This graph represents the core functionality of a document processing and question-answering system. The main flow involves ingesting documents, processing them into a searchable format, and then using a language model to answer user queries based on the ingested content. Its purpose is to provide an intelligent interface for users to retrieve information from a collection of documents.

Components

Document Ingestion

Handles the loading and initial processing of various document types.

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Text Splitter

Breaks down large documents into smaller, manageable chunks for efficient processing and embedding.

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Vector Store

Stores and retrieves document embeddings, enabling semantic search.

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Embeddings Model

Generates numerical representations (embeddings) of text chunks.

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Language Model (LLM)

Processes user queries and generates answers based on retrieved context.

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Retrieval Chain

Orchestrates the retrieval of relevant document chunks and passes them to the LLM for answer generation.

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