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40
docs/docs/concepts/architecture.mdx
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@ -3,7 +3,7 @@ import useBaseUrl from '@docusaurus/useBaseUrl';
# Architecture
LangChain as a framework consists of a number of packages.
LangChain is a framework that consists of a number of packages.
<ThemedImage
alt="Diagram outlining the hierarchical organization of the LangChain framework, displaying the interconnected parts across multiple layers."
@ -18,34 +18,34 @@ LangChain as a framework consists of a number of packages.
## langchain-core
This package contains base abstractions of different components and ways to compose them together.
The interfaces for core components like LLMs, vector stores, retrievers and more are defined here.
No third party integrations are defined here.
The dependencies are kept purposefully very lightweight.
This package contains base abstractions for different components and ways to compose them together.
The interfaces for core components like chat models, vector stores, tools and more are defined here.
No third-party integrations are defined here.
The dependencies are very lightweight.
## langchain
The main `langchain` package contains chains, agents, and retrieval strategies that make up an application's cognitive architecture.
These are NOT third party integrations.
The main `langchain` package contains chains and retrieval strategies that make up an application's cognitive architecture.
These are NOT third-party integrations.
All chains, agents, and retrieval strategies here are NOT specific to any one integration, but rather generic across all integrations.
## langchain-community
## Integration packages
This package contains third party integrations that are maintained by the LangChain community.
Key partner packages are separated out (see below).
This contains all integrations for various components (LLMs, vector stores, retrievers).
All dependencies in this package are optional to keep the package as lightweight as possible.
## Partner packages
While the long tail of integrations is in `langchain-community`, we split popular integrations into their own packages (e.g. `langchain-openai`, `langchain-anthropic`, etc). This was done in order to improve support for these important integrations.
Popular integrations have their own packages (e.g. `langchain-openai`, `langchain-anthropic`, etc) so that they can be properly versioned and appropriately lightweight.
For more information see:
* A list [LangChain integrations](/docs/integrations/providers/)
* The [LangChain API Reference](https://python.langchain.com/api_reference/) where you can find detailed information about the API reference of each partner package.
* A list [integrations packages](/docs/integrations/providers/)
* The [API Reference](https://python.langchain.com/api_reference/) where you can find detailed information about each of the integration package.
## LangGraph
## langchain-community
This package contains third-party integrations that are maintained by the LangChain community.
Key integration packages are separated out (see above).
This contains integrations for various components (chat models, vector stores, tools, etc).
All dependencies in this package are optional to keep the package as lightweight as possible.
## langgraph
`langgraph` is an extension of `langchain` aimed at building robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph.
@ -58,7 +58,7 @@ LangGraph exposes high level interfaces for creating common types of agents, as
:::
## LangServe
## langserve
A package to deploy LangChain chains as REST APIs. Makes it easy to get a production ready API up and running.

2
docs/docs/concepts/async.mdx
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@ -12,7 +12,7 @@ You are expected to be familiar with asynchronous programming in Python before r
This guide specifically focuses on what you need to know to work with LangChain in an asynchronous context, assuming that you are already familiar with asynch
:::
## Langchain asynchronous apis
## Langchain asynchronous APIs
Many LangChain APIs are designed to be asynchronous, allowing you to build efficient and responsive applications.

2
docs/docs/concepts/callbacks.mdx
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@ -4,7 +4,7 @@
- [Runnable interface](/docs/concepts/runnables)
:::
LangChain provides a callbacks system that allows you to hook into the various stages of your LLM application. This is useful for logging, monitoring, streaming, and other tasks.
LangChain provides a callback system that allows you to hook into the various stages of your LLM application. This is useful for logging, monitoring, streaming, and other tasks.
You can subscribe to these events by using the `callbacks` argument available throughout the API. This argument is list of handler objects, which are expected to implement one or more of the methods described below in more detail.

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docs/docs/concepts/chat_history.mdx
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@ -42,5 +42,5 @@ Understanding correct conversation structure is essential for being able to prop
## Related resources
- [How to trim messages](https://python.langchain.com/docs/how_to/trim_messages/)
- [How to trim messages](/docs/how_to/trim_messages/)
- [Memory guide](https://langchain-ai.github.io/langgraph/concepts/memory/) for information on implementing short-term and long-term memory in chat models using [LangGraph](https://langchain-ai.github.io/langgraph/).

