diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
new file mode 100644
index 0000000..6b8402f
--- /dev/null
+++ b/.github/workflows/ci.yml
@@ -0,0 +1,32 @@
+name: CI
+
+on:
+  push:
+  pull_request:
+
+jobs:
+  tests:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v5
+        with:
+          python-version: "3.12"
+          cache: pip
+      - name: Install dependencies
+        run: pip install -r requirements.txt pytest httpx
+      - name: Unit and API tests (LLM mocked, embeddings real)
+        run: pytest -q
+      - name: Retrieval quality gate (offline eval)
+        run: python evals/run_eval.py
+
+  docker:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      # index/ is gitignored (private); build with an empty stand-in — the
+      # build-time index check treats a missing index as "ship without one"
+      - name: Create placeholder dir
+        run: mkdir -p index
+      - name: Docker build
+        run: docker build -f DOCKERFILE -t rag-resume-api .
diff --git a/.gitignore b/.gitignore
index 1ebba88..42c0535 100644
--- a/.gitignore
+++ b/.gitignore
@@ -23,4 +23,4 @@ scripts/publish-docker.ps1
 commands.txt
 docs/plans/
 *.json
-CLAUDE.md
\ No newline at end of file
+CLAUDE.md.pytest_cache/
diff --git a/DOCKERFILE b/DOCKERFILE
index 33e22c8..8b6b36d 100644
--- a/DOCKERFILE
+++ b/DOCKERFILE
@@ -1,16 +1,24 @@
-FROM python:3.12-slim
-
-WORKDIR /app
-
-# Copy requirements and install dependencies
-COPY requirements.txt ./
-RUN pip install --no-cache-dir -r requirements.txt
-
-# Copy the application code
-COPY index/ ./index/
-COPY app.py .
-
-EXPOSE 8000
-
-# Run the server
-CMD ["python", "-m", "uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"]
\ No newline at end of file
+FROM python:3.12-slim
+
+WORKDIR /app
+
+# Copy requirements and install dependencies
+COPY requirements.txt ./
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Pre-download the local embedding + reranker models so the container doesn't fetch them at startup
+RUN python -c "from fastembed import TextEmbedding; TextEmbedding('BAAI/bge-small-en-v1.5')" \
+    && python -c "from fastembed.rerank.cross_encoder import TextCrossEncoder; TextCrossEncoder('Xenova/ms-marco-MiniLM-L-6-v2')"
+
+# Copy the application code and the pre-built index (docs/ stays out of the
+# image — the index already holds the indexed text, raw files aren't needed)
+COPY index/ ./index/
+COPY app.py retrieval.py check_index.py ./
+
+# Fail the build if the shipped index was built with a different embedding model
+RUN python check_index.py
+
+EXPOSE 8000
+
+# Run the server (requires OPENROUTER_API_KEY at runtime)
+CMD ["python", "-m", "uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"]
diff --git a/README.md b/README.md
index ece1fd9..56d6353 100644
--- a/README.md
+++ b/README.md
@@ -1,15 +1,29 @@
 ## RAG Resume API
 
+![CI](https://github.com/rahulk98/Resume-RAG-System/actions/workflows/ci.yml/badge.svg)
+
 ### Live
 Hosted and running at [rahul-krishnan.is-a.dev](https://rahul-krishnan.is-a.dev).
 
 ### Overview
-Retrieval-Augmented Generation (RAG) API that indexes documents in `docs/` and answers questions using LlamaIndex with Google Gemini (embeddings + LLM). FastAPI provides `/health` and `/query` endpoints. Containerized with Docker and deployed as an API on GCP Free tier.
+Retrieval-Augmented Generation (RAG) API that indexes documents in `docs/` and answers questions using LlamaIndex. The LLM runs on OpenRouter's free-model router (`openrouter/free`) and everything else runs locally — so there are no per-query API charges. FastAPI provides `/health`, `/query`, and `/query/stream` endpoints. Containerized with Docker and deployed as an API on GCP Free tier.
+
+**Pipeline highlights**
+- **Hybrid retrieval**: BM25 keyword search fused with vector search (reciprocal rank fusion), then reranked by a local ONNX cross-encoder (`Xenova/ms-marco-MiniLM-L-6-v2`) — better recall than embeddings alone, still zero API cost.
+- **Local embeddings**: `BAAI/bge-small-en-v1.5` via fastembed (ONNX, in-process).
+- **Multi-turn chat**: pass a `session_id` and follow-up questions are condensed into standalone queries using conversation history ("what did he do there?" → "what did Rahul do at SAP?").
+- **Degenerate-answer guard**: the free router occasionally returns a junk response; answers are validated and retried on a re-routed model.
+- **Evaluated**: a golden Q&A set gates retrieval quality in CI (offline) and scores answer faithfulness with an LLM judge (live) — see `evals/`.
+- **Production hardening**: per-IP rate limiting, daily quota, response caching, SSE streaming, query logging.
 
 ### Prerequisites
 - **Python**: 3.12+
-- **Environment**: `GOOGLE_API_KEY` set (in shell or `.env`)
-  - Optional Supabase logging: set `SUPABASE_URL` and `SUPABASE_SERVICE_ROLE_KEY` (or `SUPABASE_ANON_KEY`)
+- **Environment**: `OPENROUTER_API_KEY` set (in shell or `.env`) — get a free key at [openrouter.ai](https://openrouter.ai)
+  - Optional: `OPENROUTER_MODEL` to pin a specific model (default `openrouter/free`, which auto-routes to an available free model). Note: do **not** use `openrouter/auto:free` — it does not restrict routing to free models and can incur charges.
+  - Optional: `EMBED_MODEL_NAME` to change the local embedding model (default `BAAI/bge-small-en-v1.5`). Changing it requires rebuilding the index.
+  - Optional Sheets logging: set `SHEETS_JSON_KEY` (service-account JSON)
+
+> **Migrating from the Gemini version?** The embedding model changed (3072-dim Gemini → 384-dim local BGE), so the old persisted index is incompatible. Rebuild it once with `python build_index.py` (no API key needed — embeddings run locally). Safety nets: the Docker build fails fast if the shipped index doesn't match the embedding model (`check_index.py`), and at startup the app rebuilds a mismatched index from `docs/` when that folder is present. Raw documents are never copied into the Docker image.
 
 ### Quickstart
 ```bash
@@ -26,14 +40,15 @@ python -m uvicorn app:app --host 0.0.0.0 --port 8000
 ### API
 - **GET `/health`** → `{ "status": "ok", "model_loaded": boolean }`
 - **POST `/query`**
-  - Request body:
+  - Request body (`top_k` and `session_id` optional; send a stable `session_id` to enable follow-up questions):
 ```json
-{ "query": "Tell me about Rahul in 2 sentences.", "top_k": 5 }
+{ "query": "Tell me about Rahul in 2 sentences.", "top_k": 5, "session_id": "abc123" }
 ```
   - Response body:
 ```json
 { "query": "...", "answer": "..." }
 ```
+- **POST `/query/stream`** — same request body; responds with `text/event-stream` of `data: {"token": "..."}` events terminated by `data: [DONE]`.
 
 ### Docker
 ```bash
@@ -41,7 +56,19 @@ python -m uvicorn app:app --host 0.0.0.0 --port 8000
 docker build -t rag-resume-api .
 
 # Run
-docker run -e GOOGLE_API_KEY=YOUR_KEY -p 8000:8000 rag-resume-api
+docker run -e OPENROUTER_API_KEY=YOUR_KEY -p 8000:8000 rag-resume-api
+```
+
+### Tests & evals
+```bash
+pip install pytest httpx
+pytest tests/ -v            # unit/API tests — LLM mocked, no key needed
+python evals/run_eval.py    # offline retrieval-quality eval (also runs in CI)
+```
+With `OPENROUTER_API_KEY` set:
+```bash
+pytest tests/ -v -m live         # live OpenRouter smoke tests
+python evals/run_eval.py --live  # full answer eval: keyword coverage, abstention, LLM-judged faithfulness
 ```
 
 ### Screenshots
diff --git a/app.py b/app.py
index 605da2f..0dd6c68 100644
--- a/app.py
+++ b/app.py
@@ -7,9 +7,11 @@
 from collections import defaultdict
 from datetime import datetime, timezone
 from dotenv import load_dotenv
-from llama_index.embeddings.google_genai import GoogleGenAIEmbedding
-from llama_index.llms.google_genai import GoogleGenAI
-from llama_index.core import StorageContext, load_index_from_storage, PromptTemplate
+from llama_index.embeddings.fastembed import FastEmbedEmbedding
+from llama_index.llms.openrouter import OpenRouter
+from llama_index.core import StorageContext, load_index_from_storage
+
+import retrieval
 from contextlib import asynccontextmanager
 import uvicorn
 from fastapi.middleware.cors import CORSMiddleware
@@ -46,14 +48,17 @@ async def lifespan(app: FastAPI):
     max_age=86400,
 )
 
-GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
-if not GOOGLE_API_KEY:
-    print("WARNING: GOOGLE_API_KEY not found in environment. RAG queries will not work.")
