app.py

import os
import gradio as gr
import chromadb

from sentence_transformers import SentenceTransformer

import spaces

@spaces.GPU
def get_embeddings(query, task):
    model = SentenceTransformer("Linq-AI-Research/Linq-Embed-Mistral", use_auth_token=os.getenv("HF_TOKEN"))
    prompt = f"Instruct: {task}\nQuery: {query}" 
    query_embeddings = model.encode([prompt])  
    return query_embeddings

# Initialize a persistent Chroma client and retrieve collection
client = chromadb.PersistentClient(path="./chroma")
collection = client.get_collection(name="phil_de")

authors_list = ["Ludwig Wittgenstein", "Sigmund Freud", "Marcus Aurelius", "Friedrich Nietzsche", "Epiktet", "Ernst Jünger", "Georg Christoph Lichtenberg", "Balthasar Gracian", "Hannah Arendt", "Erich Fromm", "Albert Camus"]
#authors_list = ["Friedrich Nietzsche", "Joscha Bach"]

def query_chroma(embeddings, authors, num_results=10):
    try:
        where_filter = {"author": {"$in": authors}} if authors else {}

        embeddings_list = embeddings[0].tolist()

        results = collection.query(
            query_embeddings=[embeddings_list],
            n_results=num_results,
            where=where_filter,
            include=["documents", "metadatas", "distances"]
        )

        ids = results.get('ids', [[]])[0]
        metadatas = results.get('metadatas', [[]])[0]
        documents = results.get('documents', [[]])[0]
        distances = results.get('distances', [[]])[0]

        formatted_results = []
        for id_, metadata, document_text, distance in zip(ids, metadatas, documents, distances):
            result_dict = {
                "id": id_,
                "author": metadata.get('author', 'Unknown author'),
                "book": metadata.get('book', 'Unknown book'),
                "section": metadata.get('section', 'Unknown section'),
                "title": metadata.get('title', 'Untitled'),
                "text": document_text,
                "distance": distance
            }
            formatted_results.append(result_dict)

        return formatted_results
    except Exception as e:
        return {"error": str(e)}


# Main function
def perform_query(query, authors, num_results):
    task = "Given a question, retrieve passages that answer the question"
    embeddings = get_embeddings(query, task)
    results = query_chroma(embeddings, authors, num_results)

    if "error" in results:
        return [gr.update(visible=True, value=f"Error: {results['error']}") for _ in range(max_textboxes * 2)]

    updates = []
    for res in results:
        markdown_content = f"**{res['author']}, {res['book']}**\n\n{res['text']}"
        updates.append(gr.update(visible=True, value=markdown_content))

    updates += [gr.update(visible=False)] * (max_textboxes - len(results))

    return updates

# Gradio interface
max_textboxes = 30

with gr.Blocks(css=".custom-markdown { border: 1px solid #ccc; padding: 10px; border-radius: 5px; }") as demo:
    gr.Markdown("Enter your query, filter authors (default is all), click **Search** to search. Click **Flag** if a result is relevant to the query and interesting to you.")
    with gr.Row():
        with gr.Column():
            inp = gr.Textbox(label="query", placeholder="Enter thought...")
            author_inp = gr.Dropdown(label="authors", choices=authors_list, multiselect=True)
            num_results_inp = gr.Number(label="number of results", value=10, step=1, minimum=1, maximum=max_textboxes)
            btn = gr.Button("Search")

    components = []
    textboxes = []

    for _ in range(max_textboxes):
        with gr.Column() as col:
            text_out = gr.Markdown(visible=False, elem_classes="custom-markdown")
            components.append(text_out)
            textboxes.append(text_out)

    btn.click(
        fn=perform_query, 
        inputs=[inp, author_inp, num_results_inp],
        outputs=components
    )
    

demo.launch()