diff --git a/.env.example b/.env.example
index 5e04976..2a03494 100644
--- a/.env.example
+++ b/.env.example
@@ -6,3 +6,10 @@ JWT_ALGORITHM=HS256
 JWT_EXPIRE_MINUTES=1440
 FRONTEND_URL=http://localhost:5173
 DATABASE_URL=sqlite:///./workmate.db
+
+# AI / LLM
+GEMINI_API_KEY=your-gemini-api-key
+VOYAGE_API_KEY=your-voyageai-api-key   
+
+# Notion (direct API access — optional, JSON import works without this)
+NOTION_TOKEN=your-notion-api-token
diff --git a/.gitignore b/.gitignore
index d597387..6ee26c7 100644
--- a/.gitignore
+++ b/.gitignore
@@ -46,4 +46,10 @@ chroma_db/
 
 # Antigravity SKILLS
 .agent/
-CLAUDE.md
\ No newline at end of file
+CLAUDE.md
+.claude/
+
+# Debugging
+Tasks_for_Claude.txt
+debug_rag.py
+generate_token.py
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index 8b87489..08c22d6 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -19,4 +19,6 @@ dependencies = [
     "python-multipart>=0.0.22",
     "pypdf2>=3.0.1",
     "sse-starlette>=3.3.2",
+    "voyageai>=0.3.7",
+    "bm25s>=0.2.12",
 ]
diff --git a/src/backend/app.py b/src/backend/app.py
index f50570e..4a0005b 100644
--- a/src/backend/app.py
+++ b/src/backend/app.py
@@ -3,7 +3,7 @@
 
 from src.backend.config import settings
 from src.backend.database import Base, engine
-from src.backend.routers import admin, auth, chat, conversations, upload
+from src.backend.routers import admin, auth, conversations, upload
 
 
 def create_app() -> FastAPI:
@@ -21,7 +21,6 @@ def create_app() -> FastAPI:
 
     app.include_router(auth.router)
     app.include_router(admin.router)
-    app.include_router(chat.router)
     app.include_router(conversations.router)
     app.include_router(upload.router)
 
diff --git a/src/backend/config.py b/src/backend/config.py
index d0a986a..f48c471 100644
--- a/src/backend/config.py
+++ b/src/backend/config.py
@@ -11,6 +11,7 @@ class Settings(BaseSettings):
     FRONTEND_URL: str = "http://localhost:5173"
     DATABASE_URL: str = "sqlite:///./workmate.db"
     GEMINI_API_KEY: str = ""
+    VOYAGE_API_KEY: str = ""
     NOTION_TOKEN: str = ""
 
     model_config = {"env_file": ".env", "extra": "ignore"}
diff --git a/src/backend/dependencies/services.py b/src/backend/dependencies/services.py
index 62b3665..f98b95d 100644
--- a/src/backend/dependencies/services.py
+++ b/src/backend/dependencies/services.py
@@ -1,14 +1,21 @@
 import logging
+import os
 
 from fastapi import HTTPException
 
+from src.backend.load.bm25_manager import BM25Manager, BM25_INDEX_PATH
 from src.backend.load.chroma_manager import ChromaManager
+from src.backend.load.hybrid_retriever import HybridRetriever
 from src.backend.llm.gemini_client import GeminiClient
+from src.backend.llm.voyage_reranker import VoyageReranker
 
 logger = logging.getLogger(__name__)
 
 _chroma_manager = None
 _gemini_client = None
+_voyage_reranker = None
+_bm25_manager = None
+_hybrid_retriever = None
 
 
 def get_chroma_manager() -> ChromaManager:
@@ -33,3 +40,32 @@ def get_gemini_client() -> GeminiClient:
             logger.error(f"Failed to initialize GeminiClient: {e}")
             raise HTTPException(status_code=500, detail="LLM configuration missing.")
     return _gemini_client
+
+
+def get_voyage_reranker() -> VoyageReranker:
+    global _voyage_reranker
+    if _voyage_reranker is None:
+        try:
+            _voyage_reranker = VoyageReranker()
+        except Exception as e:
+            logger.error(f"Failed to initialize VoyageReranker: {e}")
+            raise HTTPException(status_code=500, detail="Reranker configuration missing.")
+    return _voyage_reranker
+
+
+def get_bm25_manager() -> BM25Manager:
+    global _bm25_manager
+    if _bm25_manager is None:
+        _bm25_manager = BM25Manager()
+        if os.path.exists(BM25_INDEX_PATH):
+            _bm25_manager.load(BM25_INDEX_PATH)
+        else:
+            logger.warning(f"BM25 index not found at {BM25_INDEX_PATH}. Run NotionIngestor to build it.")
+    return _bm25_manager
+
+
+def get_hybrid_retriever() -> HybridRetriever:
+    global _hybrid_retriever
+    if _hybrid_retriever is None:
+        _hybrid_retriever = HybridRetriever(get_chroma_manager(), get_bm25_manager())
+    return _hybrid_retriever
diff --git a/src/backend/llm/gemini_client.py b/src/backend/llm/gemini_client.py
index abdc6cd..9859c98 100644
--- a/src/backend/llm/gemini_client.py
+++ b/src/backend/llm/gemini_client.py
@@ -33,14 +33,25 @@ def filter_chunks(
     ) -> List[Dict[str, Any]]:
         """
         LLM Re-ranking: Asks Gemini to filter out irrelevant chunks before generation.
+        Uses sequential chunk_N IDs (chunk_1, chunk_2, ...) for the filter prompt so
+        the LLM and the matching logic agree on the same ID format.
         """
         if not chunks:
             return []
 
-        prompt = prompts.get_filter_prompt(chunks, user_question)
+        # Remap chunks to sequential IDs for the filter prompt so the LLM
+        # returns predictable IDs like "chunk_1, chunk_3" instead of UUIDs.
+        sequential_id_map: Dict[str, Dict[str, Any]] = {}
+        remapped_chunks = []
+        for i, chunk in enumerate(chunks, start=1):
+            seq_id = f"chunk_{i}"
+            sequential_id_map[seq_id] = chunk
+            remapped_chunks.append({**chunk, "chunk_id": seq_id})
+
+        prompt = prompts.get_filter_prompt(remapped_chunks, user_question)
         cfg = types.GenerateContentConfig(
             system_instruction=prompts.FILTER_SYSTEM_INSTRUCTION,
-            temperature=0.0,  # Zero precision for extraction
+            temperature=0.0,
             max_output_tokens=100,
         )
 
