Chat with Any PDF in Seconds Using Intelligent RAG.
Engineered with state-of-the-art semantic search and context-augmented neural generation for lightning-fast retrieval and zero-hallucination document interrogation.Engineered with semantic search and neural generation for instant, zero-hallucination document interrogation.
"What were the key revenue growth drivers this quarter?"
According to Section 3.2 (Page 14), revenue increased by 28% YoY driven primarily by Enterprise Cloud expansion ($4.2M) and automated subscription renewals.
How It Works
From a static document to an interactive conversation in three simple steps.
Upload & Ingestion
PyPDF multi-page parsing and recursive text splitting (1,000-char chunks with 200-char overlap) preserve document structure.
Vector Embeddings & Pinecone
Google Gemini 768-dimensional embeddings index vectorized chunks into low-latency Pinecone vector stores with metadata isolation.
LCEL Retrieval & Streaming
Top-4 contextual similarity search combined with Google Gemini LLM synthesis and real-time chunked token streaming.
System Architecture
How our Retrieval-Augmented Generation pipeline transforms static documents into instant, verified intelligence.
Pinecone 768-D Vector Space
Document chunks are transformed into 768-dimensional embeddings via Google Gemini and indexed into Pinecone with isolated metadata filtering for sub-millisecond retrieval.
Top-K Context Retrieval
Cosine-distance similarity scoring fetches the top 4 most relevant text excerpts with strict document ID isolation per conversation.
Recursive Chunking Engine
PyPDF stream extraction with LangChain recursive character splitting (1,000-char chunks, 200-char overlap) preserves semantic paragraph flow.
Grounded LCEL Neural Synthesis
LangChain Expression Language (LCEL) chain couples the retriever to Google Gemini with anti-hallucination guardrails and asynchronous chunked token streaming.
Frequently Asked Questions
Everything you need to know about the product and architecture.