mirror of
https://github.com/hwchase17/langchain.git
synced 2026-02-21 06:33:41 +00:00
Merge branch 'wip-v1.0' into cc/1.0/standard_content
This commit is contained in:
Mason Daugherty
GitHub
@@ -47,7 +47,7 @@ See [supported integrations](/docs/integrations/chat/) for details on getting st
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- [How to: use chat model to call tools](/docs/how_to/tool_calling)
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- [How to: stream tool calls](/docs/how_to/tool_streaming)
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- [How to: handle rate limits](/docs/how_to/chat_model_rate_limiting)
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- [How to: few shot prompt tool behavior](/docs/how_to/tools_few_shot)
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- [How to: few-shot prompt tool behavior](/docs/how_to/tools_few_shot)
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- [How to: bind model-specific formatted tools](/docs/how_to/tools_model_specific)
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- [How to: force a specific tool call](/docs/how_to/tool_choice)
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- [How to: pass multimodal data directly to models](/docs/how_to/multimodal_inputs/)
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@@ -64,8 +64,8 @@ See [supported integrations](/docs/integrations/chat/) for details on getting st
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[Prompt Templates](/docs/concepts/prompt_templates) are responsible for formatting user input into a format that can be passed to a language model.
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- [How to: use few shot examples](/docs/how_to/few_shot_examples)
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- [How to: use few shot examples in chat models](/docs/how_to/few_shot_examples_chat/)
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- [How to: use few-shot examples](/docs/how_to/few_shot_examples)
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- [How to: use few-shot examples in chat models](/docs/how_to/few_shot_examples_chat/)
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- [How to: partially format prompt templates](/docs/how_to/prompts_partial)
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- [How to: compose prompts together](/docs/how_to/prompts_composition)
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- [How to: use multimodal prompts](/docs/how_to/multimodal_prompts/)
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@@ -168,7 +168,7 @@ See [supported integrations](/docs/integrations/vectorstores/) for details on ge
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Indexing is the process of keeping your vectorstore in-sync with the underlying data source.
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- [How to: reindex data to keep your vectorstore in-sync with the underlying data source](/docs/how_to/indexing)
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- [How to: reindex data to keep your vectorstore in sync with the underlying data source](/docs/how_to/indexing)
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### Tools
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@@ -178,7 +178,7 @@ LangChain [Tools](/docs/concepts/tools) contain a description of the tool (to pa
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- [How to: use built-in tools and toolkits](/docs/how_to/tools_builtin)
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- [How to: use chat models to call tools](/docs/how_to/tool_calling)
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- [How to: pass tool outputs to chat models](/docs/how_to/tool_results_pass_to_model)
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- [How to: pass run time values to tools](/docs/how_to/tool_runtime)
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- [How to: pass runtime values to tools](/docs/how_to/tool_runtime)
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- [How to: add a human-in-the-loop for tools](/docs/how_to/tools_human)
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- [How to: handle tool errors](/docs/how_to/tools_error)
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- [How to: force models to call a tool](/docs/how_to/tool_choice)
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@@ -297,7 +297,7 @@ For a high-level tutorial, check out [this guide](/docs/tutorials/sql_qa/).
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You can use an LLM to do question answering over graph databases.
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For a high-level tutorial, check out [this guide](/docs/tutorials/graph/).
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- [How to: add a semantic layer over the database](/docs/how_to/graph_semantic)
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- [How to: add a semantic layer over a database](/docs/how_to/graph_semantic)
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- [How to: construct knowledge graphs](/docs/how_to/graph_constructing)
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### Summarization
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@@ -17,7 +17,7 @@
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"source": [
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"# ChatOllama\n",
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"\n",
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"[Ollama](https://ollama.com/) allows you to run open-source large language models, such as `got-oss`, locally.\n",
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"[Ollama](https://ollama.com/) allows you to run open-source large language models, such as `gpt-oss`, locally.\n",
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"\n",
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"`ollama` bundles model weights, configuration, and data into a single package, defined by a Modelfile.\n",
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"\n",
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@@ -142,8 +142,7 @@ const config = {
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respectPrefersColorScheme: true,
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},
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announcementBar: {
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content:
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'<strong>Our <a href="https://academy.langchain.com/courses/ambient-agents/?utm_medium=internal&utm_source=docs&utm_campaign=q2-2025_ambient-agents_co" target="_blank">Building Ambient Agents with LangGraph</a> course is now available on LangChain Academy!</strong>',
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content: "Our new LangChain Academy Course Deep Research with LangGraph is now live! <a href='https://academy.langchain.com/courses/deep-research-with-langgraph/?utm_medium=internal&utm_source=docs&utm_campaign=q3-2025_deep-research-course_co' target='_blank'>Enroll for free</a>.",
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backgroundColor: "#d0c9fe",
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},
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prism: {
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@@ -21,13 +21,13 @@ For full documentation see the [API reference](https://python.langchain.com/api_
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## 1️⃣ Core Interface: Runnables
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The concept of a Runnable is central to LangChain Core – it is the interface that most LangChain Core components implement, giving them
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The concept of a `Runnable` is central to LangChain Core – it is the interface that most LangChain Core components implement, giving them
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- a common invocation interface (invoke, batch, stream, etc.)
