Spaces:
Running
Running
File size: 3,351 Bytes
d66ac69 d1f5b25 d66ac69 0e12b6a d66ac69 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 |
# app_pure_rag.py
import numpy as np
import faiss
import gradio as gr
from langchain.text_splitter import CharacterTextSplitter
from sentence_transformers import SentenceTransformer
# --- Load and Prepare Data ---
with open("gen_agents.txt", "r", encoding="utf-8") as f:
full_text = f.read()
# Split text into passages
text_splitter = CharacterTextSplitter(separator="\n\n", chunk_size=512, chunk_overlap=20)
docs = text_splitter.create_documents([full_text])
passages = [doc.page_content for doc in docs]
# Initialize embedder and build FAISS index
embedder = SentenceTransformer('all-MiniLM-L6-v2')
passage_embeddings = embedder.encode(passages, convert_to_tensor=False, show_progress_bar=True)
passage_embeddings = np.array(passage_embeddings).astype("float32")
d = passage_embeddings.shape[1]
index = faiss.IndexFlatL2(d)
index.add(passage_embeddings)
# --- Provided Functions ---
def retrieve_passages(query, embedder, index, passages, top_k=3):
"""
Retrieve the top-k most relevant passages based on the query.
"""
query_embedding = embedder.encode([query], convert_to_tensor=False)
query_embedding = np.array(query_embedding).astype('float32')
distances, indices = index.search(query_embedding, top_k)
retrieved = [passages[i] for i in indices[0]]
return retrieved
# --- Gradio App Function ---
def get_pure_rag_output(query):
retrieved = retrieve_passages(query, embedder, index, passages, top_k=3)
rag_text = "\n".join([f"Passage {i+1}: {p}" for i, p in enumerate(retrieved)])
# Wrap text in a styled div
return f"<div style='white-space: pre-wrap;'>{rag_text}</div>"
def clear_output():
return ""
# --- Custom CSS for a ChatGPT-like Dark Theme ---
custom_css = """
body {
background-color: #343541 !important;
color: #ECECEC !important;
margin: 0;
padding: 0;
font-family: 'Inter', sans-serif;
}
#container {
max-width: 900px;
margin: 0 auto;
padding: 20px;
}
label {
color: #ECECEC;
font-weight: 600;
}
textarea, input {
background-color: #40414F;
color: #ECECEC;
border: 1px solid #565869;
}
button {
background-color: #565869;
color: #ECECEC;
border: none;
font-weight: 600;
transition: background-color 0.2s ease;
}
button:hover {
background-color: #6e7283;
}
.output-box {
border: 1px solid #565869;
border-radius: 4px;
padding: 10px;
margin-top: 8px;
background-color: #40414F;
}
"""
# --- Build Gradio Interface ---
with gr.Blocks(css=custom_css) as demo:
with gr.Column(elem_id="container"):
gr.Markdown("## Anonymous Chatbot\n### Loaded Article: Generative Agents - Interactive Simulacra of Human Behavior (Park et al. 2023)\n [https://arxiv.org/pdf/2304.03442](https://arxiv.org/pdf/2304.03442)")
gr.Markdown("Enter any questions about the article above in the prompt!")
query_input = gr.Textbox(label="Query", placeholder="Enter your query here...", lines=1)
with gr.Column():
submit_button = gr.Button("Submit")
clear_button = gr.Button("Clear")
output_box = gr.HTML(label="Output", elem_classes="output-box")
submit_button.click(fn=get_pure_rag_output, inputs=query_input, outputs=output_box)
clear_button.click(fn=clear_output, inputs=[], outputs=output_box)
demo.launch()
|