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Sophisticated Controllable Agent for Complex RAG Tasks 🧠📚
An advanced Retrieval-Augmented Generation (RAG) solution designed to tackle complex questions that simple semantic similarity-based retrieval cannot solve. This project showcases a sophisticated deterministic graph acting as the "brain" of a highly controllable autonomous agent capable of answering non-trivial questions from your own data.
RAG Made Simple - the 400-page visual guide that extends this repo.
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🌟 Key Features
- Sophisticated Deterministic Graph: Acts as the "brain" of the agent, enabling complex reasoning.
- Controllable Autonomous Agent: Capable of answering non-trivial questions from custom datasets.
- Hallucination Prevention: Ensures answers are solely based on provided data, avoiding AI hallucinations.
- Multi-step Reasoning: Breaks down complex queries into manageable sub-tasks.
- Adaptive Planning: Continuously updates its plan based on new information.
- Performance Evaluation: Utilizes
Ragas metrics for comprehensive quality assessment.
🧠 How It Works
- PDF Loading and Processing: Load PDF documents and split them into chapters.
- Text Preprocessing: Clean and preprocess the text for better summarization and encoding.
- Summarization: Generate extensive summaries of each chapter using large language models.
- Book Quotes Database Creation: Create a database for specific questions that will need access to quotes from the book.
- Vector Store Encoding: Encode the book content and chapter summaries into vector stores for efficient retrieval.
- Question Processing:
- Anonymize the question by replacing named entities with variables.
- Generate a high-level plan to answer the anonymized question.
- De-anonymize the plan and break it down into retrievable or answerable tasks.
- Task Execution:
- For each task, decide whether to retrieve information or answer based on context.
- If retrieving, fetch relevant information from vector stores and distill it.
- If answering, generate a response using chain-of-thought reasoning.
- Verification and Re-planning:
- Verify that generated content is grounded in the original context.
- Re-plan remaining steps based on new information.
- Final Answer Generation: Produce the final answer using accumulated context and chain-of-thought reasoning.
📊 Evaluation
The solution is evaluated using Ragas metrics:
- Answer Correctness
- Faithfulness
- Answer Relevancy
- Context Recall
- Answer Similarity
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