Fix /solve echo bug, type errors, ruff config & README inconsistencies

README.md

@@ -35,7 +35,6 @@ graph TB
     style API fill:#2563eb,color:#fff
     style NEO fill:#4581C3,color:#fff
     style OUT fill:#059669,color:#fff
-  fallback (no LLM)    (Cypher DDL)         invocations
 ```
 
 ---
@@ -45,7 +44,7 @@ graph TB
 ```
 fingraph-sentinel/
 ├── src/
-│   ├── api/main.py              # FastAPI: /solve, /agent, /schema, /domains, /health
+│   ├── api/main.py              # FastAPI: /solve, /playbooks, /schemas, /schema/{domain}, /algorithms, /algorithm/{name}, /health
 │   ├── agent/sentinel.py        # LangChain agent → loads system prompt, invokes LLM
 │   ├── graph/
 │   │   ├── schema.py            # Cypher DDL for 4 BFSI domains
@@ -60,7 +59,7 @@ fingraph-sentinel/
 ├── prompts/system_prompt.md     # FinGraph Sentinel persona (1,200+ words)
 ├── data/seed_cypher.cql         # Sample BFSI graph (parties, accounts, ownership chain)
 ├── examples/solved_aml.json     # Fully worked AML solution (1,800+ words)
-├── tests/                       # 15 tests (API + graph integration)
+├── tests/                       # 17 tests (15 API + 2 graph integration)
 ├── configs/model_config.yaml
 ├── docker-compose.yml           # API + Neo4j + optional Ollama
 ├── Dockerfile                   # Multi-stage Python 3.11-slim
@@ -106,22 +105,26 @@ make seed
 
 ### Mode 1: Deterministic (default — no LLM required)
 
-The agent matches your problem against its knowledge base of solved BFSI playbooks (4 domains × full 4-section solutions). Returns a comprehensive, production-ready answer in under 100ms — ideal for CI/CD and quick prototyping.
+The agent matches your problem against its knowledge base of solved BFSI playbooks (4 domains × full 8-section solutions). Returns a comprehensive, production-ready answer in under 100ms — ideal for CI/CD and quick prototyping.
 
 ### Mode 2: LLM-powered (Ollama or OpenAI-compatible)
 
-Set `FS_LLM_PROVIDER=ollama` in `.env`. The agent loads the full FinGraph Sentinel system prompt, invokes the LLM, and parses the output into the canonical 4-section structure. Use `docker-compose --profile llm up` to also start Ollama.
+Set `FS_LLM_PROVIDER=ollama` in `.env`. The agent loads the full FinGraph Sentinel system prompt, invokes the LLM, and parses the output into the canonical 8-section structure. Use `docker-compose --profile llm up` to also start Ollama.
 
-### The 4-Section Canon
+### The 8-Section Canon
 
-Every solution follows this exact structure:
+Every solution follows this exact structure (mirroring the system prompt and the `SolutionResponse` model):
 
 | Section | Content |
 |:--------|:--------|
-| **Problem Decomposition** | Current-state failure analysis, why tabular/SQL approaches fail, graph-theoretic problem framing |
-| **Knowledge Graph Schema** | Complete Cypher DDL (constraints, indexes, node taxonomies, relationship types) with GDS projections |
-| **Algorithms & Methodology** | Streaming + batch pipelines, specific graph algorithms with runnable Cypher, GNN architectures (layers, dims, loss) |
-| **KPIs & Compliance** | Numeric KPI targets with measurement methodology, regulatory alignment matrix (GDPR/6AMLD/Basel/FATF citations), audit trail specification |
+| **1. Problem Decomposition** | Current-state failure analysis, why tabular/SQL approaches fail, graph-theoretic problem framing, explicit assumptions |
+| **2. Knowledge Graph Schema** | Complete Cypher DDL (constraints, indexes, node taxonomies, relationship types) with GDS projections |
+| **3. Graph Algorithm Selection** | Specific algorithms with parameters & complexity, runnable Cypher, GNN architectures (layers, dims, loss, train regime) |
+| **4. Gen AI Integration** | Graph RAG pipeline: subgraph retrieval, serialization, prompt templates, anti-hallucination guardrails |
+| **5. Architecture Diagram** | Ingestion → graph storage → ML inference → Gen AI reasoning → API, with latency budgets |
+| **6. Implementation Roadmap** | Phased plan (weeks, deliverables, exit criteria) from graph construction to production hardening |
+| **7. Success Metrics** | Numeric KPI targets (baseline → target) with measurement methodology |
+| **8. Risk & Mitigation** | GDPR/PII, LLM hallucination, scalability/sharding, adversarial attacks, model drift, legacy integration |
 
