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from langchain_community.graphs import Neo4jGraph
from dotenv import load_dotenv
from utils import (
create_vector_index,
BaseLogger,
)
from chains import (
load_embedding_model,
load_llm,
configure_llm_only_chain,
configure_qa_rag_chain,
generate_ticket,
)
from fastapi import FastAPI, Depends
from pydantic import BaseModel
from langchain.callbacks.base import BaseCallbackHandler
from threading import Thread
from queue import Queue, Empty
from collections.abc import Generator
from sse_starlette.sse import EventSourceResponse
from fastapi.middleware.cors import CORSMiddleware
import json
load_dotenv(".env")
url = os.getenv("NEO4J_URI")
username = os.getenv("NEO4J_USERNAME")
password = os.getenv("NEO4J_PASSWORD")
ollama_base_url = os.getenv("OLLAMA_BASE_URL")
embedding_model_name = os.getenv("EMBEDDING_MODEL")
llm_name = os.getenv("LLM")
# Remapping for Langchain Neo4j integration
os.environ["NEO4J_URL"] = url
embeddings, dimension = load_embedding_model(
embedding_model_name,
config={"ollama_base_url": ollama_base_url},
logger=BaseLogger(),
)
# if Neo4j is local, you can go to http://localhost:7474/ to browse the database
neo4j_graph = Neo4jGraph(
url=url, username=username, password=password, refresh_schema=False
)
create_vector_index(neo4j_graph)
llm = load_llm(
llm_name, logger=BaseLogger(), config={"ollama_base_url": ollama_base_url}
)
llm_chain = configure_llm_only_chain(llm)
rag_chain = configure_qa_rag_chain(
llm, embeddings, embeddings_store_url=url, username=username, password=password
)
class QueueCallback(BaseCallbackHandler):
"""Callback handler for streaming LLM responses to a queue."""
def __init__(self, q):
self.q = q
def on_llm_new_token(self, token: str, **kwargs) -> None:
self.q.put(token)
def on_llm_end(self, *args, **kwargs) -> None:
return self.q.empty()
def stream(cb, q) -> Generator:
job_done = object()
def task():
x = cb()
q.put(job_done)
t = Thread(target=task)
t.start()
content = ""
# Get each new token from the queue and yield for our generator
while True:
try:
next_token = q.get(True, timeout=1)
if next_token is job_done:
break
content += next_token
yield next_token, content
except Empty:
continue
app = FastAPI()
origins = ["*"]
app.add_middleware(
CORSMiddleware,
allow_origins=origins,
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
@app.get("/")
async def root():
return {"message": "Hello World"}
class Question(BaseModel):
text: str
rag: bool = False
class BaseTicket(BaseModel):
text: str
@app.get("/query-stream")
def qstream(question: Question = Depends()):
output_function = llm_chain
if question.rag:
output_function = rag_chain
q = Queue()
def cb():
output_function(
{"question": question.text, "chat_history": []},
callbacks=[QueueCallback(q)],
)
def generate():
yield json.dumps({"init": True, "model": llm_name})
for token, _ in stream(cb, q):
yield json.dumps({"token": token})
return EventSourceResponse(generate(), media_type="text/event-stream")
@app.get("/query")
async def ask(question: Question = Depends()):
output_function = llm_chain
if question.rag:
output_function = rag_chain
result = output_function(
{"question": question.text, "chat_history": []}, callbacks=[]
)
return {"result": result["answer"], "model": llm_name}
@app.get("/generate-ticket")
async def generate_ticket_api(question: BaseTicket = Depends()):
new_title, new_question = generate_ticket(
neo4j_graph=neo4j_graph,
llm_chain=llm_chain,
input_question=question.text,
)
return {"result": {"title": new_title, "text": new_question}, "model": llm_name}
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