+OPENROUTER_API_KEY = os.getenv("OPENROUTER_API_KEY")
+if not OPENROUTER_API_KEY:
+    print("WARNING: OPENROUTER_API_KEY not found in environment. RAG queries will not work.")
 
 # configuration
 INDEX_PERSIST_DIR = "./index"
-EMBED_MODEL_NAME = "gemini-embedding-001"
-LLM_MODEL_NAME = "gemini-2.5-flash"
+DOCS_DIR = "./docs"
+# Local ONNX embedding model (no API cost). Changing this requires rebuilding the index.
+EMBED_MODEL_NAME = os.getenv("EMBED_MODEL_NAME", "BAAI/bge-small-en-v1.5")
+# Free OpenRouter model; override with OPENROUTER_MODEL if this one is deprecated.
+LLM_MODEL_NAME = os.getenv("OPENROUTER_MODEL", "openrouter/free")
 
 SYSTEM_PROMPT = (
     "You are a helpful assistant that answers questions about Rahul Krishnan based on his resume, "
@@ -68,13 +73,15 @@ async def lifespan(app: FastAPI):
 embed_model = None
 llm = None
 sheets_worksheet = None
+retriever = None
+reranker = None
 
 # Rate limiting: max 10 requests per IP per minute
 RATE_LIMIT = 10
 RATE_WINDOW = 60  # seconds
 _rate_store: dict[str, list[float]] = defaultdict(list)
 
-# Daily Gemini API call limit
+# Daily LLM API call limit
 DAILY_QUERY_LIMIT = 30
 _daily_count = 0
 _daily_reset_date = datetime.now(timezone.utc).date()
@@ -84,6 +91,12 @@ async def lifespan(app: FastAPI):
 CACHE_TTL = 3600  # 1 hour
 CACHE_MAX_SIZE = 100
 
+# Multi-turn chat sessions: {session_id: {"turns": [(role, text)], "ts": last_used}}
+_sessions: dict[str, dict] = {}
+SESSION_TTL = 1800  # 30 minutes
+MAX_SESSIONS = 500
+MAX_HISTORY_TURNS = 10  # question+answer pairs kept per session
+
 
 
 def check_rate_limit(ip: str) -> None:
@@ -131,6 +144,7 @@ class HealthResponse(BaseModel):
 class QueryRequest(BaseModel):
     query: str
     top_k: Optional[int] = None
+    session_id: Optional[str] = None  # enables multi-turn follow-up questions
 
 
 class QueryResponse(BaseModel):
@@ -138,43 +152,87 @@ class QueryResponse(BaseModel):
     answer: str
 
 
+def _stored_index_embedding_dim(persist_dir: str) -> int | None:
+    """Return the embedding dimension of the persisted index, or None if unknown."""
+    try:
+        with open(os.path.join(persist_dir, "default__vector_store.json")) as f:
+            data = json.load(f)
+        embedding_dict = data.get("embedding_dict", {})
+        if embedding_dict:
+            return len(next(iter(embedding_dict.values())))
+    except Exception:
+        pass
+    return None
+
+
+def _rebuild_index_from_docs():
+    """Rebuild the index from DOCS_DIR with the current embedding model, or None."""
+    from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
+
+    if not (os.path.isdir(DOCS_DIR) and os.listdir(DOCS_DIR)):
+        print(f"Cannot rebuild index: no documents in {DOCS_DIR}.")
+        return None
+    try:
+        documents = SimpleDirectoryReader(DOCS_DIR).load_data()
+        new_index = VectorStoreIndex.from_documents(documents, embed_model=embed_model)
+        new_index.storage_context.persist(persist_dir=INDEX_PERSIST_DIR)
+        print("Index rebuilt from docs and persisted.")
+        return new_index
+    except Exception as e:
+        print(f"Failed to rebuild index from docs: {e}")
+        return None
+
+
 def init_models_and_index():
     """
     Initialize embedding model, llm, and load index from disk (if exists).
+    An index built with a different embedding model (e.g. the old Gemini
+    3072-dim index) is detected by dimension mismatch and rebuilt from docs.
     Called once at startup.
     """
     global embed_model, llm, index
-    if not GOOGLE_API_KEY:
+    if not OPENROUTER_API_KEY:
         print("Skipping model initialization — no API key.")
         return
-    embed_model = GoogleGenAIEmbedding(
-        model_name=EMBED_MODEL_NAME,
-        embed_batch_size=100,
-        api_key=GOOGLE_API_KEY,
-    )
+    global retriever, reranker
+    embed_model = FastEmbedEmbedding(model_name=EMBED_MODEL_NAME)
+    reranker = retrieval.load_reranker()
 
     try:
-        llm = GoogleGenAI(
+        llm = OpenRouter(
             model=LLM_MODEL_NAME,
-            api_key=GOOGLE_API_KEY,
+            api_key=OPENROUTER_API_KEY,
+            max_tokens=1024,
         )
     except Exception as e:
         print(f"Failed to initialize LLM: {e}")
         llm = None
 
     if os.path.isdir(INDEX_PERSIST_DIR) and os.listdir(INDEX_PERSIST_DIR):
-        try:
-            storage_context = StorageContext.from_defaults(
-                persist_dir=INDEX_PERSIST_DIR
+        stored_dim = _stored_index_embedding_dim(INDEX_PERSIST_DIR)
+        model_dim = len(embed_model.get_text_embedding("dimension probe"))
+        if stored_dim is not None and stored_dim != model_dim:
+            print(
+                f"Persisted index embedding dim ({stored_dim}) does not match "
+                f"current model dim ({model_dim}); rebuilding from docs."
             )
-            index = load_index_from_storage(storage_context, embed_model=embed_model)
-            print("Index loaded from disk.")
-        except Exception as e:
-            print(f"Failed to load index from disk: {e}")
-            index = None
+            index = _rebuild_index_from_docs()
+        else:
+            try:
+                storage_context = StorageContext.from_defaults(
+                    persist_dir=INDEX_PERSIST_DIR
+                )
+                index = load_index_from_storage(storage_context, embed_model=embed_model)
+                print("Index loaded from disk.")
+            except Exception as e:
+                print(f"Failed to load index from disk: {e}")
+                index = _rebuild_index_from_docs()
     else:
-        print("No index on disk yet. Call /reindex to build it.")
-        index = None
+        print("No index on disk yet; trying to build from docs.")
+        index = _rebuild_index_from_docs()
+
+    if index is not None:
+        retriever = retrieval.build_hybrid_retriever(index)
 
 
 def init_sheets_client():
@@ -226,16 +284,79 @@ def health():
     return HealthResponse(status="ok", model_loaded=index is not None)
 
 
-def _build_query_engine(top_k: int = 5, streaming: bool = False):
-    return index.as_query_engine(
-        llm=llm,
-        similarity_top_k=top_k,
-        response_mode="compact",
-        streaming=streaming,
-        text_qa_template=PromptTemplate(
-            SYSTEM_PROMPT + "\n\nContext:\n{context_str}\n\nQuestion: {query_str}\nAnswer:"
-        ),
-    )
+QA_TEMPLATE = (
+    SYSTEM_PROMPT + "\n\nContext:\n{context_str}\n\nQuestion: {query_str}\nAnswer:"
+)
+
+CONDENSE_TEMPLATE = (
+    "Given the conversation below and a follow-up question, rephrase the "
+    "follow-up as a single standalone question about Rahul Krishnan. Return "
+    "only the rewritten question, nothing else.\n\n"
+    "Conversation:\n{history}\n\nFollow-up question: {question}\n\n"
+    "Standalone question:"
+)
+
+
+def _get_session_history(session_id: str | None) -> list[tuple[str, str]]:
+    if not session_id:
+        return []
+    now = time.time()
+    for sid in [s for s, data in _sessions.items() if now - data["ts"] > SESSION_TTL]:
+        del _sessions[sid]
+    session = _sessions.get(session_id)
+    return session["turns"] if session else []
+
+
+def _append_session_turn(session_id: str | None, question: str, answer: str) -> None:
+    if not session_id:
+        return
+    if session_id not in _sessions and len(_sessions) >= MAX_SESSIONS:
+        oldest = min(_sessions, key=lambda s: _sessions[s]["ts"])
+        del _sessions[oldest]
+    session = _sessions.setdefault(session_id, {"turns": [], "ts": 0})
+    session["turns"].append((question, answer))
+    session["turns"] = session["turns"][-MAX_HISTORY_TURNS:]
+    session["ts"] = time.time()
+
+
+def _condense_question(history: list[tuple[str, str]], question: str) -> str:
+    """Rewrite a follow-up question as standalone using the chat history, so
+    retrieval and caching work on the full meaning ('what did he do there?'
