Spaces:
Sleeping
Sleeping
File size: 14,344 Bytes
62d1e75 2311e2d 62d1e75 2311e2d 62d1e75 2311e2d 62d1e75 3b2ec72 ac3798b 3b2ec72 ac3798b 3b2ec72 ac3798b 3b2ec72 62d1e75 3b2ec72 62d1e75 2311e2d 62d1e75 38deecc 2311e2d 38deecc 2311e2d 87dead9 2311e2d 62d1e75 ff4d9c5 62d1e75 3b2ec72 ff4d9c5 3b2ec72 ff4d9c5 3b2ec72 62d1e75 ff4d9c5 62d1e75 2311e2d 62d1e75 38deecc 62d1e75 38deecc 62d1e75 38deecc f637309 38deecc 62d1e75 2311e2d 62d1e75 38deecc |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 |
import os
import json
import numpy as np
import faiss
import torch
import torch.nn as nn
from google.cloud import storage
from transformers import AutoTokenizer, AutoModel
import openai
import textwrap
import unicodedata
import streamlit as st
from utils import setup_gcp_auth, setup_openai_auth
# Force model to CPU for stability
os.environ["CUDA_VISIBLE_DEVICES"] = ""
# Create a function to initialize session state
def initialize_session_state():
if 'model_initialized' not in st.session_state:
st.session_state.model_initialized = False
st.session_state.model = None
st.session_state.tokenizer = None
st.session_state.device = torch.device("cpu")
print("Initialized session state variables")
# Call the initialization function right away
initialize_session_state()
# Load GCP authentication from utility function
def setup_gcp_client():
try:
credentials = setup_gcp_auth()
storage_client = storage.Client(credentials=credentials)
bucket_name = "indian_spiritual-1"
bucket = storage_client.bucket(bucket_name)
print("β
GCP client initialized successfully")
return bucket
except Exception as e:
print(f"β GCP client initialization error: {str(e)}")
st.error(f"GCP client initialization error: {str(e)}")
return None
# Setup OpenAI authentication
def setup_openai_client():
try:
setup_openai_auth()
print("β
OpenAI client initialized successfully")
return True
except Exception as e:
print(f"β OpenAI client initialization error: {str(e)}")
st.error(f"OpenAI client initialization error: {str(e)}")
return False
# GCS Paths
metadata_file_gcs = "metadata/metadata.jsonl"
embeddings_file_gcs = "processed/embeddings/all_embeddings.npy"
faiss_index_file_gcs = "processed/indices/faiss_index.faiss"
text_chunks_file_gcs = "processed/chunks/text_chunks.txt"
# Local Paths
local_embeddings_file = "all_embeddings.npy"
local_faiss_index_file = "faiss_index.faiss"
local_text_chunks_file = "text_chunks.txt"
local_metadata_file = "metadata.jsonl"
def load_model():
try:
# Initialize model if it doesn't exist
if 'model' not in st.session_state or st.session_state.model is None:
# Force model to CPU - more stable than GPU for this use case
os.environ["CUDA_VISIBLE_DEVICES"] = ""
print("Loading tokenizer...")
tokenizer = AutoTokenizer.from_pretrained("intfloat/e5-small-v2")
print("Loading model...")
model = AutoModel.from_pretrained(
"intfloat/e5-small-v2",
torch_dtype=torch.float16 # Use half precision
)
# Move model to CPU explicitly
model = model.to('cpu')
model.eval()
torch.set_grad_enabled(False)
# Store in session state
st.session_state.tokenizer = tokenizer
st.session_state.model = model
print("β
Model loaded successfully")
return st.session_state.tokenizer, st.session_state.model
except Exception as e:
print(f"β Error loading model: {str(e)}")
# Return None values instead of raising to avoid crashing
return None, None
def download_file_from_gcs(bucket, gcs_path, local_path):
"""Download a file from GCS to local storage."""
