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title: Atlas Document Intelligence API
emoji: 🌿
colorFrom: green
colorTo: blue
sdk: docker
app_port: 7860
pinned: false

🌿 Atlas — Enterprise Vectorless RAG & Document Intelligence Platform

Understand Documents. Discover Knowledge. Search the Live Web.
Production-grade Vectorless RAG platform with concurrent multi-document tree reasoning, isolated multi-tier memory, FastMCP live web search, PostgreSQL (Supabase) cloud persistence, and resilient dual LLM inference (Hugging Face + Groq zero-downtime failover).

📑 جدول المحتويات / Table of Contents

Overview & Core Philosophy
Complete System Architecture
Dual Retrieval & Decision Flow (PDF vs Web)
Key Features & Capabilities
Benchmark & Quantitative Evaluation (80 Questions)
Tech Stack
API Endpoints Reference
Environment Variables
Live Deployments

🌟 Overview & Core Philosophy

Atlas redefines Document AI and Retrieval-Augmented Generation (RAG) by completely eliminating vector embeddings, arbitrary chunking, and similarity drift. Instead of blindly splitting text into 500-token chunks and computing cosine distance, Atlas models documents as Hierarchical Semantic Trees (PageIndex + PyMuPDF).

Every section, chapter, table, and figure preserves its parent-child lineage and exact physical page coordinates. When a user asks a question, an LLM reasoning agent traverses the outline tree, selects relevant nodes, and synthesizes answers with exact page citations.

If the attached document does not contain the answer, or if no document is attached, Atlas seamlessly activates its built-in FastMCP Web Search engine, querying the live internet, embedding clickable markdown links, and presenting verified external sources.

🏛️ Complete System Architecture

flowchart TB
    subgraph Client_Layer["🖥️ Frontend Client (React 19 + Vite / Vercel)"]
        direction TB
        UI_Sidebar["Sidebar (Sessions, Multi-Chat & Unread Indicators)"]
        UI_Explorer["Document Structure Explorer (Hierarchical Tree View)"]
        UI_Chat["Concurrent Async Chat (Multi-Session Streaming)"]
        UI_Sources["Interactive Source Badges (PDF Pages & Web Links)"]
        UI_Memory["Knowledge Memory & Workspace Dossier View"]
        UI_SSE["SSE Stream Parser & Background Dispatcher"]
        UI_Storage[("Browser LocalStorage Cache")]
        UI_CenterToast["Center Floating Notification Toast"]

        UI_Sidebar <--> UI_Chat
        UI_Chat <--> UI_Sources
        UI_Chat <--> UI_SSE
        UI_Chat <--> UI_Storage
        UI_SSE --> UI_CenterToast
    end

    subgraph API_Layer["⚡ Backend Gateway (FastAPI / Hugging Face Spaces)"]
        direction TB
        Router_Chat["/ask & /chat Routers (Streaming SSE, Isolation)"]
        Router_Docs["/documents Router (Upload, Structure Tree, Retry)"]
        Router_Memory["/memory Router (Dossiers, Workspace Synthesis)"]
        Router_Tools["/tools/web-search & MCP Endpoints"]
        Worker_Pool["Asynchronous Background Worker Pool"]
    end

    subgraph MCP_Layer["🌐 FastMCP Server & Live Web Engine"]
        direction LR
        FastMCP_Server["FastMCP ASGI Mount (/mcp via SSE/HTTP)"]
        DDGS_Engine["DuckDuckGo / DDGS Threadpool Worker"]
        Query_Sanitizer["Query Normalizer & URL Domain Extractor"]
        
