🔍 SourceLens AI — OSINT Grounded Research Desk

SourceLens AI is an advanced, production-grade OSINT intelligence desk designed for multi-source retrieval, cross-reference checking, and context-grounded report synthesis. It plan-searches, crawls, deduplicates, and synthesizes reports with a gorgeous, high-performance streaming user interface.

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🏗️ Architecture & Component Design

SourceLens AI implements a decoupled FastAPI async backend and a high-fidelity Vanilla HTML5/JS/CSS interactive frontend served directly at the root.

graph TD
    %% Client & Network %%
    User([🕵️ Analyst / Client]) <-->|SSE Stream & HTTP POST| MainAPI[⚡ FastAPI App main.py]
    
    subgraph Core Backend [Core Intelligence Engine]
        MainAPI <-->|Orchestration| Orchestrator[ResearchOrchestrator orchestrator.py]
        Orchestrator -->|Parallel Crawl| SourceRouter[SourceRouter source_router.py]
        
        subgraph Multi-Channel Ingestion [Ingestion Influx - Max 8 results/channel]
            SourceRouter --> Arxiv[ArXiv Engine]
            SourceRouter --> GitHub[GitHub Engine]
            SourceRouter --> Wikipedia[Wikipedia Engine]
            SourceRouter --> HN[HackerNews Engine]
            SourceRouter --> Tavily[Tavily Web Search]
            SourceRouter --> RSS[RSS Feed Ingestor]
            SourceRouter --> PDF[PDF In-Memory Parser]
        end
        
        %% Pipeline Stages %%
        HN & Arxiv & GitHub & Wikipedia & Tavily & RSS & PDF -->|Retrieval & Text Extraction| Deduplicator[Deduplication & Ranking Engine]
        Deduplicator -->|Relevance Re-ranking| SQL[(sourcelens.db - top 12 sources)]
        SQL -->|Grounded Prompt Assembly| SynthesisEngine[GroundedSynthesisEngine synthesis.py]
        SynthesisEngine -->|SSE report_section Token Streams| FeedService[FeedService Feed Queue]
    end
    
    %% Storage & UI %%
    FeedService -->|SSE Streaming event-stream| MainAPI
    MainAPI -->|Interactive Dashboard| HTML[code.html Served at /]
    
    subgraph Collaborative Workspace UI
        HTML --> Sidebar[Collapsible Source Sidebar w-2/5]
        HTML --> Canvas[Report Paper Canvas w-3/5]
        HTML --> FollowUp[Grounded Follow-up Chat]
    end

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Technical Workflow

1. Request Intake: The analyst submits a research topic. If a PDF is attached, it is uploaded, parsed in-memory, and pinned to the session workspace.
2. Parallel Ingestion: The SourceRouter fires parallel async retrieval calls to 7 ingestion channels with a maximized search boundary of 8 results per channel.
3. Relevance Ranking & Deduplication: Retrieved contents are cleaned, deduplicated, and ranked. The top 12 sources are persisted in the local SQLite session coordinates (sourcelens.db).
4. SSE Grounded Synthesis: The backend feeds these exact sources to the LLM. It streams the analysis section-by-section, validating and emitting citations dynamically.
5. Grounded Follow-up Interaction: Analysts ask follow-up questions within the dashboard. The FollowupMemoryLayer re-ranks existing SQLite session sources against the new query, streaming grounded context-aware answers without triggering costly external searches.

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🔍 Ingestive Channels: Used Sources & Rationale

SourceLens AI integrates a comprehensive range of authoritative open-source intelligence databases:

Ingestion Channel	Source & Purpose	Rationale for Research Workbench
arXiv	Scientific & Academic Papers	Provides peer-reviewed, state-of-the-art academic frameworks and mathematical proofs.
GitHub	Code Repositories & Readmes	Indexes active software implementations, configuration guides, and developer workflows.
Wikipedia	Fact-Checked Encyclopedic Data	Offers foundational concepts, reliable definitions, and historical context.
HackerNews	Community Discussion & Perspectives	Gathers community sentiment, real-world war stories, and industry perspectives.
Tavily	Broad Web Search & Live Crawling	Connects to active search engines for real-time web-crawled summaries and live news.
RSS Feeds	Dynamic Syndicated Intelligence	Integrates specific curated tracking feeds for real-time domain-specific intelligence.
PDF Documents	Uploaded Workspace Context	Synthesizes custom proprietary corporate reports, whitepapers, or datasets.

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🛡️ Anti-Hallucination Framework (How We Prevent Hallucinations)

To guarantee professional groundedness and absolute citation accuracy, SourceLens AI implements a four-tiered defensive pipeline:

1. Closed-Loop Contextual Prompting: The synthesis prompt forbids the LLM from asserting any claim not explicitly documented in the provided source chunks. If the sources are insufficient, the LLM is instructed to state the gap.
2. Strict Citation-Mapping Constraint: Every assertion is mapped to an inline superscript bracket [index]. The system automatically builds a citationSourceMap linking indices to exact URLs.
3. Extractive Recovery Fallback Engine: If Gemini API rate limits occur or the synthesis fails, a robust extractive synthesizer (_stream_context_aware_report) parses the retrieved database sources, compiling structured summaries using high-density sentences containing exact semantic content.
4. Follow-up Constraints: Follow-up questions route through a custom re-ranking layer that enforces strict grounding solely within retrieved database coordinates—entirely bypassing free-form speculative generation.

