An innovative open-source solution for Retrieval-Augmented Generation (RAG) is designed to address complex problems that simple semantic similarity-based retrieval methods cannot resolve.
This framework features a sophisticated deterministic graph that acts as the “brain” of a highly controllable autonomous agent, capable of answering intricate questions derived from private domain data.
Key Features
- Complex Deterministic Graph: Serves as the agent’s “brain,” enabling advanced reasoning capabilities.
- Controllable Autonomous Agent: Effectively addresses complex questions within customized datasets.
- Hallucination Prevention: Guarantees that answers are based solely on the provided data, minimizing the risk of AI-generated inaccuracies.
- Multi-step Reasoning: Breaks down complex queries into manageable subtasks for systematic analysis.
- Adaptive Planning: Continuously refines its strategy based on new information, ensuring relevance and accuracy.
- Performance Evaluation: Employs Ragas metrics for comprehensive quality assessment, ensuring high standards of output.
Workflow Overview
- PDF Loading and Processing: Efficiently loads PDF documents and segments them into chapters for easier handling.
- Text Preprocessing: Cleans and prepares text to enhance summarization and encoding quality.
- Summarization: Utilizes large language models to generate detailed summaries for each chapter, facilitating quick comprehension.
- Book Citation Database Creation: Establishes a database for specific questions that require access to book citations, enhancing the depth of responses.
- Vector Storage Encoding: Encodes book content and chapter summaries into vector storage for efficient retrieval, optimizing response time.
Question Processing
- Anonymizes questions by replacing named entities with variables to maintain privacy.
- Generates high-level plans for anonymized questions, ensuring clarity in the response strategy.
- De-anonymizes plans and breaks them down into tasks that can be retrieved or answered effectively.
Task Execution
- For each task, the agent determines whether to retrieve information or provide an answer based on contextual cues.
- If retrieving, it extracts relevant information from vector storage and refines it for clarity.
- If answering, it employs chain-of-thought reasoning to generate coherent and logical responses.
Validation and Re-planning
- Validates that generated content aligns with the original context to ensure accuracy.
- Re-plans remaining steps based on new information, enhancing the adaptability of the agent.
Final Answer Generation
- Utilizes accumulated context and chain-of-thought reasoning to produce a comprehensive final answer.
Use Case: Analysis of Harry Potter Books
The algorithm was tested using the first Harry Potter book, allowing for a comparative analysis of the model’s reliance on retrieved information versus pre-trained knowledge. This approach enables verification of whether the model utilizes its pre-trained knowledge or strictly depends on information retrieved from vector storage.
Example Question
Q: How did the protagonist defeat the antagonist’s assistant?
To address this question, the following steps are executed:
- Identify the protagonist in the plot.
- Identify the antagonist character.
- Identify the antagonist’s assistant.
- Search for interactions or confrontations between the protagonist and the antagonist.
- Infer the reasons leading to the protagonist’s victory over the assistant.
The agent’s ability to decompose and resolve such complex queries showcases its advanced reasoning capabilities.
For more information, visit the official GitHub repository: Controllable RAG Agent.
This refined article maintains the original intent while enhancing clarity, coherence, and engagement for an international audience.