diff --git a/docs/docs/concepts/index.mdx b/docs/docs/concepts/index.mdx index 29c8578761e..7064a5e0669 100644 --- a/docs/docs/concepts/index.mdx +++ b/docs/docs/concepts/index.mdx @@ -12,43 +12,40 @@ In this section, you'll find explanations of the key concepts, providing a deepe The conceptual guide will not cover step-by-step instructions or specific implementation details — those are found in the [How-To Guides](/docs/how_to/) and [Tutorials](/docs/tutorials) sections. For detailed reference material, please visit the [API Reference](https://python.langchain.com/api_reference/). -| Concept | Description | -|----------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------| -| [Runnable interface](/docs/concepts/runnables) | A standard interface for creating and invoking custom chains, with methods like invoke, stream, and batch, both sync and async. | -| [LangChain Expression Language (LCEL)](/docs/concepts/lcel) | A declarative way to chain LangChain components with features like streaming, async support, retries, and more. | -| [Chat models](/docs/concepts/chat_models) | Models that process sequences of messages as input and output, with support for roles like 'user', 'assistant', and 'system'. | -| [LLMs](/docs/concepts/llms) | Older or lower-level models that process plain text input and output plain text, often replaced by chat models. | -| [Messages](/docs/concepts/messages) | Different message types representing the roles and content of conversational exchanges in LangChain. | -| [Prompt templates](/docs/concepts/prompts) | Templates that help guide a model's response by formatting user inputs and parameters into prompts. | -| [Output parsers](/docs/concepts/output_parsers) | Components that transform model output into structured formats, useful for LLMs generating structured data. | -| Example selectors | Classes responsible for selecting and formatting examples into prompts to improve model performance. | -| Chat history | A class that stores and manages the history of inputs and outputs in a conversation, keeping track of previous interactions. | -| Documents | An object that contains the text and metadata associated with a piece of information in LangChain. | -| Document loaders | Classes responsible for loading document data from various external sources like Slack, Google Drive, or Notion. | -| [Text splitters](/docs/concepts/text_splitters) | Tools for splitting text into smaller, semantically meaningful chunks to fit into model context windows. | -| [Embedding models](/docs/concepts/embedding_models) | Create vector representations of text for similarity search and retrieval in natural language tasks. | -| [Vector stores](/docs/concepts/vectorstores) | Tools for storing and searching embedded data, allowing you to perform vector searches based on similarity. | -| [Retrievers](/docs/concepts/retrievers) | Interfaces that return relevant documents based on an unstructured query, more general than vector stores. | -| Key-value stores | Storage mechanism for key-value pairs, helpful for caching embeddings or storing multiple vectors per document. | -| Tools | Utilities designed to be invoked by models, allowing models to interact with code or external APIs. | -| Toolkits | Collections of tools designed for specific tasks, often with convenient loading methods. | -| Agents | Systems that use LLMs as reasoning engines to choose actions and determine inputs, iterating until task completion. | -| Callbacks | LangChain's system for logging and tracking various stages of execution, including models, chains, and tools. | -| Streaming | Allows consuming partial output as it is generated, helping to reduce latency in complex chains and agents. | -| [Function/tool calling](/docs/concepts/#function-tool-calling) | Allows a model to generate output arguments that invoke external tools or functions for more complex tasks. | -| Structured output | Constrains a model's output to a specific format, such as JSON, to improve the usability of generated responses. | -| [Few-shot prompting](/docs/concepts/#few-shot-prompting) | A prompting technique where example inputs and outputs are added to a model prompt to improve performance. | -| Retrieval | The process of providing relevant data to an LLM at query time to improve its response to the user. | -| Query Translation | Techniques for improving retrieval accuracy by refining or altering the original query. | -| Routing | Methods for routing queries to appropriate data sources based on content or context. | -| Query Construction | Techniques for transforming natural language queries into queries specific to the data source's format, such as SQL. | -| Indexing | Creating and storing documents for efficient search and retrieval, often by using embedding models and vector stores. | -| Post-processing | Techniques for filtering or ranking retrieved documents to improve the quality of results passed to the LLM. | -| Generation | Methods for self-correcting errors in responses, such as hallucinations, by iterating