0.2/docs/how_to/qa_per_user)
    *   [How to track token usage](/v0.2/docs/how_to/chat_token_usage_tracking)
    *   [How to track token usage](/v0.2/docs/how_to/llm_token_usage_tracking)
    *   [How to pass through arguments from one step to the next](/v0.2/docs/how_to/passthrough)
    *   [How to compose prompts together](/v0.2/docs/how_to/prompts_composition)
    *   [How to use legacy LangChain Agents (AgentExecutor)](/v0.2/docs/how_to/agent_executor)
    *   [How to add values to a chain's state](/v0.2/docs/how_to/assign)
    *   [How to attach runtime arguments to a Runnable](/v0.2/docs/how_to/binding)
    *   [How to cache embedding results](/v0.2/docs/how_to/caching_embeddings)
    *   [How to split by character](/v0.2/docs/how_to/character_text_splitter)
    *   [How to manage memory](/v0.2/docs/how_to/chatbots_memory)
    *   [How to do retrieval](/v0.2/docs/how_to/chatbots_retrieval)
    *   [How to use tools](/v0.2/docs/how_to/chatbots_tools)
    *   [How to split code](/v0.2/docs/how_to/code_splitter)
    *   [How to do retrieval with contextual compression](/v0.2/docs/how_to/contextual_compression)
    *   [How to write a custom retriever class](/v0.2/docs/how_to/custom_retriever)
    *   [How to create custom Tools](/v0.2/docs/how_to/custom_tools)
    *   [How to debug your LLM apps](/v0.2/docs/how_to/debugging)
    *   [How to load CSV data](/v0.2/docs/how_to/document_loader_csv)
    *   [How to write a custom document loader](/v0.2/docs/how_to/document_loader_custom)
    *   [How to load data from a directory](/v0.2/docs/how_to/document_loader_directory)
    *   [How to load PDF files](/v0.2/docs/how_to/document_loader_pdf)
    *   [How to load JSON data](/v0.2/docs/how_to/document_loaders_json)
    *   [How to select examples by length](/v0.2/docs/how_to/example_selectors_length_based)
    *   [How to select examples by similarity](/v0.2/docs/how_to/example_selectors_similarity)
    *   [How to use reference examples](/v0.2/docs/how_to/extraction_examples)
    *   [How to handle long text](/v0.2/docs/how_to/extraction_long_text)
    *   [How to do extraction without using function calling](/v0.2/docs/how_to/extraction_parse)
    *   [Fallbacks](/v0.2/docs/how_to/fallbacks)
    *   [Few Shot Prompt Templates](/v0.2/docs/how_to/few_shot)
    *   [How to run custom functions](/v0.2/docs/how_to/functions)
    *   [How to construct knowledge graphs](/v0.2/docs/how_to/graph_constructing)
    *   [How to map values to a database](/v0.2/docs/how_to/graph_mapping)
    *   [How to improve results with prompting](/v0.2/docs/how_to/graph_prompting)
    *   [How to add a semantic layer over the database](/v0.2/docs/how_to/graph_semantic)
    *   [How to reindex data to keep your vectorstore in-sync with the underlying data source](/v0.2/docs/how_to/indexing)
    *   [How to get log probabilities](/v0.2/docs/how_to/logprobs)
    *   [How to add message history](/v0.2/docs/how_to/message_history)
    *   [How to generate multiple embeddings per document](/v0.2/docs/how_to/multi_vector)
    *   [How to generate multiple queries to retrieve data for](/v0.2/docs/how_to/multiple_queries)
    *   [How to parse JSON output](/v0.2/docs/how_to/output_parser_json)
    *   [How to retry when output parsing errors occur](/v0.2/docs/how_to/output_parser_retry)
    *   [How to parse XML output](/v0.2/docs/how_to/output_parser_xml)
    *   [How to invoke runnables in parallel](/v0.2/docs/how_to/parallel)
    *   [How to retrieve the whole document for a chunk](/v0.2/docs/how_to/parent_document_retriever)
    *   [How to partially format prompt templates](/v0.2/docs/how_to/prompts_partial)
    *   [How to add chat history to a question-answering chain](/v0.2/docs/how_to/qa_chat_history_how_to)
    *   [How to return citations](/v0.2/docs/how_to/qa_citations)
    *   [How to return sources](/v0.2/docs/how_to/qa_sources)
    *   [How to stream from a question-answering chain](/v0.2/docs/how_to/qa_streaming)
    *   [How to construct filters](/v0.2/docs/how_to/query_constructing_filters)
    *   [How to add examples to the prompt](/v0.2/docs/how_to/query_few_shot)
    *   [How to deal with high cardinality categorical variables](/v0.2/docs/how_to/query_high_cardinality)
    *   [How to handle multiple queries](/v0.2/docs/how_to/query_multiple_queries)
    *   [How to handle multiple retrievers](/v0.2/docs/how_to/query_multiple_retrievers)
    *   [How to handle cases where no queries are generated](/v0.2/docs/how_to/query_no_queries)
    *   [How to recursively split text by characters](/v0.2/docs/how_to/recursive_text_splitter)
    *   [How to reduce retrieval latency](/v0.2/docs/how_to/reduce_retrieval_latency)
    *   [How to route execution within a chain](/v0.2/docs/how_to/routing)
    *   [How to chain runnables](/v0.2/docs/how_to/sequence)
    *   [How to split text by tokens](/v0.2/docs/how_to/split_by_token)
    *   [How to deal with large databases](/v0.2/docs/how_to/sql_large_db)
    *   [How to use prompting to improve results](/v0.2/docs/how_to/sql_prompting)
    *   [How to do query validation](/v0.2/docs/how_to/sql_query_checking)
    *   [How to stream](/v0.2/docs/how_to/streaming)
    *   [How to create a time-weighted retriever](/v0.2/docs/how_to/time_weighted_vectorstore)
    *   [How to use a chat model to call tools](/v0.2/docs/how_to/tool_calling)
    *   [How to call tools with multi-modal data](/v0.2/docs/how_to/tool_calls_multi_modal)
    *   [How to use LangChain tools](/v0.2/docs/how_to/tools_builtin)
    *   [How use a vector store to retrieve data](/v0.2/docs/how_to/vectorstore_retriever)
    *   [How to create and query vector stores](/v0.2/docs/how_to/vectorstores)
*   [Conceptual guide](/v0.2/docs/concepts)
*   Ecosystem
    
