Agentic RAG for Documents Exploration

Overview

This project implements Agentic Retrieval-Augmented Generation (Agentic RAG) for document-centric QA and search, integrating LangChain for agent orchestration and LangGraph for workflow visualization and reasoning tracking.

Unlike traditional RAG, this system uses autonomous agents that iteratively retrieve, reason, verify, and refine answers based on document context.

See also: Semantic Query Parsing for the query understanding component and Prompt Engineering & Evaluation for prompt optimization strategies.

Implementation Details

• Frameworks: LangChain (agent orchestration), LangGraph (workflow graphing)
• Retriever: Dense embeddings (OpenAI / SentenceTransformers), BM25 sparse retrieval, hybrid ranking
• Agents:
  • Planner Agent: decomposes queries into sub-tasks
  • Retriever Agent: executes multi-strategy retrieval
  • Reasoning Agent: applies chain-of-thought reasoning, step-wise synthesis
  • Verifier Agent: validates facts, detects hallucinations
• Indexing: Chunked documents, hierarchical sections, overlapping windows
• Evidence Attribution: Each answer includes references to the original document chunks

Workflow

1. User query → Semantic Query Parser (see project)
2. Planner decomposes query → selects retrieval strategy
3. Retriever pulls relevant documents (dense + sparse)
4. Reasoning agent generates grounded answer
5. Verifier checks consistency and confidence
6. Agent iteratively refines until threshold confidence is reached

Advanced Techniques

• Iterative RAG loops: retrieve → reason → retrieve
• Self-reflection and hallucination detection
• Tool-aware prompt management via Prompt Engineering & Evaluation
• Automated answer abstention if evidence is insufficient

Evaluation & Use Cases

• Enterprise knowledge search
• Scientific and technical QA
• Legal and regulatory compliance analysis
• Human-in-the-loop verification and iterative improvement