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docs/docs/concepts/callbacks.mdx
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@ -1,7 +1,7 @@
# Callbacks
:::note Prerequisites
- [Runnable interface](/docs/concepts/#runnable-interface)
- [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.

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docs/docs/concepts/few_shot_prompting.mdx
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@ -70,7 +70,7 @@ Most state-of-the-art models these days are chat models, so we'll focus on forma
If we insert our examples into the system prompt as a string, we'll need to make sure it's clear to the model where each example begins and which parts are the input versus output. Different models respond better to different syntaxes, like [ChatML](https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/chat-markup-language), XML, TypeScript, etc.
If we insert our examples as messages, where each example is represented as a sequence of Human, AI messages, we might want to also assign [names](/docs/concepts/#messages) to our messages like `"example_user"` and `"example_assistant"` to make it clear that these messages correspond to different actors than the latest input message.
If we insert our examples as messages, where each example is represented as a sequence of Human, AI messages, we might want to also assign [names](/docs/concepts/messages) to our messages like `"example_user"` and `"example_assistant"` to make it clear that these messages correspond to different actors than the latest input message.
**Formatting tool call examples**

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docs/docs/concepts/lcel.mdx
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@ -22,7 +22,7 @@ LangChain optimizes the run-time execution of chains built with LCEL in a number
- **Optimize parallel execution**: Run Runnables in parallel using [RunnableParallel](#RunnableParallel) or run multiple inputs through a given chain in parallel using the [Runnable Batch API](/docs/concepts/runnables#batch). Parallel execution can significantly reduce the latency as processing can be done in parallel instead of sequentially.
- **Guarantee Async support**: Any chain built with LCEL can be run asynchronously using the [Runnable Async API](/docs/concepts/runnables#async-api). This can be useful when running chains in a server environment where you want to handle large number of requests concurrently.
- **Simplify streaming**: LCEL chains can be streamed, allowing for incremental output as the chain is executed. LangChain can optimize the streaming of the output to minimize the time-to-first-token(time elapsed until the first chunk of output from a [chat model](/docs/concepts/chat_models) or [llm](/docs/concepts/llms) comes out).
- **Simplify streaming**: LCEL chains can be streamed, allowing for incremental output as the chain is executed. LangChain can optimize the streaming of the output to minimize the time-to-first-token(time elapsed until the first chunk of output from a [chat model](/docs/concepts/chat_models) or [llm](/docs/concepts/text_llms) comes out).
Other benefits include:

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docs/docs/concepts/llms.mdx
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@ -1,3 +0,0 @@
# Large language models (llms)
Please see the [Chat Model Concept Guide](/docs/concepts/chat_models) page for more information.

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docs/docs/concepts/output_parsers.mdx
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@ -7,7 +7,7 @@
The information here refers to parsers that take a text output from a model try to parse it into a more structured representation.
More and more models are supporting function (or tool) calling, which handles this automatically.
It is recommended to use function/tool calling rather than output parsing.
See documentation for that [here](/docs/concepts/#function-tool-calling).
See documentation for that [here](/docs/concepts/tool_calling).
:::

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docs/docs/concepts/streaming.mdx
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@ -106,7 +106,7 @@ While this API is available for use with [LangGraph](/docs/concepts/architecture
For chains constructed using **LCEL**, the `.stream()` method only streams the output of the final step from te chain. This might be sufficient for some applications, but as you build more complex chains of several LLM calls together, you may want to use the intermediate values of the chain alongside the final output. For example, you may want to return sources alongside the final generation when building a chat-over-documents app.
There are ways to do this [using callbacks](/docs/concepts/#callbacks-1), or by constructing your chain in such a way that it passes intermediate
There are ways to do this [using callbacks](/docs/concepts/callbacks), or by constructing your chain in such a way that it passes intermediate
values to the end with something like chained [`.assign()`](/docs/how_to/passthrough/) calls, but LangChain also includes an
`.astream_events()` method that combines the flexibility of callbacks with the ergonomics of `.stream()`. When called, it returns an iterator
which yields [various types of events](/docs/how_to/streaming/#event-reference) that you can filter and process according
@ -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](https://python.langchain.com/docs/how_to/callbacks_custom_events/#astream-events-api).

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docs/docs/concepts/text_llms.mdx Normal file
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@ -0,0 +1,10 @@
# String-in, string-out llms
:::tip
You are probably looking for the [Chat Model Concept Guide](/docs/concepts/chat_models) page for more information.
:::
LangChain has implementations for older language models that take a string as input and return a string as output. These models are typically named without the "Chat" prefix (e.g., `Ollama`, `Anthropic`, `OpenAI`, etc.), and may include the "LLM" suffix (e.g., `OllamaLLM`, `AnthropicLLM`, `OpenAILLM`, etc.). These models implement the [BaseLLM](https://python.langchain.com/api_reference/core/language_models/langchain_core.language_models.llms.BaseLLM.html#langchain_core.language_models.llms.BaseLLM) interface.
Users should be using almost exclusively the newer [Chat Models](/docs/concepts/chat_models) as most
model providers have adopted a chat like interface for interacting with language models.

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docs/docs/concepts/tool_calling.mdx
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@ -141,7 +141,7 @@ For more details on usage, see our [how-to guides](/docs/how_to/#tools)!
When designing [tools](/docs/concepts/tools/) to be used by a model, it is important to keep in mind that:
* Models that have explicit [tool-calling APIs](/docs/concepts/#functiontool-calling) will be better at tool calling than non-fine-tuned models.
* Models that have explicit [tool-calling APIs](/docs/concepts/tool_calling) will be better at tool calling than non-fine-tuned models.
* Models will perform better if the tools have well-chosen names and descriptions.
* Simple, narrowly scoped tools are easier for models to use than complex tools.
* Asking the model to select from a large list of tools poses challenges for the model.

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docs/docs/contributing/documentation/style_guide.mdx
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@ -92,8 +92,8 @@ To quote the Diataxis website:
Some examples include:
- [Retrieval conceptual docs](/docs/concepts/#retrieval)
- [Chat model conceptual docs](/docs/concepts/#chat-models)
- [Retrieval conceptual docs](/docs/concepts/retrieval)
- [Chat model conceptual docs](/docs/concepts/chat_models)
Here are some high-level tips on writing a good conceptual guide:

2
docs/docs/how_to/add_scores_retriever.ipynb
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@ -75,7 +75,7 @@
"\n",
"To obtain scores from a vector store retriever, we wrap the underlying vector store's `.similarity_search_with_score` method in a short function that packages scores into the associated document's metadata.\n",
"\n",
"We add a `@chain` decorator to the function to create a [Runnable](/docs/concepts/#langchain-expression-language) that can be used similarly to a typical retriever."
"We add a `@chain` decorator to the function to create a [Runnable](/docs/concepts/lcel) that can be used similarly to a typical retriever."
]
},
{

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docs/docs/how_to/agent_executor.ipynb
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@ -31,10 +31,10 @@
"## Concepts\n",
"\n",
"Concepts we will cover are:\n",
"- Using [language models](/docs/concepts/#chat-models), in particular their tool calling ability\n",
"- Creating a [Retriever](/docs/concepts/#retrievers) to expose specific information to our agent\n",
"- Using a Search [Tool](/docs/concepts/#tools) to look up things online\n",
"- [`Chat History`](/docs/concepts/#chat-history), which allows a chatbot to \"remember\" past interactions and take them into account when responding to follow-up questions. \n",
"- Using [language models](/docs/concepts/chat_models), in particular their tool calling ability\n",
"- Creating a [Retriever](/docs/concepts/retrievers) to expose specific information to our agent\n",
"- Using a Search [Tool](/docs/concepts/tools) to look up things online\n",
"- [`Chat History`](/docs/concepts/chat_history), which allows a chatbot to \"remember\" past interactions and take them into account when responding to follow-up questions. \n",
"- Debugging and tracing your application using [LangSmith](/docs/concepts/#langsmith)\n",
"\n",
"## Setup\n",
@ -415,7 +415,7 @@
"source": [
"## Create the agent\n",
"\n",
"Now that we have defined the tools and the LLM, we can create the agent. We will be using a tool calling agent - for more information on this type of agent, as well as other options, see [this guide](/docs/concepts/#agent_types/).\n",
"Now that we have defined the tools and the LLM, we can create the agent. We will be using a tool calling agent - for more information on this type of agent, as well as other options, see [this guide](/docs/concepts/agents/).\n",
"\n",
"We can first choose the prompt we want to use to guide the agent.\n",
"\n",
@ -457,7 +457,7 @@
"id": "f8014c9d",
"metadata": {},
"source": [
"Now, we can initialize the agent with the LLM, the prompt, and the tools. The agent is responsible for taking in input and deciding what actions to take. Crucially, the Agent does not execute those actions - that is done by the AgentExecutor (next step). For more information about how to think about these components, see our [conceptual guide](/docs/concepts/#agents).\n",
"Now, we can initialize the agent with the LLM, the prompt, and the tools. The agent is responsible for taking in input and deciding what actions to take. Crucially, the Agent does not execute those actions - that is done by the AgentExecutor (next step). For more information about how to think about these components, see our [conceptual guide](/docs/concepts/agents).\n",
"\n",
"Note that we are passing in the `model`, not `model_with_tools`. That is because `create_tool_calling_agent` will call `.bind_tools` for us under the hood."
]

4
docs/docs/how_to/assign.ipynb
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@ -19,7 +19,7 @@
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following concepts:\n",
"- [LangChain Expression Language (LCEL)](/docs/concepts/#langchain-expression-language)\n",
"- [LangChain Expression Language (LCEL)](/docs/concepts/lcel)\n",
"- [Chaining runnables](/docs/how_to/sequence/)\n",
"- [Calling runnables in parallel](/docs/how_to/parallel/)\n",
"- [Custom functions](/docs/how_to/functions/)\n",
@ -29,7 +29,7 @@
"\n",
"An alternate way of [passing data through](/docs/how_to/passthrough) steps of a chain is to leave the current values of the chain state unchanged while assigning a new value under a given key. The [`RunnablePassthrough.assign()`](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.passthrough.RunnablePassthrough.html#langchain_core.runnables.passthrough.RunnablePassthrough.assign) static method takes an input value and adds the extra arguments passed to the assign function.\n",
"\n",
"This is useful in the common [LangChain Expression Language](/docs/concepts/#langchain-expression-language) pattern of additively creating a dictionary to use as input to a later step.\n",
"This is useful in the common [LangChain Expression Language](/docs/concepts/lcel) pattern of additively creating a dictionary to use as input to a later step.\n",
"\n",
"Here's an example:"
]

