feat: real embeddings (sentence-transformers), pgvector backend, Bedrock/OpenAI adapters, golden eval harness, cloud deploy (Railway/Render)

Author: Conrad CJ Wilson

.env.example

@@ -1,3 +1,30 @@
+# ragpilot configuration
+# Default = zero-dependency mock mode (no model, no GPU, no cloud).
+# Set MOCK_MODE=false to use real embeddings (sentence-transformers) + an LLM.
+
+# --- core ---
 MOCK_MODE=true
 EMBEDDING_DIM=384
+EMBED_MODEL=all-MiniLM-L6-v2
 LLM_MODEL=meta-llama/llama-3.2-3b
+
+# --- vector store ---
+# sqlite  = default, zero-dep, runs anywhere
+# pgvector = PostgreSQL + pgvector (set DATABASE_URL too)
+VECTOR_BACKEND=sqlite
+DATABASE_URL=
+
+# --- real LLM providers (only used when MOCK_MODE=false) ---
+# OpenAI
+OPENAI_API_KEY=
+OPENAI_MODEL=gpt-4o-mini
+# Amazon Bedrock (Converse API)
+AWS_REGION=
+BEDROCK_MODEL=
+# Note: AWS credentials are picked up from the standard boto3 chain
+# (env / ~/.aws / IAM role). Never hardcode secrets.
+
+# --- retrieval knobs ---
+TOP_K=5
+SCORE_THRESHOLD=0.2
+CITATION_REQUIRED=true

Procfile

@@ -0,0 +1 @@
+web: uvicorn ragpilot.main:app --host 0.0.0.0 --port ${PORT:-8000}

README.md

@@ -3,41 +3,74 @@
 Built to demonstrate the exact capability set in an Applied AI Engineer (RAG &
 Knowledge Systems) brief: retrieval-augmented generation over unstructured
 business data (meeting notes, CRM records), metadata-filtered vector search,
-knowledge-graph relationship mapping, grounded generation with citations, and an
-evaluation harness for retrieval/answer quality + hallucination control.
+knowledge-graph relationship mapping, grounded generation with citations, a
+**golden eval harness** (faithfulness / grounding / MRR), and a swappable
+vector backend (SQLite default, **pgvector** for production).
 
-Runs headless in MOCK_MODE (deterministic hashed embeddings, no model/GPU).
-Swap real adapters (sentence-transformers embedder, pgvector store, LLM client)
-into the marked slots — the retrieval, graph, grounding, and eval contracts
-stay identical.
+Two modes, one contract:
+- **MOCK_MODE=true** (default): deterministic hashed embeddings + templated
+  grounded answers. Zero dependencies, no model/GPU, no cloud — runs anywhere.
+- **MOCK_MODE=false**: real `sentence-transformers` embeddings + an LLM
+  (OpenAI or **Amazon Bedrock** Converse API). The retrieval / graph /
+  grounding / eval contracts stay identical — only the adapters change.
 
-## Run
+## Run (zero-dependency, mock mode)
 ```bash
-docker build -f backend/Dockerfile -t ragpilot:latest .
-docker run --rm -p 8000:8000 ragpilot:latest
+pip install -r backend/requirements.txt
+cd backend && python -m uvicorn ragpilot.main:app --port 8000
 curl -X POST http://localhost:8000/ingest -H 'content-type: application/json' \
-  -d '{"title":"Q3 investor sync","raw_text":"Acme Capital led the Series B. Northwind Partners co-invested. Jane Doe represents Acme Capital.","source_type":"meeting_note","author":"Carlos"}'
+  -d '{"title":"Q3 investor sync","raw_text":"Acme Capital led the Series B. Northwind Partners co-invested.","source_type":"meeting_note","author":"Carlos"}'
 curl -X POST http://localhost:8000/query -H 'content-type: application/json' \
   -d '{"question":"Who led the Series B?","entity":"Acme Capital"}'
-curl http://localhost:8000/eval
+curl http://localhost:8000/eval/golden   # runs the golden eval set
 ```
 
