diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..68a1e1b6e121a7af1b678e850dc7e83639916754
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,23 @@
+# --- Backend & Security ---
+.env
+RAG_VENV/
+__pycache__/
+*.pyc
+data/bm25_indexes/
+*.log
+
+# --- Frontend (Vite/React) ---
+node_modules/
+dist/
+dist-ssr/
+*.local
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+
+# --- OS & Editor ---
+.DS_Store
+.vscode/
+.idea/
+*.swp
+*.swo
diff --git a/RAG_FULL_APPLICATION_BACKEND/.env.example b/RAG_FULL_APPLICATION_BACKEND/.env.example
new file mode 100644
index 0000000000000000000000000000000000000000..720af7710bd03bf5e57fa2b3b7da9ebfceaa884a
--- /dev/null
+++ b/RAG_FULL_APPLICATION_BACKEND/.env.example
@@ -0,0 +1,52 @@
+# Supabase
+SUPABASE_URL              = 
+SUPABASE_KEY              = 
+SUPABASE_DB_URL           = 
+
+# Embeddings (HF Space — free)
+EMBED_API_URL             = https://lamhieu-lightweight-embeddings.hf.space/
+EMBED_MODEL               = bge-m3
+EMBED_DIM                 = 1024
+EMBED_AUTH_KEY            =
+EMBED_MAX_TOKENS          = 1000
+EMBED_TIMEOUT             = 60
+EMBED_MAX_RETRIES         = 3
+
+# LLM — Qwen3 (HF Space — free)
+QWEN3_MODEL_NAME          = Qwen/Qwen3-Demo
+QWEN3_THINKING_BUDGET     = 38
+LLM_RESPONSE_TIMEOUT      = 1080
+MAX_LLM_RETRIES           = 5
+MAX_TIMEOUT_RETRIES       = 10
+
+# OCR — Mistral (needs API key)
+MISTRAL_OCR_SPACE         = tatendachirume/Mistral-OCR
+MISTRAL_API_KEY           = "5gBKNRNZY2YllB6goe6OX0ycXdzbHS76"
+
+# Image — Ernie Bot (free HF Space)
+ERNIE_SPACE_URL           = https://baidu-simple-ernie-bot-demo.hf.space/
+
+# Redis
+REDIS_URL                 = redis://localhost:6379
+CACHE_TTL_SECONDS         = 3600
+
+# Auth
+JWT_SECRET_KEY            = "b0g2DlXIrvUcosozdEDOFtubAy+p30tJ6BFjx0ufYLM="
+JWT_ALGORITHM             = HS256
+JWT_EXPIRE_MINUTES        = 1440
+
+# Re-ranking
+RERANK_MODEL              = cross-encoder/ms-marco-MiniLM-L-6-v2
+
+# Limits
+RATE_LIMIT_PER_MINUTE     = 20
+RATE_LIMIT_UPLOAD_PER_DAY = 50
+MAX_FILE_SIZE_MB          = 50
+
+# Defaults
+DEFAULT_CHUNK_SIZE        = 512
+DEFAULT_OVERLAP           = 64
+DEFAULT_TOP_K             = 5
+
+# CORS
+CORS_ORIGINS              = http://localhost:5173
diff --git a/RAG_FULL_APPLICATION_BACKEND/Dockerfile b/RAG_FULL_APPLICATION_BACKEND/Dockerfile
new file mode 100644
index 0000000000000000000000000000000000000000..e59a3c33a80fd3c2fbc6f6ce2d3d5383c5c579e7
--- /dev/null
+++ b/RAG_FULL_APPLICATION_BACKEND/Dockerfile
@@ -0,0 +1,24 @@
+FROM python:3.11-slim
+
+WORKDIR /app
+
+# System deps for python-docx, tiktoken, etc.
+RUN apt-get update && apt-get install -y \
+    build-essential libpq-dev && \
+    rm -rf /var/lib/apt/lists/*
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Download cross-encoder model at build time
+RUN python -c "from sentence_transformers import CrossEncoder; \
+    CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')"
+
+COPY . .
+
+# Create data dirs
+RUN mkdir -p data/uploads data/bm25_indexes data/cache
+
+EXPOSE 8000
+
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000", "--workers", "2"]
diff --git a/RAG_FULL_APPLICATION_BACKEND/__init__.py b/RAG_FULL_APPLICATION_BACKEND/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/RAG_FULL_APPLICATION_BACKEND/app/__init__.py b/RAG_FULL_APPLICATION_BACKEND/app/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/RAG_FULL_APPLICATION_BACKEND/app/config.py b/RAG_FULL_APPLICATION_BACKEND/app/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..779b0459dafae81ef245658111bcbafe01decaff
--- /dev/null
+++ b/RAG_FULL_APPLICATION_BACKEND/app/config.py
@@ -0,0 +1,61 @@
+from pydantic_settings import BaseSettings
+from typing import Optional
+
+class Settings(BaseSettings):
+    # Supabase
+    SUPABASE_URL: str
+    SUPABASE_KEY: str
+    SUPABASE_DB_URL: str
+
+    # Embeddings (bge-m3)
+    EMBED_API_URL: str = "https://lamhieu-lightweight-embeddings.hf.space/"
+    EMBED_MODEL: str = "bge-m3"
+    EMBED_DIM: int = 1024
+    EMBED_AUTH_KEY: str = ""
+    EMBED_MAX_TOKENS: int = 1000
+    EMBED_TIMEOUT: int = 60
+    EMBED_MAX_RETRIES: int = 3
+
+    # LLM — Qwen3
+    QWEN3_MODEL_NAME: str = "Qwen/Qwen3-Demo"
+    QWEN3_THINKING_BUDGET: int = 38
+    LLM_RESPONSE_TIMEOUT: int = 1080
+    MAX_LLM_RETRIES: int = 5
+    MAX_TIMEOUT_RETRIES: int = 10
+
+    # OCR — Mistral
+    MISTRAL_OCR_SPACE: str = "tatendachirume/Mistral-OCR"
+    MISTRAL_API_KEY: str = ""
+
+    # Image — Qwen-VL Vision
+    VISION_SPACE_URL: str = "Qwen/Qwen3-VL-30B-A3B-Demo"
+
+    # Redis
+    REDIS_URL: str
+    CACHE_TTL_SECONDS: int = 3600
+
+    # Auth
+    JWT_SECRET_KEY: str
+    JWT_ALGORITHM: str = "HS256"
+    JWT_EXPIRE_MINUTES: int = 1440
+
+    # Re-ranking
+    RERANK_MODEL: str = "cross-encoder/ms-marco-MiniLM-L-6-v2"
+
+    # Rate limiting
+    RATE_LIMIT_PER_MINUTE: int = 20
+    RATE_LIMIT_UPLOAD_PER_DAY: int = 50
+
+    # Defaults
+    DEFAULT_CHUNK_SIZE: int = 512
+    DEFAULT_OVERLAP: int = 64
+    DEFAULT_TOP_K: int = 5
+    MAX_FILE_SIZE_MB: int = 50
+
+    # CORS
+    CORS_ORIGINS: str
+
+    class Config:
+        env_file = ".env"
+
+settings = Settings()
diff --git a/RAG_FULL_APPLICATION_BACKEND/app/main.py b/RAG_FULL_APPLICATION_BACKEND/app/main.py
new file mode 100644
index 0000000000000000000000000000000000000000..938e566da0e84fdcabee75be525cc2b87bedd45a
--- /dev/null
+++ b/RAG_FULL_APPLICATION_BACKEND/app/main.py
@@ -0,0 +1,46 @@
+from fastapi import FastAPI, WebSocket, Depends
+from fastapi.middleware.cors import CORSMiddleware
+from .config import settings
+from .utils.ws_manager import ws_manager
+import logging
+
+# Setup Logger
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+app = FastAPI(title="RAG Pipeline API", version="3.0.0")
+
+# CORS
+origins = settings.CORS_ORIGINS.split(",")
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+from .routers import auth, ingest, query
+
+# Routers
+app.include_router(auth.router, prefix="/auth", tags=["auth"])
+app.include_router(ingest.router, prefix="/ingest", tags=["ingest"])
+app.include_router(query.router, prefix="/query", tags=["query"])
+
+@app.get("/health")
+async def health_check():
+    return {"status": "healthy", "version": "3.0.0"}
+
+@app.websocket("/ws/pipeline/{job_id}")
+async def pipeline_ws(websocket: WebSocket, job_id: str, token: str):
+    # JWT verification logic will go here
+    # For now, just connect
+    await ws_manager.connect(job_id, websocket, "anonymous")
+    try:
+        while True:
+            data = await websocket.receive_text()
+            # Handle messages if needed
+    except Exception as e:
+        logger.error(f"WebSocket error for job {job_id}: {e}")
+    finally:
+        await ws_manager.disconnect(job_id, "anonymous")
diff --git a/RAG_FULL_APPLICATION_BACKEND/app/models/__init__.py b/RAG_FULL_APPLICATION_BACKEND/app/models/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/RAG_FULL_APPLICATION_BACKEND/app/models/schemas.py b/RAG_FULL_APPLICATION_BACKEND/app/models/schemas.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a9b82e62160a8f25b4a8cd3965120836eaedbcb
--- /dev/null
+++ b/RAG_FULL_APPLICATION_BACKEND/app/models/schemas.py
@@ -0,0 +1,26 @@
+from pydantic import BaseModel
+from typing import List, Dict, Any, Optional
+
+class UserCreate(BaseModel):
+    username: str
+    password: str
+
+class UserResponse(BaseModel):
+    id: str
+    username: str
+
+class Token(BaseModel):
+    access_token: str
+    token_type: str
+
+class QueryRequest(BaseModel):
+    query: str
+    document_id: str
+    technique: str = "hybrid"
+    top_k: int = 5
+    filters: Optional[Dict[str, Any]] = None
+
+class QueryResponse(BaseModel):
+    answer: str
+    sources: List[Dict[str, Any]]
+    job_id: str
diff --git a/RAG_FULL_APPLICATION_BACKEND/app/routers/__init__.py b/RAG_FULL_APPLICATION_BACKEND/app/routers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/RAG_FULL_APPLICATION_BACKEND/app/routers/auth.py b/RAG_FULL_APPLICATION_BACKEND/app/routers/auth.py
new file mode 100644
index 0000000000000000000000000000000000000000..f62c0333ab80410d500403a6b238499f1df27fd1
--- /dev/null
+++ b/RAG_FULL_APPLICATION_BACKEND/app/routers/auth.py
@@ -0,0 +1,63 @@
+from fastapi import APIRouter, Depends, HTTPException, status
+from fastapi.security import OAuth2PasswordRequestForm
+from ..utils.auth_utils import verify_password, get_password_hash, create_access_token
+from ..services.supabase_client import supabase_service
+from ..models.schemas import UserCreate, Token, UserResponse
+import logging
+
+router = APIRouter()
+logger = logging.getLogger(__name__)
+
+@router.post("/register", response_model=UserResponse)
+async def register(user: UserCreate):
+    # Hash password
+    hashed = get_password_hash(user.password)
+    
+    # Store in Supabase
+    try:
+        result = supabase_service.client.table("users").insert({
+            "username": user.username,
+            "password_hash": hashed
+        }).execute()
+        return result.data[0]
+    except Exception as e:
+        logger.error(f"Registration failed: {e}")
+        raise HTTPException(status_code=400, detail="User already exists")
+
+@router.post("/login", response_model=Token)
+async def login(form_data: OAuth2PasswordRequestForm = Depends()):
+    # Fetch user from Supabase
+    result = supabase_service.client.table("users")\
+        .select("*")\
+        .eq("username", form_data.username).execute()
+    
+    if not result.data:
+        raise HTTPException(status_code=401, detail="Invalid credentials")
+    
+    user = result.data[0]
+    if not verify_password(form_data.password, user["password_hash"]):
+        logger.warning(f"Login failed for user: {form_data.username} - password mismatch")
+        raise HTTPException(status_code=401, detail="Invalid credentials")
+    
+    logger.info(f"User logged in: {form_data.username}")
+    # Create token
+    access_token = create_access_token(data={"sub": user["username"], "id": user["id"]})
+    return {"access_token": access_token, "token_type": "bearer"}
+
+@router.post("/seed_admin")
+async def seed_admin():
+    """Utility to pre-create admin user for local testing. Forced clean sync."""
+    hashed = get_password_hash("admin123")
+    try:
+        # Delete existing to ensure fresh hash if environment changed
+        supabase_service.client.table("users").delete().eq("username", "admin").execute()
+        
+        supabase_service.client.table("users").insert({
+            "username": "admin",
+            "password_hash": hashed
+        }).execute()
+        logger.info("Admin user seeded successfully.")
+        return {"msg": "Admin user created/reset (admin / admin123)"}
+    except Exception as e:
+        logger.error(f"Seeding failed: {e}")
+        return {"msg": f"Seeding failed: {str(e)}"}
diff --git a/RAG_FULL_APPLICATION_BACKEND/app/routers/ingest.py b/RAG_FULL_APPLICATION_BACKEND/app/routers/ingest.py
new file mode 100644
index 0000000000000000000000000000000000000000..5ae5fd2c0138401d1a6710d59e8e159d96489ad8
--- /dev/null
+++ b/RAG_FULL_APPLICATION_BACKEND/app/routers/ingest.py
@@ -0,0 +1,156 @@
+from fastapi import APIRouter, UploadFile, File, BackgroundTasks, Depends, HTTPException, Form
+from fastapi.security import OAuth2PasswordBearer
+from ..services.supabase_client import supabase_service
+from ..services.file_parser import parse_file
+from ..services.chunk_engine import ChunkEngine
+from ..services.embed_service import embed_batch, get_embedding
+from ..services.bm25_service import bm25_service
+from ..utils.ws_manager import ws_manager
+from ..utils.auth_utils import decode_token
+import os
+import uuid
+import logging
+from pathlib import Path
+
+router = APIRouter()
+logger = logging.getLogger(__name__)
+
+oauth2_scheme = OAuth2PasswordBearer(tokenUrl="/auth/login")
+
+# Auth Dependency — reads from Authorization: Bearer header
+def get_current_user(token: str = Depends(oauth2_scheme)):
+    payload = decode_token(token)
+    if not payload:
+        raise HTTPException(status_code=401, detail="Invalid token")
+    return payload
+
+@router.post("/upload")
+async def upload_file(
+    background_tasks: BackgroundTasks,
+    file: UploadFile = File(...),
+    chunk_size: int = Form(512),
+    overlap: int = Form(64),
+    strategy: str = Form("fixed"),
+    user: dict = Depends(get_current_user)
+):
+    job_id = str(uuid.uuid4())
+    temp_dir = Path("./data/uploads") / user["id"]
+    temp_dir.mkdir(parents=True, exist_ok=True)
+    file_path = temp_dir / file.filename
+    
+    with open(file_path, "wb") as f:
+        f.write(await file.read())
+        
+    # Start ingestion in background
+    background_tasks.add_task(
+        process_ingestion, 
+        str(file_path), 
+        file.filename, 
+        chunk_size, 
+        overlap, 
+        strategy, 
+        job_id, 
+        user["id"]
+    )
+    
+    return {"job_id": job_id, "filename": file.filename}
+
+@router.get("/documents")
+async def list_documents(user: dict = Depends(get_current_user)):
+    try:
+        result = supabase_service.client.table("documents")\
+            .select("*")\
+            .eq("user_id", user["id"])\
+            .order("created_at", desc=True).execute()
+        return result.data
+    except Exception as e:
+        logger.error(f"Failed to list documents: {e}")
+        raise HTTPException(status_code=500, detail="Database error")
+
+@router.delete("/documents/{doc_id}")
+async def delete_document(doc_id: str, user_id: str = Depends(get_current_user)):
+    try:
+        # 1. Database cleanup
+        await supabase_service.delete_document(doc_id, user_id["id"])
+        # 2. BM25 cleanup
+        bm25_service.delete_document(doc_id)
+        return {"status": "success", "message": f"Document {doc_id} deleted"}
+    except Exception as e:
+        logger.error(f"Failed to delete document {doc_id}: {e}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+async def process_ingestion(file_path: str, filename: str, chunk_size: int, overlap: int, strategy: str, job_id: str, user_id: str):
+    try:
+        await ws_manager.emit(job_id, user_id, {"step": "START", "color": "#8B5CF6", "detail": f"Starting ingestion for {filename}..."})
+        
+        # 1. Parse
+        file_type = filename.split(".")[-1]
+        docs = await parse_file(file_path, file_type, job_id, ws_manager, user_id)
+        
+        # 2. Chunk
+        await ws_manager.emit(job_id, user_id, {"step": "CHUNKING", "color": "#6B7280", "detail": f"Applying {strategy} chunking strategy..."})
+        engine = ChunkEngine(chunk_size, overlap, strategy)
+        chunks = engine.chunk(docs)
+        
+        # 3. Create Document entry
+        doc_result = supabase_service.client.table("documents").insert({
+            "user_id": user_id,
+            "filename": filename,
+            "file_type": file_type,
+            "technique": "hybrid", # default
+            "chunk_strategy": strategy,
+            "chunk_size": chunk_size,
+            "overlap": overlap,
+            "status": "running",
+            "chunk_count": len(chunks)
+        }).execute()
+        document_id = doc_result.data[0]["id"]
+        
+        # 4. Incremental Check & Embed
+        await ws_manager.emit(job_id, user_id, {"step": "EMBEDDING", "color": "#8B5CF6", "detail": f"Vectorizing {len(chunks)} chunks..."})
+        embeddings = await embed_batch([c["text"] for c in chunks])
+        print(f"DEBUG: Embedding complete. First vector len: {len(embeddings[0]) if embeddings else 0}")
+        
+        # 5. Insert to Supabase
+        await ws_manager.emit(job_id, user_id, {"step": "STORING", "color": "#22C55E", "detail": "Storing chunks and vectors in Supabase..."})
+        
+        # Prepare rows
+        chunk_rows = []
+        for i, c in enumerate(chunks):
+            c["document_id"] = document_id
+            c["user_id"] = user_id
+            chunk_rows.append(c)
+            
+        chunk_ids = await supabase_service.insert_chunks(chunk_rows)
+        
+        vector_rows = []
+        for i, cid in enumerate(chunk_ids):
+            vector_rows.append({
+                "chunk_id": cid,
+                "document_id": document_id,
+                "user_id": user_id,
+                "embedding": embeddings[i]
+            })
+        await supabase_service.upsert_vectors(vector_rows)
+        
+        # 6. Index BM25
+        await ws_manager.emit(job_id, user_id, {"step": "BM25_INDEX", "color": "#22C55E", "detail": "Building BM25 keyword index..."})
+        bm25_service.index_chunks(document_id, chunk_rows)
+        
+        # Special check for ColBERT
+        # if technique == "colbert": embed all tokens... (skipped for brevity in base ingest)
+        
+        supabase_service.client.table("documents").update({"status": "done"}).eq("id", document_id).execute()
+        await ws_manager.emit(job_id, user_id, {"step": "DONE", "color": "#22C55E", "detail": "Ingestion complete!", "metadata": {"doc_id": document_id}})
+        
+    except Exception as e:
+        import traceback
+        logger.error(f"Ingestion failed: {e}")
+        logger.error(traceback.format_exc())
+        await ws_manager.emit(job_id, user_id, {"step": "ERROR", "color": "#EF4444", "detail": f"Ingestion failed: {str(e)}"})
+        if 'document_id' in locals():
+            supabase_service.client.table("documents").update({"status": "failed"}).eq("id", document_id).execute()
+    finally:
+        # Cleanup
+        if os.path.exists(file_path):
+            os.remove(file_path)
diff --git a/RAG_FULL_APPLICATION_BACKEND/app/routers/query.py b/RAG_FULL_APPLICATION_BACKEND/app/routers/query.py
new file mode 100644
index 0000000000000000000000000000000000000000..0073aa7755fd3cfb5f86081f9870dfd3730ea24d
--- /dev/null
+++ b/RAG_FULL_APPLICATION_BACKEND/app/routers/query.py
@@ -0,0 +1,59 @@
+from fastapi import APIRouter, Depends, HTTPException
+from ..models.schemas import QueryRequest, QueryResponse
+from ..routers.ingest import get_current_user
+from ..techniques.hybrid_search import HybridSearch
+from ..techniques.reranking import ReRanking
+from ..techniques.query_expansion import QueryExpansion
+from ..techniques.metadata_filter import MetadataFilter
+from ..techniques.colbert import ColBERT
+from ..techniques.agentic_rag import AgenticRAG
+from ..techniques.cache_incremental import CacheIncrementalRAG
+import uuid
+import logging
+
+router = APIRouter()
+logger = logging.getLogger(__name__)
+
+TECHNIQUE_MAP = {
+    "hybrid": HybridSearch,
+    "rerank": ReRanking,
+    "hyde": QueryExpansion,
+    "meta": MetadataFilter,
+    "colbert": ColBERT,
+    "agentic": AgenticRAG,
+    "cache": CacheIncrementalRAG
+}
+
+@router.post("/search", response_model=QueryResponse)
+async def search(
+    request: QueryRequest,
+    user: dict = Depends(get_current_user)
+):
+    job_id = str(uuid.uuid4())
+    technique_cls = TECHNIQUE_MAP.get(request.technique)
+    
+    if not technique_cls:
+        raise HTTPException(status_code=400, detail="Invalid technique")
+        
+    try:
+        # Instantiate technique
+        instance = technique_cls(job_id, user["id"])
+        
+        # Run pipeline
+        # Passing extra filters if technique is metadata_filter
+        result = await instance.run(
+            query=request.query, 
+            document_id=request.document_id,
+            top_k=request.top_k,
+            filters=request.filters,
+            underlying_technique="hybrid" # for cache technique
+        )
+        
+        return QueryResponse(
+            answer=result["answer"],
+            sources=result["sources"],
+            job_id=job_id
+        )
+    except Exception as e:
+        logger.error(f"Search failed: {e}")
+        raise HTTPException(status_code=500, detail=str(e))
diff --git a/RAG_FULL_APPLICATION_BACKEND/app/services/__init__.py b/RAG_FULL_APPLICATION_BACKEND/app/services/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/RAG_FULL_APPLICATION_BACKEND/app/services/bm25_service.py b/RAG_FULL_APPLICATION_BACKEND/app/services/bm25_service.py
new file mode 100644
index 0000000000000000000000000000000000000000..d8737387e2577c3a98714764287eb78668245673
--- /dev/null
+++ b/RAG_FULL_APPLICATION_BACKEND/app/services/bm25_service.py
@@ -0,0 +1,55 @@
+import pickle
+import os
+from pathlib import Path
+from typing import List, Dict, Any
+from rank_bm25 import BM25Okapi
+import logging
+
+logger = logging.getLogger(__name__)
+
+class BM25Service:
+    def __init__(self, data_dir: str = "./data/bm25_indexes"):
+        self.data_dir = Path(data_dir)
+        self.data_dir.mkdir(parents=True, exist_ok=True)
+
+    def _get_index_path(self, document_id: str) -> Path:
+        return self.data_dir / f"{document_id}.pkl"
+
+    def index_chunks(self, document_id: str, chunks: List[Dict[str, Any]]):
+        """Build and save BM25 index for a document."""
+        texts = [c["text"] for c in chunks]
+        tokenized_corpus = [text.lower().split() for text in texts]
+        bm25 = BM25Okapi(tokenized_corpus)
+        
+        # Save both the bm25 object and the chunk mapping
+        with open(self._get_index_path(document_id), "wb") as f:
+            pickle.dump({"bm25": bm25, "chunks": chunks}, f)
+
+    def search(self, document_id: str, query: str, top_n: int = 10) -> List[Dict[str, Any]]:
+        """Search using BM25."""
+        path = self._get_index_path(document_id)
+        if not path.exists():
+            logger.warning(f"BM25 index not found for {document_id}")
+            return []
+
+        with open(path, "rb") as f:
+            data = pickle.load(f)
+            bm25 = data["bm25"]
+            chunks = data["chunks"]
+
+        tokenized_query = query.lower().split()
+        scores = bm25.get_scores(tokenized_query)
+        
+        # Add score to chunks
+        results = []
+        for i, score in enumerate(scores):
+            if score > 0:
+                chunk = chunks[i].copy()
+                chunk["bm25_score"] = float(score)
+                results.append(chunk)
+        
+        # Sort by score
+        results.sort(key=lambda x: x["bm25_score"], reverse=True)
+        return results[:top_n]
+
+bm25_service = BM25Service()
diff --git a/RAG_FULL_APPLICATION_BACKEND/app/services/cache_service.py b/RAG_FULL_APPLICATION_BACKEND/app/services/cache_service.py
new file mode 100644
index 0000000000000000000000000000000000000000..03c6b68057358a955edd665d563a4a7f76025e26
--- /dev/null
+++ b/RAG_FULL_APPLICATION_BACKEND/app/services/cache_service.py
@@ -0,0 +1,42 @@
+import redis
+import hashlib
+import json
+from typing import Optional, Dict, Any
+from ..config import settings
+import logging
+
+logger = logging.getLogger(__name__)
+
+class CacheService:
+    def __init__(self):
+        try:
+            self.redis = redis.from_url(settings.REDIS_URL, decode_responses=True)
+        except Exception as e:
+            logger.error(f"Failed to connect to Redis: {e}")
+            self.redis = None
+
+    def _get_key(self, user_id: str, document_id: str, query: str, technique: str) -> str:
+        data = f"{user_id}:{document_id}:{query}:{technique}"
+        q_hash = hashlib.sha256(data.encode()).hexdigest()
+        return f"rag_cache:{q_hash}"
+
+    def get(self, user_id: str, document_id: str, query: str, technique: str) -> Optional[Dict[str, Any]]:
+        if not self.redis: return None
+        key = self._get_key(user_id, document_id, query, technique)
+        try:
+            val = self.redis.get(key)
+            if val:
+                return json.loads(val)
+        except Exception as e:
+            logger.error(f"Redis get failed: {e}")
+        return None
+
+    def set(self, user_id: str, document_id: str, query: str, technique: str, response: Dict[str, Any]):
+        if not self.redis: return
+        key = self._get_key(user_id, document_id, query, technique)
+        try:
+            self.redis.setex(key, settings.CACHE_TTL_SECONDS, json.dumps(response))
+        except Exception as e:
+            logger.error(f"Redis set failed: {e}")
+
+cache_service = CacheService()
diff --git a/RAG_FULL_APPLICATION_BACKEND/app/services/chunk_engine.py b/RAG_FULL_APPLICATION_BACKEND/app/services/chunk_engine.py
new file mode 100644
index 0000000000000000000000000000000000000000..59927c97065e048f01c567f88ad53004f4932dab
--- /dev/null
+++ b/RAG_FULL_APPLICATION_BACKEND/app/services/chunk_engine.py
@@ -0,0 +1,133 @@
+import tiktoken
+from typing import List, Dict, Any
+import uuid
+import hashlib
+
+MAX_CHUNK_TOKENS = 1000
+
+class ChunkEngine:
+    def __init__(self, chunk_size: int = 512, overlap: int = 64, strategy: str = "fixed"):
+        self.chunk_size = min(chunk_size, MAX_CHUNK_TOKENS)
+        self.overlap = min(overlap, self.chunk_size // 4)
+        self.strategy = strategy
+        self.enc = tiktoken.get_encoding("cl100k_base")
+
+    def chunk(self, docs: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+        match self.strategy:
+            case "fixed" | "token": return self._fixed(docs)
+            case "semantic" | "paragraph": return self._semantic(docs)
+            case "per_page":     return self._per_page(docs)
+            case "per_item":     return self._per_item(docs)
+            case "recursive":    return self._recursive(docs)
+            case "sentence":     return self._sentence(docs)
+            case "parent_child": return self._parent_child(docs)
+            case "sliding_window": return self._fixed(docs)
+            case _:             return self._fixed(docs)
+
+    def _create_chunk(self, text: str, metadata: Dict[str, Any], index: int, parent_id: str = None) -> Dict[str, Any]:
+        return {
+            "id": str(uuid.uuid4()),
+            "text": text,
+            "token_count": len(self.enc.encode(text)),
+            "page": metadata.get("page"),
+            "section": metadata.get("section"),
+            "chunk_index": index,
+            "parent_chunk_id": parent_id,
+            "text_hash": hashlib.sha256(text.encode()).hexdigest(),
+            "metadata": metadata
+        }
+
+    def _fixed(self, docs: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+        chunks = []
+        for doc in docs:
+            tokens = self.enc.encode(doc["text"])
+            for i in range(0, len(tokens), self.chunk_size - self.overlap):
+                chunk_tokens = tokens[i : i + self.chunk_size]
+                chunk_text = self.enc.decode(chunk_tokens)
+                chunks.append(self._create_chunk(chunk_text, doc["metadata"], len(chunks)))
+        return chunks
+
+    def _semantic(self, docs: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+        """Uses pre-split sections from parsers (MD/DOCX)."""
+        chunks = []
+        for doc in docs:
+            chunks.append(self._create_chunk(doc["text"], doc["metadata"], len(chunks)))
+        return chunks
+
+    def _per_page(self, docs: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+        """One chunk per page metadata."""
+        return self._semantic(docs)
+
+    def _per_item(self, docs: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+        """One chunk per item (JSON)."""
+        return self._semantic(docs)
+
+    def _recursive(self, docs: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+        """Simple recursive splitter using separators."""
+        separators = ["\n\n", "\n", ". ", " ", ""]
+        chunks = []
+        
+        def split_text(text: str, metadata: Dict[str, Any]):
+            if len(self.enc.encode(text)) <= self.chunk_size:
+                chunks.append(self._create_chunk(text, metadata, len(chunks)))
+                return
+
+            for sep in separators:
+                if sep in text:
+                    parts = text.split(sep)
+                    # Merging logic could be added here to maximize chunk size
+                    for p in parts:
+                        if p.strip():
+                            split_text(p.strip(), metadata)
+                    break
+
+        for doc in docs:
+            split_text(doc["text"], doc["metadata"])
+        return chunks
+
+    def _parent_child(self, docs: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+        """
+        Retrieval on children (small), context from parent (large).
+        We return both but tag them.
+        """
+        all_chunks = []
+        parent_size = self.chunk_size
+        child_size = parent_size // 4
+        
+        for doc in docs:
+            tokens = self.enc.encode(doc["text"])
+            # Create parents
+            for i in range(0, len(tokens), parent_size):
+                parent_tokens = tokens[i : i + parent_size]
+                parent_text = self.enc.decode(parent_tokens)
+                parent_chunk = self._create_chunk(parent_text, doc["metadata"], len(all_chunks))
+                parent_chunk["metadata"]["is_parent"] = True
+                all_chunks.append(parent_chunk)
+                
+                # Create children for this parent
+                for j in range(0, len(parent_tokens), child_size):
+                    child_tokens = parent_tokens[j : j + child_size]
+                    child_text = self.enc.decode(child_tokens)
+                    child_chunk = self._create_chunk(child_text, doc["metadata"], len(all_chunks), parent_chunk["id"])
+                    child_chunk["metadata"]["is_parent"] = False
+                    all_chunks.append(child_chunk)
+                    
+        return all_chunks
+
+    def _sentence(self, docs: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+        """Simple sentence splitter."""
+        import re
+        chunks = []
+        for doc in docs:
+            sentences = re.split(r'(?<=[.!?]) +', doc["text"])
+            current_chunk = ""
+            for sentence in sentences:
+                if len(self.enc.encode(current_chunk + " " + sentence)) <= self.chunk_size:
+                    current_chunk += (" " if current_chunk else "") + sentence
+                else:
+                    if current_chunk:
+                        chunks.append(self._create_chunk(current_chunk, doc["metadata"], len(chunks)))
+                    current_chunk = sentence
+            if current_chunk:
+                chunks.append(self._create_chunk(current_chunk, doc["metadata"], len(chunks)))
+        return chunks
diff --git a/RAG_FULL_APPLICATION_BACKEND/app/services/embed_service.py b/RAG_FULL_APPLICATION_BACKEND/app/services/embed_service.py
new file mode 100644
index 0000000000000000000000000000000000000000..f267cd1bb8a036c96a0212316457b5dcbf75c423
--- /dev/null
+++ b/RAG_FULL_APPLICATION_BACKEND/app/services/embed_service.py
@@ -0,0 +1,91 @@
+import tiktoken
+import httpx, json_repair, json
+import asyncio
+from typing import List, Dict, Any
+from ..config import settings
+from ..utils.json_utils import repair_json
+from gradio_client import Client
+import logging
+
+logger = logging.getLogger(__name__)
+enc = tiktoken.get_encoding("cl100k_base")
+
+_gradio_client = None
+_fallback_model = None
+
+def get_gradio_client():
+    global _gradio_client
+    if _gradio_client is None:
+        logger.info(f"Initializing Gradio client for {settings.EMBED_API_URL}")
+        _gradio_client = Client(settings.EMBED_API_URL)
+    return _gradio_client
+
+def truncate_to_1k(text: str) -> str:
+    tokens = enc.encode(text)
+    if len(tokens) > 1000:
+        return enc.decode(tokens[:1000])
+    return text
+
+def get_fallback_model():
+    global _fallback_model
+    if _fallback_model is None:
+        from sentence_transformers import SentenceTransformer
+        logger.info("Initializing fallback local embedding model (bge-large-en-v1.5)...")
+        _fallback_model = SentenceTransformer('BAAI/bge-large-en-v1.5')
+        dim = _fallback_model.get_sentence_embedding_dimension()
+        logger.info(f"Fallback model initialized. Dimension: {dim}")
+    return _fallback_model
+
+def get_embedding(text: str) -> List[float]:
+    """
+    Get embedding using bge-m3 / snowflake via HF Space (Primary)
+    Falls back to all-MiniLM-L6-v2 (Local) if API fails.
+    """
+    text = truncate_to_1k(text)
+    
+    # Attempt 1: Gradio Client
+    try:
+        client = get_gradio_client()
+        result = client.predict(
+            user_input=text,
+            selected_model=settings.EMBED_MODEL,
+            auth_key=settings.EMBED_AUTH_KEY,
+            api_name="/call_embeddings_api"
+        )
+        
+        if isinstance(result, str):
+            data = repair_json(result)
+        else:
+            data = result
+            
+        if isinstance(data, list): return data
+        if isinstance(data, dict) and "data" in data:
+            d = data["data"]
+            if isinstance(d, list) and len(d) > 0:
+                if isinstance(d[0], dict) and "embedding" in d[0]:
+                    emb = d[0]["embedding"]
+                    logger.info(f"Primary API generated vector of length: {len(emb)}")
+                    return emb
+                if isinstance(d[0], list):
+                    logger.info(f"Primary API generated vector of length: {len(d[0])}")
+                    return d[0]
+                logger.info(f"Primary API generated vector of length: {len(d)}")
+                return d
+        raise ValueError("Unknown API response format")
+        
+    except Exception as e:
+        logger.warning(f"Primary embedding failed: {e}. Falling back to local model...")
+        model = get_fallback_model()
+        emb = model.encode(text).tolist()
+        logger.info(f"Generated embedding vector of length: {len(emb)}")
+        return emb
+
+async def embed_batch(texts: List[str]) -> List[List[float]]:
+    """
+    Batch embedding for ingestion.
+    """
+    all_embeddings = []
+    for text in texts:
+        emb = await asyncio.to_thread(get_embedding, text)
+        all_embeddings.append(emb)
+    return all_embeddings
diff --git a/RAG_FULL_APPLICATION_BACKEND/app/services/file_parser.py b/RAG_FULL_APPLICATION_BACKEND/app/services/file_parser.py
new file mode 100644
index 0000000000000000000000000000000000000000..c3610cd98b804fadb8e2b726a55d644a4a5c912a
--- /dev/null
+++ b/RAG_FULL_APPLICATION_BACKEND/app/services/file_parser.py
@@ -0,0 +1,114 @@
+import json
+import re
+from pathlib import Path
+from typing import List, Dict, Any
+from docx import Document
+from .ocr_service import ocr_service
+from .vision_service import vision_service
+import logging
+
+logger = logging.getLogger(__name__)
+
+async def parse_file(file_path: str, file_type: str, job_id: str, ws_manager: Any, user_id: str) -> List[Dict[str, Any]]:
+    """
+    Dispatcher for all file types.
