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docs: update how-to index page (#29393)
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@ -25,52 +25,10 @@ This highlights functionality that is core to using LangChain.
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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](/docs/concepts/lcel) is a way to create arbitrary custom chains. It is built on the [Runnable](https://python.langchain.com/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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## Components
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These are the core building blocks you can use when building applications.
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### Prompt templates
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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
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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
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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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@ -101,6 +59,26 @@ See [supported integrations](/docs/integrations/chat/) for details on getting st
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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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### Prompt templates
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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
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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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### LLMs
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What LangChain calls [LLMs](/docs/concepts/text_llms) are older forms of language models that take a string in and output a string.
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@ -329,6 +307,36 @@ large volumes of text. For a high-level tutorial, check out [this guide](/docs/t
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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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## LangChain Expression Language (LCEL)
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:::note Should I use LCEL?
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LCEL is an orchestration solution. See our
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[concepts page](/docs/concepts/lcel/#should-i-use-lcel) for recommendations on when to
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use LCEL.
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:::
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[LangChain Expression Language](/docs/concepts/lcel) is a way to create arbitrary custom chains. It is built on the [Runnable](https://python.langchain.com/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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## [LangGraph](https://langchain-ai.github.io/langgraph)
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LangGraph is an extension of LangChain aimed at
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