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# Required modules
import os
from pinecone import Pinecone
from transformers import AutoModel
from langchain_core.prompts import ChatPromptTemplate
from langchain_groq import ChatGroq
from dotenv import load_dotenv
load_dotenv()
# Initialize clients, indexes, models etc.
pc_client = Pinecone(api_key=os.getenv("PINECONE_API_KEY"))
pc_index = pc_client.Index(os.getenv("PINECONE_INDEX"))
embedding_model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-base-en', trust_remote_code=True)
groq_llm=ChatGroq(
groq_api_key=os.getenv("GROQ_API_KEY"),
model_name="Llama3-8b-8192"
)
#context retrivel
def retrive_context(user_query:str) -> str:
"""Retrives the context for asked query from vector database
Args:
user_query (str): Questions asked by user to bot
Returns:
context (str): Question's context
"""
context = ""
try:
embedded_query = embedding_model.encode(user_query).tolist()
except Exception as e:
return 500
try:
res = pc_index.query(
vector=embedded_query,
top_k=5,
include_values=True,
include_metadata = True
)
except Exception as e:
return 500
for match in res['matches']:
context = context + match['metadata']['text'] + " "
print(context)
return context
# Prompt Engineering for LLM
prompt = ChatPromptTemplate.from_template(
"""
Hello! As a RAG agent for Biskane, your task is to answer the user's question using the provided context. Please keep your responses brief and straightforward.
<context>
{context}
<context>
Question: {query}
"""
)
# Response generator
def generate_response(query:str, context:str) -> str:
"""Generates the response for asked question from given context
Args:
query (str): Query asked by user to bot
context (str): Context, retrived from vector database
Returns:
answer (str): Generated response
"""
try:
chain = prompt | groq_llm
llm_response = chain.invoke({
"context": context,
"query": query
})
return llm_response.content
except Exception as e:
return 500
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