Create app.py

app.py

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+import uvicorn
+from fastapi import FastAPI, HTTPException, Depends
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+from sentence_transformers import SentenceTransformer
+from pinecone import Pinecone, ServerlessSpec
+import uuid
+import os
+from contextlib import asynccontextmanager
+
+# --- Environment Setup ---
+# It's best practice to get sensitive keys from environment variables
+# We will set these up in Hugging Face Spaces Secrets
+PINECONE_API_KEY = os.getenv("PINECONE_API_KEY")
+PINECONE_INDEX_NAME = os.getenv("PINECONE_INDEX_NAME", "memoria-index")
+
+# --- Global objects ---
+# We load these once at startup to save time and memory
+model = None
+pc = None
+index = None
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    """
+    Handles startup and shutdown events for the FastAPI app.
+    Loads the model and connects to Pinecone on startup.
+    """
+    global model, pc, index
+    print("Application startup...")
+
+    if not PINECONE_API_KEY:
+        raise ValueError("PINECONE_API_KEY environment variable not set.")
+
+    # 1. Load the AI Model
+    print("Loading lightweight sentence transformer model...")
+    model = SentenceTransformer('sentence-transformers/paraphrase-albert-small-v2')
+    print("Model loaded.")
+
+    # 2. Connect to Pinecone
+    print("Connecting to Pinecone...")
+    pc = Pinecone(api_key=PINECONE_API_KEY)
+
+    # 3. Get or create the Pinecone index
+    if PINECONE_INDEX_NAME not in pc.list_indexes().names():
+        print(f"Creating new Pinecone index: {PINECONE_INDEX_NAME}")
+        pc.create_index(
+            name=PINECONE_INDEX_NAME,
+            dimension=model.get_sentence_embedding_dimension(),
+            metric="cosine", # Cosine similarity is great for sentence vectors
+            spec=ServerlessSpec(cloud="aws", region="us-east-1")
+        )
+    index = pc.Index(PINECONE_INDEX_NAME)
+    print("Pinecone setup complete.")
+    yield
+    # Cleanup logic can go here if needed on shutdown
+    print("Application shutdown.")
+
+# --- Pydantic Models ---
+class Memory(BaseModel):
+    content: str
+
+class SearchQuery(BaseModel):
+    query: str
+
+# --- FastAPI App ---
+app = FastAPI(
+    title="Memoria API",
+    description="API for storing and retrieving memories.",
+    version="1.0.0",
+    lifespan=lifespan # Use the lifespan context manager
+)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"], # Allow all origins for simplicity
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# --- API Endpoints ---
+@app.get("/")
+def read_root():
+    return {"status": "ok", "message": "Welcome to the Memoria API!"}
+
+@app.post("/save_memory")
+def save_memory(memory: Memory):
+    try:
+        embedding = model.encode(memory.content).tolist()
+        memory_id = str(uuid.uuid4())
+        
+        # Upsert (update or insert) the vector into Pinecone
+        index.upsert(vectors=[{"id": memory_id, "values": embedding, "metadata": {"text": memory.content}}])
+        
+        print(f"Successfully saved memory with ID: {memory_id}")
+        return {"status": "success", "id": memory_id}
+    except Exception as e:
+        print(f"An error occurred during save: {e}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.post("/search_memory")
+def search_memory(search: SearchQuery):
+    try:
+        query_embedding = model.encode(search.query).tolist()
+        
+        # Query Pinecone for the most similar vectors
+        results = index.query(vector=query_embedding, top_k=5, include_metadata=True)
+        
+        # Extract the original text from the metadata
+        retrieved_documents = [match['metadata']['text'] for match in results['matches']]
+        
+        print(f"Found {len(retrieved_documents)} results for query: '{search.query}'")
+        return {"status": "success", "results": retrieved_documents}
+    except Exception as e:
+        print(f"An error occurred during search: {e}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+if __name__ == "__main__":
+    uvicorn.run("main:app", host="127.0.0.1", port=8000, reload=True)
+
+
+