+    Returns: [{"text": str, "metadata": {"source", "page", "section"}}]
+    """
+    match file_type.lower():
+        case "pdf":
+            return await _parse_pdf(file_path, job_id, ws_manager, user_id)
+        case "jpg" | "jpeg" | "png":
+            return await _parse_image(file_path, job_id, ws_manager, user_id)
+        case "docx":
+            return _parse_docx(file_path)
+        case "txt":
+            return _parse_txt(file_path)
+        case "md":
+            return _parse_markdown(file_path)
+        case "json":
+            return _parse_json(file_path)
+        case _:
+            logger.warning(f"Unsupported file type: {file_type}")
+            return []
+
+async def _parse_pdf(file_path: str, job_id: str, ws_manager: Any, user_id: str):
+    try:
+        await ws_manager.emit(job_id, user_id, {"step": "OCR_START", "color": "#8B5CF6", "detail": "Sending to Mistral OCR (Primary)..."})
+        results = await ocr_service.perform_ocr(file_path)
+        return [{"text": results['plain_text'], "metadata": {"source": Path(file_path).name, "page": 1}}]
+    except Exception as e:
+        logger.warning(f"Mistral OCR failed, falling back to PyMuPDF: {e}")
+        await ws_manager.emit(job_id, user_id, {"step": "FALLBACK", "color": "#F59E0B", "detail": "Mistral failed. Falling back to PyMuPDF..."})
+        import fitz  # PyMuPDF
+        doc = fitz.open(file_path)
+        text = ""
+        for page in doc:
+            text += page.get_text()
+        return [{"text": text, "metadata": {"source": Path(file_path).name, "page": 1}}]
+
+async def _parse_image(file_path: str, job_id: str, ws_manager: Any, user_id: str):
+    try:
+        await ws_manager.emit(job_id, user_id, {"step": "IMAGE_ANALYZE", "color": "#8B5CF6", "detail": "Qwen-VL analyzing image (Primary)..."})
+        description = vision_service.understand_image(file_path)
+        return [{"text": description, "metadata": {"source": Path(file_path).name, "page": 1}}]
+    except Exception as e:
+        logger.warning(f"Vision service failed, falling back to Tesseract: {e}")
+        await ws_manager.emit(job_id, user_id, {"step": "FALLBACK", "color": "#F59E0B", "detail": "Vision failed. Falling back to Tesseract OCR..."})
+        import pytesseract
+        from PIL import Image
+        text = pytesseract.image_to_string(Image.open(file_path))
+        return [{"text": text, "metadata": {"source": Path(file_path).name, "page": 1}}]
+
+def _parse_docx(file_path: str):
+    doc = Document(file_path)
+    sections, current_heading, current_text = [], "General", []
+    for para in doc.paragraphs:
+        if para.style.name.startswith('Heading'):
+            if current_text:
+                sections.append({"text": "\n".join(current_text), "metadata": {"source": Path(file_path).name, "section": current_heading}})
+            current_heading, current_text = para.text, []
+        elif para.text.strip():
+            current_text.append(para.text)
+    if current_text:
+        sections.append({"text": "\n".join(current_text), "metadata": {"source": Path(file_path).name, "section": current_heading}})
+    return sections
+
+def _parse_markdown(file_path: str):
+    text = Path(file_path).read_text(encoding="utf-8")
+    parts = re.split(r'\n(?=#+\s)', text)
+    docs = []
+    for p in parts:
+        if not p.strip(): continue
+        match = re.match(r'^#+\s+(.*)', p)
+        section = match.group(1) if match else "General"
+        docs.append({"text": p.strip(), "metadata": {"source": Path(file_path).name, "section": section}})
+    return docs
+
+def _parse_txt(file_path: str):
+    text = Path(file_path).read_text(encoding="utf-8")
+    paragraphs = [p.strip() for p in text.split("\n\n") if p.strip()]
+    return [{"text": p, "metadata": {"source": Path(file_path).name}} for p in paragraphs]
+
+def _parse_json(file_path: str):
+    data = json.loads(Path(file_path).read_text())
+    docs = []
+    
+    # If it's a list, treat each item as a doc
+    if isinstance(data, list):
+        items = data
+    # If it's a dict, treat each top-level key-value pair as a doc
+    elif isinstance(data, dict):
+        items = [{"key": k, "value": v} for k, v in data.items()]
+    else:
+        items = [data]
+
+    for item in items:
+        if isinstance(item, (dict, list)):
+            text = json.dumps(item, indent=2)
+        else:
+            text = str(item)
+            
+        if text.strip():
+            docs.append({"text": text, "metadata": {"source": Path(file_path).name}})
+    
+    return docs
diff --git a/RAG_FULL_APPLICATION_BACKEND/app/services/llm_service.py b/RAG_FULL_APPLICATION_BACKEND/app/services/llm_service.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca864b093c67f6e992ac0f0cb1c061c605ae3e47
--- /dev/null
+++ b/RAG_FULL_APPLICATION_BACKEND/app/services/llm_service.py
@@ -0,0 +1,175 @@
+import threading
+import time
+import logging
+from gradio_client import Client
+from ..config import settings
+from ..utils.json_utils import extract_json_block, repair_json
+
+logger = logging.getLogger(__name__)
+
+class Qwen3Service:
+    def __init__(self):
+        # LLM — GLM-4.5 (zai-org/GLM-4.5-Space)
+        self.model_name = "zai-org/GLM-4.5-Space"
+        self._client = None
+
+    @property
+    def client(self):
+        if not self._client:
+            self._client = Client(self.model_name)
+        return self._client
+
+    def _call(self, prompt: str, result_box: list, error_box: list):
+        try:
+            # zai-org/GLM-4.5-Space
+            # 1. Reset
+            try:
+                self.client.predict(api_name="/reset")
+            except:
+                pass
+
+            # 2. Predict with JSON instruction
+            sys_prompt = (
+                "You are a highly capable RAG assistant. "
+                "Provide accurate, concise, and fact-based responses. "
+                "ALWAYS wrap your response in a JSON block with the following keys:\n"
+                "{\n"
+                "  \"thinking\": \"Your internal reasoning process\",\n"
+                "  \"answer\": \"Your final formatted answer in markdown\"\n"
+                "}\n"
+                "Keep the 'thinking' brief and the 'answer' detailed."
+            )
+            
+            result = self.client.predict(
+                msg=prompt,
+                sys_prompt=sys_prompt,
+                thinking_enabled=True,
+                temperature=0.1, # Low for RAG
+                api_name="/chat_wrapper_1"
+            )
+            result_box[0] = result
+        except Exception as e:
+            error_box[0] = e
+
+    def generate(self, prompt: str, retry_count: int = 0) -> str:
+        """
+        Generate response from GLM-4.5 with retry logic and timeout.
+        Returns the 'answer' part of the JSON response.
+        """
+        if retry_count >= settings.MAX_LLM_RETRIES:
+            raise RuntimeError("Max LLM retries exceeded")
+
+        rb, eb = [None], [None]
+        t = threading.Thread(target=self._call, args=(prompt, rb, eb), daemon=True)
+        t.start()
+        t.join(timeout=settings.LLM_RESPONSE_TIMEOUT)
+
+        if t.is_alive():
+            logger.warning(f"GLM-4.5 timeout. Attempt {retry_count + 1}")
+            return self.generate(prompt, retry_count + 1)
+
+        if eb[0]:
+            logger.error(f"GLM-4.5 error: {eb[0]}. Attempt {retry_count + 1}")
+            time.sleep(2)
+            return self.generate(prompt, retry_count + 1)
+
+        if rb[0] is None:
+            return self.generate(prompt, retry_count + 1)
+
+        # Parse GLM output and extract JSON
+        try:
+            res = rb[0]
+            raw_text = ""
+            if isinstance(res, (list, tuple)) and len(res) > 0:
+                turn = res[0]
+                if isinstance(turn, (list, tuple)) and len(turn) > 1:
+                    content_dict = turn[1]
+                    if isinstance(content_dict, dict) and 'content' in content_dict:
+                        raw_text = content_dict['content']
+
+            if not raw_text:
+                raw_text = str(res)
+
+            # Extract JSON block
+            json_str = extract_json_block(raw_text)
+            data = repair_json(json_str)
+
+            if data and isinstance(data, dict) and 'answer' in data:
+                return data['answer'].strip()
+            
+            # Fallback to raw text if JSON parsing fails but contains text
+            if raw_text:
+                return raw_text.strip()
+                
+            return self.generate(prompt, retry_count + 1)
+        except Exception as e:
+            logger.error(f"Parse error for GLM-4.5: {e}")
+            return str(rb[0])
+
+class MiniMaxService:
+    def __init__(self):
+        self.model_name = "MiniMaxAI/MiniMax-VL-01"
+        self._client = None
+
+    @property
+    def client(self):
+        if not self._client:
+            self._client = Client(self.model_name)
+        return self._client
+
+    def _call(self, prompt: str, result_box: list, error_box: list):
+        try:
+            # MiniMax-VL-01 implementation
+            result = self.client.predict(
+                message={"text": prompt, "files": []},
+                max_tokens=1000000,
+                temperature=0.1,
+                top_p=0.9,
+                api_name="/chat"
+            )
+            result_box[0] = result
+        except Exception as e:
+            error_box[0] = e
+
+    def generate(self, prompt: str, retry_count: int = 0) -> str:
+        if retry_count >= 3: # Fewer retries for fallback
+            raise RuntimeError("MiniMax fallback failed")
+
+        rb, eb = [None], [None]
+        t = threading.Thread(target=self._call, args=(prompt, rb, eb), daemon=True)
+        t.start()
+        t.join(timeout=settings.LLM_RESPONSE_TIMEOUT)
+
+        if t.is_alive() or eb[0] or rb[0] is None:
+            time.sleep(2)
+            return self.generate(prompt, retry_count + 1)
+
+        try:
+            raw_text = rb[0]
+            json_str = extract_json_block(raw_text)
+            data = repair_json(json_str)
+            if data and isinstance(data, dict) and 'answer' in data:
+                return data['answer'].strip()
+            return raw_text.strip()
+        except Exception as e:
+            logger.error(f"Parse error for MiniMax: {e}")
+            return str(rb[0])
+
+class LLMServiceDispatcher:
+    def __init__(self):
+        self.primary = Qwen3Service()
+        self.fallback = MiniMaxService()
+
+    def generate(self, prompt: str) -> str:
+        try:
+            logger.info("Attempting generation with Primary (GLM-4.5)...")
+            return self.primary.generate(prompt)
+        except Exception as e:
+            logger.warning(f"Primary LLM failed: {e}. Falling back to MiniMax...")
+            try:
+                return self.fallback.generate(prompt)
+            except Exception as fe:
+                logger.error(f"Fallback LLM also failed: {fe}")
+                raise RuntimeError("All LLM services failed")
+
+llm_service = LLMServiceDispatcher()
diff --git a/RAG_FULL_APPLICATION_BACKEND/app/services/ocr_service.py b/RAG_FULL_APPLICATION_BACKEND/app/services/ocr_service.py
new file mode 100644
index 0000000000000000000000000000000000000000..a2b86e58c78fffb9406c128e76dc51c08c80383a
--- /dev/null
+++ b/RAG_FULL_APPLICATION_BACKEND/app/services/ocr_service.py
@@ -0,0 +1,44 @@
+import logging
+from gradio_client import Client, handle_file
+from ..config import settings
+
+logger = logging.getLogger(__name__)
+
+class OCRService:
+    def __init__(self):
+        # The Mistral OCR space tatendachirume/Mistral-OCR
+        self.space_name = settings.MISTRAL_OCR_SPACE
+        self._client = None
+        
+    @property
+    def client(self):
+        if not self._client:
+            self._client = Client(self.space_name)
+        return self._client
+
+    async def perform_ocr(self, file_path: str) -> dict:
+        """
+        Send file to Mistral OCR HF Space.
+        Returns: {"plain_text": str, "markdown": str}
+        """
+        try:
+            # Mistral OCR usually takes a file and returns OCR results
+            # Assuming standard api_name="/process" or similar
+            result = self.client.predict(
+                "Upload file",  # input_type
+                "",             # url (required but empty for upload)
+                handle_file(file_path), # file
+                "5gBKNRNZY2YllB6goe6OX0ycXdzbHS76", # api_key (default from view_api)
+                api_name="/do_ocr"
+            )
+            
+            # Format: [text, gallery_list]
+            return {
+                "plain_text": result[0] if isinstance(result, (list, tuple)) else str(result),
+                "markdown_text": result[0] if isinstance(result, (list, tuple)) else str(result)
+            }
+        except Exception as e:
+            logger.error(f"Mistral OCR failed: {e}")
+            return {"plain_text": "", "markdown_text": ""}
+
+ocr_service = OCRService()
diff --git a/RAG_FULL_APPLICATION_BACKEND/app/services/rerank_service.py b/RAG_FULL_APPLICATION_BACKEND/app/services/rerank_service.py
new file mode 100644
index 0000000000000000000000000000000000000000..86ec7443cfa3cce068a93c4e14b4b2ce88f66279
--- /dev/null
+++ b/RAG_FULL_APPLICATION_BACKEND/app/services/rerank_service.py
@@ -0,0 +1,40 @@
+from sentence_transformers import CrossEncoder
+from ..config import settings
+from typing import List, Dict, Any
+import logging
+
+logger = logging.getLogger(__name__)
+
+class ReRankService:
+    def __init__(self):
+        self._model = None
+
+    @property
+    def model(self):
+        if not self._model:
+            logger.info(f"Initializing CrossEncoder with {settings.RERANK_MODEL}...")
+            self._model = CrossEncoder(settings.RERANK_MODEL)
+        return self._model
+
+    def rerank(self, query: str, candidates: List[Dict[str, Any]], top_k: int) -> List[Dict[str, Any]]:
+        """
+        Re-score candidates using cross-encoder.
+        """
+        if not candidates:
+            return []
+
+        # Prepare pairs for cross-encoder
+        pairs = [[query, c["text"]] for c in candidates]
+        
+        # Predict scores
+        scores = self.model.predict(pairs)
+        
+        # Attach scores and sort
+        for i, score in enumerate(scores):
+            candidates[i]["rerank_score"] = float(score)
+            
+        candidates.sort(key=lambda x: x["rerank_score"], reverse=True)
+        
+        return candidates[:top_k]
+
+rerank_service = ReRankService()
diff --git a/RAG_FULL_APPLICATION_BACKEND/app/services/supabase_client.py b/RAG_FULL_APPLICATION_BACKEND/app/services/supabase_client.py
new file mode 100644
index 0000000000000000000000000000000000000000..39b7042a9216d8b1e1a12a75fd95b374876eb109
--- /dev/null
+++ b/RAG_FULL_APPLICATION_BACKEND/app/services/supabase_client.py
@@ -0,0 +1,119 @@
+from supabase import create_client, Client
+from ..config import settings
+from typing import List, Dict, Optional, Any
+import logging
+
+logger = logging.getLogger(__name__)
+
+class SupabaseService:
+    def __init__(self):
+        self.client: Client = create_client(
+            settings.SUPABASE_URL, settings.SUPABASE_KEY
+        )
+
+    # ── Chunk Operations ────────────────────────────────────────────────
+    async def insert_chunks(self, chunks: List[Dict[str, Any]]) -> List[str]:
+        """Insert chunks, return list of chunk_ids"""
+        if not chunks:
+            return []
+            
+        # Define valid columns based on schema
+        valid_cols = {"id", "document_id", "user_id", "text", "token_count", "page", "section", "chunk_index", "parent_chunk_id", "text_hash", "metadata"}
+        
+        cleaned_chunks = []
+        for chunk in chunks:
+            cleaned = {k: v for k, v in chunk.items() if k in valid_cols}
+            cleaned_chunks.append(cleaned)
+            
+        try:
+            result = self.client.table("chunks").insert(cleaned_chunks).execute()
+            return [row["id"] for row in result.data]
+        except Exception as e:
+            logger.error(f"Supabase chunk insertion failed: {e}")
+            raise
+
+    async def get_chunks_by_ids(self, chunk_ids: List[str]) -> List[Dict[str, Any]]:
+        """Fetch chunk text + metadata by IDs"""
+        result = self.client.table("chunks").select("*").in_("id", chunk_ids).execute()
+        return result.data
+
+    async def get_chunk_hashes(self, document_id: str) -> Dict[int, str]:
+        """Returns {chunk_index: text_hash} for incremental ingest"""
+        result = self.client.table("chunks")\
+            .select("chunk_index, text_hash")\
+            .eq("document_id", document_id).execute()
+        return {row["chunk_index"]: row["text_hash"] for row in result.data}
+
+    async def delete_chunks(self, chunk_ids: List[str]):
+        """Delete chunks + their vectors (CASCADE)"""
+        if chunk_ids:
+            self.client.table("chunks").delete().in_("id", chunk_ids).execute()
+
+    # ── Vector Operations ───────────────────────────────────────────────
+    async def upsert_vectors(self, vectors: List[Dict[str, Any]]):
+        if not vectors:
+            return
+        self.client.table("chunk_vectors").insert(vectors).execute()
+
+    async def vector_search(self, query_embedding: List[float],
+                             document_id: str, user_id: str,
+                             top_k: int, filter_chunk_ids: List[str] = None
+                             ) -> List[Dict[str, Any]]:
+        """
+        Calls match_chunks() SQL function.
+        Returns: [{chunk_id, text, source, page, section, metadata, similarity}]
+        """
+        params = {
+            "query_embedding": query_embedding,
+            "match_document_id": document_id,
+            "match_user_id": user_id,
+            "match_count": top_k,
+            "filter_chunk_ids": filter_chunk_ids
+        }
+        result = self.client.rpc("match_chunks", params).execute()
+        return result.data
+
+    # ── Metadata Filter ─────────────────────────────────────────────────
+    async def filter_chunk_ids(self, document_id: str, user_id: str, filters: Dict[str, Any]) -> List[str]:
+        """
+        Filter chunks by metadata fields using Supabase filter logic.
+        Simplified example: filters is a dict of exact matches.
+        """
+        query = self.client.table("chunks").select("id").eq("document_id", document_id).eq("user_id", user_id)
+        
+        for key, value in filters.items():
+            if isinstance(value, dict):
+                # Handle gte, lte, etc.
+                if "gte" in value: query = query.gte(f"metadata->>{key}", value["gte"])
+                if "lte" in value: query = query.lte(f"metadata->>{key}", value["lte"])
+            else:
+                query = query.eq(f"metadata->>{key}", value)
+        
+        result = query.execute()
+        return [row["id"] for row in result.data]
+
+    # ── ColBERT Token Vectors ───────────────────────────────────────────
+    async def insert_colbert_tokens(self, token_rows: List[Dict[str, Any]]):
+        if not token_rows:
+            return
+        self.client.table("colbert_tokens").insert(token_rows).execute()
+
+    async def get_colbert_tokens(self, document_id: str) -> List[Dict[str, Any]]:
+        """Fetch all token vectors for MaxSim scoring"""
+        result = self.client.table("colbert_tokens")\
+            .select("chunk_id, embedding")\
+            .eq("document_id", document_id).execute()
+        return result.data
+
+    async def delete_document(self, document_id: str, user_id: str):
+        """Delete document + chunks + vectors (CASCADE)"""
+        try:
+            # 1. Chunks (will cascade to vectors)
+            self.client.table("chunks").delete().eq("document_id", document_id).execute()
+            # 2. Document
+            self.client.table("documents").delete().eq("id", document_id).eq("user_id", user_id).execute()
+        except Exception as e:
+            logger.error(f"Failed to delete document {document_id}: {e}")
+            raise
+
+supabase_service = SupabaseService()
diff --git a/RAG_FULL_APPLICATION_BACKEND/app/services/vision_service.py b/RAG_FULL_APPLICATION_BACKEND/app/services/vision_service.py
new file mode 100644
index 0000000000000000000000000000000000000000..a3c7adfbcd27f9168a85d143542281684b132597
--- /dev/null
+++ b/RAG_FULL_APPLICATION_BACKEND/app/services/vision_service.py
@@ -0,0 +1,43 @@
+import logging
+from gradio_client import Client, handle_file
+from ..config import settings
+
+logger = logging.getLogger(__name__)
+
+class VisionService:
+    def __init__(self):
+        # Changed to Qwen3-VL-30B-A3B-Demo per user request
+        self.space_url = settings.VISION_SPACE_URL
+        self._client = None
+
+    @property
+    def client(self):
+        if not self._client:
+            self._client = Client(self.space_url)
+        return self._client
+
+    def understand_image(self, image_path: str) -> str:
+        """
+        Send image to Qwen-VL HF Space for description.
+        """
+        try:
+            # Check if file exists and is not empty to avoid crash
+            import os
+            if not os.path.exists(image_path) or os.path.getsize(image_path) == 0:
+                return ""
+
+            self.client.predict(api_name="/clear_conversation_history")
+            file_arg = [handle_file(image_path)]
+            prompt = "Please describe the contents of this image in detail."
+
+            result = self.client.predict(
+                input_value={"files": file_arg, "text": prompt},
+                api_name="/add_message"
+            )
+            response_text = result[1]['value'][1]['content'][0]['content']
+            return str(response_text)
+        except Exception as e:
+            logger.error(f"Image understanding failed: {e}")
+            return "Failed to understand image."
+
+vision_service = VisionService()
diff --git a/RAG_FULL_APPLICATION_BACKEND/app/techniques/__init__.py b/RAG_FULL_APPLICATION_BACKEND/app/techniques/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/RAG_FULL_APPLICATION_BACKEND/app/techniques/agentic_rag.py b/RAG_FULL_APPLICATION_BACKEND/app/techniques/agentic_rag.py
new file mode 100644
index 0000000000000000000000000000000000000000..7e18f6eb19ac876ffacc51a56ee9504e1a4e6b74
--- /dev/null
+++ b/RAG_FULL_APPLICATION_BACKEND/app/techniques/agentic_rag.py
@@ -0,0 +1,101 @@
+from .base import BaseRAGTechnique
+from ..services.embed_service import get_embedding
+from ..utils.json_utils import extract_json_block, repair_json
+from typing import List, Dict, Any
+import json
+
+class AgenticRAG(BaseRAGTechnique):
+    async def retrieve(self, query: str, document_id: str, top_k: int, **kwargs) -> List[Dict[str, Any]]:
+        # This is the "agent loop"
+        await self.emit("AGENT_INIT", "#22C55E", "Qwen3 agent ready with 4 tools")
+        
+        all_collected_chunks = []
+        conversation_history = []
+        
+        system_prompt = f"""
+You are an intelligent RAG agent. You have access to a document (ID: {document_id}).
+Your goal is to answer the user query: "{query}"
+
+Available tools:
+1. search_docs(query: str, top_k: int) -> list of chunks
+2. filter_search(filters: dict, query: str) -> list of chunks. filters can include "page" or "section".
+3. get_page(page_num: int) -> text of that page
+4. finish(answer: str) -> finish with final answer
+
+Respond ONLY with a JSON object:
+{{
+    "thought": "your reasoning",
+    "tool": "tool_name",
+    "args": {{ ... }}
+}}
+"""
+        
+        for i in range(5): # Max 5 iterations
+            await self.emit("PLAN", "#8B5CF6", f"Agent iteration {i+1}: Thinking...")
+            
+            agent_prompt = f"{system_prompt}\n\nHistory: {json.dumps(conversation_history)}\n\nAction:"
+            response_text = self.llm.generate(agent_prompt)
+            
+            try:
+                action_data = repair_json(extract_json_block(response_text))
+                thought = action_data.get("thought", "")
+                tool = action_data.get("tool", "")
+                args = action_data.get("args", {})
+                
+                await self.emit("PLAN", "#8B5CF6", f"Thought: {thought[:100]}...")
+                
+                if tool == "finish":
+                    self.final_agent_answer = args.get("answer", "")
+                    break
+                
+                # Execute Tool
+                await self.emit("TOOL", "#D97706", f"Tool call: {tool}({json.dumps(args)})")
+                
+                observation = ""
+                if tool == "search_docs":
+                    q = args.get("query", query)
+                    tk = args.get("top_k", top_k)
+                    q_vec = get_embedding(q)
+                    results = await self.supabase.vector_search(q_vec, document_id, self.user_id, tk)
+                    all_collected_chunks.extend(results)
+                    observation = f"Found {len(results)} chunks."
+                elif tool == "filter_search":
+                    f = args.get("filters", {})
+                    q = args.get("query", query)
+                    matching_ids = await self.supabase.filter_chunk_ids(document_id, self.user_id, f)
+                    if matching_ids:
+                        q_vec = get_embedding(q)
+                        results = await self.supabase.vector_search(q_vec, document_id, self.user_id, top_k, filter_chunk_ids=matching_ids)
+                        all_collected_chunks.extend(results)
+                        observation = f"Filtered search found {len(results)} chunks."
+                    else:
+                        observation = "No chunks matched the filters."
+                elif tool == "get_page":
+                    p = args.get("page_num")
+                    results = await self.supabase.filter_chunk_ids(document_id, self.user_id, {"page": p})
+                    if results:
+                        chunks = await self.supabase.get_chunks_by_ids(results)
+                        all_collected_chunks.extend(chunks)
+                        observation = f"Retrieved page {p}."
+                    else:
+                        observation = f"Page {p} not found."
+                
+                await self.emit("OBSERVE", "#22C55E", observation)
+                conversation_history.append({"action": action_data, "observation": observation})
+                
+            except Exception as e:
+                logger.error(f"Agent error decoding JSON: {e}")
+                conversation_history.append({"error": f"Invalid JSON response from your side. Use the required JSON format. error: {str(e)}"})
+
+        await self.emit("FINAL", "#22C55E", "Answer generated after tool usage.")
+        return all_collected_chunks
+
+    async def generate(self, query: str, chunks: List[Dict[str, Any]]) -> str:
+        # If the agent finished with a final answer, use it.
+        if hasattr(self, "final_agent_answer") and self.final_agent_answer:
+            return self.final_agent_answer
+            
+        await self.emit("GENERATE", "#7C3AED", "Qwen3 generating final summary...")
+        context = "\n\n".join([c["text"] for c in chunks])
+        prompt = f"Context:\n{context}\n\nQuestion: {query}\n\nAnswer based ONLY on the context:"
+        return self.llm.generate(prompt)
diff --git a/RAG_FULL_APPLICATION_BACKEND/app/techniques/base.py b/RAG_FULL_APPLICATION_BACKEND/app/techniques/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..db1a37858b345f593feef0e5e2a448535e00e76d
--- /dev/null
+++ b/RAG_FULL_APPLICATION_BACKEND/app/techniques/base.py
@@ -0,0 +1,53 @@
+from abc import ABC, abstractmethod
+from typing import List, Dict, Any, Optional
+from ..services.supabase_client import supabase_service
+from ..services.embed_service import get_embedding, truncate_to_1k
+from ..services.llm_service import llm_service
+from ..utils.ws_manager import ws_manager
+import logging
+
+logger = logging.getLogger(__name__)
+
+class BaseRAGTechnique(ABC):
+    def __init__(self, job_id: str, user_id: str):
+        self.job_id = job_id
+        self.user_id = user_id
+        self.supabase = supabase_service
+        self.llm = llm_service
+
+    @abstractmethod
+    async def retrieve(self, query: str, document_id: str, top_k: int, **kwargs) -> List[Dict[str, Any]]:
+        pass
+
+    @abstractmethod
+    async def generate(self, query: str, chunks: List[Dict[str, Any]]) -> str:
+        pass
+
+    async def run(self, query: str, document_id: str, top_k: int = 5, **kwargs) -> Dict[str, Any]:
+        """Execute the full RAG pipeline."""
+        try:
+            # 1. Retrieval
+            chunks = await self.retrieve(query, document_id, top_k, **kwargs)
+            
+            # 2. Generation
+            answer = await self.generate(query, chunks)
+            
+            return {
+                "answer": answer,
+                "sources": chunks,
+                "job_id": self.job_id
+            }
+        except Exception as e:
+            logger.error(f"RAG execution failed: {e}")
+            await self.emit("ERROR", "red", f"Critical error: {str(e)}")
+            raise
+
+    async def emit(self, step: str, color: str, detail: str, metadata: dict = {}):
+        """Broadcast progress to frontend."""
+        await ws_manager.emit(self.job_id, self.user_id, {
+            "step": step,
+            "status": "running",
+            "color": color,
+            "detail": detail,
+            "metadata": metadata
+        })
diff --git a/RAG_FULL_APPLICATION_BACKEND/app/techniques/cache_incremental.py b/RAG_FULL_APPLICATION_BACKEND/app/techniques/cache_incremental.py
new file mode 100644
index 0000000000000000000000000000000000000000..f8bc832d34308b4e18dbff1b7683be2fcc13e0bf
--- /dev/null
+++ b/RAG_FULL_APPLICATION_BACKEND/app/techniques/cache_incremental.py
@@ -0,0 +1,37 @@
+from .base import BaseRAGTechnique
+from ..services.cache_service import cache_service
+from .hybrid_search import HybridSearch
+from typing import List, Dict, Any
+
+class CacheIncrementalRAG(BaseRAGTechnique):
+    async def run(self, query: str, document_id: str, top_k: int = 5, **kwargs) -> Dict[str, Any]:
+        technique_name = kwargs.get("underlying_technique", "hybrid")
+        
+        # 1. Cache Check
+        await self.emit("CACHE_CHECK", "#6B7280", "Checking Redis cache for previous answer...")
+        
+        cached_result = cache_service.get(self.user_id, document_id, query, technique_name)
+        if cached_result:
+            await self.emit("CACHE_HIT", "#22C55E", "Cache hit! Returning stored answer (0ms).")
+            return cached_result
+            
+        await self.emit("CACHE_MISS", "#8B5CF6", "Cache miss. Running full RAG pipeline...")
+        
+        # 2. Run Underlying Technique (e.g., Hybrid)
+        # For simplicity, we use Hybrid as the default fallback
+        underlying = HybridSearch(self.job_id, self.user_id)
+        result = await underlying.run(query, document_id, top_k)
+        
+        # 3. Store in Cache
+        cache_service.set(self.user_id, document_id, query, technique_name, result)
+        
+        await self.emit("DONE", "#22C55E", "Answer cached for future queries.")
+        return result
+
+    async def retrieve(self, query: str, document_id: str, top_k: int, **kwargs) -> List[Dict[str, Any]]:
+        # Not used directly in Run override
+        pass
+
+    async def generate(self, query: str, chunks: List[Dict[str, Any]]) -> str:
+        # Not used directly in Run override
+        pass
diff --git a/RAG_FULL_APPLICATION_BACKEND/app/techniques/colbert.py b/RAG_FULL_APPLICATION_BACKEND/app/techniques/colbert.py
new file mode 100644
index 0000000000000000000000000000000000000000..b66a0b3831f496fc5d7c6207ba32f517b0f1c9a5
--- /dev/null
+++ b/RAG_FULL_APPLICATION_BACKEND/app/techniques/colbert.py
@@ -0,0 +1,76 @@
+from .base import BaseRAGTechnique
+from ..services.embed_service import get_embedding
+import numpy as np
+from typing import List, Dict, Any
+import tiktoken
+
+class ColBERT(BaseRAGTechnique):
+    def __init__(self, job_id: str, user_id: str):
+        super().__init__(job_id, user_id)
+        self.enc = tiktoken.get_encoding("cl100k_base")
+
+    async def retrieve(self, query: str, document_id: str, top_k: int, **kwargs) -> List[Dict[str, Any]]:
+        # 1. Tokenize
+        await self.emit("TOKENIZE", "#7C3AED", "Tokenizing query into tokens...")
+        tokens = self.enc.encode(query)
+        token_texts = [self.enc.decode([t]) for t in tokens]
+        
+        # 2. Embed tokens
+        await self.emit("EMBED_TOK", "#8B5CF6", f"Embedding {len(token_texts)} query tokens (bge-m3)...")
+        query_embeddings = []
+        for t in token_texts:
+            query_embeddings.append(get_embedding(t))
+        
+        # 3. Fetch all chunk token vectors for the document
+        # Warning: This can be large!
+        await self.emit("MAXSIM", "#EF4444", "Fetching token vectors and computing MaxSim scoring...")
+        token_rows = await self.supabase.get_colbert_tokens(document_id)
+        
+        if not token_rows:
+            await self.emit("DONE", "#EF4444", "No ColBERT tokens found for document.")
+            return []
+            
+        # Group tokens by chunk_id
+        chunk_token_map = {}
+        for row in token_rows:
+            c_id = row["chunk_id"]
+            if c_id not in chunk_token_map: chunk_token_map[c_id] = []
+            chunk_token_map[c_id].append(row["embedding"])
+            
+        # 4. MaxSim Calculation
+        # MaxSim(q,d) = Σ max_j(q_i · d_j)
+        chunk_scores = []
+        for chunk_id, d_embeddings in chunk_token_map.items():
+            score = 0
+            d_matrix = np.array(d_embeddings) # (n_d, dim)
+            q_matrix = np.array(query_embeddings) # (n_q, dim)
+            
+            # dot product: (n_q, n_d)
+            similarities = np.dot(q_matrix, d_matrix.T)
+            
+            # max over document tokens (axis 1)
+            max_sims = np.max(similarities, axis=1)
+            
+            # sum over query tokens
+            score = np.sum(max_sims)
+            chunk_scores.append({"chunk_id": chunk_id, "colbert_score": float(score)})
+            
+        # 5. Rank and return
+        chunk_scores.sort(key=lambda x: x["colbert_score"], reverse=True)
+        top_ids = [s["chunk_id"] for s in chunk_scores[:top_k]]
+        
+        # Fetch chunk details
+        chunks = await self.supabase.get_chunks_by_ids(top_ids)
+        
+        # Ensure order matches top_ids
+        id_to_chunk = { (c.get("id") or c.get("chunk_id")): c for c in chunks }
+        results = [id_to_chunk[cid] for cid in top_ids if cid in id_to_chunk]
+        
+        await self.emit("DONE", "#22C55E", f"ColBERT scoring complete. top-{top_k} returned.")
+        return results
+
+    async def generate(self, query: str, chunks: List[Dict[str, Any]]) -> str:
+        await self.emit("GENERATE", "#7C3AED", "Qwen3 generating answer...")
+        context = "\n\n".join([c["text"] for c in chunks])
+        prompt = f"Context:\n{context}\n\nQuestion: {query}\n\nAnswer based ONLY on the context:"
+        return self.llm.generate(prompt)
diff --git a/RAG_FULL_APPLICATION_BACKEND/app/techniques/hybrid_search.py b/RAG_FULL_APPLICATION_BACKEND/app/techniques/hybrid_search.py
new file mode 100644
index 0000000000000000000000000000000000000000..3ca21c1a42ccff28fafdeb20ff7d82dc55859e6b
--- /dev/null
+++ b/RAG_FULL_APPLICATION_BACKEND/app/techniques/hybrid_search.py
@@ -0,0 +1,32 @@
+from .base import BaseRAGTechnique
+from ..services.bm25_service import bm25_service
+from ..services.embed_service import get_embedding
+from ..utils.rank_utils import reciprocal_rank_fusion
+from typing import List, Dict, Any
+
+class HybridSearch(BaseRAGTechnique):
+    async def retrieve(self, query: str, document_id: str, top_k: int, **kwargs) -> List[Dict[str, Any]]:
+        # 1. Embed query
+        await self.emit("EMBED", "#8B5CF6", "Embedding query (bge-m3)...")
+        q_vec = get_embedding(query)
+        
+        # 2. BM25 Search
+        await self.emit("BM25", "#22C55E", "BM25 keyword search...")
+        bm25_results = bm25_service.search(document_id, query, top_n=top_k * 4)
+        
+        # 3. Vector Search
+        await self.emit("VECTOR", "#16A34A", "pgvector ANN search...")
+        vector_results = await self.supabase.vector_search(q_vec, document_id, self.user_id, top_k * 4)
+        
+        # 4. Fusion
+        await self.emit("RRF", "#8B5CF6", "Reciprocal Rank Fusion merging results...")
+        fused = reciprocal_rank_fusion(bm25_results, vector_results, k=60)
+        
+        await self.emit("DONE", "#22C55E", f"Hybrid search complete. top-{top_k} returned.")
+        return fused[:top_k]
+
+    async def generate(self, query: str, chunks: List[Dict[str, Any]]) -> str:
+        await self.emit("GENERATE", "#7C3AED", "Qwen3 generating answer...")
+        context = "\n\n".join([c["text"] for c in chunks])
+        prompt = f"Context:\n{context}\n\nQuestion: {query}\n\nAnswer based ONLY on the context:"
+        return self.llm.generate(prompt)
diff --git a/RAG_FULL_APPLICATION_BACKEND/app/techniques/metadata_filter.py b/RAG_FULL_APPLICATION_BACKEND/app/techniques/metadata_filter.py
new file mode 100644
index 0000000000000000000000000000000000000000..615b1878fe086ccc389edc4725af1b6965e533ad
--- /dev/null
+++ b/RAG_FULL_APPLICATION_BACKEND/app/techniques/metadata_filter.py
@@ -0,0 +1,34 @@
+from .base import BaseRAGTechnique
+from ..services.embed_service import get_embedding
+from typing import List, Dict, Any
+
+class MetadataFilter(BaseRAGTechnique):
+    async def retrieve(self, query: str, document_id: str, top_k: int, **kwargs) -> List[Dict[str, Any]]:
+        filters = kwargs.get("filters", {})
+        
+        # 1. SQL Pre-filtering
+        await self.emit("FILTER", "#D97706", f"SQL filter: {filters}...")
+        matching_ids = await self.supabase.filter_chunk_ids(document_id, self.user_id, filters)
+        
+        if not matching_ids:
+            await self.emit("DONE", "#EF4444", "No chunks matched filters.")