2
docs/docs/how_to/binding.ipynb
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@ -21,7 +21,7 @@
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following concepts:\n",
"- [LangChain Expression Language (LCEL)](/docs/concepts/#langchain-expression-language)\n",
"- [LangChain Expression Language (LCEL)](/docs/concepts/lcel)\n",
"- [Chaining runnables](/docs/how_to/sequence/)\n",
"- [Tool calling](/docs/how_to/tool_calling)\n",
"\n",

2
docs/docs/how_to/callbacks_async.ipynb
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@ -10,7 +10,7 @@
"\n",
"This guide assumes familiarity with the following concepts:\n",
"\n",
"- [Callbacks](/docs/concepts/#callbacks)\n",
"- [Callbacks](/docs/concepts/callbacks)\n",
"- [Custom callback handlers](/docs/how_to/custom_callbacks)\n",
":::\n",
"\n",

2
docs/docs/how_to/callbacks_attach.ipynb
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@ -10,7 +10,7 @@
"\n",
"This guide assumes familiarity with the following concepts:\n",
"\n",
"- [Callbacks](/docs/concepts/#callbacks)\n",
"- [Callbacks](/docs/concepts/callbacks)\n",
"- [Custom callback handlers](/docs/how_to/custom_callbacks)\n",
"- [Chaining runnables](/docs/how_to/sequence)\n",
"- [Attach runtime arguments to a Runnable](/docs/how_to/binding)\n",

2
docs/docs/how_to/callbacks_constructor.ipynb
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@ -10,7 +10,7 @@
"\n",
"This guide assumes familiarity with the following concepts:\n",
"\n",
"- [Callbacks](/docs/concepts/#callbacks)\n",
"- [Callbacks](/docs/concepts/callbacks)\n",
"- [Custom callback handlers](/docs/how_to/custom_callbacks)\n",
"\n",
":::\n",

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docs/docs/how_to/callbacks_custom_events.ipynb
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@ -10,13 +10,13 @@
"\n",
"This guide assumes familiarity with the following concepts:\n",
"\n",
"- [Callbacks](/docs/concepts/#callbacks)\n",
"- [Callbacks](/docs/concepts/callbacks)\n",
"- [Custom callback handlers](/docs/how_to/custom_callbacks)\n",
"- [Astream Events API](/docs/concepts/#astream_events) the `astream_events` method will surface custom callback events.\n",
"- [Astream Events API](/docs/concepts/streaming/#astream_events) the `astream_events` method will surface custom callback events.\n",
":::\n",
"\n",
"In some situations, you may want to dipsatch a custom callback event from within a [Runnable](/docs/concepts/#runnable-interface) so it can be surfaced\n",
"in a custom callback handler or via the [Astream Events API](/docs/concepts/#astream_events).\n",
"In some situations, you may want to dipsatch a custom callback event from within a [Runnable](/docs/concepts/runnables) so it can be surfaced\n",
"in a custom callback handler or via the [Astream Events API](/docs/concepts/streaming/#astream_events).\n",
"\n",
"For example, if you have a long running tool with multiple steps, you can dispatch custom events between the steps and use these custom events to monitor progress.\n",
"You could also surface these custom events to an end user of your application to show them how the current task is progressing.\n",
@ -64,7 +64,7 @@
"source": [
"## Astream Events API\n",
"\n",
"The most useful way to consume custom events is via the [Astream Events API](/docs/concepts/#astream_events).\n",
"The most useful way to consume custom events is via the [Astream Events API](/docs/concepts/streaming/#astream_events).\n",
"\n",
"We can use the `async` `adispatch_custom_event` API to emit custom events in an async setting. \n",
"\n",

2
docs/docs/how_to/callbacks_runtime.ipynb
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@ -10,7 +10,7 @@
"\n",
"This guide assumes familiarity with the following concepts:\n",
"\n",
"- [Callbacks](/docs/concepts/#callbacks)\n",
"- [Callbacks](/docs/concepts/callbacks)\n",
"- [Custom callback handlers](/docs/how_to/custom_callbacks)\n",
"\n",
":::\n",

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docs/docs/how_to/chat_model_caching.ipynb
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@ -10,8 +10,8 @@
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following concepts:\n",
"- [Chat models](/docs/concepts/#chat-models)\n",
"- [LLMs](/docs/concepts/#llms)\n",
"- [Chat models](/docs/concepts/chat_models)\n",
"- [LLMs](/docs/concepts/text_llms)\n",
"\n",
":::\n",
"\n",

4
docs/docs/how_to/chat_model_rate_limiting.ipynb
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@ -10,8 +10,8 @@
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following concepts:\n",
"- [Chat models](/docs/concepts/#chat-models)\n",
"- [LLMs](/docs/concepts/#llms)\n",
"- [Chat models](/docs/concepts/chat_models)\n",
"- [LLMs](/docs/concepts/text_llms)\n",
":::\n",
"\n",
"\n",

2
docs/docs/how_to/chat_token_usage_tracking.ipynb
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@ -10,7 +10,7 @@
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following concepts:\n",
"- [Chat models](/docs/concepts/#chat-models)\n",
"- [Chat models](/docs/concepts/chat_models)\n",
"\n",
":::\n",
"\n",

4
docs/docs/how_to/chatbots_tools.ipynb
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@ -10,9 +10,9 @@
"\n",
"This guide assumes familiarity with the following concepts:\n",
"\n",
"- [Chatbots](/docs/concepts/#messages)\n",
"- [Chatbots](/docs/concepts/messages)\n",
"- [Agents](/docs/tutorials/agents)\n",
"- [Chat history](/docs/concepts/#chat-history)\n",
"- [Chat history](/docs/concepts/chat_history)\n",
"\n",
":::\n",
"\n",

2
docs/docs/how_to/configure.ipynb
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@ -21,7 +21,7 @@
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following concepts:\n",
"- [LangChain Expression Language (LCEL)](/docs/concepts/#langchain-expression-language)\n",
"- [LangChain Expression Language (LCEL)](/docs/concepts/lcel)\n",
"- [Chaining runnables](/docs/how_to/sequence/)\n",
"- [Binding runtime arguments](/docs/how_to/binding/)\n",
"\n",

6
docs/docs/how_to/convert_runnable_to_tool.ipynb
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@ -42,7 +42,7 @@
"source": [
"LangChain [tools](/docs/concepts#tools) are interfaces that an agent, chain, or chat model can use to interact with the world. See [here](/docs/how_to/#tools) for how-to guides covering tool-calling, built-in tools, custom tools, and more information.\n",
"\n",
"LangChain tools-- instances of [BaseTool](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.BaseTool.html)-- are [Runnables](/docs/concepts/#runnable-interface) with additional constraints that enable them to be invoked effectively by language models:\n",
"LangChain tools-- instances of [BaseTool](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.BaseTool.html)-- are [Runnables](/docs/concepts/runnables) with additional constraints that enable them to be invoked effectively by language models:\n",
"\n",
"- Their inputs are constrained to be serializable, specifically strings and Python `dict` objects;\n",
"- They contain names and descriptions indicating how and when they should be used;\n",
@ -259,9 +259,9 @@
"source": [
"## In agents\n",
"\n",
"Below we will incorporate LangChain Runnables as tools in an [agent](/docs/concepts/#agents) application. We will demonstrate with:\n",
"Below we will incorporate LangChain Runnables as tools in an [agent](/docs/concepts/agents) application. We will demonstrate with:\n",
"\n",
"- a document [retriever](/docs/concepts/#retrievers);\n",
"- a document [retriever](/docs/concepts/retrievers);\n",
"- a simple [RAG](/docs/tutorials/rag/) chain, allowing an agent to delegate relevant queries to it.\n",
"\n",
"We first instantiate a chat model that supports [tool calling](/docs/how_to/tool_calling/):\n",

2
docs/docs/how_to/custom_callbacks.ipynb
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@ -10,7 +10,7 @@
"\n",
"This guide assumes familiarity with the following concepts:\n",
"\n",
"- [Callbacks](/docs/concepts/#callbacks)\n",
"- [Callbacks](/docs/concepts/callbacks)\n",
"\n",
":::\n",
"\n",

4
docs/docs/how_to/custom_chat_model.ipynb
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@ -10,7 +10,7 @@
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following concepts:\n",
"- [Chat models](/docs/concepts/#chat-models)\n",
"- [Chat models](/docs/concepts/chat_models)\n",
"\n",
":::\n",
"\n",
@ -28,7 +28,7 @@
"\n",
"Chat models take messages as inputs and return a message as output. \n",
"\n",
"LangChain has a few [built-in message types](/docs/concepts/#message-types):\n",
"LangChain has a few [built-in message types](/docs/concepts/messages):\n",
"\n",
"| Message Type | Description |\n",
"|-----------------------|-------------------------------------------------------------------------------------------------|\n",

6
docs/docs/how_to/document_loader_pdf.ipynb
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@ -50,7 +50,7 @@
"\n",
"If you are looking for a simple string representation of text that is embedded in a PDF, the method below is appropriate. It will return a list of [Document](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html) objects-- one per page-- containing a single string of the page's text in the Document's `page_content` attribute. It will not parse text in images or scanned PDF pages. Under the hood it uses the [pypydf](https://pypdf.readthedocs.io/en/stable/) Python library.\n",
"\n",
"LangChain [document loaders](/docs/concepts/#document-loaders) implement `lazy_load` and its async variant, `alazy_load`, which return iterators of `Document` objects. We will use these below."
"LangChain [document loaders](/docs/concepts/document_loaders) implement `lazy_load` and its async variant, `alazy_load`, which return iterators of `Document` objects. We will use these below."
]
},
{
@ -147,7 +147,7 @@
"\n",
"### Vector search over PDFs\n",
"\n",
"Once we have loaded PDFs into LangChain `Document` objects, we can index them (e.g., a RAG application) in the usual way. Below we use OpenAI embeddings, although any LangChain [embeddings](https://python.langchain.com/docs/concepts/#embedding-models) model will suffice."
"Once we have loaded PDFs into LangChain `Document` objects, we can index them (e.g., a RAG application) in the usual way. Below we use OpenAI embeddings, although any LangChain [embeddings](https://python.langchain.com/docs/concepts/embedding_models) model will suffice."
]
},
{
@ -804,7 +804,7 @@
"\n",
"Many modern LLMs support inference over multimodal inputs (e.g., images). In some applications-- such as question-answering over PDFs with complex layouts, diagrams, or scans-- it may be advantageous to skip the PDF parsing, instead casting a PDF page to an image and passing it to a model directly. This allows a model to reason over the two dimensional content on the page, instead of a \"one-dimensional\" string representation.\n",
"\n",
"In principle we can use any LangChain [chat model](/docs/concepts/#chat-models) that supports multimodal inputs. A list of these models is documented [here](/docs/integrations/chat/). Below we use OpenAI's `gpt-4o-mini`.\n",
"In principle we can use any LangChain [chat model](/docs/concepts/chat_models) that supports multimodal inputs. A list of these models is documented [here](/docs/integrations/chat/). Below we use OpenAI's `gpt-4o-mini`.\n",
"\n",
"First we define a short utility function to convert a PDF page to a base64-encoded image:"
]

