Create main.py

main.py

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+import json  # to work with JSON
+import threading  # to allow streaming response
+import time  # to pave the deliver of the message
+
+import faiss  # to create a search index
+import gradio  # for the interface
+import numpy  # to work with vectors
+import pandas  # to work with pandas
+import sentence_transformers  # to load an embedding model
+import spaces  # for GPU
+import transformers  # to load an LLM
+
+# Constants
+GREETING = (
+    "Howdy! "
+    "I'm an AI agent that uses [retrieval-augmented generation](https://en.wikipedia.org/wiki/Retrieval-augmented_generation) pipeline to answer questions about research by the [Design Research Collective](https://cmudrc.github.io/). "
+    "And the best part is that I always try to cite my sources! "
+    "I still make some mistakes though. " 
+    "What can I tell you about today?"
+)
+EXAMPLE_QUERIES = [
+    "Tell me about new research at the intersection of additive manufacturing and machine learning.",
+    "What is a physics-informed neural network and what can it be used for?",
+    "What can agent-based models do about climate change?",
+    "What's the difference between a markov chain and a hidden markov model?",
+    "What are the latest advancements in reinforcement learning?",
+    "What is known about different modes for human-AI teaming?",
+]
+EMBEDDING_MODEL_NAME = "allenai-specter"
+LLM_MODEL_NAME = "Qwen/Qwen2.5-7B-Instruct"
+PUBLICATIONS_TO_RETRIEVE = 5
+PARQUET_URL = "hf://datasets/ccm/publications/data/train-00000-of-00001.parquet"
+
+# Load the dataset and convert to pandas
+data = pandas.read_parquet(PARQUET_URL)
+
+# Filter out any publications without an abstract
+abstract_is_null = [
+    '"abstract": null' in json.dumps(bibdict) for bibdict in data["bib_dict"].values
+]
+data = data[~pandas.Series(abstract_is_null)]
+data.reset_index(inplace=True)
+
+# Load the model for later use in embeddings
+model = sentence_transformers.SentenceTransformer(EMBEDDING_MODEL_NAME)
+
+# Create an LLM pipeline that we can send queries to
+tokenizer = transformers.AutoTokenizer.from_pretrained(LLM_MODEL_NAME, trust_remote_code=True)
+streamer = transformers.TextIteratorStreamer(
+    tokenizer, skip_prompt=True, skip_special_tokens=True
+)
+chatmodel = transformers.AutoModelForCausalLM.from_pretrained(
+    LLM_MODEL_NAME, device_map="auto", torch_dtype="auto", trust_remote_code=True
+)
+
+# Create a FAISS index for fast similarity search
+metric = faiss.METRIC_INNER_PRODUCT
+vectors = numpy.stack(data["embedding"].tolist(), axis=0)
+index = faiss.IndexFlatL2(len(data["embedding"][0]))
+index.metric_type = metric
+faiss.normalize_L2(vectors)
+index.train(vectors)
+index.add(vectors)
+
+
+def preprocess(query: str, k: int) -> tuple[str, str]:
+    """
+    Searches the dataset for the top k most relevant papers to the query and returns a prompt and references
+    Args:
+        query (str): The user's query
+        k (int): The number of results to return
+    Returns:
+        tuple[str, str]: A tuple containing the prompt and references
+    """
+    encoded_query = numpy.expand_dims(model.encode(query), axis=0)
+    faiss.normalize_L2(encoded_query)
+    D, I = index.search(encoded_query, k)
+    top_five = data.loc[I[0]]
+
+    prompt = (
+        "You are an AI assistant who delights in helping people learn about research from the Design Research Collective, which is a research lab at Carnegie Mellon University led by Professor Chris McComb. "
+        "Your main task is to provide a concise ANSWER to the USER_QUERY that includes as many of the RESEARCH_ABSTRACTS as possible. "
