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# How-to guides
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Here you’ll find short answers to “How do I….?” types of questions.
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These how-to guides don’t cover topics in depth – you’ll find that material in the [Tutorials](/docs/tutorials) and the [API Reference](https://api.python.langchain.com/en/latest/).
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However, these guides will help you quickly accomplish common tasks.
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Here you’ll find answers to “How do I….?” types of questions.
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These guides are *goal-oriented* and *concrete*; they're meant to help you complete a specific task.
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For conceptual explanations see [Conceptual Guides](/docs/concepts/).
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For end-to-end walkthroughs see [Tutorials](/docs/tutorials).
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For comprehensive descriptions of every class and function see [API Reference](https://api.python.langchain.com/en/latest/).
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## Core Functionality
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## Key features
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This covers functionality that is core to using LangChain
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This highlights functionality that is core to using LangChain.
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- [How to return structured data from an LLM](/docs/how_to/structured_output/)
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- [How to use a chat model to call tools](/docs/how_to/tool_calling/)
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- [How to stream](/docs/how_to/streaming)
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- [How to debug your LLM apps](/docs/how_to/debugging/)
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- [How to: return structured data from an LLM](/docs/how_to/structured_output/)
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- [How to: use a chat model to call tools](/docs/how_to/tool_calling/)
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- [How to: stream runnables](/docs/how_to/streaming)
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- [How to: debug your LLM apps](/docs/how_to/debugging/)
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## LangChain Expression Language (LCEL)
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LangChain Expression Language a way to create arbitrary custom chains.
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LangChain Expression Language is a way to create arbitrary custom chains. It is built on the Runnable protocol.
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- [How to combine multiple runnables into a chain](/docs/how_to/sequence)
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- [How to invoke runnables in parallel](/docs/how_to/parallel/)
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- [How to attach runtime arguments to a runnable](/docs/how_to/binding/)
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- [How to run custom functions](/docs/how_to/functions)
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- [How to pass through arguments from one step to the next](/docs/how_to/passthrough)
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- [How to add values to a chain's state](/docs/how_to/assign)
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- [How to configure a chain at runtime](/docs/how_to/configure)
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- [How to add message history](/docs/how_to/message_history)
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- [How to route execution within a chain](/docs/how_to/routing)
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- [How to inspect your runnables](/docs/how_to/inspect)
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- [How to add fallbacks](/docs/how_to/fallbacks)
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- [How to: chain runnables](/docs/how_to/sequence)
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- [How to: stream runnables](/docs/how_to/streaming)
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- [How to: invoke runnables in parallel](/docs/how_to/parallel/)
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- [How to: attach runtime arguments to a runnable](/docs/how_to/binding/)
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- [How to: run custom functions](/docs/how_to/functions)
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- [How to: pass through arguments from one step to the next](/docs/how_to/passthrough)
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- [How to: add values to a chain's state](/docs/how_to/assign)
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- [How to: configure a chain at runtime](/docs/how_to/configure)
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- [How to: add message history](/docs/how_to/message_history)
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- [How to: route execution within a chain](/docs/how_to/routing)
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- [How to: inspect runnables](/docs/how_to/inspect)
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- [How to: add fallbacks](/docs/how_to/fallbacks)
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## Components
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@ -42,132 +45,132 @@ These are the core building blocks you can use when building applications.
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Prompt Templates are responsible for formatting user input into a format that can be passed to a language model.
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- [How to use few shot examples](/docs/how_to/few_shot_examples)
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- [How to use few shot examples in chat models](/docs/how_to/few_shot_examples_chat/)
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- [How to partially format prompt templates](/docs/how_to/prompts_partial)
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- [How to compose prompts together](/docs/how_to/prompts_composition)
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- [How to: use few shot examples](/docs/how_to/few_shot_examples)
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- [How to: use few shot examples in chat models](/docs/how_to/few_shot_examples_chat/)
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- [How to: partially format prompt templates](/docs/how_to/prompts_partial)
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- [How to: compose prompts together](/docs/how_to/prompts_composition)
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### Example selectors
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Example Selectors are responsible for selecting the correct few shot examples to pass to the prompt.
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- [How to use example selectors](/docs/how_to/example_selectors)
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- [How to select examples by length](/docs/how_to/example_selectors_length_based)
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- [How to select examples by semantic similarity](/docs/how_to/example_selectors_similarity)
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- [How to select examples by semantic ngram overlap](/docs/how_to/example_selectors_ngram)
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- [How to select examples by maximal marginal relevance](/docs/how_to/example_selectors_mmr)
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- [How to: use example selectors](/docs/how_to/example_selectors)
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- [How to: select examples by length](/docs/how_to/example_selectors_length_based)
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- [How to: select examples by semantic similarity](/docs/how_to/example_selectors_similarity)
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- [How to: select examples by semantic ngram overlap](/docs/how_to/example_selectors_ngram)
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- [How to: select examples by maximal marginal relevance](/docs/how_to/example_selectors_mmr)
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### Chat models
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Chat Models are newer forms of language models that take messages in and output a message.
