First Commit

app.py

@@ -14,22 +14,75 @@ import os
 from dotenv import load_dotenv
 from fastapi import FastAPI, HTTPException
 from pydantic import BaseModel
-from scripts.rag import get_answer, response_agent
+from scripts.rag import get_calculations, response_agent,setup_rag_system
+import os
+from typing import Optional
+from fastapi import FastAPI, HTTPException, Security, Depends
+from fastapi.security import APIKeyHeader
+from fastapi.middleware.cors import CORSMiddleware
+from dotenv import load_dotenv
+load_dotenv()
 # Load environment variables from a .env file
 
+ # Initialize OpenAI LLM and Embeddings
+llm = ChatOpenAI(model="gpt-4o-mini")  # Use the appropriate model
+embedding_function = OpenAIEmbeddings()
+
+    # Set up the RAG system
+file_path = r'docs/UpdateAI_training-company-data.md'
+rag_chain = setup_rag_system(file_path, llm, embedding_function)
 
 # Initialize FastAPI app
 app = FastAPI()
+load_dotenv()
+API_KEY = os.getenv("API_KEY")
 
+# Initialize FastAPI app
+app = FastAPI(
+    title="Update stack  AI API",
+    description="API For fire Update stack",
+    version="1.0.0"
+)
+
+# Add CORS middleware
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Setup API key authentication
+api_key_header = APIKeyHeader(name="Authorization", auto_error=False)
+
+
+async def get_api_key(api_key_header: str = Security(api_key_header)) -> str:
+    """Validate API key from header"""
+    if not api_key_header or not api_key_header.startswith('Bearer '):
+        raise HTTPException(
+            status_code=401,
+            detail={"error": "Unauthorized", "message": "API key is missing or invalid."}
+        )
+    
+    token = api_key_header.split(' ')[1]
+    if token != API_KEY:
+        raise HTTPException(
+            status_code=401,
+            detail={"error": "Unauthorized", "message": "API key does not match."}
+        )
+    
+    return token
 # Define a request model
 class QuestionRequest(BaseModel):
     question: str
 
-@app.post("/ask")
+@app.post("/update-stack/v1/ask")
-def ask_question(request: QuestionRequest):
+def ask_question(request: QuestionRequest,
+                 api_key: str = Depends(get_api_key)):
     try:
         # Use the RAG system to get the answer
-        calculation_data = get_answer(rag_chain, request.question)
+        calculation_data = get_calculations(rag_chain, request.question)
         final_response = response_agent(request.question, calculation_data)
         return {"answer": final_response}
     except Exception as e:

scripts/rag.py

@@ -70,7 +70,7 @@ def setup_rag_system(file_path, llm, embedding_function):
     question_answer_chain = create_stuff_documents_chain(llm, prompt)
     return create_retrieval_chain(retriever, question_answer_chain)
 
-def get_answer(rag_chain, user_input):
+def get_calculations(rag_chain, user_input):
     answer = rag_chain.invoke({"input": user_input})
     return answer['answer']