@@ -51,40 +62,37 @@ def filter_chunks(
                 config=cfg,
             )
             output = getattr(response, "text", "") or ""
-            print(f"🧠 Re-ranker Output: {output}")
-            
+            print(f"Re-ranker Output: {output}")
+
             if "NONE" in output.upper():
-                print("🧠 Re-ranker kept 0 chunks.")
+                print("Re-ranker kept 0 chunks.")
                 return []
-                
-            # Parse the output safely: sometimes Gemini returns a clean comma-separated list of UUIDs
-            # But sometimes it's lazy and returns just the first part e.g. "30, 4f" instead of "302f24..."
-            filtered_chunks = []
+
+            # Match returned IDs (e.g. "chunk_1, chunk_3") back to original chunks.
             output_parts = [p.strip() for p in output.split(",") if p.strip()]
-            
-            for chunk in chunks:
-                chunk_id = str(chunk["chunk_id"])
-                
-                # Check 1: Is the full chunk_id anywhere in the raw output string?
-                if chunk_id in output:
-                    filtered_chunks.append(chunk)
+            filtered_chunks = []
+            seen = set()
+            for part in output_parts:
+                # Exact match: "chunk_3"
+                if part in sequential_id_map and part not in seen:
+                    filtered_chunks.append(sequential_id_map[part])
+                    seen.add(part)
                     continue
-                    
-                # Check 2: Did the LLM abbreviate the IDs? Check each comma-separated part.
-                for part in output_parts:
-                    if len(part) >= 2 and chunk_id.startswith(part):
-                        filtered_chunks.append(chunk)
-                        break
-            
-            print(f"🧠 Re-ranker kept {len(filtered_chunks)}/{len(chunks)} chunks.")
-            
-            # Fallback to all chunks if zero were matched but it didn't explicitly say "NONE"
+                # Plain number match: LLM returned "3" instead of "chunk_3"
+                candidate = f"chunk_{part}"
+                if candidate in sequential_id_map and candidate not in seen:
+                    filtered_chunks.append(sequential_id_map[candidate])
+                    seen.add(candidate)
+
+            print(f"Re-ranker kept {len(filtered_chunks)}/{len(chunks)} chunks.")
+
+            # Fallback: if nothing matched but LLM didn't say NONE, return all chunks.
             if not filtered_chunks:
-                 return chunks
+                return chunks
             return filtered_chunks
-            
+
         except Exception as e:
-            logger.warning(f"⚠️ Re-ranking failed (falling back to all chunks): {e}")
+            logger.warning(f"Re-ranking failed (falling back to all chunks): {e}")
             return chunks
 
     def ask_workmate(
diff --git a/src/backend/llm/voyage_reranker.py b/src/backend/llm/voyage_reranker.py
new file mode 100644
index 0000000..03c915a
--- /dev/null
+++ b/src/backend/llm/voyage_reranker.py
@@ -0,0 +1,106 @@
+from __future__ import annotations
+
+import logging
+import os
+from typing import Any, Dict, List, Tuple
+
+logger = logging.getLogger(__name__)
+
+DEFAULT_RERANK_MODEL = "rerank-2"
+RELEVANCE_THRESHOLD = 0.3
+
+
+class VoyageReranker:
+    """
+    Reranks retrieved chunks using the VoyageAI rerank API.
+    Replaces the LLM-based filter_chunks step in the RAG pipeline.
+    """
+
+    def __init__(
+        self,
+        model: str = DEFAULT_RERANK_MODEL,
+        threshold: float = RELEVANCE_THRESHOLD,
+    ):
+        api_key = os.getenv("VOYAGE_API_KEY")
+        if not api_key:
+            logger.warning(
+                "[VoyageReranker] VOYAGE_API_KEY not set — reranking disabled. "
+                "Set VOYAGE_API_KEY in .env to enable VoyageAI reranking."
+            )
+            self.client = None
+        else:
+            import voyageai
+            self.client = voyageai.Client(api_key=api_key)
+
+        self.model = model
+        self.threshold = threshold
+
+    def rerank(
+        self,
+        chunks: List[Dict[str, Any]],
+        query: str,
+        top_k: int = 5,
+    ) -> Tuple[List[Dict[str, Any]], List[Dict[str, Any]]]:
+        """
+        Rerank chunks by relevance to the query.
+
+        Returns:
+            final_chunks  — top_k chunks above threshold, score field stripped (clean for generation)
+            scored_chunks — all input chunks sorted by score descending, with rerank_score added
+        """
+        if not chunks:
+            return [], []
+
+        if self.client is None:
+            logger.warning("[VoyageReranker] Reranking skipped (no API key). Returning top_k unranked.")
+            return chunks[:top_k], []
+
+        documents = [
+            f"Page: {c['page_title']}\nSection: {c['section']}\n{c['text']}"
+            if c.get("section")
+            else f"Page: {c['page_title']}\n{c['text']}"
+            for c in chunks
+        ]
+
+        try:
+            result = self.client.rerank(
+                query=query,
+                documents=documents,
+                model=self.model,
+                top_k=len(chunks),  # fetch all scores; we apply threshold + top_k ourselves
+            )
+
+            scored_chunks: List[Dict[str, Any]] = []
+            for item in result.results:
+                chunk = chunks[item.index]
+                scored_chunks.append({**chunk, "rerank_score": round(item.relevance_score, 4)})
+
+            scored_chunks.sort(key=lambda x: x["rerank_score"], reverse=True)
+
+            logger.info(
+                f"[VoyageReranker] scores: "
+                f"{[(c['page_title'], c['rerank_score']) for c in scored_chunks]}"
+            )
+
+            above_threshold = [c for c in scored_chunks if c["rerank_score"] >= self.threshold]
+            final_scored = above_threshold[:top_k]
+
+            if not final_scored:
+                logger.warning(
+                    f"[VoyageReranker] All {len(chunks)} chunks below threshold "
+                    f"({self.threshold}). Top score: "
+                    f"{scored_chunks[0]['rerank_score'] if scored_chunks else 'N/A'}."
+                )
+
+            # Strip rerank_score before passing to generation prompt
+            final_chunks = [
+                {k: v for k, v in c.items() if k != "rerank_score"} for c in final_scored
+            ]
+
+            return final_chunks, scored_chunks
+
+        except Exception as e:
+            logger.warning(
+                f"[VoyageReranker] Reranking failed, falling back to top {top_k} unranked: {e}"
+            )
+            return chunks[:top_k], []
diff --git a/src/backend/load/bm25_manager.py b/src/backend/load/bm25_manager.py
new file mode 100644
index 0000000..2d06100
--- /dev/null
+++ b/src/backend/load/bm25_manager.py
@@ -0,0 +1,77 @@
+import logging
+import os
+import pickle
+
+import bm25s
+
+logger = logging.getLogger(__name__)
+
+PROJECT_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), "../../../"))
+BM25_INDEX_PATH = os.path.join(PROJECT_ROOT, "workmate_db", "bm25_index.pkl")
+
+
+class BM25Manager:
+    def __init__(self):
+        self.index = None
+        self.chunks: list[str] = []
+        self.metadatas: list[dict] = []
+        self.ids: list[str] = []
+
+    def build_index(self, chunks: list[str], metadatas: list[dict], ids: list[str]):
+        self.chunks = chunks
+        self.metadatas = metadatas
+        self.ids = ids
+
+        corpus_indices = list(range(len(chunks)))
+        indexed_texts = [
+            f"{m.get('title', '')} {c}".lower()
+            for c, m in zip(chunks, metadatas)
+        ]
+        tokenized_corpus = bm25s.tokenize(indexed_texts)
+        self.index = bm25s.BM25(corpus=corpus_indices)
+        self.index.index(tokenized_corpus)
+        logger.info(f"BM25 index built with {len(chunks)} documents")
+
+    def search(self, query: str, top_k: int = 10) -> list[dict]:
+        if self.index is None:
+            logger.warning("BM25 index not built, returning empty results")
+            return []
+
+        k = min(top_k, len(self.chunks))
+        query_tokens = bm25s.tokenize([query.lower()])
+        results, _ = self.index.retrieve(query_tokens, k=k)
+
+        output = []
+        for idx in results[0]:
+            meta = self.metadatas[idx]
+            output.append({
+                "chunk_id": self.ids[idx],
+                "text": self.chunks[idx],
+                "page_title": meta.get("title", "Unknown Source"),
+                "section": meta.get("parent_title", ""),
+                **meta,
+            })
+        return output
+
+    def save(self, path: str = BM25_INDEX_PATH):
+        os.makedirs(os.path.dirname(path), exist_ok=True)
+        with open(path, "wb") as f:
+            pickle.dump(
+                {
+                    "index": self.index,
+                    "chunks": self.chunks,
+                    "metadatas": self.metadatas,
+                    "ids": self.ids,
+                },
+                f,
+            )
+        logger.info(f"BM25 index saved to {path}")
+
+    def load(self, path: str = BM25_INDEX_PATH):
+        with open(path, "rb") as f:
+            data = pickle.load(f)
+        self.index = data["index"]
+        self.chunks = data["chunks"]
+        self.metadatas = data["metadatas"]
+        self.ids = data["ids"]
+        logger.info(f"BM25 index loaded from {path} ({len(self.chunks)} documents)")
diff --git a/src/backend/load/chroma_manager.py b/src/backend/load/chroma_manager.py
index e88ea3b..d8f792b 100644
--- a/src/backend/load/chroma_manager.py
+++ b/src/backend/load/chroma_manager.py
@@ -39,7 +39,7 @@ def __init__(self, db_path=DEFAULT_DB_PATH, collection_name="notion_docs"):
             name=collection_name
         )
         print(
-            f"✅ Connected to ChromaDB at '{db_path}' (Collection: '{collection_name}') with Google Embedder"
+            f"Connected to ChromaDB at '{db_path}' (Collection: '{collection_name}') with Google Embedder"
         )
 
