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refactor(instructions): Streamline chat models section and remove redundant overview content

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Akash Kumar Shaw 2025-10-09 03:45:21 +05:30
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instructions/langchain-python.instructions.md
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@ -35,10 +35,7 @@ LangChain's `Runnable` interface is the foundation for composing and executing c
- For advanced configuration, expose fields and alternatives via `configurable_fields` and `configurable_alternatives`.
## Chat Models
Use LangChain's chat model integrations for conversational AI:
- Use LangChain's chat model integrations for conversational AI:
- Import from `langchain.chat_models` or `langchain_openai` (e.g., `ChatOpenAI`).
- Compose messages using `SystemMessage`, `HumanMessage`, `AIMessage`.
@ -132,27 +129,7 @@ for doc in results:
## Chat models
### Overview
Large Language Models (LLMs) power a wide range of language tasks (generation, summarization, QA, etc.). Modern LLMs are commonly exposed via a chat model interface that accepts a list of messages and returns a message or list of messages.
Newer chat models include advanced capabilities:
- Tool calling: native APIs that allow models to call external tools/services (see tool calling guides).
- Structured output: ask models to emit JSON or schema-shaped responses (use `with_structured_output` where available).
- Multimodality: support for non-text inputs (images, audio) in some models — consult provider docs for support and limits.
### Features & benefits
LangChain offers a consistent interface for chat models with additional features for monitoring, debugging, and optimization:
- Integrations with many providers (OpenAI, Anthropic, Ollama, Azure, Google Vertex, Amazon Bedrock, Hugging Face, Cohere, Groq, etc.). See the chat model integrations in the official docs for the current list.
- Support for LangChain's message format and OpenAI-style message format.
- Standardized tool-calling API for binding tools and handling tool requests/results.
- `with_structured_output` helper for structured responses.
- Async, streaming, and optimized batching support.
- LangSmith integration for tracing/monitoring.
- Standardized token usage reporting, rate limiting hooks, and caching support.
LangChain offers a consistent interface for chat models with additional features for monitoring, debugging, and optimization.
### Integrations