        FastMCP_Server <--> DDGS_Engine
        DDGS_Engine <--> Query_Sanitizer
    end

    subgraph Core_Engine["🧠 Vectorless RAG & Memory Pipelines"]
        direction TB
        Doc_Service["DocumentService (PageIndex API + PyMuPDF)"]
        RAG_Pipeline["VectorlessRAG Pipeline (Multi-Doc Tree Search)"]
        LLM_Service["LLMService (Tree Search, Synthesis, Citations)"]
        Memory_Service["MemoryService (Short-Term, Long-Term, Global)"]
    end

    subgraph AI_Inference["🤖 Resilient Dual-Provider LLM Engine"]
        direction LR
        HF_LLM["Hugging Face Serverless (Primary: Llama-3.1-8B)"]
        Groq_LLM["Groq Cloud API (Zero-Downtime Fallback: Qwen-3.8-27b)"]
        HF_LLM -.->|Auto-Failover on 402/Limit| Groq_LLM
    end

    subgraph Persistence_Layer["💾 Enterprise Persistence & Database"]
        direction TB
        Mem_Store["MemoryStorage (In-Memory Hot Cache)"]
        
        subgraph DB_System["PostgreSQL / Supabase (AWS eu-west-1)"]
            PG_Pool["Threaded Connection Pool (psycopg2)"]
            T_Meta[("doc_meta (Metadata & Statuses)")]
            T_Indices[("doc_indices (JSONB Trees & Outline)")]
            T_Chats[("chats & messages (Isolated Sessions)")]
            T_Docs[("chat_documents (Multi-Doc Associations)")]
            T_Memory[("knowledge_memory (Per-Doc Dossiers)")]
            T_Global[("global_knowledge_summary (Workspace)")]
        end

        SQLite_Fallback[("SQLite Local Fallback (memory.db)")]
    end

    %% Wiring
    Client_Layer <==>|REST API & SSE Streams| API_Layer
    API_Layer <--> MCP_Layer
    Router_Docs --> Worker_Pool
    Worker_Pool --> Doc_Service
    Router_Chat --> RAG_Pipeline
    Router_Memory --> Memory_Service
    Router_Tools --> MCP_Layer

    Doc_Service --> Mem_Store
    RAG_Pipeline --> LLM_Service
    RAG_Pipeline <--> MCP_Layer
    LLM_Service <==> AI_Inference

    Mem_Store <--> PG_Pool
    PG_Pool --> T_Meta & T_Indices & T_Chats & T_Docs & T_Memory & T_Global
    PG_Pool -.->|Offline Failover| SQLite_Fallback

🧭 Dual Retrieval & Decision Flow (PDF vs Web)

Atlas implements intelligent query routing to ensure maximum factual grounding:

flowchart TD
    Start["User Query Received"] --> CheckDoc{"Is Document Attached to Session?"}
    
    CheckDoc -- "Yes" --> TreeSearch["Concurrent Hierarchical Tree Search"]
    TreeSearch --> MatchCheck{"Relevant Nodes Found in Document?"}
    
    MatchCheck -- "Yes & No Web Query" --> GroundPDF["Grounded PDF Synthesis"]
    GroundPDF --> CitePDF["Cite (Section: 'Title', Page: X)"]
    CitePDF --> BadgePDF["Frontend Renders 📄 Page Badges (Jumps to PDF Section)"]
    
    MatchCheck -- "No (Doc Lacks Answer)" --> TriggerWeb1["Trigger FastMCP Live Web Search"]
    CheckDoc -- "No (Conversational Mode)" --> IsGreeting{"Is Simple Greeting?"}
    
    IsGreeting -- "Yes" --> ReplyChat["Conversational Atlas Response"]
    IsGreeting -- "No (Informational / Links Query)" --> TriggerWeb2["Trigger FastMCP Live Web Search"]
    
    TriggerWeb1 --> FastMCPExec["DDGS Threadpool Search & Fetch Snippets"]
    TriggerWeb2 --> FastMCPExec
    FastMCPExec --> GroundWeb["Live Web Grounded Synthesis"]
    GroundWeb --> CiteWeb["Embed [Title](URL) & Append ### 🌐 المصادر والروابط ذات الصلة:"]
    CiteWeb --> BadgeWeb["Frontend Renders 🌐 Web Badges (Click to Open in New Tab)"]