try:
blob = bucket.blob(gcs_path)
blob.download_to_filename(local_path)
print(f"β
Downloaded {gcs_path} β {local_path}")
return True
except Exception as e:
print(f"β Error downloading {gcs_path}: {str(e)}")
st.error(f"Error downloading {gcs_path}: {str(e)}")
return False
def load_data_files():
# Initialize GCP and OpenAI clients
bucket = setup_gcp_client()
openai_initialized = setup_openai_client()
if not bucket or not openai_initialized:
st.error("Failed to initialize required services")
return None, None, None
# Download necessary files - remove the spinner from here
success = True
success &= download_file_from_gcs(bucket, faiss_index_file_gcs, local_faiss_index_file)
success &= download_file_from_gcs(bucket, text_chunks_file_gcs, local_text_chunks_file)
success &= download_file_from_gcs(bucket, metadata_file_gcs, local_metadata_file)
if not success:
st.error("Failed to download required files")
return None, None, None
# Load FAISS index
try:
faiss_index = faiss.read_index(local_faiss_index_file)
except Exception as e:
print(f"β Error loading FAISS index: {str(e)}")
st.error(f"Error loading FAISS index: {str(e)}")
return None, None, None
# Load text chunks
try:
text_chunks = {} # {ID -> (Title, Author, Text)}
with open(local_text_chunks_file, "r", encoding="utf-8") as f:
for line in f:
parts = line.strip().split("\t")
if len(parts) == 4:
text_chunks[int(parts[0])] = (parts[1], parts[2], parts[3])
except Exception as e:
print(f"β Error loading text chunks: {str(e)}")
st.error(f"Error loading text chunks: {str(e)}")
return None, None, None
# Load metadata.jsonl for publisher information
try:
metadata_dict = {}
with open(local_metadata_file, "r", encoding="utf-8") as f:
for line in f:
item = json.loads(line)
metadata_dict[item["Title"]] = item # Store for easy lookup
except Exception as e:
print(f"β Error loading metadata: {str(e)}")
st.error(f"Error loading metadata: {str(e)}")
return None, None, None
print(f"β
FAISS index and text chunks loaded. {len(text_chunks)} passages available.")
return faiss_index, text_chunks, metadata_dict
def average_pool(last_hidden_states, attention_mask):
"""Average pooling for sentence embeddings."""
last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
query_embedding_cache = {}
def get_embedding(text):
if text in query_embedding_cache:
return query_embedding_cache[text]
try:
# Ensure model initialization
if 'model' not in st.session_state or st.session_state.model is None:
tokenizer, model = load_model()
if model is None:
return np.zeros((1, 384), dtype=np.float32) # Fallback for e5-small-v2
else:
tokenizer, model = st.session_state.tokenizer, st.session_state.model
input_text = f"query: {text}" if len(text) < 512 else f"passage: {text}"
# Explicitly specify truncation parameters to avoid warnings
inputs = tokenizer(
input_text,
padding=True,
truncation=True,
return_tensors="pt",
max_length=512,
return_attention_mask=True
)
# Move to CPU explicitly before processing
inputs = {k: v.to('cpu') for k, v in inputs.items()}
with torch.no_grad():
outputs = model(**inputs)
embeddings = average_pool(outputs.last_hidden_state, inputs['attention_mask'])
embeddings = nn.functional.normalize(embeddings, p=2, dim=1)
# Ensure we detach and move to numpy on CPU
embeddings = embeddings.detach().cpu().numpy()
# Explicitly clean up
del outputs
torch.cuda.empty_cache() if torch.cuda.is_available() else None
query_embedding_cache[text] = embeddings
return embeddings
except Exception as e:
print(f"β Embedding error: {str(e)}")
st.error(f"Embedding error: {str(e)}")
return np.zeros((1, 384), dtype=np.float32) # Changed from 1024 to 384 for e5-small-v2
def retrieve_passages(query, faiss_index, text_chunks, metadata_dict, top_k=5, similarity_threshold=0.5):
"""Retrieve top-k most relevant passages using FAISS with metadata."""