    *   [🦜🛠️ LangSmith](/v0.2/docs/langsmith/)
        
    *   [🦜🕸️LangGraph.js](/v0.2/docs/langgraph)
*   Versions
    
    *   [Overview](/v0.2/docs/versions/overview)
    *   [v0.2](/v0.2/docs/versions/v0_2)
    *   [Release Policy](/v0.2/docs/versions/release_policy)
    *   [Packages](/v0.2/docs/versions/packages)
*   [Security](/v0.2/docs/security)

*   [](/v0.2/)
*   How-to guides

On this page

How-to guides
=============

Here you'll find answers to “How do I….?” types of questions. These guides are _goal-oriented_ and _concrete_; they're meant to help you complete a specific task. For conceptual explanations see [Conceptual Guides](/v0.2/docs/concepts/). For end-to-end walkthroughs see [Tutorials](/v0.2/docs/tutorials). For comprehensive descriptions of every class and function see [API Reference](https://v2.v02.api.js.langchain.com/).

Installation[​](#installation "Direct link to Installation")
------------------------------------------------------------

*   [How to: install LangChain packages](/v0.2/docs/how_to/installation/)

Key features[​](#key-features "Direct link to Key features")
------------------------------------------------------------

This highlights functionality that is core to using LangChain.

*   [How to: return structured data from an LLM](/v0.2/docs/how_to/structured_output/)
*   [How to: use a chat model to call tools](/v0.2/docs/how_to/tool_calling/)
*   [How to: stream runnables](/v0.2/docs/how_to/streaming)
*   [How to: debug your LLM apps](/v0.2/docs/how_to/debugging/)

LangChain Expression Language (LCEL)[​](#langchain-expression-language-lcel "Direct link to LangChain Expression Language (LCEL)")
----------------------------------------------------------------------------------------------------------------------------------

LangChain Expression Language is a way to create arbitrary custom chains. It is built on the Runnable protocol.

