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# Read the data
import pandas as pd
df = pd.read_csv('./Automobile_data.csv')
#df = df.drop(columns = ['normalized-losses','symboling'], axis = 1)
context_data = []
for i in range(len(df)): # Loop over rows
context = ""
for j in range(26): # Loop over the first 8 columns
context += df.columns[j] # Add column name
context += ": "
context += str(df.iloc[i][j]) # Convert value to string
context += " "
context_data.append(context)
import os
# Get the secret key from the environment
groq_key = os.environ.get('groq_API_Keys')
## LLM used for RAG
from langchain_groq import ChatGroq
llm = ChatGroq(model="llama-3.1-70b-versatile",api_key=groq_key)
## Embedding model!
from langchain_huggingface import HuggingFaceEmbeddings
embed_model = HuggingFaceEmbeddings(model_name="mixedbread-ai/mxbai-embed-large-v1")
# create vector store!
from langchain_chroma import Chroma
vectorstore = Chroma(
collection_name="car_dataset_store",
embedding_function=embed_model,
persist_directory="./",
)
# add data to vector nstore
vectorstore.add_texts(context_data)
retriever = vectorstore.as_retriever()
from langchain_core.prompts import PromptTemplate
template = ("""You are a car expert.
Use the provided context to answer the question.
If you don't know the answer, say so. Explain your answer in detail.
Do not discuss the context in your response; just provide the answer directly.
Context: {context}
Question: {question}
Answer:""")
rag_prompt = PromptTemplate.from_template(template)
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
rag_chain = (
{"context": retriever, "question": RunnablePassthrough()}
| rag_prompt
| llm
| StrOutputParser()
)
import gradio as gr
# Function to process streaming responses
def rag_memory_stream(message, history):
partial_text = ""
for new_text in rag_chain.stream(message):
partial_text += new_text
yield partial_text
# Examples and app information
examples = ['I need a car', 'What is the make and fuel type of a car?']
description = "Real-time AI App with Groq API and LangChain to Answer car-related questions"
title = "Car Expert :) Try me!"
# Custom theme with black background
custom_theme = gr.themes.Base(primary_hue="blue", secondary_hue="green").set(
body_background_fill="#000000", # Black background
body_text_color="#FFFFFF", # White text for contrast
)
# Gradio interface
demo = gr.ChatInterface(
fn=rag_memory_stream,
type="messages",
title=title,
description=description,
fill_height=True,
examples=examples,
theme=custom_theme,
)
# Launch the app
if __name__ == "__main__":
demo.launch()
|