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This intelligent customer support chatbot leverages Retrieval-Augmented Generation (RAG) to provide accurate, contextual responses by combining your knowledge base with AI capabilities. The system automatically retrieves relevant documents from your Pinecone vector store and uses them to generate informed responses through OpenAI's language models.
Main Chat Flow (Agent Workflow)
User Message β Memory Retrieval β Vector Search β Context Assembly β AI Response β Memory Update β Response
Process Flow:
Message Reception: Webhook receives user chat messages with session management
Memory Retrieval: Loads conversation history for context continuity
Semantic Search: Queries Pinecone vector store for relevant documents
Context Assembly: Combines retrieved documents with conversation history
AI Generation: OpenAI generates contextual response using assembled context
Memory Storage: Updates conversation memory for future interactions
Response Delivery: Returns formatted response to user interface
Document Ingestion Flow
Document Source β Text Extraction β Chunking β Embedding β Vector Storage
Process Flow:
Document Trigger: Google Drive or manual file upload detection
Content Extraction: Extracts text from various file formats (PDF, DOC, TXT)
Text Chunking: Splits documents into optimal chunks for embedding
Embedding Generation: Creates vector embeddings using OpenAI
Vector Storage: Stores embeddings in Pinecone with metadata
Index Update: Updates search index for immediate availability