Update app.py

app.py

@@ -1,140 +1,140 @@
-import os
-from fastapi import FastAPI, Request, HTTPException, status, BackgroundTasks
-from fastapi.responses import JSONResponse
-from fastapi.middleware.cors import CORSMiddleware
-from pydantic import BaseModel
-from typing import List
-from google import genai
-from semantic_aware import load_document
-import hashlib
-import httpx
-from datetime import datetime
-import re
-from pinecone import Pinecone
-from pinecone_embeddings import PineconeVectorStore
-
-
-# Configuration
-EMBEDDING_MODEL = "BAAI/bge-base-en-v1.5"
-PINECONE_INDEX = 'policy-documents'
-CACHE_DIR = "./document_cache"
-os.makedirs(CACHE_DIR, exist_ok=True)
-pinecone = Pinecone(
-    api_key=os.getenv("PINECONE_API_KEY"),
-    environment=os.getenv("PINECONE_ENV")
-)
-
-# Initialize Gemini
-# genai.configure(api_key=os.environ["GEMINI_API_KEY"])
-# model = genai.GenerativeModel('gemini-1.5-flash')
-
-app = FastAPI()
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-class QueryRequest(BaseModel):
-    documents: str
-    questions: List[str]
-
-class QueryResponse(BaseModel):
-    answers: List[str]
-
-def document_cache_key(url: str) -> str:
-    return hashlib.md5(url.encode()).hexdigest()
-
-async def fetch_with_cache(url: str) -> str:
-    """Download with caching"""
-    cache_key = document_cache_key(url)
-    cache_path = os.path.join(CACHE_DIR, f"{cache_key}.pdf")
-    
-    if os.path.exists(cache_path):
-        return cache_path
-    
-    async with httpx.AsyncClient() as client:
-        response = await client.get(url)
-        response.raise_for_status()
-        with open(cache_path, "wb") as f:
-            f.write(response.content)
-    
-    return cache_path
-
-def build_gemini_prompt(question: str, clauses: List[dict]) -> str:
-    """Strictly formatted prompt for Gemini"""
-    context = "\n\n".join(
-        f"CLAUSE {c.get('header', '')} (Page {c.get('page', 'N/A')}):\n{c['text']}"
-        for c in clauses
-    )
-    
-    return f"""You are a strict, accurate assistant that answers insurance or policy-related questions using only provided clauses.
-
-        A user has asked the following question:
-        "{question}"
-
-        You must answer only based on the given text below, without guessing or skipping any information.
-        If an answer is partially stated or implied, respond accordingly with brief clarification.
-        If the information is not present at all, reply exactly: "Not mentioned in the provided clauses."
-
-        Clauses:
-        {context}
-
-        Respond with 1 to 3 sentences max.
-        Do not add explanations, formatting, bullet points, summaries, or any output other than the answer sentence.
-
-"""
-
-# def extract_first_sentence(text: str) -> str:
-#     """Ensure single-sentence output"""
-#     sentences = re.split(r'(?<=[.!?])\s+', text.strip())
-#     return sentences[0] if sentences else text
-
-@app.post("/query", response_model=QueryResponse)
-async def answer_questions(request: Request, body: QueryRequest):
-    # Authentication
-    if request.headers.get("Authorization") != f"Bearer {os.environ['API_KEY']}":
-        raise HTTPException(status_code=status.HTTP_401_UNAUTHORIZED)
-    
-    try:
-        # 1. Process document
-        local_path = await fetch_with_cache(body.documents)
-        doc = load_document(local_path)
-        
-        # 2. Initialize engine
-        vector_store = PineconeVectorStore(index_name=PINECONE_INDEX, pinecone=pinecone)
-        vector_store.overwrite_vectors(doc["chunks"], 'doc_a.pdf', pinecone)
-        
-        # 3. Process questions
-        answers = []
-        client = genai.Client(
-            api_key=os.environ["GEMINI_API_KEY"]
-        )
-
-        for question in body.questions:
-            # Retrieve relevant clauses
-            clauses = vector_store.retrieve_chunks(question, pinecone, top_k=5)
-        
-            # print("\n\n")
-            # print(clauses)
-
-            # Generate answer with Gemini
-            prompt = build_gemini_prompt(question, clauses)
-            response = client.models.generate_content(
-                model="gemini-2.5-flash",
-                contents=prompt
-            )
-            
-            # Strict formatting
-            # answer = extract_first_sentence(response.text)
-            # print(response.text)
-            answers.append(response.text)
-        
-        return {"answers": answers}
-        
-    except Exception as e:
-        raise HTTPException(
-            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