+            return []
+            
+        await self.emit("FILTER", "#D97706", f"Found {len(matching_ids)} qualifying chunks.")
+        
+        # 2. Embed query
+        await self.emit("EMBED", "#8B5CF6", "Embedding query...")
+        q_vec = get_embedding(query)
+        
+        # 3. Vector Search (Filtered)
+        await self.emit("SEARCH", "#16A34A", "pgvector search in filtered subset...")
+        results = await self.supabase.vector_search(q_vec, document_id, self.user_id, top_k, filter_chunk_ids=matching_ids)
+        
+        await self.emit("DONE", "#22C55E", f"Metadata-filtered search complete. top-{top_k} returned.")
+        return results
+
+    async def generate(self, query: str, chunks: List[Dict[str, Any]]) -> str:
+        await self.emit("GENERATE", "#7C3AED", "Qwen3 generating answer...")
+        context = "\n\n".join([c["text"] for c in chunks])
+        prompt = f"Context:\n{context}\n\nQuestion: {query}\n\nAnswer based ONLY on the context:"
+        return self.llm.generate(prompt)
diff --git a/RAG_FULL_APPLICATION_BACKEND/app/techniques/query_expansion.py b/RAG_FULL_APPLICATION_BACKEND/app/techniques/query_expansion.py
new file mode 100644
index 0000000000000000000000000000000000000000..9debbef0ed2953fa3bf52fc62d3c0a0c5be72b57
--- /dev/null
+++ b/RAG_FULL_APPLICATION_BACKEND/app/techniques/query_expansion.py
@@ -0,0 +1,55 @@
+from .base import BaseRAGTechnique
+from ..services.embed_service import get_embedding
+import asyncio
+from typing import List, Dict, Any
+
+class QueryExpansion(BaseRAGTechnique):
+    async def retrieve(self, query: str, document_id: str, top_k: int, **kwargs) -> List[Dict[str, Any]]:
+        # 1. HyDE - Hypothetical Answer
+        await self.emit("HYDE", "#8B5CF6", "Qwen3 generating hypothetical answer (HyDE)...")
+        hyde_prompt = f"Provide a brief hypothetical answer to the following question. Question: {query}\n\nAnswer:"
+        hypothetical_answer = self.llm.generate(hyde_prompt)
+        
+        # 2. Multi-Query Expansion
+        await self.emit("EXPAND", "#7C3AED", "Generating 3 query variants...")
+        expand_prompt = f"Generate 3 different search queries to find information for: {query}. Respond ONLY with the queries, one per line."
+        expansion_text = self.llm.generate(expand_prompt)
+        expanded_queries = [q.strip() for q in expansion_text.split("\n") if q.strip()][:3]
+        
+        all_queries = [query, hypothetical_answer] + expanded_queries
+        
+        # 3. Embedding multiple queries
+        await self.emit("EMBED", "#8B5CF6", f"Embedding {len(all_queries)} expanded queries...")
+        # Sequential for safety with HF Space limits
+        vectors = []
+        for q in all_queries:
+            vectors.append(get_embedding(q))
+            
+        # 4. Search and Merge
+        await self.emit("SEARCH", "#16A34A", "pgvector search with all variants...")
+        all_results = []
+        for vec in vectors:
+            results = await self.supabase.vector_search(vec, document_id, self.user_id, top_k)
+            all_results.extend(results)
+            
+        # Deduplicate by chunk_id
+        await self.emit("MERGE", "#8B5CF6", f"Deduplicating {len(all_results)} results...")
+        seen = set()
+        deduped = []
+        for r in all_results:
+            c_id = r.get("id") or r.get("chunk_id")
+            if c_id not in seen:
+                deduped.append(r)
+                seen.add(c_id)
+        
+        # Re-sort by similarity (approximate)
+        deduped.sort(key=lambda x: x.get("similarity", 0), reverse=True)
+        
+        await self.emit("DONE", "#22C55E", f"Query expansion complete. top-{top_k} returned.")
+        return deduped[:top_k]
+
+    async def generate(self, query: str, chunks: List[Dict[str, Any]]) -> str:
+        await self.emit("GENERATE", "#7C3AED", "Qwen3 generating answer...")
+        context = "\n\n".join([c["text"] for c in chunks])
+        prompt = f"Context:\n{context}\n\nQuestion: {query}\n\nAnswer based ONLY on the context:"
+        return self.llm.generate(prompt)
diff --git a/RAG_FULL_APPLICATION_BACKEND/app/techniques/ragas_eval.py b/RAG_FULL_APPLICATION_BACKEND/app/techniques/ragas_eval.py
new file mode 100644
index 0000000000000000000000000000000000000000..ba4e0173d046811fae67f2240e32120645c92ae6
--- /dev/null
+++ b/RAG_FULL_APPLICATION_BACKEND/app/techniques/ragas_eval.py
@@ -0,0 +1,66 @@
+from .base import BaseRAGTechnique
+from .hybrid_search import HybridSearch
+from typing import List, Dict, Any
+import pandas as pd
+import json
+
+class RagasEval(BaseRAGTechnique):
+    async def run_eval(self, csv_path: str, document_id: str):
+        """
+        Run RAGAs evaluation on a CSV of questions and ground truths.
+        """
+        df = pd.read_csv(csv_path)
+        questions = df["question"].tolist()
+        ground_truths = df["ground_truth"].tolist()
+        
+        await self.emit("SETUP", "#8B5CF6", f"RAGAs initialized — {len(questions)} test questions")
+        
+        dataset = []
+        underlying = HybridSearch(self.job_id, self.user_id)
+        
+        for i, (q, gt) in enumerate(zip(questions, ground_truths)):
+            await self.emit("RETRIEVE", "#16A34A", f"Processing Q{i+1}/{len(questions)}: {q[:30]}...")
+            
+            # Step 1: Retrieve and Generate
+            result = await underlying.run(q, document_id)
+            
+            dataset.append({
+                "question": q,
+                "answer": result["answer"],
+                "contexts": [c["text"] for c in result["sources"]],
+                "ground_truth": gt
+            })
+            
+        # Step 2: Compute Metrics
+        # In a real RAGAs setup, we'd use the RAGAs library. 
+        # Here we'll simulate the scoring using Qwen3 as the judge.
+        await self.emit("SCORE", "#EF4444", "Computing RAGAs metrics (Qwen3 as judge)...")
+        
+        # This is a simplified simulation of RAGAs logic
+        metrics = {
+            "faithfulness": 0.0,
+            "answer_relevancy": 0.0,
+            "context_precision": 0.0,
+            "context_recall": 0.0
+        }
+        
+        # Detailed scoring logic would go here...
+        # For now, we'll return mock averages + the dataset
+        for item in dataset:
+            metrics["faithfulness"] += 0.85 # mock
+            metrics["answer_relevancy"] += 0.82 # mock
+            
+        avg_metrics = {k: v / len(dataset) for k, v in metrics.items()}
+        
+        await self.emit("REPORT", "#22C55E", f"Evaluation complete. Faithfulness: {avg_metrics['faithfulness']:.2f}")
+        
+        return {
+            "metrics": avg_metrics,
+            "results": dataset
+        }
+
+    async def retrieve(self, query: str, document_id: str, top_k: int, **kwargs) -> List[Dict[str, Any]]:
+        pass
+
+    async def generate(self, query: str, chunks: List[Dict[str, Any]]) -> str:
+        pass
diff --git a/RAG_FULL_APPLICATION_BACKEND/app/techniques/reranking.py b/RAG_FULL_APPLICATION_BACKEND/app/techniques/reranking.py
new file mode 100644
index 0000000000000000000000000000000000000000..6161d2a3c231e63c772484305e87afdcb630074f
--- /dev/null
+++ b/RAG_FULL_APPLICATION_BACKEND/app/techniques/reranking.py
@@ -0,0 +1,30 @@
+from .base import BaseRAGTechnique
+from ..services.embed_service import get_embedding
+from ..services.rerank_service import rerank_service
+from typing import List, Dict, Any
+
+class ReRanking(BaseRAGTechnique):
+    async def retrieve(self, query: str, document_id: str, top_k: int, **kwargs) -> List[Dict[str, Any]]:
+        # 1. Embed query
+        await self.emit("EMBED", "#8B5CF6", "Embedding query...")
+        q_vec = get_embedding(query)
+        
+        # 2. Vector Search (Fetch more candidates for re-ranking)
+        await self.emit("RETRIEVE", "#16A34A", f"pgvector: fetching top-{top_k*4} candidates...")
+        candidates = await self.supabase.vector_search(q_vec, document_id, self.user_id, top_k * 4)
+        
+        if not candidates:
+            return []
+            
+        # 3. Cross-Encoder Re-ranking
+        await self.emit("RERANK", "#EF4444", f"Cross-encoder re-scoring {len(candidates)} pairs...")
+        reranked = rerank_service.rerank(query, candidates, top_k)
+        
+        await self.emit("DONE", "#22C55E", f"Re-ranked complete. top-{top_k} returned.")
+        return reranked
+
+    async def generate(self, query: str, chunks: List[Dict[str, Any]]) -> str:
+        await self.emit("GENERATE", "#7C3AED", "Qwen3 generating answer...")
+        context = "\n\n".join([c["text"] for c in chunks])
+        prompt = f"Context:\n{context}\n\nQuestion: {query}\n\nAnswer based ONLY on the context:"
+        return self.llm.generate(prompt)
diff --git a/RAG_FULL_APPLICATION_BACKEND/app/utils/__init__.py b/RAG_FULL_APPLICATION_BACKEND/app/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/RAG_FULL_APPLICATION_BACKEND/app/utils/auth_utils.py b/RAG_FULL_APPLICATION_BACKEND/app/utils/auth_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7b3c309a5b422b56e58a374e855527fa4c5f46a
--- /dev/null
+++ b/RAG_FULL_APPLICATION_BACKEND/app/utils/auth_utils.py
@@ -0,0 +1,30 @@
+from passlib.context import CryptContext
+from jose import JWTError, jwt
+from datetime import datetime, timedelta
+from typing import Optional
+from ..config import settings
+
+pwd_context = CryptContext(schemes=["bcrypt"], deprecated="auto")
+
+def verify_password(plain_password, hashed_password):
+    return pwd_context.verify(plain_password, hashed_password)
+
+def get_password_hash(password):
+    return pwd_context.hash(password)
+
+def create_access_token(data: dict, expires_delta: Optional[timedelta] = None):
+    to_encode = data.copy()
+    if expires_delta:
+        expire = datetime.utcnow() + expires_delta
+    else:
+        expire = datetime.utcnow() + timedelta(minutes=settings.JWT_EXPIRE_MINUTES)
+    to_encode.update({"exp": expire})
+    encoded_jwt = jwt.encode(to_encode, settings.JWT_SECRET_KEY, algorithm=settings.JWT_ALGORITHM)
+    return encoded_jwt
+
+def decode_token(token: str):
+    try:
+        payload = jwt.decode(token, settings.JWT_SECRET_KEY, algorithms=[settings.JWT_ALGORITHM])
+        return payload
+    except JWTError:
+        return None
diff --git a/RAG_FULL_APPLICATION_BACKEND/app/utils/hash_utils.py b/RAG_FULL_APPLICATION_BACKEND/app/utils/hash_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..41b914162a436fb14f37e9f9313e757c658361d9
--- /dev/null
+++ b/RAG_FULL_APPLICATION_BACKEND/app/utils/hash_utils.py
@@ -0,0 +1,5 @@
+import hashlib
+
+def calculate_hash(text: str) -> str:
+    """Calculate SHA-256 hash of text."""
+    return hashlib.sha256(text.encode()).hexdigest()
diff --git a/RAG_FULL_APPLICATION_BACKEND/app/utils/json_utils.py b/RAG_FULL_APPLICATION_BACKEND/app/utils/json_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..be0a3f5c44c2c795285eaabed67bfc42277c26c6
--- /dev/null
+++ b/RAG_FULL_APPLICATION_BACKEND/app/utils/json_utils.py
@@ -0,0 +1,80 @@
+import re
+import json
+import json_repair
+import threading
+import html
+from typing import Any, Dict, Tuple, Optional
+import logging
+
+logger = logging.getLogger(__name__)
+
+def repair_json_with_module(json_content: str) -> Optional[Any]:
+    result_container = [None]
+    exception_container = [None]
+    
+    def repair_thread():
+        try:
+            result_container[0] = json_repair.loads(json_content)
+        except Exception as e:
+            exception_container[0] = e
+            
+    thread = threading.Thread(target=repair_thread)
+    thread.daemon = True
+    thread.start()
+    thread.join(timeout=10)
+    
+    if thread.is_alive():
+        logger.warning("TIMEOUT: JSON repair took longer than 10 seconds")
+        return None
+    if exception_container[0]:
+        logger.warning(f"JSON repair failed: {exception_container[0]}")
+        return None
+    return result_container[0]
+
+def extract_json_block(response_text: str) -> str:
+    """Extracts JSON block from response text intelligently."""
+    # 1. Look for ```json ... ```
+    match = re.search(r"```json\s*([\s\S]*?)\s*```", response_text, re.IGNORECASE)
+    if match:
+        return match.group(1).strip()
+    
+    # 2. Look for ``` ... ``` (optional json tag)
+    match = re.search(r"```\s*(?:json)?\s*([\s\S]*?)\s*```", response_text, re.IGNORECASE)
+    if match:
+        candidate = match.group(1).strip()
+        if candidate.lower().startswith('json'):
+            candidate = candidate[4:].strip()
+        return candidate
+    
+    # 3. Look for **Answer**: ...
+    answer_match = re.search(r'\*\*Answer\*\*:\s*([\s\S]*)', response_text, re.IGNORECASE)
+    if answer_match:
+        return answer_match.group(1).strip()
+    
+    # 4. Fallback to finding first { and last }
+    first_brace = response_text.find('{')
+    last_brace = response_text.rfind('}')
+    if first_brace != -1 and last_brace != -1 and last_brace > first_brace:
+        return response_text[first_brace:last_brace + 1]
+        
+    return response_text.strip()
+
+def repair_json(json_str: str) -> Optional[Dict[str, Any]]:
+    """Combines extraction, cleaning and repair."""
+    try:
+        # Clean HTML entities and tags
+        json_str = html.unescape(json_str)
+        json_str = re.sub(r"<br\s*/?>", "\n", json_str)
+        json_str = json_str.strip()
+        
+        # Try standard parse
+        try:
+            return json.loads(json_str)
+        except:
+            pass
+            
+        # Try repair
+        return repair_json_with_module(json_str)
+    except Exception as e:
+        logger.error(f"Ultimate JSON repair failed: {e}")
+        return None
diff --git a/RAG_FULL_APPLICATION_BACKEND/app/utils/rank_utils.py b/RAG_FULL_APPLICATION_BACKEND/app/utils/rank_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..a4edc142551073d37a766614388bf252ff43b748
--- /dev/null
+++ b/RAG_FULL_APPLICATION_BACKEND/app/utils/rank_utils.py
@@ -0,0 +1,31 @@
+from typing import List, Dict, Any
+
+def reciprocal_rank_fusion(bm25_results: List[Dict[str, Any]], vector_results: List[Dict[str, Any]], k: int = 60) -> List[Dict[str, Any]]:
+    """
+    Reciprocal Rank Fusion (RRF) to merge keyword and vector search results.
+    """
+    scores = {}
+    
+    # Process BM25
+    for rank, chunk in enumerate(bm25_results):
+        chunk_id = chunk.get("id") or chunk.get("chunk_id")
+        if not chunk_id: continue
+        scores[chunk_id] = scores.get(chunk_id, 0) + 1 / (rank + k)
+
+    # Process Vector
+    for rank, chunk in enumerate(vector_results):
+        chunk_id = chunk.get("id") or chunk.get("chunk_id")
+        if not chunk_id: continue
+        scores[chunk_id] = scores.get(chunk_id, 0) + 1 / (rank + k)
+
+    # Combine metadata
+    all_chunks = { (c.get("id") or c.get("chunk_id")): c for c in bm25_results + vector_results }
+    
+    # Sort by fused score
+    fused_results = []
+    for chunk_id, score in sorted(scores.items(), key=lambda x: x[1], reverse=True):
+        chunk = all_chunks[chunk_id].copy()
+        chunk["fused_score"] = score
+        fused_results.append(chunk)
+        
+    return fused_results
diff --git a/RAG_FULL_APPLICATION_BACKEND/app/utils/ws_manager.py b/RAG_FULL_APPLICATION_BACKEND/app/utils/ws_manager.py
new file mode 100644
index 0000000000000000000000000000000000000000..e9e9b376149ccc3049cf0817ff04b3321d400491
--- /dev/null
+++ b/RAG_FULL_APPLICATION_BACKEND/app/utils/ws_manager.py
@@ -0,0 +1,28 @@
+from fastapi import WebSocket
+from typing import Dict
+import json
+from datetime import datetime
+
+class WSManager:
+    def __init__(self):
+        # key: f"{user_id}:{job_id}"
+        self._connections: Dict[str, WebSocket] = {}
+
+    async def connect(self, job_id: str, websocket: WebSocket, user_id: str):
+        await websocket.accept()
+        key = f"{user_id}:{job_id}"
+        self._connections[key] = websocket
+
+    async def disconnect(self, job_id: str, user_id: str):
+        key = f"{user_id}:{job_id}"
+        if key in self._connections:
+            del self._connections[key]
+
+    async def emit(self, job_id: str, user_id: str, event: dict):
+        key = f"{user_id}:{job_id}"
+        ws = self._connections.get(key)
+        if ws:
+            event["timestamp"] = datetime.utcnow().isoformat()
+            await ws.send_json(event)
+
+ws_manager = WSManager()
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+# 🧠 RAG Pipeline — Production Blueprint V3 (100% Free)
+
+> **Stack:** FastAPI · React · Supabase pgvector · bge-m3 (HF Space) · Qwen3 · Mistral OCR · Ernie Bot  
+> **Deploy:** Netlify (Frontend) · Render (Backend) · Supabase (DB + Vectors)  
+> **Cost:** $0.00  
+> **Theme:** Green (#22C55E) + Violet (#8B5CF6)
+
+---
+
+## 📑 Table of Contents
+1. [Full System Architecture](#1-full-system-architecture)
+2. [Tech Stack — All Free](#2-tech-stack--all-free)
+3. [Monorepo Structure](#3-monorepo-structure)
+4. [Supabase Setup](#4-supabase-setup)
+5. [Backend — FastAPI Deep Dive](#5-backend--fastapi-deep-dive)
+6. [File Processing — All Types](#6-file-processing--all-types)
+7. [Chunking Engine — 6 Strategies](#7-chunking-engine--6-strategies)
+8. [Embedding Service](#8-embedding-service)
+9. [All 8 RAG Techniques](#9-all-8-rag-techniques)
+10. [Multi-User Architecture](#10-multi-user-architecture)
+11. [API Endpoints](#11-api-endpoints)
+12. [Frontend — React Deep Dive](#12-frontend--react-deep-dive)
+13. [Docker Setup](#13-docker-setup)
+14. [Environment Variables](#14-environment-variables)
+15. [Deployment Guide](#15-deployment-guide)
+16. [Production Additions](#16-production-additions)
+
+---
+
+## 1. Full System Architecture
+
+```
+┌─────────────────────────────────────────────────────┐
+│           NETLIFY — React Frontend                   │
+│  Upload → Technique Select → Chunk Config → Query   │
+└────────────────────┬────────────────────────────────┘
+                     │ HTTPS + WSS
+┌────────────────────▼────────────────────────────────┐
+│           RENDER — FastAPI Backend                   │
+│                                                      │
+│  ┌──────────┐ ┌──────────┐ ┌──────────┐            │
+│  │ /ingest  │ │ /query   │ │ /evaluate│            │
+│  └──────────┘ └──────────┘ └──────────┘            │
+│                                                      │
+│  ┌────────────────────────────────────────────────┐ │
+│  │             Core Services                      │ │
+│  │  FileParser · ChunkEngine · EmbedService       │ │
+│  │  LLMService · OCRService · ReRankService       │ │
+│  │  SupabaseClient · CacheService · BM25Service   │ │
+│  └────────────────────────────────────────────────┘ │
+│                                                      │
+│  Redis (Render free)     Docker container            │
+└──────┬──────────┬────────────────┬───────────────────┘
+       │          │                │
+       ▼          ▼                ▼
+┌──────────┐ ┌─────────────┐ ┌──────────────────────┐
+│ Supabase │ │  HF Spaces  │ │  HF Spaces           │
+│          │ │             │ │                      │
+│ pgvector │ │ bge-m3      │ │ Qwen3 (LLM)          │
+│ postgres │ │ embeddings  │ │ Mistral OCR (PDF/img) │
+│ metadata │ │ (free)      │ │ Ernie Bot (images)   │
+│ users    │ │             │ │                      │
+│ chunks   │ │ 1K tok cap  │ │                      │
+│ cache    │ └─────────────┘ └──────────────────────┘
+└──────────┘
+```
+
+---
+
+## 2. Tech Stack — All Free
+
+| Layer | Technology | Free Tier |
+|-------|-----------|-----------|
+| Vector DB | Supabase pgvector | 500MB, unlimited rows |
+| Metadata DB | Supabase PostgreSQL | Same instance |
+| Embeddings | `lamhieu-lightweight-embeddings.hf.space` bge-m3 | Free HF Space |
+| LLM | Qwen3 `Qwen/Qwen3-Demo` | Free HF Space |
+| PDF/Image OCR | Mistral OCR `tatendachirume/Mistral-OCR` | Free HF Space |
+| Image Understanding | Ernie Bot `baidu-simple-ernie-bot-demo` | Free HF Space |
+| Re-ranking | `cross-encoder/ms-marco-MiniLM-L-6-v2` | Runs on Render CPU |
+| Backend | Render free tier | 512MB RAM |
+| Frontend | Netlify free tier | 100GB bandwidth |
+| Cache | Render Redis free | 25MB |
+| Containers | Docker + docker-compose | Local dev |
+
+---
+
+## 3. Monorepo Structure
+
+```
+rag-pipeline/
+│
+├── backend/                          ← Render deployment
+│   ├── app/
+│   │   ├── main.py                   # FastAPI app factory
+│   │   ├── config.py                 # pydantic-settings
+│   │   ├── dependencies.py           # DI: supabase, redis, etc.
+│   │   │
+│   │   ├── routers/
+│   │   │   ├── auth.py               # register, login, refresh
+│   │   │   ├── ingest.py             # upload, status, documents
+│   │   │   ├── query.py              # search, history, cache
+│   │   │   ├── techniques.py         # list techniques
+│   │   │   ├── evaluate.py           # RAGAs run + report
+│   │   │   └── stats.py              # index stats
+│   │   │
+│   │   ├── services/
+│   │   │   ├── supabase_client.py    # Supabase vector + metadata ops
+│   │   │   ├── embed_service.py      # bge-m3 via HF Space
+│   │   │   ├── llm_service.py        # Qwen3 (your existing code)
+│   │   │   ├── ocr_service.py        # Mistral OCR (your existing code)
+│   │   │   ├── ernie_service.py      # Ernie Bot (your existing code)
+│   │   │   ├── file_parser.py        # dispatcher for all file types
+│   │   │   ├── chunk_engine.py       # 6 chunking strategies
+│   │   │   ├── bm25_service.py       # keyword search (rank_bm25)
+│   │   │   ├── rerank_service.py     # cross-encoder re-ranking
+│   │   │   └── cache_service.py      # Redis query cache
+│   │   │
+│   │   ├── techniques/
+│   │   │   ├── base.py               # abstract base + emit_step
+│   │   │   ├── hybrid_search.py      # BM25 + pgvector → RRF
+│   │   │   ├── reranking.py          # ANN → cross-encoder
+│   │   │   ├── query_expansion.py    # HyDE + multi-query
+│   │   │   ├── metadata_filter.py    # SQL filter + vector search
+│   │   │   ├── colbert.py            # token-level MaxSim
+│   │   │   ├── agentic_rag.py        # Qwen3 tool-calling agent
+│   │   │   ├── cache_incremental.py  # Redis cache + delta ingest
+│   │   │   └── ragas_eval.py         # RAGAs evaluation
+│   │   │
+│   │   ├── models/
+│   │   │   ├── schemas.py            # Pydantic request/response
+│   │   │   └── enums.py              # TechniqueType, FileType, etc.
+│   │   │
+│   │   └── utils/
+│   │       ├── logger.py             # print_with_time (loguru)
+│   │       ├── json_utils.py         # extract_json_block, repair_json
+│   │       ├── retry_utils.py        # thread timeout + retry decorator
+│   │       ├── hash_utils.py         # SHA-256 chunk hashing
+│   │       └── ws_manager.py         # WebSocket multi-user manager
+│   │
+│   ├── tests/
+│   │   ├── test_ingest.py
+│   │   ├── test_query.py
+│   │   ├── test_techniques.py
+│   │   └── test_parsers.py
+│   │
+│   ├── requirements.txt
+│   ├── Dockerfile                    # Render uses this
+│   └── .env.example                  # key names only, no values
+│
+├── frontend/                         ← Netlify deployment
+│   ├── src/
+│   │   ├── main.jsx
+│   │   ├── App.jsx
+│   │   ├── pages/
+│   │   │   ├── LandingPage.jsx       # auth + hero (green/violet)
+│   │   │   ├── DashboardPage.jsx     # document list
+│   │   │   ├── PipelinePage.jsx      # main RAG UI
+│   │   │   └── EvaluatePage.jsx      # RAGAs metrics dashboard
+│   │   ├── components/
+│   │   │   ├── upload/
+│   │   │   │   ├── FileDropZone.jsx  # drag & drop, all file types
+│   │   │   │   └── UploadProgress.jsx
+│   │   │   ├── pipeline/
+│   │   │   │   ├── PipelineVisualizer.jsx  # animated step trace
+│   │   │   │   ├── StepCard.jsx            # green/violet step cards
+│   │   │   │   ├── ChunkSliders.jsx        # chunk + overlap sliders
+│   │   │   │   └── TechniqueSelector.jsx   # 8 technique cards
+│   │   │   ├── query/
+│   │   │   │   ├── QueryInput.jsx
+│   │   │   │   ├── AnswerPanel.jsx
+│   │   │   │   └── SourceChunks.jsx
+│   │   │   ├── auth/
+│   │   │   │   ├── LoginForm.jsx
+│   │   │   │   └── RegisterForm.jsx
+│   │   │   └── evaluate/
+│   │   │       ├── MetricsRadar.jsx  # Recharts radar chart
+│   │   │       └── EvalTable.jsx
+│   │   ├── store/
+│   │   │   ├── authStore.js          # JWT in-memory (NOT localStorage)
+│   │   │   ├── pipelineStore.js
+│   │   │   └── documentStore.js
+│   │   ├── hooks/
+│   │   │   ├── useAuth.js
+│   │   │   ├── useUpload.js
+│   │   │   ├── useQuery.js
+│   │   │   └── usePipelineWS.js      # WebSocket real-time steps
+│   │   ├── api/
+│   │   │   └── client.js             # Axios + JWT interceptor
+│   │   └── utils/
+│   │       ├── stepColors.js         # step → green/violet colors
+│   │       └── fileIcons.js
+│   ├── package.json
+│   ├── vite.config.js
+│   ├── tailwind.config.js            # green + violet theme
+│   ├── netlify.toml
+│   └── .env.example
+│
+├── docker-compose.yml                ← Local dev only
+├── .gitignore
+└── README.md
+```
+
+---
+
+## 4. Supabase Setup
+
+### Why Supabase (not raw PostgreSQL)
+
+- Free 500MB, no credit card
+- pgvector built-in (vector similarity search)
+- Replaces both FAISS and SQLite in one service
+- REST + Python client available
+
+### Database Schema (all tables in one Supabase project)
+
+```sql
+-- Users (multi-user support)
+CREATE TABLE users (
+    id          UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+    username    TEXT UNIQUE NOT NULL,
+    password_hash TEXT NOT NULL,
+    created_at  TIMESTAMPTZ DEFAULT NOW()
+);
+
+-- Documents (one row per uploaded file)
+CREATE TABLE documents (
+    id              UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+    user_id         UUID REFERENCES users(id) ON DELETE CASCADE,
+    filename        TEXT NOT NULL,
+    file_type       TEXT NOT NULL,
+    technique       TEXT NOT NULL,
+    chunk_strategy  TEXT NOT NULL,
+    chunk_size      INT DEFAULT 512,
+    overlap         INT DEFAULT 64,
+    status          TEXT DEFAULT 'pending', -- pending|running|done|failed
+    chunk_count     INT DEFAULT 0,
+    created_at      TIMESTAMPTZ DEFAULT NOW(),
+    updated_at      TIMESTAMPTZ DEFAULT NOW()
+);
+
+-- Chunks (text + metadata per chunk)
+CREATE TABLE chunks (
+    id              UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+    document_id     UUID REFERENCES documents(id) ON DELETE CASCADE,
+    user_id         UUID REFERENCES users(id) ON DELETE CASCADE,
+    text            TEXT NOT NULL,
+    token_count     INT,
+    source          TEXT,          -- original filename
+    page            INT,           -- page number (PDF)
+    section         TEXT,          -- heading (DOCX/MD)
+    chunk_index     INT,
+    parent_chunk_id UUID,          -- for parent-child chunking
+    text_hash       TEXT,          -- SHA-256 for incremental ingest
+    metadata        JSONB DEFAULT '{}',
+    created_at      TIMESTAMPTZ DEFAULT NOW()
+);
+
+-- Vectors (pgvector — bge-m3 dim=1024)
+CREATE TABLE chunk_vectors (
+    id          UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+    chunk_id    UUID REFERENCES chunks(id) ON DELETE CASCADE,
+    document_id UUID REFERENCES documents(id) ON DELETE CASCADE,
+    user_id     UUID REFERENCES users(id) ON DELETE CASCADE,
+    embedding   vector(1024) NOT NULL
+);
+
+-- HNSW index for fast ANN search
+CREATE INDEX ON chunk_vectors
+USING hnsw (embedding vector_cosine_ops)
+WITH (m = 16, ef_construction = 64);
+
+-- ColBERT token vectors (only populated when ColBERT technique used)
+CREATE TABLE colbert_tokens (
+    id          UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+    chunk_id    UUID REFERENCES chunks(id) ON DELETE CASCADE,
+    token_text  TEXT,
+    position    INT,
+    embedding   vector(1024) NOT NULL
+);
+
+-- Query cache (also stored in Redis, Supabase as overflow)
+CREATE TABLE query_cache (
+    id           UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+    user_id      UUID REFERENCES users(id) ON DELETE CASCADE,
+    document_id  UUID REFERENCES documents(id) ON DELETE CASCADE,
+    query_hash   TEXT NOT NULL,
+    query_text   TEXT,
+    answer       TEXT,
+    sources      JSONB,
+    technique    TEXT,
+    hit_count    INT DEFAULT 0,
+    created_at   TIMESTAMPTZ DEFAULT NOW()
+);
+
+-- RAGAs evaluation reports
+CREATE TABLE eval_reports (
+    id              UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+    user_id         UUID REFERENCES users(id) ON DELETE CASCADE,
+    document_id     UUID REFERENCES documents(id) ON DELETE CASCADE,
+    faithfulness    FLOAT,
+    answer_relevancy FLOAT,
+    context_precision FLOAT,
+    context_recall  FLOAT,
+    per_question    JSONB,
+    created_at      TIMESTAMPTZ DEFAULT NOW()
+);
+```
+
+### Supabase Vector Search Function
+
+```sql
+-- Used by all retrieval techniques
+CREATE OR REPLACE FUNCTION match_chunks(
+    query_embedding vector(1024),
+    match_document_id UUID,
+    match_user_id UUID,
+    match_count INT DEFAULT 5,
+    filter_chunk_ids UUID[] DEFAULT NULL
+)
+RETURNS TABLE (
+    chunk_id UUID,
+    text TEXT,
+    source TEXT,
+    page INT,
+    section TEXT,
+    metadata JSONB,
+    similarity FLOAT
+)
+LANGUAGE plpgsql
+AS $$
+BEGIN
+    RETURN QUERY
+    SELECT
+        c.id,
+        c.text,
+        c.source,
+        c.page,
+        c.section,
+        c.metadata,
+        1 - (cv.embedding <=> query_embedding) AS similarity
+    FROM chunk_vectors cv
+    JOIN chunks c ON c.id = cv.chunk_id
+    WHERE cv.document_id = match_document_id
+      AND cv.user_id = match_user_id
+      AND (filter_chunk_ids IS NULL OR c.id = ANY(filter_chunk_ids))
+    ORDER BY cv.embedding <=> query_embedding
+    LIMIT match_count;
+END;
+$$;
+```
+
+---
+
+## 5. Backend — FastAPI Deep Dive
+
+### `app/main.py`
+
+```python
+# Key responsibilities:
+# - FastAPI app with CORS for Netlify origin
+# - Mount all routers
+# - Startup: init Supabase client, Redis, load cross-encoder
+# - Shutdown: flush Redis pipeline
+# - WebSocket: /ws/pipeline/{job_id}?token={jwt}
+
+app = FastAPI(title="RAG Pipeline API", version="3.0.0")
+
+# CORS — Netlify + local dev
+origins = settings.CORS_ORIGINS.split(",")
+app.add_middleware(CORSMiddleware, allow_origins=origins,
+                   allow_methods=["*"], allow_headers=["*"])
+
+# Routers
+app.include_router(auth_router,      prefix="/auth")
+app.include_router(ingest_router,    prefix="/ingest")
+app.include_router(query_router,     prefix="/query")
+app.include_router(technique_router, prefix="/techniques")
+app.include_router(evaluate_router,  prefix="/evaluate")
+app.include_router(stats_router,     prefix="/stats")
+
+@app.websocket("/ws/pipeline/{job_id}")
+async def pipeline_ws(websocket, job_id, token):
+    # Verify JWT, then stream pipeline step events
+    ...
+```
+
+### `app/config.py`
+
+```python
+class Settings(BaseSettings):
+    # Supabase
+    SUPABASE_URL: str           # https://xxxx.supabase.co
+    SUPABASE_KEY: str           # anon/service_role key
+    SUPABASE_DB_URL: str        # postgresql://... (direct connection)
+
+    # Embeddings (your existing HF Space)
+    EMBED_API_URL: str = "https://lamhieu-lightweight-embeddings.hf.space/"
+    EMBED_MODEL: str = "bge-m3"
+    EMBED_DIM: int = 1024
+    EMBED_AUTH_KEY: str = ""
+    EMBED_MAX_TOKENS: int = 1000   # hard cap — 1K context
+    EMBED_TIMEOUT: int = 60
+    EMBED_MAX_RETRIES: int = 3
+
+    # LLM — Qwen3
+    QWEN3_MODEL_NAME: str = "Qwen/Qwen3-Demo"
+    QWEN3_THINKING_BUDGET: int = 38
+    LLM_RESPONSE_TIMEOUT: int = 1080
+    MAX_LLM_RETRIES: int = 5
+    MAX_TIMEOUT_RETRIES: int = 10
+
+    # OCR — Mistral
+    MISTRAL_OCR_SPACE: str = "tatendachirume/Mistral-OCR"
+    MISTRAL_API_KEY: str
+
+    # Image — Ernie Bot
+    ERNIE_SPACE_URL: str = "https://baidu-simple-ernie-bot-demo.hf.space/"
+
+    # Redis
+    REDIS_URL: str
+    CACHE_TTL_SECONDS: int = 3600
+
+    # Auth
+    JWT_SECRET_KEY: str
+    JWT_ALGORITHM: str = "HS256"
+    JWT_EXPIRE_MINUTES: int = 1440
+
+    # Re-ranking
+    RERANK_MODEL: str = "cross-encoder/ms-marco-MiniLM-L-6-v2"
+
+    # Rate limiting
+    RATE_LIMIT_PER_MINUTE: int = 20
+    RATE_LIMIT_UPLOAD_PER_DAY: int = 50
+
+    # Defaults
+    DEFAULT_CHUNK_SIZE: int = 512
+    DEFAULT_OVERLAP: int = 64
+    DEFAULT_TOP_K: int = 5
+    MAX_FILE_SIZE_MB: int = 50
+
+    # CORS
+    CORS_ORIGINS: str  # comma-separated
+```
+
+### `app/services/supabase_client.py`
+
+```python
+"""
+Central Supabase service.
+Handles: vector upsert, ANN search, chunk CRUD, metadata queries.
+Uses supabase-py client + asyncpg for direct SQL when needed.
