initialize project

.gitignore

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+__pycache__
+.DS_Store
+faiss_index/*

app.py

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+import os
+from langchain_community.vectorstores import FAISS
+from langchain_mongodb.chat_message_histories import MongoDBChatMessageHistory
+from langchain_nvidia_ai_endpoints import NVIDIAEmbeddings, ChatNVIDIA
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain_core.messages import HumanMessage
+from langchain_core.runnables.history import RunnableWithMessageHistory
+import gradio as gr
+import time
+
+
+embedder = NVIDIAEmbeddings(model="NV-Embed-QA", model_type=None)
+db = FAISS.load_local("faiss_index", embedder, allow_dangerous_deserialization=True)
+model = ChatNVIDIA(model="meta/llama3-70b-instruct")
+
+retriever = db.as_retriever(search_kwargs={"k": 8})
+
+retrieved_docs = retriever.invoke("Seafood restaurants in Phuket")
+print(len(retrieved_docs))
+for doc in retrieved_docs:
+    print(doc.metadata)
+    
+
+
+def get_session_history(session_id):
+    return MongoDBChatMessageHistory(
+        session_id=session_id,
+        connection_string=os.environ["MONGODB_URI"],
+        database_name="tour_planner_db",
+        collection_name="chat_histories",
+    )
+    
+
+prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", """
+### [INST] Instruction: Answer the question based on your knowledge about places in Thailand. You are Roam Mate which is a chat bot to help users with their travel and recommending places according to their reference. Here is context to help:
+Also provides your rationale for generating the places you are recommending.
+Context:\n{context}\n
+(Answer from retrieval if they are relevant to the question. Only cite sources that are used. Make your response conversational.)
+
+
+### QUESTION:
+{question} [/INST]
+ """),
+        MessagesPlaceholder(variable_name="history")
+    ]
+)
+
+runnable = prompt | model
+
+runnable_with_history = RunnableWithMessageHistory(
+    runnable,
+    get_session_history,
+    input_messages_key="question",
+    history_messages_key="history",
+)
+
+initial_msg = (
+    "Hello! I am a chatbot to help with vacation."
+    f"\nHow can I help you?"
+)
+
+def chat_gen(message, history, session_id, return_buffer=True):
+    print(session_id)
+    buffer = ""
+    for token in runnable_with_history.stream(
+        {"question": message, "context": db.as_retriever(search_type="similarity", search_kwargs={"k": 5})},
+        config={"configurable": {"session_id": session_id}},
+    ):
+        buffer += token.content
+        time.sleep(0.05)
+        yield buffer
+
+with gr.Blocks(fill_height=True) as demo:
+    session_id = gr.Textbox("1", label="Session ID")
+    chatbot = gr.Chatbot(value = [[None, initial_msg]], bubble_full_width=True, scale=1)
+    gr.ChatInterface(chat_gen, chatbot=chatbot, additional_inputs=[session_id]).queue()
+
+    
+if __name__ == "__main__":
+    demo.launch()
+
+

requirements.txt

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+langchain
+langchain-community
+faiss-cpu
+flashrank
+langchain-nvidia-ai-endpoints
+langchain-mongodb