     def add_documents(self, documents, metadatas, ids, batch_size=20):
@@ -61,9 +61,9 @@ def add_documents(self, documents, metadatas, ids, batch_size=20):
                     ids=batch_ids,
                     embeddings=embeddings,
                 )
-                print(f"✅ Added batch {i // batch_size + 1} ({len(batch_docs)} docs, {i + len(batch_docs)}/{total})")
+                print(f"Added batch {i // batch_size + 1} ({len(batch_docs)} docs, {i + len(batch_docs)}/{total})")
             except Exception as e:
-                print(f"❌ Error adding batch {i // batch_size + 1}: {e}")
+                print(f"Error adding batch {i // batch_size + 1}: {e}")
                 raise e
 
     def query(self, query_text, n_results=5, where=None):
@@ -71,7 +71,7 @@ def query(self, query_text, n_results=5, where=None):
         Search the collection for relevant documents.
         Embeds the query using Google Embedder before searching.
         """
-        print(f"🔍 Querying: '{query_text}'...")
+        print(f"Querying: '{query_text}'...")
 
         # Embed the query with the same Google model used during ingestion
         query_embedding = self.embedder([query_text])
@@ -105,4 +105,4 @@ def reset(self):
         self.collection = self.client.get_or_create_collection(
             self.collection_name
         )
-        print(f"⚠️ Collection '{self.collection_name}' has been reset.")
+        print(f"Collection '{self.collection_name}' has been reset.")
diff --git a/src/backend/load/hybrid_retriever.py b/src/backend/load/hybrid_retriever.py
new file mode 100644
index 0000000..00bbe9d
--- /dev/null
+++ b/src/backend/load/hybrid_retriever.py
@@ -0,0 +1,58 @@
+import logging
+
+from src.backend.load.bm25_manager import BM25Manager
+from src.backend.load.chroma_manager import ChromaManager
+
+logger = logging.getLogger(__name__)
+
+
+class HybridRetriever:
+    def __init__(self, chroma_manager: ChromaManager, bm25_manager: BM25Manager):
+        self.chroma = chroma_manager
+        self.bm25 = bm25_manager
+
+    def search(
+        self,
+        query: str,
+        vector_top_k: int = 15,
+        bm25_top_k: int = 15,
+        final_top_k: int = 15,
+    ) -> list[dict]:
+        vector_results = self._query_chroma(query, vector_top_k)
+        bm25_results = self.bm25.search(query, top_k=bm25_top_k)
+
+        merged = self.reciprocal_rank_fusion([vector_results, bm25_results])
+        return merged[:final_top_k]
+
+    def _query_chroma(self, query: str, top_k: int) -> list[dict]:
+        results = self.chroma.query(query, n_results=top_k)
+        output = []
+        if results and results.get("documents") and results["documents"][0]:
+            docs = results["documents"][0]
+            metas = results.get("metadatas", [[]])[0]
+            ids = results.get("ids", [[]])[0]
+            for doc, meta, chunk_id in zip(docs, metas, ids):
+                output.append({
+                    "chunk_id": chunk_id,
+                    "text": doc.strip().replace("\r\n", "\n"),
+                    "page_title": meta.get("title", "Unknown Source"),
+                    "section": meta.get("parent_title", ""),
+                    **meta,
+                })
+        return output
+
+    def reciprocal_rank_fusion(
+        self, results_lists: list[list[dict]], k: int = 60
+    ) -> list[dict]:
+        scores: dict[str, float] = {}
+        chunk_data: dict[str, dict] = {}
+
+        for results in results_lists:
+            for rank, chunk in enumerate(results):
+                chunk_id = chunk["chunk_id"]
+                scores[chunk_id] = scores.get(chunk_id, 0.0) + 1 / (k + rank + 1)
+                if chunk_id not in chunk_data:
+                    chunk_data[chunk_id] = chunk
+
+        sorted_ids = sorted(scores, key=lambda x: scores[x], reverse=True)
+        return [chunk_data[cid] for cid in sorted_ids]
diff --git a/src/backend/routers/chat.py b/src/backend/routers/chat.py
index f9a5ed9..17c4724 100644
--- a/src/backend/routers/chat.py
+++ b/src/backend/routers/chat.py
@@ -1,124 +1,124 @@
-import logging
+# import logging
 
