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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 the [Conceptual guide](/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 the [API Reference](https://python.langchain.com/v0.2/api_reference/). |
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- [How to: install LangChain packages](/docs/how_to/installation/) |
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- [How to: use LangChain with different Pydantic versions](/docs/how_to/pydantic_compatibility) |
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This highlights functionality that is core to using LangChain. |
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- [How to: return structured data from a model](/docs/how_to/structured_output/) |
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- [How to: use a 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](/docs/concepts/#langchain-expression-language-lcel) is a way to create arbitrary custom chains. It is built on the [Runnable](https://python.langchain.com/v0.2/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html) protocol. |
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[**LCEL cheatsheet**](/docs/how_to/lcel_cheatsheet/): For a quick overview of how to use the main LCEL primitives. |
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[**Migration guide**](/docs/versions/migrating_chains): For migrating legacy chain abstractions to LCEL. |
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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: add default invocation args to runnables](/docs/how_to/binding/) |
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- [How to: turn any function into a runnable](/docs/how_to/functions) |
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- [How to: pass through inputs from one chain step to the next](/docs/how_to/passthrough) |
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- [How to: configure runnable behavior at runtime](/docs/how_to/configure) |
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- [How to: add message history (memory) to a chain](/docs/how_to/message_history) |
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- [How to: route between sub-chains](/docs/how_to/routing) |
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- [How to: create a dynamic (self-constructing) chain](/docs/how_to/dynamic_chain/) |
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- [How to: inspect runnables](/docs/how_to/inspect) |
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- [How to: add fallbacks to a runnable](/docs/how_to/fallbacks) |
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- [How to: pass runtime secrets to a runnable](/docs/how_to/runnable_runtime_secrets) |
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These are the core building blocks you can use when building applications. |
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[Prompt Templates](/docs/concepts/#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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[Example Selectors](/docs/concepts/#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: select examples from LangSmith few-shot datasets](/docs/how_to/example_selectors_langsmith/) |
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[Chat Models](/docs/concepts/#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](/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: use chat model to call tools](/docs/how_to/tool_calling) |
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- [How to: stream tool calls](/docs/how_to/tool_streaming) |
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- [How to: handle rate limits](/docs/how_to/chat_model_rate_limiting) |
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- [How to: few shot prompt tool behavior](/docs/how_to/tools_few_shot) |
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- [How to: bind model-specific formatted tools](/docs/how_to/tools_model_specific) |
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- [How to: force a specific tool call](/docs/how_to/tool_choice) |
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- [How to: work with local models](/docs/how_to/local_llms) |
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- [How to: init any model in one line](/docs/how_to/chat_models_universal_init/) |
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[Messages](/docs/concepts/#messages) are the input and output of chat models. They have some `content` and a `role`, which describes the source of the message. |
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- [How to: trim messages](/docs/how_to/trim_messages/) |
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- [How to: filter messages](/docs/how_to/filter_messages/) |
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- [How to: merge consecutive messages of the same type](/docs/how_to/merge_message_runs/) |
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What LangChain calls [LLMs](/docs/concepts/#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 models](/docs/how_to/local_llms) |
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[Output Parsers](/docs/concepts/#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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[Document Loaders](/docs/concepts/#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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[Text Splitters](/docs/concepts/#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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[Embedding Models](/docs/concepts/#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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[Vector stores](/docs/concepts/#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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[Retrievers](/docs/concepts/#retrievers) are responsible for taking a query and returning relevant documents. |
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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: add similarity scores to retriever results](/docs/how_to/add_scores_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 mitigate the "lost in the middle" effect](/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 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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LangChain [Tools](/docs/concepts/#tools) contain a description of the tool (to pass to the language model) as well as the implementation of the function to call. Refer [here](/docs/integrations/tools/) for a list of pre-buit tools. |
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- [How to: create tools](/docs/how_to/custom_tools) |
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- [How to: use built-in tools and toolkits](/docs/how_to/tools_builtin) |
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- [How to: use chat models to call tools](/docs/how_to/tool_calling) |
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- [How to: pass tool outputs to chat models](/docs/how_to/tool_results_pass_to_model) |
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- [How to: pass run time values to tools](/docs/how_to/tool_runtime) |
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- [How to: add a human-in-the-loop for tools](/docs/how_to/tools_human) |
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- [How to: handle tool errors](/docs/how_to/tools_error) |
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- [How to: force models to call a tool](/docs/how_to/tool_choice) |
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- [How to: disable parallel tool calling](/docs/how_to/tool_calling_parallel) |
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- [How to: access the `RunnableConfig` from a tool](/docs/how_to/tool_configure) |
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- [How to: stream events from a tool](/docs/how_to/tool_stream_events) |
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- [How to: return artifacts from a tool](/docs/how_to/tool_artifacts/) |
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- [How to: convert Runnables to tools](/docs/how_to/convert_runnable_to_tool) |
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- [How to: add ad-hoc tool calling capability to models](/docs/how_to/tools_prompting) |
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- [How to: pass in runtime secrets](/docs/how_to/runnable_runtime_secrets) |
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- [How to: pass multimodal data directly to models](/docs/how_to/multimodal_inputs/) |
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- [How to: use multimodal prompts](/docs/how_to/multimodal_prompts/) |
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:::note |
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For in depth how-to guides for agents, please check out [LangGraph](https://langchain-ai.github.io/langgraph/) documentation. |
