main.py

import os
from operator import itemgetter

import chainlit as cl
from dotenv import load_dotenv
from langchain.retrievers.contextual_compression import ContextualCompressionRetriever
from langchain_cohere import CohereRerank
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnableLambda
from langchain_google_genai import GoogleGenerativeAIEmbeddings
from langchain_openai import ChatOpenAI
from langchain_qdrant import FastEmbedSparse, QdrantVectorStore, RetrievalMode
from qdrant_client import QdrantClient


load_dotenv()

client = QdrantClient(
    url=os.environ["QDRANT_URL"], api_key=os.environ["QDRANT_API_KEY"]
)
embeddings = GoogleGenerativeAIEmbeddings(model="models/gemini-embedding-exp-03-07")
vectorstore = QdrantVectorStore(
    client=client,
    collection_name="collection_v_0_3",
    embedding=embeddings,
    sparse_embedding=FastEmbedSparse(model_name="Qdrant/bm25"),
    retrieval_mode=RetrievalMode.HYBRID,
    vector_name="dense",
    sparse_vector_name="sparse",
)
retriever = vectorstore.as_retriever(search_kwargs={"k": 20})
compressor = CohereRerank(model="rerank-v3.5")
compression_retriever = ContextualCompressionRetriever(
    base_compressor=compressor, base_retriever=retriever
)
prompt = """\
You are a helpful and kind assistant. Use the context provided below to answer the question.

If you do not know the answer, or are unsure, say you don't know.

Query:
{question}

Context:
{context}
"""


def format_docs(docs):
    return [doc.page_content for doc in docs]


llm_model = ChatOpenAI(model="gpt-4.1-mini", temperature=0, streaming=True)
chain = (
    {
        "context": itemgetter("question")
        | compression_retriever
        | RunnableLambda(format_docs),
        "question": itemgetter("question"),
    }
    | ChatPromptTemplate.from_template(prompt)
    | llm_model
)


@cl.on_chat_start
async def start_chat():
    await cl.Message(content="Ask your questions...").send()


@cl.on_message
async def main(message: cl.Message):
    response = cl.Message("")
    await response.send()
    async for chunk in chain.astream({"question": message.content}):
        response.content += chunk.content
        await response.update()