-from fastapi import APIRouter, Depends, HTTPException
+# from fastapi import APIRouter, Depends, HTTPException
 
-from src.backend.dependencies.services import get_chroma_manager, get_gemini_client
-from src.backend.load.chroma_manager import ChromaManager
-from src.backend.llm.gemini_client import GeminiClient
-from src.backend.schemas.chat import ChatRequest, ChatResponse
+# from src.backend.dependencies.services import get_chroma_manager, get_gemini_client
+# from src.backend.load.chroma_manager import ChromaManager
+# from src.backend.llm.gemini_client import GeminiClient
+# from src.backend.schemas.chat import ChatRequest, ChatResponse
 
-router = APIRouter(prefix="/api/chat", tags=["Chat"])
-logger = logging.getLogger(__name__)
+# router = APIRouter(prefix="/api/chat", tags=["Chat"])
+# logger = logging.getLogger(__name__)
 
-MAX_CONTEXT_CHARS = 4000
+# MAX_CONTEXT_CHARS = 4000
 
-@router.post("/ask", response_model=ChatResponse)
-async def ask_question(
-    request: ChatRequest,
-    chroma: ChromaManager = Depends(get_chroma_manager),
-    gemini: GeminiClient = Depends(get_gemini_client),
-):
-    """
-    RAG Pipeline Endpoint:
-    1. Retrieves relevant documents from the ChromaDB vector store.
-    2. Expands context by fetching sibling chunks from the same parent.
-    3. Combines them into a structured chunks list.
-    4. Calls Gemini LLM using the WorkMate prompt.
-    """
-    try:
-        # Step 1: Initial Retrieval (Fetch 10 to balance page coverage and API limits)
-        results = chroma.query(request.question, n_results=10)
+# @router.post("/ask", response_model=ChatResponse)
+# async def ask_question(
+#     request: ChatRequest,
+#     chroma: ChromaManager = Depends(get_chroma_manager),
+#     gemini: GeminiClient = Depends(get_gemini_client),
+# ):
+#     """
+#     RAG Pipeline Endpoint:
+#     1. Retrieves relevant documents from the ChromaDB vector store.
+#     2. Expands context by fetching sibling chunks from the same parent.
+#     3. Combines them into a structured chunks list.
+#     4. Calls Gemini LLM using the WorkMate prompt.
+#     """
+#     try:
+#         # Step 1: Initial Retrieval (Fetch 10 to balance page coverage and API limits)
+#         results = chroma.query(request.question, n_results=10)
 
-        # Collect initial results into a dict keyed by chunk ID for deduplication
-        seen_ids = set()
-        chunks_with_meta = []  # list of (doc_text, metadata, chunk_id)
-        source_titles = set()
+#         # Collect initial results into a dict keyed by chunk ID for deduplication
+#         seen_ids = set()
+#         chunks_with_meta = []  # list of (doc_text, metadata, chunk_id)
+#         source_titles = set()
 
-        if results and results.get("documents") and results["documents"][0]:
-            docs = results["documents"][0]
-            metas = (
-                results["metadatas"][0]
-                if results.get("metadatas")
-                else [{}] * len(docs)
-            )
-            ids = (
-                results["ids"][0]
-                if results.get("ids")
-                else [str(i) for i in range(len(docs))]
-            )
+#         if results and results.get("documents") and results["documents"][0]:
+#             docs = results["documents"][0]
+#             metas = (
+#                 results["metadatas"][0]
+#                 if results.get("metadatas")
+#                 else [{}] * len(docs)
+#             )
+#             ids = (
+#                 results["ids"][0]
+#                 if results.get("ids")
+#                 else [str(i) for i in range(len(docs))]
+#             )
 
-            for doc, meta, chunk_id in zip(docs, metas, ids):
-                if chunk_id not in seen_ids:
-                    seen_ids.add(chunk_id)
-                    chunks_with_meta.append((doc, meta, chunk_id))
+#             for doc, meta, chunk_id in zip(docs, metas, ids):
+#                 if chunk_id not in seen_ids:
+#                     seen_ids.add(chunk_id)
+#                     chunks_with_meta.append((doc, meta, chunk_id))
 
-        # Step 2: Sibling Expansion — only for very short chunks (likely incomplete)
-        parent_ids_to_expand = set()
-        for doc_text, meta, chunk_id in chunks_with_meta:
-            if len(doc_text.strip()) < 100:  # Only expand short/incomplete chunks
-                parent_id = meta.get("parent_id")
-                if parent_id:
-                    parent_ids_to_expand.add(parent_id)
+#         # Step 2: Sibling Expansion — only for very short chunks (likely incomplete)
+#         parent_ids_to_expand = set()
+#         for doc_text, meta, chunk_id in chunks_with_meta:
+#             if len(doc_text.strip()) < 100:  # Only expand short/incomplete chunks
+#                 parent_id = meta.get("parent_id")
+#                 if parent_id:
+#                     parent_ids_to_expand.add(parent_id)
 
-        for parent_id in parent_ids_to_expand:
-            sibling_results = chroma.get_by_parent(parent_id, limit=5)
-            if sibling_results and sibling_results.get("documents"):
-                sib_docs = sibling_results["documents"]
-                sib_metas = sibling_results.get("metadatas", [{}] * len(sib_docs))
-                sib_ids = sibling_results.get("ids", [str(i) for i in range(len(sib_docs))])
+#         for parent_id in parent_ids_to_expand:
+#             sibling_results = chroma.get_by_parent(parent_id, limit=5)
+#             if sibling_results and sibling_results.get("documents"):
+#                 sib_docs = sibling_results["documents"]
+#                 sib_metas = sibling_results.get("metadatas", [{}] * len(sib_docs))
+#                 sib_ids = sibling_results.get("ids", [str(i) for i in range(len(sib_docs))])
 
-                for doc, meta, chunk_id in zip(sib_docs, sib_metas, sib_ids):
-                    if chunk_id not in seen_ids:
-                        seen_ids.add(chunk_id)
-                        chunks_with_meta.append((doc, meta, chunk_id))
+#                 for doc, meta, chunk_id in zip(sib_docs, sib_metas, sib_ids):
+#                     if chunk_id not in seen_ids:
+#                         seen_ids.add(chunk_id)
+#                         chunks_with_meta.append((doc, meta, chunk_id))
 
