Multi-agent RAG system for Goldman Sachs SEC filings — with citations, hallucination checks, and production-grade security
Ask a natural-language question about Goldman Sachs' public SEC filings. Get a precise, source-cited answer — or a clean refusal if the question is out of scope or a prompt injection attempt.
> What was Goldman Sachs diluted EPS in Q1 2025?
Goldman Sachs' diluted Earnings Per Common Share (EPS) for the first quarter ended
March 31, 2025, was $14.12 [CITE AS: GS Q1 2025 Earnings Release].
Disclaimer: This information is sourced from Goldman Sachs public SEC filings and is
for informational purposes only. It does not constitute investment advice...
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Live deployment: Vertex AI Agent Runtime Console
Most RAG demos retrieve from a single document store and answer in one pass. Real financial filings don't work that way:
- A question about quarterly EPS belongs in the Q1 earnings release (8-K EX-99.1)
- A question about risk factors belongs in the annual report (10-K)
- A question about a board appointment belongs in the announcement filings (8-K)
- Some questions genuinely need multiple corpora — and need RRF to merge them correctly
This project builds a system that:
- Decides which corpus to search — not everything every time
- Retries when retrieval quality is weak — with a different rewritten query
- Merges results across corpora using Reciprocal Rank Fusion
- Refuses cleanly when a question is off-topic or a prompt injection attempt
- Cites every fact with the exact filing source
User Query
│
▼
┌─────────────────────────────────────────────────────────────────┐
│ RootAgent (SequentialAgent) │
│ │
│ before_agent_callback (callbacks.py) │
│ ├── Regex gate: injection / jailbreak patterns ─── BLOCK │
│ ├── Finance-topic gate: off-topic queries ──────── BLOCK │
│ └── Greeting detector ──────────────────────────── REDIRECT │
│ │
│ ┌─────────────────────────────────────────────────────────┐ │
│ │ ReasoningLoop (LoopAgent, max 2 iterations) │ │
│ │ │ │
│ │ QueryRewriteAgent ──► SearchPlanAgent │ │
│ │ │ │ │ │
│ │ Expands abbreviations Selects corpora │ │
│ │ Broadens on retry (1–3 of EARNINGS / │ │
│ │ temperature=0 ANNUAL / ANNOUNCEMENTS) │ │
│ │ │ │ │
│ │ CustomRetrievalAgent │ │
│ │ Vertex AI RAG (hybrid search) │ │
│ │ alpha=0.5 (dense + sparse) │ │
│ │ RRF merge across corpora │ │
│ │ │ │ │
│ │ CriticAgent │ │
│ │ STRONG / PARTIAL ──► exit loop │ │
│ │ WEAK ──────────────► retry │ │
│ └─────────────────────────────────────────────────────────┘ │
│ │
│ AnswerAgent │
│ ├── Guard A: injection check (semantic, LLM-level) │
│ ├── Guard B: off-topic check (semantic, LLM-level) │
│ └── Answer with [CITE AS: ...] labels + disclaimer │
└─────────────────────────────────────────────────────────────────┘
| Corpus | Filing type | Coverage |
|---|---|---|
EARNINGS_RELEASES |
SEC 8-K EX-99.1 | Quarterly EPS, revenue, ROE, ROTCE, segment results — Q4 2024 to present |
ANNUAL_REPORTS |
SEC 10-K | Full-year audited financials, risk factors, MD&A, capital disclosures, strategy |
MAJOR_ANNOUNCEMENTS |
SEC 8-K | Board/executive appointments, M&A, strategic partnerships, compensation |
Query → Hybrid Search (Vertex AI RAG)
│ dense vectors (text-embedding-004)
│ sparse BM25 keywords
│ alpha=0.5 (equal weight)
↓
Per-corpus ranked lists
↓
RRF Merge → rank-based cross-corpus fusion → Top-20 chunks
↓
CriticAgent → STRONG / PARTIAL / WEAK verdict
RRF (Reciprocal Rank Fusion, k=60) ensures that a chunk ranked #1 from one corpus isn't buried by lower-ranked chunks from another just because they have marginally higher similarity scores. For single-corpus queries, RRF degenerates to the original Vertex AI rank order — no behavior change.
Two independent layers, deliberately redundant:
Layer 1 — Deterministic pre-LLM gate (callbacks.py, runs before any model call)
- Regex patterns for instruction-override attacks, role-reassignment, newline injection, Llama-style token delimiters (
[INST],<<SYS>>) - Finance-topic relevance check — off-topic queries blocked before touching any model
- Retrieved document content wrapped with an explicit anti-injection header
Layer 2 — LLM semantic check (AnswerAgent, Guard Checks A + B)
- Catches injection phrasing that a fixed regex list can't anticipate
- Second independent verdict before any answer is generated
Tested against 8 simulated attack categories: 100% blocked, 0 false positives on 50 legitimate financial queries.
