Legal Discovery Intelligence Graph

A public synthetic-matter investigation workflow combines vector and graph retrieval, cited evidence, timelines, privilege/PII flags, calibrated refusal, and an auditable case brief.

Open project Source

Abstract investigation graph with connected entities and evidence paths

STAKEHOLDER VIEW

What this project is for.

Problem: Legal discovery teams need to connect people, events, documents, and evidence without losing the source trail. This project demonstrates that workflow on a fully fictional matter: reviewers can search, traverse relationships, inspect a timeline, and open cited documents while the product clearly distinguishes evidence from generated structure.
Intended user: A legal-technology reviewer or discovery analyst evaluating evidence retrieval and relationship navigation.
Decision supported: Whether a relationship or event is supported by cited evidence in the fictional matter.
Outcome: A public, evidence-first Graph RAG case workspace built on a reproducible synthetic legal matter.
What to try: Open the fictional matter and choose a guided investigation question
Important limitation: Synthetic templated text is materially cleaner than real discovery data.

Investigate a relationship while retaining citations

01Open the fictional matter and choose a guided investigation question
02Review hybrid search results and the calibrated evidence/refusal state
03Traverse related entities and events in the graph and timeline
04Open cited synthetic documents and compare the result with committed evaluation

Technical reviewArchitecture, evidence, controls, deployment, and trade-offs

ARCHITECTURE

One source, one rendered system view.

A deterministic generator emits a 455-document corpus and gold labels. Offline pipelines extract entities/events, create pgvector embeddings, and load Neo4j relationships. The deployed Flask app uses an ONNX embedding backend within Render's memory limit, fuses vector and graph retrieval, applies a calibrated refusal threshold, and renders cited results, graphs, timelines, flags, and evaluation artifacts.

System architecture showing a reviewer, synthetic discovery inputs, validation and extraction, vector and graph stores, hybrid retrieval, security boundary, observability, Render deployment, cited outputs, and the evaluation loop. — Pan to inspect the system; zoom controls are available when detail is needed.

End-to-end flow

01Deterministic synthetic corpus and gold-label generation
02Real-file ingestion for PDF, DOCX, and EML
03Entity, event, privilege, and synthetic-PII extraction
04PostgreSQL pgvector indexing and Neo4j graph loading
05Hybrid retrieval with calibrated refusal and cited evidence
06Flask investigation UI with graph, timeline, evaluation, and case brief

Technology stack

PythonFlaskLangChainsentence-transformersONNX RuntimePostgreSQL + pgvectorNeo4j AuraDBSupabasePlotly

Technology decisions

Decision	Why	Alternative	Trade-off
PostgreSQL pgvector plus Neo4j	Vector similarity and explicit relationship traversal serve complementary discovery questions.	Vector search alone	Two stores add operational complexity, and graph expansion requires category-level regression measurement.
ONNX Runtime on Render	Fits the embedding model inside the free-tier memory envelope while preserving measured vector parity.	PyTorch sentence-transformers runtime	Requires an exported model and parity testing but avoids the deployed worker's torch memory failure.

Evaluation and evidence

Metric	Plain-language meaning	Score / value	Dataset / scenario	Threshold / baseline	Interpretation	Evidence
strict entity F1	What strict entity F1 measures for the stated evaluation scope.	0.887	2,223 entity mentions in a deterministic 455-document synthetic corpus, seed 42	No threshold or baseline recorded.	Clean templated text materially overstates expected performance on real discovery documents.	evaluation.entity-extraction
relationship hit@5	What relationship hit@5 measures for the stated evaluation scope.	0.833	38 categorized retrieval queries over the 455-document synthetic corpus	No threshold or baseline recorded.	Graph expansion improves relationship queries but can dilute dense-corpus ranking at larger k.	evaluation.hybrid-retrieval

These are deterministic local evaluation on a synthetic corpus plus a live reachability observation results, not a production service-level objective. Unknown values are shown as “Not recorded”; units and special characters retain their source meaning.

Technical terms and value conventions

Term	Plain-language use	How this project uses it
Evaluation score	A measured result from a defined test or benchmark, not a general promise.	Read it with the dataset, scenario, and limitation shown in the evaluation table.
Unknown / null	A value that was not observed or is intentionally not claimed.	Rendered as “Not recorded” rather than silently converted to zero or omitted.
Special characters	Symbols such as %, currency, Unicode, and escaped input retain their source meaning.	Units and encoding are explained where they affect the decision.

Data boundary

Classification	Source	Permitted use	Excluded data
synthetic	Repository-generated Project Falcon synthetic legal-discovery corpus, seed 42	Portfolio demonstration, deterministic evaluation, and engineering research	Real client, employee, custodian, matter, communication, and personal data; Production legal advice, privilege determinations, and discovery decisions

All documents, people, companies, amounts, identifiers, and events are synthetic and fictional. Metrics are reproducible results on templated synthetic text and are not claims about real legal matters, privilege determinations, or production discovery performance.

Security and privacy controls

Control	Implementation	Evidence	Limitation
Synthetic-only public corpus	The generator creates fictional documents, people, organizations, events, identifiers, and gold labels.	disclosure.synthetic-corpus	Synthetic templated text is cleaner than real discovery material.
Evidence threshold and explicit refusal	The retriever returns an explicit unsupported state when the calibrated evidence threshold is not met.	evaluation.hybrid-retrieval	A calibrated threshold on this corpus is not a legal-correctness guarantee.
Read-only public presentation	The public application exposes the fictional matter and aggregate evidence without a real-client upload path.	deployment.render-root, disclosure.synthetic-corpus	The demo has no authenticated matter isolation or production confidentiality posture.

Deployment and cost boundary

Provider	Runtime	State	Exposure	Verified	Production claim
Render	Flask + gunicorn with an ONNX embedding backend	live	anonymous	2026-07-23T20:52:19Z	No

Component	Boundary	Implication
Render web service	Free-tier, memory-constrained, sleep-prone deployment using ONNX instead of PyTorch.	Cold starts and availability variation are expected; no SLO is claimed.
Supabase and Neo4j AuraDB	Portfolio-scale hosted data services for a synthetic corpus.	A real matter would require an owner-approved capacity, retention, and security budget.

Known limitations

Synthetic templated text is materially cleaner than real discovery data.
The public demo has no real-client security or matter-isolation posture.
Free-tier services can sleep or pause and do not establish an availability SLO.
Privilege/PII flags are research aids, not legal or privacy determinations.

Scalability roadmap

Add authenticated matter isolation, role-based access, audit retention, and encrypted document storage
Introduce OCR and robust parsing for scans, forwarded chains, and production document variance
Move ingestion and indexing to background workers with versioned index promotion
Add real-world benchmark sets under appropriate legal/data agreements and revalidate thresholds
Retire free-tier keep-alive behavior under an owner-approved paid reliability envelope

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