DocuSync AI: Context-Aware Multimodal RAG Engine

DocuSync AI is an enterprise-grade, multi-agent Retrieval-Augmented Generation (RAG) system. Unlike standard RAG pipelines that blindly retrieve text, DocuSync utilizes a 4-Agent Orchestration Flow to read a user's environment profile (OS, Framework, Language), dynamically rewrite search queries, strictly filter vector databases, and synthesize highly contextual answers with zero hallucination.

Automated Evaluation Metrics (Ragas & LangSmith)

This system was mathematically evaluated using the ragas framework against a ground-truth dataset, proving its enterprise readiness and guardrail efficacy.

• Faithfulness (Hallucination Guardrail): 97.06% (System successfully refuses to invent code when documentation is missing for a specific OS/Framework).
• Context Precision: 100% (On Valid Contexts) (Cross-Encoder reranking ensures the top returned node is the exact match for the user's environment).
• Observability: 100% of pipeline executions, token usage, and cross-encoder latencies are traced and monitored via LangSmith.

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System Architecture

graph TD
    subgraph Frontend [UI Layer]
        UI[Web Interface]
        Profile[User Persona Profile]
    end

    subgraph API [FastAPI Backend]
        Router[API Router]
    end

    subgraph Agentic_Flow [4-Agent Orchestration]
        A1[Agent A: Context Extractor]
        A2[Agent B: Query Rewriter & Retriever]
        A3[Agent C: Cross-Encoder Reranker]
        A4[Agent D: Contextual Synthesizer]
    end

    subgraph Data_Layer [Vector Infrastructure]
        Parser[Context-Aware Chunking]
        Embed[BAAI/bge-large-en-v1.5]
        DB[(Qdrant Vector Database)]
    end

    %% Ingestion Flow
    Docs((Raw Docs)) --> Parser
    Parser -->|Intelligent Metadata Tagging| Embed
    Embed --> DB

    %% Query Flow
    UI -->|Raw Query| Router
    Profile -->|JSON Context| Router
    Router --> A1
    
    A1 -->|Structured Constraints| A2
    A2 -->|Optimized Query + Strict Filters| DB
    DB -->|Top 20 Nodes| A3
    A3 -->|Top 5 High-Precision Nodes| A4
    
    A4 -->|Gemini 2.5 Synthesis| Router
    Router -->|Final Answer| UI

    %% Styling
    classDef primary fill:#0f172a,stroke:#38bdf8,stroke-width:2px,color:#f8fafc;
    classDef secondary fill:#1e293b,stroke:#a78bfa,stroke-width:2px,color:#f8fafc;
    
    class A1,A2,A3,A4 primary;
    class DB,Embed,Parser secondary;

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Core Features

1. Intelligent Ingestion (document_parser.py): Deep content analysis that scans markdown headers and code blocks to dynamically assign os, language, and framework metadata tags to vector chunks before embedding.
2. Dynamic Metadata Filtering: Prevents context contamination. A macOS/React developer will never be served Windows/Python installation instructions.
3. Cross-Encoder Reranking: Utilizes HuggingFace ms-marco-MiniLM-L-6-v2 to mathematically score and rerank retrieved nodes, boosting precision by filtering out semantically similar but contextually irrelevant chunks.
4. Asynchronous Backend: Built on FastAPI with asyncio.to_thread execution, ensuring the API remains highly responsive during computationally heavy ML embedding and reranking tasks.

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Tech Stack

• LLM: Google Gemini 2.5 Flash/ (You can also switch to ollama if Gemini API is not feasible)
• Orchestration: LlamaIndex
• Vector Database: Qdrant (Local Backend)
• Embeddings: HuggingFace (BAAI/bge-large-en-v1.5)
• Reranker: Sentence Transformers (cross-encoder/ms-marco-MiniLM-L-6-v2)
• Backend: FastAPI, Uvicorn, Pydantic
• MLOps / Evaluation: LangSmith, Ragas, Datasets
• Frontend: Vanilla HTML/CSS/JS (Glassmorphism UI)

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Quick Start

1. Prerequisites

Ensure you have Python 3.10+ installed. Clone the repository and install the dependencies.

git clone https://github.com/AbdullahWali007/DocuSync.git
cd DocuSync
pip install -r requirements.txt

2. Environment Variables

Create a .env file in the root directory:

GEMINI_API_KEY="your_gemini_key"
LANGCHAIN_API_KEY="your_langsmith_key"   # Only required for Phase 4 tracing
LANGCHAIN_PROJECT="DocuSync_AI_Production"

3. Initialize the Vector Database (Phase 1)

Run the ingestion script to parse your documents, generate embeddings, and populate Qdrant.

python ingest.py

Note: This single command internally imports and uses the supporting modules config.py, document_parser.py, and qdrant_setup.py – you do not need to run them manually.

4. Start the Application (Phase 5)

Spin up the FastAPI backend server:

python phase5_api.py

Once the server is running on http://localhost:8000, open index.html in your web browser to interact with the Multimodal UI.

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Pipeline Execution Order (Detailed)

For those who wish to run the optional evaluation or tracing phases, below is the full recommended sequence:

Step	File	Purpose
1	ingest.py	(Mandatory) Loads, chunks, enriches metadata, embeds, and stores documents in Qdrant.
2 (Optional)	phase2_baseline.py	Runs a set of benchmark queries against a simple Python-only retriever; exports results to phase2_control_state.json. Useful for before/after comparisons.
3 (Optional)	phase3_orchestration.py	Can be run directly (has __main__) to test a single query against the 4‑Agent pipeline without the API. Mainly used for development.
4 (Optional)	phase4_tracing.py	Wraps the orchestrator with LangSmith @traceable decorators and executes a sample query. Requires LANGCHAIN_API_KEY.
5 (Optional)	phase4_ragas_eval.py	Runs the Ragas evaluation suite against a ground‑truth dataset; outputs a CSV with per‑sample metrics (faithfulness, context precision, answer relevancy).
6	phase5_api.py	(Production) Starts the FastAPI server. All other phases are integrated; this is the final service entrypoint.

Important: The supporting library files (config.py, document_parser.py, qdrant_setup.py) are not executed directly; they are imported by the scripts above.

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Evaluation & Observability (Phases 4)

To run the automated evaluation and tracing:

# Trace a query with LangSmith (ensure LANGCHAIN_API_KEY is set)
python phase4_tracing.py

# Evaluate the pipeline with Ragas on a ground-truth dataset
python phase4_ragas_eval.py

Results are saved locally (phase4_ragas_metrics.csv) and visible in your LangSmith dashboard.

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Engineered by M.Abdullah Wali.