File size: 2,564 Bytes
07037b8 51782e8 086f34e 90c6fe9 07037b8 feba6f5 90c6fe9 feba6f5 07037b8 4d09653 619c894 4d09653 07037b8 4d09653 07037b8 5be2eda 07037b8 5be2eda 07037b8 015e894 07037b8 c7a20f3 07037b8 c7a20f3 07037b8 5be2eda 4d09653 07037b8 eb3640e 07037b8 015e894 |
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 96 97 98 99 |
# 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
def rag_memory_stream(message, history):
partial_text = ""
for new_text in rag_chain.stream(message):
partial_text += new_text
yield partial_text
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 medical questions"
title = "Medical Expert :) Try me!"
demo = gr.ChatInterface(fn=rag_memory_stream,
type="messages",
title=title,
description=description,
fill_height=True,
examples=examples,
theme="glass",
)
if __name__ == "__main__":
demo.launch()
|