The Customer Support Chatbot with Retrieval-Augmented Generation (RAG) project delivers an end-to-ai solution for accurate, context-aware customer interactions, automating support and reducing resolution times. It ingests support documents, indexes via LangChain, retrieves semantically with Pinecone/Weaviate embeddings, generates responses using OpenAI GPT/Llama 3, and exposes via FastAPI for website/CRM integration. The system achieves 92% accuracy, <1.5s latency, cuts tickets by 50%, minimizes hallucinations through RAG, and ensures brand-aligned rules, completed over 9.5 months from February to November 2025 for scalable service automation.
The architecture follows a RAG pipeline: support documents are ingested and chunked via LangChain loaders, embedded and stored in vector DBs (Pinecone/Weaviate), retrieved (top-k=5) with hybrid semantic/keyword search for queries, augmented into LLM prompts for generation, and served through FastAPI endpoints with async handling. This design ensures factual responses, scalability with caching, security via JWT/rate limiting, and integrations (e.g., JS widgets, Salesforce webhooks), focusing on low-latency, compliant support for diverse queries like troubleshooting or policies.
The system uses OpenAI GPT/Llama 3 for language generation, LangChain for orchestration (loaders, splitters, chains, prompts), Pinecone/Weaviate for vector storage and search, and FastAPI for API deployment. Additional libraries include OpenAI/Hugging Face embeddings, Docker for containerization, Prometheus for monitoring; alternatives like HuggingFaceHub for local Llama inference support on-premise options.
The RAG pipeline indexes documents (PDFs/Markdown/web) with 500-token chunking and embeddings (text-embedding-ada-002 or Hugging Face), stores in vector DB namespaces/schemas, retrieves via LangChain retrievers with metadata filtering/re-ranking, and injects contexts into chains like RetrievalQA. Features include custom prompts ("Use {context} to answer {question} politely"), system rules (e.g., no medical advice, escalate if unsure), moderation guardrails, and hybrid search for precision, achieving 92% accuracy on test queries with BLEU/ROUGE evaluation.
Data processing loads documents from directories using LangChain loaders, splits recursively, generates embeddings, and upserts ~10,000 chunks into vector DBs with namespaces. For queries, compute embeddings, retrieve/filter contexts (k=5), inject into prompts for LLM generation, cache frequent responses for efficiency, and log interactions via API. Ensures privacy anonymization, handles ~500 docs initially, scalable to production with async orchestration and cost management via Llama fallback.
Testing includes unit for indexing/chains, integration for end-to-end query flow, load for 500+ queries/day (<2s latency), and accuracy via human/BLEU review (>85%). Deployment containerizes with Docker, hosts on AWS/Heroku with auto-scaling, integrates JS widgets/webhooks, uses phased rollout with anonymization/guardrails, and supports rollback via model versions if issues arise.
Post-deployment, monitor latency/accuracy via Prometheus, query logs for drift, and vector DB usage, aiming for 99.9% uptime and <2s responses. Maintenance includes quarterly prompt/rule updates, monthly security audits, and cost optimizations (caching, Llama switch), with alerts for high-escalation rates to trigger reviews.
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