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- a common invocation interface (`invoke()`, `batch()`, `stream()`, etc.)
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- built-in utilities for retries, fallbacks, schemas and runtime configurability
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- easy deployment with [LangServe](https://github.com/langchain-ai/langserve)
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- easy deployment with [LangGraph](https://github.com/langchain-ai/langgraph)
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For more check out the [runnable docs](https://python.langchain.com/docs/expression_language/interface). Examples of components that implement the interface include: LLMs, Chat Models, Prompts, Retrievers, Tools, Output Parsers.
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For more check out the [runnable docs](https://python.langchain.com/docs/concepts/runnables/). Examples of components that implement the interface include: LLMs, Chat Models, Prompts, Retrievers, Tools, Output Parsers.
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You can use LangChain Core objects in two ways:
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@@ -51,7 +51,7 @@ LangChain Expression Language (LCEL) is a _declarative language_ for composing L
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LangChain Core compiles LCEL sequences to an _optimized execution plan_, with automatic parallelization, streaming, tracing, and async support.
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For more check out the [LCEL docs](https://python.langchain.com/docs/expression_language/).
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For more check out the [LCEL docs](https://python.langchain.com/docs/concepts/lcel/).
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@@ -59,8 +59,6 @@ For more advanced use cases, also check out [LangGraph](https://github.com/langc
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## 📕 Releases & Versioning
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`langchain-core` is currently on version `0.1.x`.
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As `langchain-core` contains the base abstractions and runtime for the whole LangChain ecosystem, we will communicate any breaking changes with advance notice and version bumps. The exception for this is anything in `langchain_core.beta`. The reason for `langchain_core.beta` is that given the rate of change of the field, being able to move quickly is still a priority, and this module is our attempt to do so.
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Minor version increases will occur for:
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@@ -3,28 +3,21 @@
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⚡ Building applications with LLMs through composability ⚡
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[](https://github.com/langchain-ai/langchain/releases)
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[](https://github.com/langchain-ai/langchain/actions/workflows/lint.yml)
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[](https://github.com/langchain-ai/langchain/actions/workflows/test.yml)
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[](https://pepy.tech/project/langchain)
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[](https://opensource.org/licenses/MIT)
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[](https://twitter.com/langchainai)
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[](https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/langchain-ai/langchain)
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[](https://codespaces.new/langchain-ai/langchain)
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[](https://star-history.com/#langchain-ai/langchain)
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[](https://libraries.io/github/langchain-ai/langchain)
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[](https://github.com/langchain-ai/langchain/issues)
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Looking for the JS/TS version? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).
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To help you ship LangChain apps to production faster, check out [LangSmith](https://smith.langchain.com).
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[LangSmith](https://smith.langchain.com) is a unified developer platform for building, testing, and monitoring LLM applications.
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Fill out [this form](https://www.langchain.com/contact-sales) to speak with our sales team.
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## Quick Install
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`pip install langchain`
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or
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`pip install langsmith && conda install langchain -c conda-forge`
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## 🤔 What is this?
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@@ -34,22 +27,22 @@ This library aims to assist in the development of those types of applications. C
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**❓ Question answering with RAG**
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- [Documentation](https://python.langchain.com/docs/use_cases/question_answering/)
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- [Documentation](https://python.langchain.com/docs/tutorials/rag/)
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- End-to-end Example: [Chat LangChain](https://chat.langchain.com) and [repo](https://github.com/langchain-ai/chat-langchain)
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**🧱 Extracting structured output**
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- [Documentation](https://python.langchain.com/docs/use_cases/extraction/)
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- [Documentation](https://python.langchain.com/docs/tutorials/extraction/)
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- End-to-end Example: [SQL Llama2 Template](https://github.com/langchain-ai/langchain-extract/)
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**🤖 Chatbots**
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- [Documentation](https://python.langchain.com/docs/use_cases/chatbots)
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- [Documentation](https://python.langchain.com/docs/tutorials/chatbot/)
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- End-to-end Example: [Web LangChain (web researcher chatbot)](https://weblangchain.vercel.app) and [repo](https://github.com/langchain-ai/weblangchain)
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## 📖 Documentation
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Please see [here](https://python.langchain.com) for full documentation on:
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Please see [our full documentation](https://python.langchain.com) on:
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- Getting started (installation, setting up the environment, simple examples)
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- How-To examples (demos, integrations, helper functions)
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@@ -79,7 +72,7 @@ Agents involve an LLM making decisions about which Actions to take, taking that
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**🧐 Evaluation:**
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[BETA] Generative models are notoriously hard to evaluate with traditional metrics. One new way of evaluating them is using language models themselves to do the evaluation. LangChain provides some prompts/chains for assisting in this.