+    -> 'what did Rahul do at SAP?')."""
+    history_str = "\n".join(f"User: {q}\nAssistant: {a}" for q, a in history)
+    try:
+        condensed = str(
+            llm.complete(CONDENSE_TEMPLATE.format(history=history_str, question=question))
+        ).strip()
+        return condensed or question
+    except Exception as e:
+        print(f"Condense step failed ({e}); using original question.")
+        return question
+
+
+def _build_prompt(query_text: str, top_k: int) -> str:
+    """Hybrid retrieve (BM25 + vector fusion), rerank locally, build QA prompt."""
+    nodes = retrieval.retrieve(retriever, reranker, query_text, top_k)
+    context_str = "\n\n".join(n.get_content() for n in nodes)
+    return QA_TEMPLATE.format(context_str=context_str, query_str=query_text)
+
+
+def _complete_with_retry(prompt: str, attempts: int = 2, min_chars: int = 20) -> str:
+    """The free router occasionally lands on a model that returns a degenerate
+    non-answer (e.g. a bare moderation verdict). Retry once — a re-route
+    almost always picks a different model."""
+    answer = ""
+    for _ in range(attempts):
+        answer = str(llm.complete(prompt)).strip()
+        if len(answer) >= min_chars:
+            return answer
+    return answer
+
+
+def _stream_llm_tokens(prompt: str):
+    for chunk in llm.stream_complete(prompt):
+        if chunk.delta:
+            yield chunk.delta
 
 
 def _validate_query(request: QueryRequest, req: Request) -> tuple[str, str, int]:
@@ -255,17 +376,21 @@ def _validate_query(request: QueryRequest, req: Request) -> tuple[str, str, int]
 def query(request: QueryRequest, background_tasks: BackgroundTasks, req: Request):
     global _daily_count
     query_text, client_ip, top_k = _validate_query(request, req)
-    cached = get_cached_response(query_text)
-    if cached is not None:
-        return QueryResponse(query=query_text, answer=cached)
+    history = _get_session_history(request.session_id)
     try:
-        query_engine = _build_query_engine(top_k)
-        response = query_engine.query(query_text)
+        standalone = _condense_question(history, query_text) if history else query_text
+        cached = get_cached_response(standalone)
+        if cached is not None:
+            _append_session_turn(request.session_id, query_text, cached)
+            return QueryResponse(query=query_text, answer=cached)
+        answer = _complete_with_retry(_build_prompt(standalone, top_k))
         _daily_count += 1
-        answer = str(response)
-        set_cache(query_text, answer)
+        set_cache(standalone, answer)
+        _append_session_turn(request.session_id, query_text, answer)
         background_tasks.add_task(log_to_sheets, query_text, answer, client_ip)
         return QueryResponse(query=query_text, answer=answer)
+    except HTTPException:
+        raise
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
 
@@ -274,29 +399,40 @@ def query(request: QueryRequest, background_tasks: BackgroundTasks, req: Request
 def query_stream(request: QueryRequest, background_tasks: BackgroundTasks, req: Request):
     global _daily_count
     query_text, client_ip, top_k = _validate_query(request, req)
-
-    cached = get_cached_response(query_text)
-    if cached is not None:
-        def cached_gen():
-            yield f"data: {json.dumps({'token': cached})}\n\n"
-            yield "data: [DONE]\n\n"
-        return StreamingResponse(cached_gen(), media_type="text/event-stream")
+    history = _get_session_history(request.session_id)
 
     try:
-        query_engine = _build_query_engine(top_k, streaming=True)
-        streaming_response = query_engine.query(query_text)
+        standalone = _condense_question(history, query_text) if history else query_text
+        cached = get_cached_response(standalone)
+        if cached is not None:
+            _append_session_turn(request.session_id, query_text, cached)
+
+            def cached_gen():
+                yield f"data: {json.dumps({'token': cached})}\n\n"
+                yield "data: [DONE]\n\n"
+            return StreamingResponse(cached_gen(), media_type="text/event-stream")
+
+        token_stream = _stream_llm_tokens(_build_prompt(standalone, top_k))
         _daily_count += 1
 
         def token_generator():
             full_response = ""
-            for token in streaming_response.response_gen:
-                full_response += token
-                yield f"data: {json.dumps({'token': token})}\n\n"
+            try:
+                for token in token_stream:
+                    full_response += token
+                    yield f"data: {json.dumps({'token': token})}\n\n"
+            except Exception as e:
+                print(f"Stream interrupted: {e}")
+                yield f"data: {json.dumps({'error': 'stream interrupted'})}\n\n"
             yield "data: [DONE]\n\n"
-            set_cache(query_text, full_response)
-            background_tasks.add_task(log_to_sheets, query_text, full_response, client_ip)
+            if full_response:
+                set_cache(standalone, full_response)
+                _append_session_turn(request.session_id, query_text, full_response)
+                background_tasks.add_task(log_to_sheets, query_text, full_response, client_ip)
 
         return StreamingResponse(token_generator(), media_type="text/event-stream")
+    except HTTPException:
+        raise
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
 
diff --git a/build_index.py b/build_index.py
index 2f6dc11..4170ac0 100644
--- a/build_index.py
+++ b/build_index.py
@@ -1,41 +1,46 @@
-from llama_index.core import SimpleDirectoryReader
-from llama_index.core import VectorStoreIndex
-from llama_index.embeddings.google_genai import GoogleGenAIEmbedding
-from llama_index.llms.google_genai import GoogleGenAI
-from dotenv import load_dotenv
-import os
-
-load_dotenv()
-GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
-if not GOOGLE_API_KEY:
-    raise RuntimeError("GOOGLE_API_KEY not found in .env")
-
-
-def build_index():
-    # Read all the relevant documents in docs folder
-    documents = SimpleDirectoryReader("./docs").load_data()
-
-    # Setup the gemini model for embeddings
-    embed_model = GoogleGenAIEmbedding(
-        model_name="gemini-embedding-001", embed_batch_size=100, api_key=GOOGLE_API_KEY
-    )
-    # Build the vector store index from the documents in docs folder
-    index = VectorStoreIndex.from_documents(documents, embed_model=embed_model)
-
-    # Store the index for reuse
-    index.storage_context.persist(persist_dir="./index")
-
-    # Initliase the LLM
-    llm = GoogleGenAI(
-        model="gemini-2.5-flash",
-        api_key=GOOGLE_API_KEY,  # uses GOOGLE_API_KEY env var by default
-    )
-
-    # create a query engine and query
-    query_engine = index.as_query_engine(llm=llm)
-    response = query_engine.query("What are Rahul's skills?")
-    print(response)
-
-
-if __name__ == "__main__":
-    build_index()
+from llama_index.core import SimpleDirectoryReader
+from llama_index.core import VectorStoreIndex
+from llama_index.embeddings.fastembed import FastEmbedEmbedding
+from llama_index.llms.openrouter import OpenRouter
+from dotenv import load_dotenv
+import os
+
+load_dotenv()
+# Only needed for the test query at the end — embeddings are local and free.
+OPENROUTER_API_KEY = os.getenv("OPENROUTER_API_KEY")
+
+EMBED_MODEL_NAME = os.getenv("EMBED_MODEL_NAME", "BAAI/bge-small-en-v1.5")
+LLM_MODEL_NAME = os.getenv("OPENROUTER_MODEL", "openrouter/free")
+
+
+def build_index():
+    # Read all the relevant documents in docs folder
+    documents = SimpleDirectoryReader("./docs").load_data()
+
+    # Local ONNX embedding model — runs in-process, no API cost
+    embed_model = FastEmbedEmbedding(model_name=EMBED_MODEL_NAME)
+    # Build the vector store index from the documents in docs folder
+    index = VectorStoreIndex.from_documents(documents, embed_model=embed_model)
+
+    # Store the index for reuse
+    index.storage_context.persist(persist_dir="./index")
+
+    if not OPENROUTER_API_KEY:
+        print("Index built. Set OPENROUTER_API_KEY to also run a test query.")
+        return
+
+    # Initliase the LLM (free OpenRouter model)
+    llm = OpenRouter(
+        model=LLM_MODEL_NAME,
+        api_key=OPENROUTER_API_KEY,
+        max_tokens=1024,
+    )
+
+    # create a query engine and query
+    query_engine = index.as_query_engine(llm=llm)
+    response = query_engine.query("What are Rahul's skills?")
+    print(response)
+
+
+if __name__ == "__main__":
+    build_index()
diff --git a/check_index.py b/check_index.py
new file mode 100644
index 0000000..67f51ba
--- /dev/null
+++ b/check_index.py
@@ -0,0 +1,46 @@
+"""Build-time guard used by the Dockerfile: refuse to ship an index that was
+built with a different embedding model than the app will use at runtime
+(e.g. an old 3072-dim Gemini index vs the 384-dim local model).