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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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[Callbacks](/docs/concepts/#callbacks) allow you to hook into the various stages of your LLM application's execution. |
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- [How to: pass in callbacks at runtime](/docs/how_to/callbacks_runtime) |
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- [How to: attach callbacks to a module](/docs/how_to/callbacks_attach) |
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- [How to: pass callbacks into a module constructor](/docs/how_to/callbacks_constructor) |
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- [How to: create custom callback handlers](/docs/how_to/custom_callbacks) |
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- [How to: use callbacks in async environments](/docs/how_to/callbacks_async) |
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- [How to: dispatch custom callback events](/docs/how_to/callbacks_custom_events) |
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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: create custom callback handlers](/docs/how_to/custom_callbacks) |
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- [How to: define a custom tool](/docs/how_to/custom_tools) |
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- [How to: dispatch custom callback events](/docs/how_to/callbacks_custom_events) |
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- [How to: save and load LangChain objects](/docs/how_to/serialization) |
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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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For a high-level tutorial on RAG, check out [this guide](/docs/tutorials/rag/). |
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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 is when you use LLMs to extract structured information from unstructured text. |
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For a high level tutorial on extraction, check out [this guide](/docs/tutorials/extraction/). |
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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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Chatbots involve using an LLM to have a conversation. |
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For a high-level tutorial on building chatbots, check out [this guide](/docs/tutorials/chatbot/). |
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- [How to: manage memory](/docs/how_to/chatbots_memory) |
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- [How to: do retrieval](/docs/how_to/chatbots_retrieval) |
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- [How to: use tools](/docs/how_to/chatbots_tools) |
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- [How to: manage large chat history](/docs/how_to/trim_messages/) |
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Query Analysis is the task of using an LLM to generate a query to send to a retriever. |
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For a high-level tutorial on query analysis, check out [this guide](/docs/tutorials/query_analysis/). |
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- [How to: add examples to the prompt](/docs/how_to/query_few_shot) |
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- [How to: handle cases where no queries are generated](/docs/how_to/query_no_queries) |
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- [How to: handle multiple queries](/docs/how_to/query_multiple_queries) |
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- [How to: handle multiple retrievers](/docs/how_to/query_multiple_retrievers) |
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- [How to: construct filters](/docs/how_to/query_constructing_filters) |
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- [How to: deal with high cardinality categorical variables](/docs/how_to/query_high_cardinality) |
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You can use LLMs to do question answering over tabular data. |
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For a high-level tutorial, check out [this guide](/docs/tutorials/sql_qa/). |
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- [How to: use prompting to improve results](/docs/how_to/sql_prompting) |
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- [How to: do query validation](/docs/how_to/sql_query_checking) |
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- [How to: deal with large databases](/docs/how_to/sql_large_db) |
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- [How to: deal with CSV files](/docs/how_to/sql_csv) |
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You can use an LLM to do question answering over graph databases. |
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For a high-level tutorial, check out [this guide](/docs/tutorials/graph/). |
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- [How to: map values to a database](/docs/how_to/graph_mapping) |
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- [How to: add a semantic layer over the database](/docs/how_to/graph_semantic) |
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- [How to: improve results with prompting](/docs/how_to/graph_prompting) |
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- [How to: construct knowledge graphs](/docs/how_to/graph_constructing) |
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LLMs can summarize and otherwise distill desired information from text, including |
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large volumes of text. For a high-level tutorial, check out [this guide](/docs/tutorials/summarization). |
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- [How to: summarize text in a single LLM call](/docs/how_to/summarize_stuff) |
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- [How to: summarize text through parallelization](/docs/how_to/summarize_map_reduce) |
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- [How to: summarize text through iterative refinement](/docs/how_to/summarize_refine) |
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LangGraph is an extension of LangChain aimed at |
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building robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph. |
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LangGraph documentation is currently hosted on a separate site. |
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You can peruse [LangGraph how-to guides here](https://langchain-ai.github.io/langgraph/how-tos/). |
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LangSmith allows you to closely trace, monitor and evaluate your LLM application. |
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It seamlessly integrates with LangChain and LangGraph, and you can use it to inspect and debug individual steps of your chains and agents as you build. |
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LangSmith documentation is hosted on a separate site. |
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You can peruse [LangSmith how-to guides here](https://docs.smith.langchain.com/how_to_guides/), but we'll highlight a few sections that are particularly |
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relevant to LangChain below: |
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<span data-heading-keywords="evaluation,evaluate"></span> |
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Evaluating performance is a vital part of building LLM-powered applications. |
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LangSmith helps with every step of the process from creating a dataset to defining metrics to running evaluators. |
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To learn more, check out the [LangSmith evaluation how-to guides](https://docs.smith.langchain.com/how_to_guides#evaluation). |
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<span data-heading-keywords="trace,tracing"></span> |
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Tracing gives you observability inside your chains and agents, and is vital in diagnosing issues. |
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- [How to: trace with LangChain](https://docs.smith.langchain.com/how_to_guides/tracing/trace_with_langchain) |
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- [How to: add metadata and tags to traces](https://docs.smith.langchain.com/how_to_guides/tracing/trace_with_langchain#add-metadata-and-tags-to-traces) |
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You can see general tracing-related how-tos [in this section of the LangSmith docs](https://docs.smith.langchain.com/how_to_guides/tracing). |
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