app.py

"""
This script sets up a Gradio interface for querying an AI assistant about additive manufacturing research.
It uses a vectorstore to retrieve relevant research excerpts and a language model to generate responses.

Modules:
    - gradio: Interface handling
    - spaces: For GPU
    - transformers: LLM Loading
    - langchain_community.vectorstores: Vectorstore for publications
    - langchain_huggingface: Embeddings

Constants:
    - PUBLICATIONS_TO_RETRIEVE: The number of publications to retrieve for the prompt
    - RAG_TEMPLATE: The template for the RAG prompt

Functions:
    - preprocess(query: str) -> str: Generates a prompt based on the top k documents matching the query.
    - reply(message: str, history: list[str]) -> str: Generates a response to the user’s message.

Example Queries:
    - "What is multi-material 3D printing?"
    - "How is additive manufacturing being applied in aerospace?"
    - "Tell me about innovations in metal 3D printing techniques."
    - "What are some sustainable materials for 3D printing?"
    - "What are the biggest challenges with support structures in additive manufacturing?"
    - "How is 3D printing impacting the medical field?"
    - "What are some common applications of additive manufacturing in industry?"
    - "What are the benefits and limitations of using polymers in 3D printing?"
    - "Tell me about the environmental impacts of additive manufacturing."
    - "What are the primary limitations of current 3D printing technologies?"
    - "How are researchers improving the speed of 3D printing processes?"
    - "What are the best practices for managing post-processing in additive manufacturing?"
"""

import gradio  # Interface handling
import spaces  # For GPU
import langchain_community.vectorstores  # Vectorstore for publications
import langchain_huggingface  # Embeddings
import llama_cpp

# The number of publications to retrieve for the prompt
PUBLICATIONS_TO_RETRIEVE = 5

# The template for the RAG prompt
RAG_TEMPLATE = """You are an AI assistant who enjoys helping users learn about research. 
Answer the USER_QUERY on additive manufacturing research using the RESEARCH_EXCERPTS. 
Provide a concise ANSWER based on these excerpts. Avoid listing references.

===== RESEARCH_EXCERPTS =====
{research_excerpts}

===== USER_QUERY =====
{query}

===== ANSWER =====
"""

# Load vectorstore of SFF publications
publication_vectorstore = langchain_community.vectorstores.FAISS.load_local(
    folder_path="publication_vectorstore",
    embeddings=langchain_huggingface.HuggingFaceEmbeddings(
        model_name="all-MiniLM-L12-v2",
        model_kwargs={"device": "cuda"},
        encode_kwargs={"normalize_embeddings": False},
    ),
    allow_dangerous_deserialization=True,
)

# Create the callable LLM
# llm = transformers.pipeline(
#     task="text-generation", model="Qwen/Qwen2.5-7B-Instruct-GPTQ-Int4", device="cuda"
# )
llm = llama_cpp.Llama.from_pretrained(
    repo_id="bartowski/Qwen2.5-7B-Instruct-GGUF", 
    filename="Qwen2.5-7B-Instruct-Q4_K_M.gguf", 
    n_gpu_layers=-1,
    n_ctx=2048,
    verbose=True
)


def preprocess(query: str) -> str:
    """
    Generates a prompt based on the top k documents matching the query.

    Args:
        query (str): The user's query.

    Returns:
        str: The formatted prompt containing research excerpts and the user's query.
    """

    # Search for the top k documents matching the query
    documents = publication_vectorstore.search(
        query, k=PUBLICATIONS_TO_RETRIEVE, search_type="similarity"
    )

    # Extract the page content from the documents
    research_excerpts = [f'"... {doc.page_content}..."' for doc in documents]

    # Format the prompt with the research excerpts and the user's query
    prompt = RAG_TEMPLATE.format(
        research_excerpts="\n\n".join(research_excerpts), query=query
    )

    # Print the prompt for debugging purposes
    print(prompt)

    return prompt


@spaces.GPU
def reply(message: str, history: list[str]) -> str:
    """
    Generates a response to the user’s message.

    Args:
        message (str): The user's message or query.
        history (list[str]): The conversation history.

    Returns:
        str: The generated response from the language model.
    """

    # return llm(
    #     preprocess(message),
    #     max_new_tokens=512,
    #     return_full_text=False,
    # )[
    #     0
    # ]["generated_text"]

    return llm(preprocess(message))["choices"][0]["text"]

# Example Queries for Interface
EXAMPLE_QUERIES = [
    "What is multi-material 3D printing?",
    "How is additive manufacturing being applied in aerospace?",
    "Tell me about innovations in metal 3D printing techniques.",
    "What are some sustainable materials for 3D printing?",
    "What are the biggest challenges with support structures in additive manufacturing?",
    "How is 3D printing impacting the medical field?",
    "What are some common applications of additive manufacturing in industry?",
    "What are the benefits and limitations of using polymers in 3D printing?",
    "Tell me about the environmental impacts of additive manufacturing.",
    "What are the primary limitations of current 3D printing technologies?",
    "How are researchers improving the speed of 3D printing processes?",
    "What are the best practices for managing post-processing in additive manufacturing?",
]

# Run the Gradio Interface
gradio.ChatInterface(
    reply,
    examples=EXAMPLE_QUERIES,
    cache_examples=False,
    chatbot=gradio.Chatbot(
        show_label=False,
        show_share_button=False,
        show_copy_button=False,
        bubble_full_width=False,
    ),
).launch(debug=True)