Streaming Output from AutoGPTQForCausalLM and RetrievalQA.from_chain_type to Chainlit
Problem: Developers often struggle to visually track the real-time progress of their large language models (LLMs) when utilizing libraries like AutoGPTQForCausalLM and RetrievalQA.from_chain_type. This lack of visibility hinders debugging and understanding the model's reasoning process.
Rephrased: Imagine you're building a powerful AI chatbot. You want to see exactly how it's thinking and generating responses, step by step. But the tools you're using only show the final result. This article shows you how to display the model's inner workings in a user-friendly interface, using Chainlit.
Scenario and Code:
Let's say we're building a Q&A chatbot that retrieves information from a knowledge base and then uses a language model to provide a comprehensive answer. Here's a basic example using AutoGPTQForCausalLM and RetrievalQA.from_chain_type:
from langchain.chains import RetrievalQA
from langchain.llms import AutoGPTQForCausalLM
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
# Load your knowledge base
knowledge_base = ... # Load from your data source
# Initialize the embedding model and vector store
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
vectorstore = FAISS.from_texts(knowledge_base, embeddings)
# Load the quantized GPTQ model
llm = AutoGPTQForCausalLM(model_path="path/to/model.safetensors", # Replace with your model path
model_type="gptq_for_causal_lm",
use_cuda=True,
quantize_config=dict(bits=4, group_size=128))
# Initialize the RetrievalQA chain
chain = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=vectorstore.as_retriever(),
return_source_documents=True,
)
# Get an answer
question = "What is the capital of France?"
result = chain(question)
print(result['answer'])
Insights and Improvements:
This code works, but it only shows the final answer. To gain deeper insights into the model's process, we can utilize Chainlit:
-
Install Chainlit:
pip install chainlit -
Import and Configure Chainlit:
from chainlit import Chainlit from chainlit.callbacks import StreamingCallback # Initialize Chainlit with your project name Chainlit.set_project(project_name="My-Q&A-Chatbot") -
Enable Streaming Output:
chain = RetrievalQA.from_chain_type( llm=llm, chain_type="stuff", retriever=vectorstore.as_retriever(), return_source_documents=True, ) chain.add_callback(StreamingCallback()) # ... rest of the code -
Run your code: When you execute your script, Chainlit will launch a web interface displaying the following information:
- Input: The question you asked.
- Retrieval: The retrieved documents from your knowledge base.
- LLM Generation: The model's step-by-step reasoning process, including the generated text.
- Output: The final answer.
Benefits of Streaming Output:
- Improved Debugging: Quickly identify issues with your model's reasoning, retrieval, or generation process.
- Enhanced Understanding: Gain valuable insights into how your model works, making it easier to refine and improve its performance.
- Better Collaboration: Share your model's inner workings with other developers or stakeholders for easier collaboration and understanding.
Additional Considerations:
- Privacy: Ensure that any sensitive information in your knowledge base or model output is appropriately handled and protected.
- Performance: Streaming output might slightly affect your model's execution speed. For very large models, you might consider using a dedicated streaming server.
References:
Conclusion:
By integrating Chainlit into your code, you can unlock the full potential of your LLMs by visualizing their reasoning process. This allows you to understand your model's strengths and weaknesses, improve its performance, and collaborate more effectively with others.
