Create app.py

app.py

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+import torch
+from fastapi import FastAPI
+from pydantic import BaseModel
+from sentence_transformers import SentenceTransformer
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+from qdrant_client import models
+import logging
+
+# --- Setup Logging ---
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# --- Configuration ---
+# Local models for vector generation
+DENSE_MODEL_ID = 'sentence-transformers/all-MiniLM-L6-v2'
+# Use the corresponding QUERY encoder for SPLADE, which is optimized for search queries
+SPLADE_QUERY_MODEL_ID = 'naver/efficient-splade-VI-BT-large-query'
+DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+# --- Global Variables for Models ---
+# These will be loaded once when the application starts
+dense_model = None
+splade_tokenizer = None
+splade_model = None
+
+# --- FastAPI Application ---
+app = FastAPI(
+    title="Hybrid Vector Generation API",
+    description="An API to generate dense and sparse vectors for a given text query.",
+    version="1.0.0"
+)
+
+# --- Pydantic Models for API ---
+class QueryRequest(BaseModel):
+    """Request model for the API, expecting a single text query."""
+    query_text: str
+
+class SparseVectorResponse(BaseModel):
+    """Response model for the sparse vector."""
+    indices: list[int]
+    values: list[float]
+
+class VectorResponse(BaseModel):
+    """The final JSON response model containing both vectors."""
+    dense_vector: list[float]
+    sparse_vector: SparseVectorResponse
+
+
+@app.on_event("startup")
+async def load_models():
+    """
+    Asynchronous event to load ML models on application startup.
+    This ensures models are loaded only once.
+    """
+    global dense_model, splade_tokenizer, splade_model
+    logger.info(f"Loading models onto device: {DEVICE}")
+    try:
+        dense_model = SentenceTransformer(DENSE_MODEL_ID, device=DEVICE)
+        splade_tokenizer = AutoTokenizer.from_pretrained(SPLADE_QUERY_MODEL_ID)
+        splade_model = AutoModelForMaskedLM.from_pretrained(SPLADE_QUERY_MODEL_ID).to(DEVICE)
+        logger.info("Models initialized successfully.")
+    except Exception as e:
+        logger.fatal(f"FATAL: Could not initialize models. Error: {e}")
+        # In a real-world scenario, you might want the app to fail startup if models don't load.
+        raise e
+
+def compute_splade_vector(text: str) -> models.SparseVector:
+    """
+    Computes a SPLADE sparse vector from a given text query.
+    
+    Args:
+        text: The input text string.
+    Returns:
+        A Qdrant SparseVector object.
+    """
+    tokens = splade_tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
+    tokens = {key: val.to(DEVICE) for key, val in tokens.items()} # Move tensors to the correct device
+
+    with torch.no_grad():
+        output = splade_model(**tokens)
+    
+    logits, attention_mask = output.logits, tokens.attention_mask
+    relu_log = torch.log(1 + torch.relu(logits))
+    weighted_log = relu_log * attention_mask.unsqueeze(-1)
+    max_val, _ = torch.max(weighted_log, dim=1)
+    vec = max_val.squeeze()
+
+    indices = vec.nonzero().squeeze().cpu().tolist()
+    values = vec[indices].cpu().tolist()
+    
+    # Ensure indices and values are always lists, even for a single-element tensor
+    if not isinstance(indices, list):
+        indices = [indices]
+        values = [values]
+        
+    return models.SparseVector(indices=indices, values=values)
+
+
+@app.post("/vectorize", response_model=VectorResponse)
+async def vectorize_query(request: QueryRequest):
+    """
+    API endpoint to generate and return dense and sparse vectors for a text query.
+    
+    Args:
+        request: A QueryRequest object containing the 'query_text'.
+        
+    Returns:
+        A JSON response containing the dense and sparse vectors.
+    """
+    logger.info(f"Received query for vectorization: '{request.query_text}'")
+
+    # 1. Generate Dense Vector
+    logger.info("Generating dense vector...")
+    dense_query_vector = dense_model.encode(request.query_text).tolist()
+    logger.info("Dense vector generated.")
+
+    # 2. Generate Sparse Vector
+    logger.info("Generating sparse vector...")
+    sparse_query_vector = compute_splade_vector(request.query_text)
+    logger.info("Sparse vector generated.")
+
+    # 3. Construct and return the response
+    return VectorResponse(
+        dense_vector=dense_query_vector,
+        sparse_vector=SparseVectorResponse(
+            indices=sparse_query_vector.indices,
+            values=sparse_query_vector.values
+        )
+    )
+
+@app.get("/", include_in_schema=False)
+async def root():
+    return {"message": "Vector Generation API is running. POST to /vectorize to get vectors."}