2
docs/docs/how_to/document_loader_web.ipynb
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@ -389,7 +389,7 @@
"source": [
"### Vector search over page content\n",
"\n",
"Once we have loaded the page contents into LangChain `Document` objects, we can index them (e.g., for a RAG application) in the usual way. Below we use OpenAI [embeddings](/docs/concepts/#embedding-models), although any LangChain embeddings model will suffice."
"Once we have loaded the page contents into LangChain `Document` objects, we can index them (e.g., for a RAG application) in the usual way. Below we use OpenAI [embeddings](/docs/concepts/embedding_models), although any LangChain embeddings model will suffice."
]
},
{

2
docs/docs/how_to/dynamic_chain.ipynb
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@ -10,7 +10,7 @@
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following:\n",
"- [LangChain Expression Language (LCEL)](/docs/concepts/#langchain-expression-language)\n",
"- [LangChain Expression Language (LCEL)](/docs/concepts/lcel)\n",
"- [How to turn any function into a runnable](/docs/how_to/functions)\n",
"\n",
":::\n",

4
docs/docs/how_to/example_selectors_langsmith.ipynb
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@ -11,8 +11,8 @@
"import Compatibility from \"@theme/Compatibility\";\n",
"\n",
"<Prerequisites titlesAndLinks={[\n",
" [\"Chat models\", \"/docs/concepts/#chat-models\"],\n",
" [\"Few-shot-prompting\", \"/docs/concepts/#few-shot-prompting\"],\n",
" [\"Chat models\", \"/docs/concepts/chat_models\"],\n",
" [\"Few-shot-prompting\", \"/docs/concepts/few-shot-prompting\"],\n",
" [\"LangSmith\", \"/docs/concepts/#langsmith\"],\n",
"]} />\n",
"\n",

6
docs/docs/how_to/few_shot_examples.ipynb
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@ -20,9 +20,9 @@
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following concepts:\n",
"- [Prompt templates](/docs/concepts/#prompt-templates)\n",
"- [Example selectors](/docs/concepts/#example-selectors)\n",
"- [LLMs](/docs/concepts/#llms)\n",
"- [Prompt templates](/docs/concepts/prompt_templates)\n",
"- [Example selectors](/docs/concepts/example_selectors)\n",
"- [LLMs](/docs/concepts/text_llms)\n",
"- [Vectorstores](/docs/concepts/#vector-stores)\n",
"\n",
":::\n",

8
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@ -20,9 +20,9 @@
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following concepts:\n",
"- [Prompt templates](/docs/concepts/#prompt-templates)\n",
"- [Example selectors](/docs/concepts/#example-selectors)\n",
"- [Chat models](/docs/concepts/#chat-model)\n",
"- [Prompt templates](/docs/concepts/prompt_templates)\n",
"- [Example selectors](/docs/concepts/example_selectors)\n",
"- [Chat models](/docs/concepts/chat_models)\n",
"- [Vectorstores](/docs/concepts/#vector-stores)\n",
"\n",
":::\n",
@ -33,7 +33,7 @@
"\n",
"The goal of few-shot prompt templates are to dynamically select examples based on an input, and then format the examples in a final prompt to provide for the model.\n",
"\n",
"**Note:** The following code examples are for chat models only, since `FewShotChatMessagePromptTemplates` are designed to output formatted [chat messages](/docs/concepts/#message-types) rather than pure strings. For similar few-shot prompt examples for pure string templates compatible with completion models (LLMs), see the [few-shot prompt templates](/docs/how_to/few_shot_examples/) guide."
"**Note:** The following code examples are for chat models only, since `FewShotChatMessagePromptTemplates` are designed to output formatted [chat messages](/docs/concepts/messages) rather than pure strings. For similar few-shot prompt examples for pure string templates compatible with completion models (LLMs), see the [few-shot prompt templates](/docs/how_to/few_shot_examples/) guide."
]
},
{

2
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@ -21,7 +21,7 @@
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following concepts:\n",
"- [LangChain Expression Language (LCEL)](/docs/concepts/#langchain-expression-language)\n",
"- [LangChain Expression Language (LCEL)](/docs/concepts/lcel)\n",
"- [Chaining runnables](/docs/how_to/sequence/)\n",
"\n",
":::\n",

26
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@ -27,7 +27,7 @@ This highlights functionality that is core to using LangChain.
## LangChain Expression Language (LCEL)
[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/api_reference/core/runnables/langchain_core.runnables.base.Runnable.html) protocol.
[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.
[**LCEL cheatsheet**](/docs/how_to/lcel_cheatsheet/): For a quick overview of how to use the main LCEL primitives.
@ -53,7 +53,7 @@ These are the core building blocks you can use when building applications.
### Prompt templates
[Prompt Templates](/docs/concepts/#prompt-templates) are responsible for formatting user input into a format that can be passed to a language model.
[Prompt Templates](/docs/concepts/prompt_templates) are responsible for formatting user input into a format that can be passed to a language model.
- [How to: use few shot examples](/docs/how_to/few_shot_examples)
- [How to: use few shot examples in chat models](/docs/how_to/few_shot_examples_chat/)
@ -62,7 +62,7 @@ These are the core building blocks you can use when building applications.
### Example selectors
[Example Selectors](/docs/concepts/#example-selectors) are responsible for selecting the correct few shot examples to pass to the prompt.
[Example Selectors](/docs/concepts/example_selectors) are responsible for selecting the correct few shot examples to pass to the prompt.
- [How to: use example selectors](/docs/how_to/example_selectors)
- [How to: select examples by length](/docs/how_to/example_selectors_length_based)
@ -73,7 +73,7 @@ These are the core building blocks you can use when building applications.
### Chat models
[Chat Models](/docs/concepts/#chat-models) are newer forms of language models that take messages in and output a message.
[Chat Models](/docs/concepts/chat_models) are newer forms of language models that take messages in and output a message.
- [How to: do function/tool calling](/docs/how_to/tool_calling)
- [How to: get models to return structured output](/docs/how_to/structured_output)
@ -94,7 +94,7 @@ These are the core building blocks you can use when building applications.
### Messages
[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.
[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.
- [How to: trim messages](/docs/how_to/trim_messages/)
- [How to: filter messages](/docs/how_to/filter_messages/)
@ -102,7 +102,7 @@ These are the core building blocks you can use when building applications.
### LLMs
What LangChain calls [LLMs](/docs/concepts/#llms) are older forms of language models that take a string in and output a string.
What LangChain calls [LLMs](/docs/concepts/text_llms) are older forms of language models that take a string in and output a string.
- [How to: cache model responses](/docs/how_to/llm_caching)
- [How to: create a custom LLM class](/docs/how_to/custom_llm)
@ -112,7 +112,7 @@ What LangChain calls [LLMs](/docs/concepts/#llms) are older forms of language mo
### Output parsers
[Output Parsers](/docs/concepts/#output-parsers) are responsible for taking the output of an LLM and parsing into more structured format.
[Output Parsers](/docs/concepts/output_parsers) are responsible for taking the output of an LLM and parsing into more structured format.
- [How to: use output parsers to parse an LLM response into structured format](/docs/how_to/output_parser_structured)
- [How to: parse JSON output](/docs/how_to/output_parser_json)
@ -124,7 +124,7 @@ What LangChain calls [LLMs](/docs/concepts/#llms) are older forms of language mo
### Document loaders
[Document Loaders](/docs/concepts/#document-loaders) are responsible for loading documents from a variety of sources.
[Document Loaders](/docs/concepts/document_loaders) are responsible for loading documents from a variety of sources.
- [How to: load PDF files](/docs/how_to/document_loader_pdf)
- [How to: load web pages](/docs/how_to/document_loader_web)
@ -138,7 +138,7 @@ What LangChain calls [LLMs](/docs/concepts/#llms) are older forms of language mo
### Text splitters
[Text Splitters](/docs/concepts/#text-splitters) take a document and split into chunks that can be used for retrieval.
[Text Splitters](/docs/concepts/text_splitters) take a document and split into chunks that can be used for retrieval.
- [How to: recursively split text](/docs/how_to/recursive_text_splitter)
- [How to: split by HTML headers](/docs/how_to/HTML_header_metadata_splitter)
@ -152,7 +152,7 @@ What LangChain calls [LLMs](/docs/concepts/#llms) are older forms of language mo
### Embedding models
[Embedding Models](/docs/concepts/#embedding-models) take a piece of text and create a numerical representation of it.
[Embedding Models](/docs/concepts/embedding_models) take a piece of text and create a numerical representation of it.
- [How to: embed text data](/docs/how_to/embed_text)
- [How to: cache embedding results](/docs/how_to/caching_embeddings)
@ -165,7 +165,7 @@ What LangChain calls [LLMs](/docs/concepts/#llms) are older forms of language mo
### Retrievers
[Retrievers](/docs/concepts/#retrievers) are responsible for taking a query and returning relevant documents.
[Retrievers](/docs/concepts/retrievers) are responsible for taking a query and returning relevant documents.
- [How to: use a vector store to retrieve data](/docs/how_to/vectorstore_retriever)
- [How to: generate multiple queries to retrieve data for](/docs/how_to/MultiQueryRetriever)
@ -188,7 +188,7 @@ Indexing is the process of keeping your vectorstore in-sync with the underlying
### Tools
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.
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.
- [How to: create tools](/docs/how_to/custom_tools)
- [How to: use built-in tools and toolkits](/docs/how_to/tools_builtin)
@ -225,7 +225,7 @@ For in depth how-to guides for agents, please check out [LangGraph](https://lang
### Callbacks
[Callbacks](/docs/concepts/#callbacks) allow you to hook into the various stages of your LLM application's execution.
[Callbacks](/docs/concepts/callbacks) allow you to hook into the various stages of your LLM application's execution.
- [How to: pass in callbacks at runtime](/docs/how_to/callbacks_runtime)
- [How to: attach callbacks to a module](/docs/how_to/callbacks_attach)