+"""
+from supabase import create_client, Client
+
+class SupabaseService:
+    def __init__(self):
+        self.client: Client = create_client(
+            settings.SUPABASE_URL, settings.SUPABASE_KEY
+        )
+
+    # ── Chunk Operations ────────────────────────────────────────────────
+    async def insert_chunks(self, chunks: list[dict]) -> list[str]:
+        """Insert chunks, return list of chunk_ids"""
+
+    async def get_chunks_by_ids(self, chunk_ids: list[str]) -> list[dict]:
+        """Fetch chunk text + metadata by IDs"""
+
+    async def get_chunk_hashes(self, document_id: str) -> dict[str, str]:
+        """Returns {chunk_index: text_hash} for incremental ingest"""
+
+    async def delete_chunks(self, chunk_ids: list[str]):
+        """Delete chunks + their vectors (CASCADE)"""
+
+    # ── Vector Operations ───────────────────────────────────────────────
+    async def upsert_vectors(self, vectors: list[dict]):
+        """
+        vectors: [{"chunk_id": uuid, "document_id": uuid,
+                   "user_id": uuid, "embedding": [...1024 floats...]}]
+        """
+
+    async def vector_search(self, query_embedding: list[float],
+                             document_id: str, user_id: str,
+                             top_k: int, filter_chunk_ids: list = None
+                             ) -> list[dict]:
+        """
+        Calls match_chunks() SQL function.
+        Returns: [{chunk_id, text, source, page, section, metadata, similarity}]
+        """
+        result = self.client.rpc("match_chunks", {
+            "query_embedding": query_embedding,
+            "match_document_id": document_id,
+            "match_user_id": user_id,
+            "match_count": top_k,
+            "filter_chunk_ids": filter_chunk_ids
+        }).execute()
+        return result.data
+
+    # ── Metadata Filter ─────────────────────────────────────────────────
+    async def filter_chunk_ids(self, document_id: str, filters: dict) -> list[str]:
+        """
+        Filter chunks by metadata fields.
+        filters: {"page": {"gte": 5, "lte": 10}, "section": "Intro"}
+        Returns list of chunk_ids matching the filter.
+        """
+
+    # ── ColBERT Token Vectors ───────────────────────────────────────────
+    async def insert_colbert_tokens(self, token_rows: list[dict]):
+        """Store token-level vectors for ColBERT technique"""
+
+    async def get_colbert_tokens(self, document_id: str) -> list[dict]:
+        """Fetch all token vectors for MaxSim scoring"""
+
+    # ── Cache ────────────────────────────────────────────────────────────
+    async def get_cached_query(self, user_id: str,
+                                document_id: str, query_hash: str) -> dict | None:
+        """Check Supabase query_cache table (overflow from Redis)"""
+
+    async def store_cached_query(self, cache_row: dict):
+        """Store answer in query_cache table"""
+```
+
+### `app/services/embed_service.py` — Your exact code, integrated
+
+```python
+"""
+Direct port of your get_embedding_with_retry() function.
+Extended to support batch embedding for ingestion.
+1K token hard cap applied before every call.
+"""
+import tiktoken
+enc = tiktoken.get_encoding("cl100k_base")
+
+def truncate_to_1k(text: str) -> str:
+    tokens = enc.encode(text)
+    return enc.decode(tokens[:1000]) if len(tokens) > 1000 else text
+
+def get_embedding(text: str) -> list[float]:
+    """
+    Your existing get_embedding_with_retry() — unchanged.
+    Truncates to 1K tokens before calling HF Space.
+    Model: bge-m3, dim: 1024
+    """
+    text = truncate_to_1k(text)
+    # ... your exact code from get_embedding_with_retry()
+
+async def embed_batch(texts: list[str]) -> list[list[float]]:
+    """
+    Batch embedding for ingestion.
+    Processes sequentially in groups of 8 (HF Space rate limit safety).
+    Each text truncated to 1K tokens.
+    """
+    all_embeddings = []
+    for i in range(0, len(texts), 8):
+        batch = [truncate_to_1k(t) for t in texts[i:i+8]]
+        for text in batch:
+            emb = get_embedding(text)
+            all_embeddings.append(emb)
+    return all_embeddings
+```
+
+---
+
+## 6. File Processing — All Types
+
+```
+PDF        → Mistral OCR (your perform_ocr()) → text per page
+JPG/PNG/JPEG → Ernie Bot (your ernie code)    → image description text
+DOCX       → python-docx                      → paragraphs by heading
+TXT        → raw read                         → paragraph split
+MD         → regex heading split              → section chunks
+JSON       → flatten keys/values              → one text per item
+```
+
+### `app/services/file_parser.py`
+
+```python
+async def parse_file(file_path, file_type, job_id, ws_manager) -> list[dict]:
+    """
+    Returns: [{"text": str, "metadata": {"source", "page", "section"}}]
+    Emits WebSocket steps for every file type.
+    """
+    match file_type:
+        case "pdf":
+            return await parse_pdf(file_path, job_id, ws_manager)
+        case "jpg" | "jpeg" | "png":
+            return await parse_image(file_path, job_id, ws_manager)
+        case "docx":
+            return parse_docx(file_path)
+        case "txt":
+            return parse_txt(file_path)
+        case "md":
+            return parse_markdown(file_path)
+        case "json":
+            return parse_json(file_path)
+
+# PDF — uses your perform_ocr() unchanged
+async def parse_pdf(file_path, job_id, ws_manager):
+    await ws_manager.emit(job_id, step="OCR_START", color="#8B5CF6",
+        detail=f"Sending to Mistral OCR...")
+    plain_text, markdown_text, images = perform_ocr(
+        file_path, api_key=settings.MISTRAL_API_KEY)
+    await ws_manager.emit(job_id, step="OCR_DONE", color="#22C55E",
+        detail=f"OCR complete: {len(plain_text)} chars")
+    return split_to_pages(plain_text, markdown_text, str(file_path))
+
+# Image — uses your Ernie Bot code unchanged
+async def parse_image(file_path, job_id, ws_manager):
+    await ws_manager.emit(job_id, step="IMAGE_ANALYZE", color="#8B5CF6",
+        detail="Ernie Bot analyzing image...")
+    description = understand_image(file_path)
+    return [{"text": description, "metadata": {"source": str(file_path), "page": 1}}]
+
+# DOCX — python-docx, split by headings
+def parse_docx(file_path):
+    doc = Document(file_path)
+    sections, current_heading, current_text = [], "", []
+    for para in doc.paragraphs:
+        if para.style.name.startswith('Heading'):
+            if current_text:
+                sections.append({"text": " ".join(current_text),
+                                  "metadata": {"source": str(file_path),
+                                               "section": current_heading}})
+            current_heading, current_text = para.text, []
+        elif para.text.strip():
+            current_text.append(para.text)
+    if current_text:
+        sections.append({"text": " ".join(current_text),
+                         "metadata": {"source": str(file_path),
+                                      "section": current_heading}})
+    return sections
+
+# MD — split at headings
+def parse_markdown(file_path):
+    text = Path(file_path).read_text(encoding="utf-8")
+    parts = re.split(r'\n(?=#+\s)', text)
+    return [{"text": p.strip(), "metadata": {"source": str(file_path),
+             "section": re.match(r'^#+\s+(.*)', p).group(1) if re.match(r'^#+\s', p) else ""}}
+            for p in parts if p.strip()]
+
+# TXT — paragraph split
+def parse_txt(file_path):
+    text = Path(file_path).read_text(encoding="utf-8")
+    paragraphs = [p.strip() for p in text.split("\n\n") if p.strip()]
+    return [{"text": p, "metadata": {"source": str(file_path)}} for p in paragraphs]
+
+# JSON — flatten per item
+def parse_json(file_path):
+    data = json.loads(Path(file_path).read_text())
+    items = data if isinstance(data, list) else [data]
+    docs = []
+    for item in items:
+        def flatten(obj, prefix=""):
+            parts = []
+            for k, v in obj.items() if isinstance(obj, dict) else enumerate(obj):
+                full_key = f"{prefix}.{k}" if prefix else str(k)
+                if isinstance(v, (dict, list)):
+                    parts.extend(flatten(v, full_key))
+                else:
+                    parts.append(f"{full_key}: {v}")
+            return parts
+        text = " | ".join(flatten(item))
+        docs.append({"text": text, "metadata": {"source": str(file_path),
+                     "original": item}})
+    return docs
+```
+
+---
+
+## 7. Chunking Engine — 6 Strategies
+
+```python
+"""
+All strategies hard-cap at 1K tokens per chunk.
+bge-m3 recommended context: up to 8192, but we cap at 1K for speed/cost.
+"""
+MAX_CHUNK_TOKENS = 1000
+
+class ChunkEngine:
+    def __init__(self, chunk_size: int, overlap: int, strategy: str):
+        self.chunk_size = min(chunk_size, MAX_CHUNK_TOKENS)
+        self.overlap = min(overlap, self.chunk_size // 4)
+        self.strategy = strategy
+        self.enc = tiktoken.get_encoding("cl100k_base")
+
+    def chunk(self, docs: list[dict]) -> list[dict]:
+        # Each output chunk:
+        # {chunk_id, text, token_count, source, page, section,
+        #  chunk_index, parent_chunk_id, text_hash, metadata}
+        match self.strategy:
+            case "fixed":        return self._fixed(docs)
+            case "semantic":     return self._semantic(docs)
+            case "per_page":     return self._per_page(docs)
+            case "per_item":     return self._per_item(docs)
+            case "recursive":    return self._recursive(docs)
+            case "parent_child": return self._parent_child(docs)
+
+    def _fixed(self, docs):
+        """Sliding window: step = chunk_size - overlap. Token-accurate."""
+
+    def _semantic(self, docs):
+        """Use heading sections as natural boundaries. Fixed fallback if too large."""
+
+    def _per_page(self, docs):
+        """One chunk per PDF page. Fixed fallback for long pages."""
+
+    def _per_item(self, docs):
+        """One chunk per JSON item (parser already splits)."""
+
+    def _recursive(self, docs):
+        """Split at: \\n\\n → \\n → '. ' → ' ' until fits in chunk_size."""
+
+    def _parent_child(self, docs):
+        """
+        child:  chunk_size // 4 tokens → stored in Supabase, used for retrieval
+        parent: chunk_size tokens     → stored in Supabase, sent to LLM
+        child.parent_chunk_id → parent.id
+        """
+```
+
+---
+
+## 8. Embedding Service
+
+```python
+# app/services/embed_service.py
+# Your exact get_embedding_with_retry() function — zero changes
+# Calling convention matches your existing code:
+#
+# get_embedding_with_retry(
+#     text=text,
+#     model="bge-m3",
+#     auth_key=settings.EMBED_AUTH_KEY,
+#     max_retries=settings.EMBED_MAX_RETRIES,
+#     timeout_seconds=settings.EMBED_TIMEOUT
+# )
+#
+# Returns: {"data": [[...1024 floats...]], "usage": {...}}
+# We extract: result["data"][0]
+#
+# 1K token truncation applied BEFORE calling — see truncate_to_1k()
+```
+
+---
+
+## 9. All 8 RAG Techniques
+
+### Base class
+
+```python
+# app/techniques/base.py
+class BaseRAGTechnique(ABC):
+    def __init__(self, supabase, embed_svc, llm_svc, redis, job_id, ws_manager):
+        ...
+
+    @abstractmethod
+    async def retrieve(self, query, document_id, user_id, top_k, **kwargs) -> list[dict]:
+        ...
+
+    @abstractmethod
+    async def generate(self, query, chunks) -> str:
+        ...
+
+    async def run(self, request: QueryRequest) -> QueryResponse:
+        chunks = await self.retrieve(...)
+        answer = await self.generate(...)
+        return QueryResponse(...)
+
+    async def emit(self, step, status, color, detail, metadata={}):
+        """Broadcast step event to frontend via WebSocket"""
+        await ws_manager.emit(self.job_id, {
+            "step": step, "status": status,
+            "color": color, "detail": detail,
+            "timestamp": datetime.utcnow().isoformat(),
+            "metadata": metadata
+        })
+```
+
+---
+
+### Technique 1 — Hybrid Search
+
+```python
+# Algorithm: BM25 keyword + pgvector ANN → Reciprocal Rank Fusion (k=60)
+# BM25 index built from chunk texts at ingest time, stored as pickle on Render disk
+
+# Steps emitted:
+#  🟣 EMBED      "Embedding query (bge-m3)..."
+#  🟢 BM25       "BM25 keyword search → {n} candidates"
+#  🟢 VECTOR     "pgvector ANN search → top-{n}"
+#  🟣 RRF        "Reciprocal Rank Fusion merging results..."
+#  🟢 DONE       "Hybrid search → top-{k} returned"
+
+async def retrieve(self, query, document_id, user_id, top_k, bm25_weight=0.5):
+    q_vec = get_embedding(truncate_to_1k(query))
+    bm25_results = bm25_service.search(document_id, query, top_n=top_k * 4)
+    vector_results = await supabase.vector_search(q_vec, document_id, user_id, top_k * 4)
+    fused = reciprocal_rank_fusion(bm25_results, vector_results, k=60)
+    return fused[:top_k]
+```
+
+---
+
+### Technique 2 — Re-ranking
+
+```python
+# Algorithm: pgvector top-20 → cross-encoder/ms-marco-MiniLM-L-6-v2 → top-K
+# Cross-encoder runs on Render CPU. ~3-8s for 20 pairs. Model cached after first load.
+
+# Steps emitted:
+#  🟣 EMBED      "Embedding query..."
+#  🟢 RETRIEVE   "pgvector: fetching top-20 candidates..."
+#  🔴 RERANK     "Cross-encoder re-scoring 20 pairs..."
+#  🟢 DONE       "Re-ranked → top-{k}"
+
+async def retrieve(self, query, document_id, user_id, top_k):
+    q_vec = get_embedding(truncate_to_1k(query))
+    candidates = await supabase.vector_search(q_vec, document_id, user_id, top_k * 4)
+    pairs = [(query, c["text"]) for c in candidates]
+    scores = cross_encoder.predict(pairs)
+    reranked = sorted(zip(candidates, scores), key=lambda x: x[1], reverse=True)
+    return [c for c, _ in reranked[:top_k]]
+```
+
+---
+
+### Technique 3 — Query Expansion (HyDE)
+
+```python
+# Algorithm:
+#  1. Qwen3 generates hypothetical answer → embed it (HyDE)
+#  2. Qwen3 generates 3 query variants → embed each
+#  3. FAISS search with all 4 vectors, deduplicate, rank
+
+# Steps emitted:
+#  🟣 HYDE       "Qwen3 generating hypothetical answer..."
+#  🟣 EXPAND     "Generating 3 query variants..."
+#  🟢 EMBED      "Embedding 4 expanded queries..."
+#  🟢 SEARCH     "pgvector search with all variants..."
+#  🟣 MERGE      "Deduplicating {n} results..."
+#  🟢 DONE       "Query expansion → top-{k}"
+```
+
+---
+
+### Technique 4 — Metadata Filtering
+
+```python
+# Algorithm:
+#  1. User sets filters (page range, section, source file, custom JSON fields)
+#  2. Supabase SQL pre-filters chunk IDs
+#  3. pgvector search restricted to those IDs
+
+# Supported filters:
+#  page: {gte: 5, lte: 10}
+#  section: "Introduction"
+#  source: "contract.docx"
+#  file_type: "pdf"
+#  metadata->>'custom_key': "value"   (JSONB field)
+
+# Steps emitted:
+#  🟤 FILTER     "SQL filter: {filters} → {n} qualifying chunks"
+#  🟣 EMBED      "Embedding query..."
+#  🟢 SEARCH     "pgvector search in filtered subset..."
+#  🟢 DONE       "Metadata-filtered → top-{k}"
+```
+
+---
+
+### Technique 5 — ColBERT (Multi-vector MaxSim)
+
+```python
+# Algorithm:
+#  INGEST: each chunk → tokenize → embed each token → store in colbert_tokens table
+#  QUERY:  tokenize query → embed each token → MaxSim scoring
+#  MaxSim(q,d) = Σ max_j(q_i · d_j) for each query token i
+
+# ⚠️ WARNING shown in UI before selecting:
+# "ColBERT embeds every token individually. For a 50-chunk doc,
+#  expect 500-5000 extra embedding calls. Ingestion will be slow."
+
+# Steps emitted:
+#  🟣 TOKENIZE   "Tokenizing query into {n} tokens..."
+#  🟢 EMBED_TOK  "Embedding {n} query tokens (bge-m3)..."
+#  🔴 MAXSIM     "MaxSim scoring {n_chunks} × {n_tokens} token vectors..."
+#  🟢 DONE       "ColBERT scoring → top-{k}"
+```
+
+---
+
+### Technique 6 — Agentic RAG
+
+```python
+# Algorithm: Qwen3 agent with 4 tools, max 5 iterations
+# Tools:
+#  search_docs(query, top_k)        → pgvector search
+#  filter_search(filters, query)    → metadata-filtered search
+#  get_page(page_num)               → retrieve specific page
+#  summarize_chunks(chunk_ids)      → Qwen3 summarizes chunk set
+
+# Uses your existing Qwen3 wrapper (llm_service.py)
+# Tool call JSON parsed with your extract_json_block() + repair_json_with_module()
+
+# Steps emitted (one per agent iteration):
+#  🟢 AGENT_INIT "Qwen3 agent ready with 4 tools"
+#  🟣 PLAN       "Agent: '{thought[:80]}...'"
+#  🟤 TOOL       "Tool call: {tool_name}({args})"
+#  🟢 OBSERVE    "Tool returned {n} chunks"
+#  🟢 FINAL      "Answer generated after {n} tool calls"
+```
+
+---
+
+### Technique 7 — Caching & Incremental Ingestion
+
+```python
+# SUB-FEATURE A — Redis Query Cache:
+#  key = SHA-256(user_id + document_id + query + technique)
+#  hit  → return stored QueryResponse instantly
+#  miss → run pipeline → store in Redis (TTL: 1hr) + Supabase overflow
+#
+# SUB-FEATURE B — Incremental Ingestion:
+#  On re-upload: hash each chunk text
+#  Compare vs stored hashes in Supabase chunks table
+#  NEW chunks     → embed + insert to Supabase
+#  CHANGED chunks → delete old vectors, re-embed, insert new
+#  UNCHANGED      → skip entirely (0 embedding calls)
+#  DELETED chunks → remove from Supabase (CASCADE deletes vectors)
+#  Saves 80-95% of embedding calls on document updates
+
+# Steps emitted:
+#  🟤 CACHE_CHECK "Checking Redis cache..."
+#  🟢 CACHE_HIT   "Cache hit — returning stored answer (0ms)" OR
+#  🟣 CACHE_MISS  "Cache miss. Running pipeline..."
+#  ──── Incremental ────
+#  🟤 DIFF        "Comparing {n} new chunks vs {m} stored..."
+#  🟢 DELTA       "{new} new, {changed} changed, {same} unchanged"
+#  🟣 EMBED_DELTA "Embedding {n} delta chunks only..."
+#  🟢 DONE        "Incremental update complete"
+```
+
+---
+
+### Technique 8 — RAGAs Evaluation
+
+```python
+# User uploads CSV: question,ground_truth
+# For each question:
+#   1. Retrieve top-K chunks (standard vector search)
+#   2. Generate answer via Qwen3
+#   3. Collect dataset: (question, answer, contexts, ground_truth)
+# RAGAs metrics (Qwen3 as judge):
+#   faithfulness, answer_relevancy, context_precision, context_recall
+# Results saved to Supabase eval_reports table
+# Frontend shows Recharts radar chart + per-question table
+
+# Steps emitted:
+#  🟣 SETUP      "RAGAs initialized — {n} test questions"
+#  🟢 RETRIEVE   "Retrieving context for Q{i}/{n}..."
+#  🟣 GENERATE   "Qwen3 generating answer {i}/{n}..."
+#  🔴 SCORE      "Computing RAGAs metrics (Qwen3 as judge)..."
+#  🟢 REPORT     "Faithfulness:{f:.2f} Relevancy:{r:.2f} ..."
+```
+
+---
+
+## 10. Multi-User Architecture
+
+### User Isolation
+
+```
+Supabase row-level security (RLS) policies:
+  All tables have user_id column
+  RLS enabled: users can only see their own rows
+  Enforced at DB level — even if API has a bug, data stays isolated
+
+FAISS → replaced by Supabase pgvector → isolation via user_id column
+BM25 index files → ./data/bm25_indexes/{user_id}_{doc_id}.pkl
+Upload temp files → ./data/uploads/{user_id}/{filename}
+Redis cache keys → cache:{user_id}:{doc_id}:{query_hash}
+```
+
+### JWT Auth Flow
+
+```
+POST /auth/register  →  username + password → bcrypt hash → Supabase users table
+POST /auth/login     →  verify password → return JWT (24h expiry)
+All protected routes → Authorization: Bearer {token}
+Frontend → JWT stored in Zustand memory (NOT localStorage — XSS safe)
+POST /auth/refresh   → return new JWT before expiry
+```
+
+### WebSocket Isolation
+
+```python
+# app/utils/ws_manager.py
+# One WebSocket connection per (user_id, job_id)
+# Job ownership verified before connecting
+# Users only receive their own pipeline events
+
+class WSManager:
+    _connections: dict[str, WebSocket] = {}  # key = f"{user_id}:{job_id}"
+
+    async def connect(self, job_id, websocket, user_id):
+        key = f"{user_id}:{job_id}"
+        self._connections[key] = websocket
+
+    async def emit(self, job_id, user_id, event: dict):
+        key = f"{user_id}:{job_id}"
+        ws = self._connections.get(key)
+        if ws:
+            await ws.send_json(event)
+```
+
+---
+
+## 11. API Endpoints
+
+### Auth
+| Method | Endpoint | Description |
+|--------|----------|-------------|
+| POST | `/auth/register` | Create account |
+| POST | `/auth/login` | Get JWT |
+| POST | `/auth/refresh` | Refresh JWT |
+
+### Ingestion
+| Method | Endpoint | Description |
+|--------|----------|-------------|
+| POST | `/ingest/upload` | Upload file → background job |
+| GET | `/ingest/status/{job_id}` | Job status + pipeline steps |
+| GET | `/ingest/documents` | User's document list |
+| DELETE | `/ingest/document/{doc_id}` | Delete doc + vectors |
+| POST | `/ingest/reindex/{doc_id}` | Incremental re-ingest |
+
+### Query
+| Method | Endpoint | Description |
+|--------|----------|-------------|
+| POST | `/query/search` | RAG query with technique |
+| GET | `/query/history/{doc_id}` | Query history |
+| DELETE | `/query/cache/{doc_id}` | Clear Redis cache |
+
+### Evaluate
+| Method | Endpoint | Description |
+|--------|----------|-------------|
+| POST | `/evaluate/run` | Run RAGAs (CSV upload) |
+| GET | `/evaluate/report/{doc_id}` | Latest report |
+
+### Stats & Health
+| Method | Endpoint | Description |
+|--------|----------|-------------|
+| GET | `/stats/documents` | Docs with chunk counts |
+| GET | `/health` | Backend + Supabase + Redis status |
+
+### WebSocket
+| Endpoint | Description |
+|----------|-------------|
+| `WS /ws/pipeline/{job_id}?token={jwt}` | Real-time pipeline steps |
+
+---
+
+## 12. Frontend — React Deep Dive
+
+### Tailwind Green + Violet Theme
+
+```js
+// tailwind.config.js
+module.exports = {
+  theme: {
+    extend: {
+      colors: {
+        primary: {   // Green
+          50: '#f0fdf4', 400: '#4ade80',
+          500: '#22c55e', 600: '#16a34a', 700: '#15803d'
+        },
+        accent: {    // Violet
+          50: '#f5f3ff', 400: '#a78bfa',
+          500: '#8b5cf6', 600: '#7c3aed', 700: '#6d28d9'
+        },
+        surface: {   // Dark base for dashboard
+          900: '#0a0f0a', 800: '#111a11', 700: '#1a2b1a'
+        }
+      },
+      boxShadow: {
+        'glow-green':  '0 0 20px rgba(34,197,94,0.25)',
+        'glow-violet': '0 0 20px rgba(139,92,246,0.25)',
+      }
+    }
+  }
+}
+```
+
+### Step Color Mapping
+
+```js
+// src/utils/stepColors.js
+export const STEP_COLORS = {
+  // Violet — LLM / AI ops
+  EMBED: '#8B5CF6', HYDE: '#8B5CF6', EXPAND: '#7C3AED',
+  PLAN: '#8B5CF6', GENERATE: '#7C3AED', SCORE: '#6D28D9',
+  CACHE_MISS: '#8B5CF6', EMBED_DELTA: '#8B5CF6',
+  SETUP: '#8B5CF6', EMBED_TOK: '#8B5CF6', TOKENIZE: '#7C3AED',
+  OCR_START: '#8B5CF6', IMAGE_ANALYZE: '#8B5CF6',
+
+  // Green — retrieval / data ops
+  BM25: '#22C55E', VECTOR: '#16A34A', DONE: '#22C55E',
+  CACHE_HIT: '#22C55E', RETRIEVE: '#16A34A', OBSERVE: '#22C55E',
+  DELTA: '#22C55E', AGENT_INIT: '#22C55E', OCR_DONE: '#22C55E',
+  FINAL: '#22C55E', REPORT: '#22C55E',
+
+  // Special
+  RERANK: '#EF4444',  // red — heavy compute, distinct
+  MAXSIM: '#EF4444',  // red — heavy compute
+  FILTER: '#D97706',  // amber — metadata ops
+  TOOL:   '#D97706',  // amber — tool calls
+  DIFF:   '#6B7280',  // gray — neutral checks
+  CACHE_CHECK: '#6B7280',
+  RRF:    '#8B5CF6',  // violet — fusion
+  ERROR:  '#EF4444',  // red
+}
+```
+
+### Pipeline Page UI Layout
+
+```
+┌─────────────────────────────────────────────────────────────┐
+│ 🟢 RAG Pipeline                      [user ▾]  [Logout]    │
+├─────────────────────────────────────────────────────────────┤
+│  📄 document.pdf   142 chunks   ✅ Indexed                  │
+├───────────────────────────┬─────────────────────────────────┤
+│  SELECT TECHNIQUE         │  CHUNKING CONFIG                │
+│  ┌────────┐  ┌────────┐  │  Chunk  ──────●────── 512 tok  │
+│  │Hybrid  │  │ReRank  │  │  Overlap ───●──────── 64 tok   │
+│  └────────┘  └────────┘  │  Strategy [Fixed ▾]            │
+│  ┌────────┐  ┌────────┐  │  Est. chunks: ~148              │
+│  │ HyDE  │  │ Meta   │  │  [Apply & Re-chunk]             │
+│  └────────┘  └────────┘  │                                 │
+│  ┌────────┐  ┌────────┐  │                                 │
+│  │ColBERT │  │Agentic │  │                                 │
+│  └────────┘  └────────┘  │                                 │
+│  ┌────────┐  ┌────────┐  │                                 │
+│  │ Cache  │  │ RAGAs  │  │                                 │
+│  └────────┘  └────────┘  │                                 │
+├───────────────────────────┴─────────────────────────────────┤
+│  QUERY                                                       │
+│  ┌──────────────────────────────────────┐  [🔍 Search]     │
+│  └──────────────────────────────────────┘                   │
+├─────────────────────────────────────────────────────────────┤
+│  PIPELINE TRACE  ● LIVE                                      │
+│  🟣 EMBED     Embedding query (bge-m3)...          ✅ 2.1s │
+│  🟢 BM25      Keyword search → 22 candidates       ✅ 0.1s │
+│  🟢 VECTOR    pgvector ANN → top-20                ✅ 0.3s │
+│  🟣 RRF       Reciprocal Rank Fusion...             ⏳      │
+├─────────────────────────────────────────────────────────────┤
+│  ANSWER                                                      │
+│  The contract was signed on April 3rd, 2024...              │
+│  SOURCES                                                     │
+│  📄 contract.docx §3   Score: 0.94  ██████████ 94%         │
+│  📄 contract.docx §1   Score: 0.81  ████████── 81%         │
+└─────────────────────────────────────────────────────────────┘
+```
+
+### Key React Hooks
+
+```js
+// usePipelineWS.js — WebSocket for real-time steps
+// Connects to: wss://{backend}/ws/pipeline/{job_id}?token={jwt}
+// Each message → add to pipelineStore.steps
+// Auto-reconnects (max 3 attempts)
+// Shows "LIVE" green dot while connected
+
+// useUpload.js — Upload + job polling
+// POST /ingest/upload (multipart form)
+// Polls /ingest/status/{job_id} every 2s until done/failed
+
+// useQuery.js — RAG search
+// POST /query/search → streams answer via WebSocket
+// Updates answerPanel + sourceChunks + pipeline steps simultaneously
+```
+
+---
+
+## 13. Docker Setup
+
+### `backend/Dockerfile`
+
+```dockerfile
+FROM python:3.11-slim
+
+WORKDIR /app
+
+# System deps for python-docx, tiktoken
+RUN apt-get update && apt-get install -y \
+    build-essential libpq-dev && \
+    rm -rf /var/lib/apt/lists/*
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Download cross-encoder model at build time (not runtime)
+RUN python -c "from sentence_transformers import CrossEncoder; \
+    CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')"
+
+COPY . .
+
+# Create data dirs
+RUN mkdir -p data/uploads data/bm25_indexes data/cache
+
+EXPOSE 8000
+
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", \
+     "--port", "8000", "--workers", "2"]
+```
+
+### `docker-compose.yml` — Local Dev
+
+```yaml
+version: "3.9"
+
+services:
+  backend:
+    build: ./backend
+    ports:
+      - "8000:8000"
+    environment:
+      - SUPABASE_URL=${SUPABASE_URL}
+      - SUPABASE_KEY=${SUPABASE_KEY}
+      - REDIS_URL=redis://redis:6379
+      - JWT_SECRET_KEY=${JWT_SECRET_KEY}
+      - MISTRAL_API_KEY=${MISTRAL_API_KEY}
+      # ... other env vars from .env
+    env_file:
+      - ./backend/.env
+    volumes:
+      - ./backend/data:/app/data       # BM25 indexes + uploads persist locally
+    depends_on:
+      - redis
+    restart: unless-stopped
+
+  redis:
+    image: redis:7-alpine
+    ports:
+      - "6379:6379"
+    restart: unless-stopped
+
+  # Optional: local frontend dev server
+  frontend:
+    build:
+      context: ./frontend
+      dockerfile: Dockerfile.dev
+    ports:
+      - "5173:5173"
+    environment:
+      - VITE_API_BASE_URL=http://localhost:8000
+      - VITE_WS_BASE_URL=ws://localhost:8000
+    volumes:
+      - ./frontend/src:/app/src        # hot reload
+    restart: unless-stopped
+```
+
+### `backend/requirements.txt`
+
+```txt
+fastapi==0.111.0
+uvicorn[standard]==0.30.0
+gunicorn==22.0.0
+pydantic-settings==2.3.0
+supabase==2.5.0
+asyncpg==0.29.0
+redis==5.0.6
+python-jose[cryptography]==3.3.0
+passlib[bcrypt]==1.7.4
+python-multipart==0.0.9
+httpx==0.27.0
+gradio_client==0.17.0
+tiktoken==0.7.0
+python-docx==1.1.2
+rank_bm25==0.2.2
+sentence-transformers==3.0.1
+ragas==0.1.14
+json_repair==0.25.2
+loguru==0.7.2
+slowapi==0.1.9
+```
+
+---
+
+## 14. Environment Variables
+
+> All values go into **Render Dashboard → Environment tab**.  
+> Never committed to Git. `.env.example` has key names only.
+
+### Render Backend
+
+```env
+# Supabase
+SUPABASE_URL              = https://xxxx.supabase.co
+SUPABASE_KEY              = your_service_role_key
+SUPABASE_DB_URL           = postgresql://postgres:pass@db.xxxx.supabase.co:5432/postgres
+
+# Embeddings (HF Space — free)
+EMBED_API_URL             = https://lamhieu-lightweight-embeddings.hf.space/
+EMBED_MODEL               = bge-m3
+EMBED_DIM                 = 1024
+EMBED_AUTH_KEY            =
+EMBED_MAX_TOKENS          = 1000
+EMBED_TIMEOUT             = 60
+EMBED_MAX_RETRIES         = 3
+
+# LLM — Qwen3 (HF Space — free)
+QWEN3_MODEL_NAME          = Qwen/Qwen3-Demo
+QWEN3_THINKING_BUDGET     = 38
+LLM_RESPONSE_TIMEOUT      = 1080
+MAX_LLM_RETRIES           = 5
+MAX_TIMEOUT_RETRIES       = 10
+
+# OCR — Mistral (needs API key)
+MISTRAL_OCR_SPACE         = tatendachirume/Mistral-OCR
+MISTRAL_API_KEY           = your_mistral_api_key
+
+# Image — Ernie Bot (free HF Space)
+ERNIE_SPACE_URL           = https://baidu-simple-ernie-bot-demo.hf.space/
+
+# Redis (auto-filled by Render when Redis added)
+REDIS_URL                 = redis://...
+CACHE_TTL_SECONDS         = 3600
+
+# Auth
+JWT_SECRET_KEY            = generate_with: openssl rand -hex 32
+JWT_ALGORITHM             = HS256
+JWT_EXPIRE_MINUTES        = 1440
+
+# Re-ranking
+RERANK_MODEL              = cross-encoder/ms-marco-MiniLM-L-6-v2
+
+# Limits
+RATE_LIMIT_PER_MINUTE     = 20
+RATE_LIMIT_UPLOAD_PER_DAY = 50
+MAX_FILE_SIZE_MB          = 50
+
+# Defaults
+DEFAULT_CHUNK_SIZE        = 512
+DEFAULT_OVERLAP           = 64
+DEFAULT_TOP_K             = 5
+
+# CORS
+CORS_ORIGINS              = https://your-app.netlify.app,http://localhost:5173
+```
+
+### Netlify Frontend
+
+```env
+VITE_API_BASE_URL = https://your-backend.onrender.com
+VITE_WS_BASE_URL  = wss://your-backend.onrender.com
+```
+
+### Local Dev (`backend/.env`)
+
+```env
+# Same as Render vars above +
+REDIS_URL = redis://localhost:6379
+CORS_ORIGINS = http://localhost:5173
+```
+
+---
+
+## 15. Deployment Guide
+
+### Step 1 — Supabase Setup (10 min)
+
+```
+1. supabase.com → New project (free)
+2. Settings → Database → Copy connection string → SUPABASE_DB_URL
+3. Settings → API → Copy URL + service_role key
+4. SQL Editor → run the schema SQL from Section 4
+5. SQL Editor → run the match_chunks() function SQL from Section 4
+6. Authentication → Disable (we handle auth ourselves with JWT)
+7. Table Editor → Enable RLS on all tables
+```
+
+### Step 2 — Render Backend (15 min)
+
+```
+1. render.com → New Web Service → Connect GitHub → select backend/
+2. Runtime: Python / Docker (choose Docker — uses our Dockerfile)
+3. Build command: (auto from Dockerfile)
+4. Start command: (auto from Dockerfile CMD)
+5. Add Redis: New → Redis → Free tier → auto-links REDIS_URL
+6. Environment tab: add all vars from Section 14
+7. Deploy → wait ~5 min
+8. Test: curl https://your-app.onrender.com/health
+```
+
+### Step 3 — Netlify Frontend (5 min)
+
+```
+1. netlify.com → New site → Import from GitHub → select frontend/
+2. Build command: npm run build
+3. Publish dir: dist
+4. Environment vars: VITE_API_BASE_URL, VITE_WS_BASE_URL
+5. Deploy
+6. Copy Netlify URL → update CORS_ORIGINS in Render env
+```
+
+### Step 4 — Local Dev
+
+```bash
+# Clone repo
+git clone https://github.com/you/rag-pipeline.git
+cd rag-pipeline
+
+# Copy env files
+cp backend/.env.example backend/.env
+# Fill in values
+
+# Start with Docker Compose
+docker-compose up --build
+
+# Frontend available: http://localhost:5173
+# Backend available:  http://localhost:8000
+# Redis:              localhost:6379
+```
+
+---
+
+## 16. Production Additions
+
+Items added beyond what you mentioned — all included in this blueprint:
+
+| # | Item | Why |
+|---|------|-----|
+| 1 | JWT auth + multi-user | You said multi-user needed |
+| 2 | Supabase Row Level Security | Data isolation at DB level |
+| 3 | Rate limiting (slowapi) | Prevent abuse on free Render tier |
+| 4 | Docker + docker-compose | Local dev, portfolio quality, Render deployment |
+| 5 | Cross-encoder model pre-downloaded in Dockerfile | Avoid cold download on first query |
+| 6 | File cleanup after ingestion | Prevent disk fill on Render |
+| 7 | JWT in Zustand memory (not localStorage) | XSS attack prevention |
+| 8 | WebSocket user isolation | Multi-user safety |
+| 9 | ColBERT warning dialog | Prevent accidental slow ingestion |
+| 10 | /health endpoint | Shows Supabase + Redis status to frontend |
+| 11 | BM25 pickle persisted on Render disk | Hybrid search needs it across restarts |
+| 12 | Render cold start note | Free tier sleeps after 15 min — warn interviewer |
+
+### ⚠️ One Render Free Tier Limitation
+
+Render free tier **sleeps after 15 minutes of inactivity**. First request takes 30-60 seconds to wake up. For an interview demo, either:
+- Upgrade to Starter ($7/mo) — keeps it warm
+- OR ping `/health` from frontend every 5 min to prevent sleep
+- OR just open the app 2 min before the interview
+
+---
+
+> **Next step:** Confirm this blueprint and tell me which module to code first.  
