LangChain 03 - astream_events Streaming Output with FAISS Practice

This article mainly introduces two key technologies:

FAISS Introduction: Facebook AI Similarity Search is an efficient similarity search library developed by Meta, suitable for large-scale vector data processing, supports GPU acceleration, multiple index structures (L2 distance, inner product distance).

langchain astream_events: Used for real-time monitoring and processing intermediate events during language model generation, supports async streaming processing, provides event types like on_llm_start, on_llm_new_token, on_llm_end.

Install Dependencies

pip install --upgrade --quiet langchain-core langchain-community langchain-openai
pip install faiss  # or for mac use faiss-cpu

Core Code Example

Use DocArrayInMemorySearch to create vector store, combine with ChatOpenAI and prompt template, implement streaming output through astream_events.

Running Result Example

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Streaming output effect shows the progressive generation process of each token.