RAG & Knowledge SystemsSTATUS: Open Source 0 0

Atlas Knowledge RAG Platform

Hybrid RAG retrieval and vector search platform

A grounded RAG system that ingests document collections, splits text into embeddings, and queries vector stores to retrieve context for LLM generation with source citations.

Inspect GitHub Source Code

01 // THE BUSINESS PROBLEM & SITUATION

Operational Context

Enterprise teams need accurate answers from large document repositories without ungrounded hallucinations.

CLIENT / ENVIRONMENT: Designed for knowledge management and internal document querying.

02 // SYSTEM ARCHITECTURE & SOLUTION

What Was Built

Built a hybrid RAG pipeline combining vector similarity search with reranking to inject verified context into model prompts.

ATLAS.KNOWLEDGE // HYBRID_RAGSystem Architecture Diagram

CORPUS

PDF Ingest

VECTOR STORE

Qdrant DB

RERANK

CrossEncoder

RESPONSE

Citations

SYSTEMS_ARCHITECTURE_EXPLODER // LAYERED_STACKFULL-STACK SYSTEM DECOUPLING

Frontend Layer

Next.js 16 App Router, React 19, Tailwind CSS, TypeScript

API & Middleware

FastAPI, Pydantic Schema Validation, Rate Limiters

Orchestration

LangGraph Multi-Agent Supervisor & State Machines

Model Providers

OpenAI GPT-4o, Anthropic Claude 3.5, Vision AI

Data Stores

PostgreSQL (Prisma), Qdrant Vector Store, Redis Caching

Observability

LangSmith traces, Pydantic audit logs, Error retries

03 // ENGINEERING CONTRIBUTION & FEATURES

Exact Contribution

Built document chunking and vector ingestion pipeline
Implemented Qdrant vector database integration
Designed citation tracking and grounded response generator

Key Capabilities Built

✓ Hybrid Vector Search

✓ Source Document Citations

✓ Chunking & Reranking Pipeline

✓ FastAPI Query Endpoint

DATA_PROVENANCE_TIMELINE // AUDIT_TRACETRACE_ID: #TR-992041

14:22:01.002Source Ingestion

Received webhook payloadOK

14:22:01.045Schema Validation

Pydantic structured field checkOK

14:22:01.210LangGraph Reasoning

Evaluated criteria rulesOK

14:22:01.350Human Audit Check

Confidence threshold evaluationPASSED

14:22:01.480Database Commit

Created audit record in PostgreSQLCOMMITTED

*Illustrative system trace — demonstration data, not a client result.

04 // EXECUTION PIPELINE

Step-by-Step Workflow

STEP 01User uploads document corpus.

STEP 02Pipeline chunks text and generates embeddings.

STEP 03Query triggers vector retrieval and reranking.

STEP 04LLM generates answer constrained strictly to retrieved context.

05 // ENGINEERING LESSONS & OUTCOMES

Technical Challenges Overcome

• Balancing chunk size between semantic context depth and retrieval precision.

Measurable Outcomes

→ Provided verifiable citations for every generated response.

→ Sub-second context retrieval across document sets.

Lessons & Engineering Rules

"Grounded RAG architectures require explicit citation tracking to maintain user trust."

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PROJECT METADATA

ARSLAN'S ROLERAG & Search Engineer

YEAR & STATUS2025 // Open Source

CATEGORYRAG & Knowledge Systems

TECHNOLOGY STACK

PythonQdrantFastAPILangChainOpenAI APIsPydantic

GITHUB METRICS

Stars: 0

Forks: 0

Verified GitHub Update: 7/28/2026

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