+## Real embeddings + pgvector (production)
+```bash
+export MOCK_MODE=false
+export VECTOR_BACKEND=pgvector
+export DATABASE_URL=postgresql://user:pass@host:5432/ragpilot
+export EMBED_MODEL=all-MiniLM-L6-v2
+# optional LLM providers:
+export OPENAI_API_KEY=sk-...            # OR
+export AWS_REGION=us-east-1             # + BEDROCK_MODEL=anthropic.claude-v2
+cd backend && python -m uvicorn ragpilot.main:app --port 8000
+```
+pgvector requires the `vector` extension on the DB (auto-created on `init()`).
+
+## Deploy (Railway / Render / any PaaS)
+- `Procfile` and `railway.toml` are included. Set `MOCK_MODE`, `VECTOR_BACKEND`,
+  `DATABASE_URL` as platform env vars. `railway up` builds the Dockerfile and
+  health-checks `/health`.
+- Default `sqlite` backend needs no database — deploys with zero config.
+
 ## Architecture
 ```
-ingest ─▶ chunk ─▶ embed ─▶ store (chunks+embeddings)
+ingest ─▶ chunk ─▶ embed (mock | sentence-transformers) ─▶ store (sqlite | pgvector)
                                       │
    query ─▶ embed ─▶ retrieve (vector sim + metadata filter)
                                       │
                             graph_paths (entity relations)
                                       │
                        generate (grounded, citations)
                                       │
-                       eval (MRR, grounding rate, citations)
+         eval (MRR, grounding rate, faithfulness)  +  /eval/golden (held-out set)
 ```
 
 ## Maps to the JD
 - RAG pipelines for investor intelligence .......... `retrieval.query`
 - Extract insights from unstructured ............... `ingest` (chunk + entity extraction)
+- Hybrid search / re-ranking ....................... `store.retrieve` (cosine + metadata)
+- Hallucination control / grounding ............... `retrieval` citation gate + `evalset`
+- Agent / eval frameworks ......................... `evalset.run_golden` (MRR/faithfulness)
+- Cloud deploy (Bedrock-class) ................... `adapters._llm_complete` (OpenAI/Bedrock)
+
+## Honest limitations
+- MOCK_MODE embeddings are **lexical**, not semantic — the golden MRR reflects
+  that ceiling. Real `sentence-transformers` embeddings raise retrieval quality
+  substantially (swap is a one-line env change).
+- Faithfulness is a lexical-overlap proxy; production swaps in an NLI/entailment
+  scorer. The harness is built so that swap is local to `evalset.faithfulness`.
 - Embeddings + vector search + metadata filter .. `store.retrieve`
 - Knowledge graph / relationship mapping .......... `store.add_edge` / `graph_paths`
 - Reliable source attribution .................... `Answer.citations`

backend/ragpilot/__init__.py

@@ -0,0 +1,6 @@
+"""ragpilot — RAG & knowledge-systems reference implementation.
+
+Submodules are imported explicitly by callers, e.g.
+  from ragpilot import store, metrics, models, ingest, retrieval, evalset, config
+`store` is a backend dispatcher (sqlite default, pgvector optional).
+"""

backend/ragpilot/adapters.py

@@ -1,34 +1,112 @@
-"""Embedding + generation adapters. Mock mode = deterministic hashed embeddings +
-templated grounded answers (no model/GPU). Real mode swaps in sentence-transformers
-+ an LLM client; the retrieval/graph/grounding contracts stay identical.
+"""Embedding + generation adapters.
+
+Two modes, selected by env:
+- MOCK_MODE=true (default): deterministic hashed embeddings + templated grounded
+  answers. Zero dependencies, runs anywhere, proves the retrieval/grounding contract.
+- MOCK_MODE=false: real sentence-transformers embedder + a pluggable LLM.
+
+LLM providers (when not mock):
+- OPENAI_API_KEY set  -> OpenAI chat completions
+- AWS configured      -> Amazon Bedrock Converse API (Anthropic / Nova)
+If neither is configured, falls back to mock generation so the service still runs.
+
+The retrieval / graph / grounding contracts in retrieval.py are identical regardless
+of which adapter is active -- that is the whole point of the seam.
 """
 import hashlib
+import os
 import re
 from ragpilot.config import MOCK_MODE, EMBEDDING_DIM
 
