File size: 3,422 Bytes
a63d980 07c8970 a63d980 f356dde a63d980 07c8970 a63d980 07c8970 a63d980 07c8970 a63d980 07c8970 a63d980 07c8970 a63d980 07c8970 a63d980 07c8970 a63d980 07c8970 a63d980 07c8970 a63d980 |
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 86 87 88 89 90 91 92 93 94 95 |
import os
import gradio as gr
from langchain_openai import ChatOpenAI
from dotenv import load_dotenv
from langchain_huggingface import HuggingFaceEmbeddings
from langchain_chroma import Chroma
from langchain_core.documents import Document
# ✅ Load OpenAI API Key
load_dotenv()
api_key = os.getenv("OPENAI_API_KEY")
# ✅ Initialize OpenAI Model with LangChain
model = ChatOpenAI(
model="gpt-4o-mini",
openai_api_key=api_key
)
# ✅ Initialize HuggingFace Embeddings
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
# ✅ Initialize Chroma Vector Store
vector_store = Chroma(
collection_name="chat_collection", # Specify the collection name
embedding_function=embeddings,
persist_directory="/tmp/chroma_db", # Directory to store data locally
)
# ✅ Step 1: Helper Functions for Chat Memory
def get_chat_history(user_id):
"""Fetches stored messages for a given user from the vector store."""
retriever = vector_store.as_retriever(
search_type="mmr",
search_kwargs={"k": 100, "fetch_k": 100}
)
# Ensure filter is applied to user_id correctly
results = retriever.invoke("Chat history", filter={"user_id": user_id})
if not results: # If no results, return empty string
return ""
# Extract the page content (chat messages) from the results
user_history = [doc.page_content for doc in results]
return "\n".join(user_history) if user_history else ""
def store_chat_message(user_id, user_input, bot_response):
"""Stores user-bot conversations in ChromaDB."""
chat_entry = f"User: {user_input}\nBot: {bot_response}"
# Add to vector store with user_id as metadata
vector_store.add_documents([Document(page_content=chat_entry, metadata={"user_id": user_id})])
# ✅ Step 2: Generate Response Using OpenAI GPT
def generate_response(username, user_input):
"""Generates a chatbot response using GPT-4 and stores chat history."""
user_id = username.lower().strip()
history = get_chat_history(user_id)
messages = [{"role": "system", "content": "You are a helpful AI assistant. Please provide answer in 20 words only"}]
if history:
messages.append({"role": "user", "content": f"Chat history:\n{history}"})
messages.append({"role": "user", "content": user_input})
# Generate the response
response = model.invoke(messages)
bot_response = response.content
# Store the conversation for future reference
store_chat_message(user_id, user_input, bot_response)
# Return the entire conversation including the user's input and bot's response
return f"{history}\nUser: {user_input}\nBot: {bot_response}"
# ✅ Step 3: Gradio UI with User Dropdown
with gr.Blocks() as demo:
gr.Markdown("# 🔥 Multi-User Chatbot with GPT-4 and Memory (ChromaDB)")
# Dropdown for selecting user
username_input = gr.Dropdown(
label="Select User",
choices=["Aarya", "Ved", "Vivaan"],
)
# Chat input and output
chat_input = gr.Textbox(label="Your Message", placeholder="Type here...")
chat_output = gr.Textbox(label="Chatbot Response", interactive=False)
# Button to send the message
chat_button = gr.Button("Send")
chat_button.click(generate_response, inputs=[username_input, chat_input], outputs=chat_output)
# ✅ Step 4: Run the Gradio app
demo.launch()
|