*   [How to: chain runnables](/v0.2/docs/how_to/sequence)
*   [How to: stream runnables](/v0.2/docs/how_to/streaming)
*   [How to: invoke runnables in parallel](/v0.2/docs/how_to/parallel/)
*   [How to: attach runtime arguments to a runnable](/v0.2/docs/how_to/binding/)
*   [How to: run custom functions](/v0.2/docs/how_to/functions)
*   [How to: pass through arguments from one step to the next](/v0.2/docs/how_to/passthrough)
*   [How to: add values to a chain's state](/v0.2/docs/how_to/assign)
*   [How to: add message history](/v0.2/docs/how_to/message_history)
*   [How to: route execution within a chain](/v0.2/docs/how_to/routing)
*   [How to: add fallbacks](/v0.2/docs/how_to/fallbacks)

Components[​](#components "Direct link to Components")
------------------------------------------------------

These are the core building blocks you can use when building applications.

### Prompt templates[​](#prompt-templates "Direct link to Prompt templates")

Prompt Templates are responsible for formatting user input into a format that can be passed to a language model.

*   [How to: use few shot examples](/v0.2/docs/how_to/few_shot_examples)
*   [How to: use few shot examples in chat models](/v0.2/docs/how_to/few_shot_examples_chat/)
*   [How to: partially format prompt templates](/v0.2/docs/how_to/prompts_partial)
*   [How to: compose prompts together](/v0.2/docs/how_to/prompts_composition)

### Example selectors[​](#example-selectors "Direct link to Example selectors")

Example Selectors are responsible for selecting the correct few shot examples to pass to the prompt.

*   [How to: use example selectors](/v0.2/docs/how_to/example_selectors)
*   [How to: select examples by length](/v0.2/docs/how_to/example_selectors_length_based)
*   [How to: select examples by semantic similarity](/v0.2/docs/how_to/example_selectors_similarity)

### Chat models[​](#chat-models "Direct link to Chat models")

Chat Models are newer forms of language models that take messages in and output a message.

*   [How to: do function/tool calling](/v0.2/docs/how_to/tool_calling)
*   [How to: get models to return structured output](/v0.2/docs/how_to/structured_output)
*   [How to: cache model responses](/v0.2/docs/how_to/chat_model_caching)
*   [How to: create a custom chat model class](/v0.2/docs/how_to/custom_chat)
*   [How to: get log probabilities](/v0.2/docs/how_to/logprobs)
*   [How to: stream a response back](/v0.2/docs/how_to/chat_streaming)
*   [How to: track token usage](/v0.2/docs/how_to/chat_token_usage_tracking)

### LLMs[​](#llms "Direct link to LLMs")

What LangChain calls LLMs are older forms of language models that take a string in and output a string.

*   [How to: cache model responses](/v0.2/docs/how_to/llm_caching)
*   [How to: create a custom LLM class](/v0.2/docs/how_to/custom_llm)
*   [How to: stream a response back](/v0.2/docs/how_to/streaming_llm)
*   [How to: track token usage](/v0.2/docs/how_to/llm_token_usage_tracking)

### Output parsers[​](#output-parsers "Direct link to Output parsers")

Output Parsers are responsible for taking the output of an LLM and parsing into more structured format.

*   [How to: use output parsers to parse an LLM response into structured format](/v0.2/docs/how_to/output_parser_structured)
*   [How to: parse JSON output](/v0.2/docs/how_to/output_parser_json)
*   [How to: parse XML output](/v0.2/docs/how_to/output_parser_xml)
*   [How to: retry when output parsing errors occur](/v0.2/docs/how_to/output_parser_retry)

### Document loaders[​](#document-loaders "Direct link to Document loaders")

Document Loaders are responsible for loading documents from a variety of sources.

*   [How to: load CSV data](/v0.2/docs/how_to/document_loader_csv)
*   [How to: load data from a directory](/v0.2/docs/how_to/document_loader_directory)
*   [How to: load PDF files](/v0.2/docs/how_to/document_loader_pdf)
*   [How to: write a custom document loader](/v0.2/docs/how_to/document_loader_custom)

### Text splitters[​](#text-splitters "Direct link to Text splitters")

Text Splitters take a document and split into chunks that can be used for retrieval.