4
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@ -10,12 +10,12 @@
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following concepts:\n",
"- [LangChain Expression Language (LCEL)](/docs/concepts/#langchain-expression-language)\n",
"- [LangChain Expression Language (LCEL)](/docs/concepts/lcel)\n",
"- [Chaining runnables](/docs/how_to/sequence/)\n",
"\n",
":::\n",
"\n",
"Once you create a runnable with [LangChain Expression Language](/docs/concepts/#langchain-expression-language), you may often want to inspect it to get a better sense for what is going on. This notebook covers some methods for doing so.\n",
"Once you create a runnable with [LangChain Expression Language](/docs/concepts/lcel), you may often want to inspect it to get a better sense for what is going on. This notebook covers some methods for doing so.\n",
"\n",
"This guide shows some ways you can programmatically introspect the internal steps of chains. If you are instead interested in debugging issues in your chain, see [this section](/docs/how_to/debugging) instead.\n",
"\n",

2
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@ -13,7 +13,7 @@
"\n",
"This guide assumes familiarity with the following concepts:\n",
"\n",
"- [LLMs](/docs/concepts/#llms)\n",
"- [LLMs](/docs/concepts/text_llms)\n",
":::\n",
"\n",
"## Using LangSmith\n",

2
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@ -68,7 +68,7 @@
"\n",
"### Formatting prompts\n",
"\n",
"Some providers have [chat model](/docs/concepts/#chat-models) wrappers that takes care of formatting your input prompt for the specific local model you're using. However, if you are prompting local models with a [text-in/text-out LLM](/docs/concepts/#llms) wrapper, you may need to use a prompt tailed for your specific model.\n",
"Some providers have [chat model](/docs/concepts/chat_models) wrappers that takes care of formatting your input prompt for the specific local model you're using. However, if you are prompting local models with a [text-in/text-out LLM](/docs/concepts/text_llms) wrapper, you may need to use a prompt tailed for your specific model.\n",
"\n",
"This can [require the inclusion of special tokens](https://huggingface.co/blog/llama2#how-to-prompt-llama-2). [Here's an example for LLaMA 2](https://smith.langchain.com/hub/rlm/rag-prompt-llama).\n",
"\n",

2
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@ -10,7 +10,7 @@
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following concepts:\n",
"- [Chat models](/docs/concepts/#chat-models)\n",
"- [Chat models](/docs/concepts/chat_models)\n",
"\n",
":::\n",
"\n",

2
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@ -9,7 +9,7 @@
"\n",
"Substantial performance degradations in [RAG](/docs/tutorials/rag) applications have been [documented](https://arxiv.org/abs/2307.03172) as the number of retrieved documents grows (e.g., beyond ten). In brief: models are liable to miss relevant information in the middle of long contexts.\n",
"\n",
"By contrast, queries against vector stores will typically return documents in descending order of relevance (e.g., as measured by cosine similarity of [embeddings](/docs/concepts/#embedding-models)).\n",
"By contrast, queries against vector stores will typically return documents in descending order of relevance (e.g., as measured by cosine similarity of [embeddings](/docs/concepts/embedding_models)).\n",
"\n",
"To mitigate the [\"lost in the middle\"](https://arxiv.org/abs/2307.03172) effect, you can re-order documents after retrieval such that the most relevant documents are positioned at extrema (e.g., the first and last pieces of context), and the least relevant documents are positioned in the middle. In some cases this can help surface the most relevant information to LLMs.\n",
"\n",

6
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@ -25,8 +25,8 @@
"\n",
"This guide assumes familiarity with the following concepts:\n",
"- [Chaining runnables](/docs/how_to/sequence/)\n",
"- [Prompt templates](/docs/concepts/#prompt-templates)\n",
"- [Chat Messages](/docs/concepts/#message-types)\n",
"- [Prompt templates](/docs/concepts/prompt_templates)\n",
"- [Chat Messages](/docs/concepts/messages)\n",
"- [LangGraph persistence](https://langchain-ai.github.io/langgraph/how-tos/persistence/)\n",
"\n",
":::\n",
@ -85,7 +85,7 @@
"source": [
"## Example: message inputs\n",
"\n",
"Adding memory to a [chat model](/docs/concepts/#chat-models) provides a simple example. Chat models accept a list of messages as input and output a message. LangGraph includes a built-in `MessagesState` that we can use for this purpose.\n",
"Adding memory to a [chat model](/docs/concepts/chat_models) provides a simple example. Chat models accept a list of messages as input and output a message. LangGraph includes a built-in `MessagesState` that we can use for this purpose.\n",
"\n",
"Below, we:\n",
"1. Define the graph state to be a list of messages;\n",

4
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@ -24,7 +24,7 @@
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following concepts:\n",
"- [Agents](/docs/concepts/#agents)\n",
"- [Agents](/docs/concepts/agents)\n",
"- [LangGraph](https://langchain-ai.github.io/langgraph/)\n",
"- [Tool calling](/docs/how_to/tool_calling/)\n",
"\n",
@ -298,7 +298,7 @@
"- A `SystemMessage`, which is added to the beginning of the list of messages.\n",
"- A `string`, which is converted to a `SystemMessage` and added to the beginning of the list of messages.\n",
"- A `Callable`, which should take in full graph state. The output is then passed to the language model.\n",
"- Or a [`Runnable`](/docs/concepts/#langchain-expression-language-lcel), which should take in full graph state. The output is then passed to the language model.\n",
"- Or a [`Runnable`](/docs/concepts/lcel), which should take in full graph state. The output is then passed to the language model.\n",
"\n",
"Here's how it looks in action:"
]

2
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@ -162,7 +162,7 @@
"source": [
"## Tool calls\n",
"\n",
"Some multimodal models support [tool calling](/docs/concepts/#functiontool-calling) features as well. To call tools using such models, simply bind tools to them in the [usual way](/docs/how_to/tool_calling), and invoke the model using content blocks of the desired type (e.g., containing image data)."
"Some multimodal models support [tool calling](/docs/concepts/tool_calling) features as well. To call tools using such models, simply bind tools to them in the [usual way](/docs/how_to/tool_calling), and invoke the model using content blocks of the desired type (e.g., containing image data)."
]
},
{

6
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@ -10,9 +10,9 @@
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following concepts:\n",
"- [Chat models](/docs/concepts/#chat-models)\n",
"- [Output parsers](/docs/concepts/#output-parsers)\n",
"- [Prompt templates](/docs/concepts/#prompt-templates)\n",
"- [Chat models](/docs/concepts/chat_models)\n",
"- [Output parsers](/docs/concepts/output_parsers)\n",
"- [Prompt templates](/docs/concepts/prompt_templates)\n",
"- [Structured output](/docs/how_to/structured_output)\n",
"- [Chaining runnables together](/docs/how_to/sequence/)\n",
"\n",

6
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@ -10,9 +10,9 @@
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following concepts:\n",
"- [Chat models](/docs/concepts/#chat-models)\n",
"- [Output parsers](/docs/concepts/#output-parsers)\n",
"- [Prompt templates](/docs/concepts/#prompt-templates)\n",
"- [Chat models](/docs/concepts/chat_models)\n",
"- [Output parsers](/docs/concepts/output_parsers)\n",
"- [Prompt templates](/docs/concepts/prompt_templates)\n",
"- [Structured output](/docs/how_to/structured_output)\n",
"- [Chaining runnables together](/docs/how_to/sequence/)\n",
"\n",

6
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@ -10,9 +10,9 @@
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following concepts:\n",
"- [Chat models](/docs/concepts/#chat-models)\n",
"- [Output parsers](/docs/concepts/#output-parsers)\n",
"- [Prompt templates](/docs/concepts/#prompt-templates)\n",
"- [Chat models](/docs/concepts/chat_models)\n",
"- [Output parsers](/docs/concepts/output_parsers)\n",
"- [Prompt templates](/docs/concepts/prompt_templates)\n",
"- [Structured output](/docs/how_to/structured_output)\n",
"- [Chaining runnables together](/docs/how_to/sequence/)\n",
"\n",

2
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@ -21,7 +21,7 @@
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following concepts:\n",
"- [LangChain Expression Language (LCEL)](/docs/concepts/#langchain-expression-language)\n",
"- [LangChain Expression Language (LCEL)](/docs/concepts/lcel)\n",
"- [Chaining runnables](/docs/how_to/sequence)\n",
"\n",
":::\n",

2
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@ -21,7 +21,7 @@
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following concepts:\n",
"- [LangChain Expression Language (LCEL)](/docs/concepts/#langchain-expression-language)\n",
"- [LangChain Expression Language (LCEL)](/docs/concepts/lcel)\n",
"- [Chaining runnables](/docs/how_to/sequence/)\n",
"- [Calling runnables in parallel](/docs/how_to/parallel/)\n",
"- [Custom functions](/docs/how_to/functions/)\n",

2
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@ -20,7 +20,7 @@
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following concepts:\n",
"- [Prompt templates](/docs/concepts/#prompt-templates)\n",
"- [Prompt templates](/docs/concepts/prompt_templates)\n",
"\n",
":::\n",
"\n",

2
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@ -20,7 +20,7 @@
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following concepts:\n",
"- [Prompt templates](/docs/concepts/#prompt-templates)\n",
"- [Prompt templates](/docs/concepts/prompt_templates)\n",
"\n",
":::\n",
"\n",

2
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@ -161,7 +161,7 @@
"id": "15f8ad59-19de-42e3-85a8-3ba95ee0bd43",
"metadata": {},
"source": [
"For the retriever, we will use [WebBaseLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.web_base.WebBaseLoader.html) to load the content of a web page. Here we instantiate a `InMemoryVectorStore` vectorstore and then use its [.as_retriever](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.VectorStore.html#langchain_core.vectorstores.VectorStore.as_retriever) method to build a retriever that can be incorporated into [LCEL](/docs/concepts/#langchain-expression-language) chains."
"For the retriever, we will use [WebBaseLoader](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.web_base.WebBaseLoader.html) to load the content of a web page. Here we instantiate a `InMemoryVectorStore` vectorstore and then use its [.as_retriever](https://python.langchain.com/api_reference/core/vectorstores/langchain_core.vectorstores.VectorStore.html#langchain_core.vectorstores.VectorStore.as_retriever) method to build a retriever that can be incorporated into [LCEL](/docs/concepts/lcel) chains."
]
},
{

2
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@ -326,7 +326,7 @@
"\n",
"Up to this point, we've simply propagated the documents returned from the retrieval step through to the final response. But this may not illustrate what subset of information the model relied on when generating its answer. Below, we show how to structure sources into the model response, allowing the model to report what specific context it relied on for its answer.\n",
"\n",
"Because the above LCEL implementation is composed of [Runnable](/docs/concepts/#runnable-interface) primitives, it is straightforward to extend. Below, we make a simple change:\n",
"Because the above LCEL implementation is composed of [Runnable](/docs/concepts/runnables) primitives, it is straightforward to extend. Below, we make a simple change:\n",
"\n",
"- We use the model's tool-calling features to generate [structured output](/docs/how_to/structured_output/), consisting of an answer and list of sources. The schema for the response is represented in the `AnswerWithSources` TypedDict, below.\n",
"- We remove the `StrOutputParser()`, as we expect `dict` output in this scenario."