Two independent frameworks, 100 total test cases:
| Evalset | Cases | Result | Notes |
|---|---|---|---|
| Set 1 — Earnings metrics | 10 | 9/10 | One hallucinations_v1 judge-variance edge case |
| Set 2 — Corporate announcements | 10 | 10/10 | Clean |
| Set 3 — Annual report / 10-K | 10 | 10/10 | Clean |
| Set 4 — Security / edge cases | 10 | 10/10 | All 8 attack categories blocked |
| Set 5 — Multi-corpus queries | 10 | 10/10 | RRF fusion fix closed citation-breadth gap |
| Total | 50 | 49/50 | hallucinations_v1 = 1.0 on all 50 cases |
Metrics: rubric_based_tool_use_quality_v1, rubric_based_final_response_quality_v1, hallucinations_v1
| Set | Cases | Faithfulness | Context Precision |
|---|---|---|---|
| Set 1 — Core queries | 20 | 0.950 | 0.950 |
| Set 2 — Annual / announcements | 20 | 0.972 | 1.000 |
| Set 3 — Multi-corpus | 10 | 0.842 | 0.900 |
Full breakdown, bug history, root-cause writeups, before/after scores: tests/eval/EVAL_RESULTS_SET1.md
| Layer | Technology |
|---|---|
| Agent framework | Google ADK — SequentialAgent, LoopAgent, BaseAgent |
| LLMs | Gemini Flash (answer, critic), Gemini 2.5 Flash Lite (query rewrite, search plan) |
| Vector store | Vertex AI RAG Engine — serverless, text-embedding-004, hybrid search |
| Deployment | Vertex AI Agent Runtime (source-based, no Docker) |
| Session management | Vertex AI Session Service (persistent, managed) |
| Eval | Google ADK Eval + RAGAS with Groq judge |
| Package management | uv |
# Install package manager and CLI
curl -LsSf https://astral.sh/uv/install.sh | sh
uv tool install google-agents-cli
agents-cli installAuthenticate with GCP:
gcloud auth application-default login
gcloud config set project YOUR_PROJECT_IDCreate a .env file:
GOOGLE_CLOUD_PROJECT=your-project-id
GOOGLE_CLOUD_LOCATION=global
RAG_CORPUS_EARNINGS_RELEASES=projects/YOUR_PROJECT/locations/us-central1/ragCorpora/...
RAG_CORPUS_ANNUAL_REPORTS=projects/YOUR_PROJECT/locations/us-central1/ragCorpora/...
RAG_CORPUS_MAJOR_ANNOUNCEMENTS=projects/YOUR_PROJECT/locations/us-central1/ragCorpora/...
GROQ_API_KEY=your-groq-key # only needed for RAGAS evalAdd your SEC filing source PDFs to data/, then:
uv run python create_corpus_earnings.py
uv run python create_corpus_annual.py
uv run python create_corpus_major.pyagents-cli playground# ADK eval (5 evalsets)
uv run adk eval app tests/eval/evalsets/gs-eval-set-1-earnings.json \
--config_file_path tests/eval/eval_config_reference.json --print_detailed_results
# RAGAS eval
uv run python tests/eval/ragas/run_eval.py --set 1 # cases 0-19
uv run python tests/eval/ragas/run_eval.py --set 2 # cases 20-39
uv run python tests/eval/ragas/run_eval.py --set 3 # cases 40-49Deploy to Vertex AI Agent Runtime:
# Move eval history out first (56MB exceeds Agent Runtime's 8MB package limit)
mv app/.adk .adk_backup
agents-cli deploy --no-wait \
--project YOUR_PROJECT_ID \
--region us-central1 \
--update-env-vars "RAG_CORPUS_EARNINGS_RELEASES=...,RAG_CORPUS_ANNUAL_REPORTS=...,RAG_CORPUS_MAJOR_ANNOUNCEMENTS=..."
# Restore after deploy starts
mv .adk_backup app/.adk
# Grant Agent Runtime SA permission to query RAG corpora
gcloud projects add-iam-policy-binding YOUR_PROJECT_ID \
--member="serviceAccount:service-PROJECT_NUMBER@gcp-sa-aiplatform-re.iam.gserviceaccount.com" \
--role="roles/aiplatform.user"
# Check status
agents-cli deploy --statusTest the deployed agent:
agents-cli run --url YOUR_ENGINE_URL --mode adk "What was Goldman Sachs EPS in Q1 2025?"app/
├── agent.py # All agent definitions (QueryRewrite → SearchPlan → Retrieval → Critic → Answer)
├── callbacks.py # Security gate + session state init
├── rag_tools.py # RAG retrieval, RRF merge, citation labeling, injection sanitization
└── agent_runtime_app.py # Vertex AI Agent Runtime entrypoint
tests/eval/
├── evalsets/ # 5 ADK evalsets (50 cases total)
├── eval_config_*.json # Scoring criteria per evalset
├── ragas/ # RAGAS golden dataset (50 cases) + runner
└── EVAL_RESULTS_SET1.md # Full eval history with before/after numbers
create_corpus_*.py # One-time corpus ingestion scripts