Spaces:
Running
on
Zero
Running
on
Zero
import os | |
import gradio as gr | |
import chromadb | |
from sentence_transformers import SentenceTransformer | |
import spaces | |
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_de = client.get_collection(name="phil_de") | |
collection_en = client.get_collection(name="phil_en") | |
authors_list_de = ["Ludwig Wittgenstein", "Sigmund Freud", "Marcus Aurelius", "Friedrich Nietzsche", "Epiktet", "Ernst Jünger", "Georg Christoph Lichtenberg", "Balthasar Gracian", "Hannah Arendt", "Erich Fromm", "Albert Camus"] | |
authors_list_en = ["Friedrich Nietzsche", "Joscha Bach"] | |
def query_chroma(collection, 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, database): | |
task = "Given a question, retrieve passages that answer the question" | |
embeddings = get_embeddings(query, task) | |
collection = collection_de if database == "German" else collection_en | |
results = query_chroma(collection, 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 | |
def update_authors(database): | |
return gr.update(choices=authors_list_de if database == "German" else authors_list_en) | |
# 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(): | |
database_inp = gr.Dropdown(label="Database", choices=["English", "German"], value="German") | |
inp = gr.Textbox(label="query", placeholder="Enter question...") | |
author_inp = gr.Dropdown(label="authors", choices=authors_list_de, 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 = [] | |
for _ in range(max_textboxes): | |
with gr.Column() as col: | |
text_out = gr.Markdown(visible=False, elem_classes="custom-markdown") | |
components.append(text_out) | |
btn.click( | |
fn=perform_query, | |
inputs=[inp, author_inp, num_results_inp, database_inp], | |
outputs=components | |
) | |
database_inp.change( | |
fn=update_authors, | |
inputs=database_inp, | |
outputs=author_inp | |
) | |
demo.launch() | |