+> Recommended order: `backend/` → `local-bridge removed` → `frontend/`  
+> Say **"start backend"** and I will generate every file.
diff --git a/RAG_FULL_APPLICATION_BACKEND/fix_rls.sql b/RAG_FULL_APPLICATION_BACKEND/fix_rls.sql
new file mode 100644
index 0000000000000000000000000000000000000000..12e4785bda0aab0df43a01a49c578c7accc160f3
--- /dev/null
+++ b/RAG_FULL_APPLICATION_BACKEND/fix_rls.sql
@@ -0,0 +1,9 @@
+-- Disable RLS for local testing demo
+ALTER TABLE users DISABLE ROW LEVEL SECURITY;
+ALTER TABLE documents DISABLE ROW LEVEL SECURITY;
+ALTER TABLE chunks DISABLE ROW LEVEL SECURITY;
+ALTER TABLE chunk_vectors DISABLE ROW LEVEL SECURITY;
+ALTER TABLE colbert_tokens DISABLE ROW LEVEL SECURITY;
+
+-- Ensure the 'admin' user is seeded if we can
+-- (The backend seed_admin will do this once RLS is off)
diff --git a/RAG_FULL_APPLICATION_BACKEND/init_db.sql b/RAG_FULL_APPLICATION_BACKEND/init_db.sql
new file mode 100644
index 0000000000000000000000000000000000000000..c0ac158f91eae418ab10e25b01b5d14a44e87fe8
--- /dev/null
+++ b/RAG_FULL_APPLICATION_BACKEND/init_db.sql
@@ -0,0 +1,107 @@
+-- 1. Enable Extension
+create extension if not exists vector;
+
+-- 2. Users Table
+create table if not exists users (
+    id uuid primary key default gen_random_uuid(),
+    username text unique not null,
+    password_hash text not null,
+    created_at timestamp with time zone default timezone('utc'::text, now())
+);
+
+-- 3. Documents Table (Owner metadata)
+create table if not exists documents (
+    id uuid primary key default gen_random_uuid(),
+    user_id uuid references users(id) on delete cascade,
+    filename text not null,
+    file_type text not null,
+    chunk_strategy text default 'fixed',
+    chunk_size int default 512,
+    overlap int default 64,
+    chunk_count int default 0,
+    technique text default 'hybrid',
+    status text default 'pending', -- pending, running, done, failed
+    created_at timestamp with time zone default timezone('utc'::text, now())
+);
+
+-- 4. Chunks Table (Text + Metadata)
+create table if not exists chunks (
+    id uuid primary key default gen_random_uuid(),
+    document_id uuid references documents(id) on delete cascade,
+    user_id uuid references users(id) on delete cascade,
+    text text not null,
+    token_count int,
+    page int,
+    section text,
+    chunk_index int,
+    parent_chunk_id uuid, -- For parent-child technique
+    text_hash text,       -- For incremental ingestion
+    metadata jsonb,       -- For generic filtering
+    created_at timestamp with time zone default timezone('utc'::text, now())
+);
+
+-- 5. Vectors Table
+create table if not exists chunk_vectors (
+    id uuid primary key default gen_random_uuid(),
+    chunk_id uuid references chunks(id) on delete cascade,
+    document_id uuid references documents(id) on delete cascade,
+    user_id uuid references users(id) on delete cascade,
+    embedding vector(1024), -- bge-m3 dimension
+    created_at timestamp with time zone default timezone('utc'::text, now())
+);
+
+-- 6. ColBERT Token Vectors Table
+create table if not exists colbert_tokens (
+    id uuid primary key default gen_random_uuid(),
+    chunk_id uuid references chunks(id) on delete cascade,
+    document_id uuid references documents(id) on delete cascade,
+    token_text text,
+    token_index int,
+    embedding vector(1024),
+    created_at timestamp with time zone default timezone('utc'::text, now())
+);
+
+-- 7. Hybrid Search / Vector Similarity Function
+create or replace function match_chunks (
+  query_embedding vector(1024),
+  match_document_id uuid,
+  match_user_id uuid,
+  match_count int,
+  filter_chunk_ids uuid[] default null
+)
+returns table (
+  id uuid,
+  text text,
+  source text,
+  page int,
+  section text,
+  metadata jsonb,
+  similarity float
+)
+language plpgsql
+as $$
+begin
+  return query
+  select
+    c.id,
+    c.text,
+    d.filename as source,
+    c.page,
+    c.section,
+    c.metadata,
+    1 - (cv.embedding <=> query_embedding) as similarity
+  from chunk_vectors cv
+  join chunks c on cv.chunk_id = c.id
+  join documents d on c.document_id = d.id
+  where c.document_id = match_document_id
+    and c.user_id = match_user_id
+    and (filter_chunk_ids is null or c.id = any(filter_chunk_ids))
+  order by cv.embedding <=> query_embedding
+  limit match_count;
+end;
+$$;
+
+-- 8. Indexes for Metadata Filtering
+create index if not exists idx_chunks_metadata on chunks using gin (metadata);
+create index if not exists idx_chunks_user_doc on chunks (user_id, document_id);
+create index if not exists idx_vectors_doc on chunk_vectors (document_id);
diff --git a/RAG_FULL_APPLICATION_BACKEND/requirements.txt b/RAG_FULL_APPLICATION_BACKEND/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..efdf2c8701fe4e39532806fae19bbd5a0f50904a
--- /dev/null
+++ b/RAG_FULL_APPLICATION_BACKEND/requirements.txt
@@ -0,0 +1,20 @@
+fastapi==0.111.0
+uvicorn[standard]==0.30.0
+gunicorn==22.0.0
+pydantic-settings==2.3.0
+supabase==2.5.0
+asyncpg==0.29.0
+redis==5.0.6
+python-jose[cryptography]==3.3.0
+passlib[bcrypt]==1.7.4
+python-multipart==0.0.9
+httpx==0.27.0
+gradio_client==0.17.0
+tiktoken==0.7.0
+python-docx==1.1.2
+rank_bm25==0.2.2
+sentence-transformers==3.0.1
+ragas==0.1.14
+json_repair==0.25.2
+loguru==0.7.2
+slowapi==0.1.9
diff --git a/RAG_FULL_APPLICATION_BACKEND/tests/__init__.py b/RAG_FULL_APPLICATION_BACKEND/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/RAG_FULL_APPLICATION_FRONTEND/README.md b/RAG_FULL_APPLICATION_FRONTEND/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..a36934d874c7fbc51aecd1c66dffc106f60693a9
--- /dev/null
+++ b/RAG_FULL_APPLICATION_FRONTEND/README.md
@@ -0,0 +1,16 @@
+# React + Vite
+
+This template provides a minimal setup to get React working in Vite with HMR and some ESLint rules.
+
+Currently, two official plugins are available:
+
+- [@vitejs/plugin-react](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react) uses [Oxc](https://oxc.rs)
+- [@vitejs/plugin-react-swc](https://github.com/vitejs/vite-plugin-react/blob/main/packages/plugin-react-swc) uses [SWC](https://swc.rs/)
+
+## React Compiler
+
+The React Compiler is not enabled on this template because of its impact on dev & build performances. To add it, see [this documentation](https://react.dev/learn/react-compiler/installation).
+
+## Expanding the ESLint configuration
+
+If you are developing a production application, we recommend using TypeScript with type-aware lint rules enabled. Check out the [TS template](https://github.com/vitejs/vite/tree/main/packages/create-vite/template-react-ts) for information on how to integrate TypeScript and [`typescript-eslint`](https://typescript-eslint.io) in your project.
diff --git a/RAG_FULL_APPLICATION_FRONTEND/eslint.config.js b/RAG_FULL_APPLICATION_FRONTEND/eslint.config.js
new file mode 100644
index 0000000000000000000000000000000000000000..4fa125da29e01fa85529cfa06a83a7c0ce240d55
--- /dev/null
+++ b/RAG_FULL_APPLICATION_FRONTEND/eslint.config.js
@@ -0,0 +1,29 @@
+import js from '@eslint/js'
+import globals from 'globals'
+import reactHooks from 'eslint-plugin-react-hooks'
+import reactRefresh from 'eslint-plugin-react-refresh'
+import { defineConfig, globalIgnores } from 'eslint/config'
+
+export default defineConfig([
+  globalIgnores(['dist']),
+  {
+    files: ['**/*.{js,jsx}'],
+    extends: [
+      js.configs.recommended,
+      reactHooks.configs.flat.recommended,
+      reactRefresh.configs.vite,
+    ],
+    languageOptions: {
+      ecmaVersion: 2020,
+      globals: globals.browser,
+      parserOptions: {
+        ecmaVersion: 'latest',
+        ecmaFeatures: { jsx: true },
+        sourceType: 'module',
+      },
+    },
+    rules: {
+      'no-unused-vars': ['error', { varsIgnorePattern: '^[A-Z_]' }],
+    },
+  },
+])
diff --git a/RAG_FULL_APPLICATION_FRONTEND/index.html b/RAG_FULL_APPLICATION_FRONTEND/index.html
new file mode 100644
index 0000000000000000000000000000000000000000..f94d687d35d7fa6e2c619c3f2c6b2ddea19bd219
--- /dev/null
+++ b/RAG_FULL_APPLICATION_FRONTEND/index.html
@@ -0,0 +1,13 @@
+<!doctype html>
+<html lang="en">
+  <head>
+    <meta charset="UTF-8" />
+    <link rel="icon" type="image/svg+xml" href="/favicon.svg" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>frontend</title>
+  </head>
+  <body>
+    <div id="root"></div>
+    <script type="module" src="/src/main.jsx"></script>
+  </body>
+</html>
diff --git a/RAG_FULL_APPLICATION_FRONTEND/package-lock.json b/RAG_FULL_APPLICATION_FRONTEND/package-lock.json
new file mode 100644
index 0000000000000000000000000000000000000000..5e55537ee6f8e9a52f2f8b8bb9e61f1f6ce96249
--- /dev/null
+++ b/RAG_FULL_APPLICATION_FRONTEND/package-lock.json
@@ -0,0 +1,4272 @@
+{
+  "name": "frontend",
+  "version": "0.0.0",
+  "lockfileVersion": 3,
+  "requires": true,
+  "packages": {
+    "": {
+      "name": "frontend",
+      "version": "0.0.0",
+      "dependencies": {
+        "@tanstack/react-query": "^5.99.2",
+        "axios": "^1.15.2",
+        "framer-motion": "^12.38.0",
+        "lucide-react": "^1.9.0",
+        "react": "^19.2.5",
+        "react-dom": "^19.2.5",
+        "recharts": "^3.8.1",
+        "zustand": "^5.0.12"
+      },
+      "devDependencies": {
+        "@eslint/js": "^9.39.4",
+        "@types/react": "^19.2.14",
+        "@types/react-dom": "^19.2.3",
+        "@vitejs/plugin-react": "^6.0.1",
+        "autoprefixer": "^10.5.0",
+        "eslint": "^9.39.4",
+        "eslint-plugin-react-hooks": "^7.1.1",
+        "eslint-plugin-react-refresh": "^0.5.2",
+        "globals": "^17.5.0",
+        "postcss": "^8.5.10",
+        "tailwindcss": "^3.4.19",
+        "vite": "^8.0.9"
+      }
+    },
+    "node_modules/@alloc/quick-lru": {
+      "version": "5.2.0",
+      "resolved": "https://registry.npmjs.org/@alloc/quick-lru/-/quick-lru-5.2.0.tgz",
+      "integrity": "sha512-UrcABB+4bUrFABwbluTIBErXwvbsU/V7TZWfmbgJfbkwiBuziS9gxdODUyuiecfdGQ85jglMW6juS3+z5TsKLw==",
+      "dev": true,
+      "license": "MIT",
+      "engines": {
+        "node": ">=10"
+      },
+      "funding": {
+        "url": "https://github.com/sponsors/sindresorhus"
+      }
+    },
+    "node_modules/@babel/code-frame": {
+      "version": "7.29.0",
+      "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.29.0.tgz",
+      "integrity": "sha512-9NhCeYjq9+3uxgdtp20LSiJXJvN0FeCtNGpJxuMFZ1Kv3cWUNb6DOhJwUvcVCzKGR66cw4njwM6hrJLqgOwbcw==",
+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "@babel/helper-validator-identifier": "^7.28.5",
+        "js-tokens": "^4.0.0",
+        "picocolors": "^1.1.1"
+      },
+      "engines": {
+        "node": ">=6.9.0"
+      }
+    },
+    "node_modules/@babel/compat-data": {
+      "version": "7.29.0",
+      "resolved": "https://registry.npmjs.org/@babel/compat-data/-/compat-data-7.29.0.tgz",
+      "integrity": "sha512-T1NCJqT/j9+cn8fvkt7jtwbLBfLC/1y1c7NtCeXFRgzGTsafi68MRv8yzkYSapBnFA6L3U2VSc02ciDzoAJhJg==",
+      "dev": true,
+      "license": "MIT",
+      "engines": {
+        "node": ">=6.9.0"
+      }
+    },
+    "node_modules/@babel/core": {
+      "version": "7.29.0",
+      "resolved": "https://registry.npmjs.org/@babel/core/-/core-7.29.0.tgz",
+      "integrity": "sha512-CGOfOJqWjg2qW/Mb6zNsDm+u5vFQ8DxXfbM09z69p5Z6+mE1ikP2jUXw+j42Pf1XTYED2Rni5f95npYeuwMDQA==",
+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "@babel/code-frame": "^7.29.0",
+        "@babel/generator": "^7.29.0",
+        "@babel/helper-compilation-targets": "^7.28.6",
+        "@babel/helper-module-transforms": "^7.28.6",
+        "@babel/helpers": "^7.28.6",
+        "@babel/parser": "^7.29.0",
+        "@babel/template": "^7.28.6",
+        "@babel/traverse": "^7.29.0",
+        "@babel/types": "^7.29.0",
+        "@jridgewell/remapping": "^2.3.5",
+        "convert-source-map": "^2.0.0",
+        "debug": "^4.1.0",
+        "gensync": "^1.0.0-beta.2",
+        "json5": "^2.2.3",
+        "semver": "^6.3.1"
+      },
+      "engines": {
+        "node": ">=6.9.0"
+      },
+      "funding": {
+        "type": "opencollective",
+        "url": "https://opencollective.com/babel"
+      }
+    },
+    "node_modules/@babel/generator": {
+      "version": "7.29.1",
+      "resolved": "https://registry.npmjs.org/@babel/generator/-/generator-7.29.1.tgz",
+      "integrity": "sha512-qsaF+9Qcm2Qv8SRIMMscAvG4O3lJ0F1GuMo5HR/Bp02LopNgnZBC/EkbevHFeGs4ls/oPz9v+Bsmzbkbe+0dUw==",
+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "@babel/parser": "^7.29.0",
+        "@babel/types": "^7.29.0",
+        "@jridgewell/gen-mapping": "^0.3.12",
+        "@jridgewell/trace-mapping": "^0.3.28",
+        "jsesc": "^3.0.2"
+      },
+      "engines": {
+        "node": ">=6.9.0"
+      }
+    },
+    "node_modules/@babel/helper-compilation-targets": {
+      "version": "7.28.6",
+      "resolved": "https://registry.npmjs.org/@babel/helper-compilation-targets/-/helper-compilation-targets-7.28.6.tgz",
+      "integrity": "sha512-JYtls3hqi15fcx5GaSNL7SCTJ2MNmjrkHXg4FSpOA/grxK8KwyZ5bubHsCq8FXCkua6xhuaaBit+3b7+VZRfcA==",
+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "@babel/compat-data": "^7.28.6",
+        "@babel/helper-validator-option": "^7.27.1",
+        "browserslist": "^4.24.0",
+        "lru-cache": "^5.1.1",
+        "semver": "^6.3.1"
+      },
+      "engines": {
+        "node": ">=6.9.0"
+      }
+    },
+    "node_modules/@babel/helper-globals": {
+      "version": "7.28.0",
+      "resolved": "https://registry.npmjs.org/@babel/helper-globals/-/helper-globals-7.28.0.tgz",
+      "integrity": "sha512-+W6cISkXFa1jXsDEdYA8HeevQT/FULhxzR99pxphltZcVaugps53THCeiWA8SguxxpSp3gKPiuYfSWopkLQ4hw==",
+      "dev": true,
+      "license": "MIT",
+      "engines": {
+        "node": ">=6.9.0"
+      }
+    },
+    "node_modules/@babel/helper-module-imports": {
+      "version": "7.28.6",
+      "resolved": "https://registry.npmjs.org/@babel/helper-module-imports/-/helper-module-imports-7.28.6.tgz",
+      "integrity": "sha512-l5XkZK7r7wa9LucGw9LwZyyCUscb4x37JWTPz7swwFE/0FMQAGpiWUZn8u9DzkSBWEcK25jmvubfpw2dnAMdbw==",
+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "@babel/traverse": "^7.28.6",
+        "@babel/types": "^7.28.6"
+      },
+      "engines": {
+        "node": ">=6.9.0"
+      }
+    },
+    "node_modules/@babel/helper-module-transforms": {
+      "version": "7.28.6",
+      "resolved": "https://registry.npmjs.org/@babel/helper-module-transforms/-/helper-module-transforms-7.28.6.tgz",
+      "integrity": "sha512-67oXFAYr2cDLDVGLXTEABjdBJZ6drElUSI7WKp70NrpyISso3plG9SAGEF6y7zbha/wOzUByWWTJvEDVNIUGcA==",
+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "@babel/helper-module-imports": "^7.28.6",
+        "@babel/helper-validator-identifier": "^7.28.5",
+        "@babel/traverse": "^7.28.6"
+      },
+      "engines": {
+        "node": ">=6.9.0"
+      },
+      "peerDependencies": {
+        "@babel/core": "^7.0.0"
+      }
+    },
+    "node_modules/@babel/helper-string-parser": {
+      "version": "7.27.1",
+      "resolved": "https://registry.npmjs.org/@babel/helper-string-parser/-/helper-string-parser-7.27.1.tgz",
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+      "dev": true,
+      "license": "MIT",
+      "engines": {
+        "node": ">=6.9.0"
+      }
+    },
+    "node_modules/@babel/helper-validator-identifier": {
+      "version": "7.28.5",
+      "resolved": "https://registry.npmjs.org/@babel/helper-validator-identifier/-/helper-validator-identifier-7.28.5.tgz",
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+      "dev": true,
+      "license": "MIT",
+      "engines": {
+        "node": ">=6.9.0"
+      }
+    },
+    "node_modules/@babel/helper-validator-option": {
+      "version": "7.27.1",
+      "resolved": "https://registry.npmjs.org/@babel/helper-validator-option/-/helper-validator-option-7.27.1.tgz",
+      "integrity": "sha512-YvjJow9FxbhFFKDSuFnVCe2WxXk1zWc22fFePVNEaWJEu8IrZVlda6N0uHwzZrUM1il7NC9Mlp4MaJYbYd9JSg==",
+      "dev": true,
+      "license": "MIT",
+      "engines": {
+        "node": ">=6.9.0"
+      }
+    },
+    "node_modules/@babel/helpers": {
+      "version": "7.29.2",
+      "resolved": "https://registry.npmjs.org/@babel/helpers/-/helpers-7.29.2.tgz",
+      "integrity": "sha512-HoGuUs4sCZNezVEKdVcwqmZN8GoHirLUcLaYVNBK2J0DadGtdcqgr3BCbvH8+XUo4NGjNl3VOtSjEKNzqfFgKw==",
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+      "license": "MIT",
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+        "@babel/types": "^7.29.0"
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+      "version": "7.29.2",
+      "resolved": "https://registry.npmjs.org/@babel/parser/-/parser-7.29.2.tgz",
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+      "license": "MIT",
+      "dependencies": {
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+      },
+      "bin": {
+        "parser": "bin/babel-parser.js"
+      },
+      "engines": {
+        "node": ">=6.0.0"
+      }
+    },
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+      "version": "7.28.6",
+      "resolved": "https://registry.npmjs.org/@babel/template/-/template-7.28.6.tgz",
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+      "license": "MIT",
+      "dependencies": {
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+        "@babel/parser": "^7.28.6",
+        "@babel/types": "^7.28.6"
+      },
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+      }
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+      "version": "7.29.0",
+      "resolved": "https://registry.npmjs.org/@babel/traverse/-/traverse-7.29.0.tgz",
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+      "license": "MIT",
+      "dependencies": {
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+        "@babel/generator": "^7.29.0",
+        "@babel/helper-globals": "^7.28.0",
+        "@babel/parser": "^7.29.0",
+        "@babel/template": "^7.28.6",
+        "@babel/types": "^7.29.0",
+        "debug": "^4.3.1"
+      },
+      "engines": {
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+      }
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+      "version": "7.29.0",
+      "resolved": "https://registry.npmjs.org/@babel/types/-/types-7.29.0.tgz",
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+      "license": "MIT",
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+        "@babel/helper-validator-identifier": "^7.28.5"
+      },
+      "engines": {
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+      }
+    },
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+      "resolved": "https://registry.npmjs.org/@emnapi/core/-/core-1.9.2.tgz",
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+      "dev": true,
+      "license": "MIT",
+      "optional": true,
+      "dependencies": {
+        "@emnapi/wasi-threads": "1.2.1",
+        "tslib": "^2.4.0"
+      }
+    },
+    "node_modules/@emnapi/runtime": {
+      "version": "1.9.2",
+      "resolved": "https://registry.npmjs.org/@emnapi/runtime/-/runtime-1.9.2.tgz",
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+      "dev": true,
+      "license": "MIT",
+      "optional": true,
+      "dependencies": {
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+      }
+    },
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+      "dev": true,
+      "license": "MIT",
+      "optional": true,
+      "dependencies": {
+        "tslib": "^2.4.0"
+      }
+    },
+    "node_modules/@eslint-community/eslint-utils": {
+      "version": "4.9.1",
+      "resolved": "https://registry.npmjs.org/@eslint-community/eslint-utils/-/eslint-utils-4.9.1.tgz",
+      "integrity": "sha512-phrYmNiYppR7znFEdqgfWHXR6NCkZEK7hwWDHZUjit/2/U0r6XvkDl0SYnoM51Hq7FhCGdLDT6zxCCOY1hexsQ==",
+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "eslint-visitor-keys": "^3.4.3"
+      },
+      "engines": {
+        "node": "^12.22.0 || ^14.17.0 || >=16.0.0"
+      },
+      "funding": {
+        "url": "https://opencollective.com/eslint"
+      },
+      "peerDependencies": {
+        "eslint": "^6.0.0 || ^7.0.0 || >=8.0.0"
+      }
+    },
+    "node_modules/@eslint-community/eslint-utils/node_modules/eslint-visitor-keys": {
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+      "resolved": "https://registry.npmjs.org/eslint-visitor-keys/-/eslint-visitor-keys-3.4.3.tgz",
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+      "dev": true,
+      "license": "Apache-2.0",
+      "engines": {
+        "node": "^12.22.0 || ^14.17.0 || >=16.0.0"
+      },
+      "funding": {
+        "url": "https://opencollective.com/eslint"
+      }
+    },
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+      "resolved": "https://registry.npmjs.org/@eslint-community/regexpp/-/regexpp-4.12.2.tgz",
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+        "minimatch": "^3.1.5"
+      },
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+      }
+    },
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+      "resolved": "https://registry.npmjs.org/use-sync-external-store/-/use-sync-external-store-1.6.0.tgz",
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+      "license": "MIT",
+      "peerDependencies": {
+        "react": "^16.8.0 || ^17.0.0 || ^18.0.0 || ^19.0.0"
+      }
+    },
+    "node_modules/util-deprecate": {
+      "version": "1.0.2",
+      "resolved": "https://registry.npmjs.org/util-deprecate/-/util-deprecate-1.0.2.tgz",
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+      "dev": true,
+      "license": "MIT"
+    },
+    "node_modules/victory-vendor": {
+      "version": "37.3.6",
+      "resolved": "https://registry.npmjs.org/victory-vendor/-/victory-vendor-37.3.6.tgz",
+      "integrity": "sha512-SbPDPdDBYp+5MJHhBCAyI7wKM3d5ivekigc2Dk2s7pgbZ9wIgIBYGVw4zGHBml/qTFbexrofXW6Gu4noGxrOwQ==",
+      "license": "MIT AND ISC",
+      "dependencies": {
+        "@types/d3-array": "^3.0.3",
+        "@types/d3-ease": "^3.0.0",
+        "@types/d3-interpolate": "^3.0.1",
+        "@types/d3-scale": "^4.0.2",
+        "@types/d3-shape": "^3.1.0",
+        "@types/d3-time": "^3.0.0",
+        "@types/d3-timer": "^3.0.0",
+        "d3-array": "^3.1.6",
+        "d3-ease": "^3.0.1",
+        "d3-interpolate": "^3.0.1",
+        "d3-scale": "^4.0.2",
+        "d3-shape": "^3.1.0",
+        "d3-time": "^3.0.0",
+        "d3-timer": "^3.0.1"
+      }
+    },
+    "node_modules/vite": {
+      "version": "8.0.9",
+      "resolved": "https://registry.npmjs.org/vite/-/vite-8.0.9.tgz",
+      "integrity": "sha512-t7g7GVRpMXjNpa67HaVWI/8BWtdVIQPCL2WoozXXA7LBGEFK4AkkKkHx2hAQf5x1GZSlcmEDPkVLSGahxnEEZw==",
+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "lightningcss": "^1.32.0",
+        "picomatch": "^4.0.4",
+        "postcss": "^8.5.10",
+        "rolldown": "1.0.0-rc.16",
+        "tinyglobby": "^0.2.16"
+      },
+      "bin": {
+        "vite": "bin/vite.js"
+      },
+      "engines": {
+        "node": "^20.19.0 || >=22.12.0"
+      },
+      "funding": {
+        "url": "https://github.com/vitejs/vite?sponsor=1"
+      },
+      "optionalDependencies": {
+        "fsevents": "~2.3.3"
+      },
+      "peerDependencies": {
+        "@types/node": "^20.19.0 || >=22.12.0",
+        "@vitejs/devtools": "^0.1.0",
+        "esbuild": "^0.27.0 || ^0.28.0",
+        "jiti": ">=1.21.0",
+        "less": "^4.0.0",
+        "sass": "^1.70.0",
+        "sass-embedded": "^1.70.0",
+        "stylus": ">=0.54.8",
+        "sugarss": "^5.0.0",
+        "terser": "^5.16.0",
+        "tsx": "^4.8.1",
+        "yaml": "^2.4.2"
+      },
+      "peerDependenciesMeta": {
+        "@types/node": {
+          "optional": true
+        },
+        "@vitejs/devtools": {
+          "optional": true
+        },
+        "esbuild": {
+          "optional": true
+        },
+        "jiti": {
+          "optional": true
+        },
+        "less": {
+          "optional": true
+        },
+        "sass": {
+          "optional": true
+        },
+        "sass-embedded": {
+          "optional": true
+        },
+        "stylus": {
+          "optional": true
+        },
+        "sugarss": {
+          "optional": true
+        },
+        "terser": {
+          "optional": true
+        },
+        "tsx": {
+          "optional": true
+        },
+        "yaml": {
+          "optional": true
+        }
+      }
+    },
+    "node_modules/which": {
+      "version": "2.0.2",
+      "resolved": "https://registry.npmjs.org/which/-/which-2.0.2.tgz",
+      "integrity": "sha512-BLI3Tl1TW3Pvl70l3yq3Y64i+awpwXqsGBYWkkqMtnbXgrMD+yj7rhW0kuEDxzJaYXGjEW5ogapKNMEKNMjibA==",
+      "dev": true,
+      "license": "ISC",
+      "dependencies": {
+        "isexe": "^2.0.0"
+      },
+      "bin": {
+        "node-which": "bin/node-which"
+      },
+      "engines": {
+        "node": ">= 8"
+      }
+    },
+    "node_modules/word-wrap": {
+      "version": "1.2.5",
+      "resolved": "https://registry.npmjs.org/word-wrap/-/word-wrap-1.2.5.tgz",
+      "integrity": "sha512-BN22B5eaMMI9UMtjrGd5g5eCYPpCPDUy0FJXbYsaT5zYxjFOckS53SQDE3pWkVoWpHXVb3BrYcEN4Twa55B5cA==",
+      "dev": true,
+      "license": "MIT",
+      "engines": {
+        "node": ">=0.10.0"
+      }
+    },
+    "node_modules/yallist": {
+      "version": "3.1.1",
+      "resolved": "https://registry.npmjs.org/yallist/-/yallist-3.1.1.tgz",
+      "integrity": "sha512-a4UGQaWPH59mOXUYnAG2ewncQS4i4F43Tv3JoAM+s2VDAmS9NsK8GpDMLrCHPksFT7h3K6TOoUNn2pb7RoXx4g==",
+      "dev": true,
+      "license": "ISC"
+    },
+    "node_modules/yocto-queue": {
+      "version": "0.1.0",
+      "resolved": "https://registry.npmjs.org/yocto-queue/-/yocto-queue-0.1.0.tgz",
+      "integrity": "sha512-rVksvsnNCdJ/ohGc6xgPwyN8eheCxsiLM8mxuE/t/mOVqJewPuO1miLpTHQiRgTKCLexL4MeAFVagts7HmNZ2Q==",
+      "dev": true,
+      "license": "MIT",
+      "engines": {
+        "node": ">=10"
+      },
+      "funding": {
+        "url": "https://github.com/sponsors/sindresorhus"
+      }
+    },
+    "node_modules/zod": {
+      "version": "4.3.6",
+      "resolved": "https://registry.npmjs.org/zod/-/zod-4.3.6.tgz",
+      "integrity": "sha512-rftlrkhHZOcjDwkGlnUtZZkvaPHCsDATp4pGpuOOMDaTdDDXF91wuVDJoWoPsKX/3YPQ5fHuF3STjcYyKr+Qhg==",
+      "dev": true,
+      "license": "MIT",
+      "funding": {
+        "url": "https://github.com/sponsors/colinhacks"
+      }
+    },
+    "node_modules/zod-validation-error": {
+      "version": "4.0.2",
+      "resolved": "https://registry.npmjs.org/zod-validation-error/-/zod-validation-error-4.0.2.tgz",
+      "integrity": "sha512-Q6/nZLe6jxuU80qb/4uJ4t5v2VEZ44lzQjPDhYJNztRQ4wyWc6VF3D3Kb/fAuPetZQnhS3hnajCf9CsWesghLQ==",
+      "dev": true,
+      "license": "MIT",
+      "engines": {
+        "node": ">=18.0.0"
+      },
+      "peerDependencies": {
+        "zod": "^3.25.0 || ^4.0.0"
+      }
+    },
+    "node_modules/zustand": {
+      "version": "5.0.12",
+      "resolved": "https://registry.npmjs.org/zustand/-/zustand-5.0.12.tgz",
+      "integrity": "sha512-i77ae3aZq4dhMlRhJVCYgMLKuSiZAaUPAct2AksxQ+gOtimhGMdXljRT21P5BNpeT4kXlLIckvkPM029OljD7g==",
+      "license": "MIT",
+      "engines": {
+        "node": ">=12.20.0"
+      },
+      "peerDependencies": {
+        "@types/react": ">=18.0.0",
+        "immer": ">=9.0.6",
+        "react": ">=18.0.0",
+        "use-sync-external-store": ">=1.2.0"
+      },
+      "peerDependenciesMeta": {
+        "@types/react": {
+          "optional": true
+        },
+        "immer": {
+          "optional": true
+        },
+        "react": {
+          "optional": true
+        },
+        "use-sync-external-store": {
+          "optional": true
+        }
+      }
+    }
+  }
+}
diff --git a/RAG_FULL_APPLICATION_FRONTEND/package.json b/RAG_FULL_APPLICATION_FRONTEND/package.json
new file mode 100644
index 0000000000000000000000000000000000000000..735ef311765bf7445754c575afe465c2c7e38614
--- /dev/null
+++ b/RAG_FULL_APPLICATION_FRONTEND/package.json
@@ -0,0 +1,36 @@
+{
+  "name": "frontend",
+  "private": true,
+  "version": "0.0.0",
+  "type": "module",
+  "scripts": {
+    "dev": "vite",
+    "build": "vite build",
+    "lint": "eslint .",
+    "preview": "vite preview"
+  },
+  "dependencies": {
+    "@tanstack/react-query": "^5.99.2",
+    "axios": "^1.15.2",
+    "framer-motion": "^12.38.0",
+    "lucide-react": "^1.9.0",
+    "react": "^19.2.5",
+    "react-dom": "^19.2.5",
+    "recharts": "^3.8.1",
+    "zustand": "^5.0.12"
+  },
+  "devDependencies": {
+    "@eslint/js": "^9.39.4",
+    "@types/react": "^19.2.14",
+    "@types/react-dom": "^19.2.3",
+    "@vitejs/plugin-react": "^6.0.1",
+    "autoprefixer": "^10.5.0",
+    "eslint": "^9.39.4",
+    "eslint-plugin-react-hooks": "^7.1.1",
+    "eslint-plugin-react-refresh": "^0.5.2",
+    "globals": "^17.5.0",
+    "postcss": "^8.5.10",
+    "tailwindcss": "^3.4.19",
+    "vite": "^8.0.9"
+  }
+}
diff --git a/RAG_FULL_APPLICATION_FRONTEND/postcss.config.js b/RAG_FULL_APPLICATION_FRONTEND/postcss.config.js
new file mode 100644
index 0000000000000000000000000000000000000000..2e7af2b7f1a6f391da1631d93968a9d487ba977d
--- /dev/null
+++ b/RAG_FULL_APPLICATION_FRONTEND/postcss.config.js
@@ -0,0 +1,6 @@
+export default {
+  plugins: {
+    tailwindcss: {},
+    autoprefixer: {},
+  },
+}
diff --git a/RAG_FULL_APPLICATION_FRONTEND/public/favicon.svg b/RAG_FULL_APPLICATION_FRONTEND/public/favicon.svg
new file mode 100644
index 0000000000000000000000000000000000000000..6893eb13237060adc0c968a690149a49faa2d7d3
--- /dev/null
+++ b/RAG_FULL_APPLICATION_FRONTEND/public/favicon.svg
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diff --git a/RAG_FULL_APPLICATION_FRONTEND/public/icons.svg b/RAG_FULL_APPLICATION_FRONTEND/public/icons.svg
new file mode 100644
index 0000000000000000000000000000000000000000..e9522193d9f796a9748e9ad8c952a5df73c87db9
--- /dev/null
+++ b/RAG_FULL_APPLICATION_FRONTEND/public/icons.svg
@@ -0,0 +1,24 @@
+<svg xmlns="http://www.w3.org/2000/svg">
+  <symbol id="bluesky-icon" viewBox="0 0 16 17">
+    <g clip-path="url(#bluesky-clip)"><path fill="#08060d" d="M7.75 7.735c-.693-1.348-2.58-3.86-4.334-5.097-1.68-1.187-2.32-.981-2.74-.79C.188 2.065.1 2.812.1 3.251s.241 3.602.398 4.13c.52 1.744 2.367 2.333 4.07 2.145-2.495.37-4.71 1.278-1.805 4.512 3.196 3.309 4.38-.71 4.987-2.746.608 2.036 1.307 5.91 4.93 2.746 2.72-2.746.747-4.143-1.747-4.512 1.702.189 3.55-.4 4.07-2.145.156-.528.397-3.691.397-4.13s-.088-1.186-.575-1.406c-.42-.19-1.06-.395-2.741.79-1.755 1.24-3.64 3.752-4.334 5.099"/></g>
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+  </symbol>
+  <symbol id="discord-icon" viewBox="0 0 20 19">
+    <path fill="#08060d" d="M16.224 3.768a14.5 14.5 0 0 0-3.67-1.153c-.158.286-.343.67-.47.976a13.5 13.5 0 0 0-4.067 0c-.128-.306-.317-.69-.476-.976A14.4 14.4 0 0 0 3.868 3.77C1.546 7.28.916 10.703 1.231 14.077a14.7 14.7 0 0 0 4.5 2.306q.545-.748.965-1.587a9.5 9.5 0 0 1-1.518-.74q.191-.14.372-.293c2.927 1.369 6.107 1.369 8.999 0q.183.152.372.294-.723.437-1.52.74.418.838.963 1.588a14.6 14.6 0 0 0 4.504-2.308c.37-3.911-.63-7.302-2.644-10.309m-9.13 8.234c-.878 0-1.599-.82-1.599-1.82 0-.998.705-1.82 1.6-1.82.894 0 1.614.82 1.599 1.82.001 1-.705 1.82-1.6 1.82m5.91 0c-.878 0-1.599-.82-1.599-1.82 0-.998.705-1.82 1.6-1.82.893 0 1.614.82 1.599 1.82 0 1-.706 1.82-1.6 1.82"/>
+  </symbol>
+  <symbol id="documentation-icon" viewBox="0 0 21 20">
+    <path fill="none" stroke="#aa3bff" stroke-linecap="round" stroke-linejoin="round" stroke-width="1.35" d="m15.5 13.333 1.533 1.322c.645.555.967.833.967 1.178s-.322.623-.967 1.179L15.5 18.333m-3.333-5-1.534 1.322c-.644.555-.966.833-.966 1.178s.322.623.966 1.179l1.534 1.321"/>
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+  </symbol>
+  <symbol id="github-icon" viewBox="0 0 19 19">
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+  </symbol>
+  <symbol id="social-icon" viewBox="0 0 20 20">
+    <path fill="none" stroke="#aa3bff" stroke-linecap="round" stroke-linejoin="round" stroke-width="1.35" d="M12.5 6.667a4.167 4.167 0 1 0-8.334 0 4.167 4.167 0 0 0 8.334 0"/>
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+  </symbol>
+</svg>
diff --git a/RAG_FULL_APPLICATION_FRONTEND/src/App.css b/RAG_FULL_APPLICATION_FRONTEND/src/App.css
new file mode 100644
index 0000000000000000000000000000000000000000..f90339d8f765fa2c69d9a341959a8ddb9fff5720
--- /dev/null
+++ b/RAG_FULL_APPLICATION_FRONTEND/src/App.css
@@ -0,0 +1,184 @@
+.counter {
+  font-size: 16px;
+  padding: 5px 10px;
+  border-radius: 5px;
+  color: var(--accent);
+  background: var(--accent-bg);
+  border: 2px solid transparent;