*   [How to: recursively split text](/v0.2/docs/how_to/recursive_text_splitter)
*   [How to: split by character](/v0.2/docs/how_to/character_text_splitter)
*   [How to: split code](/v0.2/docs/how_to/code_splitter)
*   [How to: split by tokens](/v0.2/docs/how_to/split_by_token)

### Embedding models[​](#embedding-models "Direct link to Embedding models")

Embedding Models take a piece of text and create a numerical representation of it.

*   [How to: embed text data](/v0.2/docs/how_to/embed_text)
*   [How to: cache embedding results](/v0.2/docs/how_to/caching_embeddings)

### Vector stores[​](#vector-stores "Direct link to Vector stores")

Vector stores are databases that can efficiently store and retrieve embeddings.

*   [How to: create and query vector stores](/v0.2/docs/how_to/vectorstores)

### Retrievers[​](#retrievers "Direct link to Retrievers")

Retrievers are responsible for taking a query and returning relevant documents.

*   [How to: use a vector store to retrieve data](/v0.2/docs/how_to/vectorstore_retriever)
*   [How to: generate multiple queries to retrieve data for](/v0.2/docs/how_to/multiple_queries)
*   [How to: use contextual compression to compress the data retrieved](/v0.2/docs/how_to/contextual_compression)
*   [How to: write a custom retriever class](/v0.2/docs/how_to/custom_retriever)
*   [How to: generate multiple embeddings per document](/v0.2/docs/how_to/multi_vector)
*   [How to: retrieve the whole document for a chunk](/v0.2/docs/how_to/parent_document_retriever)
*   [How to: create a time-weighted retriever](/v0.2/docs/how_to/time_weighted_vectorstore)
*   [How to: reduce retrieval latency](/v0.2/docs/how_to/reduce_retrieval_latency)

### Indexing[​](#indexing "Direct link to Indexing")

Indexing is the process of keeping your vectorstore in-sync with the underlying data source.

*   [How to: reindex data to keep your vectorstore in-sync with the underlying data source](/v0.2/docs/how_to/indexing)

### Tools[​](#tools "Direct link to Tools")

LangChain Tools contain a description of the tool (to pass to the language model) as well as the implementation of the function to call).

*   [How to: create custom tools](/v0.2/docs/how_to/custom_tools)
*   [How to: use built-in tools and built-in toolkits](/v0.2/docs/how_to/tools_builtin)
*   [How to: use a chat model to call tools](/v0.2/docs/how_to/tool_calling/)
*   [How to: add ad-hoc tool calling capability to LLMs and Chat Models](/v0.2/docs/how_to/tools_prompting)
*   [How to: call tools using multi-modal data](/v0.2/docs/how_to/tool_calls_multi_modal)

### Agents[​](#agents "Direct link to Agents")

note

For in depth how-to guides for agents, please check out [LangGraph](https://langchain-ai.github.io/langgraphjs/) documentation.

*   [How to: use legacy LangChain Agents (AgentExecutor)](/v0.2/docs/how_to/agent_executor)

### Custom[​](#custom "Direct link to Custom")

All of LangChain components can easily be extended to support your own versions.

*   [How to: create a custom LLM class](/v0.2/docs/how_to/custom_llm)
*   [How to: write a custom retriever class](/v0.2/docs/how_to/custom_retriever)
*   [How to: write a custom document loader](/v0.2/docs/how_to/document_loader_custom)
*   [How to: define a custom tool](/v0.2/docs/how_to/custom_tools)

Use cases[​](#use-cases "Direct link to Use cases")
---------------------------------------------------

These guides cover use-case specific details.

### Q&A with RAG[​](#qa-with-rag "Direct link to Q&A with RAG")

Retrieval Augmented Generation (RAG) is a way to connect LLMs to external sources of data.