+
+Exit codes: 0 = index matches or no index shipped; 1 = dimension mismatch.
+"""
+
+import json
+import os
+import sys
+
+INDEX_DIR = os.getenv("INDEX_PERSIST_DIR", "./index")
+EMBED_MODEL_NAME = os.getenv("EMBED_MODEL_NAME", "BAAI/bge-small-en-v1.5")
+
+
+def main() -> int:
+    path = os.path.join(INDEX_DIR, "default__vector_store.json")
+    if not os.path.exists(path):
+        print(
+            f"No index found at {INDEX_DIR}; the app will start without one. "
+            "Run `python build_index.py` and rebuild the image to ship it."
+        )
+        return 0
+    with open(path) as f:
+        embedding_dict = json.load(f).get("embedding_dict", {})
+    if not embedding_dict:
+        print("Index contains no embeddings; treating as empty.")
+        return 0
+    stored_dim = len(next(iter(embedding_dict.values())))
+
+    from fastembed import TextEmbedding
+
+    model_dim = len(next(iter(TextEmbedding(EMBED_MODEL_NAME).embed(["probe"]))))
+    if stored_dim != model_dim:
+        print(
+            f"ERROR: shipped index is {stored_dim}-dim but {EMBED_MODEL_NAME} "
+            f"produces {model_dim}-dim embeddings. Rebuild the index with "
+            "`python build_index.py` before building the image."
+        )
+        return 1
+    print(f"Index OK: {stored_dim}-dim matches {EMBED_MODEL_NAME}.")
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/evals/__init__.py b/evals/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/evals/golden_qa.jsonl b/evals/golden_qa.jsonl
new file mode 100644
index 0000000..f714d23
--- /dev/null
+++ b/evals/golden_qa.jsonl
@@ -0,0 +1,10 @@
+{"id": "skills", "question": "What are Rahul's skills?", "retrieval_keywords": ["Python", "FastAPI"], "expected_keywords": [["Python"], ["FastAPI", "LlamaIndex"]], "should_abstain": false}
+{"id": "education", "question": "What is Rahul studying and where?", "retrieval_keywords": ["Trier", "Data Science"], "expected_keywords": [["Trier"], ["Data Science"]], "should_abstain": false}
+{"id": "past-role", "question": "What did Rahul do before his Master's degree?", "retrieval_keywords": ["SAP"], "expected_keywords": [["SAP"]], "should_abstain": false}
+{"id": "main-project", "question": "Tell me about Rahul's resume Q&A project.", "retrieval_keywords": ["LlamaIndex", "RAG"], "expected_keywords": [["RAG", "retrieval"], ["LlamaIndex"]], "should_abstain": false}
+{"id": "deployment", "question": "How is the resume assistant deployed?", "retrieval_keywords": ["Docker", "GCP"], "expected_keywords": [["Docker", "container"], ["GCP", "Google Cloud"]], "should_abstain": false}
+{"id": "embeddings", "question": "What embedding technology does the project use?", "retrieval_keywords": ["fastembed"], "expected_keywords": [["fastembed", "bge"]], "should_abstain": false}
+{"id": "streaming", "question": "Does the API support streaming responses?", "retrieval_keywords": ["stream"], "expected_keywords": [["stream"]], "should_abstain": false}
+{"id": "website", "question": "Where is Rahul's portfolio website hosted?", "retrieval_keywords": ["rahul-krishnan.is-a.dev"], "expected_keywords": [["rahul-krishnan.is-a.dev", "GitHub Pages"]], "should_abstain": false}
+{"id": "abstain-salary", "question": "What is Rahul's salary?", "retrieval_keywords": [], "expected_keywords": [], "should_abstain": true}
+{"id": "abstain-address", "question": "What is Rahul's home address?", "retrieval_keywords": [], "expected_keywords": [], "should_abstain": true}
diff --git a/evals/run_eval.py b/evals/run_eval.py
new file mode 100644
index 0000000..8407f0d
--- /dev/null
+++ b/evals/run_eval.py
@@ -0,0 +1,177 @@
+"""RAG evaluation harness for the Resume RAG System.
+
+Offline mode (default, CI-safe — no API key needed):
+    python evals/run_eval.py
+  Builds the full hybrid retrieval pipeline (local embeddings + BM25 fusion +
+  cross-encoder rerank) over the sample docs and scores retrieval quality
+  against the golden Q&A set. Fails (exit 1) below the hit-rate threshold.
+
+Live mode (needs OPENROUTER_API_KEY):
+    python evals/run_eval.py --live
+  Additionally generates real answers through OpenRouter and scores:
+  - keyword coverage: expected facts present in the answer
+  - abstention: questions outside the docs get an honest "don't know"
+  - faithfulness: an LLM judge checks the answer is grounded in the context
+"""
+
+import argparse
+import json
+import os
+import sys
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+RETRIEVAL_THRESHOLD = 0.9
+ANSWER_THRESHOLD = 0.8
+TOP_K = 5
+
+EVALS_DIR = os.path.dirname(os.path.abspath(__file__))
+GOLDEN_PATH = os.path.join(EVALS_DIR, "golden_qa.jsonl")
+DEFAULT_DOCS = os.path.join(EVALS_DIR, "sample_docs")
+
+QA_TEMPLATE = (
+    "You are a helpful assistant that answers questions about Rahul Krishnan based on his resume, "
+    "CV, and project documents. Be concise, professional, and friendly. "
+    "If the provided context doesn't contain enough information to answer, say so honestly. "
+    "Do not make up information that isn't in the documents."
+    "\n\nContext:\n{context_str}\n\nQuestion: {query_str}\nAnswer:"
+)
+
+JUDGE_TEMPLATE = (
+    "You are a strict evaluator. Given a context and an answer, decide whether "
+    "every factual claim in the answer is supported by the context. Reply with "
+    "exactly one word: YES if fully supported, NO otherwise.\n\n"
+    "Context:\n{context}\n\nAnswer:\n{answer}\n\nFully supported (YES/NO):"
+)
+
+ABSTAIN_MARKERS = [
+    "don't", "do not", "doesn't", "does not", "no information", "not mentioned",
+    "not included", "not provided", "not specified", "sorry", "unable",
+    "cannot", "can't", "not contain",
+]
+
+
+def load_golden(path: str = GOLDEN_PATH) -> list[dict]:
+    with open(path) as f:
+        return [json.loads(line) for line in f if line.strip()]
+
+
+def build_pipeline(docs_dir: str):
+    from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
+    from llama_index.embeddings.fastembed import FastEmbedEmbedding
+
+    import retrieval
+
+    documents = SimpleDirectoryReader(docs_dir).load_data()
+    embed_model = FastEmbedEmbedding(
+        model_name=os.getenv("EMBED_MODEL_NAME", "BAAI/bge-small-en-v1.5")
+    )
+    index = VectorStoreIndex.from_documents(documents, embed_model=embed_model)
+    retriever = retrieval.build_hybrid_retriever(index)
+    reranker = retrieval.load_reranker()
+    return retriever, reranker
+
+
+def get_context(retriever, reranker, question: str, top_k: int = TOP_K) -> str:
+    import retrieval
+
+    nodes = retrieval.retrieve(retriever, reranker, question, top_k)
+    return "\n\n".join(n.get_content() for n in nodes)
+
+
+def eval_retrieval(retriever, reranker, golden: list[dict]) -> tuple[float, list[dict]]:
+    """Fraction of questions whose expected keywords all appear in the
+    retrieved context. Deterministic and fully offline."""
+    rows = []
+    scored = [g for g in golden if g["retrieval_keywords"]]
+    for item in scored:
+        context = get_context(retriever, reranker, item["question"])
+        hit = all(kw.lower() in context.lower() for kw in item["retrieval_keywords"])
+        rows.append({"id": item["id"], "retrieval_hit": hit})
+    hit_rate = sum(r["retrieval_hit"] for r in rows) / len(rows) if rows else 1.0
+    return hit_rate, rows
+
+
+def eval_answers_live(retriever, reranker, golden: list[dict]) -> tuple[float, list[dict]]:
+    """Generate real answers via OpenRouter and score keyword coverage,
+    abstention behavior, and LLM-judged faithfulness."""
