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import os
from dotenv import load_dotenv
from qdrant_client import QdrantClient
from langchain_openai import OpenAIEmbeddings, OpenAI
from langchain_community.vectorstores import Qdrant as QdrantVectorStore
import chainlit as cl
from langchain_openai import ChatOpenAI
from langchain_qdrant import Qdrant
# Load environment variables
load_dotenv()
# Initialize Qdrant client
openai_api_key = os.getenv("OPENAI_API_KEY")
qdrant_api_key = os.getenv("QDRANT_API_KEY")
qdrant_url = os.getenv("QDRANT_URL")
qdrant_client = QdrantClient(
url=qdrant_url,
api_key=qdrant_api_key
)
# Initialize OpenAI
embeddings = OpenAIEmbeddings(model="text-embedding-3-small",openai_api_key=openai_api_key)
llm = ChatOpenAI(temperature=0, openai_api_key=openai_api_key)
# Initialize vector store
collection_name = "ai_info_collection"
vector_store = Qdrant(
client=qdrant_client,
collection_name=collection_name,
embeddings=embeddings,
)
@cl.on_chat_start
async def start():
await cl.Message(content="Welcome! Ask me anything about AI ethics, regulations, or policies.").send()
@cl.on_message
async def main(message: cl.Message):
query = message.content
print(f"Received query: {query}") # Basic console logging
try:
docs = vector_store.similarity_search(query, k=3)
print(f"Retrieved {len(docs)} documents") # Basic console logging
context = "\n".join(doc.page_content for doc in docs if doc.page_content)
prompt = f"Based on the following context, answer the question: {query}\n\nContext: {context}"
response = await llm.ainvoke(prompt)
print(f"Generated response: {response}") # Basic console logging
await cl.Message(content=response.content).send()
except Exception as e:
error_message = f"An error occurred: {str(e)}"
print(f"Error: {error_message}") # Basic console logging
await cl.Message(content=error_message).send()
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