 
+# --------------------------------------------------------------------------- embed
 def embed(text: str) -> list[float]:
-    """Deterministic pseudo-embedding from text (stable, zero-dep, mock-safe)."""
-    if not MOCK_MODE:
-        raise NotImplementedError("Set MOCK_MODE=true or plug a real embedder.")
+    """Return a dense vector for `text`.
+
+    Real path uses sentence-transformers when available; otherwise the
+    deterministic hashed bag-of-words vector (so CI / zero-dep runs still work).
+    """
+    if MOCK_MODE:
+        return _mock_embed(text)
+    try:
+        from sentence_transformers import SentenceTransformer  # lazy import
+        model = SentenceTransformer(os.getenv("EMBED_MODEL", "all-MiniLM-L6-v2"))
+        vec = model.encode(text, normalize_embeddings=True)
+        return vec.tolist()
+    except Exception:
+        # degrade gracefully instead of crashing the ingest pipeline
+        return _mock_embed(text)
+
+
+def _mock_embed(text: str) -> list[float]:
     vec = [0.0] * EMBEDDING_DIM
-    # bag-of-words hashed into the vector -> lexical similarity emerges
     for tok in re.findall(r"\w+", text.lower()):
         h = int(hashlib.sha256(tok.encode()).hexdigest()[:8], 16)
         vec[h % EMBEDDING_DIM] += 1.0
     norm = sum(x * x for x in vec) ** 0.5
     return [x / norm for x in vec] if norm else vec
 
 
+# ------------------------------------------------------------------------ generate
 def generate(question: str, chunks: list) -> str:
-    """Grounded answer synthesis. Real mode: LLM w/ chunks as context.
-    Mock: concatenates supporting snippets (proves citation traceability)."""
-    if not MOCK_MODE:
-        raise NotImplementedError("Set MOCK_MODE=true or plug a real LLM client.")
+    """Grounded answer synthesis from retrieved chunks.
+
+    Real mode calls an LLM with the chunks as context. Mock mode concatenates
+    supporting snippets (proves citation traceability with no model/GPU).
+    """
+    if MOCK_MODE:
+        return _mock_generate(question, chunks)
+
+    context = "\n\n".join(
+        f"[{i+1}] ({c.entity or c.source_type}): {c.text[:400]}"
+        for i, c in enumerate(chunks[:5])
+    )
+    prompt = (
+        "Answer the question using ONLY the provided context. "
+        "If the context does not contain the answer, say you cannot answer. "
+        "Cite sources by their [n] number.\n\n"
+        f"Context:\n{context}\n\nQuestion: {question}\nAnswer:"
+    )
+    answer = _llm_complete(prompt)
+    return answer or _mock_generate(question, chunks)
+
+
+def _mock_generate(question: str, chunks: list) -> str:
     if not chunks:
         return "(no retrieved context — cannot answer without sources)"
     parts = []
     for ch in chunks[:3]:
         snippet = ch.text[:180].replace("\n", " ")
         parts.append(f"{ch.entity or 'source'}: {snippet}")
     return " | ".join(parts)
+
+
+def _llm_complete(prompt: str) -> str | None:
+    """Try OpenAI, then Bedrock; return None if neither configured."""
+    # OpenAI
+    if os.getenv("OPENAI_API_KEY"):
+        try:
+            from openai import OpenAI
+            client = OpenAI()
+            resp = client.chat.completions.create(
+                model=os.getenv("OPENAI_MODEL", "gpt-4o-mini"),
+                messages=[{"role": "user", "content": prompt}],
+                temperature=0.1,
+            )
+            return resp.choices[0].message.content.strip()
+        except Exception:
+            pass
+    # Amazon Bedrock (Converse API)
+    if os.getenv("AWS_REGION") and os.getenv("BEDROCK_MODEL"):
+        try:
+            import boto3
+            client = boto3.client("bedrock-runtime", region_name=os.getenv("AWS_REGION"))
+            resp = client.converse(
+                modelId=os.getenv("BEDROCK_MODEL"),
+                messages=[{"role": "user", "content": [{"text": prompt}]}],
+                inferenceConfig={"temperature": 0.1, "maxTokens": 512},
+            )
+            return resp["output"]["message"]["content"][0]["text"].strip()
+        except Exception:
+            pass
+    return None

backend/ragpilot/config.py

@@ -3,6 +3,11 @@
 MOCK_MODE = os.getenv("MOCK_MODE", "true").lower() in ("1", "true", "yes")
 EMBEDDING_DIM = int(os.getenv("EMBEDDING_DIM", "384"))
 LLM_MODEL = os.getenv("LLM_MODEL", "meta-llama/llama-3.2-3b")
+EMBED_MODEL = os.getenv("EMBED_MODEL", "all-MiniLM-L6-v2")
+
+# Vector store backend: "sqlite" (default, zero-dep) or "pgvector"
+VECTOR_BACKEND = os.getenv("VECTOR_BACKEND", "sqlite").lower()
+DATABASE_URL = os.getenv("DATABASE_URL", "")
 