+    from llama_index.llms.openrouter import OpenRouter
+
+    llm = OpenRouter(
+        model=os.getenv("OPENROUTER_MODEL", "openrouter/free"),
+        api_key=os.environ["OPENROUTER_API_KEY"],
+        max_tokens=1024,
+    )
+    def complete_with_retry(prompt: str, attempts: int = 2, min_chars: int = 20) -> str:
+        # the free router occasionally returns a degenerate non-answer; re-route
+        answer = ""
+        for _ in range(attempts):
+            answer = str(llm.complete(prompt)).strip()
+            if len(answer) >= min_chars:
+                return answer
+        return answer
+
+    rows = []
+    for item in golden:
+        context = get_context(retriever, reranker, item["question"])
+        answer = complete_with_retry(
+            QA_TEMPLATE.format(context_str=context, query_str=item["question"])
+        )
+        row = {"id": item["id"], "answer": answer}
+        if item["should_abstain"]:
+            row["pass"] = any(m in answer.lower() for m in ABSTAIN_MARKERS)
+            row["check"] = "abstained" if row["pass"] else "DID NOT ABSTAIN"
+        else:
+            coverage = all(
+                any(kw.lower() in answer.lower() for kw in group)
+                for group in item["expected_keywords"]
+            )
+            verdict = ""
+            for _ in range(2):  # judge is also served by the free router
+                verdict = str(
+                    llm.complete(JUDGE_TEMPLATE.format(context=context, answer=answer))
+                ).strip().upper()
+                if verdict.startswith(("YES", "NO")):
+                    break
+            faithful = verdict.startswith("YES")
+            row["pass"] = coverage and faithful
+            row["check"] = f"coverage={coverage} faithful={faithful}"
+        rows.append(row)
+    score = sum(r["pass"] for r in rows) / len(rows) if rows else 1.0
+    return score, rows
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--docs", default=DEFAULT_DOCS, help="docs dir to index")
+    parser.add_argument("--live", action="store_true", help="also eval real answers via OpenRouter")
+    args = parser.parse_args()
+
+    golden = load_golden()
+    print(f"Golden set: {len(golden)} questions | docs: {args.docs}")
+    retriever, reranker = build_pipeline(args.docs)
+
+    hit_rate, rows = eval_retrieval(retriever, reranker, golden)
+    print("\n== Retrieval eval (offline) ==")
+    for r in rows:
+        print(f"  {'PASS' if r['retrieval_hit'] else 'FAIL'}  {r['id']}")
+    print(f"Hit rate: {hit_rate:.0%} (threshold {RETRIEVAL_THRESHOLD:.0%})")
+    failed = hit_rate < RETRIEVAL_THRESHOLD
+
+    if args.live:
+        if not os.getenv("OPENROUTER_API_KEY"):
+            print("OPENROUTER_API_KEY not set; cannot run --live eval.")
+            return 2
+        score, rows = eval_answers_live(retriever, reranker, golden)
+        print("\n== Answer eval (live, via OpenRouter) ==")
+        for r in rows:
+            print(f"  {'PASS' if r['pass'] else 'FAIL'}  {r['id']}: {r['check']}")
+        print(f"Answer score: {score:.0%} (threshold {ANSWER_THRESHOLD:.0%})")
+        failed = failed or score < ANSWER_THRESHOLD
+
+    print("\nRESULT:", "FAIL" if failed else "PASS")
+    return 1 if failed else 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/evals/sample_docs/projects.md b/evals/sample_docs/projects.md
new file mode 100644
index 0000000..9bf7798
--- /dev/null
+++ b/evals/sample_docs/projects.md
@@ -0,0 +1,14 @@
+# Rahul Krishnan — Projects
+
+## AI-Powered Resume Q&A System (RAG Resume API)
+A context-aware question-answering tool that retrieves information from Rahul's resume, projects, and notes, hosted live at rahul-krishnan.is-a.dev.
+
+- Retrieval-Augmented Generation (RAG) pipeline built with **LlamaIndex**
+- Answers are generated by an LLM served through **OpenRouter** (free-model router); embeddings run locally with **fastembed** (BAAI/bge-small-en-v1.5, ONNX)
+- **FastAPI** backend exposing `/health`, `/query`, and `/query/stream` (server-sent events streaming) endpoints
+- Production hardening: per-IP rate limiting, daily query quota, response caching, and query logging to Google Sheets
+- Containerized with **Docker** and deployed on the **GCP free tier**
+- Embedded as a chat widget on his personal portfolio website
+
+## Personal Portfolio Website
+Personal site at rahul-krishnan.is-a.dev showcasing his background and projects, hosted via GitHub Pages, integrated with the RAG resume assistant above.
diff --git a/evals/sample_docs/resume.md b/evals/sample_docs/resume.md
new file mode 100644
index 0000000..394b797
--- /dev/null
+++ b/evals/sample_docs/resume.md
@@ -0,0 +1,22 @@
+# Rahul Krishnan — Resume
+
+## Summary
+Former SAP Developer now pursuing a Data Science Master's degree at the University of Trier, Germany. Builds AI solutions that solve real-world problems through machine learning and data science.
+
+## Education
+- **M.Sc. Data Science** — University of Trier, Germany (in progress)
+
+## Work Experience
+- **SAP Developer** — worked as an SAP developer before transitioning into data science. Experience with enterprise software development, ABAP, and business process integration.
+
+## Skills
+- **Programming**: Python, SQL, ABAP
+- **AI / ML**: Machine learning, retrieval-augmented generation (RAG), LLM-powered applications, vector search, embeddings
+- **Frameworks & Tools**: LlamaIndex, FastAPI, Docker, Git
+- **Cloud**: Google Cloud Platform (GCP), deployment on GCP free tier
+- **Data**: Data engineering, data science workflows
+
+## Links
+- GitHub: https://github.com/rahulk98
+- LinkedIn: https://linkedin.com/in/rahulk98
+- Website: https://rahul-krishnan.is-a.dev
diff --git a/pyproject.toml b/pyproject.toml
index b0ba401..d909c64 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -6,11 +6,24 @@ readme = "README.md"
 requires-python = ">=3.12"
 dependencies = [
     "fastapi>=0.120.0",
-    "google-generativeai>=0.3.0",
     "llama-index>=0.14.5",
-    "llama-index-embeddings-google-genai>=0.3.1",
-    "llama-index-llms-google-genai>=0.6.2",
+    "llama-index-embeddings-fastembed>=0.4.0",
+    "llama-index-retrievers-bm25>=0.7.0",
+    "fastembed>=0.4.0",
+    "llama-index-llms-openrouter>=0.4.0",
     "python-dotenv>=1.1.1",
     "uvicorn>=0.38.0",
     "supabase",
 ]
+
+[dependency-groups]
+dev = [
+    "pytest>=8.0.0",
+    "httpx>=0.27.0",
+]
+
+[tool.pytest.ini_options]
+markers = [
+    "live: tests that call the real OpenRouter API (need OPENROUTER_API_KEY)",
+]
+addopts = "-m 'not live'"
diff --git a/query_index.py b/query_index.py
index 453e669..2cbe402 100644
--- a/query_index.py
+++ b/query_index.py
@@ -1,37 +1,39 @@
-from llama_index.embeddings.google_genai import GoogleGenAIEmbedding
-from llama_index.llms.google_genai import GoogleGenAI
-from llama_index.core import StorageContext, load_index_from_storage
-from dotenv import load_dotenv
-import os
-
-load_dotenv()
-GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
-if not GOOGLE_API_KEY:
-    raise RuntimeError("GOOGLE_API_KEY not found in .env")
-
-
-def query_index(query_text: str) -> str:
-    # Setup the gemini model for embeddings
-    embed_model = GoogleGenAIEmbedding(
-        model_name="text-embedding-004", embed_batch_size=100, api_key=GOOGLE_API_KEY
-    )
-    # rebuild storage context
-    storage_context = StorageContext.from_defaults(persist_dir="./index")
-    # load index
-    index = load_index_from_storage(storage_context, embed_model=embed_model)
-
-    # Initliase the LLM
-    llm = GoogleGenAI(
-        model="gemini-2.5-flash",
-        api_key=GOOGLE_API_KEY,  # uses GOOGLE_API_KEY env var by default
-    )
-
-    # create a query engine and query
-    query_engine = index.as_query_engine(llm=llm)
-    response = query_engine.query(query_text)
-    print(response)
-    return response
-    
-    
-if __name__ == "__main__":
-    query_index("Tell me about Rahul in 3 sentences.")