2
docs/docs/concepts/chat_models.mdx
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@ -136,7 +136,7 @@ If you hit a rate limit, you will typically receive a rate limit error response
You have a few options to deal with rate limits:
1. Try to avoid hitting rate limits by spacing out requests: Chat models accept a `rate_limiter` parameter that can be provided during initialization. This parameter is used to control the rate at which requests are made to the model provider. Spacing out the requests to a given model is a particularly useful strategy when benchmarking models to evaluate their performance. Please see the [how to handle rate limits](https://python.langchain.com/docs/how_to/chat_model_rate_limiting/) for more information on how to use this feature.
1. Try to avoid hitting rate limits by spacing out requests: Chat models accept a `rate_limiter` parameter that can be provided during initialization. This parameter is used to control the rate at which requests are made to the model provider. Spacing out the requests to a given model is a particularly useful strategy when benchmarking models to evaluate their performance. Please see the [how to handle rate limits](/docs/how_to/chat_model_rate_limiting/) for more information on how to use this feature.
2. Try to recover from rate limit errors: If you receive a rate limit error, you can wait a certain amount of time before retrying the request. The amount of time to wait can be increased with each subsequent rate limit error. Chat models have a `max_retries` parameter that can be used to control the number of retries. See the [standard parameters](#standard-parameters) section for more information.
3. Fallback to another chat model: If you hit a rate limit with one chat model, you can switch to another chat model that is not rate-limited.

10
docs/docs/concepts/document_loaders.mdx
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@ -3,14 +3,14 @@
:::info[Prerequisites]
* [Document loaders API reference](https://python.langchain.com/docs/how_to/#document-loaders)
* [Document loaders API reference](/docs/how_to/#document-loaders)
:::
Document loaders are designed to load document objects. LangChain has hundreds of integrations with various data sources to load data from: Slack, Notion, Google Drive, etc.
## Integrations
You can find available integrations on the [Document Loaders Integrations page](https://python.langchain.com/docs/integrations/document_loaders/).
You can find available integrations on the [Document loaders integrations page](/docs/integrations/document_loaders/).
## Interface
@ -40,6 +40,6 @@ for document in loader.lazy_load():
Please see the following resources for more information:
* [How-to guides for document loaders](https://python.langchain.com/docs/how_to/#document-loaders)
* [Document API reference](https://python.langchain.com/docs/how_to/#document-loaders)
* [Document loaders integrations](https://python.langchain.com/docs/integrations/document_loaders/)
* [How-to guides for document loaders](/docs/how_to/#document-loaders)
* [Document API reference](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html)
* [Document loaders integrations](/docs/integrations/document_loaders/)

4
docs/docs/concepts/key_value_stores.mdx
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@ -21,8 +21,8 @@ The key-value store interface in LangChain is used primarily for:
Please see these how-to guides for more information:
* [How to cache embeddings guide](https://python.langchain.com/docs/how_to/caching_embeddings/).
* [How to retriever using multiple vectors per document](https://python.langchain.com/docs/how_to/custom_retriever/).
* [How to cache embeddings guide](/docs/how_to/caching_embeddings/).
* [How to retriever using multiple vectors per document](/docs/how_to/custom_retriever/).
## Interface