on the answer. | -| Text splitting | Mechanisms for splitting text into smaller chunks, either by character, sentence, or semantically related units. | -| Evaluation | The process of assessing the quality and performance of an LLM's responses in an application. | -| Tracing | A system for tracking and observing the sequence of operations in a LangChain application to help diagnose issues. | - +- [Runnable interface](/docs/concepts/runnables): A standard interface for creating and invoking custom chains, with methods like invoke, stream, and batch, both sync and async. +- [LangChain Expression Language (LCEL)](/docs/concepts/lcel): A declarative way to chain LangChain components with features like streaming, async support, retries, and more. +- [Chat models](/docs/concepts/chat_models): Models that process sequences of messages as input and output, with support for roles like 'user', 'assistant', and 'system'. +- [LLMs](/docs/concepts/llms): Older or lower-level models that process plain text input and output plain text, often replaced by chat models. +- [Messages](/docs/concepts/messages): Different message types representing the roles and content of conversational exchanges in LangChain. +- [Prompt templates](/docs/concepts/prompts): Templates that help guide a model's response by formatting user inputs and parameters into prompts. +- [Output parsers](/docs/concepts/output_parsers): Components that transform model output into structured formats, useful for LLMs generating structured data. +- Example selectors: Classes responsible for selecting and formatting examples into prompts to improve model performance. +- Chat history: A class that stores and manages the history of inputs and outputs in a conversation, keeping track of previous interactions. +- Documents: An object that contains the text and metadata associated with a piece of information in LangChain. +- Document loaders: Classes responsible for loading document data from various external sources like Slack, Google Drive, or Notion. +- [Text splitters](/docs/concepts/text_splitters): Tools for splitting text into smaller, semantically meaningful chunks to fit into model context windows. +- [Embedding models](/docs/concepts/embedding_models): Create vector representations of text for similarity search and retrieval in natural language tasks. +- [Vector stores](/docs/concepts/vectorstores): Tools for storing and searching embedded data, allowing you to perform vector searches based on similarity. +- [Retrievers](/docs/concepts/retrievers): Interfaces that return relevant documents based on an unstructured query, more general than vector stores. +- Key-value stores: Storage mechanism for key-value pairs, helpful for caching embeddings or storing multiple vectors per document. +- Tools: Utilities designed to be invoked by models, allowing models to interact with code or external APIs. +- Toolkits: Collections of tools designed for specific tasks, often with convenient loading methods. +- Agents: Systems that use LLMs as reasoning engines to choose actions and determine inputs, iterating until task completion. +- Callbacks: LangChain's system for logging and tracking various stages of execution, including models, chains, and tools. +- Streaming: Allows consuming partial output as it is generated, helping to reduce latency in complex chains and agents. +- [Function/tool calling](/docs/concepts/#function-tool-calling): Allows a model to generate output arguments that invoke external tools or functions for more complex tasks. +- Structured output: Constrains a model's output to a specific format, such as JSON, to improve the usability of generated responses. +- [Few-shot prompting](/docs/concepts/#few-shot-prompting): A prompting technique where example inputs and outputs are added to a model prompt to improve performance. +- Retrieval: The process of providing relevant data to an LLM at query time to improve its response to the user. +- Query Translation: Techniques for improving retrieval accuracy by refining or altering the original query. +- Routing: Methods for routing queries to appropriate data sources based on content or context. +- Query Construction: Techniques for transforming natural language queries into queries specific to the data source's format, such as SQL. +- Indexing: Creating and storing documents for efficient search and retrieval, often by using embedding models and vector stores. +- Post-processing: Techniques for filtering or ranking retrieved documents to improve the quality of results passed to the LLM. +- Generation: Methods for self-correcting errors in responses, such as hallucinations, by iterating on the answer. +- Text splitting: Mechanisms for splitting text into smaller chunks, either by character, sentence, or semantically related units. +- Evaluation: The process of assessing the quality and performance of an LLM's responses in an application. +- Tracing: A system for tracking and observing the sequence of operations in a LangChain application to help diagnose issues.