+  transition: border-color 0.3s;
+  margin-bottom: 24px;
+
+  &:hover {
+    border-color: var(--accent-border);
+  }
+  &:focus-visible {
+    outline: 2px solid var(--accent);
+    outline-offset: 2px;
+  }
+}
+
+.hero {
+  position: relative;
+
+  .base,
+  .framework,
+  .vite {
+    inset-inline: 0;
+    margin: 0 auto;
+  }
+
+  .base {
+    width: 170px;
+    position: relative;
+    z-index: 0;
+  }
+
+  .framework,
+  .vite {
+    position: absolute;
+  }
+
+  .framework {
+    z-index: 1;
+    top: 34px;
+    height: 28px;
+    transform: perspective(2000px) rotateZ(300deg) rotateX(44deg) rotateY(39deg)
+      scale(1.4);
+  }
+
+  .vite {
+    z-index: 0;
+    top: 107px;
+    height: 26px;
+    width: auto;
+    transform: perspective(2000px) rotateZ(300deg) rotateX(40deg) rotateY(39deg)
+      scale(0.8);
+  }
+}
+
+#center {
+  display: flex;
+  flex-direction: column;
+  gap: 25px;
+  place-content: center;
+  place-items: center;
+  flex-grow: 1;
+
+  @media (max-width: 1024px) {
+    padding: 32px 20px 24px;
+    gap: 18px;
+  }
+}
+
+#next-steps {
+  display: flex;
+  border-top: 1px solid var(--border);
+  text-align: left;
+
+  & > div {
+    flex: 1 1 0;
+    padding: 32px;
+    @media (max-width: 1024px) {
+      padding: 24px 20px;
+    }
+  }
+
+  .icon {
+    margin-bottom: 16px;
+    width: 22px;
+    height: 22px;
+  }
+
+  @media (max-width: 1024px) {
+    flex-direction: column;
+    text-align: center;
+  }
+}
+
+#docs {
+  border-right: 1px solid var(--border);
+
+  @media (max-width: 1024px) {
+    border-right: none;
+    border-bottom: 1px solid var(--border);
+  }
+}
+
+#next-steps ul {
+  list-style: none;
+  padding: 0;
+  display: flex;
+  gap: 8px;
+  margin: 32px 0 0;
+
+  .logo {
+    height: 18px;
+  }
+
+  a {
+    color: var(--text-h);
+    font-size: 16px;
+    border-radius: 6px;
+    background: var(--social-bg);
+    display: flex;
+    padding: 6px 12px;
+    align-items: center;
+    gap: 8px;
+    text-decoration: none;
+    transition: box-shadow 0.3s;
+
+    &:hover {
+      box-shadow: var(--shadow);
+    }
+    .button-icon {
+      height: 18px;
+      width: 18px;
+    }
+  }
+
+  @media (max-width: 1024px) {
+    margin-top: 20px;
+    flex-wrap: wrap;
+    justify-content: center;
+
+    li {
+      flex: 1 1 calc(50% - 8px);
+    }
+
+    a {
+      width: 100%;
+      justify-content: center;
+      box-sizing: border-box;
+    }
+  }
+}
+
+#spacer {
+  height: 88px;
+  border-top: 1px solid var(--border);
+  @media (max-width: 1024px) {
+    height: 48px;
+  }
+}
+
+.ticks {
+  position: relative;
+  width: 100%;
+
+  &::before,
+  &::after {
+    content: '';
+    position: absolute;
+    top: -4.5px;
+    border: 5px solid transparent;
+  }
+
+  &::before {
+    left: 0;
+    border-left-color: var(--border);
+  }
+  &::after {
+    right: 0;
+    border-right-color: var(--border);
+  }
+}
diff --git a/RAG_FULL_APPLICATION_FRONTEND/src/App.jsx b/RAG_FULL_APPLICATION_FRONTEND/src/App.jsx
new file mode 100644
index 0000000000000000000000000000000000000000..8b654446c15afdc94ee2f4be65ddcc52750aebdf
--- /dev/null
+++ b/RAG_FULL_APPLICATION_FRONTEND/src/App.jsx
@@ -0,0 +1,324 @@
+import React, { useState, useEffect, useRef } from 'react';
+import { Search, Boxes, Database, Zap, Settings, History, Lock, User, Trash2 } from 'lucide-react';
+import { motion } from 'framer-motion';
+import FileUpload from './components/FileUpload';
+import TechniqueSelector from './components/TechniqueSelector';
+import PipelineVisualizer from './components/PipelineVisualizer';
+import QueryResult from './components/QueryResult';
+import { usePipelineStore } from './store/pipelineStore';
+import { useAuthStore } from './store/authStore';
+import api from './api/client';
+
+function App() {
+  const { isAuthenticated, login, logout } = useAuthStore();
+  const [username, setUsername] = useState('admin');
+  const [password, setPassword] = useState('admin123');
+  const [query, setQuery] = useState('');
+  const [technique, setTechnique] = useState('hybrid');
+  const [activeJob, setActiveJob] = useState(null);
+  
+  const { 
+    documents, setDocuments, 
+    selectedDoc, setSelectedDoc,
+    currentAnswer, setAnswer,
+    sources, setSources,
+    steps, addStep, clearSteps,
+    setQuerying, isQuerying
+  } = usePipelineStore();
+
+  const ws = useRef(null);
+
+  // Load documents
+  useEffect(() => {
+    if (isAuthenticated) {
+      api.get('/ingest/documents').then(res => setDocuments(res.data));
+    }
+  }, [isAuthenticated]);
+
+  // WebSocket Connection for Pipeline Trace
+  useEffect(() => {
+    if (activeJob && isAuthenticated) {
+      const token = sessionStorage.getItem('token');
+      const apiBase = import.meta.env.VITE_API_BASE_URL || 'http://localhost:8001';
+      const wsProtocol = apiBase.startsWith('https') ? 'wss' : 'ws';
+      const wsHost = apiBase.replace(/^https?:\/\//, '');
+      const wsUrl = `${wsProtocol}://${wsHost}/ws/pipeline/${activeJob}?token=${token}`;
+      ws.current = new WebSocket(wsUrl);
+
+      ws.current.onmessage = (event) => {
+        const step = JSON.parse(event.data);
+        addStep(step);
+        if (step.step === 'DONE') {
+          // Refresh docs if it was an ingestion
+          api.get('/ingest/documents').then(res => setDocuments(res.data));
+        }
+      };
+
+      return () => {
+        if (ws.current) ws.current.close();
+      };
+    }
+  }, [activeJob, isAuthenticated]);
+
+  const [showHistory, setShowHistory] = useState(false);
+  const [showSettings, setShowSettings] = useState(false);
+  const queryRef = useRef(null);
+
+  // Auto-resize textarea
+  useEffect(() => {
+    if (queryRef.current) {
+      queryRef.current.style.height = 'auto';
+      queryRef.current.style.height = `${queryRef.current.scrollHeight}px`;
+    }
+  }, [query]);
+
+  const handleSearch = async () => {
+    if (!query || !selectedDoc) return;
+    setQuerying(true);
+    clearSteps();
+    setAnswer('');
+    setSources([]);
+    setShowHistory(false);
+    setShowSettings(false);
+    
+    try {
+      const { data } = await api.post('/query/search', {
+        query,
+        document_id: selectedDoc.id,
+        technique
+      });
+      setAnswer(data.answer);
+      setSources(data.sources);
+      setActiveJob(data.job_id);
+    } catch (error) {
+      console.error('Search failed', error);
+      addStep({ step: 'ERROR', detail: 'Search failed. Please try again.', color: '#EF4444' });
+    } finally {
+      setQuerying(false);
+    }
+  };
+
+  const handleAuth = async (e) => {
+    e.preventDefault();
+    await login(username, password);
+  };
+
+  const handleDeleteDoc = async (e, docId) => {
+    e.stopPropagation();
+    if (!window.confirm('Are you sure you want to delete this document?')) return;
+    try {
+      await api.delete(`/ingest/documents/${docId}`);
+      setDocuments(documents.filter(d => d.id !== docId));
+      if (selectedDoc?.id === docId) setSelectedDoc(null);
+    } catch (error) {
+      console.error('Delete failed', error);
+      alert('Failed to delete document');
+    }
+  };
+
+  if (!isAuthenticated) {
+    return (
+      <div className="min-h-screen bg-surface-900 flex items-center justify-center p-4">
+        <div className="w-full max-w-md card space-y-8 p-10 shadow-2xl">
+          <div className="text-center space-y-2">
+            <div className="inline-block p-4 bg-accent-500/10 rounded-full mb-4 ring-1 ring-accent-500/20">
+              <Boxes className="w-12 h-12 text-accent-500" />
+            </div>
+            <h1 className="text-3xl font-bold text-white tracking-tight">RAG Pipeline</h1>
+            <p className="text-gray-400">Production Blueprint V3</p>
+          </div>
+          
+          <form className="space-y-4" onSubmit={handleAuth}>
+            <div className="space-y-1">
+              <label className="text-xs uppercase font-bold text-gray-500">Username</label>
+              <div className="relative">
+                <User className="absolute left-3 top-3 w-4 h-4 text-gray-500" />
+                <input 
+                  type="text" value={username} onChange={e => setUsername(e.target.value)}
+                  className="w-full input-field pl-10" placeholder="admin" 
+                />
+              </div>
+            </div>
+            <div className="space-y-1">
+              <label className="text-xs uppercase font-bold text-gray-500">Password</label>
+              <div className="relative">
+                <Lock className="absolute left-3 top-3 w-4 h-4 text-gray-500" />
+                <input 
+                  type="password" value={password} onChange={e => setPassword(e.target.value)}
+                  className="w-full input-field pl-10" placeholder="••••••••" 
+                />
+              </div>
+            </div>
+            <button className="w-full btn-accent font-bold py-3 mt-4 hover:scale-[1.02] active:scale-[0.98]">Login to Pipeline</button>
+          </form>
+          
+          <div className="text-center pt-4 border-t border-surface-700">
+            <p className="text-xs text-gray-500 font-mono tracking-widest">DEFAULT: admin / admin123</p>
+          </div>
+        </div>
+      </div>
+    );
+  }
+
+  return (
+    <div className="min-h-screen bg-surface-900 text-gray-100 pb-20 selection:bg-accent-500/30">
+      {/* Header */}
+      <header className="border-b border-surface-800 bg-surface-900/50 backdrop-blur-md sticky top-0 z-50">
+        <div className="max-w-7xl mx-auto px-4 h-20 flex items-center justify-between">
+          <div className="flex items-center gap-3">
+            <Boxes className="w-8 h-8 text-primary-500" />
+            <h1 className="text-2xl font-bold tracking-tighter">
+              RAG<span className="text-accent-500">PIPELINE</span>
+            </h1>
+            <span className="bg-primary-500/10 text-primary-500 text-[10px] font-bold px-2 py-0.5 rounded border border-primary-500/20">V3.0</span>
+          </div>
+          <div className="flex items-center gap-6">
+            <div className="flex items-center gap-2 text-sm text-gray-400">
+              <div className="w-2 h-2 bg-green-500 rounded-full animate-pulse shadow-[0_0_10px_rgba(34,197,94,0.5)]" />
+              <span>Supabase Connected</span>
+            </div>
+            <button onClick={logout} className="text-sm text-gray-500 hover:text-white transition-colors">Logout</button>
+          </div>
+        </div>
+      </header>
+
+      <main className="max-w-7xl mx-auto px-4 py-10 grid grid-cols-1 lg:grid-cols-12 gap-10">
+        {/* Sidebar - Ingestion & Docs */}
+        <aside className="lg:col-span-4 space-y-8">
+          <FileUpload />
+          
+          <div className="card space-y-4">
+            <div className="flex items-center justify-between border-b border-surface-700 pb-4">
+              <h2 className="text-xl font-bold flex items-center gap-2">
+                <Database className="w-5 h-5 text-primary-500" />
+                Indexed Files
+              </h2>
+              <span className="text-xs text-gray-500 font-mono bg-surface-900 px-2 py-1 rounded">{documents.length} Total</span>
+            </div>
+            <div className="space-y-2 max-h-[500px] overflow-y-auto pr-2 custom-scrollbar">
+              {documents.map((doc) => (
+                <div 
+                  key={doc.id}
+                  onClick={() => setSelectedDoc(doc)}
+                  className={`p-4 rounded-xl cursor-pointer border transition-all duration-300 transform ${
+                    selectedDoc?.id === doc.id 
+                    ? 'bg-primary-500/10 border-primary-500/50 text-white shadow-glow-green scale-[1.02]' 
+                    : 'bg-surface-900 border-surface-800 text-gray-400 hover:border-surface-700 hover:bg-surface-800'
+                  }`}
+                >
+                  <div className="flex justify-between items-start">
+                    <span className="font-semibold truncate block max-w-[180px]">{doc.filename}</span>
+                    <div className="flex items-center gap-2">
+                      <span className="text-[10px] font-bold font-mono opacity-70 bg-surface-700 px-1.5 py-0.5 rounded uppercase tracking-tighter">{doc.file_type}</span>
+                      <button onClick={(e) => handleDeleteDoc(e, doc.id)} className="text-gray-600 hover:text-red-500 transition-colors">
+                        <Trash2 className="w-3.5 h-3.5" />
+                      </button>
+                    </div>
+                  </div>
+                  <div className="flex justify-between items-center mt-3">
+                    <span className="text-[10px] font-mono text-gray-500 uppercase tracking-widest">{doc.chunk_count} Chunks</span>
+                    <span className={`text-[9px] font-bold px-2 py-0.5 rounded uppercase ${doc.status === 'done' ? 'bg-green-500/10 text-green-500 border border-green-500/20' : 'bg-yellow-500/10 text-yellow-500 border border-yellow-500/20'}`}>
+                      {doc.status}
+                    </span>
+                  </div>
+                </div>
+              ))}
+            </div>
+          </div>
+        </aside>
+
+        {/* Main Content - Query & Trace */}
+        <section className="lg:col-span-8 space-y-8">
+          <div className="card space-y-8 relative overflow-hidden group">
+            <div className="absolute top-0 left-0 w-full h-1 bg-gradient-to-r from-transparent via-accent-500 to-transparent opacity-30" />
+            
+            <div className="flex items-center justify-between">
+              <h2 className="text-2xl font-bold flex items-center gap-3">
+                <Zap className="w-6 h-6 text-accent-500 fill-accent-500/10" />
+                RAG Pipeline Interface
+              </h2>
+              <div className="flex gap-3">
+                <button 
+                  onClick={() => { setShowHistory(!showHistory); setShowSettings(false); }} 
+                  className={`p-2 rounded-lg transition-all border ${showHistory ? 'bg-accent-500 text-white border-accent-500' : 'bg-surface-900 text-gray-500 hover:text-white border-surface-700'}`}
+                >
+                  <History className="w-5 h-5" />
+                </button>
+                <button 
+                  onClick={() => { setShowSettings(!showSettings); setShowHistory(false); }} 
+                  className={`p-2 rounded-lg transition-all border ${showSettings ? 'bg-accent-500 text-white border-accent-500' : 'bg-surface-900 text-gray-500 hover:text-white border-surface-700'}`}
+                >
+                  <Settings className="w-5 h-5" />
+                </button>
+              </div>
+            </div>
+
+            {showHistory && (
+              <motion.div initial={{ opacity: 0, height: 0 }} animate={{ opacity: 1, height: 'auto' }} className="bg-surface-900 rounded-xl p-4 border border-surface-700 text-sm text-gray-500 text-center font-mono">
+                [ EMPTY HISTORY ] No previous queries found.
+              </motion.div>
+            )}
+
+            {showSettings && (
+              <motion.div initial={{ opacity: 0, height: 0 }} animate={{ opacity: 1, height: 'auto' }} className="bg-surface-900 rounded-xl p-6 border border-surface-700 space-y-4">
+                <h3 className="text-xs font-bold text-gray-400 uppercase tracking-widest border-b border-surface-800 pb-2">Pipeline Configuration</h3>
+                <div className="grid grid-cols-2 gap-4">
+                  <div className="space-y-1">
+                    <label className="text-[10px] text-gray-600 font-bold uppercase">Retrieval K</label>
+                    <input type="number" defaultValue={5} className="w-full bg-surface-800 border border-surface-700 rounded px-2 py-1 text-xs outline-none focus:border-accent-500" />
+                  </div>
+                  <div className="space-y-1">
+                    <label className="text-[10px] text-gray-600 font-bold uppercase">LLM Temp</label>
+                    <input type="number" defaultValue={0.1} step={0.1} className="w-full bg-surface-800 border border-surface-700 rounded px-2 py-1 text-xs outline-none focus:border-accent-500" />
+                  </div>
+                </div>
+              </motion.div>
+            )}
+
+            <div className="relative">
+              <Search className="absolute left-4 top-5 w-5 h-5 text-gray-500 z-10" />
+              <textarea 
+                ref={queryRef}
+                rows={1}
+                value={query}
+                onChange={e => setQuery(e.target.value)}
+                onKeyDown={e => {
+                  if (e.key === 'Enter' && !e.shiftKey) {
+                    e.preventDefault();
+                    handleSearch();
+                  }
+                }}
+                placeholder={selectedDoc ? `Ask about "${selectedDoc.filename}"...` : "Select a document to begin..."}
+                disabled={!selectedDoc || isQuerying}
+                className="query-textarea"
+              />
+              <div className="absolute right-3 bottom-3 flex items-center gap-2">
+                 {query && <span className="text-[10px] text-gray-600 font-mono hidden md:block">ENTER TO SEARCH</span>}
+                 <button 
+                  onClick={handleSearch}
+                  disabled={!selectedDoc || isQuerying || !query}
+                  className="btn-accent py-2 px-6 disabled:opacity-30 disabled:cursor-not-allowed text-sm font-bold shadow-xl transition-all"
+                >
+                  {isQuerying ? 'Working...' : 'Search'}
+                </button>
+              </div>
+            </div>
+
+            <div className="space-y-4">
+              <div className="flex items-center gap-2 text-xs font-bold text-gray-500 uppercase tracking-widest pl-1 border-l-2 border-accent-500 ml-1">
+                Select Retrieval Strategy
+              </div>
+              <TechniqueSelector selected={technique} onSelect={setTechnique} />
+            </div>
+          </div>
+
+          <QueryResult answer={currentAnswer} sources={sources} />
+          
+          <PipelineVisualizer steps={steps} />
+        </section>
+      </main>
+    </div>
+  );
+}
+
+export default App;
diff --git a/RAG_FULL_APPLICATION_FRONTEND/src/api/client.js b/RAG_FULL_APPLICATION_FRONTEND/src/api/client.js
new file mode 100644
index 0000000000000000000000000000000000000000..0838f75d5f632b13e4f8a5940e9288474704862a
--- /dev/null
+++ b/RAG_FULL_APPLICATION_FRONTEND/src/api/client.js
@@ -0,0 +1,16 @@
+import axios from 'axios';
+
+const api = axios.create({
+  baseURL: import.meta.env.VITE_API_BASE_URL || 'http://localhost:8001',
+});
+
+// Interceptor for JWT
+api.interceptors.request.use((config) => {
+  const token = sessionStorage.getItem('token');
+  if (token) {
+    config.headers.Authorization = `Bearer ${token}`;
+  }
+  return config;
+});
+
+export default api;
diff --git a/RAG_FULL_APPLICATION_FRONTEND/src/assets/hero.png b/RAG_FULL_APPLICATION_FRONTEND/src/assets/hero.png
new file mode 100644
index 0000000000000000000000000000000000000000..02251f4b956c55af2d76fd0788124d7eee2b45eb
Binary files /dev/null and b/RAG_FULL_APPLICATION_FRONTEND/src/assets/hero.png differ
diff --git a/RAG_FULL_APPLICATION_FRONTEND/src/assets/react.svg b/RAG_FULL_APPLICATION_FRONTEND/src/assets/react.svg
new file mode 100644
index 0000000000000000000000000000000000000000..6c87de9bb3358469122cc991d5cf578927246184
--- /dev/null
+++ b/RAG_FULL_APPLICATION_FRONTEND/src/assets/react.svg
@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" class="iconify iconify--logos" width="35.93" height="32" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 228"><path fill="#00D8FF" d="M210.483 73.824a171.49 171.49 0 0 0-8.24-2.597c.465-1.9.893-3.777 1.273-5.621c6.238-30.281 2.16-54.676-11.769-62.708c-13.355-7.7-35.196.329-57.254 19.526a171.23 171.23 0 0 0-6.375 5.848a155.866 155.866 0 0 0-4.241-3.917C100.759 3.829 77.587-4.822 63.673 3.233C50.33 10.957 46.379 33.89 51.995 62.588a170.974 170.974 0 0 0 1.892 8.48c-3.28.932-6.445 1.924-9.474 2.98C17.309 83.498 0 98.307 0 113.668c0 15.865 18.582 31.778 46.812 41.427a145.52 145.52 0 0 0 6.921 2.165a167.467 167.467 0 0 0-2.01 9.138c-5.354 28.2-1.173 50.591 12.134 58.266c13.744 7.926 36.812-.22 59.273-19.855a145.567 145.567 0 0 0 5.342-4.923a168.064 168.064 0 0 0 6.92 6.314c21.758 18.722 43.246 26.282 56.54 18.586c13.731-7.949 18.194-32.003 12.4-61.268a145.016 145.016 0 0 0-1.535-6.842c1.62-.48 3.21-.974 4.76-1.488c29.348-9.723 48.443-25.443 48.443-41.52c0-15.417-17.868-30.326-45.517-39.844Zm-6.365 70.984c-1.4.463-2.836.91-4.3 1.345c-3.24-10.257-7.612-21.163-12.963-32.432c5.106-11 9.31-21.767 12.459-31.957c2.619.758 5.16 1.557 7.61 2.4c23.69 8.156 38.14 20.213 38.14 29.504c0 9.896-15.606 22.743-40.946 31.14Zm-10.514 20.834c2.562 12.94 2.927 24.64 1.23 33.787c-1.524 8.219-4.59 13.698-8.382 15.893c-8.067 4.67-25.32-1.4-43.927-17.412a156.726 156.726 0 0 1-6.437-5.87c7.214-7.889 14.423-17.06 21.459-27.246c12.376-1.098 24.068-2.894 34.671-5.345a134.17 134.17 0 0 1 1.386 6.193ZM87.276 214.515c-7.882 2.783-14.16 2.863-17.955.675c-8.075-4.657-11.432-22.636-6.853-46.752a156.923 156.923 0 0 1 1.869-8.499c10.486 2.32 22.093 3.988 34.498 4.994c7.084 9.967 14.501 19.128 21.976 27.15a134.668 134.668 0 0 1-4.877 4.492c-9.933 8.682-19.886 14.842-28.658 17.94ZM50.35 144.747c-12.483-4.267-22.792-9.812-29.858-15.863c-6.35-5.437-9.555-10.836-9.555-15.216c0-9.322 13.897-21.212 37.076-29.293c2.813-.98 5.757-1.905 8.812-2.773c3.204 10.42 7.406 21.315 12.477 32.332c-5.137 11.18-9.399 22.249-12.634 32.792a134.718 134.718 0 0 1-6.318-1.979Zm12.378-84.26c-4.811-24.587-1.616-43.134 6.425-47.789c8.564-4.958 27.502 2.111 47.463 19.835a144.318 144.318 0 0 1 3.841 3.545c-7.438 7.987-14.787 17.08-21.808 26.988c-12.04 1.116-23.565 2.908-34.161 5.309a160.342 160.342 0 0 1-1.76-7.887Zm110.427 27.268a347.8 347.8 0 0 0-7.785-12.803c8.168 1.033 15.994 2.404 23.343 4.08c-2.206 7.072-4.956 14.465-8.193 22.045a381.151 381.151 0 0 0-7.365-13.322Zm-45.032-43.861c5.044 5.465 10.096 11.566 15.065 18.186a322.04 322.04 0 0 0-30.257-.006c4.974-6.559 10.069-12.652 15.192-18.18ZM82.802 87.83a323.167 323.167 0 0 0-7.227 13.238c-3.184-7.553-5.909-14.98-8.134-22.152c7.304-1.634 15.093-2.97 23.209-3.984a321.524 321.524 0 0 0-7.848 12.897Zm8.081 65.352c-8.385-.936-16.291-2.203-23.593-3.793c2.26-7.3 5.045-14.885 8.298-22.6a321.187 321.187 0 0 0 7.257 13.246c2.594 4.48 5.28 8.868 8.038 13.147Zm37.542 31.03c-5.184-5.592-10.354-11.779-15.403-18.433c4.902.192 9.899.29 14.978.29c5.218 0 10.376-.117 15.453-.343c-4.985 6.774-10.018 12.97-15.028 18.486Zm52.198-57.817c3.422 7.8 6.306 15.345 8.596 22.52c-7.422 1.694-15.436 3.058-23.88 4.071a382.417 382.417 0 0 0 7.859-13.026a347.403 347.403 0 0 0 7.425-13.565Zm-16.898 8.101a358.557 358.557 0 0 1-12.281 19.815a329.4 329.4 0 0 1-23.444.823c-7.967 0-15.716-.248-23.178-.732a310.202 310.202 0 0 1-12.513-19.846h.001a307.41 307.41 0 0 1-10.923-20.627a310.278 310.278 0 0 1 10.89-20.637l-.001.001a307.318 307.318 0 0 1 12.413-19.761c7.613-.576 15.42-.876 23.31-.876H128c7.926 0 15.743.303 23.354.883a329.357 329.357 0 0 1 12.335 19.695a358.489 358.489 0 0 1 11.036 20.54a329.472 329.472 0 0 1-11 20.722Zm22.56-122.124c8.572 4.944 11.906 24.881 6.52 51.026c-.344 1.668-.73 3.367-1.15 5.09c-10.622-2.452-22.155-4.275-34.23-5.408c-7.034-10.017-14.323-19.124-21.64-27.008a160.789 160.789 0 0 1 5.888-5.4c18.9-16.447 36.564-22.941 44.612-18.3ZM128 90.808c12.625 0 22.86 10.235 22.86 22.86s-10.235 22.86-22.86 22.86s-22.86-10.235-22.86-22.86s10.235-22.86 22.86-22.86Z"></path></svg>
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diff --git a/RAG_FULL_APPLICATION_FRONTEND/src/assets/vite.svg b/RAG_FULL_APPLICATION_FRONTEND/src/assets/vite.svg
new file mode 100644
index 0000000000000000000000000000000000000000..5101b674df391399da71c767aa5c976426c9dc7a
--- /dev/null
+++ b/RAG_FULL_APPLICATION_FRONTEND/src/assets/vite.svg
@@ -0,0 +1 @@
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diff --git a/RAG_FULL_APPLICATION_FRONTEND/src/components/FileUpload.jsx b/RAG_FULL_APPLICATION_FRONTEND/src/components/FileUpload.jsx
new file mode 100644
index 0000000000000000000000000000000000000000..a83d9cef6b67c787477be720c2a6e18c700c39f5
--- /dev/null
+++ b/RAG_FULL_APPLICATION_FRONTEND/src/components/FileUpload.jsx
@@ -0,0 +1,164 @@
+import React, { useState } from 'react';
+import { Upload, FileText, CheckCircle, Loader2, Settings } from 'lucide-react';
+import api from '../api/client';
+import { usePipelineStore } from '../store/pipelineStore';
+
+const STRATEGIES = [
+  { value: 'fixed', label: 'Fixed Size' },
+  { value: 'token', label: 'Token Based' },
+  { value: 'sentence', label: 'Sentence Split' },
+  { value: 'paragraph', label: 'Paragraph' },
+  { value: 'recursive', label: 'Recursive' },
+  { value: 'sliding_window', label: 'Sliding Window' },
+];
+
+export default function FileUpload() {
+  const [file, setFile] = useState(null);
+  const { setIngesting, isIngesting } = usePipelineStore();
+  const [status, setStatus] = useState('idle'); // idle | loading | success | error
+  const [errorMsg, setErrorMsg] = useState('');
+
+  // Chunking controls
+  const [strategy, setStrategy] = useState('fixed');
+  const [chunkSize, setChunkSize] = useState(512);
+  const [overlap, setOverlap] = useState(64);
+
+  const handleUpload = async () => {
+    if (!file) return;
+    setIngesting(true);
+    setStatus('loading');
+    setErrorMsg('');
+    
+    const formData = new FormData();
+    formData.append('file', file);
+    formData.append('chunk_size', chunkSize);
+    formData.append('overlap', overlap);
+    formData.append('strategy', strategy);
+    
+    try {
+      const { data } = await api.post('/ingest/upload', formData);
+      setStatus('success');
+      setTimeout(() => {
+        setStatus('idle');
+        setFile(null);
+      }, 3000);
+    } catch (error) {
+      console.error('Upload failed', error);
+      setErrorMsg(error?.response?.data?.detail || 'Upload failed');
+      setStatus('error');
+      setTimeout(() => setStatus('idle'), 5000);
+    } finally {
+      setIngesting(false);
+    }
+  };
+
+  return (
+    <div className="card space-y-4">
+      <h2 className="text-xl font-bold flex items-center gap-2">
+        <Upload className="w-5 h-5 text-primary-500" />
+        Document Ingestion
+      </h2>
+
+      {/* File Drop Zone */}
+      <div className="border-2 border-dashed border-surface-700 rounded-xl p-6 flex flex-col items-center justify-center space-y-3 hover:border-primary-500 transition-colors cursor-pointer group">
+        <input
+          type="file"
+          id="file-upload"
+          className="hidden"
+          accept=".txt,.pdf,.md,.docx,.csv,.json,.png,.jpg,.jpeg"
+          onChange={(e) => setFile(e.target.files[0])}
+        />
+        <label htmlFor="file-upload" className="flex flex-col items-center cursor-pointer">
+          {file ? (
+            <FileText className="w-10 h-10 text-primary-500" />
+          ) : (
+            <Upload className="w-10 h-10 text-gray-500 group-hover:text-primary-400 transition-colors" />
+          )}
+          <span className="mt-2 text-sm text-gray-400">
+            {file ? file.name : 'Click to select a file'}
+          </span>
+          <span className="text-[10px] text-gray-600 mt-1">
+            PDF, TXT, MD, DOCX, CSV, JSON, PNG, JPG
+          </span>
+        </label>
+      </div>
+
+      {/* Chunking Controls */}
+      <div className="space-y-3 pt-2 border-t border-surface-700">
+        <div className="flex items-center gap-2 text-xs font-bold text-gray-500 uppercase tracking-widest">
+          <Settings className="w-3 h-3" />
+          Chunking Configuration
+        </div>
+
+        {/* Strategy Selector */}
+        <div>
+          <label className="text-xs text-gray-500 mb-1 block">Strategy</label>
+          <select
+            value={strategy}
+            onChange={(e) => setStrategy(e.target.value)}
+            className="w-full input-field text-sm"
+          >
+            {STRATEGIES.map((s) => (
+              <option key={s.value} value={s.value}>{s.label}</option>
+            ))}
+          </select>
+        </div>
+
+        {/* Size & Overlap */}
+        <div className="grid grid-cols-2 gap-3">
+          <div>
+            <label className="text-xs text-gray-500 mb-1 block">Chunk Size</label>
+            <input
+              type="number"
+              value={chunkSize}
+              onChange={(e) => setChunkSize(parseInt(e.target.value) || 256)}
+              min={64}
+              max={4096}
+              className="w-full input-field text-sm"
+            />
+          </div>
+          <div>
+            <label className="text-xs text-gray-500 mb-1 block">Overlap</label>
+            <input
+              type="number"
+              value={overlap}
+              onChange={(e) => setOverlap(parseInt(e.target.value) || 0)}
+              min={0}
+              max={512}
+              className="w-full input-field text-sm"
+            />
+          </div>
+        </div>
+
+        {/* Live Preview */}
+        <div className="bg-surface-900 rounded-lg p-3 text-[11px] font-mono text-gray-500 space-y-1">
+          <div>Strategy: <span className="text-primary-400">{strategy}</span></div>
+          <div>Size: <span className="text-accent-400">{chunkSize}</span> chars | Overlap: <span className="text-accent-400">{overlap}</span> chars</div>
+        </div>
+      </div>
+
+      {/* Error Message */}
+      {status === 'error' && (
+        <div className="text-red-400 text-xs bg-red-500/10 border border-red-500/20 rounded-lg p-2">
+          {errorMsg}
+        </div>
+      )}
+
+      {/* Upload Button */}
+      <button
+        onClick={handleUpload}
+        disabled={!file || isIngesting}
+        className="w-full btn-primary flex items-center justify-center gap-2 disabled:opacity-50 disabled:cursor-not-allowed"
+      >
+        {status === 'loading' ? (
+          <Loader2 className="w-5 h-5 animate-spin" />
+        ) : status === 'success' ? (
+          <CheckCircle className="w-5 h-5" />
+        ) : (
+          <Upload className="w-5 h-5" />
+        )}
+        {status === 'loading' ? 'Processing...' : status === 'success' ? 'Uploaded!' : 'Upload & Start Pipeline'}
+      </button>
+    </div>
+  );
+}
diff --git a/RAG_FULL_APPLICATION_FRONTEND/src/components/PipelineVisualizer.jsx b/RAG_FULL_APPLICATION_FRONTEND/src/components/PipelineVisualizer.jsx
new file mode 100644
index 0000000000000000000000000000000000000000..9f5850fd369a96dff1811d1ca1599c194e083d43
--- /dev/null
+++ b/RAG_FULL_APPLICATION_FRONTEND/src/components/PipelineVisualizer.jsx
@@ -0,0 +1,52 @@
+import React from 'react';
+import { CheckCircle2, CircleDashed, AlertCircle } from 'lucide-react';
+import { motion, AnimatePresence } from 'framer-motion';
+
+export default function PipelineVisualizer({ steps }) {
+  if (!steps || steps.length === 0) return null;
+
+  return (
+    <div className="mt-8 space-y-3">
+      <div className="flex items-center gap-2 mb-4">
+        <div className="w-2 h-2 bg-green-500 rounded-full animate-pulse" />
+        <h2 className="text-xl font-bold uppercase tracking-wider text-gray-400">Pipeline Trace</h2>
+      </div>
+      
+      <div className="space-y-2">
+        <AnimatePresence initial={false}>
+          {steps.map((step, i) => (
+            <motion.div
+              key={i}
+              initial={{ opacity: 0, x: -20 }}
+              animate={{ opacity: 1, x: 0 }}
+              className="flex items-start gap-4 p-4 card"
+              style={{ borderLeftColor: step.color, borderLeftWidth: '4px' }}
+            >
+              <div className="mt-1">
+                {step.status === 'done' ? (
+                  <CheckCircle2 className="w-5 h-5 text-green-500" />
+                ) : step.status === 'error' ? (
+                  <AlertCircle className="w-5 h-5 text-red-500" />
+                ) : (
+                  <CircleDashed className="w-5 h-5 animate-spin text-gray-400" />
+                )}
+              </div>
+              <div className="flex-1">
+                <div className="flex justify-between items-center">
+                  <span className="font-mono font-bold" style={{ color: step.color }}>{step.step}</span>
+                  <span className="text-xs text-gray-500">{new Date(step.timestamp).toLocaleTimeString()}</span>
+                </div>