6
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@ -21,11 +21,11 @@
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following concepts:\n",
"- [LangChain Expression Language (LCEL)](/docs/concepts/#langchain-expression-language)\n",
"- [LangChain Expression Language (LCEL)](/docs/concepts/lcel)\n",
"- [Chaining runnables](/docs/how_to/sequence/)\n",
"- [Configuring chain parameters at runtime](/docs/how_to/configure)\n",
"- [Prompt templates](/docs/concepts/#prompt-templates)\n",
"- [Chat Messages](/docs/concepts/#message-types)\n",
"- [Prompt templates](/docs/concepts/prompt_templates)\n",
"- [Chat Messages](/docs/concepts/messages)\n",
"\n",
":::\n",
"\n",

10
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@ -22,14 +22,14 @@
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following concepts:\n",
"- [LangChain Expression Language (LCEL)](/docs/concepts/#langchain-expression-language)\n",
"- [Prompt templates](/docs/concepts/#prompt-templates)\n",
"- [Chat models](/docs/concepts/#chat-models)\n",
"- [Output parser](/docs/concepts/#output-parsers)\n",
"- [LangChain Expression Language (LCEL)](/docs/concepts/lcel)\n",
"- [Prompt templates](/docs/concepts/prompt_templates)\n",
"- [Chat models](/docs/concepts/chat_models)\n",
"- [Output parser](/docs/concepts/output_parsers)\n",
"\n",
":::\n",
"\n",
"One point about [LangChain Expression Language](/docs/concepts/#langchain-expression-language) is that any two runnables can be \"chained\" together into sequences. The output of the previous runnable's `.invoke()` call is passed as input to the next runnable. This can be done using the pipe operator (`|`), or the more explicit `.pipe()` method, which does the same thing.\n",
"One point about [LangChain Expression Language](/docs/concepts/lcel) is that any two runnables can be \"chained\" together into sequences. The output of the previous runnable's `.invoke()` call is passed as input to the next runnable. This can be done using the pipe operator (`|`), or the more explicit `.pipe()` method, which does the same thing.\n",
"\n",
"The resulting [`RunnableSequence`](https://python.langchain.com/api_reference/core/runnables/langchain_core.runnables.base.RunnableSequence.html) is itself a runnable, which means it can be invoked, streamed, or further chained just like any other runnable. Advantages of chaining runnables in this way are efficient streaming (the sequence will stream output as soon as it is available), and debugging and tracing with tools like [LangSmith](/docs/how_to/debugging).\n",
"\n",

2
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@ -14,7 +14,7 @@
"\n",
"To save and load LangChain objects using this system, use the `dumpd`, `dumps`, `load`, and `loads` functions in the [load module](https://python.langchain.com/api_reference/core/load.html) of `langchain-core`. These functions support JSON and JSON-serializable objects.\n",
"\n",
"All LangChain objects that inherit from [Serializable](https://python.langchain.com/api_reference/core/load/langchain_core.load.serializable.Serializable.html) are JSON-serializable. Examples include [messages](https://python.langchain.com/api_reference//python/core_api_reference.html#module-langchain_core.messages), [document objects](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html) (e.g., as returned from [retrievers](/docs/concepts/#retrievers)), and most [Runnables](/docs/concepts/#langchain-expression-language-lcel), such as chat models, retrievers, and [chains](/docs/how_to/sequence) implemented with the LangChain Expression Language.\n",
"All LangChain objects that inherit from [Serializable](https://python.langchain.com/api_reference/core/load/langchain_core.load.serializable.Serializable.html) are JSON-serializable. Examples include [messages](https://python.langchain.com/api_reference//python/core_api_reference.html#module-langchain_core.messages), [document objects](https://python.langchain.com/api_reference/core/documents/langchain_core.documents.base.Document.html) (e.g., as returned from [retrievers](/docs/concepts/retrievers)), and most [Runnables](/docs/concepts/lcel), such as chat models, retrievers, and [chains](/docs/how_to/sequence) implemented with the LangChain Expression Language.\n",
"\n",
"Below we walk through an example with a simple [LLM chain](/docs/tutorials/llm_chain).\n",
"\n",

12
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@ -24,15 +24,15 @@
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following concepts:\n",
"- [Chat models](/docs/concepts/#chat-models)\n",
"- [LangChain Expression Language](/docs/concepts/#langchain-expression-language)\n",
"- [Output parsers](/docs/concepts/#output-parsers)\n",
"- [Chat models](/docs/concepts/chat_models)\n",
"- [LangChain Expression Language](/docs/concepts/lcel)\n",
"- [Output parsers](/docs/concepts/output_parsers)\n",
"\n",
":::\n",
"\n",
"Streaming is critical in making applications based on LLMs feel responsive to end-users.\n",
"\n",
"Important LangChain primitives like [chat models](/docs/concepts/#chat-models), [output parsers](/docs/concepts/#output-parsers), [prompts](/docs/concepts/#prompt-templates), [retrievers](/docs/concepts/#retrievers), and [agents](/docs/concepts/#agents) implement the LangChain [Runnable Interface](/docs/concepts#interface).\n",
"Important LangChain primitives like [chat models](/docs/concepts/chat_models), [output parsers](/docs/concepts/output_parsers), [prompts](/docs/concepts/prompt_templates), [retrievers](/docs/concepts/retrievers), and [agents](/docs/concepts/agents) implement the LangChain [Runnable Interface](/docs/concepts#interface).\n",
"\n",
"This interface provides two general approaches to stream content:\n",
"\n",
@ -42,7 +42,7 @@
"Let's take a look at both approaches, and try to understand how to use them.\n",
"\n",
":::info\n",
"For a higher-level overview of streaming techniques in LangChain, see [this section of the conceptual guide](/docs/concepts/#streaming).\n",
"For a higher-level overview of streaming techniques in LangChain, see [this section of the conceptual guide](/docs/concepts/streaming).\n",
":::\n",
"\n",
"## Using Stream\n",
@ -1510,7 +1510,7 @@
"\n",
"Now you've learned some ways to stream both final outputs and internal steps with LangChain.\n",
"\n",
"To learn more, check out the other how-to guides in this section, or the [conceptual guide on Langchain Expression Language](/docs/concepts/#langchain-expression-language/)."
"To learn more, check out the other how-to guides in this section, or the [conceptual guide on Langchain Expression Language](/docs/concepts/lcel/)."
]
}
],

6
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@ -25,8 +25,8 @@
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following concepts:\n",
"- [Chat models](/docs/concepts/#chat-models)\n",
"- [Function/tool calling](/docs/concepts/#functiontool-calling)\n",
"- [Chat models](/docs/concepts/chat_models)\n",
"- [Function/tool calling](/docs/concepts/tool_calling)\n",
":::\n",
"\n",
"It is often useful to have a model return output that matches a specific schema. One common use-case is extracting data from text to insert into a database or use with some other downstream system. This guide covers a few strategies for getting structured outputs from a model.\n",
@ -776,7 +776,7 @@
"\n",
"### Custom Parsing\n",
"\n",
"You can also create a custom prompt and parser with [LangChain Expression Language (LCEL)](/docs/concepts/#langchain-expression-language), using a plain function to parse the output from the model:"
"You can also create a custom prompt and parser with [LangChain Expression Language (LCEL)](/docs/concepts/lcel), using a plain function to parse the output from the model:"
]
},
{

2
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@ -33,7 +33,7 @@
"\n",
"- LangGraph allows for individual steps (such as successive summarizations) to be streamed, allowing for greater control of execution;\n",
"- LangGraph's [checkpointing](https://langchain-ai.github.io/langgraph/how-tos/persistence/) supports error recovery, extending with human-in-the-loop workflows, and easier incorporation into conversational applications.\n",
"- Because it is assembled from modular components, it is also simple to extend or modify (e.g., to incorporate [tool calling](/docs/concepts/#functiontool-calling) or other behavior).\n",
"- Because it is assembled from modular components, it is also simple to extend or modify (e.g., to incorporate [tool calling](/docs/concepts/tool_calling) or other behavior).\n",
"\n",
"Below, we demonstrate how to summarize text via iterative refinement."
]

2
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@ -117,7 +117,7 @@
"source": [
"## Invoke chain\n",
"\n",
"Because the chain is a [Runnable](/docs/concepts/#runnable-interface), it implements the usual methods for invocation:"
"Because the chain is a [Runnable](/docs/concepts/runnables), it implements the usual methods for invocation:"
]
},
{

6
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@ -10,9 +10,9 @@
":::info Prerequisites\n",
"This guide assumes familiarity with the following concepts:\n",
"\n",
"- [ToolMessage](/docs/concepts/#toolmessage)\n",
"- [Tools](/docs/concepts/#tools)\n",
"- [Function/tool calling](/docs/concepts/#functiontool-calling)\n",
"- [ToolMessage](/docs/concepts/messages/#toolmessage)\n",
"- [Tools](/docs/concepts/tools)\n",
"- [Function/tool calling](/docs/concepts/tool_calling)\n",
"\n",
":::\n",
"\n",

10
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@ -23,13 +23,13 @@
"\n",
"This guide assumes familiarity with the following concepts:\n",
"\n",
"- [Chat models](/docs/concepts/#chat-models)\n",
"- [Tool calling](/docs/concepts/#functiontool-calling)\n",
"- [Tools](/docs/concepts/#tools)\n",
"- [Output parsers](/docs/concepts/#output-parsers)\n",
"- [Chat models](/docs/concepts/chat_models)\n",
"- [Tool calling](/docs/concepts/tool_calling)\n",
"- [Tools](/docs/concepts/tools)\n",
"- [Output parsers](/docs/concepts/output_parsers)\n",
":::\n",
"\n",
"[Tool calling](/docs/concepts/#functiontool-calling) allows a chat model to respond to a given prompt by \"calling a tool\".\n",
"[Tool calling](/docs/concepts/tool_calling) allows a chat model to respond to a given prompt by \"calling a tool\".\n",
"\n",
"Remember, while the name \"tool calling\" implies that the model is directly performing some action, this is actually not the case! The model only generates the arguments to a tool, and actually running the tool (or not) is up to the user.\n",
"\n",

