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from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import os
import requests
from langchain.chains import RetrievalQA
from langchain.llms import HuggingFaceHub
from ..utils.vector_store import get_vector_store
app = FastAPI()
class QueryRequest(BaseModel):
query: str
# Option 1: Hugging Face Inference API (Free with rate limits)
def setup_hf_api_model():
"""Uses Hugging Face's free Inference API"""
# Get free token from https://huggingface.co/settings/tokens
# Set this in your deployment
hf_token = os.getenv("HUGGINGFACE_API_TOKEN")
if not hf_token:
raise ValueError("HUGGINGFACE_API_TOKEN environment variable required")
llm = HuggingFaceHub(
repo_id="microsoft/DialoGPT-medium", # Free model
model_kwargs={
"temperature": 0.1,
"max_length": 512
},
huggingfacehub_api_token=hf_token
)
return llm
# Option 2: Cohere API (Free tier: 100 API calls/month)
def setup_cohere_model():
"""Uses Cohere's free tier"""
from langchain.llms import Cohere
cohere_api_key = os.getenv("COHERE_API_KEY")
if not cohere_api_key:
raise ValueError("COHERE_API_KEY required")
llm = Cohere(
cohere_api_key=cohere_api_key,
model="command-light", # Free tier model
temperature=0.1
)
return llm
# Option 3: Together AI (Free credits)
def setup_together_model():
"""Uses Together AI's free credits"""
from langchain.llms import Together
together_api_key = os.getenv("TOGETHER_API_KEY")
if not together_api_key:
raise ValueError("TOGETHER_API_KEY required")
llm = Together(
together_api_key=together_api_key,
model="meta-llama/Llama-2-7b-chat-hf",
temperature=0.1
)
return llm
# Initialize model (try different options in order of preference)
llm = None
model_used = "none"
try:
llm = setup_hf_api_model()
model_used = "huggingface"
print("✅ Using Hugging Face Inference API")
except:
try:
llm = setup_cohere_model()
model_used = "cohere"
print("✅ Using Cohere API")
except:
try:
llm = setup_together_model()
model_used = "together"
print("✅ Using Together AI")
except Exception as e:
print(f"❌ Failed to initialize any model: {e}")
# Setup QA chain
qa_chain = None
if llm:
try:
retriever = get_vector_store().as_retriever()
qa_chain = RetrievalQA.from_chain_type(
llm=llm,
retriever=retriever,
return_source_documents=True
)
print("✅ QA chain ready")
except Exception as e:
print(f"❌ QA chain failed: {e}")
@app.get("/")
def root():
return {
"status": "running",
"model": model_used,
"qa_ready": qa_chain is not None
}
@app.post("/ask")
def ask_question(request: QueryRequest):
if qa_chain is None:
raise HTTPException(status_code=500, detail="Service not ready")
try:
result = qa_chain({"query": request.query})
return {
"answer": result["result"],
"model_used": model_used,
"sources": [
{
"content": doc.page_content[:200] + "...",
"metadata": doc.metadata
}
for doc in result["source_documents"][:3]
]
}
except Exception as e:
raise HTTPException(status_code=500, detail=f"Error: {str(e)}")
@app.post("/ask/{store_type}")
def ask_specific_store(store_type: str, request: QueryRequest):
if llm is None:
raise HTTPException(status_code=500, detail="LLM not available")
store_paths = {
"mes": "./vector_stores/mes_db",
"general": "./vector_stores/general_db",
"tech": "./vector_stores/tech_db"
}
if store_type not in store_paths:
raise HTTPException(status_code=400, detail="Invalid store type")
try:
vector_store = get_vector_store(
persist_directory=store_paths[store_type])
retriever = vector_store.as_retriever()
store_qa_chain = RetrievalQA.from_chain_type(
llm=llm,
retriever=retriever,
return_source_documents=True
)
result = store_qa_chain({"query": request.query})
return {
"answer": result["result"],
"store_used": store_type,
"model_used": model_used,
"sources": [doc.page_content[:200] + "..." for doc in result["source_documents"][:3]]
}
except Exception as e:
raise HTTPException(status_code=500, detail=str(e))
if __name__ == "__main__":
import uvicorn
port = int(os.environ.get("PORT", 8000))
uvicorn.run(app, host="0.0.0.0", port=port)
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