\ No newline at end of file
+from llama_index.embeddings.fastembed import FastEmbedEmbedding
+from llama_index.llms.openrouter import OpenRouter
+from llama_index.core import StorageContext, load_index_from_storage
+from dotenv import load_dotenv
+import os
+
+load_dotenv()
+OPENROUTER_API_KEY = os.getenv("OPENROUTER_API_KEY")
+if not OPENROUTER_API_KEY:
+    raise RuntimeError("OPENROUTER_API_KEY not found in .env")
+
+EMBED_MODEL_NAME = os.getenv("EMBED_MODEL_NAME", "BAAI/bge-small-en-v1.5")
+LLM_MODEL_NAME = os.getenv("OPENROUTER_MODEL", "openrouter/free")
+
+
+def query_index(query_text: str) -> str:
+    # Local ONNX embedding model — must match the model used in build_index.py
+    embed_model = FastEmbedEmbedding(model_name=EMBED_MODEL_NAME)
+    # rebuild storage context
+    storage_context = StorageContext.from_defaults(persist_dir="./index")
+    # load index
+    index = load_index_from_storage(storage_context, embed_model=embed_model)
+
+    # Initliase the LLM (free OpenRouter model)
+    llm = OpenRouter(
+        model=LLM_MODEL_NAME,
+        api_key=OPENROUTER_API_KEY,
+        max_tokens=1024,
+    )
+
+    # create a query engine and query
+    query_engine = index.as_query_engine(llm=llm)
+    response = query_engine.query(query_text)
+    print(response)
+    return response
+
+
+if __name__ == "__main__":
+    query_index("Tell me about Rahul in 3 sentences.")
diff --git a/requirements.txt b/requirements.txt
index 4a662e0..9de794e 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,8 +1,9 @@
-fastapi>=0.120.0
-google-generativeai>=0.3.0
-llama-index>=0.14.5
-llama-index-embeddings-google-genai>=0.3.1
-llama-index-llms-google-genai>=0.6.2
-python-dotenv>=1.1.1
-uvicorn>=0.38.0
-gspread>=6.0.0
+fastapi>=0.120.0
+llama-index>=0.14.5
+llama-index-embeddings-fastembed>=0.4.0
+llama-index-retrievers-bm25>=0.7.0
+fastembed>=0.4.0
+llama-index-llms-openrouter>=0.4.0
+python-dotenv>=1.1.1
+uvicorn>=0.38.0
+gspread>=6.0.0
diff --git a/retrieval.py b/retrieval.py
new file mode 100644
index 0000000..c203937
--- /dev/null
+++ b/retrieval.py
@@ -0,0 +1,63 @@
+"""Hybrid retrieval pipeline: BM25 + vector fusion, reranked by a local
+ONNX cross-encoder. Everything runs in-process — no API calls, no cost.
+
+Used by both the API (app.py) and the eval harness (evals/run_eval.py).
+"""
+
+import os
+
+from llama_index.core.llms import MockLLM
+from llama_index.core.retrievers import QueryFusionRetriever
+
+RERANK_MODEL_NAME = os.getenv("RERANK_MODEL_NAME", "Xenova/ms-marco-MiniLM-L-6-v2")
+# candidates fetched per retriever before fusion + reranking narrows them down
+CANDIDATE_POOL = 8
+
+
+def build_hybrid_retriever(index, candidate_pool: int = CANDIDATE_POOL):
+    """Fuse vector search (semantic) with BM25 (keyword) via reciprocal rank
+    fusion. Falls back to vector-only if the BM25 index can't be built."""
+    vector_retriever = index.as_retriever(similarity_top_k=candidate_pool)
+    try:
+        from llama_index.retrievers.bm25 import BM25Retriever
+
+        bm25_retriever = BM25Retriever.from_defaults(
+            docstore=index.docstore, similarity_top_k=candidate_pool
+        )
+    except Exception as e:
+        print(f"BM25 unavailable ({e}); falling back to vector-only retrieval.")
+        return vector_retriever
+    return QueryFusionRetriever(
+        [vector_retriever, bm25_retriever],
+        mode="reciprocal_rerank",
+        similarity_top_k=candidate_pool,
+        num_queries=1,  # no LLM query expansion — keep retrieval free and fast
+        use_async=False,
+        llm=MockLLM(),  # required by the constructor, never called with num_queries=1
+    )
+
+
+def load_reranker():
+    """Local cross-encoder reranker (ONNX via fastembed), or None if unavailable."""
+    try:
+        from fastembed.rerank.cross_encoder import TextCrossEncoder
+
+        return TextCrossEncoder(model_name=RERANK_MODEL_NAME)
+    except Exception as e:
+        print(f"Reranker unavailable ({e}); skipping rerank stage.")
+        return None
+
+
+def rerank_nodes(reranker, query_text: str, nodes: list, top_k: int) -> list:
+    """Order retrieved nodes by cross-encoder relevance and keep the top_k."""
+    if reranker is None or not nodes:
+        return nodes[:top_k]
+    scores = list(reranker.rerank(query_text, [n.get_content() for n in nodes]))
+    ranked = sorted(zip(nodes, scores), key=lambda pair: pair[1], reverse=True)
+    return [node for node, _ in ranked[:top_k]]
+
+
+def retrieve(retriever, reranker, query_text: str, top_k: int) -> list:
+    """Full pipeline: hybrid retrieve a candidate pool, then rerank to top_k."""
+    nodes = retriever.retrieve(query_text)
+    return rerank_nodes(reranker, query_text, nodes, top_k)
diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/test_app.py b/tests/test_app.py
new file mode 100644
index 0000000..3618d2c
--- /dev/null
+++ b/tests/test_app.py
@@ -0,0 +1,383 @@
+"""Tests for the RAG Resume API after the Gemini -> OpenRouter migration.
+
+The LLM is mocked (MockLLM) so no API key or network is needed, while the
+embedding path is exercised for real with the local fastembed model.
+
+Run the live OpenRouter smoke tests with:  pytest -m live  (needs OPENROUTER_API_KEY)
+"""
+
+import json
+import os
+
+import pytest
+from fastapi.testclient import TestClient
+from llama_index.core import Document, VectorStoreIndex
+from llama_index.core.llms import MockLLM
+from llama_index.embeddings.fastembed import FastEmbedEmbedding
+
+import app as app_module
+import retrieval
+
+SAMPLE_DOCS = [
+    Document(text="Rahul Krishnan is a software engineer with experience in Python, "
+                  "FastAPI, and machine learning. He built a RAG-based resume assistant."),
+    Document(text="Rahul's skills include Python, SQL, Docker, GCP, LlamaIndex, "
+                  "and building REST APIs with FastAPI."),
+    Document(text="Rahul studied computer science and has worked on data engineering "
+                  "and LLM-powered applications."),
+]
+
+
+class CountingMockLLM(MockLLM):
+    """MockLLM that counts completion calls, to verify caching behavior."""
+
+    call_count: int = 0
+
+    def complete(self, *args, **kwargs):
+        CountingMockLLM.call_count += 1
+        return super().complete(*args, **kwargs)
+
+    def stream_complete(self, *args, **kwargs):
+        CountingMockLLM.call_count += 1
+        return super().stream_complete(*args, **kwargs)
+
+
+@pytest.fixture(scope="session")
+def embed_model():
+    return FastEmbedEmbedding(model_name="BAAI/bge-small-en-v1.5")
+
+
+@pytest.fixture(scope="session")
+def vector_index(embed_model):
+    return VectorStoreIndex.from_documents(SAMPLE_DOCS, embed_model=embed_model)
+
+
+@pytest.fixture(scope="session")
+def hybrid_retriever(vector_index):
+    return retrieval.build_hybrid_retriever(vector_index)
+
+
+@pytest.fixture()
+def client(vector_index, embed_model, hybrid_retriever):
+    """TestClient with a real (local-embedding) hybrid index and a mocked LLM."""
+    app_module.index = vector_index
+    app_module.embed_model = embed_model
+    app_module.retriever = hybrid_retriever
+    app_module.reranker = None  # cross-encoder covered by test_retrieval tests
+    app_module.llm = CountingMockLLM(max_tokens=32)
+    CountingMockLLM.call_count = 0
+    # reset per-test state
+    app_module._rate_store.clear()
+    app_module._query_cache.clear()
+    app_module._sessions.clear()
+    app_module._daily_count = 0
+    yield TestClient(app_module.app)
+    app_module.index = None
+    app_module.retriever = None
+    app_module.llm = None
+
+
+def test_health_with_index(client):
+    r = client.get("/health")
+    assert r.status_code == 200
+    assert r.json() == {"status": "ok", "model_loaded": True}
+
+
+def test_health_without_index(client):
+    app_module.index = None
+    r = client.get("/health")
+    assert r.status_code == 200
+    assert r.json()["model_loaded"] is False
+
+
+def test_query_happy_path(client):
+    r = client.post("/query", json={"query": "What are Rahul's skills?"})
+    assert r.status_code == 200
+    body = r.json()
+    assert body["query"] == "What are Rahul's skills?"