4
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@ -9,8 +9,8 @@
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following concepts:\n",
"- [Chat models](/docs/concepts/#chat-models)\n",
"- [LangChain Tools](/docs/concepts/#tools)\n",
"- [Chat models](/docs/concepts/chat_models)\n",
"- [LangChain Tools](/docs/concepts/tools)\n",
"- [How to use a model to call tools](/docs/how_to/tool_calling)\n",
":::\n",
"\n",

4
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@ -10,9 +10,9 @@
"\n",
"This guide assumes familiarity with the following concepts:\n",
"\n",
"- [LangChain Tools](/docs/concepts/#tools)\n",
"- [LangChain Tools](/docs/concepts/tools)\n",
"- [Custom tools](/docs/how_to/custom_tools)\n",
"- [LangChain Expression Language (LCEL)](/docs/concepts/#langchain-expression-language-lcel)\n",
"- [LangChain Expression Language (LCEL)](/docs/concepts/lcel)\n",
"- [Configuring runnable behavior](/docs/how_to/configure/)\n",
"\n",
":::\n",

6
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@ -9,14 +9,14 @@
":::info Prerequisites\n",
"This guide assumes familiarity with the following concepts:\n",
"\n",
"- [LangChain Tools](/docs/concepts/#tools)\n",
"- [Function/tool calling](/docs/concepts/#functiontool-calling)\n",
"- [LangChain Tools](/docs/concepts/tools)\n",
"- [Function/tool calling](/docs/concepts/tool_calling)\n",
"- [Using chat models to call tools](/docs/how_to/tool_calling)\n",
"- [Defining custom tools](/docs/how_to/custom_tools/)\n",
"\n",
":::\n",
"\n",
"Some models are capable of [**tool calling**](/docs/concepts/#functiontool-calling) - generating arguments that conform to a specific user-provided schema. This guide will demonstrate how to use those tool cals to actually call a function and properly pass the results back to the model.\n",
"Some models are capable of [**tool calling**](/docs/concepts/tool_calling) - generating arguments that conform to a specific user-provided schema. This guide will demonstrate how to use those tool cals to actually call a function and properly pass the results back to the model.\n",
"\n",
"![Diagram of a tool call invocation](/img/tool_invocation.png)\n",
"\n",

4
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@ -10,8 +10,8 @@
"import Compatibility from \"@theme/Compatibility\";\n",
"\n",
"<Prerequisites titlesAndLinks={[\n",
" [\"Chat models\", \"/docs/concepts/#chat-models\"],\n",
" [\"LangChain Tools\", \"/docs/concepts/#tools\"],\n",
" [\"Chat models\", \"/docs/concepts/chat_models\"],\n",
" [\"LangChain Tools\", \"/docs/concepts/tools\"],\n",
" [\"How to create tools\", \"/docs/how_to/custom_tools\"],\n",
" [\"How to use a model to call tools\", \"/docs/how_to/tool_calling\"],\n",
"]} />\n",

2
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@ -9,7 +9,7 @@
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following concepts:\n",
"- [LangChain Tools](/docs/concepts/#tools)\n",
"- [LangChain Tools](/docs/concepts/tools)\n",
"- [Custom tools](/docs/how_to/custom_tools)\n",
"- [Using stream events](/docs/how_to/streaming/#using-stream-events)\n",
"- [Accessing RunnableConfig within a custom tool](/docs/how_to/tool_configure/)\n",

4
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@ -23,8 +23,8 @@
"\n",
"This guide assumes familiarity with the following concepts:\n",
"\n",
"- [LangChain Tools](/docs/concepts/#tools)\n",
"- [LangChain Toolkits](/docs/concepts/#tools)\n",
"- [LangChain Tools](/docs/concepts/tools)\n",
"- [LangChain Toolkits](/docs/concepts/tools)\n",
"\n",
":::\n",
"\n",

4
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@ -10,8 +10,8 @@
":::info Prerequisites\n",
"\n",
"This guide assumes familiarity with the following concepts:\n",
"- [Chat models](/docs/concepts/#chat-models)\n",
"- [LangChain Tools](/docs/concepts/#tools)\n",
"- [Chat models](/docs/concepts/chat_models)\n",
"- [LangChain Tools](/docs/concepts/tools)\n",
"- [How to use a model to call tools](/docs/how_to/tool_calling)\n",
"\n",
":::\n",

8
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@ -27,10 +27,10 @@
"\n",
"This guide assumes familiarity with the following concepts:\n",
"\n",
"- [LangChain Tools](/docs/concepts/#tools)\n",
"- [Function/tool calling](https://python.langchain.com/docs/concepts/#functiontool-calling)\n",
"- [Chat models](/docs/concepts/#chat-models)\n",
"- [LLMs](/docs/concepts/#llms)\n",
"- [LangChain Tools](/docs/concepts/tools)\n",
"- [Function/tool calling](https://python.langchain.com/docs/concepts/tool_calling)\n",
"- [Chat models](/docs/concepts/chat_models)\n",
"- [LLMs](/docs/concepts/text_llms)\n",
"\n",
":::\n",
"\n",

8
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@ -11,10 +11,10 @@
"\n",
"This guide assumes familiarity with the following concepts:\n",
"\n",
"- [Messages](/docs/concepts/#messages)\n",
"- [Chat models](/docs/concepts/#chat-models)\n",
"- [Messages](/docs/concepts/messages)\n",
"- [Chat models](/docs/concepts/chat_models)\n",
"- [Chaining](/docs/how_to/sequence/)\n",
"- [Chat history](/docs/concepts/#chat-history)\n",
"- [Chat history](/docs/concepts/chat_history)\n",
"\n",
"The methods in this guide also require `langchain-core>=0.2.9`.\n",
"\n",
@ -28,7 +28,7 @@
"If passing the trimmed chat history back into a chat model directly, the trimmed chat history should satisfy the following properties:\n",
"\n",
"1. The resulting chat history should be **valid**. Usually this means that the following properties should be satisfied:\n",
" - The chat history **starts** with either (1) a `HumanMessage` or (2) a [SystemMessage](/docs/concepts/#systemmessage) followed by a `HumanMessage`.\n",
" - The chat history **starts** with either (1) a `HumanMessage` or (2) a [SystemMessage](/docs/concepts/messages/#systemmessage) followed by a `HumanMessage`.\n",
" - The chat history **ends** with either a `HumanMessage` or a `ToolMessage`.\n",
" - A `ToolMessage` can only appear after an `AIMessage` that involved a tool call. \n",
" This can be achieved by setting `start_on=\"human\"` and `ends_on=(\"human\", \"tool\")`.\n",

2
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@ -17,7 +17,7 @@
"source": [
"# ChatAnthropic\n",
"\n",
"This notebook provides a quick overview for getting started with Anthropic [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatAnthropic features and configurations head to the [API reference](https://python.langchain.com/api_reference/anthropic/chat_models/langchain_anthropic.chat_models.ChatAnthropic.html).\n",
"This notebook provides a quick overview for getting started with Anthropic [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatAnthropic features and configurations head to the [API reference](https://python.langchain.com/api_reference/anthropic/chat_models/langchain_anthropic.chat_models.ChatAnthropic.html).\n",
"\n",
"Anthropic has several chat models. You can find information about their latest models and their costs, context windows, and supported input types in the [Anthropic docs](https://docs.anthropic.com/en/docs/models-overview).\n",
"\n",

2
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@ -17,7 +17,7 @@
"source": [
"# AzureChatOpenAI\n",
"\n",
"This guide will help you get started with AzureOpenAI [chat models](/docs/concepts/#chat-models). For detailed documentation of all AzureChatOpenAI features and configurations head to the [API reference](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.azure.AzureChatOpenAI.html).\n",
"This guide will help you get started with AzureOpenAI [chat models](/docs/concepts/chat_models). For detailed documentation of all AzureChatOpenAI features and configurations head to the [API reference](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.azure.AzureChatOpenAI.html).\n",
"\n",
"Azure OpenAI has several chat models. You can find information about their latest models and their costs, context windows, and supported input types in the [Azure docs](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models).\n",
"\n",

2
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@ -17,7 +17,7 @@
"source": [
"# ChatBedrock\n",
"\n",
"This doc will help you get started with AWS Bedrock [chat models](/docs/concepts/#chat-models). Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon via a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI. Using Amazon Bedrock, you can easily experiment with and evaluate top FMs for your use case, privately customize them with your data using techniques such as fine-tuning and Retrieval Augmented Generation (RAG), and build agents that execute tasks using your enterprise systems and data sources. Since Amazon Bedrock is serverless, you don't have to manage any infrastructure, and you can securely integrate and deploy generative AI capabilities into your applications using the AWS services you are already familiar with.\n",
"This doc will help you get started with AWS Bedrock [chat models](/docs/concepts/chat_models). Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon via a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI. Using Amazon Bedrock, you can easily experiment with and evaluate top FMs for your use case, privately customize them with your data using techniques such as fine-tuning and Retrieval Augmented Generation (RAG), and build agents that execute tasks using your enterprise systems and data sources. Since Amazon Bedrock is serverless, you don't have to manage any infrastructure, and you can securely integrate and deploy generative AI capabilities into your applications using the AWS services you are already familiar with.\n",
"\n",
"For more information on which models are accessible via Bedrock, head to the [AWS docs](https://docs.aws.amazon.com/bedrock/latest/userguide/models-features.html).\n",
"\n",

2
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@ -17,7 +17,7 @@
"source": [
"# ChatCerebras\n",
"\n",
"This notebook provides a quick overview for getting started with Cerebras [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatCerebras features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/chat_models/langchain_cerebras.chat_models.ChatCerebras.html).\n",
"This notebook provides a quick overview for getting started with Cerebras [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatCerebras features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/chat_models/langchain_cerebras.chat_models.ChatCerebras.html).\n",
"\n",
"At Cerebras, we've developed the world's largest and fastest AI processor, the Wafer-Scale Engine-3 (WSE-3). The Cerebras CS-3 system, powered by the WSE-3, represents a new class of AI supercomputer that sets the standard for generative AI training and inference with unparalleled performance and scalability.\n",
"\n",

2
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@ -21,7 +21,7 @@
"\n",
"> [Databricks](https://www.databricks.com/) Lakehouse Platform unifies data, analytics, and AI on one platform. \n",
"\n",
"This notebook provides a quick overview for getting started with Databricks [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatDatabricks features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.databricks.ChatDatabricks.html).\n",
"This notebook provides a quick overview for getting started with Databricks [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatDatabricks features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.databricks.ChatDatabricks.html).\n",
"\n",
"## Overview\n",
"\n",

