app.py

import os
import gradio as gr
from langchain_community.vectorstores import FAISS
from langchain_nvidia_ai_endpoints import NVIDIAEmbeddings
import pymongo

from langchain_community.vectorstores import MongoDBAtlasVectorSearch
from langchain_core.runnables.passthrough import RunnableAssign, RunnablePassthrough
from langchain.memory import ConversationBufferMemory
from langchain_core.messages import get_buffer_string
from langchain_nvidia_ai_endpoints import ChatNVIDIA, NVIDIAEmbeddings

from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.output_parsers import StrOutputParser
from langchain.chains import create_history_aware_retriever, create_retrieval_chain
from langchain_core.chat_history import BaseChatMessageHistory
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_community.chat_message_histories import ChatMessageHistory
from langchain_core.runnables.history import RunnableWithMessageHistory
from langchain_core.messages import HumanMessage


embedder = NVIDIAEmbeddings(model="nvolveqa_40k", model_type=None)
db = FAISS.load_local("vms_faiss_index", embedder, allow_dangerous_deserialization=True)

# docs = new_db.similarity_search(query)

nvidia_api_key = os.environ.get("NVIDIA_API_KEY", "")



def get_mongo_client(mongo_uri):
  """Establish connection to the MongoDB."""
  try:
    client = pymongo.MongoClient(mongo_uri)
    print("Connection to MongoDB successful")
    return client
  except pymongo.errors.ConnectionFailure as e:
    print(f"Connection failed: {e}")
    return None

mongo_uri = os.environ.get('MyCluster_MONGO_URI')
if not mongo_uri:
  print("MONGO_URI not set in environment variables")

mongo_client = get_mongo_client(mongo_uri)

DB_NAME="vms_courses"
COLLECTION_NAME="courses"

db = mongo_client[DB_NAME]
collection = db[COLLECTION_NAME]
ATLAS_VECTOR_SEARCH_INDEX_NAME = "vector_index"


vector_search = MongoDBAtlasVectorSearch.from_connection_string(
    mongo_uri,
    DB_NAME + "." + COLLECTION_NAME,
    embedder,
    index_name=ATLAS_VECTOR_SEARCH_INDEX_NAME,
)


llm = ChatNVIDIA(model="mixtral_8x7b")

retriever = vector_search.as_retriever(
    search_type="similarity",
    search_kwargs={"k": 12},
)



### 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
)


### Answer question ###
qa_system_prompt = """You are a VMS assistant for helping students with their academic. \
Answer the question using only the context provided. Do not include based on the context or based on the documents provided in your answer. \
Please help them with their question. Remember that your job is to represent Vicent Mary School of Science and Technology (VMS) at Assumption University. \
Do not hallucinate any details, and make sure the knowledge base is not redundant.\
If you don't know the answer, just say that you don't know. \

{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",
)

c_history = []

def chat_gen(message, history):
    buffer = ""

    ai_message = rag_chain.invoke({"input": message, "chat_history": c_history})
    c_history.extend([HumanMessage(content=message), ai_message["answer"]])
    print(c_history)
    yield ai_message["answer"]

    # for doc in ai_message["context"]:
    #     yield doc

initial_msg = (
    "Hello! I am VMS bot here to help you with your academic issues!"
    f"\nHow can I help you?"
)


chatbot = gr.Chatbot(value = [[None, initial_msg]], bubble_full_width=False)
demo = gr.ChatInterface(chat_gen, chatbot=chatbot).queue()

try:
    demo.launch(debug=True, share=True, show_api=False)
    demo.close()
except Exception as e:
    demo.close()
    print(e)
    raise e

# available models names
# mixtral_8x7b
# llama2_13b
# llm = ChatNVIDIA(model="mixtral_8x7b") | StrOutputParser()

# initial_msg = (
#     "Hello! I am VMS bot here to help you with your academic issues!"
#     f"\nHow can I help you?"
# )

# context_prompt = ChatPromptTemplate.from_messages([
#     ('system',
#         "You are a VMS chatbot, and you are helping students with their academic issues."
#         "Answer the question using only the context provided. Do not include based on the context or based on the documents provided in your answer."
#         "Please help them with their question. Remember that your job is to represent Vicent Mary School of Science and Technology (VMS) at Assumption University."
#         "Do not hallucinate any details, and make sure the knowledge base is not redundant."
#         "Please say you do not know if you do not know or you cannot find the information needed."
#         "\n\nQuestion: {question}\n\nContext: {context}"),
#         ('user', "{question}"
# )])

# chain = (
#     {
#         'context': db.as_retriever(search_type="similarity"),
#         'question': (lambda x:x)
#     }
#     | context_prompt
#     # | RPrint()
#     | llm
#     | StrOutputParser()
# )

# conv_chain = (
#     context_prompt
#     # | RPrint()
#     | llm
#     | StrOutputParser()
# )

# def chat_gen(message, history, return_buffer=True):
#     buffer = ""

#     doc_retriever = db.as_retriever(search_type="similarity_score_threshold", search_kwargs={"score_threshold": 0.2})
#     retrieved_docs = doc_retriever.invoke(message)
#     print(len(retrieved_docs))
#     print(retrieved_docs)

#     if len(retrieved_docs) > 0:
#         state = {
#                 'question': message,
#                 'context': retrieved_docs
#         }
#         for token in conv_chain.stream(state):
#             buffer += token
#             yield buffer
#     else:
#       passage = "I am sorry. I do not have relevant information to answer on that specific topic. Please try another question."
#       buffer += passage
#       yield buffer if return_buffer else passage


# chatbot = gr.Chatbot(value = [[None, initial_msg]])
# iface = gr.ChatInterface(chat_gen, chatbot=chatbot).queue()
# iface.launch()