AI Document Agent
RAG-based PDF Analyzer
The Challenge
Extracting specific, contextual answers from thousands of pages of static PDF documentation is a manual, error-prone task for most engineering teams.
The Innovation
We built a Retrieval-Augmented Generation (RAG) agent that converts unstructured data into a high-performance searchable knowledge graph.
Semantic Search
Goes beyond keywords to understand the intent and context of queries.
Vector DB Pipeline
Automated ingestion pipeline with FAISS and high-dimensional embeddings.
Contextual AI
Responses grounded in your specific documents to eliminate hallucinations.
Multi-format Support
Extensible parsing for PDF, Markdown, and raw TXT data.
How it Works
Chunks documents into semantically meaningful pieces.
Generates vector embeddings using OpenAI/OpenSource models.
Stores in a high-speed FAISS index for sub-second retrieval.
LLM synthesizes retrieved context into an accurate response.
What I Learned
Semantic Accuracy
Mastered the nuances of chunking strategies and overlap parameters to maintain document context during vectorization.
Vector Database Tuning
Explored the performance trade-offs between different indexing methods in FAISS for large-scale document sets.