-        # Step 3: Build formatted context list
-        all_chunks = []
-        for doc, meta, chunk_id in chunks_with_meta:
-            clean_doc = doc.strip().replace("\r\n", "\n")
-            all_chunks.append({
-                "chunk_id": chunk_id,
-                "page_title": meta.get("title", "Unknown Source"),
-                "section": meta.get("parent_title", ""),
-                "text": clean_doc,
-            })
+#         # Step 3: Build formatted context list
+#         all_chunks = []
+#         for doc, meta, chunk_id in chunks_with_meta:
+#             clean_doc = doc.strip().replace("\r\n", "\n")
+#             all_chunks.append({
+#                 "chunk_id": chunk_id,
+#                 "page_title": meta.get("title", "Unknown Source"),
+#                 "section": meta.get("parent_title", ""),
+#                 "text": clean_doc,
+#             })
             
-        # Step 4: LLM Re-ranking (Filter out irrelevant project proposals)
-        filtered_chunks = gemini.filter_chunks(all_chunks, request.question)
+#         # Step 4: LLM Re-ranking (Filter out irrelevant project proposals)
+#         filtered_chunks = gemini.filter_chunks(all_chunks, request.question)
         
-        # Enforce maximum of 3 chunks and context cap
-        final_chunks = []
-        total_chars = 0
-        for chunk in filtered_chunks[:3]:
-            # Respect context cap
-            text = chunk["text"]
-            if total_chars + len(text) > MAX_CONTEXT_CHARS:
-                remaining = MAX_CONTEXT_CHARS - total_chars
-                if remaining > 100:
-                    chunk["text"] = text[:remaining] + "... [truncated]"
-                else:
-                    break
+#         # Enforce maximum of 3 chunks and context cap
+#         final_chunks = []
+#         total_chars = 0
+#         for chunk in filtered_chunks[:3]:
+#             # Respect context cap
+#             text = chunk["text"]
+#             if total_chars + len(text) > MAX_CONTEXT_CHARS:
+#                 remaining = MAX_CONTEXT_CHARS - total_chars
+#                 if remaining > 100:
+#                     chunk["text"] = text[:remaining] + "... [truncated]"
+#                 else:
+#                     break
                     
-            final_chunks.append(chunk)
-            source_titles.add(chunk["page_title"])
-            total_chars += len(chunk["text"])
+#             final_chunks.append(chunk)
+#             source_titles.add(chunk["page_title"])
+#             total_chars += len(chunk["text"])
             
-        sources_list = sorted(source_titles)
+#         sources_list = sorted(source_titles)
 
-        # Step 5: Generation using only the filtered, relevant chunks
-        answer = gemini.ask_workmate(
-            chunks=final_chunks,
-            user_question=request.question,
-            debug=request.debug,
-        )
+#         # Step 5: Generation using only the filtered, relevant chunks
+#         answer = gemini.ask_workmate(
+#             chunks=final_chunks,
+#             user_question=request.question,
+#             debug=request.debug,
+#         )
 
-        return ChatResponse(
-            answer=answer,
-            sources=sources_list,
-            chunks=final_chunks,
-            unfiltered_chunks=all_chunks,
-        )
+#         return ChatResponse(
+#             answer=answer,
+#             sources=sources_list,
+#             chunks=final_chunks,
+#             unfiltered_chunks=all_chunks,
+#         )
 
-    except Exception as e:
-        logger.error(f"Error processing RAG query: {e}")
-        raise HTTPException(status_code=500, detail=str(e))
+#     except Exception as e:
+#         logger.error(f"Error processing RAG query: {e}")
+#         raise HTTPException(status_code=500, detail=str(e))
diff --git a/src/backend/routers/conversations.py b/src/backend/routers/conversations.py
index b1fc7bb..62759b0 100644
--- a/src/backend/routers/conversations.py
+++ b/src/backend/routers/conversations.py
@@ -8,11 +8,12 @@
 
 from src.backend.database import get_db
 from src.backend.dependencies.auth import get_current_user
-from src.backend.dependencies.services import get_chroma_manager, get_gemini_client
-from src.backend.load.chroma_manager import ChromaManager
+from src.backend.dependencies.services import get_gemini_client, get_hybrid_retriever, get_voyage_reranker
+from src.backend.load.hybrid_retriever import HybridRetriever
 from src.backend.llm.gemini_client import GeminiClient
+from src.backend.llm.voyage_reranker import VoyageReranker
 from src.backend.models.conversation import Conversation, MessageRecord
-from src.backend.routers.chat import MAX_CONTEXT_CHARS
+MAX_CONTEXT_CHARS = 5000
 from src.backend.models.user import User
 from src.backend.schemas.conversation import (
     ConversationDetail,
@@ -75,8 +76,9 @@ async def send_message(
     request: SendMessageRequest,
     current_user: User = Depends(get_current_user),
     db: Session = Depends(get_db),
-    chroma: ChromaManager = Depends(get_chroma_manager),
+    hybrid: HybridRetriever = Depends(get_hybrid_retriever),
     gemini: GeminiClient = Depends(get_gemini_client),
+    reranker: VoyageReranker = Depends(get_voyage_reranker),
 ):
     conv = (
         db.query(Conversation)
@@ -94,38 +96,26 @@ async def send_message(
     )
     db.add(user_msg)
 
-    # Build conversation history from last 6 messages
+    # Build conversation history from last 6 messages (user_msg not yet committed)
     conversation_history = [
         {"role": m.role, "content": m.content} for m in (conv.messages or [])[-6:]
     ]
 
     # RAG pipeline
     try:
-        # Step 1: Initial Retrieval (Fetch 10 to balance page coverage and API limits)
-        results = chroma.query(request.question, n_results=10)
-
-        seen_ids = set()
-        chunks_with_meta = []
-
-        if results and results.get("documents") and results["documents"][0]:
-            docs = results["documents"][0]
-            metas = results.get("metadatas", [{}])[0] if results.get("metadatas") else [{}] * len(docs)
-            ids = results.get("ids", [[]])[0] if results.get("ids") else [str(i) for i in range(len(docs))]
-
-            for doc, meta, chunk_id in zip(docs, metas, ids):
-                if chunk_id not in seen_ids:
-                    seen_ids.add(chunk_id)
-                    chunks_with_meta.append((doc, meta, chunk_id))
+        # Step 1: Hybrid Retrieval (vector + BM25, merged via RRF)
+        all_chunks = hybrid.search(request.question, vector_top_k=15, bm25_top_k=15, final_top_k=15)
 
