Spaces:
Sleeping
Sleeping
File size: 2,774 Bytes
e249f66 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 |
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()
|