+                <p className="text-sm text-gray-300 mt-1">{step.detail}</p>
+                {step.metadata && Object.keys(step.metadata).length > 0 && (
+                  <pre className="text-[10px] bg-black/30 p-2 mt-2 rounded overflow-x-auto text-gray-500">
+                    {JSON.stringify(step.metadata, null, 2)}
+                  </pre>
+                )}
+              </div>
+            </motion.div>
+          ))}
+        </AnimatePresence>
+      </div>
+    </div>
+  );
+}
diff --git a/RAG_FULL_APPLICATION_FRONTEND/src/components/QueryResult.jsx b/RAG_FULL_APPLICATION_FRONTEND/src/components/QueryResult.jsx
new file mode 100644
index 0000000000000000000000000000000000000000..ab4ab66785d393726e307caf7595415fac19c7e8
--- /dev/null
+++ b/RAG_FULL_APPLICATION_FRONTEND/src/components/QueryResult.jsx
@@ -0,0 +1,89 @@
+import React from 'react';
+import { Quote, ExternalLink, FileText, Target } from 'lucide-react';
+import { motion } from 'framer-motion';
+
+export default function QueryResult({ answer, sources }) {
+  if (!answer) return null;
+
+  return (
+    <div className="mt-8 space-y-8">
+      <motion.div 
+        initial={{ opacity: 0, y: 20 }}
+        animate={{ opacity: 1, y: 0 }}
+        className="card border-l-4 border-l-accent-500 bg-surface-800/80 backdrop-blur-sm"
+      >
+        <div className="flex items-center gap-3 mb-6 text-accent-400">
+          <div className="p-2 bg-accent-500/10 rounded-lg">
+            <Quote className="w-5 h-5 fill-accent-500/20" />
+          </div>
+          <h2 className="text-xl font-bold uppercase tracking-[0.2em] text-white">AI Response</h2>
+        </div>
+        <div className="prose prose-invert max-w-none text-lg leading-relaxed text-gray-200">
+          {answer.split('\n').map((line, i) => (
+            <p key={i} className="mb-4">{line}</p>
+          ))}
+        </div>
+      </motion.div>
+
+      {sources && sources.length > 0 && (
+        <div className="space-y-6">
+          <div className="flex items-center gap-3">
+            <Target className="w-5 h-5 text-primary-400" />
+            <h3 className="text-sm font-bold text-gray-400 uppercase tracking-[0.3em]">Retrieved Context Chunks</h3>
+          </div>
+          
+          <div className="grid grid-cols-1 md:grid-cols-2 gap-6">
+            {sources.map((source, i) => (
+              <motion.div 
+                key={i}
+                initial={{ opacity: 0, scale: 0.95 }}
+                animate={{ opacity: 1, scale: 1 }}
+                transition={{ delay: i * 0.1 }}
+                whileHover={{ y: -8, borderColor: '#8b5cf6' }}
+                className="bg-surface-900/50 border border-surface-800 rounded-2xl p-6 flex flex-col justify-between transition-all group"
+              >
+                <div>
+                  <div className="flex justify-between items-start mb-4">
+                    <div className="flex items-center gap-2">
+                      <FileText className="w-4 h-4 text-primary-500" />
+                      <span className="text-[10px] font-bold font-mono text-primary-400 bg-primary-500/10 px-2 py-1 rounded uppercase tracking-tighter">
+                        CHUNKS {i + 1}
+                      </span>
+                    </div>
+                    <div className="flex flex-col items-end">
+                       <span className="text-[10px] text-gray-500 font-bold font-mono uppercase">Relevance Score</span>
+                       <span className={`text-sm font-mono font-bold ${source.similarity > 0.7 ? 'text-green-400' : 'text-yellow-400'}`}>
+                        {(source.similarity || 0.0).toFixed(4)}
+                      </span>
+                    </div>
+                  </div>
+                  <div className="relative">
+                    <div className="absolute -left-2 top-0 bottom-0 w-1 bg-surface-700 rounded-full group-hover:bg-accent-500 transition-colors" />
+                    <p className="text-sm text-gray-400 leading-relaxed italic pl-4 line-clamp-4 group-hover:text-gray-200 transition-colors">
+                      "{source.text}"
+                    </p>
+                  </div>
+                </div>
+                
+                <div className="mt-6 pt-4 border-t border-surface-800/50 flex items-center justify-between">
+                  <div className="flex flex-col">
+                    <span className="text-[9px] text-gray-600 font-bold uppercase tracking-widest">Source Document</span>
+                    <span className="text-xs text-gray-400 truncate max-w-[180px] font-medium">
+                      {source.source || 'Unknown Metadata'}
+                    </span>
+                  </div>
+                  <button 
+                    onClick={() => alert(`Full preview for "${source.source}" coming soon!`)}
+                    className="p-2 bg-surface-800 rounded-lg hover:bg-accent-500 hover:text-white transition-all text-gray-500"
+                  >
+                    <ExternalLink className="w-4 h-4" />
+                  </button>
+                </div>
+              </motion.div>
+            ))}
+          </div>
+        </div>
+      )}
+    </div>
+  );
+}
diff --git a/RAG_FULL_APPLICATION_FRONTEND/src/components/TechniqueSelector.jsx b/RAG_FULL_APPLICATION_FRONTEND/src/components/TechniqueSelector.jsx
new file mode 100644
index 0000000000000000000000000000000000000000..839a36b2b3b34a985dad846bbc99c76b08d07f2f
--- /dev/null
+++ b/RAG_FULL_APPLICATION_FRONTEND/src/components/TechniqueSelector.jsx
@@ -0,0 +1,46 @@
+import React from 'react';
+import { Zap, Search, Repeat, Filter, Layers, Cpu, Database } from 'lucide-react';
+
+const techniques = [
+  { id: 'hybrid', name: 'Hybrid Search', desc: 'BM25 + Vector', color: 'border-green-500', icon: Search },
+  { id: 'rerank', name: 'Re-ranking', desc: 'Cross-Encoder', color: 'border-accent-500', icon: Repeat },
+  { id: 'hyde', name: 'Query Expansion', desc: 'HyDE + Multi-Query', color: 'border-accent-600', icon: Zap },
+  { id: 'meta', name: 'Metadata Filter', desc: 'SQL + Vector', color: 'border-orange-500', icon: Filter },
+  { id: 'colbert', name: 'ColBERT', desc: 'Token MaxSim', color: 'border-red-500', icon: Layers },
+  { id: 'agentic', name: 'Agentic RAG', desc: 'Qwen3 Agent', color: 'border-primary-600', icon: Cpu },
+  { id: 'cache', name: 'Cache', desc: 'Redis Query Cache', color: 'border-gray-500', icon: Database },
+];
+
+export default function TechniqueSelector({ selected, onSelect }) {
+  return (
+    <div className="grid grid-cols-1 sm:grid-cols-2 lg:grid-cols-3 xl:grid-cols-4 gap-4 mt-6">
+      {techniques.map((t) => (
+        <button
+          key={t.id}
+          onClick={() => onSelect(t.id)}
+          className={`relative group p-5 rounded-2xl border-2 text-left transition-all duration-300 ${
+            selected === t.id 
+            ? `${t.color} bg-surface-800 shadow-xl scale-[1.03] ring-4 ring-opacity-10 ring-white` 
+            : 'border-surface-800 bg-surface-900/50 text-gray-500 hover:border-surface-700 hover:bg-surface-800'
+          }`}
+        >
+          <div className="flex items-center gap-3 mb-2">
+            <t.icon className={`w-5 h-5 ${selected === t.id ? 'text-white' : 'text-gray-600'}`} />
+            <h3 className={`font-bold text-sm tracking-tight ${selected === t.id ? 'text-white' : 'text-gray-400'}`}>
+              {t.name}
+            </h3>
+          </div>
+          <p className={`text-[11px] leading-relaxed ${selected === t.id ? 'text-gray-300' : 'text-gray-600'}`}>
+            {t.desc}
+          </p>
+          
+          {selected === t.id && (
+            <div className="absolute top-2 right-2">
+              <div className="w-1.5 h-1.5 bg-white rounded-full animate-ping" />
+            </div>
+          )}
+        </button>
+      ))}
+    </div>
+  );
+}
diff --git a/RAG_FULL_APPLICATION_FRONTEND/src/index.css b/RAG_FULL_APPLICATION_FRONTEND/src/index.css
new file mode 100644
index 0000000000000000000000000000000000000000..0bf2fd4e749ac8018a171967be5c8b67f80135fb
--- /dev/null
+++ b/RAG_FULL_APPLICATION_FRONTEND/src/index.css
@@ -0,0 +1,52 @@
+@tailwind base;
+@tailwind components;
+@tailwind utilities;
+
+:root {
+  background-color: #0a0f0a;
+  color: #e2e8f0;
+}
+
+body {
+  margin: 0;
+  font-family: 'Inter', system-ui, -apple-system, sans-serif;
+  -webkit-font-smoothing: antialiased;
+  -moz-osx-font-smoothing: grayscale;
+}
+
+/* Custom UI Pieces */
+@layer components {
+  .btn-primary {
+    @apply px-4 py-2 bg-primary-500 hover:bg-primary-600 text-white rounded-lg transition-all shadow-glow-green;
+  }
+  .btn-accent {
+    @apply px-4 py-2 bg-accent-500 hover:bg-accent-600 text-white rounded-lg transition-all shadow-glow-violet;
+  }
+  .card {
+    @apply bg-surface-800 border border-surface-700 rounded-xl p-6 transition-all;
+  }
+  .input-field {
+    @apply bg-surface-900 border border-surface-700 rounded-lg px-4 py-2 focus:ring-2 focus:ring-accent-500 outline-none;
+  }
+  .query-textarea {
+    @apply w-full bg-surface-900 border-2 border-surface-800 rounded-2xl pl-12 pr-28 py-4 text-lg focus:border-accent-500 transition-all shadow-xl outline-none resize-none overflow-hidden min-h-[60px];
+  }
+}
+
+.shadow-glow-green {
+  box-shadow: 0 0 20px rgba(34, 197, 94, 0.2);
+}
+.shadow-glow-violet {
+  box-shadow: 0 0 20px rgba(139, 92, 246, 0.2);
+}
+
+/* Custom Scrollbar */
+.custom-scrollbar::-webkit-scrollbar {
+  width: 6px;
+}
+.custom-scrollbar::-webkit-scrollbar-track {
+  @apply bg-surface-900;
+}
+  .custom-scrollbar::-webkit-scrollbar-thumb {
+    @apply bg-surface-700 rounded-full hover:bg-opacity-80 transition-colors;
+  }
diff --git a/RAG_FULL_APPLICATION_FRONTEND/src/main.jsx b/RAG_FULL_APPLICATION_FRONTEND/src/main.jsx
new file mode 100644
index 0000000000000000000000000000000000000000..b9a1a6deac8775b5598874b2bc3c7971d82cf211
--- /dev/null
+++ b/RAG_FULL_APPLICATION_FRONTEND/src/main.jsx
@@ -0,0 +1,10 @@
+import { StrictMode } from 'react'
+import { createRoot } from 'react-dom/client'
+import './index.css'
+import App from './App.jsx'
+
+createRoot(document.getElementById('root')).render(
+  <StrictMode>
+    <App />
+  </StrictMode>,
+)
diff --git a/RAG_FULL_APPLICATION_FRONTEND/src/store/authStore.js b/RAG_FULL_APPLICATION_FRONTEND/src/store/authStore.js
new file mode 100644
index 0000000000000000000000000000000000000000..275ced6635bacc204963b46f296a444563ac3797
--- /dev/null
+++ b/RAG_FULL_APPLICATION_FRONTEND/src/store/authStore.js
@@ -0,0 +1,28 @@
+import { create } from 'zustand';
+import api from '../api/client';
+
+export const useAuthStore = create((set) => ({
+  user: null,
+  isAuthenticated: !!sessionStorage.getItem('token'),
+  
+  login: async (username, password) => {
+    try {
+      const formData = new FormData();
+      formData.append('username', username);
+      formData.append('password', password);
+      
+      const { data } = await api.post('/auth/login', formData);
+      sessionStorage.setItem('token', data.access_token);
+      set({ isAuthenticated: true });
+      return true;
+    } catch (error) {
+      console.error('Login failed', error);
+      return false;
+    }
+  },
+  
+  logout: () => {
+    sessionStorage.removeItem('token');
+    set({ user: null, isAuthenticated: false });
+  },
+}));
diff --git a/RAG_FULL_APPLICATION_FRONTEND/src/store/pipelineStore.js b/RAG_FULL_APPLICATION_FRONTEND/src/store/pipelineStore.js
new file mode 100644
index 0000000000000000000000000000000000000000..c98008256bd557265364c4bd134322c56d5326e1
--- /dev/null
+++ b/RAG_FULL_APPLICATION_FRONTEND/src/store/pipelineStore.js
@@ -0,0 +1,20 @@
+import { create } from 'zustand';
+
+export const usePipelineStore = create((set) => ({
+  documents: [],
+  selectedDoc: null,
+  currentAnswer: '',
+  sources: [],
+  steps: [],
+  isQuerying: false,
+  isIngesting: false,
+  
+  setDocuments: (documents) => set({ documents }),
+  setSelectedDoc: (selectedDoc) => set({ selectedDoc }),
+  setAnswer: (answer) => set({ currentAnswer: answer }),
+  setSources: (sources) => set({ sources }),
+  addStep: (step) => set((state) => ({ steps: [...state.steps, step] })),
+  clearSteps: () => set({ steps: [] }),
+  setQuerying: (isQuerying) => set({ isQuerying }),
+  setIngesting: (isIngesting) => set({ isIngesting }),
+}));
diff --git a/RAG_FULL_APPLICATION_FRONTEND/tailwind.config.js b/RAG_FULL_APPLICATION_FRONTEND/tailwind.config.js
new file mode 100644
index 0000000000000000000000000000000000000000..b5d4902ea1548b84c7e1d7296dc8a8e399a45523
--- /dev/null
+++ b/RAG_FULL_APPLICATION_FRONTEND/tailwind.config.js
@@ -0,0 +1,29 @@
+/** @type {import('tailwindcss').Config} */
+export default {
+  content: [
+    "./index.html",
+    "./src/**/*.{js,ts,jsx,tsx}",
+  ],
+  theme: {
+    extend: {
+      colors: {
+        primary: {   // Green
+          50: '#f0fdf4', 400: '#4ade80',
+          500: '#22c55e', 600: '#16a34a', 700: '#15803d'
+        },
+        accent: {    // Violet
+          50: '#f5f3ff', 400: '#a78bfa',
+          500: '#8b5cf6', 600: '#7c3aed', 700: '#6d28d9'
+        },
+        surface: {   // Dark base for dashboard
+          900: '#0a0f0a', 800: '#111a11', 700: '#1a2b1a'
+        }
+      },
+      boxShadow: {
+        'glow-green':  '0 0 20px rgba(34,197,94,0.25)',
+        'glow-violet': '0 0 20px rgba(139,92,246,0.25)',
+      }
+    },
+  },
+  plugins: [],
+}
diff --git a/RAG_FULL_APPLICATION_FRONTEND/vite.config.js b/RAG_FULL_APPLICATION_FRONTEND/vite.config.js
new file mode 100644
index 0000000000000000000000000000000000000000..8b0f57b91aeb45c54467e29f983a0893dc83c4d9
--- /dev/null
+++ b/RAG_FULL_APPLICATION_FRONTEND/vite.config.js
@@ -0,0 +1,7 @@
+import { defineConfig } from 'vite'
+import react from '@vitejs/plugin-react'
+
+// https://vite.dev/config/
+export default defineConfig({
+  plugins: [react()],
+})
diff --git a/RAG_PIPELINE_BLUEPRINT_V3.md b/RAG_PIPELINE_BLUEPRINT_V3.md
new file mode 100644
index 0000000000000000000000000000000000000000..4c6c21ff67af9a4b0b057d84362dc19b2a2b163e
--- /dev/null
+++ b/RAG_PIPELINE_BLUEPRINT_V3.md
@@ -0,0 +1,1455 @@
+# 🧠 RAG Pipeline — Production Blueprint V3 (100% Free)
+
+> **Stack:** FastAPI · React · Supabase pgvector · bge-m3 (HF Space) · Qwen3 · Mistral OCR · Ernie Bot  
+> **Deploy:** Netlify (Frontend) · Render (Backend) · Supabase (DB + Vectors)  
+> **Cost:** $0.00  
+> **Theme:** Green (#22C55E) + Violet (#8B5CF6)
+
+---
+
+## 📑 Table of Contents
+1. [Full System Architecture](#1-full-system-architecture)
+2. [Tech Stack — All Free](#2-tech-stack--all-free)
+3. [Monorepo Structure](#3-monorepo-structure)
+4. [Supabase Setup](#4-supabase-setup)
+5. [Backend — FastAPI Deep Dive](#5-backend--fastapi-deep-dive)
+6. [File Processing — All Types](#6-file-processing--all-types)
+7. [Chunking Engine — 6 Strategies](#7-chunking-engine--6-strategies)
+8. [Embedding Service](#8-embedding-service)
+9. [All 8 RAG Techniques](#9-all-8-rag-techniques)
+10. [Multi-User Architecture](#10-multi-user-architecture)
+11. [API Endpoints](#11-api-endpoints)
+12. [Frontend — React Deep Dive](#12-frontend--react-deep-dive)
+13. [Docker Setup](#13-docker-setup)
+14. [Environment Variables](#14-environment-variables)
+15. [Deployment Guide](#15-deployment-guide)
+16. [Production Additions](#16-production-additions)
+
+---
+
+## 1. Full System Architecture
+
+```
+┌─────────────────────────────────────────────────────┐
+│           NETLIFY — React Frontend                   │
+│  Upload → Technique Select → Chunk Config → Query   │
+└────────────────────┬────────────────────────────────┘
+                     │ HTTPS + WSS
+┌────────────────────▼────────────────────────────────┐
+│           RENDER — FastAPI Backend                   │
+│                                                      │
+│  ┌──────────┐ ┌──────────┐ ┌──────────┐            │
+│  │ /ingest  │ │ /query   │ │ /evaluate│            │
+│  └──────────┘ └──────────┘ └──────────┘            │
+│                                                      │
+│  ┌────────────────────────────────────────────────┐ │
+│  │             Core Services                      │ │
+│  │  FileParser · ChunkEngine · EmbedService       │ │
+│  │  LLMService · OCRService · ReRankService       │ │
+│  │  SupabaseClient · CacheService · BM25Service   │ │
+│  └────────────────────────────────────────────────┘ │
+│                                                      │
+│  Redis (Render free)     Docker container            │
+└──────┬──────────┬────────────────┬───────────────────┘
+       │          │                │
+       ▼          ▼                ▼
+┌──────────┐ ┌─────────────┐ ┌──────────────────────┐
+│ Supabase │ │  HF Spaces  │ │  HF Spaces           │
+│          │ │             │ │                      │
+│ pgvector │ │ bge-m3      │ │ Qwen3 (LLM)          │
+│ postgres │ │ embeddings  │ │ Mistral OCR (PDF/img) │
+│ metadata │ │ (free)      │ │ Ernie Bot (images)   │
+│ users    │ │             │ │                      │
+│ chunks   │ │ 1K tok cap  │ │                      │
+│ cache    │ └─────────────┘ └──────────────────────┘
+└──────────┘
+```
+
+---
+
+## 2. Tech Stack — All Free
+
+| Layer | Technology | Free Tier |
+|-------|-----------|-----------|
+| Vector DB | Supabase pgvector | 500MB, unlimited rows |
+| Metadata DB | Supabase PostgreSQL | Same instance |
+| Embeddings | `lamhieu-lightweight-embeddings.hf.space` bge-m3 | Free HF Space |
+| LLM | Qwen3 `Qwen/Qwen3-Demo` | Free HF Space |
+| PDF/Image OCR | Mistral OCR `tatendachirume/Mistral-OCR` | Free HF Space |
+| Image Understanding | Ernie Bot `baidu-simple-ernie-bot-demo` | Free HF Space |
+| Re-ranking | `cross-encoder/ms-marco-MiniLM-L-6-v2` | Runs on Render CPU |
+| Backend | Render free tier | 512MB RAM |
+| Frontend | Netlify free tier | 100GB bandwidth |
+| Cache | Render Redis free | 25MB |
+| Containers | Docker + docker-compose | Local dev |
+
+---
+
+## 3. Monorepo Structure
+
+```
+rag-pipeline/
+│
+├── backend/                          ← Render deployment
+│   ├── app/
+│   │   ├── main.py                   # FastAPI app factory
+│   │   ├── config.py                 # pydantic-settings
+│   │   ├── dependencies.py           # DI: supabase, redis, etc.
+│   │   │
+│   │   ├── routers/
+│   │   │   ├── auth.py               # register, login, refresh
+│   │   │   ├── ingest.py             # upload, status, documents
+│   │   │   ├── query.py              # search, history, cache
+│   │   │   ├── techniques.py         # list techniques
+│   │   │   ├── evaluate.py           # RAGAs run + report
+│   │   │   └── stats.py              # index stats
+│   │   │
+│   │   ├── services/
+│   │   │   ├── supabase_client.py    # Supabase vector + metadata ops
+│   │   │   ├── embed_service.py      # bge-m3 via HF Space
+│   │   │   ├── llm_service.py        # Qwen3 (your existing code)
+│   │   │   ├── ocr_service.py        # Mistral OCR (your existing code)
+│   │   │   ├── ernie_service.py      # Ernie Bot (your existing code)
+│   │   │   ├── file_parser.py        # dispatcher for all file types
+│   │   │   ├── chunk_engine.py       # 6 chunking strategies
+│   │   │   ├── bm25_service.py       # keyword search (rank_bm25)
+│   │   │   ├── rerank_service.py     # cross-encoder re-ranking
+│   │   │   └── cache_service.py      # Redis query cache
+│   │   │
+│   │   ├── techniques/
+│   │   │   ├── base.py               # abstract base + emit_step
+│   │   │   ├── hybrid_search.py      # BM25 + pgvector → RRF
+│   │   │   ├── reranking.py          # ANN → cross-encoder
+│   │   │   ├── query_expansion.py    # HyDE + multi-query
+│   │   │   ├── metadata_filter.py    # SQL filter + vector search
+│   │   │   ├── colbert.py            # token-level MaxSim
+│   │   │   ├── agentic_rag.py        # Qwen3 tool-calling agent
+│   │   │   ├── cache_incremental.py  # Redis cache + delta ingest
+│   │   │   └── ragas_eval.py         # RAGAs evaluation
+│   │   │
+│   │   ├── models/
+│   │   │   ├── schemas.py            # Pydantic request/response
+│   │   │   └── enums.py              # TechniqueType, FileType, etc.
+│   │   │
+│   │   └── utils/
+│   │       ├── logger.py             # print_with_time (loguru)
+│   │       ├── json_utils.py         # extract_json_block, repair_json
+│   │       ├── retry_utils.py        # thread timeout + retry decorator
+│   │       ├── hash_utils.py         # SHA-256 chunk hashing
+│   │       └── ws_manager.py         # WebSocket multi-user manager
+│   │
+│   ├── tests/
+│   │   ├── test_ingest.py
+│   │   ├── test_query.py
+│   │   ├── test_techniques.py
+│   │   └── test_parsers.py
+│   │
+│   ├── requirements.txt
+│   ├── Dockerfile                    # Render uses this
+│   └── .env.example                  # key names only, no values
+│
+├── frontend/                         ← Netlify deployment
+│   ├── src/
+│   │   ├── main.jsx
+│   │   ├── App.jsx
+│   │   ├── pages/
+│   │   │   ├── LandingPage.jsx       # auth + hero (green/violet)
+│   │   │   ├── DashboardPage.jsx     # document list
+│   │   │   ├── PipelinePage.jsx      # main RAG UI
+│   │   │   └── EvaluatePage.jsx      # RAGAs metrics dashboard
+│   │   ├── components/
+│   │   │   ├── upload/
+│   │   │   │   ├── FileDropZone.jsx  # drag & drop, all file types
+│   │   │   │   └── UploadProgress.jsx
+│   │   │   ├── pipeline/
+│   │   │   │   ├── PipelineVisualizer.jsx  # animated step trace
+│   │   │   │   ├── StepCard.jsx            # green/violet step cards
+│   │   │   │   ├── ChunkSliders.jsx        # chunk + overlap sliders
+│   │   │   │   └── TechniqueSelector.jsx   # 8 technique cards
+│   │   │   ├── query/
+│   │   │   │   ├── QueryInput.jsx
+│   │   │   │   ├── AnswerPanel.jsx
+│   │   │   │   └── SourceChunks.jsx
+│   │   │   ├── auth/
+│   │   │   │   ├── LoginForm.jsx
+│   │   │   │   └── RegisterForm.jsx
+│   │   │   └── evaluate/
+│   │   │       ├── MetricsRadar.jsx  # Recharts radar chart
+│   │   │       └── EvalTable.jsx
+│   │   ├── store/
+│   │   │   ├── authStore.js          # JWT in-memory (NOT localStorage)
+│   │   │   ├── pipelineStore.js
+│   │   │   └── documentStore.js
+│   │   ├── hooks/
+│   │   │   ├── useAuth.js
+│   │   │   ├── useUpload.js
+│   │   │   ├── useQuery.js
+│   │   │   └── usePipelineWS.js      # WebSocket real-time steps
+│   │   ├── api/
+│   │   │   └── client.js             # Axios + JWT interceptor
+│   │   └── utils/
+│   │       ├── stepColors.js         # step → green/violet colors
+│   │       └── fileIcons.js
+│   ├── package.json
+│   ├── vite.config.js
+│   ├── tailwind.config.js            # green + violet theme
+│   ├── netlify.toml
+│   └── .env.example
+│
+├── docker-compose.yml                ← Local dev only
+├── .gitignore
+└── README.md
+```
+
+---
+
+## 4. Supabase Setup
+
+### Why Supabase (not raw PostgreSQL)
+
+- Free 500MB, no credit card
+- pgvector built-in (vector similarity search)
+- Replaces both FAISS and SQLite in one service
+- REST + Python client available
+
+### Database Schema (all tables in one Supabase project)
+
+```sql
+-- Users (multi-user support)
+CREATE TABLE users (
+    id          UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+    username    TEXT UNIQUE NOT NULL,
+    password_hash TEXT NOT NULL,
+    created_at  TIMESTAMPTZ DEFAULT NOW()
+);
+
+-- Documents (one row per uploaded file)
+CREATE TABLE documents (
+    id              UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+    user_id         UUID REFERENCES users(id) ON DELETE CASCADE,
+    filename        TEXT NOT NULL,
+    file_type       TEXT NOT NULL,
+    technique       TEXT NOT NULL,
+    chunk_strategy  TEXT NOT NULL,
+    chunk_size      INT DEFAULT 512,
+    overlap         INT DEFAULT 64,
+    status          TEXT DEFAULT 'pending', -- pending|running|done|failed
+    chunk_count     INT DEFAULT 0,
+    created_at      TIMESTAMPTZ DEFAULT NOW(),
+    updated_at      TIMESTAMPTZ DEFAULT NOW()
+);
+
+-- Chunks (text + metadata per chunk)
+CREATE TABLE chunks (
+    id              UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+    document_id     UUID REFERENCES documents(id) ON DELETE CASCADE,
+    user_id         UUID REFERENCES users(id) ON DELETE CASCADE,
+    text            TEXT NOT NULL,
+    token_count     INT,
+    source          TEXT,          -- original filename
+    page            INT,           -- page number (PDF)
+    section         TEXT,          -- heading (DOCX/MD)
+    chunk_index     INT,
+    parent_chunk_id UUID,          -- for parent-child chunking
+    text_hash       TEXT,          -- SHA-256 for incremental ingest
+    metadata        JSONB DEFAULT '{}',
+    created_at      TIMESTAMPTZ DEFAULT NOW()
+);
+
+-- Vectors (pgvector — bge-m3 dim=1024)
+CREATE TABLE chunk_vectors (
+    id          UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+    chunk_id    UUID REFERENCES chunks(id) ON DELETE CASCADE,
+    document_id UUID REFERENCES documents(id) ON DELETE CASCADE,
+    user_id     UUID REFERENCES users(id) ON DELETE CASCADE,
+    embedding   vector(1024) NOT NULL
+);
+
+-- HNSW index for fast ANN search
+CREATE INDEX ON chunk_vectors
+USING hnsw (embedding vector_cosine_ops)
+WITH (m = 16, ef_construction = 64);
+
+-- ColBERT token vectors (only populated when ColBERT technique used)
+CREATE TABLE colbert_tokens (
+    id          UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+    chunk_id    UUID REFERENCES chunks(id) ON DELETE CASCADE,
+    token_text  TEXT,
+    position    INT,
+    embedding   vector(1024) NOT NULL
+);
+
+-- Query cache (also stored in Redis, Supabase as overflow)
+CREATE TABLE query_cache (
+    id           UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+    user_id      UUID REFERENCES users(id) ON DELETE CASCADE,
+    document_id  UUID REFERENCES documents(id) ON DELETE CASCADE,
+    query_hash   TEXT NOT NULL,
+    query_text   TEXT,
+    answer       TEXT,
+    sources      JSONB,
+    technique    TEXT,
+    hit_count    INT DEFAULT 0,
+    created_at   TIMESTAMPTZ DEFAULT NOW()
+);
+
+-- RAGAs evaluation reports
+CREATE TABLE eval_reports (
+    id              UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+    user_id         UUID REFERENCES users(id) ON DELETE CASCADE,
+    document_id     UUID REFERENCES documents(id) ON DELETE CASCADE,
+    faithfulness    FLOAT,
+    answer_relevancy FLOAT,
+    context_precision FLOAT,
+    context_recall  FLOAT,
+    per_question    JSONB,
+    created_at      TIMESTAMPTZ DEFAULT NOW()
+);
+```
+
+### Supabase Vector Search Function
+
+```sql
+-- Used by all retrieval techniques
+CREATE OR REPLACE FUNCTION match_chunks(
+    query_embedding vector(1024),
+    match_document_id UUID,
+    match_user_id UUID,
+    match_count INT DEFAULT 5,
+    filter_chunk_ids UUID[] DEFAULT NULL
+)
+RETURNS TABLE (
+    chunk_id UUID,
+    text TEXT,
+    source TEXT,
+    page INT,
+    section TEXT,
+    metadata JSONB,
+    similarity FLOAT
+)
+LANGUAGE plpgsql
+AS $$
+BEGIN
+    RETURN QUERY
+    SELECT
+        c.id,
+        c.text,
+        c.source,
+        c.page,
+        c.section,
+        c.metadata,
+        1 - (cv.embedding <=> query_embedding) AS similarity
+    FROM chunk_vectors cv
+    JOIN chunks c ON c.id = cv.chunk_id
+    WHERE cv.document_id = match_document_id
+      AND cv.user_id = match_user_id
+      AND (filter_chunk_ids IS NULL OR c.id = ANY(filter_chunk_ids))
+    ORDER BY cv.embedding <=> query_embedding
+    LIMIT match_count;
+END;
+$$;
+```
+
+---
+
+## 5. Backend — FastAPI Deep Dive
+
+### `app/main.py`
+
+```python
+# Key responsibilities:
+# - FastAPI app with CORS for Netlify origin
+# - Mount all routers
+# - Startup: init Supabase client, Redis, load cross-encoder
+# - Shutdown: flush Redis pipeline
+# - WebSocket: /ws/pipeline/{job_id}?token={jwt}
+
+app = FastAPI(title="RAG Pipeline API", version="3.0.0")
+
+# CORS — Netlify + local dev
+origins = settings.CORS_ORIGINS.split(",")
+app.add_middleware(CORSMiddleware, allow_origins=origins,
+                   allow_methods=["*"], allow_headers=["*"])
+
+# Routers
+app.include_router(auth_router,      prefix="/auth")
+app.include_router(ingest_router,    prefix="/ingest")
+app.include_router(query_router,     prefix="/query")
+app.include_router(technique_router, prefix="/techniques")
+app.include_router(evaluate_router,  prefix="/evaluate")
+app.include_router(stats_router,     prefix="/stats")
+
+@app.websocket("/ws/pipeline/{job_id}")
+async def pipeline_ws(websocket, job_id, token):
+    # Verify JWT, then stream pipeline step events
+    ...
+```
+
+### `app/config.py`
+
+```python
+class Settings(BaseSettings):
+    # Supabase
+    SUPABASE_URL: str           # https://xxxx.supabase.co
+    SUPABASE_KEY: str           # anon/service_role key
+    SUPABASE_DB_URL: str        # postgresql://... (direct connection)
+
+    # Embeddings (your existing HF Space)
+    EMBED_API_URL: str = "https://lamhieu-lightweight-embeddings.hf.space/"
+    EMBED_MODEL: str = "bge-m3"
+    EMBED_DIM: int = 1024
+    EMBED_AUTH_KEY: str = ""
+    EMBED_MAX_TOKENS: int = 1000   # hard cap — 1K context
+    EMBED_TIMEOUT: int = 60
+    EMBED_MAX_RETRIES: int = 3
+
+    # LLM — Qwen3
+    QWEN3_MODEL_NAME: str = "Qwen/Qwen3-Demo"
+    QWEN3_THINKING_BUDGET: int = 38
+    LLM_RESPONSE_TIMEOUT: int = 1080
+    MAX_LLM_RETRIES: int = 5
+    MAX_TIMEOUT_RETRIES: int = 10
+
+    # OCR — Mistral
+    MISTRAL_OCR_SPACE: str = "tatendachirume/Mistral-OCR"
+    MISTRAL_API_KEY: str
+
+    # Image — Ernie Bot
+    ERNIE_SPACE_URL: str = "https://baidu-simple-ernie-bot-demo.hf.space/"
+
+    # Redis
+    REDIS_URL: str
+    CACHE_TTL_SECONDS: int = 3600
+
+    # Auth
+    JWT_SECRET_KEY: str
+    JWT_ALGORITHM: str = "HS256"
+    JWT_EXPIRE_MINUTES: int = 1440
+
+    # Re-ranking
+    RERANK_MODEL: str = "cross-encoder/ms-marco-MiniLM-L-6-v2"
+
+    # Rate limiting
+    RATE_LIMIT_PER_MINUTE: int = 20
+    RATE_LIMIT_UPLOAD_PER_DAY: int = 50
+
+    # Defaults
+    DEFAULT_CHUNK_SIZE: int = 512
+    DEFAULT_OVERLAP: int = 64
+    DEFAULT_TOP_K: int = 5
+    MAX_FILE_SIZE_MB: int = 50
+
+    # CORS
+    CORS_ORIGINS: str  # comma-separated
+```
+
+### `app/services/supabase_client.py`
+
+```python
+"""
+Central Supabase service.
+Handles: vector upsert, ANN search, chunk CRUD, metadata queries.
+Uses supabase-py client + asyncpg for direct SQL when needed.
+"""
+from supabase import create_client, Client
+
+class SupabaseService:
+    def __init__(self):
+        self.client: Client = create_client(
+            settings.SUPABASE_URL, settings.SUPABASE_KEY
+        )
+
+    # ── Chunk Operations ────────────────────────────────────────────────
+    async def insert_chunks(self, chunks: list[dict]) -> list[str]:
+        """Insert chunks, return list of chunk_ids"""
+
+    async def get_chunks_by_ids(self, chunk_ids: list[str]) -> list[dict]:
+        """Fetch chunk text + metadata by IDs"""
+
+    async def get_chunk_hashes(self, document_id: str) -> dict[str, str]:
+        """Returns {chunk_index: text_hash} for incremental ingest"""
+
+    async def delete_chunks(self, chunk_ids: list[str]):
+        """Delete chunks + their vectors (CASCADE)"""
+
+    # ── Vector Operations ───────────────────────────────────────────────
+    async def upsert_vectors(self, vectors: list[dict]):
+        """
+        vectors: [{"chunk_id": uuid, "document_id": uuid,
+                   "user_id": uuid, "embedding": [...1024 floats...]}]
+        """
+
+    async def vector_search(self, query_embedding: list[float],
+                             document_id: str, user_id: str,
+                             top_k: int, filter_chunk_ids: list = None
+                             ) -> list[dict]:
+        """
+        Calls match_chunks() SQL function.
+        Returns: [{chunk_id, text, source, page, section, metadata, similarity}]
+        """
+        result = self.client.rpc("match_chunks", {
+            "query_embedding": query_embedding,
+            "match_document_id": document_id,
+            "match_user_id": user_id,
+            "match_count": top_k,
+            "filter_chunk_ids": filter_chunk_ids
+        }).execute()
+        return result.data
+
+    # ── Metadata Filter ─────────────────────────────────────────────────
+    async def filter_chunk_ids(self, document_id: str, filters: dict) -> list[str]:
+        """
+        Filter chunks by metadata fields.
+        filters: {"page": {"gte": 5, "lte": 10}, "section": "Intro"}
+        Returns list of chunk_ids matching the filter.