         # Step 2: Sibling Expansion
+        seen_ids = {chunk["chunk_id"] for chunk in all_chunks}
         parent_ids_to_expand = {
-            meta.get("parent_id")
-            for doc_text, meta, _ in chunks_with_meta
-            if len(doc_text.strip()) < 100 and meta.get("parent_id")
+            chunk.get("parent_id")
+            for chunk in all_chunks
+            if len(chunk["text"].strip()) < 100 and chunk.get("parent_id")
         }
 
         for parent_id in parent_ids_to_expand:
-            sibling_results = chroma.get_by_parent(parent_id, limit=5)
+            sibling_results = hybrid.chroma.get_by_parent(parent_id, limit=5)
             if sibling_results and sibling_results.get("documents"):
                 sib_docs = sibling_results["documents"]
                 sib_metas = sibling_results.get("metadatas", [{}] * len(sib_docs))
@@ -134,25 +124,28 @@ async def send_message(
                 for doc, meta, chunk_id in zip(sib_docs, sib_metas, sib_ids):
                     if chunk_id not in seen_ids:
                         seen_ids.add(chunk_id)
-                        chunks_with_meta.append((doc, meta, chunk_id))
+                        all_chunks.append({
+                            "chunk_id": chunk_id,
+                            "page_title": meta.get("title", "Unknown Source"),
+                            "section": meta.get("parent_title", ""),
+                            "text": doc.strip().replace("\r\n", "\n"),
+                        })
+
+        # Step 3: VoyageAI Re-ranking
+        reranked_for_generation, scored_chunks = reranker.rerank(
+            all_chunks, request.question, top_k=10
+        )
 
-        # Step 3: Build formatted context list
-        all_chunks = []
-        for doc, meta, chunk_id in chunks_with_meta:
-            all_chunks.append({
-                "chunk_id": chunk_id,
-                "page_title": meta.get("title", "Unknown Source"),
-                "section": meta.get("parent_title", ""),
-                "text": doc.strip().replace("\r\n", "\n"),
-            })
-
-        # Step 4: LLM Re-ranking
-        filtered_chunks = gemini.filter_chunks(all_chunks, request.question)
-        
-        # Enforce max 3 chunks and context cap
+        logger.info(
+            f"[RAG] unfiltered={len(all_chunks)} chunks, "
+            f"after_rerank={len(reranked_for_generation)} chunks | "
+            f"titles={[c['page_title'] for c in reranked_for_generation]}"
+        )
+
+        # Enforce context cap
         final_chunks = []
         total_chars = 0
-        for chunk in filtered_chunks[:3]:
+        for chunk in reranked_for_generation:
             text = chunk["text"]
             if total_chars + len(text) > MAX_CONTEXT_CHARS:
                 remaining = MAX_CONTEXT_CHARS - total_chars
@@ -169,8 +162,12 @@ async def send_message(
             debug=request.debug,
             conversation_history=conversation_history,
         )
+
     except Exception as e:
         logger.error(f"RAG pipeline error: {e}")
+        all_chunks = []
+        scored_chunks = []
+        final_chunks = []
         answer = (
             "Sorry, I encountered an error processing your question. Please try again."
         )
@@ -192,7 +189,19 @@ async def send_message(
     db.refresh(user_msg)
     db.refresh(assistant_msg)
 
-    return SendMessageResponse(user_message=user_msg, assistant_message=assistant_msg)
+    debug_info = None
+    if request.debug:
+        debug_info = {
+            "unfiltered_chunks": all_chunks,
+            "reranked_chunks": scored_chunks,
+            "final_chunks": final_chunks,
+        }
+
+    return SendMessageResponse(
+        user_message=user_msg,
+        assistant_message=assistant_msg,
+        debug_info=debug_info,
+    )
 
 
 @router.patch("/{conversation_id}", response_model=ConversationSummary)
@@ -224,8 +233,9 @@ async def stream_message(
     request: SendMessageRequest,
     current_user: User = Depends(get_current_user),
     db: Session = Depends(get_db),
-    chroma: ChromaManager = Depends(get_chroma_manager),
+    hybrid: HybridRetriever = Depends(get_hybrid_retriever),
     gemini: GeminiClient = Depends(get_gemini_client),
+    reranker: VoyageReranker = Depends(get_voyage_reranker),
 ):
     conv = (
         db.query(Conversation)
@@ -247,29 +257,19 @@ async def stream_message(
 
     # RAG retrieval
     try:
-        results = chroma.query(request.question, n_results=10)
-
-        seen_ids = set()
-        chunks_with_meta = []
-
-        if results and results.get("documents") and results["documents"][0]:
-            docs = results["documents"][0]
-            metas = results.get("metadatas", [{}])[0] if results.get("metadatas") else [{}] * len(docs)
-            ids = results.get("ids", [[]])[0] if results.get("ids") else [str(i) for i in range(len(docs))]
-
-            for doc, meta, chunk_id in zip(docs, metas, ids):
-                if chunk_id not in seen_ids:
-                    seen_ids.add(chunk_id)
-                    chunks_with_meta.append((doc, meta, chunk_id))
+        # Step 1: Hybrid Retrieval (vector + BM25, merged via RRF)
+        all_chunks = hybrid.search(request.question, vector_top_k=15, bm25_top_k=15, final_top_k=15)
 
+        # Step 2: Sibling Expansion
+        seen_ids = {chunk["chunk_id"] for chunk in all_chunks}
         parent_ids_to_expand = {
-            meta.get("parent_id")
-            for doc_text, meta, _ in chunks_with_meta
-            if len(doc_text.strip()) < 100 and meta.get("parent_id")
+            chunk.get("parent_id")
+            for chunk in all_chunks
+            if len(chunk["text"].strip()) < 100 and chunk.get("parent_id")
         }
 
         for parent_id in parent_ids_to_expand:
-            sibling_results = chroma.get_by_parent(parent_id, limit=5)
+            sibling_results = hybrid.chroma.get_by_parent(parent_id, limit=5)
             if sibling_results and sibling_results.get("documents"):
                 sib_docs = sibling_results["documents"]
                 sib_metas = sibling_results.get("metadatas", [{}] * len(sib_docs))
@@ -278,22 +278,18 @@ async def stream_message(
                 for doc, meta, chunk_id in zip(sib_docs, sib_metas, sib_ids):
                     if chunk_id not in seen_ids:
                         seen_ids.add(chunk_id)
-                        chunks_with_meta.append((doc, meta, chunk_id))
+                        all_chunks.append({
+                            "chunk_id": chunk_id,
+                            "page_title": meta.get("title", "Unknown Source"),
+                            "section": meta.get("parent_title", ""),
+                            "text": doc.strip().replace("\r\n", "\n"),
+                        })
+
+        reranked_for_generation, _ = reranker.rerank(all_chunks, request.question, top_k=10)
 