2
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@ -17,7 +17,7 @@
"source": [
"# ChatFireworks\n",
"\n",
"This doc help you get started with Fireworks AI [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatFireworks features and configurations head to the [API reference](https://python.langchain.com/api_reference/fireworks/chat_models/langchain_fireworks.chat_models.ChatFireworks.html).\n",
"This doc help you get started with Fireworks AI [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatFireworks features and configurations head to the [API reference](https://python.langchain.com/api_reference/fireworks/chat_models/langchain_fireworks.chat_models.ChatFireworks.html).\n",
"\n",
"Fireworks AI is an AI inference platform to run and customize models. For a list of all models served by Fireworks see the [Fireworks docs](https://fireworks.ai/models).\n",
"\n",

2
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@ -17,7 +17,7 @@
"source": [
"# ChatGoogleGenerativeAI\n",
"\n",
"This docs will help you get started with Google AI [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatGoogleGenerativeAI features and configurations head to the [API reference](https://python.langchain.com/api_reference/google_genai/chat_models/langchain_google_genai.chat_models.ChatGoogleGenerativeAI.html).\n",
"This docs will help you get started with Google AI [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatGoogleGenerativeAI features and configurations head to the [API reference](https://python.langchain.com/api_reference/google_genai/chat_models/langchain_google_genai.chat_models.ChatGoogleGenerativeAI.html).\n",
"\n",
"Google AI offers a number of different chat models. For information on the latest models, their features, context windows, etc. head to the [Google AI docs](https://ai.google.dev/gemini-api/docs/models/gemini).\n",
"\n",

2
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@ -17,7 +17,7 @@
"source": [
"# ChatVertexAI\n",
"\n",
"This page provides a quick overview for getting started with VertexAI [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatVertexAI features and configurations head to the [API reference](https://python.langchain.com/api_reference/google_vertexai/chat_models/langchain_google_vertexai.chat_models.ChatVertexAI.html).\n",
"This page provides a quick overview for getting started with VertexAI [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatVertexAI features and configurations head to the [API reference](https://python.langchain.com/api_reference/google_vertexai/chat_models/langchain_google_vertexai.chat_models.ChatVertexAI.html).\n",
"\n",
"ChatVertexAI exposes all foundational models available in Google Cloud, like `gemini-1.5-pro`, `gemini-1.5-flash`, etc. For a full and updated list of available models visit [VertexAI documentation](https://cloud.google.com/vertex-ai/docs/generative-ai/model-reference/overview).\n",
"\n",

2
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@ -6,7 +6,7 @@
"source": [
"# ChatHuggingFace\n",
"\n",
"This will help you getting started with `langchain_huggingface` [chat models](/docs/concepts/#chat-models). For detailed documentation of all `ChatHuggingFace` features and configurations head to the [API reference](https://python.langchain.com/api_reference/huggingface/chat_models/langchain_huggingface.chat_models.huggingface.ChatHuggingFace.html). For a list of models supported by Hugging Face check out [this page](https://huggingface.co/models).\n",
"This will help you getting started with `langchain_huggingface` [chat models](/docs/concepts/chat_models). For detailed documentation of all `ChatHuggingFace` features and configurations head to the [API reference](https://python.langchain.com/api_reference/huggingface/chat_models/langchain_huggingface.chat_models.huggingface.ChatHuggingFace.html). For a list of models supported by Hugging Face check out [this page](https://huggingface.co/models).\n",
"\n",
"## Overview\n",
"### Integration details\n",

2
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@ -6,7 +6,7 @@ keywords: [compatibility]
# Chat models
[Chat models](/docs/concepts/#chat-models) are language models that use a sequence of [messages](/docs/concepts/#messages) as inputs and return messages as outputs (as opposed to using plain text). These are generally newer models.
[Chat models](/docs/concepts/chat_models) are language models that use a sequence of [messages](/docs/concepts/messages) as inputs and return messages as outputs (as opposed to using plain text). These are generally newer models.
:::info

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@ -17,7 +17,7 @@
"source": [
"# ChatMistralAI\n",
"\n",
"This will help you getting started with Mistral [chat models](/docs/concepts/#chat-models). For detailed documentation of all `ChatMistralAI` features and configurations head to the [API reference](https://python.langchain.com/api_reference/mistralai/chat_models/langchain_mistralai.chat_models.ChatMistralAI.html). The `ChatMistralAI` class is built on top of the [Mistral API](https://docs.mistral.ai/api/). For a list of all the models supported by Mistral, check out [this page](https://docs.mistral.ai/getting-started/models/).\n",
"This will help you getting started with Mistral [chat models](/docs/concepts/chat_models). For detailed documentation of all `ChatMistralAI` features and configurations head to the [API reference](https://python.langchain.com/api_reference/mistralai/chat_models/langchain_mistralai.chat_models.ChatMistralAI.html). The `ChatMistralAI` class is built on top of the [Mistral API](https://docs.mistral.ai/api/). For a list of all the models supported by Mistral, check out [this page](https://docs.mistral.ai/getting-started/models/).\n",
"\n",
"## Overview\n",
"### Integration details\n",

2
docs/docs/integrations/chat/nvidia_ai_endpoints.ipynb
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@ -17,7 +17,7 @@
"source": [
"# ChatNVIDIA\n",
"\n",
"This will help you getting started with NVIDIA [chat models](/docs/concepts/#chat-models). For detailed documentation of all `ChatNVIDIA` features and configurations head to the [API reference](https://python.langchain.com/api_reference/nvidia_ai_endpoints/chat_models/langchain_nvidia_ai_endpoints.chat_models.ChatNVIDIA.html).\n",
"This will help you getting started with NVIDIA [chat models](/docs/concepts/chat_models). For detailed documentation of all `ChatNVIDIA` features and configurations head to the [API reference](https://python.langchain.com/api_reference/nvidia_ai_endpoints/chat_models/langchain_nvidia_ai_endpoints.chat_models.ChatNVIDIA.html).\n",
"\n",
"## Overview\n",
"The `langchain-nvidia-ai-endpoints` package contains LangChain integrations building applications with models on \n",

2
docs/docs/integrations/chat/oci_data_science.ipynb
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@ -19,7 +19,7 @@
"source": [
"# ChatOCIModelDeployment\n",
"\n",
"This will help you getting started with OCIModelDeployment [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatOCIModelDeployment features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/chat_models/langchain_community.chat_models.ChatOCIModelDeployment.html).\n",
"This will help you getting started with OCIModelDeployment [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatOCIModelDeployment features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/chat_models/langchain_community.chat_models.ChatOCIModelDeployment.html).\n",
"\n",
"[OCI Data Science](https://docs.oracle.com/en-us/iaas/data-science/using/home.htm) is a fully managed and serverless platform for data science teams to build, train, and manage machine learning models in the Oracle Cloud Infrastructure. You can use [AI Quick Actions](https://blogs.oracle.com/ai-and-datascience/post/ai-quick-actions-in-oci-data-science) to easily deploy LLMs on [OCI Data Science Model Deployment Service](https://docs.oracle.com/en-us/iaas/data-science/using/model-dep-about.htm). You may choose to deploy the model with popular inference frameworks such as vLLM or TGI. By default, the model deployment endpoint mimics the OpenAI API protocol.\n",
"\n",

2
docs/docs/integrations/chat/oci_generative_ai.ipynb
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@ -17,7 +17,7 @@
"source": [
"# ChatOCIGenAI\n",
"\n",
"This notebook provides a quick overview for getting started with OCIGenAI [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatOCIGenAI features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.oci_generative_ai.ChatOCIGenAI.html).\n",
"This notebook provides a quick overview for getting started with OCIGenAI [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatOCIGenAI features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/chat_models/langchain_community.chat_models.oci_generative_ai.ChatOCIGenAI.html).\n",
"\n",
"Oracle Cloud Infrastructure (OCI) Generative AI is a fully managed service that provides a set of state-of-the-art, customizable large language models (LLMs) that cover a wide range of use cases, and which is available through a single API.\n",
"Using the OCI Generative AI service you can access ready-to-use pretrained models, or create and host your own fine-tuned custom models based on your own data on dedicated AI clusters. Detailed documentation of the service and API is available __[here](https://docs.oracle.com/en-us/iaas/Content/generative-ai/home.htm)__ and __[here](https://docs.oracle.com/en-us/iaas/api/#/en/generative-ai/20231130/)__.\n",

2
docs/docs/integrations/chat/openai.ipynb
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@ -17,7 +17,7 @@
"source": [
"# ChatOpenAI\n",
"\n",
"This notebook provides a quick overview for getting started with OpenAI [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatOpenAI features and configurations head to the [API reference](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html).\n",
"This notebook provides a quick overview for getting started with OpenAI [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatOpenAI features and configurations head to the [API reference](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html).\n",
"\n",
"OpenAI has several chat models. You can find information about their latest models and their costs, context windows, and supported input types in the [OpenAI docs](https://platform.openai.com/docs/models).\n",
"\n",

2
docs/docs/integrations/chat/sambanova.ipynb
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@ -19,7 +19,7 @@
"source": [
"# ChatSambaNovaCloud\n",
"\n",
"This will help you getting started with SambaNovaCloud [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatSambaNovaCloud features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/chat_models/langchain_community.chat_models.sambanova.ChatSambaNovaCloud.html).\n",
"This will help you getting started with SambaNovaCloud [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatSambaNovaCloud features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/chat_models/langchain_community.chat_models.sambanova.ChatSambaNovaCloud.html).\n",
"\n",
"**[SambaNova](https://sambanova.ai/)'s** [SambaNova Cloud](https://cloud.sambanova.ai/) is a platform for performing inference with open-source models\n",
"\n",

2
docs/docs/integrations/chat/sambastudio.ipynb
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@ -19,7 +19,7 @@
"source": [
"# ChatSambaStudio\n",
"\n",
"This will help you getting started with SambaStudio [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatStudio features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/chat_models/langchain_community.chat_models.sambanova.ChatSambaStudio.html).\n",
"This will help you getting started with SambaStudio [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatStudio features and configurations head to the [API reference](https://api.python.langchain.com/en/latest/chat_models/langchain_community.chat_models.sambanova.ChatSambaStudio.html).\n",
"\n",
"**[SambaNova](https://sambanova.ai/)'s** [SambaStudio](https://docs.sambanova.ai/sambastudio/latest/sambastudio-intro.html) SambaStudio is a rich, GUI-based platform that provides the functionality to train, deploy, and manage models in SambaNova [DataScale](https://sambanova.ai/products/datascale) systems.\n",
"\n",