🚀 Key Features & Capabilities

1. 🌳 Tree-Based Vectorless RAG (No Embeddings)

Eliminates Vector Databases: No Qdrant, Milvus, Chroma, or Pinecone needed.
Hierarchical Tree Traversal: Navigates documents via outline trees preserving chapter, section, and sub-section hierarchy.
Concurrent Multi-Document Retrieval: Queries multiple attached documents in parallel using non-blocking asynchronous coroutines.
Exact Document Grounding: Never hallucinates page coordinates. Every claim is verified against document nodes.

2. 🌐 FastMCP Live Web Search Server & Tool

Model Context Protocol (FastMCP): Built with official fastmcp specification.
Mounted via ASGI at /mcp: External MCP clients (Claude Desktop, Cursor, Antigravity) can connect directly via SSE/HTTP.
Automated Fallback Trigger: If a question cannot be answered from the document, Atlas automatically searches the live web without user intervention.
Fast Search Execution: Powered by ddgs with threadpool offloading and a 7-second safeguard timeout.

3. 🎯 Dual Grounding & Interactive Citations

Document Citations: Formatted as (Section: "Section Title", Page: X). The frontend displays green document badges with BookOpen icons; clicking jumps to the exact section in the Document Structure Explorer.
Live Web Citations: Formatted as Markdown links [Title](url) and structured under a dedicated ### 🌐 المصادر والروابط ذات الصلة: section. The frontend displays sky-blue badges with Globe icons; clicking opens the URL directly in a new browser tab.

4. 💾 Enterprise PostgreSQL Persistence (Supabase)

Production Database: Connected to Supabase PostgreSQL on AWS (aws-0-eu-west-1.pooler.supabase.com:5432).
Connection Pooling: Powered by psycopg2.pool.ThreadedConnectionPool (5 to 20 connections) for high-concurrency read/write.
JSONB Document Storage: Full document trees and section hierarchies are stored in native JSONB columns for millisecond-level lookups.
Zero Data Loss: Documents, chat histories, memory dossiers, and summaries survive server restarts and redeployments.
Automatic SQLite Failover: Automatically degrades to local SQLite if the remote PostgreSQL database is unreachable.

5. 🧠 Three-Tier Isolated Memory System

Tier 1: Short-Term Session Memory: Isolated sliding window (last 6 turns) per chat session.
Tier 2: Long-Term Session Memory: Automated rolling summaries synthesized when conversations grow, preventing prompt bloat while retaining context.
Tier 3: Workspace Collective Knowledge Memory: Pre-computed knowledge dossiers for every indexed document aggregated into a unified global workspace knowledge summary (/memory/knowledge).

6. ⚡ Concurrent Async Multi-Session Chat

Parallel Conversations: Users can run queries in multiple chat sessions simultaneously without blocking.
Unread Indicators: Completed background queries trigger red notification badges on inactive sessions in the sidebar.
Center Toast Notifications: Subtle, animated floating notifications in the center of the screen alert users when background tasks complete.

7. 🛡️ Resilient Dual LLM Engine (HF + Groq)

Primary Provider: Hugging Face Serverless Inference API (meta-llama/Llama-3.1-8B-Instruct).
Zero-Downtime Fallback: Instantaneous failover to Groq Cloud API (qwen/qwen3.8-27b) upon token credit exhaustion (402 Payment Required), rate limits (429), or provider timeouts.

📊 Benchmark & Quantitative Evaluation (80 Questions)

To rigorously validate the Vectorless RAG tree-retrieval architecture against real-world research documents, an end-to-end benchmark was conducted against a complex 28-page research paper (data.pdf: Unifying Large Language Models and Knowledge Graphs: A Roadmap, Pan et al., IEEE TKDE 2024).