*   [How to: add chat history](/v0.2/docs/how_to/qa_chat_history_how_to/)
*   [How to: stream](/v0.2/docs/how_to/qa_streaming/)
*   [How to: return sources](/v0.2/docs/how_to/qa_sources/)
*   [How to: return citations](/v0.2/docs/how_to/qa_citations/)
*   [How to: do per-user retrieval](/v0.2/docs/how_to/qa_per_user/)

### Extraction[​](#extraction "Direct link to Extraction")

Extraction is when you use LLMs to extract structured information from unstructured text.

*   [How to: use reference examples](/v0.2/docs/how_to/extraction_examples/)
*   [How to: handle long text](/v0.2/docs/how_to/extraction_long_text/)
*   [How to: do extraction without using function calling](/v0.2/docs/how_to/extraction_parse)

### Chatbots[​](#chatbots "Direct link to Chatbots")

Chatbots involve using an LLM to have a conversation.

*   [How to: manage memory](/v0.2/docs/how_to/chatbots_memory)
*   [How to: do retrieval](/v0.2/docs/how_to/chatbots_retrieval)
*   [How to: use tools](/v0.2/docs/how_to/chatbots_tools)

### Query analysis[​](#query-analysis "Direct link to Query analysis")

Query Analysis is the task of using an LLM to generate a query to send to a retriever.

*   [How to: add examples to the prompt](/v0.2/docs/how_to/query_few_shot)
*   [How to: handle cases where no queries are generated](/v0.2/docs/how_to/query_no_queries)
*   [How to: handle multiple queries](/v0.2/docs/how_to/query_multiple_queries)
*   [How to: handle multiple retrievers](/v0.2/docs/how_to/query_multiple_retrievers)
*   [How to: construct filters](/v0.2/docs/how_to/query_constructing_filters)
*   [How to: deal with high cardinality categorical variables](/v0.2/docs/how_to/query_high_cardinality)

### Q&A over SQL + CSV[​](#qa-over-sql--csv "Direct link to Q&A over SQL + CSV")

You can use LLMs to do question answering over tabular data.

*   [How to: use prompting to improve results](/v0.2/docs/how_to/sql_prompting)
*   [How to: do query validation](/v0.2/docs/how_to/sql_query_checking)
*   [How to: deal with large databases](/v0.2/docs/how_to/sql_large_db)

### Q&A over graph databases[​](#qa-over-graph-databases "Direct link to Q&A over graph databases")

You can use an LLM to do question answering over graph databases.

*   [How to: map values to a database](/v0.2/docs/how_to/graph_mapping)
*   [How to: add a semantic layer over the database](/v0.2/docs/how_to/graph_semantic)
*   [How to: improve results with prompting](/v0.2/docs/how_to/graph_prompting)
*   [How to: construct knowledge graphs](/v0.2/docs/how_to/graph_constructing)

* * *

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](/v0.2/docs/how_to/)

*   [Installation](#installation)
*   [Key features](#key-features)
*   [LangChain Expression Language (LCEL)](#langchain-expression-language-lcel)
*   [Components](#components)
    *   [Prompt templates](#prompt-templates)
    *   [Example selectors](#example-selectors)
    *   [Chat models](#chat-models)
    *   [LLMs](#llms)
    *   [Output parsers](#output-parsers)
    *   [Document loaders](#document-loaders)
    *   [Text splitters](#text-splitters)
    *   [Embedding models](#embedding-models)
    *   [Vector stores](#vector-stores)
    *   [Retrievers](#retrievers)
    *   [Indexing](#indexing)
    *   [Tools](#tools)
    *   [Agents](#agents)
    *   [Custom](#custom)
*   [Use cases](#use-cases)
    *   [Q&A with RAG](#qa-with-rag)
    *   [Extraction](#extraction)
    *   [Chatbots](#chatbots)
    *   [Query analysis](#query-analysis)
    *   [Q&A over SQL + CSV](#qa-over-sql--csv)
    *   [Q&A over graph databases](#qa-over-graph-databases)

Community

*   [Discord](https://discord.gg/cU2adEyC7w)
*   [Twitter](https://twitter.com/LangChainAI)

GitHub

*   [Python](https://github.com/langchain-ai/langchain)
*   [JS/TS](https://github.com/langchain-ai/langchainjs)

More

*   [Homepage](https://langchain.com)
*   [Blog](https://blog.langchain.dev)

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