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ragSYSTEM_ID: enterprise-rag-system
Enterprise Document RAG System
High-precision PDF semantic search engine with verified source citations and FAISS vector index.
Enterprise Document RAG System // Audio BriefingSTUDIO MP3
0:00 / 0:45
VISUAL ARCHITECTURE // N8N WORKFLOW GRAPH
CLICK ANY NODE TO INSPECT LIVE BLUEPRINT🌐 TRIGGER
Enterprise PDF Upload
Dynamic Ingestion
⚙ EXECUTION TOOL
RecursiveTextSplitter
2500 Chunks / 250 Overlap
⚡ COGNITIVE CORE
Gemini Embeddings
models/gemini-embedding-001
🗄 PERSISTENCE / RAG
FAISS Vector Store
Cosine Similarity Index
🌐 TRIGGER
Recruiter Query Console
Streamlit UI Port 8501
⚡ COGNITIVE CORE
Gemini 2.5 Flash Synthesis
Zero-Hallucination Prompt
🛡 SAFETY GATE
Ragas Citation Auditor
Faithfulness >= 0.95
Gemini Embeddings// models/gemini-embedding-001
⚡ COGNITIVE CORETransforms text fragments into 768-dimensional semantic dense vectors.
EXECUTION SPEC // PYTHON IMPLEMENTATION
from langchain_google_genai import GoogleGenerativeAIEmbeddings
embeddings = GoogleGenerativeAIEmbeddings(model="models/gemini-embedding-001")