try:
print(f"\nπ Retrieving passages for query: {query}")
query_embedding = get_embedding(query)
distances, indices = faiss_index.search(query_embedding, top_k * 2)
print(f"Found {len(distances[0])} potential matches")
retrieved_passages = []
retrieved_sources = []
cited_titles = set()
for dist, idx in zip(distances[0], indices[0]):
print(f"Distance: {dist:.4f}, Index: {idx}")
if idx in text_chunks and dist >= similarity_threshold:
title_with_txt, author, text = text_chunks[idx]
# Normalize title and remove .txt
clean_title = title_with_txt.replace(".txt", "") if title_with_txt.endswith(".txt") else title_with_txt
clean_title = unicodedata.normalize("NFC", clean_title)
# Ensure unique citations
if clean_title in cited_titles:
continue
metadata_entry = metadata_dict.get(clean_title, {})
author = metadata_entry.get("Author", "Unknown")
publisher = metadata_entry.get("Publisher", "Unknown")
cited_titles.add(clean_title)
retrieved_passages.append(text)
retrieved_sources.append((clean_title, author, publisher))
if len(retrieved_passages) == top_k:
break
print(f"Retrieved {len(retrieved_passages)} passages")
return retrieved_passages, retrieved_sources
except Exception as e:
print(f"β Error in retrieve_passages: {str(e)}")
st.error(f"Error in retrieve_passages: {str(e)}")
return [], []
def answer_with_llm(query, context=None, word_limit=100):
"""
Generate an answer using OpenAI GPT model with formatted citations.
"""
try:
if context:
formatted_contexts = []
total_chars = 0
max_context_chars = 4000
for (title, author, publisher), text in context:
remaining_space = max(0, max_context_chars - total_chars)
excerpt_len = min(150, remaining_space)
if excerpt_len > 50:
excerpt = text[:excerpt_len].strip() + "..." if len(text) > excerpt_len else text
formatted_context = f"[{title} by {author}, Published by {publisher}] {excerpt}"
formatted_contexts.append(formatted_context)
total_chars += len(formatted_context)
if total_chars >= max_context_chars:
break
formatted_context = "\n".join(formatted_contexts)
else:
formatted_context = "No relevant information available."
# System message
system_message = (
"You are an AI specialized in Indian spiritual texts. "
"Answer based on context, summarizing ideas rather than quoting verbatim. "
"Ensure proper citation and do not include direct excerpts."
)
user_message = f"""
Context:
{formatted_context}
Question:
{query}
"""
response = openai.chat.completions.create(
model="gpt-3.5-turbo",
messages=[
{"role": "system", "content": system_message},
{"role": "user", "content": user_message}
],
max_tokens=200,
temperature=0.7
)
answer = response.choices[0].message.content.strip()
# Enforce word limit
words = answer.split()
if len(words) > word_limit:
answer = " ".join(words[:word_limit])
if not answer.endswith((".", "!", "?")):
answer += "."
return answer
except Exception as e:
print(f"β LLM API error: {str(e)}")
st.error(f"LLM API error: {str(e)}")
return "I apologize, but I'm unable to answer at the moment."
def format_citations(sources):
"""Format citations to display each one on a new line with a full stop if needed."""
formatted_citations = []
for title, author, publisher in sources:
# Check if the publisher already ends with a period, question mark, or exclamation mark
if publisher.endswith(('.', '!', '?')):
formatted_citations.append(f"π {title} by {author}, Published by {publisher}")
else:
formatted_citations.append(f"π {title} by {author}, Published by {publisher}.")
return "\n".join(formatted_citations)
def process_query(query, top_k=5, word_limit=100):
"""Process a query through the RAG pipeline with proper formatting."""
print(f"\nπ Processing query: {query}")
# Load data files if not already loaded
if not hasattr(st.session_state, 'data_loaded') or not st.session_state.data_loaded:
st.session_state.faiss_index, st.session_state.text_chunks, st.session_state.metadata_dict = load_data_files()
st.session_state.data_loaded = True
# Check if data loaded successfully
if not st.session_state.faiss_index or not st.session_state.text_chunks or not st.session_state.metadata_dict:
return {"query": query, "answer_with_rag": "β οΈ System error: Data files not loaded properly.", "citations": "No citations available."}
retrieved_context, retrieved_sources = retrieve_passages(
query,
st.session_state.faiss_index,
st.session_state.text_chunks,
st.session_state.metadata_dict,
top_k=top_k
)
sources = format_citations(retrieved_sources) if retrieved_sources else "No citation available."
if retrieved_context:
context_with_sources = list(zip(retrieved_sources, retrieved_context))
llm_answer_with_rag = answer_with_llm(query, context_with_sources, word_limit=word_limit)
else:
llm_answer_with_rag = "β οΈ No relevant context found."
return {"query": query, "answer_with_rag": llm_answer_with_rag, "citations": sources} |