+        "The RESEARCH_ABSTRACTS are provided in the `.bibtex` format. Your ANSWER should contain citations to the RESEARCH_ABSTRACTS using (AUTHOR, YEAR) format. "
+        "DO NOT list references at the end of the answer.\n\n"
+        "RESEARCH_ABSTRACTS:\n```bibtex\n{{ABSTRACTS_GO_HERE}}\n```\n\n"
+        "USER_GUERY:\n{{QUERY_GOES_HERE}}\n\n"
+        "ANSWER:\n"
+    )
+
+    references = []
+    research_abstracts = ""
+
+    for i in range(k):
+        year = str(int(top_five["bib_dict"].values[i]["pub_year"]))
+        abstract = top_five["bib_dict"].values[i]["abstract"]
+        url = "https://scholar.google.com/citations?view_op=view_citation&citation_for_view=" + top_five["author_pub_id"].values[i]
+        title = top_five["bib_dict"].values[i]["title"]
+        last_names = [
+                    author.split(" ")[-1]
+                    for author in top_five["bib_dict"]
+                    .values[i]["author"]
+                    .split(" and ")
+                ]
+        authors = ", ".join(
+                last_names
+            )
+
+        first_authors_last_name = last_names[0]
+
+        research_abstracts += top_five["bibtex"].values[i] + "\n"
+        references.append(f"<a href=\"{url}\">{first_authors_last_name} {year}</a>")
+        
+    prompt = prompt.replace("{{ABSTRACTS_GO_HERE}}", research_abstracts)
+    prompt = prompt.replace("{{QUERY_GOES_HERE}}", query)
+
+    print(prompt)
+    
+    return prompt, "; ".join(references)
+
+
+@spaces.GPU
+def reply(message: str, history: list[str]) -> str:
+    """
+    This function is responsible for crafting a response
+    Args:
+        message (str): The user's message
+        history (list[str]): The conversation history
+    Returns:
+        str: The AI's response
+    """
+
+    # Apply preprocessing
+    message, bypass = preprocess(message, PUBLICATIONS_TO_RETRIEVE)
+
+    # This is some handling that is applied to the history variable to put it in a good format
+    history_transformer_format = [
+        {"role": role, "content": message_pair[idx]}
+        for message_pair in history
+        for idx, role in enumerate(["user", "assistant"])
+        if message_pair[idx] is not None
+    ] + [{"role": "user", "content": message}]
+
+    # Stream a response from pipe
+    text = tokenizer.apply_chat_template(
+        history_transformer_format, tokenize=False, add_generation_prompt=True
+    )
+    model_inputs = tokenizer([text], return_tensors="pt").to("cuda:0")
+
+    generate_kwargs = dict(model_inputs, streamer=streamer, max_new_tokens=512)
+    t = threading.Thread(target=chatmodel.generate, kwargs=generate_kwargs)
+    t.start()
+
+    partial_message = ""
+    for new_token in streamer:
+        if new_token != "<":
+            partial_message += new_token
+            time.sleep(0.01)
+            yield partial_message
+
+    yield partial_message + "\n\n" + bypass
+
+    
+
+# Create and run the gradio interface
+gradio.ChatInterface(
+    reply,
+    examples=EXAMPLE_QUERIES,
+    chatbot=gradio.Chatbot(
+        show_label=False,
+        show_share_button=False,
+        show_copy_button=False,
+        value=[[None, GREETING]],
+        avatar_images=[
+            "https://cdn.dribbble.com/users/316121/screenshots/2333676/11-04_scotty-plaid_dribbble.png",
+            "https://media.thetab.com/blogs.dir/90/files/2021/06/screenshot-2021-06-10-at-110730-1024x537.png",
+        ],
+        height="60vh",
+        bubble_full_width=False,
+    ),
+    retry_btn=None,
+    undo_btn=None,
+    clear_btn=None,
+    theme=gradio.themes.Default(
+        font=[gradio.themes.GoogleFont("Zilla Slab")]
+    )
+).launch(debug=True)