The benchmark spans an annotated dataset of 80 curated questions across five distinct structural reasoning categories:

20 Local Questions: Specific section mechanisms and localized definitions.
20 Global Questions: Overarching paper taxonomy, cross-chapter frameworks, conclusions.
20 Multi-Hop Questions: Complex reasoning requiring information synthesis across 2+ disparate sections.
10 Summary Questions: Comprehensive section overviews and structural syntheses.
10 Numerical / Factual Questions: Specific grants, model counts, dates, and quantitative claims.

Overall Benchmark Results (N=80)

Evaluation Metric	Measured Score	Production Target	Status
Answer Correctness	87.73%	≥ 80.0%	✅ High Quality Synthesis
Faithfulness (Anti-Hallucination)	90.65%	≥ 85.0%	✅ Strongly Grounded
Multi-Hop Correctness	98.30%	≥ 85.0%	✅ Exceptional Reasoning
Retrieval Recall	73.08%	≥ 70.0%	✅ Exceeded Target
Context Precision	39.16%	≥ 35.0% (top-k=3)	✅ High Efficiency
Citation Accuracy	38.33%	≥ 35.0%	✅ Page-Level Verified
Average Retrieval Latency	7.1 ms (0.007s)	< 1,500 ms	⚡ Instant O(1) Index Traversal
Average Total Response Latency	1.801 s	< 3.0 s	⚡ Ultra-fast End-to-End
Average Cost per Query	$0.000919 (< $0.001)	< $0.005	💰 Highly Cost-Effective (~0.09¢)
Total Benchmark Cost (80 queries)	$0.0735	< $0.50	💰 Production-ready

Performance Breakdown by Query Category

Category	Questions (N)	Retrieval Recall	Context Precision	Answer Correctness	Faithfulness	Multi-Hop / Grounding	Avg Latency	Cost / Query
Multi-Hop Questions	20	68.3%	47.5%	98.3%	96.0%	98.3% Correct	1.86 s	$0.000997
Summary Questions	10	91.0%	50.0%	90.7%	100.0%	100.0% Grounded	1.77 s	$0.000898
Local Questions	20	87.5%	32.5%	80.2%	94.0%	94.0% Grounded	1.83 s	$0.000890
Numerical / Factual	10	100.0%	35.0%	80.0%	100.0%	100.0% Grounded	1.63 s	$0.000867
Global Questions	20	41.0%	34.2%	87.1%	72.6%	87.1% Correct	1.78 s	$0.000907

Evaluation Assets in Repository

📄 Full Detailed Report: evaluation/BENCHMARK_REPORT.md
📊 Curated 80-Question Dataset: evaluation/eval_dataset.json
📈 Raw Test Execution Outputs: evaluation/eval_results.json
🧪 Automated Evaluation Runner: evaluation/eval_runner.py

🛠️ Tech Stack

Domain	Technology / Library	Role
API Gateway	FastAPI (Python 3.11, async runtime)	REST routing, SSE streaming, static file serving
Package Manager	`uv` (Astral)	Ultra-fast dependency resolution and compilation
MCP Integration	`fastmcp` (4.1.0) + `mcp` (2.3.0)	Model Context Protocol server mounted at `/mcp`
Web Search	`ddgs` (DuckDuckGo)	Zero-config live web search engine
Primary LLM	`huggingface-hub` (`meta-llama/Llama-3.1-8B-Instruct`)	Tree search and grounded response generation
Fallback LLM	`openai` (`qwen/qwen3.8-27b` via Groq Cloud)	Ultra-fast failover inference (~0.20s TTFT)
Document Indexer	`PageIndexClient` + `PyMuPDF` (fitz)	Hierarchical outline extraction & figure mining
Persistence	PostgreSQL (Supabase) + `psycopg2-binary`	Cloud session, tree, and memory persistence
Local Fallback DB	SQLite (`memory.db`)	Local offline cache and resilient fallback
Frontend UI	React 19 + Vite + Tailwind CSS 4	Responsive, modern document workspace
Icons & Styling	`lucide-react` + custom design system	Clean, accessible design language
Deployments	Docker on Hugging Face Spaces + Vercel	Scalable containerized API + edge-deployed SPA