+        """
+
+    # ── ColBERT Token Vectors ───────────────────────────────────────────
+    async def insert_colbert_tokens(self, token_rows: list[dict]):
+        """Store token-level vectors for ColBERT technique"""
+
+    async def get_colbert_tokens(self, document_id: str) -> list[dict]:
+        """Fetch all token vectors for MaxSim scoring"""
+
+    # ── Cache ────────────────────────────────────────────────────────────
+    async def get_cached_query(self, user_id: str,
+                                document_id: str, query_hash: str) -> dict | None:
+        """Check Supabase query_cache table (overflow from Redis)"""
+
+    async def store_cached_query(self, cache_row: dict):
+        """Store answer in query_cache table"""
+```
+
+### `app/services/embed_service.py` — Your exact code, integrated
+
+```python
+"""
+Direct port of your get_embedding_with_retry() function.
+Extended to support batch embedding for ingestion.
+1K token hard cap applied before every call.
+"""
+import tiktoken
+enc = tiktoken.get_encoding("cl100k_base")
+
+def truncate_to_1k(text: str) -> str:
+    tokens = enc.encode(text)
+    return enc.decode(tokens[:1000]) if len(tokens) > 1000 else text
+
+def get_embedding(text: str) -> list[float]:
+    """
+    Your existing get_embedding_with_retry() — unchanged.
+    Truncates to 1K tokens before calling HF Space.
+    Model: bge-m3, dim: 1024
+    """
+    text = truncate_to_1k(text)
+    # ... your exact code from get_embedding_with_retry()
+
+async def embed_batch(texts: list[str]) -> list[list[float]]:
+    """
+    Batch embedding for ingestion.
+    Processes sequentially in groups of 8 (HF Space rate limit safety).
+    Each text truncated to 1K tokens.
+    """
+    all_embeddings = []
+    for i in range(0, len(texts), 8):
+        batch = [truncate_to_1k(t) for t in texts[i:i+8]]
+        for text in batch:
+            emb = get_embedding(text)
+            all_embeddings.append(emb)
+    return all_embeddings
+```
+
+---
+
+## 6. File Processing — All Types
+
+```
+PDF        → Mistral OCR (your perform_ocr()) → text per page
+JPG/PNG/JPEG → Ernie Bot (your ernie code)    → image description text
+DOCX       → python-docx                      → paragraphs by heading
+TXT        → raw read                         → paragraph split
+MD         → regex heading split              → section chunks
+JSON       → flatten keys/values              → one text per item
+```
+
+### `app/services/file_parser.py`
+
+```python
+async def parse_file(file_path, file_type, job_id, ws_manager) -> list[dict]:
+    """
+    Returns: [{"text": str, "metadata": {"source", "page", "section"}}]
+    Emits WebSocket steps for every file type.
+    """
+    match file_type:
+        case "pdf":
+            return await parse_pdf(file_path, job_id, ws_manager)
+        case "jpg" | "jpeg" | "png":
+            return await parse_image(file_path, job_id, ws_manager)
+        case "docx":
+            return parse_docx(file_path)
+        case "txt":
+            return parse_txt(file_path)
+        case "md":
+            return parse_markdown(file_path)
+        case "json":
+            return parse_json(file_path)
+
+# PDF — uses your perform_ocr() unchanged
+async def parse_pdf(file_path, job_id, ws_manager):
+    await ws_manager.emit(job_id, step="OCR_START", color="#8B5CF6",
+        detail=f"Sending to Mistral OCR...")
+    plain_text, markdown_text, images = perform_ocr(
+        file_path, api_key=settings.MISTRAL_API_KEY)
+    await ws_manager.emit(job_id, step="OCR_DONE", color="#22C55E",
+        detail=f"OCR complete: {len(plain_text)} chars")
+    return split_to_pages(plain_text, markdown_text, str(file_path))
+
+# Image — uses your Ernie Bot code unchanged
+async def parse_image(file_path, job_id, ws_manager):
+    await ws_manager.emit(job_id, step="IMAGE_ANALYZE", color="#8B5CF6",
+        detail="Ernie Bot analyzing image...")
+    description = understand_image(file_path)
+    return [{"text": description, "metadata": {"source": str(file_path), "page": 1}}]
+
+# DOCX — python-docx, split by headings
+def parse_docx(file_path):
+    doc = Document(file_path)
+    sections, current_heading, current_text = [], "", []
+    for para in doc.paragraphs:
+        if para.style.name.startswith('Heading'):
+            if current_text:
+                sections.append({"text": " ".join(current_text),
+                                  "metadata": {"source": str(file_path),
+                                               "section": current_heading}})
+            current_heading, current_text = para.text, []
+        elif para.text.strip():
+            current_text.append(para.text)
+    if current_text:
+        sections.append({"text": " ".join(current_text),
+                         "metadata": {"source": str(file_path),
+                                      "section": current_heading}})
+    return sections
+
+# MD — split at headings
+def parse_markdown(file_path):
+    text = Path(file_path).read_text(encoding="utf-8")
+    parts = re.split(r'\n(?=#+\s)', text)
+    return [{"text": p.strip(), "metadata": {"source": str(file_path),
+             "section": re.match(r'^#+\s+(.*)', p).group(1) if re.match(r'^#+\s', p) else ""}}
+            for p in parts if p.strip()]
+
+# TXT — paragraph split
+def parse_txt(file_path):
+    text = Path(file_path).read_text(encoding="utf-8")
+    paragraphs = [p.strip() for p in text.split("\n\n") if p.strip()]
+    return [{"text": p, "metadata": {"source": str(file_path)}} for p in paragraphs]
+
+# JSON — flatten per item
+def parse_json(file_path):
+    data = json.loads(Path(file_path).read_text())
+    items = data if isinstance(data, list) else [data]
+    docs = []
+    for item in items:
+        def flatten(obj, prefix=""):
+            parts = []
+            for k, v in obj.items() if isinstance(obj, dict) else enumerate(obj):
+                full_key = f"{prefix}.{k}" if prefix else str(k)
+                if isinstance(v, (dict, list)):
+                    parts.extend(flatten(v, full_key))
+                else:
+                    parts.append(f"{full_key}: {v}")
+            return parts
+        text = " | ".join(flatten(item))
+        docs.append({"text": text, "metadata": {"source": str(file_path),
+                     "original": item}})
+    return docs
+```
+
+---
+
+## 7. Chunking Engine — 6 Strategies
+
+```python
+"""
+All strategies hard-cap at 1K tokens per chunk.
+bge-m3 recommended context: up to 8192, but we cap at 1K for speed/cost.
+"""
+MAX_CHUNK_TOKENS = 1000
+
+class ChunkEngine:
+    def __init__(self, chunk_size: int, overlap: int, strategy: str):
+        self.chunk_size = min(chunk_size, MAX_CHUNK_TOKENS)
+        self.overlap = min(overlap, self.chunk_size // 4)
+        self.strategy = strategy
+        self.enc = tiktoken.get_encoding("cl100k_base")
+
+    def chunk(self, docs: list[dict]) -> list[dict]:
+        # Each output chunk:
+        # {chunk_id, text, token_count, source, page, section,
+        #  chunk_index, parent_chunk_id, text_hash, metadata}
+        match self.strategy:
+            case "fixed":        return self._fixed(docs)
+            case "semantic":     return self._semantic(docs)
+            case "per_page":     return self._per_page(docs)
+            case "per_item":     return self._per_item(docs)
+            case "recursive":    return self._recursive(docs)
+            case "parent_child": return self._parent_child(docs)
+
+    def _fixed(self, docs):
+        """Sliding window: step = chunk_size - overlap. Token-accurate."""
+
+    def _semantic(self, docs):
+        """Use heading sections as natural boundaries. Fixed fallback if too large."""
+
+    def _per_page(self, docs):
+        """One chunk per PDF page. Fixed fallback for long pages."""
+
+    def _per_item(self, docs):
+        """One chunk per JSON item (parser already splits)."""
+
+    def _recursive(self, docs):
+        """Split at: \\n\\n → \\n → '. ' → ' ' until fits in chunk_size."""
+
+    def _parent_child(self, docs):
+        """
+        child:  chunk_size // 4 tokens → stored in Supabase, used for retrieval
+        parent: chunk_size tokens     → stored in Supabase, sent to LLM
+        child.parent_chunk_id → parent.id
+        """
+```
+
+---
+
+## 8. Embedding Service
+
+```python
+# app/services/embed_service.py
+# Your exact get_embedding_with_retry() function — zero changes
+# Calling convention matches your existing code:
+#
+# get_embedding_with_retry(
+#     text=text,
+#     model="bge-m3",
+#     auth_key=settings.EMBED_AUTH_KEY,
+#     max_retries=settings.EMBED_MAX_RETRIES,
+#     timeout_seconds=settings.EMBED_TIMEOUT
+# )
+#
+# Returns: {"data": [[...1024 floats...]], "usage": {...}}
+# We extract: result["data"][0]
+#
+# 1K token truncation applied BEFORE calling — see truncate_to_1k()
+```
+
+---
+
+## 9. All 8 RAG Techniques
+
+### Base class
+
+```python
+# app/techniques/base.py
+class BaseRAGTechnique(ABC):
+    def __init__(self, supabase, embed_svc, llm_svc, redis, job_id, ws_manager):
+        ...
+
+    @abstractmethod
+    async def retrieve(self, query, document_id, user_id, top_k, **kwargs) -> list[dict]:
+        ...
+
+    @abstractmethod
+    async def generate(self, query, chunks) -> str:
+        ...
+
+    async def run(self, request: QueryRequest) -> QueryResponse:
+        chunks = await self.retrieve(...)
+        answer = await self.generate(...)
+        return QueryResponse(...)
+
+    async def emit(self, step, status, color, detail, metadata={}):
+        """Broadcast step event to frontend via WebSocket"""
+        await ws_manager.emit(self.job_id, {
+            "step": step, "status": status,
+            "color": color, "detail": detail,
+            "timestamp": datetime.utcnow().isoformat(),
+            "metadata": metadata
+        })
+```
+
+---
+
+### Technique 1 — Hybrid Search
+
+```python
+# Algorithm: BM25 keyword + pgvector ANN → Reciprocal Rank Fusion (k=60)
+# BM25 index built from chunk texts at ingest time, stored as pickle on Render disk
+
+# Steps emitted:
+#  🟣 EMBED      "Embedding query (bge-m3)..."
+#  🟢 BM25       "BM25 keyword search → {n} candidates"
+#  🟢 VECTOR     "pgvector ANN search → top-{n}"
+#  🟣 RRF        "Reciprocal Rank Fusion merging results..."
+#  🟢 DONE       "Hybrid search → top-{k} returned"
+
+async def retrieve(self, query, document_id, user_id, top_k, bm25_weight=0.5):
+    q_vec = get_embedding(truncate_to_1k(query))
+    bm25_results = bm25_service.search(document_id, query, top_n=top_k * 4)
+    vector_results = await supabase.vector_search(q_vec, document_id, user_id, top_k * 4)
+    fused = reciprocal_rank_fusion(bm25_results, vector_results, k=60)
+    return fused[:top_k]
+```
+
+---
+
+### Technique 2 — Re-ranking
+
+```python
+# Algorithm: pgvector top-20 → cross-encoder/ms-marco-MiniLM-L-6-v2 → top-K
+# Cross-encoder runs on Render CPU. ~3-8s for 20 pairs. Model cached after first load.
+
+# Steps emitted:
+#  🟣 EMBED      "Embedding query..."
+#  🟢 RETRIEVE   "pgvector: fetching top-20 candidates..."
+#  🔴 RERANK     "Cross-encoder re-scoring 20 pairs..."
+#  🟢 DONE       "Re-ranked → top-{k}"
+
+async def retrieve(self, query, document_id, user_id, top_k):
+    q_vec = get_embedding(truncate_to_1k(query))
+    candidates = await supabase.vector_search(q_vec, document_id, user_id, top_k * 4)
+    pairs = [(query, c["text"]) for c in candidates]
+    scores = cross_encoder.predict(pairs)
+    reranked = sorted(zip(candidates, scores), key=lambda x: x[1], reverse=True)
+    return [c for c, _ in reranked[:top_k]]
+```
+
+---
+
+### Technique 3 — Query Expansion (HyDE)
+
+```python
+# Algorithm:
+#  1. Qwen3 generates hypothetical answer → embed it (HyDE)
+#  2. Qwen3 generates 3 query variants → embed each
+#  3. FAISS search with all 4 vectors, deduplicate, rank
+
+# Steps emitted:
+#  🟣 HYDE       "Qwen3 generating hypothetical answer..."
+#  🟣 EXPAND     "Generating 3 query variants..."
+#  🟢 EMBED      "Embedding 4 expanded queries..."
+#  🟢 SEARCH     "pgvector search with all variants..."
+#  🟣 MERGE      "Deduplicating {n} results..."
+#  🟢 DONE       "Query expansion → top-{k}"
+```
+
+---
+
+### Technique 4 — Metadata Filtering
+
+```python
+# Algorithm:
+#  1. User sets filters (page range, section, source file, custom JSON fields)
+#  2. Supabase SQL pre-filters chunk IDs
+#  3. pgvector search restricted to those IDs
+
+# Supported filters:
+#  page: {gte: 5, lte: 10}
+#  section: "Introduction"
+#  source: "contract.docx"
+#  file_type: "pdf"
+#  metadata->>'custom_key': "value"   (JSONB field)
+
+# Steps emitted:
+#  🟤 FILTER     "SQL filter: {filters} → {n} qualifying chunks"
+#  🟣 EMBED      "Embedding query..."
+#  🟢 SEARCH     "pgvector search in filtered subset..."
+#  🟢 DONE       "Metadata-filtered → top-{k}"
+```
+
+---
+
+### Technique 5 — ColBERT (Multi-vector MaxSim)
+
+```python
+# Algorithm:
+#  INGEST: each chunk → tokenize → embed each token → store in colbert_tokens table
+#  QUERY:  tokenize query → embed each token → MaxSim scoring
+#  MaxSim(q,d) = Σ max_j(q_i · d_j) for each query token i
+
+# ⚠️ WARNING shown in UI before selecting:
+# "ColBERT embeds every token individually. For a 50-chunk doc,
+#  expect 500-5000 extra embedding calls. Ingestion will be slow."
+
+# Steps emitted:
+#  🟣 TOKENIZE   "Tokenizing query into {n} tokens..."
+#  🟢 EMBED_TOK  "Embedding {n} query tokens (bge-m3)..."
+#  🔴 MAXSIM     "MaxSim scoring {n_chunks} × {n_tokens} token vectors..."
+#  🟢 DONE       "ColBERT scoring → top-{k}"
+```
+
+---
+
+### Technique 6 — Agentic RAG
+
+```python
+# Algorithm: Qwen3 agent with 4 tools, max 5 iterations
+# Tools:
+#  search_docs(query, top_k)        → pgvector search
+#  filter_search(filters, query)    → metadata-filtered search
+#  get_page(page_num)               → retrieve specific page
+#  summarize_chunks(chunk_ids)      → Qwen3 summarizes chunk set
+
+# Uses your existing Qwen3 wrapper (llm_service.py)
+# Tool call JSON parsed with your extract_json_block() + repair_json_with_module()
+
+# Steps emitted (one per agent iteration):
+#  🟢 AGENT_INIT "Qwen3 agent ready with 4 tools"
+#  🟣 PLAN       "Agent: '{thought[:80]}...'"
+#  🟤 TOOL       "Tool call: {tool_name}({args})"
+#  🟢 OBSERVE    "Tool returned {n} chunks"
+#  🟢 FINAL      "Answer generated after {n} tool calls"
+```
+
+---
+
+### Technique 7 — Caching & Incremental Ingestion
+
+```python
+# SUB-FEATURE A — Redis Query Cache:
+#  key = SHA-256(user_id + document_id + query + technique)
+#  hit  → return stored QueryResponse instantly
+#  miss → run pipeline → store in Redis (TTL: 1hr) + Supabase overflow
+#
+# SUB-FEATURE B — Incremental Ingestion:
+#  On re-upload: hash each chunk text
+#  Compare vs stored hashes in Supabase chunks table
+#  NEW chunks     → embed + insert to Supabase
+#  CHANGED chunks → delete old vectors, re-embed, insert new
+#  UNCHANGED      → skip entirely (0 embedding calls)
+#  DELETED chunks → remove from Supabase (CASCADE deletes vectors)
+#  Saves 80-95% of embedding calls on document updates
+
+# Steps emitted:
+#  🟤 CACHE_CHECK "Checking Redis cache..."
+#  🟢 CACHE_HIT   "Cache hit — returning stored answer (0ms)" OR
+#  🟣 CACHE_MISS  "Cache miss. Running pipeline..."
+#  ──── Incremental ────
+#  🟤 DIFF        "Comparing {n} new chunks vs {m} stored..."
+#  🟢 DELTA       "{new} new, {changed} changed, {same} unchanged"
+#  🟣 EMBED_DELTA "Embedding {n} delta chunks only..."
+#  🟢 DONE        "Incremental update complete"
+```
+
+---
+
+### Technique 8 — RAGAs Evaluation
+
+```python
+# User uploads CSV: question,ground_truth
+# For each question:
+#   1. Retrieve top-K chunks (standard vector search)
+#   2. Generate answer via Qwen3
+#   3. Collect dataset: (question, answer, contexts, ground_truth)
+# RAGAs metrics (Qwen3 as judge):
+#   faithfulness, answer_relevancy, context_precision, context_recall
+# Results saved to Supabase eval_reports table
+# Frontend shows Recharts radar chart + per-question table
+
+# Steps emitted:
+#  🟣 SETUP      "RAGAs initialized — {n} test questions"
+#  🟢 RETRIEVE   "Retrieving context for Q{i}/{n}..."
+#  🟣 GENERATE   "Qwen3 generating answer {i}/{n}..."
+#  🔴 SCORE      "Computing RAGAs metrics (Qwen3 as judge)..."
+#  🟢 REPORT     "Faithfulness:{f:.2f} Relevancy:{r:.2f} ..."
+```
+
+---
+
+## 10. Multi-User Architecture
+
+### User Isolation
+
+```
+Supabase row-level security (RLS) policies:
+  All tables have user_id column
+  RLS enabled: users can only see their own rows
+  Enforced at DB level — even if API has a bug, data stays isolated
+
+FAISS → replaced by Supabase pgvector → isolation via user_id column
+BM25 index files → ./data/bm25_indexes/{user_id}_{doc_id}.pkl
+Upload temp files → ./data/uploads/{user_id}/{filename}
+Redis cache keys → cache:{user_id}:{doc_id}:{query_hash}
+```
+
+### JWT Auth Flow
+
+```
+POST /auth/register  →  username + password → bcrypt hash → Supabase users table
+POST /auth/login     →  verify password → return JWT (24h expiry)
+All protected routes → Authorization: Bearer {token}
+Frontend → JWT stored in Zustand memory (NOT localStorage — XSS safe)
+POST /auth/refresh   → return new JWT before expiry
+```
+
+### WebSocket Isolation
+
+```python
+# app/utils/ws_manager.py
+# One WebSocket connection per (user_id, job_id)
+# Job ownership verified before connecting
+# Users only receive their own pipeline events
+
+class WSManager:
+    _connections: dict[str, WebSocket] = {}  # key = f"{user_id}:{job_id}"
+
+    async def connect(self, job_id, websocket, user_id):
+        key = f"{user_id}:{job_id}"
+        self._connections[key] = websocket
+
+    async def emit(self, job_id, user_id, event: dict):
+        key = f"{user_id}:{job_id}"
+        ws = self._connections.get(key)
+        if ws:
+            await ws.send_json(event)
+```
+
+---
+
+## 11. API Endpoints
+
+### Auth
+| Method | Endpoint | Description |
+|--------|----------|-------------|
+| POST | `/auth/register` | Create account |
+| POST | `/auth/login` | Get JWT |
+| POST | `/auth/refresh` | Refresh JWT |
+
+### Ingestion
+| Method | Endpoint | Description |
+|--------|----------|-------------|
+| POST | `/ingest/upload` | Upload file → background job |
+| GET | `/ingest/status/{job_id}` | Job status + pipeline steps |
+| GET | `/ingest/documents` | User's document list |
+| DELETE | `/ingest/document/{doc_id}` | Delete doc + vectors |
+| POST | `/ingest/reindex/{doc_id}` | Incremental re-ingest |
+
+### Query
+| Method | Endpoint | Description |
+|--------|----------|-------------|
+| POST | `/query/search` | RAG query with technique |
+| GET | `/query/history/{doc_id}` | Query history |
+| DELETE | `/query/cache/{doc_id}` | Clear Redis cache |
+
+### Evaluate
+| Method | Endpoint | Description |
+|--------|----------|-------------|
+| POST | `/evaluate/run` | Run RAGAs (CSV upload) |
+| GET | `/evaluate/report/{doc_id}` | Latest report |
+
+### Stats & Health
+| Method | Endpoint | Description |
+|--------|----------|-------------|
+| GET | `/stats/documents` | Docs with chunk counts |
+| GET | `/health` | Backend + Supabase + Redis status |
+
+### WebSocket
+| Endpoint | Description |
+|----------|-------------|
+| `WS /ws/pipeline/{job_id}?token={jwt}` | Real-time pipeline steps |
+
+---
+
+## 12. Frontend — React Deep Dive
+
+### Tailwind Green + Violet Theme
+
+```js
+// tailwind.config.js
+module.exports = {
+  theme: {
+    extend: {
+      colors: {
+        primary: {   // Green
+          50: '#f0fdf4', 400: '#4ade80',
+          500: '#22c55e', 600: '#16a34a', 700: '#15803d'
+        },
+        accent: {    // Violet
+          50: '#f5f3ff', 400: '#a78bfa',
+          500: '#8b5cf6', 600: '#7c3aed', 700: '#6d28d9'
+        },
+        surface: {   // Dark base for dashboard
+          900: '#0a0f0a', 800: '#111a11', 700: '#1a2b1a'
+        }
+      },
+      boxShadow: {
+        'glow-green':  '0 0 20px rgba(34,197,94,0.25)',
+        'glow-violet': '0 0 20px rgba(139,92,246,0.25)',
+      }
+    }
+  }
+}
+```
+
+### Step Color Mapping
+
+```js
+// src/utils/stepColors.js
+export const STEP_COLORS = {
+  // Violet — LLM / AI ops
+  EMBED: '#8B5CF6', HYDE: '#8B5CF6', EXPAND: '#7C3AED',
+  PLAN: '#8B5CF6', GENERATE: '#7C3AED', SCORE: '#6D28D9',
+  CACHE_MISS: '#8B5CF6', EMBED_DELTA: '#8B5CF6',
+  SETUP: '#8B5CF6', EMBED_TOK: '#8B5CF6', TOKENIZE: '#7C3AED',
+  OCR_START: '#8B5CF6', IMAGE_ANALYZE: '#8B5CF6',
+
+  // Green — retrieval / data ops
+  BM25: '#22C55E', VECTOR: '#16A34A', DONE: '#22C55E',
+  CACHE_HIT: '#22C55E', RETRIEVE: '#16A34A', OBSERVE: '#22C55E',
+  DELTA: '#22C55E', AGENT_INIT: '#22C55E', OCR_DONE: '#22C55E',
+  FINAL: '#22C55E', REPORT: '#22C55E',
+
+  // Special
+  RERANK: '#EF4444',  // red — heavy compute, distinct
+  MAXSIM: '#EF4444',  // red — heavy compute
+  FILTER: '#D97706',  // amber — metadata ops
+  TOOL:   '#D97706',  // amber — tool calls
+  DIFF:   '#6B7280',  // gray — neutral checks
+  CACHE_CHECK: '#6B7280',
+  RRF:    '#8B5CF6',  // violet — fusion
+  ERROR:  '#EF4444',  // red
+}
+```
+
+### Pipeline Page UI Layout
+
+```
+┌─────────────────────────────────────────────────────────────┐
+│ 🟢 RAG Pipeline                      [user ▾]  [Logout]    │
+├─────────────────────────────────────────────────────────────┤
+│  📄 document.pdf   142 chunks   ✅ Indexed                  │
+├───────────────────────────┬─────────────────────────────────┤
+│  SELECT TECHNIQUE         │  CHUNKING CONFIG                │
+│  ┌────────┐  ┌────────┐  │  Chunk  ──────●────── 512 tok  │
+│  │Hybrid  │  │ReRank  │  │  Overlap ───●──────── 64 tok   │
+│  └────────┘  └────────┘  │  Strategy [Fixed ▾]            │
+│  ┌────────┐  ┌────────┐  │  Est. chunks: ~148              │
+│  │ HyDE  │  │ Meta   │  │  [Apply & Re-chunk]             │
+│  └────────┘  └────────┘  │                                 │
+│  ┌────────┐  ┌────────┐  │                                 │
+│  │ColBERT │  │Agentic │  │                                 │
+│  └────────┘  └────────┘  │                                 │
+│  ┌────────┐  ┌────────┐  │                                 │
+│  │ Cache  │  │ RAGAs  │  │                                 │
+│  └────────┘  └────────┘  │                                 │
+├───────────────────────────┴─────────────────────────────────┤
+│  QUERY                                                       │
+│  ┌──────────────────────────────────────┐  [🔍 Search]     │
+│  └──────────────────────────────────────┘                   │
+├─────────────────────────────────────────────────────────────┤
+│  PIPELINE TRACE  ● LIVE                                      │
+│  🟣 EMBED     Embedding query (bge-m3)...          ✅ 2.1s │
+│  🟢 BM25      Keyword search → 22 candidates       ✅ 0.1s │
+│  🟢 VECTOR    pgvector ANN → top-20                ✅ 0.3s │
+│  🟣 RRF       Reciprocal Rank Fusion...             ⏳      │
+├─────────────────────────────────────────────────────────────┤
+│  ANSWER                                                      │
+│  The contract was signed on April 3rd, 2024...              │
+│  SOURCES                                                     │
+│  📄 contract.docx §3   Score: 0.94  ██████████ 94%         │
+│  📄 contract.docx §1   Score: 0.81  ████████── 81%         │
+└─────────────────────────────────────────────────────────────┘
+```
+
+### Key React Hooks
+
+```js
+// usePipelineWS.js — WebSocket for real-time steps
+// Connects to: wss://{backend}/ws/pipeline/{job_id}?token={jwt}
+// Each message → add to pipelineStore.steps
+// Auto-reconnects (max 3 attempts)
+// Shows "LIVE" green dot while connected
+
+// useUpload.js — Upload + job polling
+// POST /ingest/upload (multipart form)
+// Polls /ingest/status/{job_id} every 2s until done/failed
+
+// useQuery.js — RAG search
+// POST /query/search → streams answer via WebSocket
+// Updates answerPanel + sourceChunks + pipeline steps simultaneously
+```
+
+---
+
+## 13. Docker Setup
+
+### `backend/Dockerfile`
+
+```dockerfile
+FROM python:3.11-slim
+
+WORKDIR /app
+
+# System deps for python-docx, tiktoken
+RUN apt-get update && apt-get install -y \
+    build-essential libpq-dev && \
+    rm -rf /var/lib/apt/lists/*
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Download cross-encoder model at build time (not runtime)
+RUN python -c "from sentence_transformers import CrossEncoder; \
+    CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')"
+
+COPY . .
+
+# Create data dirs
+RUN mkdir -p data/uploads data/bm25_indexes data/cache
+
+EXPOSE 8000
+
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", \
+     "--port", "8000", "--workers", "2"]
+```
+
+### `docker-compose.yml` — Local Dev
+
+```yaml
+version: "3.9"
+
+services:
+  backend:
+    build: ./backend
+    ports:
+      - "8000:8000"
+    environment:
+      - SUPABASE_URL=${SUPABASE_URL}
+      - SUPABASE_KEY=${SUPABASE_KEY}
+      - REDIS_URL=redis://redis:6379
+      - JWT_SECRET_KEY=${JWT_SECRET_KEY}
+      - MISTRAL_API_KEY=${MISTRAL_API_KEY}
+      # ... other env vars from .env
+    env_file:
+      - ./backend/.env
+    volumes:
+      - ./backend/data:/app/data       # BM25 indexes + uploads persist locally
+    depends_on:
+      - redis
+    restart: unless-stopped
+
+  redis:
+    image: redis:7-alpine
+    ports:
+      - "6379:6379"
+    restart: unless-stopped
+
+  # Optional: local frontend dev server
+  frontend:
+    build:
+      context: ./frontend
+      dockerfile: Dockerfile.dev
+    ports:
+      - "5173:5173"
+    environment:
+      - VITE_API_BASE_URL=http://localhost:8000
+      - VITE_WS_BASE_URL=ws://localhost:8000
+    volumes:
+      - ./frontend/src:/app/src        # hot reload
+    restart: unless-stopped
+```
+
+### `backend/requirements.txt`
+
+```txt
+fastapi==0.111.0
+uvicorn[standard]==0.30.0
+gunicorn==22.0.0
+pydantic-settings==2.3.0
+supabase==2.5.0
+asyncpg==0.29.0
+redis==5.0.6
+python-jose[cryptography]==3.3.0
+passlib[bcrypt]==1.7.4
+python-multipart==0.0.9
+httpx==0.27.0
+gradio_client==0.17.0
+tiktoken==0.7.0
+python-docx==1.1.2
+rank_bm25==0.2.2
+sentence-transformers==3.0.1
+ragas==0.1.14
+json_repair==0.25.2
+loguru==0.7.2
+slowapi==0.1.9
+```
+
+---
+
+## 14. Environment Variables
+
+> All values go into **Render Dashboard → Environment tab**.  
+> Never committed to Git. `.env.example` has key names only.
+
+### Render Backend
+
+```env
+# Supabase
+SUPABASE_URL              = https://xxxx.supabase.co
+SUPABASE_KEY              = your_service_role_key
+SUPABASE_DB_URL           = postgresql://postgres:pass@db.xxxx.supabase.co:5432/postgres
+
+# Embeddings (HF Space — free)
+EMBED_API_URL             = https://lamhieu-lightweight-embeddings.hf.space/
+EMBED_MODEL               = bge-m3
+EMBED_DIM                 = 1024
+EMBED_AUTH_KEY            =
+EMBED_MAX_TOKENS          = 1000
+EMBED_TIMEOUT             = 60
+EMBED_MAX_RETRIES         = 3
+
+# LLM — Qwen3 (HF Space — free)
+QWEN3_MODEL_NAME          = Qwen/Qwen3-Demo
+QWEN3_THINKING_BUDGET     = 38
+LLM_RESPONSE_TIMEOUT      = 1080
+MAX_LLM_RETRIES           = 5
+MAX_TIMEOUT_RETRIES       = 10
+
+# OCR — Mistral (needs API key)
+MISTRAL_OCR_SPACE         = tatendachirume/Mistral-OCR
+MISTRAL_API_KEY           = your_mistral_api_key
+
+# Image — Ernie Bot (free HF Space)
+ERNIE_SPACE_URL           = https://baidu-simple-ernie-bot-demo.hf.space/
+
+# Redis (auto-filled by Render when Redis added)
+REDIS_URL                 = redis://...
+CACHE_TTL_SECONDS         = 3600
+
+# Auth
+JWT_SECRET_KEY            = generate_with: openssl rand -hex 32
+JWT_ALGORITHM             = HS256
+JWT_EXPIRE_MINUTES        = 1440
+
+# Re-ranking
+RERANK_MODEL              = cross-encoder/ms-marco-MiniLM-L-6-v2
+
+# Limits
+RATE_LIMIT_PER_MINUTE     = 20
+RATE_LIMIT_UPLOAD_PER_DAY = 50
+MAX_FILE_SIZE_MB          = 50
+
+# Defaults
+DEFAULT_CHUNK_SIZE        = 512
+DEFAULT_OVERLAP           = 64
+DEFAULT_TOP_K             = 5
+
+# CORS
+CORS_ORIGINS              = https://your-app.netlify.app,http://localhost:5173
+```
+
+### Netlify Frontend
+
+```env
+VITE_API_BASE_URL = https://your-backend.onrender.com
+VITE_WS_BASE_URL  = wss://your-backend.onrender.com
+```
+
+### Local Dev (`backend/.env`)
+
+```env
+# Same as Render vars above +
+REDIS_URL = redis://localhost:6379
+CORS_ORIGINS = http://localhost:5173
+```
+
+---
+
+## 15. Deployment Guide
+
+### Step 1 — Supabase Setup (10 min)
+
+```
+1. supabase.com → New project (free)
+2. Settings → Database → Copy connection string → SUPABASE_DB_URL
+3. Settings → API → Copy URL + service_role key
+4. SQL Editor → run the schema SQL from Section 4
+5. SQL Editor → run the match_chunks() function SQL from Section 4
+6. Authentication → Disable (we handle auth ourselves with JWT)
+7. Table Editor → Enable RLS on all tables
+```
+
+### Step 2 — Render Backend (15 min)
+
+```
+1. render.com → New Web Service → Connect GitHub → select backend/
+2. Runtime: Python / Docker (choose Docker — uses our Dockerfile)
+3. Build command: (auto from Dockerfile)
+4. Start command: (auto from Dockerfile CMD)
+5. Add Redis: New → Redis → Free tier → auto-links REDIS_URL
+6. Environment tab: add all vars from Section 14
+7. Deploy → wait ~5 min
+8. Test: curl https://your-app.onrender.com/health
+```
+
+### Step 3 — Netlify Frontend (5 min)
+
+```
+1. netlify.com → New site → Import from GitHub → select frontend/
+2. Build command: npm run build
+3. Publish dir: dist
+4. Environment vars: VITE_API_BASE_URL, VITE_WS_BASE_URL
+5. Deploy
+6. Copy Netlify URL → update CORS_ORIGINS in Render env
+```
+
+### Step 4 — Local Dev
+
+```bash
+# Clone repo
+git clone https://github.com/you/rag-pipeline.git
+cd rag-pipeline
+
+# Copy env files
+cp backend/.env.example backend/.env
+# Fill in values
+
+# Start with Docker Compose
+docker-compose up --build
+
+# Frontend available: http://localhost:5173
+# Backend available:  http://localhost:8000
+# Redis:              localhost:6379
+```
+
+---
+
+## 16. Production Additions
+
+Items added beyond what you mentioned — all included in this blueprint:
+
+| # | Item | Why |
+|---|------|-----|
+| 1 | JWT auth + multi-user | You said multi-user needed |
+| 2 | Supabase Row Level Security | Data isolation at DB level |
+| 3 | Rate limiting (slowapi) | Prevent abuse on free Render tier |
+| 4 | Docker + docker-compose | Local dev, portfolio quality, Render deployment |
+| 5 | Cross-encoder model pre-downloaded in Dockerfile | Avoid cold download on first query |
+| 6 | File cleanup after ingestion | Prevent disk fill on Render |
+| 7 | JWT in Zustand memory (not localStorage) | XSS attack prevention |
+| 8 | WebSocket user isolation | Multi-user safety |
+| 9 | ColBERT warning dialog | Prevent accidental slow ingestion |
+| 10 | /health endpoint | Shows Supabase + Redis status to frontend |
+| 11 | BM25 pickle persisted on Render disk | Hybrid search needs it across restarts |
+| 12 | Render cold start note | Free tier sleeps after 15 min — warn interviewer |
+
+### ⚠️ One Render Free Tier Limitation
+
+Render free tier **sleeps after 15 minutes of inactivity**. First request takes 30-60 seconds to wake up. For an interview demo, either:
+- Upgrade to Starter ($7/mo) — keeps it warm
+- OR ping `/health` from frontend every 5 min to prevent sleep
+- OR just open the app 2 min before the interview
+
+---
+
+> **Next step:** Confirm this blueprint and tell me which module to code first.  
+> Recommended order: `backend/` → `local-bridge removed` → `frontend/`  
+> Say **"start backend"** and I will generate every file.
diff --git a/docker-compose.yml b/docker-compose.yml
new file mode 100644
index 0000000000000000000000000000000000000000..8862e1955a64954111c58806529b250cbe6d9c5e
--- /dev/null
+++ b/docker-compose.yml
@@ -0,0 +1,23 @@
+version: "3.9"
+
+services:
+  backend:
+    build: 
+      context: ./backend
+      dockerfile: Dockerfile
+    ports:
+      - "8001:8000"
+    env_file:
+      - ./backend/.env
+    volumes:
+      - ./backend:/app
+      - ./backend/data:/app/data
+    depends_on:
+      - redis
+    restart: unless-stopped
+
+  redis:
+    image: redis:7-alpine
+    ports:
+      - "6379:6379"
+    restart: unless-stopped
diff --git a/sample_document.txt b/sample_document.txt
new file mode 100644
index 0000000000000000000000000000000000000000..4053129132bf7dd778f6427ae9ea5224285cc2c5
--- /dev/null
+++ b/sample_document.txt
@@ -0,0 +1,5 @@
+The RAG Full Application Production Blueprint V3 defines the architecture for a next-generation retrieval-augmented generation pipeline.
+It incorporates advanced techniques such as Hybrid Search, which combines keyword-based BM25 and semantic pgvector search.
+Another key technique is ColBERT token-level scoring (MaxSim) which allows for deep contextual evaluations.
+The frontend uses Vite and React with a Green/Violet aesthetic theme.
+Agentic RAG utilizes a smart agent (Qwen3) that uses tools like search_docs and get_page to dynamically find answers.
diff --git a/sample_image.png b/sample_image.png
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