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import os
import openai
import chainlit as cl
from llama_index.core import Settings, ServiceContext
from llama_index.core.callbacks import CallbackManager
from llama_index.core.indices.vector_store.base import VectorStoreIndex
from llama_index.core.query_engine.retriever_query_engine import RetrieverQueryEngine
from llama_index.vector_stores.qdrant import QdrantVectorStore
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.postprocessor.flag_embedding_reranker import FlagEmbeddingReranker
import qdrant_client
# from llama_index.core import Settings
# from llama_index.core.indices.vector_store.base import VectorStoreIndex
# from llama_index.vector_stores.qdrant import QdrantVectorStore
# from llama_index.llms.openai import OpenAI
# from llama_index.embeddings.openai import OpenAIEmbedding
# from llama_index.core.query_engine.retriever_query_engine import RetrieverQueryEngine
# from llama_index.core.callbacks import CallbackManager
# from llama_index.core.service_context import ServiceContext
# from llama_index.postprocessor.flag_embedding_reranker import (
# FlagEmbeddingReranker,
# )
import qdrant_client
openai.api_key = os.environ.get("OPENAI_API_KEY")
# Connect to Qdrant vector database
client = qdrant_client.QdrantClient(
url="https://24f997d6-02df-4889-a352-1eac83e0bd37.us-east4-0.gcp.cloud.qdrant.io:6333",
api_key=os.environ["QDRANT_API_KEY"],
)
# Load the existing index from Qdrant
try:
vector_store = QdrantVectorStore(client=client, collection_name="meta_10k_filingsV5")
index = VectorStoreIndex.from_vector_store(vector_store=vector_store)
except Exception as e:
print(f"Failed to load the index from Qdrant: {e}")
@cl.on_chat_start
async def start():
Settings.llm = OpenAI(model="gpt-3.5-turbo-0125", temperature=0, max_tokens=1024, streaming=True)
Settings.embed_model = OpenAIEmbedding(model="text-embedding-3-small")
Settings.context_window = 1536
service_context = ServiceContext.from_defaults(callback_manager=CallbackManager([cl.LlamaIndexCallbackHandler()]))
#initialize reranker
reranker = FlagEmbeddingReranker(
top_n=5,
model="BAAI/bge-reranker-large",
)
query_engine = index.as_query_engine(streaming=True,
similarity_top_k=15, node_postprocessors=[reranker], verbose=True, service_context=service_context
)
cl.user_session.set("query_engine", query_engine)
await cl.Message(author="Assistant", content="Hello! I'm an AI assistant. How may I help you?").send()
@cl.on_message
async def main(message: cl.Message):
query_engine = cl.user_session.get("query_engine") # type: RetrieverQueryEngine
msg = cl.Message(content="", author="Assistant")
res = await cl.make_async(query_engine.query)(message.content)
for token in res.response_gen:
await msg.stream_token(token)
await msg.send() |