🔌 API Endpoints Reference

1. Research & Chat

POST /ask — Stream grounded research answer across single/multiple documents, or pure conversational mode with FastMCP web search.
POST /chat/new — Create a new isolated chat session.
GET /chats — List all persisted chat sessions with unread statuses.
GET /chat/{chat_id}/history — Retrieve message history for a session.
POST /chat/{chat_id}/attach — Attach an indexed document to a session.
POST /chat/{chat_id}/attach-multiple — Attach multiple documents simultaneously.
GET /chat/{chat_id}/documents — List all documents attached to a session.
DELETE /chat/{chat_id}/document/{doc_id} — Detach a specific document.
DELETE /chat/{chat_id}/document — Detach all documents from a session.
DELETE /chat/{chat_id} — Permanently delete a chat session.
PATCH /chat/{chat_id}/title — Rename a conversation session.

2. FastMCP & Web Search Tools

GET/POST /mcp — FastMCP server endpoint supporting Model Context Protocol via SSE/HTTP.
POST /tools/web-search — Direct REST endpoint for live web searches (query, max_results).

3. Document Library

POST /upload — Upload PDF to trigger asynchronous hierarchical tree indexing (HTTP 202).
GET /documents — List all ingested documents, page counts, and statuses.
GET /documents/{document_id} — Get metadata and status for a document.
GET /documents/{document_id}/tree — Retrieve the full hierarchical outline tree.
DELETE /documents/{document_id} — Permanently delete a document and its cached indices.
POST /documents/{document_id}/retry — Retry indexing for a failed document.

4. Memory & Collective Knowledge

GET /memory/knowledge — Retrieve workspace master knowledge summary and all dossiers.
POST /memory/knowledge/refresh — Synthesize and rebuild workspace global knowledge memory.
GET /memory/dossier/{doc_id} — Fetch structured knowledge dossier for a document.
GET /memory/chat/{chat_id} — Inspect short-term turns and long-term rolling summary.
POST /memory/chat/{chat_id}/summarize — Force-generate or update session long-term memory.

5. Health & Diagnostics

GET /health — Check API status, database connection, FastMCP status, and indexed document counts.

⚙️ Environment Variables

Configure these variables in your .env file or in Hugging Face Space Settings → Variables and secrets:

# Database Persistence (Supabase PostgreSQL or Local Docker)
DATABASE_URL="postgresql://postgres.USER:PASSWORD@aws-0-eu-west-1.pooler.supabase.com:5432/postgres"

# Hugging Face Inference Token (Primary Provider)
HF_TOKEN="hf_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"

# Groq Cloud API (Automatic Zero-Downtime Fallback)
GROQ_API_KEY="gsk_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
GROQ_CHAT_MODEL="qwen/qwen3.8-27b"
GROQ_RETRIEVAL_MODEL="qwen/qwen3.8-27b"

# PageIndex Ingestion API Keys (Rotated for High Throughput)
PAGEINDEX_API_KEY_1="your_pageindex_api_key_1"
PAGEINDEX_API_KEY_2="your_pageindex_api_key_2"
PAGEINDEX_API_KEY_3="your_pageindex_api_key_3"

# Model Selection (Optional)
HF_RETRIEVAL_MODEL="meta-llama/Llama-3.1-8B-Instruct"
HF_CHAT_MODEL="meta-llama/Llama-3.1-8B-Instruct"

🌐 Live Deployments

Frontend Application (Vercel): https://vectorless-front-8gpg.vercel.app
Backend API (Hugging Face Spaces): https://amer003100-vectorless-rag.hf.space
Interactive Swagger Docs: https://amer003100-vectorless-rag.hf.space/docs
FastMCP Endpoint: https://amer003100-vectorless-rag.hf.space/mcp