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from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import Chroma
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.document_loaders import TextLoader
from langchain.chains import RetrievalQA
from langchain.llms.base import LLM
from typing import List, Optional
from groq import Groq
import os
loader = TextLoader("./Project.txt")
documents = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
docs = text_splitter.split_documents(documents)
embedding = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
vectorstore = Chroma.from_documents(docs, embedding, persist_directory="rag_chroma_groq")
class GroqLLM(LLM):
model: str = "llama3-8b-8192"
api_key: str = "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" # Replace with your actual API key
temperature: float = 0.0
def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
client = Groq(api_key=self.api_key)
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt}
]
response = client.chat.completions.create(
model=self.model,
messages=messages,
temperature=self.temperature,
)
return response.choices[0].message.content
@property
def _llm_type(self) -> str:
return "groq-llm"
retriever = vectorstore.as_retriever()
groq_llm = GroqLLM(api_key="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")
qa_chain = RetrievalQA.from_chain_type(
llm=groq_llm,
retriever=retriever,
return_source_documents=True
)
query = "Explain the whole project in points and sections"
result = qa_chain({"query": query})
print("Answer:", result["result"])
import gradio as gr
# Ensure qa_chain is defined (from your code above)
# Define the function that will be called when the user submits a question
def answer_query(query):
result = qa_chain({"query": query})
return result["result"]
# Create the Gradio interface
interface = gr.Interface(
fn=answer_query,
inputs=gr.Textbox(lines=2, placeholder="Ask me anything about the project..."),
outputs="text",
title="🧠 Project Summariser",
description="Ask questions based on my projects"
)
# Launch the interface
interface.launch()
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