+    assert isinstance(body["answer"], str) and len(body["answer"]) > 0
+
+
+def test_query_empty_string_rejected(client):
+    r = client.post("/query", json={"query": ""})
+    assert r.status_code == 400
+
+
+def test_query_missing_field_rejected(client):
+    r = client.post("/query", json={})
+    assert r.status_code == 422
+
+
+def test_query_without_index_returns_500(client):
+    app_module.index = None
+    r = client.post("/query", json={"query": "anything"})
+    assert r.status_code == 500
+    assert "Index not built" in r.json()["detail"]
+
+
+def test_rate_limit_kicks_in_at_11th_request(client):
+    for i in range(app_module.RATE_LIMIT):
+        r = client.post("/query", json={"query": f"question number {i}"})
+        assert r.status_code == 200, f"request {i} unexpectedly failed: {r.text}"
+    r = client.post("/query", json={"query": "one too many"})
+    assert r.status_code == 429
+    assert "Too many requests" in r.json()["detail"]
+
+
+def test_rate_limit_is_per_ip(client):
+    for i in range(app_module.RATE_LIMIT):
+        client.post("/query", json={"query": f"q{i}"})
+    r = client.post(
+        "/query",
+        json={"query": "from another ip"},
+        headers={"X-Forwarded-For": "203.0.113.7"},
+    )
+    assert r.status_code == 200
+
+
+def test_daily_limit_blocks_requests(client):
+    app_module._daily_count = app_module.DAILY_QUERY_LIMIT
+    r = client.post("/query", json={"query": "over the daily limit"})
+    assert r.status_code == 429
+    assert "Daily query limit" in r.json()["detail"]
+
+
+def test_daily_limit_resets_on_new_day(client):
+    from datetime import date
+    app_module._daily_count = app_module.DAILY_QUERY_LIMIT
+    app_module._daily_reset_date = date(2000, 1, 1)
+    r = client.post("/query", json={"query": "new day query"})
+    assert r.status_code == 200
+    assert app_module._daily_count == 1
+
+
+def test_cache_serves_repeat_query_without_llm_call(client):
+    q = {"query": "Tell me about Rahul"}
+    r1 = client.post("/query", json=q)
+    assert r1.status_code == 200
+    assert CountingMockLLM.call_count == 1
+    count_after_first = app_module._daily_count
+
+    r2 = client.post("/query", json=q)
+    assert r2.status_code == 200
+    assert r2.json()["answer"] == r1.json()["answer"]
+    assert CountingMockLLM.call_count == 1  # served from cache
+    assert app_module._daily_count == count_after_first  # no quota consumed
+
+
+def test_cache_is_case_insensitive(client):
+    client.post("/query", json={"query": "Who is Rahul?"})
+    client.post("/query", json={"query": "  who is rahul?  "})
+    assert CountingMockLLM.call_count == 1
+
+
+def test_stream_endpoint_sse_format(client):
+    r = client.post("/query/stream", json={"query": "What did Rahul build?"})
+    assert r.status_code == 200
+    assert r.headers["content-type"].startswith("text/event-stream")
+    body = r.text
+    # real token-by-token streaming: several token events, not one buffered blob
+    assert body.count("data: ") > 3
+    assert body.rstrip().endswith("data: [DONE]")
+    tokens = [
+        json.loads(line[6:])["token"]
+        for line in body.splitlines()
+        if line.startswith("data: ") and line != "data: [DONE]"
+    ]
+    assert len("".join(tokens)) > 0
+
+
+def test_stream_endpoint_caches_response(client):
+    client.post("/query/stream", json={"query": "streaming cache test"})
+    assert CountingMockLLM.call_count == 1
+    r2 = client.post("/query/stream", json={"query": "streaming cache test"})
+    assert r2.status_code == 200
+    assert CountingMockLLM.call_count == 1  # second call served from cache
+    assert "data: [DONE]" in r2.text
+
+
+def test_index_persist_and_reload_roundtrip(tmp_path, embed_model, vector_index):
+    """The persisted index reloads with the same local embedding model (what
+    app startup does) and still retrieves relevant nodes."""
+    from llama_index.core import StorageContext, load_index_from_storage
+
+    persist_dir = str(tmp_path / "index")
+    vector_index.storage_context.persist(persist_dir=persist_dir)
+
+    storage_context = StorageContext.from_defaults(persist_dir=persist_dir)
+    reloaded = load_index_from_storage(storage_context, embed_model=embed_model)
+    nodes = reloaded.as_retriever(similarity_top_k=2).retrieve("What are Rahul's skills?")
+    assert len(nodes) == 2
+    assert any("skills" in n.text.lower() or "python" in n.text.lower() for n in nodes)
+
+
+def test_multi_turn_session_condenses_followup(client):
+    r1 = client.post("/query", json={"query": "Where does Rahul work?", "session_id": "s1"})
+    assert r1.status_code == 200
+    assert CountingMockLLM.call_count == 1  # no history yet -> no condense call
+
+    r2 = client.post("/query", json={"query": "What did he do there?", "session_id": "s1"})
+    assert r2.status_code == 200
+    # follow-up: one condense call + one answer call
+    assert CountingMockLLM.call_count == 3
+    assert len(app_module._sessions["s1"]["turns"]) == 2
+
+
+def test_sessions_are_isolated(client):
+    client.post("/query", json={"query": "Question one", "session_id": "a"})
+    client.post("/query", json={"query": "Question two", "session_id": "b"})
+    assert len(app_module._sessions["a"]["turns"]) == 1
+    assert len(app_module._sessions["b"]["turns"]) == 1
+
+
+def test_session_without_id_is_stateless(client):
+    client.post("/query", json={"query": "No session here"})
+    assert app_module._sessions == {}
+
+
+def test_session_history_capped(client):
+    for i in range(app_module.MAX_HISTORY_TURNS + 5):
+        client.post("/query", json={"query": f"unique question {i}", "session_id": "cap"})
+    assert len(app_module._sessions["cap"]["turns"]) == app_module.MAX_HISTORY_TURNS
+
+
+def test_expired_session_forgotten(client):
+    client.post("/query", json={"query": "Old question", "session_id": "expiring"})
+    app_module._sessions["expiring"]["ts"] -= app_module.SESSION_TTL + 1
+    assert app_module._get_session_history("expiring") == []
+
+
+def test_hybrid_retrieval_finds_keyword_and_semantic_matches(hybrid_retriever):
+    """BM25 catches exact keywords; vector side catches paraphrases."""
+    keyword_nodes = retrieval.retrieve(hybrid_retriever, None, "LlamaIndex", 2)
+    assert any("LlamaIndex" in n.get_content() for n in keyword_nodes)
+    semantic_nodes = retrieval.retrieve(
+        hybrid_retriever, None, "What web backend technologies does he know?", 2
+    )
+    assert any("FastAPI" in n.get_content() for n in semantic_nodes)
+
+
+def test_reranker_orders_by_relevance(hybrid_retriever):
+    """The local cross-encoder puts the most relevant chunk first."""
+    reranker = retrieval.load_reranker()
+    assert reranker is not None
+    nodes = retrieval.retrieve(hybrid_retriever, reranker, "What are Rahul's skills?", 3)
+    assert len(nodes) == 3
+    assert "skills" in nodes[0].get_content().lower()
+
+
+@pytest.fixture()
+def old_gemini_style_index_dir(tmp_path):
+    """Persist an index built with 3072-dim embeddings, like the old Gemini index."""
+    from llama_index.core.embeddings import MockEmbedding
+
+    persist_dir = str(tmp_path / "old_index")
+    old_index = VectorStoreIndex.from_documents(
+        SAMPLE_DOCS, embed_model=MockEmbedding(embed_dim=3072)
+    )
+    old_index.storage_context.persist(persist_dir=persist_dir)
+    return persist_dir
+
+
+def _run_startup(monkeypatch, persist_dir, docs_dir):
+    monkeypatch.setattr(app_module, "OPENROUTER_API_KEY", "dummy-key")
+    monkeypatch.setattr(app_module, "INDEX_PERSIST_DIR", persist_dir)
+    monkeypatch.setattr(app_module, "DOCS_DIR", docs_dir)
+    app_module.index = None
+    app_module.init_models_and_index()
+
+
+def test_startup_detects_stored_dim(old_gemini_style_index_dir):
+    assert app_module._stored_index_embedding_dim(old_gemini_style_index_dir) == 3072
+
+
+def test_startup_rebuilds_old_gemini_index_from_docs(
+    monkeypatch, tmp_path, old_gemini_style_index_dir
+):
+    """Backwards compatibility: a Docker rebuild that ships the old Gemini-built
+    index must not break — the app rebuilds it from docs/ at startup."""
+    docs_dir = tmp_path / "docs"
+    docs_dir.mkdir()
+    (docs_dir / "resume.txt").write_text(
+        "Rahul Krishnan is a software engineer skilled in Python and FastAPI."
+    )
+
+    _run_startup(monkeypatch, old_gemini_style_index_dir, str(docs_dir))
+
+    assert app_module.index is not None
+    # rebuilt index was re-persisted with the new model's dimension
+    assert app_module._stored_index_embedding_dim(old_gemini_style_index_dir) == 384
+    nodes = app_module.index.as_retriever(similarity_top_k=1).retrieve("Rahul's skills?")
+    assert len(nodes) == 1
+    app_module.index = None
+    app_module.llm = None
+
+
+def test_startup_old_index_without_docs_degrades_gracefully(
+    monkeypatch, tmp_path, old_gemini_style_index_dir
+):
+    """No docs to rebuild from: the app must still boot with index unset."""