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Generative models are notoriously hard to evaluate with traditional metrics. One new way of evaluating them is using language models themselves to do the evaluation. LangChain provides some prompts/chains for assisting in this.
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For more information on these concepts, please see our [full documentation](https://python.langchain.com).
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@@ -3,28 +3,21 @@
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⚡ Building applications with LLMs through composability ⚡
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||||
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||||
[](https://github.com/langchain-ai/langchain/releases)
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||||
[](https://github.com/langchain-ai/langchain/actions/workflows/lint.yml)
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||||
[](https://github.com/langchain-ai/langchain/actions/workflows/test.yml)
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||||
[](https://pepy.tech/project/langchain)
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||||
[](https://opensource.org/licenses/MIT)
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||||
[](https://twitter.com/langchainai)
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||||
[](https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/langchain-ai/langchain)
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||||
[](https://codespaces.new/langchain-ai/langchain)
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||||
[](https://star-history.com/#langchain-ai/langchain)
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||||
[](https://libraries.io/github/langchain-ai/langchain)
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||||
[](https://github.com/langchain-ai/langchain/issues)
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||||
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||||
Looking for the JS/TS version? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).
|
||||
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To help you ship LangChain apps to production faster, check out [LangSmith](https://smith.langchain.com).
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||||
[LangSmith](https://smith.langchain.com) is a unified developer platform for building, testing, and monitoring LLM applications.
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||||
Fill out [this form](https://www.langchain.com/contact-sales) to speak with our sales team.
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## Quick Install
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`pip install langchain`
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or
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`pip install langsmith && conda install langchain -c conda-forge`
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## 🤔 What is this?
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@@ -34,22 +27,22 @@ This library aims to assist in the development of those types of applications. C
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**❓ Question answering with RAG**
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- [Documentation](https://python.langchain.com/docs/use_cases/question_answering/)
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- [Documentation](https://python.langchain.com/docs/tutorials/rag/)
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- End-to-end Example: [Chat LangChain](https://chat.langchain.com) and [repo](https://github.com/langchain-ai/chat-langchain)
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**🧱 Extracting structured output**
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- [Documentation](https://python.langchain.com/docs/use_cases/extraction/)
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- [Documentation](https://python.langchain.com/docs/tutorials/extraction/)
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- End-to-end Example: [SQL Llama2 Template](https://github.com/langchain-ai/langchain-extract/)
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**🤖 Chatbots**
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- [Documentation](https://python.langchain.com/docs/use_cases/chatbots)
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- [Documentation](https://python.langchain.com/docs/tutorials/chatbot/)
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- End-to-end Example: [Web LangChain (web researcher chatbot)](https://weblangchain.vercel.app) and [repo](https://github.com/langchain-ai/weblangchain)
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## 📖 Documentation
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||||
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Please see [here](https://python.langchain.com) for full documentation on:
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Please see [our full documentation](https://python.langchain.com) on:
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- Getting started (installation, setting up the environment, simple examples)
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||||
- How-To examples (demos, integrations, helper functions)
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||||
@@ -79,7 +72,7 @@ Agents involve an LLM making decisions about which Actions to take, taking that
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**🧐 Evaluation:**
|
||||
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[BETA] Generative models are notoriously hard to evaluate with traditional metrics. One new way of evaluating them is using language models themselves to do the evaluation. LangChain provides some prompts/chains for assisting in this.
|
||||
Generative models are notoriously hard to evaluate with traditional metrics. One new way of evaluating them is using language models themselves to do the evaluation. LangChain provides some prompts/chains for assisting in this.
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||||
|
||||
For more information on these concepts, please see our [full documentation](https://python.langchain.com).
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@@ -18,12 +18,6 @@ Pip:
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pip install -U langchain-tests
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```
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Poetry:
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```bash
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poetry add langchain-tests
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```
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uv:
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```bash
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@@ -14,12 +14,10 @@ pip install langchain-text-splitters
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LangChain Text Splitters contains utilities for splitting into chunks a wide variety of text documents.
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For full documentation see the [API reference](https://python.langchain.com/api_reference/text_splitters/index.html)
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and the [Text Splitters](https://python.langchain.com/docs/modules/data_connection/document_transformers/) module in the main docs.
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and the [Text Splitters](https://python.langchain.com/docs/how_to/#text-splitters) module in the main docs.
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## 📕 Releases & Versioning
|
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`langchain-text-splitters` is currently on version `0.0.x`.
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Minor version increases will occur for:
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- Breaking changes for any public interfaces NOT marked `beta`
|
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