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docs/docs/concepts/lcel.mdx
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@ -11,7 +11,7 @@ This means that you describe what you want to happen, rather than how you want i
We often refer to a `Runnable` created using LCEL as a "chain". It's important to remember that a "chain" is `Runnable` and it implements the full [Runnable Interface](/docs/concepts/runnables).
:::note
* The [LCEL cheatsheet](https://python.langchain.com/docs/how_to/lcel_cheatsheet/) shows common patterns that involve the Runnable interface and LCEL expressions.
* The [LCEL cheatsheet](/docs/how_to/lcel_cheatsheet/) shows common patterns that involve the Runnable interface and LCEL expressions.
* Please see the following list of [how-to guides](/docs/how_to/#langchain-expression-language-lcel) that cover common tasks with LCEL.
* A list of built-in `Runnables` can be found in the [LangChain Core API Reference](https://python.langchain.com/api_reference/core/runnables.html). Many of these Runnables are useful when composing custom "chains" in LangChain using LCEL.
:::
@ -210,7 +210,7 @@ lambda x: x + 1.invoke(some_input)
```
:::
## Legacy Chains
## Legacy chains
LCEL aims to provide consistency around behavior and customization over legacy subclassed chains such as `LLMChain` and
`ConversationalRetrievalChain`. Many of these legacy chains hide important details like prompts, and as a wider variety

2
docs/docs/concepts/messages.mdx
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@ -30,7 +30,7 @@ Roles are used to distinguish between different types of messages in a conversat
| **user** | Represents input from a user interacting with the model, usually in the form of text or other interactive input. |
| **assistant** | Represents a response from the model, which can include text or a request to invoke tools. |
| **tool** | A message used to pass the results of a tool invocation back to the model after external data or processing has been retrieved. Used with chat models that support [tool calling](/docs/concepts/tool_calling). |
| **function (legacy)** | This is a legacy role, corresponding to OpenAI's legacy function-calling API. **tool** role should be used instead. |
| **function** (legacy) | This is a legacy role, corresponding to OpenAI's legacy function-calling API. **tool** role should be used instead. |
### Content

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docs/docs/concepts/multimodality.mdx
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@ -26,7 +26,7 @@ Multimodal support is still relatively new and less common, model providers have
#### Inputs
Some models can accept multimodal inputs, such as images, audio, video, or files. The types of multimodal inputs supported depend on the model provider. For instance, [Google's Gemini](https://python.langchain.com/docs/integrations/chat/google_generative_ai/) supports documents like PDFs as inputs.
Some models can accept multimodal inputs, such as images, audio, video, or files. The types of multimodal inputs supported depend on the model provider. For instance, [Google's Gemini](/docs/integrations/chat/google_generative_ai/) supports documents like PDFs as inputs.
Most chat models that support **multimodal inputs** also accept those values in OpenAI's content blocks format. So far this is restricted to image inputs. For models like Gemini which support video and other bytes input, the APIs also support the native, model-specific representations.
@ -53,7 +53,7 @@ integration documentation for the correct format. Find the integration in the [c
Virtually no popular chat models support multimodal outputs at the time of writing (October 2024).
The only exception is OpenAI's chat model ([gpt-4o-audio-preview](https://python.langchain.com/docs/integrations/chat/openai/)), which can generate audio outputs.
The only exception is OpenAI's chat model ([gpt-4o-audio-preview](/docs/integrations/chat/openai/)), which can generate audio outputs.
Multimodal outputs will appear as part of the [AIMessage](/docs/concepts/messages/#aimessage) response object.
@ -80,7 +80,7 @@ As use cases involving multimodal search and retrieval tasks become more common,
## Multimodality in vector stores
:::info Prerequisites
* [Vectorstores](/docs/concepts/vectorstores)
* [Vector stores](/docs/concepts/vectorstores)
:::
Vector stores are databases for storing and retrieving embeddings, which are typically used in search and retrieval tasks. Similar to embeddings, vector stores are currently optimized for text-based data.

2
docs/docs/concepts/retrieval.mdx
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@ -3,7 +3,7 @@
:::info[Prerequisites]
* [Retrievers](/docs/concepts/retrievers/)
* [Vectorstores](/docs/concepts/vectorstores/)
* [Vector stores](/docs/concepts/vectorstores/)
* [Embeddings](/docs/concepts/embedding_models/)
* [Text splitters](/docs/concepts/text_splitters/)

8
docs/docs/concepts/retrievers.mdx
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@ -4,7 +4,7 @@
:::info[Prerequisites]
* [Vectorstores](/docs/concepts/vectorstores/)
* [Vector stores](/docs/concepts/vectorstores/)
* [Embeddings](/docs/concepts/embedding_models/)
* [Text splitters](/docs/concepts/text_splitters/)
@ -58,7 +58,7 @@ Despite the flexibility of the retriever interface, a few common types of retrie
It's important to note that retrievers don't need to actually *store* documents.
For example, we can be built retrievers on top of search APIs that simply return search results!
See our retriever integrations with [Amazon Kendra](https://python.langchain.com/docs/integrations/retrievers/amazon_kendra_retriever/) or [Wikipedia Search](https://python.langchain.com/docs/integrations/retrievers/wikipedia/).
See our retriever integrations with [Amazon Kendra](/docs/integrations/retrievers/amazon_kendra_retriever/) or [Wikipedia Search](/docs/integrations/retrievers/wikipedia/).
### Relational or graph database
@ -87,9 +87,9 @@ LangChain has retrievers for many popular lexical search algorithms / engines.
:::
### Vectorstore
### Vector store
[Vectorstores](/docs/concepts/vectorstores/) are a powerful and efficient way to index and retrieve unstructured data.
[Vector stores](/docs/concepts/vectorstores/) are a powerful and efficient way to index and retrieve unstructured data.
An vectorstore can be used as a retriever by calling the `as_retriever()` method.
```python

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docs/docs/concepts/runnables.mdx
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@ -274,7 +274,7 @@ If using the `batch` or `batch_as_completed` methods, you can set the `max_concu
:::tip
If you're trying to rate limit the number of requests made by a **Chat Model**, you can use the built-in [rate limiter](/docs/concepts/chat_models#rate-limiting) instead of setting `max_concurrency`, which will be more effective.
See the [How to handle rate limits](https://python.langchain.com/docs/how_to/chat_model_rate_limiting/) guide for more information.
See the [How to handle rate limits](/docs/how_to/chat_model_rate_limiting/) guide for more information.
:::
### Setting configurable

