add conversation history

app.py

@@ -2,14 +2,22 @@ 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
+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)
@@ -20,70 +28,195 @@ db = FAISS.load_local("vms_faiss_index", embedder, allow_dangerous_deserializati
 nvidia_api_key = os.environ.get("NVIDIA_API_KEY", "")
 
 
-from operator import itemgetter
 
+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
 
-# available models names
-# mixtral_8x7b
-# llama2_13b
-llm = ChatNVIDIA(model="mixtral_8x7b") | StrOutputParser()
+mongo_uri = os.environ.get('MyCluster_MONGO_URI')
+if not mongo_uri:
+  print("MONGO_URI not set in environment variables")
 
-initial_msg = (
-    "Hello! I am VMS bot here to help you with your academic issues!"
-    f"\nHow can I help you?"
+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,
 )
 
-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()
+
+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]
 
-conv_chain = (
-    context_prompt
-    # | RPrint()
-    | llm
-    | StrOutputParser()
+
+conversational_rag_chain = RunnableWithMessageHistory(
+    rag_chain,
+    get_session_history,
+    input_messages_key="input",
+    history_messages_key="chat_history",
+    output_messages_key="answer",
 )
 
-def chat_gen(message, history, return_buffer=True):
+c_history = []
+
+def chat_gen(message, history):
     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()
+    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()

requirements.txt

@@ -1,4 +1,7 @@
 langchain
 langchain-nvidia-ai-endpoints
 gradio
-faiss-cpu
+faiss-cpu
+pymongo
+llama-index
+llama-index-vector-stores-mongodb