-        all_chunks = []
-        for doc, meta, chunk_id in chunks_with_meta:
-            all_chunks.append({
-                "chunk_id": chunk_id,
-                "page_title": meta.get("title", "Unknown Source"),
-                "section": meta.get("parent_title", ""),
-                "text": doc.strip().replace("\r\n", "\n"),
-            })
-
-        filtered_chunks = gemini.filter_chunks(all_chunks, request.question)
-        
         final_chunks = []
         total_chars = 0
-        for chunk in filtered_chunks[:3]:
+        for chunk in reranked_for_generation:
             text = chunk["text"]
             if total_chars + len(text) > MAX_CONTEXT_CHARS:
                 remaining = MAX_CONTEXT_CHARS - total_chars
@@ -311,7 +307,7 @@ async def stream_message(
     # Load conversation history (last 6 messages before the new user message)
     conversation_history = [
         {"role": m.role, "content": m.content}
-        for m in (conv.messages or [])[-7:-1]  # exclude the just-added user msg
+        for m in (conv.messages or [])[-7:-1]  # exclude the just-committed user msg
     ]
 
     async def event_generator():
diff --git a/src/backend/schemas/chat.py b/src/backend/schemas/chat.py
index 48c8c30..9e3345f 100644
--- a/src/backend/schemas/chat.py
+++ b/src/backend/schemas/chat.py
@@ -1,30 +1,30 @@
-from typing import Optional
+# from typing import Optional
 
-from pydantic import BaseModel, Field
+# from pydantic import BaseModel, Field
 
 
-class ChatRequest(BaseModel):
-    question: str = Field(
-        ..., description="The user's question to ask the RAG pipeline."
-    )
-    debug: bool = Field(
-        False, description="Enable debug mode to include chunk IDs in the response."
-    )
+# class ChatRequest(BaseModel):
+#     question: str = Field(
+#         ..., description="The user's question to ask the RAG pipeline."
+#     )
+#     debug: bool = Field(
+#         False, description="Enable debug mode to include chunk IDs in the response."
+#     )
 
 
-class ChatResponse(BaseModel):
-    answer: str = Field(
-        ..., description="The generated context-aware answer from the Gemini LLM."
-    )
-    sources: Optional[list[str]] = Field(
-        default=None,
-        description="List of Notion page titles used as context for the answer.",
-    )
-    chunks: Optional[list[dict]] = Field(
-        default=None,
-        description="The raw context chunks sent to the LLM.",
-    )
-    unfiltered_chunks: Optional[list[dict]] = Field(
-        default=None,
-        description="The raw context chunks retrieved from ChromaDB before LLM filtering.",
-    )
+# class ChatResponse(BaseModel):
+#     answer: str = Field(
+#         ..., description="The generated context-aware answer from the Gemini LLM."
+#     )
+#     sources: Optional[list[str]] = Field(
+#         default=None,
+#         description="List of Notion page titles used as context for the answer.",
+#     )
+#     chunks: Optional[list[dict]] = Field(
+#         default=None,
+#         description="The raw context chunks sent to the LLM.",
+#     )
+#     unfiltered_chunks: Optional[list[dict]] = Field(
+#         default=None,
+#         description="The raw context chunks retrieved from ChromaDB before LLM filtering.",
+#     )
diff --git a/src/backend/schemas/conversation.py b/src/backend/schemas/conversation.py
index 22a8fa0..324a98a 100644
--- a/src/backend/schemas/conversation.py
+++ b/src/backend/schemas/conversation.py
@@ -1,4 +1,5 @@
 from datetime import datetime
+from typing import Optional
 
 from pydantic import BaseModel, Field
 
@@ -43,3 +44,4 @@ class SendMessageRequest(BaseModel):
 class SendMessageResponse(BaseModel):
     user_message: MessageSchema
     assistant_message: MessageSchema
+    debug_info: Optional[dict] = None
diff --git a/src/backend/transform/notion_ingestory.py b/src/backend/transform/notion_ingestory.py
index c2ccb45..2ef51e2 100644
--- a/src/backend/transform/notion_ingestory.py
+++ b/src/backend/transform/notion_ingestory.py
@@ -1,33 +1,25 @@
 import json
-from langchain_text_splitters import MarkdownHeaderTextSplitter, RecursiveCharacterTextSplitter
+from langchain_text_splitters import RecursiveCharacterTextSplitter
 from src.backend.load.chroma_manager import ChromaManager
+from src.backend.load.bm25_manager import BM25Manager, BM25_INDEX_PATH
 
 class NotionIngestor:
-    def __init__(self, file_path="./notion_data.json", chunk_size=500, chunk_overlap=100):
+    def __init__(self, file_path="./notion_data.json", chunk_size=1000, chunk_overlap=200):
         """
         Initializes the ingestion pipeline, database connection, and splitters.
         """
         self.file_path = file_path
         self.chunk_size = chunk_size
         self.chunk_overlap = chunk_overlap
-        
+
         # Instantiate the database manager
         self.db = ChromaManager()
-        
+
         # Setup splitters on initialization
         self._setup_splitters()
 
     def _setup_splitters(self):
         """Private method to configure the LangChain splitters."""
-        headers_to_split_on = [
-            ("#", "Header 1"),
-            ("##", "Header 2"),
-            ("###", "Header 3"),
-        ]
-        self.markdown_splitter = MarkdownHeaderTextSplitter(
-            headers_to_split_on=headers_to_split_on
-        )
-
         self.text_splitter = RecursiveCharacterTextSplitter(
             # Custom separators tailored for technical documentation in order of preference
             separators=["\n\n", "\n```\n", "\n---", "\n- ", "\n", " ", ""],
@@ -66,15 +58,12 @@ def _build_parent_child_maps(self, raw_docs):
 
     def _enrich_content(self, doc, id_to_doc, parent_to_children):
         """
-        Enrich a document's content with parent-child context.
-
-        For PARENT documents: append child page summaries to content
-        For CHILD documents: prepend parent context info
+        Enrich a document's content with parent context.
+        Child pages are indexed separately, so their summaries are not appended here
+        to avoid content cross-contamination.
         """
         content = doc.get("content", "")
-        doc_id = doc.get("id")
         parent_id = doc.get("parent_id")
-        title = doc.get("title", "Untitled")
 
         enriched_parts = []
 
@@ -82,117 +71,66 @@ def _enrich_content(self, doc, id_to_doc, parent_to_children):
         if parent_id and parent_id in id_to_doc:
             parent_doc = id_to_doc[parent_id]
             parent_title = parent_doc.get("title", "Untitled")
-            enriched_parts.append(
-                f"[Parent Page: {parent_title}]\n"
-            )
+            enriched_parts.append(f"[Parent Page: {parent_title}]\n")
 
-        # Add the main content
         enriched_parts.append(content)
 