+    _run_startup(monkeypatch, old_gemini_style_index_dir, str(tmp_path / "missing_docs"))
+
+    assert app_module.index is None
+    client = TestClient(app_module.app)
+    r = client.get("/health")
+    assert r.status_code == 200
+    assert r.json()["model_loaded"] is False
+    app_module.llm = None
+
+
+def test_startup_loads_matching_index_without_rebuild(monkeypatch, tmp_path, vector_index):
+    """A current-format (384-dim) index loads directly, no rebuild attempted."""
+    persist_dir = str(tmp_path / "current_index")
+    vector_index.storage_context.persist(persist_dir=persist_dir)
+
+    _run_startup(monkeypatch, persist_dir, str(tmp_path / "missing_docs"))
+
+    assert app_module.index is not None
+    app_module.index = None
+    app_module.llm = None
+
+
+def test_openrouter_llm_configuration():
+    """The OpenRouter LLM wires up with the free-router default and streaming support."""
+    from llama_index.llms.openrouter import OpenRouter
+
+    llm = OpenRouter(model="openrouter/free", api_key="dummy-key", max_tokens=1024)
+    assert llm.model == "openrouter/free"
+    assert llm.metadata.is_chat_model
+
+
+@pytest.mark.live
+@pytest.mark.skipif(
+    not os.getenv("OPENROUTER_API_KEY"),
+    reason="OPENROUTER_API_KEY not set; skipping live OpenRouter smoke test",
+)
+def test_openrouter_live_completion():
+    from llama_index.llms.openrouter import OpenRouter
+
+    llm = OpenRouter(
+        model=os.getenv("OPENROUTER_MODEL", "openrouter/free"),
+        api_key=os.getenv("OPENROUTER_API_KEY"),
+        max_tokens=64,
+    )
+    resp = llm.complete("Reply with the single word: pong")
+    assert len(str(resp).strip()) > 0
+
+
+@pytest.mark.live
+@pytest.mark.skipif(
+    not os.getenv("OPENROUTER_API_KEY"),
+    reason="OPENROUTER_API_KEY not set; skipping live OpenRouter streaming test",
+)
+def test_openrouter_live_streaming():
+    from llama_index.llms.openrouter import OpenRouter
+
+    llm = OpenRouter(
+        model=os.getenv("OPENROUTER_MODEL", "openrouter/free"),
+        api_key=os.getenv("OPENROUTER_API_KEY"),
+        max_tokens=64,
+    )
+    chunks = [c.delta for c in llm.stream_complete("Count from 1 to 5.")]
+    assert len(chunks) >= 1
+    assert len("".join(filter(None, chunks)).strip()) > 0
diff --git a/tests/test_check_index.py b/tests/test_check_index.py
new file mode 100644
index 0000000..8a12442
--- /dev/null
+++ b/tests/test_check_index.py
@@ -0,0 +1,43 @@
+"""Tests for the Docker build-time index compatibility guard."""
+
+import json
+import subprocess
+import sys
+from pathlib import Path
+
+CHECK_SCRIPT = str(Path(__file__).resolve().parent.parent / "check_index.py")
+
+
+def run_check(index_dir: Path) -> subprocess.CompletedProcess:
+    return subprocess.run(
+        [sys.executable, CHECK_SCRIPT],
+        env={"INDEX_PERSIST_DIR": str(index_dir), "PATH": "/usr/bin:/bin"},
+        capture_output=True,
+        text=True,
+    )
+
+
+def write_vector_store(index_dir: Path, dim: int) -> None:
+    index_dir.mkdir(parents=True, exist_ok=True)
+    (index_dir / "default__vector_store.json").write_text(
+        json.dumps({"embedding_dict": {"node-1": [0.0] * dim}})
+    )
+
+
+def test_missing_index_passes(tmp_path):
+    result = run_check(tmp_path / "empty")
+    assert result.returncode == 0, result.stdout + result.stderr
+
+
+def test_matching_index_passes(tmp_path):
+    write_vector_store(tmp_path / "index", 384)  # bge-small dimension
+    result = run_check(tmp_path / "index")
+    assert result.returncode == 0, result.stdout + result.stderr
+    assert "Index OK" in result.stdout
+
+
+def test_old_gemini_index_fails_build(tmp_path):
+    write_vector_store(tmp_path / "index", 3072)  # old Gemini dimension
+    result = run_check(tmp_path / "index")
+    assert result.returncode == 1
+    assert "Rebuild the index" in result.stdout
diff --git a/tests/test_eval.py b/tests/test_eval.py
new file mode 100644
index 0000000..58bb881
--- /dev/null
+++ b/tests/test_eval.py
@@ -0,0 +1,12 @@
+"""Retrieval-quality gate: the hybrid pipeline must surface the expected
+facts for the golden questions. Offline — no API key needed."""
+
+from evals import run_eval
+
+
+def test_retrieval_hit_rate_meets_threshold():
+    golden = run_eval.load_golden()
+    retriever, reranker = run_eval.build_pipeline(run_eval.DEFAULT_DOCS)
+    hit_rate, rows = run_eval.eval_retrieval(retriever, reranker, golden)
+    misses = [r["id"] for r in rows if not r["retrieval_hit"]]
+    assert hit_rate >= run_eval.RETRIEVAL_THRESHOLD, f"retrieval misses: {misses}"
diff --git a/uv.lock b/uv.lock
index 110253c..a5f7a21 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,5 @@
 version = 1
-revision = 2
+revision = 3
 requires-python = ">=3.12"
 resolution-markers = [
     "python_full_version >= '3.14'",
@@ -197,12 +197,15 @@ wheels = [
 ]
 
 [[package]]
-name = "cachetools"
-version = "6.2.1"
+name = "bm25s"
+version = "0.3.10"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/cc/7e/b975b5814bd36faf009faebe22c1072a1fa1168db34d285ef0ba071ad78c/cachetools-6.2.1.tar.gz", hash = "sha256:3f391e4bd8f8bf0931169baf7456cc822705f4e2a31f840d218f445b9a854201", size = 31325, upload-time = "2025-10-12T14:55:30.139Z" }
+dependencies = [
+    { name = "numpy" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/34/ab/8fac50e2095d26dd9ccaf3c35bddb96ff45a8cca9fc3ac0e98209971828f/bm25s-0.3.10.tar.gz", hash = "sha256:fafdfc45e6c064fc117b08de8f59b47564a3879d3ca593daa0b2a40b2c05da43", size = 80762, upload-time = "2026-07-22T20:54:28.824Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/96/c5/1e741d26306c42e2bf6ab740b2202872727e0f606033c9dd713f8b93f5a8/cachetools-6.2.1-py3-none-any.whl", hash = "sha256:09868944b6dde876dfd44e1d47e18484541eaf12f26f29b7af91b26cc892d701", size = 11280, upload-time = "2025-10-12T14:55:28.382Z" },
+    { url = "https://files.pythonhosted.org/packages/d4/06/7ec2175c185661a976589fffde16eb7c36d69eaa43eed26fe5ad0ce3cd25/bm25s-0.3.10-py3-none-any.whl", hash = "sha256:d271d4e1ad7ffdacb224f41bc54aba55159438ecf06439ffe929f088efa96858", size = 74687, upload-time = "2026-07-22T20:54:27.447Z" },
 ]
 
 [[package]]
@@ -330,14 +333,14 @@ wheels = [
 
 [[package]]
 name = "click"
-version = "8.3.0"
+version = "8.4.2"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "colorama", marker = "sys_platform == 'win32'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/46/61/de6cd827efad202d7057d93e0fed9294b96952e188f7384832791c7b2254/click-8.3.0.tar.gz", hash = "sha256:e7b8232224eba16f4ebe410c25ced9f7875cb5f3263ffc93cc3e8da705e229c4", size = 276943, upload-time = "2025-09-18T17:32:23.696Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/76/d4/81420972a676e8ffea40450d8c8c92943e7218a78fe9b64359836cc9876b/click-8.4.2.tar.gz", hash = "sha256:9a6cea6e60b17ebe0a44c5cc636d94f09bd66142c1cd7d8b4cd731c4917a15f6", size = 338000, upload-time = "2026-06-24T17:45:15.148Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/db/d3/9dcc0f5797f070ec8edf30fbadfb200e71d9db6b84d211e3b2085a7589a0/click-8.3.0-py3-none-any.whl", hash = "sha256:9b9f285302c6e3064f4330c05f05b81945b2a39544279343e6e7c5f27a9baddc", size = 107295, upload-time = "2025-09-18T17:32:22.42Z" },
+    { url = "https://files.pythonhosted.org/packages/fb/e2/79c688af8b210d232694e31e59da9f6ec747bae31c3f5946e4e9b98860d5/click-8.4.2-py3-none-any.whl", hash = "sha256:e6f9f66136c816745b9d65817da91d61d957fb16e02e4dcd0552553c5a197b76", size = 119243, upload-time = "2026-06-24T17:45:13.73Z" },
 ]
 
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