 ---
 
@@ -196,4 +199,3 @@ poetry run pytest tests/ -v --cov=src --cov-report=term-missing
 ## 📄 License
 
 MIT — see [LICENSE](LICENSE).
-# Community Update

pyproject.toml

@@ -38,6 +38,8 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.ruff]
 line-length = 100
+
+[tool.ruff.lint]
 select = ["E", "F", "I", "B", "UP"]
 ignore = ["E501"]
 

src/agent/sentinel.py

@@ -10,7 +10,7 @@
 import json
 import os
 import re
-from dataclasses import dataclass, field
+from dataclasses import dataclass, field, replace
 from pathlib import Path
 from typing import Any
 
@@ -152,19 +152,27 @@ def _llm_solve(problem: str, domain: str = "") -> SentinelSolution:
     try:
         if model_provider == "openai":
             from langchain_openai import ChatOpenAI
-
-            llm = ChatOpenAI(model=model_name, base_url=api_base or None, api_key=api_key, temperature=0.2)
+            from pydantic import SecretStr
+
+            llm = ChatOpenAI(
+                model=model_name,
+                base_url=api_base or None,
+                api_key=SecretStr(api_key),
+                temperature=0.2,
+            )
         else:
-            from langchain_ollama import ChatOllama
+            from langchain_ollama import ChatOllama  # type: ignore[import-not-found]
 
             llm = ChatOllama(model=model_name, base_url=api_base or "http://localhost:11434", temperature=0.2)
 
         response = llm.invoke(messages)
-        raw = response.content if hasattr(response, "content") else str(response)
+        content = response.content if hasattr(response, "content") else str(response)
+        raw = content if isinstance(content, str) else str(content)
     except Exception as exc:
-        # Fall back to rule-based on any LLM error
-        sol = _rule_match(problem)
-        sol.raw = f"(LLM unavailable: {exc})\n\n{_rule_match(problem).raw}"
+        # Fall back to rule-based on any LLM error. Copy so the cached KB
+        # singleton is not mutated, and keep the caller's domain/problem.
+        sol = replace(_rule_match(problem, domain), problem=problem)
+        sol.raw = f"(LLM unavailable: {exc})\n\n{sol.raw}"
         return sol
 
     return _parse_llm_output(raw, problem, domain)
@@ -251,7 +259,9 @@ def solve(problem: str, domain: str = "", use_llm: bool | None = None) -> Sentin
 
     if use_llm:
         return _llm_solve(problem, domain)
-    return _rule_match(problem, domain)
+    # Return a copy so the cached KB singleton is not mutated; always echo the
+    # caller's problem rather than the canned example problem.
+    return replace(_rule_match(problem, domain), problem=problem)
 
 
 def get_playbooks() -> list[dict[str, str]]:

src/graph/neo4j_client.py

@@ -24,7 +24,7 @@ async def get_driver():
     global _DRIVER
     if _DRIVER is None:
         try:
-            from neo4j import AsyncGraphDatabase
+            from neo4j import AsyncGraphDatabase  # type: ignore[import-not-found]
 
             uri, (user, pwd) = _get_uri(), _get_auth()
             _DRIVER = AsyncGraphDatabase.driver(uri, auth=(user, pwd))

tests/test_api.py

@@ -1,4 +1,4 @@
-"FinGraph Sentinel — BFSI Graph + GenAI Agent API tests."
+"""FinGraph Sentinel — BFSI Graph + GenAI Agent API tests."""
 
 from fastapi.testclient import TestClient
 
@@ -90,6 +90,17 @@ def test_solve_auto_detect_domain():
     assert data["domain"] in ("aml_fraud", "general_bfsi")
 
 
+def test_solve_echoes_submitted_problem():
+    """The response must echo the caller's problem, not the canned playbook example."""
+    problem = (
+        "Detect money-mule networks across 2M accounts with real-time transaction "
+        "screening for layering rings and smurfing patterns across 4 jurisdictions."
+    )
+    resp = client.post("/api/v1/solve", json={"problem": problem, "domain": "aml_fraud"})
+    assert resp.status_code == 200
+    assert resp.json()["problem"] == problem
+
+
 def test_solve_rejects_short_problem():
     resp = client.post("/api/v1/solve", json={"problem": "too short"})
     assert resp.status_code == 422