Delete api/main.py

api/main.py

@@ -1,50 +0,0 @@
-from fastapi import FastAPI, Depends, Body
-from typing import List, Dict
-from RAG.Retriever import Retriever, load_vector_store
-from RAG.llm import GeminiLLM
-import os
-
-app = FastAPI()
-
-# Retrieve API keys from environment variables
-userdata = {
-    "GEMINI_API_KEY":os.getenv("GEMINI_API_KEY"),
-}
-
-GEMINI_KEY = userdata.get("GEMINI_API_KEY")
-
-PERSIST_DIR = os.path.join(PROJECT_ROOT, 'RAG/') # Make this configurable if needed
-v_store = load_vector_store(GEMINI_KEY, PERSIST_DIR)
-retriever = Retriever(v_store)
-gemini_llm = GeminiLLM(GEMINI_KEY)
-
-
-@app.post("/rag")
-async def rag_endpoint(query: str = Body(...)):
-    # First retrieve relevant documents
-    docs = retriever.retrieve_documents(query)
-
-    # Create a clean message list with only role and content keys
-    messages = [
-        {
-            "role": "user",
-            "content": str(query)
-        },
-        {
-            "role": "assistant",
-            "content": f"Based on the retrieved documents: {str(docs)}, I will now answer your question."
-        },
-        {
-            "role": "user",
-            "content": "Please provide a clear and concise answer based on the above documents."
-        }
-    ]
-
-    # Generate response using the formatted messages
-    # formatted_messages = gemini_llm.format_messages(messages)
-    response = gemini_llm.generate_response(messages)
-
-    return {
-        "query": query,
-        "response": response
-    }