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AI & ML, RAGCompleted
Ready4RAG: High-Precision Dual-Layer RAG Pipeline
A next-generation ingestion system converting complex PDFs into value using Vision LLMs and Hybrid Memory (Vector + Graph) for grounded, high-precision answers.
Key Details
Vision-Powered Extraction: Converts PDF to Markdown with near-perfect layout preservation using multimodal Vision LLMs.
Dual-Layer Memory: Hybrid storage system using Qdrant for vector similarity and NetworkX for graph-based reasoning.
Auto-Graph Construction: Automatically extracts entities (People, Locations, Concepts) and relationships to build a knowledge graph.
Hybrid Chatbot Engine: Interactive interface that retrieves and merges context from both vector and graph layers.
Multi-Provider Infrastructure: Plug-and-play support for Google Gemini, OpenAI, Groq, and local Ollama models.
Highlights
- •Vision-Powered PDF to Markdown conversion
- •Hybrid Memory: Vector + Knowledge Graph
- •Automatic Entity & Relationship Extraction
- •Multi-Model Support (Gemini, OpenAI, Groq)
- •Interactive Hybrid Context Chatbot
Technologies
PythonQdrantNetworkXGoogle GeminiOpenAIOllamaLangChain