2
docs/docs/integrations/chat/vllm.ipynb
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@ -20,7 +20,7 @@
"vLLM can be deployed as a server that mimics the OpenAI API protocol. This allows vLLM to be used as a drop-in replacement for applications using OpenAI API. This server can be queried in the same format as OpenAI API.\n",
"\n",
"## Overview\n",
"This will help you getting started with vLLM [chat models](/docs/concepts/#chat-models), which leverage the `langchain-openai` package. For detailed documentation of all `ChatOpenAI` features and configurations head to the [API reference](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html).\n",
"This will help you getting started with vLLM [chat models](/docs/concepts/chat_models), which leverage the `langchain-openai` package. For detailed documentation of all `ChatOpenAI` features and configurations head to the [API reference](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html).\n",
"\n",
"### Integration details\n",
"\n",

2
docs/docs/integrations/chat/yi.ipynb
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@ -6,7 +6,7 @@
"source": [
"# ChatYI\n",
"\n",
"This will help you getting started with Yi [chat models](/docs/concepts/#chat-models). For detailed documentation of all ChatYi features and configurations head to the [API reference](https://python.langchain.com/api_reference/lanchain_community/chat_models/lanchain_community.chat_models.yi.ChatYi.html).\n",
"This will help you getting started with Yi [chat models](/docs/concepts/chat_models). For detailed documentation of all ChatYi features and configurations head to the [API reference](https://python.langchain.com/api_reference/lanchain_community/chat_models/lanchain_community.chat_models.yi.ChatYi.html).\n",
"\n",
"[01.AI](https://www.lingyiwanwu.com/en), founded by Dr. Kai-Fu Lee, is a global company at the forefront of AI 2.0. They offer cutting-edge large language models, including the Yi series, which range from 6B to hundreds of billions of parameters. 01.AI also provides multimodal models, an open API platform, and open-source options like Yi-34B/9B/6B and Yi-VL.\n",
"\n",

2
docs/docs/integrations/document_loaders/bshtml.ipynb
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@ -7,7 +7,7 @@
"# BSHTMLLoader\n",
"\n",
"\n",
"This notebook provides a quick overview for getting started with BeautifulSoup4 [document loader](https://python.langchain.com/docs/concepts/#document-loaders). For detailed documentation of all __ModuleName__Loader features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.html_bs.BSHTMLLoader.html).\n",
"This notebook provides a quick overview for getting started with BeautifulSoup4 [document loader](https://python.langchain.com/docs/concepts/document_loaders). For detailed documentation of all __ModuleName__Loader features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.html_bs.BSHTMLLoader.html).\n",
"\n",
"\n",
"## Overview\n",

2
docs/docs/integrations/document_loaders/json.ipynb
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@ -6,7 +6,7 @@
"source": [
"# JSONLoader\n",
"\n",
"This notebook provides a quick overview for getting started with JSON [document loader](https://python.langchain.com/docs/concepts/#document-loaders). For detailed documentation of all JSONLoader features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.json_loader.JSONLoader.html).\n",
"This notebook provides a quick overview for getting started with JSON [document loader](https://python.langchain.com/docs/concepts/document_loaders). For detailed documentation of all JSONLoader features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.json_loader.JSONLoader.html).\n",
"\n",
"- TODO: Add any other relevant links, like information about underlying API, etc.\n",
"\n",

2
docs/docs/integrations/document_loaders/langsmith.ipynb
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@ -15,7 +15,7 @@
"source": [
"# LangSmithLoader\n",
"\n",
"This notebook provides a quick overview for getting started with the LangSmith [document loader](https://python.langchain.com/docs/concepts/#document-loaders). For detailed documentation of all LangSmithLoader features and configurations head to the [API reference](https://python.langchain.com/api_reference/core/document_loaders/langchain_core.document_loaders.langsmith.LangSmithLoader.html).\n",
"This notebook provides a quick overview for getting started with the LangSmith [document loader](https://python.langchain.com/docs/concepts/document_loaders). For detailed documentation of all LangSmithLoader features and configurations head to the [API reference](https://python.langchain.com/api_reference/core/document_loaders/langchain_core.document_loaders.langsmith.LangSmithLoader.html).\n",
"\n",
"## Overview\n",
"### Integration details\n",

2
docs/docs/integrations/document_loaders/pypdfium2.ipynb
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@ -7,7 +7,7 @@
"# PyPDFium2Loader\n",
"\n",
"\n",
"This notebook provides a quick overview for getting started with PyPDFium2 [document loader](https://python.langchain.com/docs/concepts/#document-loaders). For detailed documentation of all __ModuleName__Loader features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyPDFium2Loader.html).\n",
"This notebook provides a quick overview for getting started with PyPDFium2 [document loader](https://python.langchain.com/docs/concepts/document_loaders). For detailed documentation of all __ModuleName__Loader features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyPDFium2Loader.html).\n",
"\n",
"## Overview\n",
"### Integration details\n",

2
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@ -6,7 +6,7 @@
"source": [
"# PyPDFLoader\n",
"\n",
"This notebook provides a quick overview for getting started with `PyPDF` [document loader](https://python.langchain.com/docs/concepts/#document-loaders). For detailed documentation of all DocumentLoader features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyPDFLoader.html).\n",
"This notebook provides a quick overview for getting started with `PyPDF` [document loader](https://python.langchain.com/docs/concepts/document_loaders). For detailed documentation of all DocumentLoader features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.pdf.PyPDFLoader.html).\n",
"\n",
"\n",
"## Overview\n",

2
docs/docs/integrations/document_loaders/unstructured_file.ipynb
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@ -7,7 +7,7 @@
"source": [
"# Unstructured\n",
"\n",
"This notebook covers how to use `Unstructured` [document loader](https://python.langchain.com/docs/concepts/#document-loaders) to load files of many types. `Unstructured` currently supports loading of text files, powerpoints, html, pdfs, images, and more.\n",
"This notebook covers how to use `Unstructured` [document loader](https://python.langchain.com/docs/concepts/document_loaders) to load files of many types. `Unstructured` currently supports loading of text files, powerpoints, html, pdfs, images, and more.\n",
"\n",
"Please see [this guide](../../integrations/providers/unstructured.mdx) for more instructions on setting up Unstructured locally, including setting up required system dependencies.\n",
"\n",

2
docs/docs/integrations/document_loaders/unstructured_markdown.ipynb
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@ -6,7 +6,7 @@
"source": [
"# UnstructuredMarkdownLoader\n",
"\n",
"This notebook provides a quick overview for getting started with UnstructuredMarkdown [document loader](https://python.langchain.com/docs/concepts/#document-loaders). For detailed documentation of all __ModuleName__Loader features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.markdown.UnstructuredMarkdownLoader.html).\n",
"This notebook provides a quick overview for getting started with UnstructuredMarkdown [document loader](https://python.langchain.com/docs/concepts/document_loaders). For detailed documentation of all __ModuleName__Loader features and configurations head to the [API reference](https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.markdown.UnstructuredMarkdownLoader.html).\n",
"\n",
"## Overview\n",
"### Integration details\n",

2
docs/docs/integrations/document_loaders/xml.ipynb
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@ -7,7 +7,7 @@
"source": [
"# UnstructuredXMLLoader\n",
"\n",
"This notebook provides a quick overview for getting started with UnstructuredXMLLoader [document loader](https://python.langchain.com/docs/concepts/#document-loaders). The `UnstructuredXMLLoader` is used to load `XML` files. The loader works with `.xml` files. The page content will be the text extracted from the XML tags.\n",
"This notebook provides a quick overview for getting started with UnstructuredXMLLoader [document loader](https://python.langchain.com/docs/concepts/document_loaders). The `UnstructuredXMLLoader` is used to load `XML` files. The loader works with `.xml` files. The page content will be the text extracted from the XML tags.\n",
"\n",
"\n",
"## Overview\n",

2
docs/docs/integrations/llms/anthropic.ipynb
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@ -23,7 +23,7 @@
"# AnthropicLLM\n",
"\n",
":::caution\n",
"You are currently on a page documenting the use of Anthropic legacy Claude 2 models as [text completion models](/docs/concepts/#llms). The latest and most popular Anthropic models are [chat completion models](/docs/concepts/#chat-models), and the text completion models have been deprecated.\n",
"You are currently on a page documenting the use of Anthropic legacy Claude 2 models as [text completion models](/docs/concepts/text_llms). The latest and most popular Anthropic models are [chat completion models](/docs/concepts/chat_models), and the text completion models have been deprecated.\n",
"\n",
"You are probably looking for [this page instead](/docs/integrations/chat/anthropic/).\n",
":::\n",

2
docs/docs/integrations/llms/azure_openai.ipynb
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@ -8,7 +8,7 @@
"# Azure OpenAI\n",
"\n",
":::caution\n",
"You are currently on a page documenting the use of Azure OpenAI [text completion models](/docs/concepts/#llms). The latest and most popular Azure OpenAI models are [chat completion models](/docs/concepts/#chat-models).\n",
"You are currently on a page documenting the use of Azure OpenAI [text completion models](/docs/concepts/text_llms). The latest and most popular Azure OpenAI models are [chat completion models](/docs/concepts/chat_models).\n",
"\n",
"Unless you are specifically using `gpt-3.5-turbo-instruct`, you are probably looking for [this page instead](/docs/integrations/chat/azure_chat_openai/).\n",
":::\n",

2
docs/docs/integrations/llms/bedrock.ipynb
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@ -12,7 +12,7 @@
"metadata": {},
"source": [
":::caution\n",
"You are currently on a page documenting the use of Amazon Bedrock models as [text completion models](/docs/concepts/#llms). Many popular models available on Bedrock are [chat completion models](/docs/concepts/#chat-models).\n",
"You are currently on a page documenting the use of Amazon Bedrock models as [text completion models](/docs/concepts/text_llms). Many popular models available on Bedrock are [chat completion models](/docs/concepts/chat_models).\n",
"\n",
"You may be looking for [this page instead](/docs/integrations/chat/bedrock/).\n",
":::\n",

2
docs/docs/integrations/llms/cohere.ipynb
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@ -8,7 +8,7 @@
"# Cohere\n",
"\n",
":::caution\n",
"You are currently on a page documenting the use of Cohere models as [text completion models](/docs/concepts/#llms). Many popular Cohere models are [chat completion models](/docs/concepts/#chat-models).\n",
"You are currently on a page documenting the use of Cohere models as [text completion models](/docs/concepts/text_llms). Many popular Cohere models are [chat completion models](/docs/concepts/chat_models).\n",
"\n",
"You may be looking for [this page instead](/docs/integrations/chat/cohere/).\n",
":::\n",

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