questions = [
    "What are Heat-bath random walks with Markov base?",
    "What is the ImageBind model?",
    "How does Compositional Reasoning with Large Language Models works?",   
] 
chat_history = []
for question in questions:  
    result = qa({"question": question, "chat_history": chat_history})
    chat_history.append((question, result['answer']))
    print(f"-> **Question**: {question} \n")
    print(f"**Answer**: {result['answer']} \n")
-> **Question**: What are Heat-bath random walks with Markov base? 
**Answer**: I'm not sure, as I don't have enough context to provide a definitive answer. The term "Heat-bath random walks with Markov base" is not mentioned in the given text. Could you provide more information or context about where you encountered this term? 
-> **Question**: What is the ImageBind model? 
**Answer**: ImageBind is an approach developed by Facebook AI Research to learn a joint embedding across six different modalities, including images, text, audio, depth, thermal, and IMU data. The approach uses the binding property of images to align each modality's embedding to image embeddings and achieve an emergent alignment across all modalities. This enables novel multimodal capabilities, including cross-modal retrieval, embedding-space arithmetic, and audio-to-image generation, among others. The approach sets a new state-of-the-art on emergent zero-shot recognition tasks across modalities, outperforming specialist supervised models. Additionally, it shows strong few-shot recognition results and serves as a new way to evaluate vision models for visual and non-visual tasks. 
-> **Question**: How does Compositional Reasoning with Large Language Models works?
rtdocs_stable/api.python.langchain.com/en/stable/modules/indexes/retrievers/examples/arxiv.html