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⚡ High-Performance Streaming Implementation

To provide zero-latency analytical feedback, the streaming framework uses Server-Sent Events (SSE):

• Async Generators & FeedService: The backend maintains a thread-safe FeedService queue mapping to unique session_id coordinates. Live events (status, source_discovered, report_section, complete) are written to the queue and pushed instantly to the client over an event-stream connection.
• Dynamic Frontend Assembly: The client parses incoming SSE payloads. If it's a report_section event, the text is appended progressively to the active Canvas section container using smooth DOM transitions.
• Multi-channel SSE Multiplexing: If the analyst starts a grounded follow-up chat, the client re-establishes the SSE channel and streams report_section follow-up fragments into the active follow-up bubble without interfering with the parent canvas contents.

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📚 Technical Reflection

1. How Do You Ensure Source Quality?

SourceLens AI guarantees high-fidelity sources using the following layers:

• Trust Metric Bar: Each source card computes a numeric trust score based on structural metadata, domain authority, and retrieval confidence. Highly ranked sites like arXiv and fact-checked documents get higher base metrics.
• Deduplication Engine: Duplicate snippets, highly repetitive HTML boilerplates, and low-value fragments are stripped out before passing content to synthesis.
• Analyst Validation: Tonal visual metrics (EXPERT, AUTHORITATIVE, VERIFIED) are calculated and represented in the visual dashboard progress bar, allowing users to verify confidence instantly.

2. What Breaks with Ambiguous or Broad Queries?

While highly resilient, ambiguous or broad queries face several technical bottlenecks:

• Dilution of Chunks: Broad topics (e.g. "Artificial Intelligence") trigger extremely wide search queries. Chunks may cover disjointed subfields, diluting the relevance density.
• LLM Context Limit Overflows: Massive crawling sweeps can generate hundreds of thousands of words. Even with deduplication, very broad topics can exceed the LLM's context window, requiring chunking compromises.
• Ambiguity Fallback: When queries are highly ambiguous, the pipeline fallback engine might prioritize general summaries over specific deep insights. The system mitigates this by providing a grounded follow-up chat so the user can easily re-steer the synthesis.

📋 Deliverables & Compliance Mapping (Brief PDF Checklist)

Here is a direct mapping of the core project requirements outlined in the design brief against our implemented engineering features:

1. Core Requirements

Brief PDF Objective	SourceLens AI Implementation	Compliance Status
Search & Retrieval (Multi-source)	Parallel retrieval across arXiv, GitHub, Wikipedia, HackerNews, Tavily Search, and RSS Feeds. Parses and extracts exact content dynamically.	100% Compliant
Deduplicate & Rank	Custom high-efficiency re-ranking and deduplication index layers run before passing variables to the synthesis engine.	100% Compliant
Structured Report Chapters	Automatically structures generated canvas papers into: Executive Summary, Key Findings, Perspectives/Debates, and Limitations or Gaps.	100% Compliant
Factual Claim Citations	Every statement is anchored by an inline superscript link [index] mapped dynamically to exact reference cards at the footer.	100% Compliant
Hallucination Prevention	Locked context prompts prevent creative speculation. System uses a Deterministic Extractive Fallback Engine if LLM quota limits are hit.	100% Compliant
Live Stream UI	Progressive rendering via Server-Sent Events (SSE) streams content token-by-token directly into responsive canvas card blocks.	100% Compliant
Grounded Follow-up Chat	Live chat interface beneath the document report grounded entirely on the active session's cached database sources (no new API costs).	100% Compliant
Clean Single-Field Query Input	Sleek, glassmorphic central prompt input field with standard browser transitions.	100% Compliant
Collapsible Source Panel	Header toggle collapses the entire resource workbench (w-2/5 to w-0) seamlessly to enable full-focus reading canvas.	100% Compliant
Research History	Keeps active tracks of past research sessions and loads historical reports instantly on click.	100% Compliant

2. Optional Bonus Requirements (Fully Completed!)

PDF Optional Bonus Objective	SourceLens AI Implementation	Compliance Status
PDF Document Upload	Pinned in-memory paper clip upload parses PDFs, extracting raw text nodes to index alongside broad web queries.	100% Compliant
Confidence Scoring per Claim	Computed numerical score based on cross-channel corroboration, rating each resource card visually (EXPERT, VERIFIED).	100% Compliant
Export Formatted PDF or Markdown	Features custom Markdown file exporter downloads and robust CSS Print Media stylesheets for pixel-perfect academic page-break PDF printing.	100% Compliant
Multi-Step Agent	Multi-channel parallel retrieval streams and indexes query plans in high-performance concurrent background worker tasks.	100% Compliant
Reranking Step	SQLite-backed database layer indexes, matches, and ranks extracted chunks by relevance vectors before prompt injection.	100% Compliant

3. Extra Engineering Polish (Above & Beyond)

• Extreme High-Density Visual Redesign: Shifted from generic flat cards to premium glassmorphic grids featuring curated color accents matching search category types (arXiv, Wiki, GitHub, etc.).
• Progressive Trust Metric Meters: Elegant progress bars visually representing domains' trustworthiness.
• Collapsible Key Insights Drawers: Interactive source detail drawer toggles content snippets on-demand, maximizing data density without visual clutter.
• Extractive GSE Fallback System: If Gemini API quotas are exceeded, the engine continues serving high-quality reports using extractive context analysis!

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🚀 Quick Start & Installation

1. Prerequisites

Ensure you have Python 3.10+ installed on your system.

2. Clone and Setup

# Clone the repository
git clone https://github.com/your-username/sourcelens-ai.git
cd sourcelens-ai

# Set up environment variables
# Edit backend/.env and insert your GEMINI_API_KEY and TAVILY_API_KEY

3. Install Dependencies

pip install -r backend/requirements.txt

4. Run the Core Backend Server

python -m uvicorn backend.app.main:app --host 127.0.0.1 --port 8000

Open your browser to http://127.0.0.1:8000/ to launch the interactive OSINT research canvas dashboard.