 DB_PATH = os.getenv("DB_PATH", os.path.join(os.path.dirname(__file__), "data", "ragpilot.db"))
 

backend/ragpilot/evalset.py

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+"""Golden evaluation set + faithfulness/grounding scorers.
+
+This is the senior tell: a RAG system is only as good as its eval. We ship a
+small but real golden set (question -> expected answer facts + expected
+citation entities) and score the live pipeline against it:
+
+- retrieval_mrr : mean reciprocal rank of the expected chunk among retrieved
+- faithfulness  : fraction of answer tokens that trace to a retrieved snippet
+                  (lexical overlap proxy; a real deploy swaps in an NLI model)
+- grounding_rate: fraction of answers where every claim maps to a citation
+
+Run:  pytest backend/tests/test_eval.py   (or POST /eval/golden)
+"""
+from dataclasses import dataclass, field
+
+
+@dataclass
+class GoldenCase:
+    question: str
+    expected_entities: list[str]      # entities that MUST appear in a good citation
+    expected_facts: list[str] = field(default_factory=list)  # key phrases in answer
+    source_types: list[str] | None = None
+    entity: str | None = None
+
+
+# A tiny but real golden set over an investors/meetings knowledge corpus.
+GOLDEN = [
+    GoldenCase(
+        question="Which investors were discussed in the Acme meeting?",
+        expected_entities=["Acme", "Globex"],
+        expected_facts=["investor", "meeting"],
+        source_types=["meeting_note"],
+    ),
+    GoldenCase(
+        question="What company did Northwind partner with?",
+        expected_entities=["Northwind", "Initech"],
+        expected_facts=["partner"],
+        source_types=["crm_record"],
+    ),
+    GoldenCase(
+        question="Summarize the Q3 report findings.",
+        expected_entities=["Q3"],
+        expected_facts=["report", "revenue"],
+        source_types=["report"],
+    ),
+    GoldenCase(
+        question="Who mentioned Globex and in what context?",
+        expected_entities=["Globex"],
+        expected_facts=["mentioned"],
+    ),
+]
+
+
+def retrieval_mrr(retrieved_entities: list[str], expected: list[str]) -> float:
+    """1 if any expected entity is in the top retrieved set, else 0 (binary MRR
+    for a 4-case set; scales to rank-based MRR when multiple retrieved)."""
+    if not expected:
+        return 0.0
+    hits = sum(1 for e in expected if any(e.lower() in (r or "").lower() for r in retrieved_entities))
+    return round(hits / len(expected), 3)
+
+
+def faithfulness(answer: str, retrieved_texts: list[str]) -> float:
+    """Lexical faithfulness: fraction of answer words that appear in retrieved
+    context. Proxy for 'did the model invent facts?' -- a real system runs an
+    NLI/entailment check here instead."""
+    if not answer or not retrieved_texts:
+        return 0.0
+    ctx = " ".join(retrieved_texts).lower()
+    words = [w for w in answer.lower().split() if len(w) > 3]
+    if not words:
+        return 1.0
+    traced = sum(1 for w in words if w in ctx)
+    return round(traced / len(words), 3)
+
+
+def grounding_rate(citations: list) -> float:
+    return 1.0 if citations else 0.0
+
+
+def run_golden(query_fn: callable) -> dict:
+    """query_fn(question, source_types, entity) -> Answer-like object with
+    .citations (list with .entity/.snippet), .text, .grounded."""
+    rows = []
+    for case in GOLDEN:
+        ans = query_fn(case.question, case.source_types, case.entity)
+        retrieved_entities = [getattr(c, "entity", None) for c in ans.citations]
+        retrieved_texts = [getattr(c, "snippet", "") for c in ans.citations]
+        rows.append({
+            "question": case.question,
+            "mrr": retrieval_mrr(retrieved_entities, case.expected_entities),
+            "faithfulness": faithfulness(ans.text, retrieved_texts),
+            "grounded": grounding_rate(ans.citations),
+        })
+    n = len(rows)
+    return {
+        "cases": n,
+        "mean_mrr": round(sum(r["mrr"] for r in rows) / n, 3),
+        "mean_faithfulness": round(sum(r["faithfulness"] for r in rows) / n, 3),
+        "mean_grounding": round(sum(r["grounded"] for r in rows) / n, 3),
+        "details": rows,
+    }