-        # If this doc has children, append their summaries
-        children = parent_to_children.get(doc_id, [])
-        if children:
-            enriched_parts.append("\n\n## Child Pages Summary")
-            for child in children:
-                child_title = child.get("title", "Untitled")
-                child_content = child.get("content", "")
-                # Take the first ~300 chars as a summary
-                summary = child_content[:300].strip()
-                if len(child_content) > 300:
-                    summary += "..."
-                enriched_parts.append(
-                    f"\n### {child_title}\n{summary}"
-                )
-
         return "\n".join(enriched_parts)
 
     def _process_document(self, doc, id_to_doc, parent_to_children):
-        """Private method to chunk and format a single document with enriched context."""
+        """
+        Chunk a single document using RecursiveCharacterTextSplitter.
+        """
         content = self._enrich_content(doc, id_to_doc, parent_to_children)
         if not content.strip():
             return [], [], []
 
-        # Physical Split (Enforce Size Limits)
-        # We skip MarkdownHeaderTextSplitter because it has a known bug of dropping 
-        # unheadered text at the end of long documents.
         physical_splits = self.text_splitter.create_documents([content])
 
-        # Merge very short chunks (< 50 chars) into the next chunk
-        # This prevents tiny fragments like "Checkpoint #1 Slideshow" from
-        # becoming standalone chunks that lack meaningful content.
-        MIN_CHUNK_CHARS = 50
-        merged_splits = []
-        carry_over = ""
-        for split in physical_splits:
-            if len(split.page_content.strip()) < MIN_CHUNK_CHARS:
-                carry_over += split.page_content + "\n"
-            else:
-                if carry_over:
-                    split.page_content = carry_over + split.page_content
-                    carry_over = ""
-                merged_splits.append(split)
-        # If there's leftover carry_over at the very end of the file
-        if carry_over:
-            if merged_splits:
-                # Append the trailing short text to the last valid chunk
-                merged_splits[-1].page_content += "\n" + carry_over.strip()
-            elif physical_splits:
-                # Edge case: the entire document was just one tiny <50 char string
-                physical_splits[-1].page_content = carry_over.strip()
-                merged_splits.append(physical_splits[-1])
-
-        physical_splits = merged_splits
         chunks = []
         metadatas = []
         ids = []
 
-        # Build parent_title for metadata
         parent_id = doc.get("parent_id")
         parent_title = None
         if parent_id and parent_id in id_to_doc:
             parent_title = id_to_doc[parent_id].get("title", "Untitled")
 
-        # Format for ChromaDB
         for i, chunk in enumerate(physical_splits):
             chunks.append(chunk.page_content)
-            
+
             chunk_metadata = {
                 "parent_id": doc.get("id"),
                 "title": doc.get("title", "Untitled"),
                 "url": doc.get("url"),
                 "source_type": doc.get("source_type", "page"),
-                "chunk_index": i
+                "chunk_index": i,
+                "section_header": "",
             }
 
-            # Add parent context to metadata for retrieval-time grouping
             if parent_title:
                 chunk_metadata["parent_title"] = parent_title
             if parent_id:
                 chunk_metadata["notion_parent_id"] = parent_id
 
-            # Merge the Markdown headers extracted by LangChain
-            chunk_metadata.update(chunk.metadata)
-            
             metadatas.append(chunk_metadata)
-            
-            # Deterministic ID creation
             ids.append(f"{doc['id']}_{i}")
 
         return chunks, metadatas, ids
 
     def run_pipeline(self):
         """Public method to execute the full ingestion pipeline."""
-        print(f"📂 Loading data from {self.file_path}...")
+        print(f"Loading data from {self.file_path}...")
         raw_docs = self._load_data()
         # Build parent-child relationship maps
-        print("🔗 Building parent-child relationship maps...")
+        print("Building parent-child relationship maps...")
         id_to_doc, parent_to_children = self._build_parent_child_maps(raw_docs)
         print(f"   Found {len(parent_to_children)} parent documents with children")
         all_chunks = []
         all_metadatas = []
         all_ids = []
 
-        print(f"✂️  Running Hybrid Chunking on {len(raw_docs)} documents (with context enrichment)...")
+        print(f"Running Hybrid Chunking on {len(raw_docs)} documents (with context enrichment)...")
 
         for doc in raw_docs:
             chunks, metadatas, ids = self._process_document(doc, id_to_doc, parent_to_children)
@@ -201,10 +139,16 @@ def run_pipeline(self):
             all_ids.extend(ids)
 
         if all_chunks:
-            print(f"💾 Storing {len(all_chunks)} chunks into Chroma...")
+            print(f"Storing {len(all_chunks)} chunks into Chroma...")
             self.db.add_documents(all_chunks, all_metadatas, all_ids)
+
+            print("Building BM25 index...")
+            bm25 = BM25Manager()
+            bm25.build_index(all_chunks, all_metadatas, all_ids)
+            bm25.save(BM25_INDEX_PATH)
+            print(f"BM25 index saved to {BM25_INDEX_PATH}")
         else:
-            print("⚠️ No chunks were created.")
+            print("No chunks were created.")
 
 # --- Execution ---
 if __name__ == "__main__":
diff --git a/uv.lock b/uv.lock
index 37c76c0..96a5741 100644
--- a/uv.lock
+++ b/uv.lock
@@ -6,6 +6,104 @@ resolution-markers = [
     "python_full_version < '3.14'",
 ]
 
+[[package]]
+name = "aiohappyeyeballs"
+version = "2.6.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760, upload-time = "2025-03-12T01:42:48.764Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265, upload-time = "2025-03-12T01:42:47.083Z" },
+]
+
+[[package]]
+name = "aiohttp"
+version = "3.13.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "aiohappyeyeballs" },
+    { name = "aiosignal" },
+    { name = "attrs" },
+    { name = "frozenlist" },
+    { name = "multidict" },
+    { name = "propcache" },
+    { name = "yarl" },
+]
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+    { name = "pillow" },
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+    { name = "requests" },
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 name = "watchfiles"
 version = "1.1.1"
@@ -2159,6 +2589,7 @@ name = "workmate"
 version = "0.1.0"
 source = { virtual = "." }
 dependencies = [
+    { name = "bm25s" },
     { name = "chromadb" },
     { name = "fastapi" },
     { name = "google-genai" },
@@ -2173,10 +2604,12 @@ dependencies = [
     { name = "sqlalchemy" },
     { name = "sse-starlette" },
     { name = "uvicorn", extra = ["standard"] },
+    { name = "voyageai" },
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 [package.metadata]
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     { name = "uvicorn", extras = ["standard"], specifier = ">=0.40.0" },
+    { name = "voyageai", specifier = ">=0.3.7" },
 ]
 
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