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docs/docs/concepts/streaming.mdx
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@ -140,7 +140,7 @@ See [this guide](/docs/how_to/streaming/#using-stream-events) for more detailed
To write custom data to the stream, you will need to choose one of the following methods based on the component you are working with:
1. LangGraph's [StreamWriter](https://langchain-ai.github.io/langgraph/reference/types/#langgraph.types.StreamWriter) can be used to write custom data that will surface through **stream** and **astream** APIs when working with LangGraph. **Important** this is a LangGraph feature, so it is not available when working with pure LCEL. See [how to streaming custom data](https://langchain-ai.github.io/langgraph/how-tos/streaming-content/) for more information.
2. [dispatch_events](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.manager.dispatch_custom_event.html#) / [adispatch_events](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.manager.adispatch_custom_event.html) can be used to write custom data that will be surfaced through the **astream_events** API. See [how to dispatch custom callback events](https://python.langchain.com/docs/how_to/callbacks_custom_events/#astream-events-api) for more information.
2. [dispatch_events](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.manager.dispatch_custom_event.html#) / [adispatch_events](https://python.langchain.com/api_reference/core/callbacks/langchain_core.callbacks.manager.adispatch_custom_event.html) can be used to write custom data that will be surfaced through the **astream_events** API. See [how to dispatch custom callback events](/docs/how_to/callbacks_custom_events/#astream-events-api) for more information.
## "Auto-Streaming" Chat Models
@ -188,4 +188,4 @@ Please see the following how-to guides for specific examples of streaming in Lan
For writing custom data to the stream, please see the following resources:
* If using LangGraph, see [how to stream custom data](https://langchain-ai.github.io/langgraph/how-tos/streaming-content/).
* If using LCEL, see [how to dispatch custom callback events](https://python.langchain.com/docs/how_to/callbacks_custom_events/#astream-events-api).
* If using LCEL, see [how to dispatch custom callback events](/docs/how_to/callbacks_custom_events/#astream-events-api).

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docs/docs/concepts/tools.mdx
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@ -134,7 +134,7 @@ def user_specific_tool(input_data: str, user_id: InjectedToolArg) -> str:
Annotating the `user_id` argument with `InjectedToolArg` tells LangChain that this argument should not be exposed as part of the
tool's schema.
See [how to pass run time values to tools](https://python.langchain.com/docs/how_to/tool_runtime/) for more details on how to use `InjectedToolArg`.
See [how to pass run time values to tools](/docs/how_to/tool_runtime/) for more details on how to use `InjectedToolArg`.
### RunnableConfig
@ -203,8 +203,8 @@ tools = toolkit.get_tools()
See the following resources for more information:
- [API Reference for @tool](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.convert.tool.html)
- [How to create custom tools](https://python.langchain.com/docs/how_to/custom_tools/)
- [How to pass run time values to tools](https://python.langchain.com/docs/how_to/tool_runtime/)
- [How to create custom tools](/docs/how_to/custom_tools/)
- [How to pass run time values to tools](/docs/how_to/tool_runtime/)
- [All LangChain tool how-to guides](https://docs.langchain.com/docs/how_to/#tools)
- [Additional how-to guides that show usage with LangGraph](https://langchain-ai.github.io/langgraph/how-tos/tool-calling/)
- Tool integrations, see the [tool integration docs](https://docs.langchain.com/docs/integrations/tools/).

6
docs/docs/concepts/vectorstores.mdx
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@ -22,7 +22,7 @@ These vectors, called [embeddings](/docs/concepts/embedding_models/), capture th
Vector stores are frequently used to search over unstructured data, such as text, images, and audio, to retrieve relevant information based on semantic similarity rather than exact keyword matches.
![Vectorstores](/img/vectorstores.png)
![Vector stores](/img/vectorstores.png)
## Integrations
@ -97,7 +97,7 @@ vector_store.delete_documents(ids=["doc1"])
## Search
Vectorstores embed and store the documents that added.
Vector stores embed and store the documents that added.
If we pass in a query, the vectorstore will embed the query, perform a similarity search over the embedded documents, and return the most similar ones.
This captures two important concepts: first, there needs to be a way to measure the similarity between the query and *any* [embedded](/docs/concepts/embedding_models/) document.
Second, there needs to be an algorithm to efficiently perform this similarity search across *all* embedded documents.
@ -156,7 +156,7 @@ This allows structured filters to reduce the size of the similarity search space
1. **Semantic search**: Query the unstructured data directly, often using via embedding or keyword similarity.
2. **Metadata search**: Apply structured query to the metadata, filering specific documents.
Vectorstore support for metadata filtering is typically dependent on the underlying vector store implementation.
Vector store support for metadata filtering is typically dependent on the underlying vector store implementation.
Here is example usage with [Pinecone](/docs/integrations/vectorstores/pinecone/#query-directly), showing that we filter for all documents that have the metadata key `source` with value `tweet`.