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- [How to do function/tool calling](/docs/how_to/tool_calling)
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- [How to get models to return structured output](/docs/how_to/structured_output)
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- [How to cache model responses](/docs/how_to/chat_model_caching)
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- [How to get log probabilities from model calls](/docs/how_to/logprobs)
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- [How to create a custom chat model class](/docs/how_to/custom_chat_model)
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- [How to stream a response back](/docs/how_to/chat_streaming)
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- [How to track token usage](/docs/how_to/chat_token_usage_tracking)
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- [How to track response metadata across providers](/docs/how_to/response_metadata)
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- [How to: do function/tool calling](/docs/how_to/tool_calling)
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- [How to: get models to return structured output](/docs/how_to/structured_output)
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- [How to: cache model responses](/docs/how_to/chat_model_caching)
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- [How to: get log probabilities](/docs/how_to/logprobs)
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- [How to: create a custom chat model class](/docs/how_to/custom_chat_model)
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- [How to: stream a response back](/docs/how_to/chat_streaming)
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- [How to: track token usage](/docs/how_to/chat_token_usage_tracking)
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- [How to: track response metadata across providers](/docs/how_to/response_metadata)
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### LLMs
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What LangChain calls LLMs are older forms of language models that take a string in and output a string.
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- [How to cache model responses](/docs/how_to/llm_caching)
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- [How to create a custom LLM class](/docs/how_to/custom_llm)
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- [How to stream a response back](/docs/how_to/streaming_llm)
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- [How to track token usage](/docs/how_to/llm_token_usage_tracking)
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- [How to work with local LLMs](/docs/how_to/local_llms)
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- [How to: cache model responses](/docs/how_to/llm_caching)
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- [How to: create a custom LLM class](/docs/how_to/custom_llm)
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- [How to: stream a response back](/docs/how_to/streaming_llm)
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- [How to: track token usage](/docs/how_to/llm_token_usage_tracking)
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- [How to: work with local LLMs](/docs/how_to/local_llms)
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### Output parsers
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Output Parsers are responsible for taking the output of an LLM and parsing into more structured format.
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- [How to use output parsers to parse an LLM response into structured format](/docs/how_to/output_parser_structured)
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- [How to parse JSON output](/docs/how_to/output_parser_json)
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- [How to parse XML output](/docs/how_to/output_parser_xml)
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- [How to parse YAML output](/docs/how_to/output_parser_yaml)
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- [How to retry when output parsing errors occur](/docs/how_to/output_parser_retry)
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- [How to try to fix errors in output parsing](/docs/how_to/output_parser_fixing)
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- [How to write a custom output parser class](/docs/how_to/output_parser_custom)
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- [How to: use output parsers to parse an LLM response into structured format](/docs/how_to/output_parser_structured)
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- [How to: parse JSON output](/docs/how_to/output_parser_json)
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- [How to: parse XML output](/docs/how_to/output_parser_xml)
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- [How to: parse YAML output](/docs/how_to/output_parser_yaml)
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- [How to: retry when output parsing errors occur](/docs/how_to/output_parser_retry)
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- [How to: try to fix errors in output parsing](/docs/how_to/output_parser_fixing)
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- [How to: write a custom output parser class](/docs/how_to/output_parser_custom)
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### Document loaders
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Document Loaders are responsible for loading documents from a variety of sources.
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- [How to load CSV data](/docs/how_to/document_loader_csv)
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- [How to load data from a directory](/docs/how_to/document_loader_directory)
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- [How to load HTML data](/docs/how_to/document_loader_html)
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- [How to load JSON data](/docs/how_to/document_loader_json)
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- [How to load Markdown data](/docs/how_to/document_loader_markdown)
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- [How to load Microsoft Office data](/docs/how_to/document_loader_office_file)
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- [How to load PDF files](/docs/how_to/document_loader_pdf)
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- [How to write a custom document loader](/docs/how_to/document_loader_custom)
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- [How to: load CSV data](/docs/how_to/document_loader_csv)
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- [How to: load data from a directory](/docs/how_to/document_loader_directory)
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- [How to: load HTML data](/docs/how_to/document_loader_html)
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- [How to: load JSON data](/docs/how_to/document_loader_json)
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- [How to: load Markdown data](/docs/how_to/document_loader_markdown)
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- [How to: load Microsoft Office data](/docs/how_to/document_loader_office_file)
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- [How to: load PDF files](/docs/how_to/document_loader_pdf)
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- [How to: write a custom document loader](/docs/how_to/document_loader_custom)
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### Text splitters
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Text Splitters take a document and split into chunks that can be used for retrieval.
