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from langchain_community.vectorstores import Qdrant
from langchain_groq import ChatGroq
from langchain_huggingface import HuggingFaceEmbeddings
import os
from dotenv import load_dotenv
from langchain.prompts import ChatPromptTemplate
from langchain.schema.runnable import RunnablePassthrough
from langchain.schema.output_parser import StrOutputParser
from qdrant_client import QdrantClient, models
from langchain_qdrant import Qdrant
import gradio as gr
# Load environment variables
load_dotenv()
os.environ["GROQ_API_KEY"] = os.getenv("GROQ_API")
# HuggingFace Embeddings
embeddings = HuggingFaceEmbeddings(model_name="BAAI/bge-large-en-v1.5")
# Qdrant Client Setup
client = QdrantClient(
url=os.getenv("QDRANT_URL"),
api_key=os.getenv("QDRANT_API_KEY"),
prefer_grpc=True
)
collection_name = "mawared"
# Try to create collection, handle if it already exists
try:
client.create_collection(
collection_name=collection_name,
vectors_config=models.VectorParams(
size=768, # GTE-large embedding size
distance=models.Distance.COSINE
),
)
print(f"Created new collection: {collection_name}")
except Exception as e:
if "already exists" in str(e):
print(f"Collection {collection_name} already exists, continuing...")
else:
raise e
# Create Qdrant vector store
db = Qdrant(
client=client,
collection_name=collection_name,
embeddings=embeddings,
)
# Create retriever
retriever = db.as_retriever(
search_type="similarity",
search_kwargs={"k": 5}
)
# LLM setup
llm = ChatGroq(
model="llama-3.3-70b-versatile",
temperature=0.1,
max_tokens=None,
timeout=None,
max_retries=2,
)
# Create prompt template
template = """
You are an expert assistant specializing in the LONG COT RAG. Your task is to answer the user's question strictly based on the provided context...
Context:
{context}
Question:
{question}
Answer:
"""
prompt = ChatPromptTemplate.from_template(template)
# Create the RAG chain
rag_chain = (
{"context": retriever, "question": RunnablePassthrough()}
| prompt
| llm
| StrOutputParser()
)
# Define the Gradio function
def ask_question_gradio(question):
result = ""
for chunk in rag_chain.stream(question):
result += chunk
return result
# Create the Gradio interface
interface = gr.Interface(
fn=ask_question_gradio,
inputs="text",
outputs="text",
title="Mawared Expert Assistant",
description="Ask questions about the Mawared HR System or any related topic using Chain-of-Thought (CoT) and RAG principles.",
theme="compact",
)
# Launch Gradio app
if __name__ == "__main__":
interface.launch()
|