financebench-rag-agent 0.1.6

Multi-agent RAG system for RBAC-secured financial document Q&A. 72.7% on FinanceBench. Ships a CLI client + self-hostable FastAPI backend.

FinanceBench RAG Agent

A multi-agent RAG system for role-based access-controlled financial document Q&A. Achieves 72.7% correctness pass rate on the public FinanceBench benchmark using selective agentic retrieval, a BGE cross-encoder reranker, and a self-hosted LLM observability stack.

Try it

pip install financebench-rag-agent
financebench setup     # brings up the 4-service docker stack, seeds a sample corpus
financebench login -u analyst    # password analyst123
financebench chat

Multi-party HITL approval workflow and conversation memory have their own walkthroughs in docs/cli.md. Self-hosting the backend (env vars, full vs minimal stack, production hardening) is in docs/deploy.md.

Architecture

The mermaid diagram below renders as a flowchart on GitHub. PyPI's markdown renderer doesn't support mermaid — readers there see the source and the prose summary that follows.

flowchart TD
    Q([Query + JWT]) --> RBAC[rbac_gate<br/>JWT to Qdrant filter]
    RBAC --> Guard[guardrails<br/>regex to LLM Guard to LLM classifier]
    Guard -->|blocked| Block([blocked])
    Guard --> Route{router}
    Route -->|simple_lookup| Direct[retrieval → reranker → grader → generator]
    Route -->|research_required| Agent[[research_agent subgraph<br/>decompose → retrieve → grade → sufficiency → synthesize<br/>5-turn cap]]
    Direct --> Halu[hallucination_checker]
    Agent --> Halu
    Halu -->|ungrounded, retry up to 2| Direct
    Halu --> HITL{hitl_gate}
    HITL -->|amount above role threshold| Pause([pause for human approval])
    HITL --> Out([Answer + sources])

A router classifies each query as a simple lookup or research-required. Simple lookups take the fast direct path (retrieval → BGE reranker → grader → Claude generator); research queries enter a multi-turn subgraph that decomposes the question, retrieves per sub-question, grades sufficiency, and synthesizes a final answer. RBAC is enforced at the Qdrant payload-filter level — agentic queries cannot bypass access control. High-stakes answers (above a per-role dollar threshold) pause via LangGraph's interrupt() for multi-party human approval, with state checkpointed to Postgres so the workflow survives container restarts.

Tech stack

• Backend — FastAPI · LangGraph · Qdrant · PostgreSQL · Redis · PyJWT
• Client — financebench CLI: typer · rich · prompt_toolkit · httpx-sse · token-streaming over SSE
• LLMs — Claude Sonnet 4.6 · gpt-4o-mini · Llama 3.3 (via Groq, optional)
• Retrieval — OpenAI text-embedding-3-small or voyage-finance-2 · BGE-reranker-v2-m3 cross-encoder
• Observability — self-hosted LiteLLM proxy + Langfuse v3 + Redis semantic cache (full stack only)
• Safety — Microsoft Presidio PII detection · LLM Guard · LLM classifier (3-layer cascade)
• Evaluation — RAGAS · DeepEval · custom LLM correctness judge

Evaluation results

Evaluated on the FinanceBench benchmark (150 questions across 32 companies):

Metric	Value
Correctness pass rate	72.7% (109/150)
Refusal rate	6.7% (10/150)
RAGAS faithfulness	0.747
DeepEval faithfulness	0.844
DeepEval contextual recall	0.768

Per-slice pass rate: lookup 68.6% (n=86), multi-hop 84.6% (n=13), calc 76.5% (n=51).

The correctness judge is a Claude Sonnet 4.6 + structured-prompt setup calibrated to Cohen's κ = 0.932 against an 89-question hand-labeled set with an adversarial leniency guard. Full methodology, per-judge scores, and reproduction commands in docs/evaluation.md.

Comparison with published systems on FinanceBench

System	Approach	Accuracy
Mafin 2.5 / PageIndex	Vectorless reasoning over hierarchical document tree	98.7%
DANA	Domain-aware neurosymbolic agent with deterministic operators	94.3%
GPT-4-Turbo · long context (128k)	Whole-document prompting	~79%
Claude-2 · long context (100k)	Whole-document prompting	~76%
This project	Multi-agent RAG with selective research-agent subgraph + RBAC + HITL	72.7%
FinanceBench paper baselines	Vector retrieval + GPT-4 / Llama-2	38–43%
GPT-4-Turbo · top-k vector RAG	Standard retrieval, no agent	~19%

Long-context approaches score higher but are not enterprise-deployable — 10-K filings frequently exceed 128k tokens, and whole-document prompting is impractical at scale due to latency and cost. The 72.7% here is measured on a production-shaped pipeline (fixed institutional corpus, batched retrieval, RBAC at the storage layer, HITL on high-stakes outputs).

Known limitations

• Not deployed to production — runs locally via docker compose up -d. No public URL or live traffic.
• CLI is the canonical client today. A Next.js web frontend is in progress in web/ but not wired into the deployment story.
• Below the top-published systems (Mafin 2.5 at 98.7%, DANA at 94.3%) — see comparison table above for context.

Running from source

git clone https://github.com/Rishabhmannu/financebench-rag-agent.git
cd financebench-rag-agent
pip install -e ".[backend,dev]" && cp .env.example .env   # backend extras + dev tools
financebench setup                                         # docker compose + seed corpus

For self-hosting the full 11-service stack (LiteLLM + Langfuse), upgrade flows, and production hardening, see docs/deploy.md and docs/upgrade.md.

Documentation

• docs/cli.md — CLI reference, slash commands, multi-party HITL workflow
• docs/deploy.md — Self-host: stack profiles, env vars, backup, hardening
• docs/upgrade.md — Upgrade cookbook by change type
• docs/evaluation.md — Methodology, results, reproduction
• docs/engineering-log.md — Engineering decisions and tradeoffs
• docs/setup.md — Test accounts, environment, dev commands
• docs/architecture.md · docs/api-reference.md · docs/rbac-matrix.md · web/README.md

License

MIT