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- [How to recursively split text](/docs/how_to/recursive_text_splitter)
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- [How to split by HTML headers](/docs/how_to/HTML_header_metadata_splitter)
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- [How to split by HTML sections](/docs/how_to/HTML_section_aware_splitter)
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- [How to split by character](/docs/how_to/character_text_splitter)
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- [How to split code](/docs/how_to/code_splitter)
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- [How to split Markdown by headers](/docs/how_to/markdown_header_metadata_splitter)
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- [How to recursively split JSON](/docs/how_to/recursive_json_splitter)
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- [How to split text into semantic chunks](/docs/how_to/semantic-chunker)
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- [How to split by tokens](/docs/how_to/split_by_token)
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- [How to: recursively split text](/docs/how_to/recursive_text_splitter)
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- [How to: split by HTML headers](/docs/how_to/HTML_header_metadata_splitter)
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- [How to: split by HTML sections](/docs/how_to/HTML_section_aware_splitter)
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- [How to: split by character](/docs/how_to/character_text_splitter)
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- [How to: split code](/docs/how_to/code_splitter)
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- [How to: split Markdown by headers](/docs/how_to/markdown_header_metadata_splitter)
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- [How to: recursively split JSON](/docs/how_to/recursive_json_splitter)
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- [How to: split text into semantic chunks](/docs/how_to/semantic-chunker)
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- [How to: split by tokens](/docs/how_to/split_by_token)
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### Embedding models
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Embedding Models take a piece of text and create a numerical representation of it.
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- [How to embed text data](/docs/how_to/embed_text)
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- [How to cache embedding results](/docs/how_to/caching_embeddings)
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- [How to: embed text data](/docs/how_to/embed_text)
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- [How to: cache embedding results](/docs/how_to/caching_embeddings)
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### Vector stores
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Vector stores are databases that can efficiently store and retrieve embeddings.
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- [How to use a vector store to retrieve data](/docs/how_to/vectorstores)
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- [How to: use a vector store to retrieve data](/docs/how_to/vectorstores)
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### Retrievers
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Retrievers are responsible for taking a query and returning relevant documents.
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- [How use a vector store to retrieve data](/docs/how_to/vectorstore_retriever)
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- [How to generate multiple queries to retrieve data for](/docs/how_to/MultiQueryRetriever)
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- [How to use contextual compression to compress the data retrieved](/docs/how_to/contextual_compression)
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- [How to write a custom retriever class](/docs/how_to/custom_retriever)
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- [How to combine the results from multiple retrievers](/docs/how_to/ensemble_retriever)
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- [How to reorder retrieved results to put most relevant documents not in the middle](/docs/how_to/long_context_reorder)
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- [How to generate multiple embeddings per document](/docs/how_to/multi_vector)
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- [How to retrieve the whole document for a chunk](/docs/how_to/parent_document_retriever)
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- [How to generate metadata filters](/docs/how_to/self_query)
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- [How to create a time-weighted retriever](/docs/how_to/time_weighted_vectorstore)
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- [How to use hybrid vector and keyword retrieval](/docs/how_to/hybrid)
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- [How to: use a vector store to retrieve data](/docs/how_to/vectorstore_retriever)
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- [How to: generate multiple queries to retrieve data for](/docs/how_to/MultiQueryRetriever)
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- [How to: use contextual compression to compress the data retrieved](/docs/how_to/contextual_compression)
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- [How to: write a custom retriever class](/docs/how_to/custom_retriever)
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- [How to: combine the results from multiple retrievers](/docs/how_to/ensemble_retriever)
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- [How to: reorder retrieved results to put most relevant documents not in the middle](/docs/how_to/long_context_reorder)
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- [How to: generate multiple embeddings per document](/docs/how_to/multi_vector)
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- [How to: retrieve the whole document for a chunk](/docs/how_to/parent_document_retriever)
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- [How to: generate metadata filters](/docs/how_to/self_query)
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- [How to: create a time-weighted retriever](/docs/how_to/time_weighted_vectorstore)
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- [How to: use hybrid vector and keyword retrieval](/docs/how_to/hybrid)
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### Indexing
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Indexing is the process of keeping your vectorstore in-sync with the underlying data source.
