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import os
from dotenv import load_dotenv
load_dotenv(".env")
os.environ['USER_AGENT'] = os.getenv("USER_AGENT")
os.environ["GROQ_API_KEY"] = os.getenv("GROQ_API_KEY")
os.environ["TOKENIZERS_PARALLELISM"]='true'
import nltk
nltk.download('punkt_tab')
from langchain.chains import create_history_aware_retriever, create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_community.chat_message_histories import ChatMessageHistory
from langchain_community.document_loaders import WebBaseLoader
from langchain_core.chat_history import BaseChatMessageHistory
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.runnables.history import RunnableWithMessageHistory
from pinecone import Pinecone
from pinecone_text.sparse import BM25Encoder
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_community.retrievers import PineconeHybridSearchRetriever
from langchain_groq import ChatGroq
import gradio as gr
import spaces
import torch
try:
pc = Pinecone(api_key=os.getenv("PINECONE_API_KEY"))
index_name = "traveler-demo-website-vectorstore"
# connect to index
pinecone_index = pc.Index(index_name)
except:
pc = Pinecone(api_key=os.getenv("PINECONE_API_KEY"))
index_name = "traveler-demo-website-vectorstore"
# connect to index
pinecone_index = pc.Index(index_name)
bm25 = BM25Encoder().load("./bm25_traveler_website.json")
embed_model = HuggingFaceEmbeddings(model_name="Alibaba-NLP/gte-large-en-v1.5", model_kwargs={"trust_remote_code":True, 'device': 'cuda'})
retriever = PineconeHybridSearchRetriever(
embeddings=embed_model,
sparse_encoder=bm25,
index=pinecone_index,
top_k=20,
alpha=0.5,
)
llm = ChatGroq(model="llama-3.1-70b-versatile", temperature=0.1, max_tokens=1024, max_retries=2)
### Contextualize question ###
contextualize_q_system_prompt = """Given a chat history and the latest user question \
which might reference context in the chat history, formulate a standalone question \
which can be understood without the chat history. Do NOT answer the question, \
just reformulate it if needed and otherwise return it as is.
"""
contextualize_q_prompt = ChatPromptTemplate.from_messages(
[
("system", contextualize_q_system_prompt),
MessagesPlaceholder("chat_history"),
("human", "{input}")
]
)
history_aware_retriever = create_history_aware_retriever(
llm, retriever, contextualize_q_prompt
)
qa_system_prompt = """You are a highly skilled information retrieval assistant. Use the following pieces of retrieved context to answer the question. \
Provide links to sources provided in the answer. \
If you don't know the answer, just say that you don't know. \
Do not give extra long answers. \
When responding to queries, your responses should be comprehensive and well-organized. For each response: \
1. Provide Clear Answers \
2. Include Detailed References: \
- Include links to sources and any links or sites where there is a mentioned in the answer.
- Links to Sources: Provide URLs to credible sources where users can verify the information or explore further. \
- Downloadable Materials: Include links to any relevant downloadable resources if applicable. \
- Reference Sites: Mention specific websites or platforms that offer additional information. \
3. Formatting for Readability: \
- Bullet Points or Lists: Where applicable, use bullet points or numbered lists to present information clearly. \
- Emphasize Important Information: Use bold or italics to highlight key details. \
4. Organize Content Logically \
Do not include anything about context in the answer. \
{context}
"""
qa_prompt = ChatPromptTemplate.from_messages(
[
("system", qa_system_prompt),
MessagesPlaceholder("chat_history"),
("human", "{input}")
]
)
question_answer_chain = create_stuff_documents_chain(llm, qa_prompt)
rag_chain = create_retrieval_chain(history_aware_retriever, question_answer_chain)
### Statefully manage chat history ###
store = {}
def get_session_history(session_id: str) -> BaseChatMessageHistory:
if session_id not in store:
store[session_id] = ChatMessageHistory()
return store[session_id]
conversational_rag_chain = RunnableWithMessageHistory(
rag_chain,
get_session_history,
input_messages_key="input",
history_messages_key="chat_history",
output_messages_key="answer",
)
@spaces.GPU(duration=5)
def handle_message(question, history={}):
response = ''
chain = conversational_rag_chain.pick("answer")
for chunk in chain.stream(
{"input": question},
config={
"configurable": {"session_id": "abc123"}
},
):
response += chunk
yield response
if __name__ == '__main__':
demo = gr.ChatInterface(fn=handle_message)
demo.launch() |