-            detail=str(e)
+import os
+from fastapi import FastAPI, Request, HTTPException, status, BackgroundTasks
+from fastapi.responses import JSONResponse
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+from typing import List
+from google import genai
+from semantic_aware import load_document
+import hashlib
+import httpx
+from datetime import datetime
+import re
+from pinecone import Pinecone
+from pinecone_embeddings import PineconeVectorStore
+
+
+# Configuration
+EMBEDDING_MODEL = "BAAI/bge-base-en-v1.5"
+PINECONE_INDEX = 'policy-documents'
+CACHE_DIR = "./document_cache"
+os.makedirs(CACHE_DIR, exist_ok=True)
+pinecone = Pinecone(
+    api_key=os.getenv("PINECONE_API_KEY"),
+    environment=os.getenv("PINECONE_ENV")
+)
+
+# Initialize Gemini
+# genai.configure(api_key=os.environ["GEMINI_API_KEY"])
+# model = genai.GenerativeModel('gemini-1.5-flash')
+
+app = FastAPI()
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+class QueryRequest(BaseModel):
+    documents: str
+    questions: List[str]
+
+class QueryResponse(BaseModel):
+    answers: List[str]
+
+def document_cache_key(url: str) -> str:
+    return hashlib.md5(url.encode()).hexdigest()
+
+async def fetch_with_cache(url: str) -> str:
+    """Download with caching"""
+    cache_key = document_cache_key(url)
+    cache_path = os.path.join(CACHE_DIR, f"{cache_key}.pdf")
+    
+    if os.path.exists(cache_path):
+        return cache_path
+    
+    async with httpx.AsyncClient() as client:
+        response = await client.get(url)
+        response.raise_for_status()
+        with open(cache_path, "wb") as f:
+            f.write(response.content)
+    
+    return cache_path
+
+def build_gemini_prompt(question: str, clauses: List[dict]) -> str:
+    """Strictly formatted prompt for Gemini"""
+    context = "\n\n".join(
+        f"CLAUSE {c.get('header', '')} (Page {c.get('page', 'N/A')}):\n{c['text']}"
+        for c in clauses
+    )
+    
+    return f"""
+    You are a strict, accurate assistant that answers insurance or policy-related questions using only provided clauses.
+
+        A user has asked the following question:
+        '{question}'
+    
+        You must answer only based on the given text below, without guessing or skipping any information.
+        If an answer is partially stated or implied, respond accordingly with brief clarification.
+        If the information is not present at all, reply exactly: 'Not mentioned in the provided clauses.'
+    
+        Clauses:
+        {context}
+    
+        Respond with 1 to 3 sentences max.
+        Do not add explanations, formatting, bullet points, summaries, or any output other than the answer sentence.
+    """
+
+# def extract_first_sentence(text: str) -> str:
+#     """Ensure single-sentence output"""
+#     sentences = re.split(r'(?<=[.!?])\s+', text.strip())
+#     return sentences[0] if sentences else text
+
+@app.post("/query", response_model=QueryResponse)
+async def answer_questions(request: Request, body: QueryRequest):
+    # Authentication
+    if request.headers.get("Authorization") != f"Bearer {os.environ['API_KEY']}":
+        raise HTTPException(status_code=status.HTTP_401_UNAUTHORIZED)
+    
+    try:
+        # 1. Process document
+        local_path = await fetch_with_cache(body.documents)
+        doc = load_document(local_path)
+        
+        # 2. Initialize engine
+        vector_store = PineconeVectorStore(index_name=PINECONE_INDEX, pinecone=pinecone)
+        vector_store.overwrite_vectors(doc["chunks"], 'doc_a.pdf', pinecone)
+        
+        # 3. Process questions
+        answers = []
+        client = genai.Client(
+            api_key=os.environ["GEMINI_API_KEY"]
+        )
+
+        for question in body.questions:
+            # Retrieve relevant clauses
+            clauses = vector_store.retrieve_chunks(question, pinecone, top_k=5)
+        
+            # print("\n\n")
+            # print(clauses)
+
+            # Generate answer with Gemini
+            prompt = build_gemini_prompt(question, clauses)
+            response = client.models.generate_content(
+                model="gemini-2.5-flash",
+                contents=prompt
+            )
+            
+            # Strict formatting
+            # answer = extract_first_sentence(response.text)
+            # print(response.text)
+            answers.append(response.text)
+        
+        return {"answers": answers}
+        
+    except Exception as e:
+        raise HTTPException(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            detail=str(e)
         )