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- [How to reindex data to keep your vectorstore in-sync with the underlying data source](/docs/how_to/indexing)
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- [How to: reindex data to keep your vectorstore in-sync with the underlying data source](/docs/how_to/indexing)
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### Tools
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LangChain Tools contain a description of the tool (to pass to the language model) as well as the implementation of the function to call).
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- [How to use LangChain tools](/docs/how_to/tools)
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- [How to use a chat model to call tools](/docs/how_to/tool_calling/)
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- [How to use LangChain toolkits](/docs/how_to/toolkits)
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- [How to define a custom tool](/docs/how_to/custom_tools)
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- [How to convert LangChain tools to OpenAI functions](/docs/how_to/tools_as_openai_functions)
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- [How to use tools without function calling](/docs/how_to/tools_prompting)
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- [How to let the LLM choose between multiple tools](/docs/how_to/tools_multiple)
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- [How to add a human in the loop to tool usage](/docs/how_to/tools_human)
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- [How to do parallel tool use](/docs/how_to/tools_parallel)
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- [How to handle errors when calling tools](/docs/how_to/tools_error)
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- [How to: use LangChain tools](/docs/how_to/tools)
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- [How to: use a chat model to call tools](/docs/how_to/tool_calling/)
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- [How to: use LangChain toolkits](/docs/how_to/toolkits)
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- [How to: define a custom tool](/docs/how_to/custom_tools)
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- [How to: convert LangChain tools to OpenAI functions](/docs/how_to/tools_as_openai_functions)
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- [How to: use tools without function calling](/docs/how_to/tools_prompting)
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- [How to: let the LLM choose between multiple tools](/docs/how_to/tools_multiple)
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- [How to: add a human in the loop to tool usage](/docs/how_to/tools_human)
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- [How to: do parallel tool use](/docs/how_to/tools_parallel)
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- [How to: handle errors when calling tools](/docs/how_to/tools_error)
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### Agents
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@ -177,20 +180,19 @@ For in depth how-to guides for agents, please check out [LangGraph](https://gith
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:::
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- [How to use legacy LangChain Agents (AgentExecutor)](/docs/how_to/agent_executor)
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- [How to migrate from legacy LangChain agents to LangGraph](/docs/how_to/migrate_agent)
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- [How to: use legacy LangChain Agents (AgentExecutor)](/docs/how_to/agent_executor)
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- [How to: migrate from legacy LangChain agents to LangGraph](/docs/how_to/migrate_agent)
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### Custom
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All of LangChain components can easily be extended to support your own versions.
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- [How to create a custom chat model class](/docs/how_to/custom_chat_model)
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- [How to create a custom LLM class](/docs/how_to/custom_llm)
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- [How to write a custom retriever class](/docs/how_to/custom_retriever)
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- [How to write a custom document loader](/docs/how_to/document_loader_custom)
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- [How to write a custom output parser class](/docs/how_to/output_parser_custom)
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- [How to define a custom tool](/docs/how_to/custom_tools)
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- [How to: create a custom chat model class](/docs/how_to/custom_chat_model)
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- [How to: create a custom LLM class](/docs/how_to/custom_llm)
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- [How to: write a custom retriever class](/docs/how_to/custom_retriever)
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- [How to: write a custom document loader](/docs/how_to/document_loader_custom)
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- [How to: write a custom output parser class](/docs/how_to/output_parser_custom)
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- [How to: define a custom tool](/docs/how_to/custom_tools)
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## Use cases
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@ -201,54 +203,54 @@ These guides cover use-case specific details.
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Retrieval Augmented Generation (RAG) is a way to connect LLMs to external sources of data.
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- [How to add chat history](/docs/how_to/qa_chat_history_how_to/)
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- [How to stream](/docs/how_to/qa_streaming/)
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- [How to return sources](/docs/how_to/qa_sources/)
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- [How to return citations](/docs/how_to/qa_citations/)
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- [How to do per-user retrieval](/docs/how_to/qa_per_user/)
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- [How to: add chat history](/docs/how_to/qa_chat_history_how_to/)
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- [How to: stream](/docs/how_to/qa_streaming/)
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- [How to: return sources](/docs/how_to/qa_sources/)
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- [How to: return citations](/docs/how_to/qa_citations/)
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- [How to: do per-user retrieval](/docs/how_to/qa_per_user/)
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### Extraction
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Extraction is when you use LLMs to extract structured information from unstructured text.
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- [How to use reference examples](/docs/how_to/extraction_examples/)
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- [How to handle long text](/docs/how_to/extraction_long_text/)
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- [How to do extraction without using function calling](/docs/how_to/extraction_parse)
|
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- [How to: use reference examples](/docs/how_to/extraction_examples/)
|
||||
- [How to: handle long text](/docs/how_to/extraction_long_text/)
|
||||
- [How to: do extraction without using function calling](/docs/how_to/extraction_parse)
|
||||
|
||||
### Chatbots
|
||||
|
||||
Chatbots involve using an LLM to have a conversation.
|
||||
|
||||
- [How to manage memory](/docs/how_to/chatbots_memory)
|
||||
- [How to do retrieval](/docs/how_to/chatbots_retrieval)
|
||||
- [How to use tools](/docs/how_to/chatbots_tools)
|
||||
- [How to: manage memory](/docs/how_to/chatbots_memory)
|
||||
- [How to: do retrieval](/docs/how_to/chatbots_retrieval)
|
||||
- [How to: use tools](/docs/how_to/chatbots_tools)
|
||||
|
||||
### 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](/docs/how_to/query_few_shot)
|
||||
- [How to handle cases where no queries are generated](/docs/how_to/query_no_queries)
|
||||
- [How to handle multiple queries](/docs/how_to/query_multiple_queries)
|
||||
- [How to handle multiple retrievers](/docs/how_to/query_multiple_retrievers)
|
||||
- [How to construct filters](/docs/how_to/query_constructing_filters)
|
||||
- [How to deal with high cardinality categorical variables](/docs/how_to/query_high_cardinality)
|
||||
- [How to: add examples to the prompt](/docs/how_to/query_few_shot)
|
||||
- [How to: handle cases where no queries are generated](/docs/how_to/query_no_queries)
|
||||
- [How to: handle multiple queries](/docs/how_to/query_multiple_queries)
|
||||
- [How to: handle multiple retrievers](/docs/how_to/query_multiple_retrievers)
|
||||
- [How to: construct filters](/docs/how_to/query_constructing_filters)
|
||||
- [How to: deal with high cardinality categorical variables](/docs/how_to/query_high_cardinality)
|
||||
|
||||
### Q&A over SQL + CSV
|
||||
|
||||
You can use LLMs to do question answering over tabular data.
|
||||
|
||||
- [How to use prompting to improve results](/docs/how_to/sql_prompting)
|
||||
- [How to do query validation](/docs/how_to/sql_query_checking)
|
||||
- [How to deal with large databases](/docs/how_to/sql_large_db)
|
||||
- [How to deal with CSV files](/docs/how_to/sql_csv)
|
||||
- [How to: use prompting to improve results](/docs/how_to/sql_prompting)
|
||||
- [How to: do query validation](/docs/how_to/sql_query_checking)
|
||||
- [How to: deal with large databases](/docs/how_to/sql_large_db)
|
||||
- [How to: deal with CSV files](/docs/how_to/sql_csv)
|
||||
|
||||
### Q&A over graph databases
|
||||
|
||||
You can use an LLM to do question answering over graph databases.
|
||||
|
||||
- [How to map values to a database](/docs/how_to/graph_mapping)
|
||||
- [How to add a semantic layer over the database](/docs/how_to/graph_semantic)
|
||||
- [How to improve results with prompting](/docs/how_to/graph_prompting)
|
||||
- [How to construct knowledge graphs](/docs/how_to/graph_constructing)
|
||||
- [How to: map values to a database](/docs/how_to/graph_mapping)
|
||||
- [How to: add a semantic layer over the database](/docs/how_to/graph_semantic)
|
||||
- [How to: improve results with prompting](/docs/how_to/graph_prompting)
|
||||
- [How to: construct knowledge graphs](/docs/how_to/graph_constructing)
|
||||
|
||||
@ -5,7 +5,7 @@
|
||||
"id": "8c5eb99a",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# How to inspect your runnables\n",
|
||||
"# How to inspect runnables\n",
|
||||
"\n",
|
||||
":::info Prerequisites\n",
|
||||
"\n",
|
||||
|
||||
@ -5,7 +5,7 @@
|
||||
"id": "78b45321-7740-4399-b2ad-459811131de3",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# How to get log probabilities from model calls\n",
|
||||
"# How to get log probabilities\n",
|
||||
"\n",
|
||||
":::info Prerequisites\n",
|
||||
"\n",
|
||||
|
||||
@ -15,7 +15,7 @@
|
||||
"id": "bb7d49db-04d3-4399-bfe1-09f82bbe6015",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# How to stream\n",
|
||||
"# How to stream runnables\n",
|
||||
"\n",
|
||||
":::info Prerequisites\n",
|
||||
"\n",
|
||||
|
||||
Loading…
Reference in New Issue
Block a user