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aad6058 | 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 | # Main Multimodal RAG System
import os
import json
import uuid
import numpy as np
import faiss
import torch
from datetime import datetime
from pathlib import Path
from typing import List, Dict
import streamlit as st
from config import (DATA_DIRECTORY, DATA_FILES, DEFAULT_TOP_K, MAX_CONTEXT_LENGTH, LLM_TEMPERATURE, FAISS_TEMP_MEMORY)
from models import load_models
from document_processor import DocumentProcessor
from device_setup import clear_gpu_memory
class MultimodalRAG:
"""Main RAG system that handles document storage, search, and generation"""
def __init__(self):
# load all models
model_components = load_models()
if model_components[0] is None:
raise Exception("Failed to load models")
(self.text_encoder, self.clip_model, self.clip_processor, self.whisper_model, self.ocr_reader, self.llm, self.device, self.has_gpu) = model_components
# initialize document processor
self.processor = DocumentProcessor(
self.clip_model, self.clip_processor, self.whisper_model,
self.ocr_reader, self.text_encoder, self.device
)
# initialize storage
self.documents = []
self.metadata = []
self.embeddings = None
self.index = None
# setup data directory
self.data_dir = DATA_DIRECTORY
self.data_dir.mkdir(exist_ok=True)
# load existing data
self.load_existing_data()
def clear_knowledge_base(self):
"""clear all stored documents and embeddings"""
self.documents = []
self.metadata = []
self.embeddings = None
self.index = None
try:
# remove all files in data directory
for file in self.data_dir.glob("*"):
file.unlink()
except Exception as e:
st.error(f"Error clearing saved data files: {e}")
def save_data(self):
"""Save documents, metadata, embeddings, and FAISS index to disk"""
try:
# save documents and metadata as JSON
with open(self.data_dir / DATA_FILES["documents"], "w") as f:
json.dump(self.documents, f, default=str)
with open(self.data_dir / DATA_FILES["metadata"], "w") as f:
json.dump(self.metadata, f, default=str)
# save FAISS index
if self.index is not None:
if self.has_gpu:
# convert GPU index back to CPU for saving
cpu_index = faiss.index_gpu_to_cpu(self.index)
faiss.write_index(cpu_index, str(self.data_dir / DATA_FILES["faiss_index"]))
else:
faiss.write_index(self.index, str(self.data_dir / DATA_FILES["faiss_index"]))
# save embeddings
if self.embeddings is not None:
np.save(self.data_dir / DATA_FILES["embeddings"], self.embeddings)
except Exception as e:
st.error(f"Error saving data: {e}")
def load_existing_data(self):
"""Load existing documents and metadata from disk"""
try:
# load documents
doc_file = self.data_dir / DATA_FILES["documents"]
if doc_file.exists():
with open(doc_file, "r") as f:
self.documents = json.load(f)
# load metadata
meta_file = self.data_dir / DATA_FILES["metadata"]
if meta_file.exists():
with open(meta_file, "r") as f:
self.metadata = json.load(f)
# load FAISS index
index_file = self.data_dir / DATA_FILES["faiss_index"]
if index_file.exists():
if self.has_gpu:
cpu_index = faiss.read_index(str(index_file))
res = faiss.StandardGpuResources()
self.index = faiss.index_cpu_to_gpu(res, 0, cpu_index)
else:
self.index = faiss.read_index(str(index_file))
# load embeddings
emb_file = self.data_dir / DATA_FILES["embeddings"]
if emb_file.exists():
self.embeddings = np.load(emb_file)
except Exception as e:
st.error(f"Error loading existing data: {e}")
def process_file(self, uploaded_file, file_type: str) -> bool:
"""Process uploaded file and add to knowledge base"""
try:
# process the file
processed_data = self.processor.process_uploaded_file(uploaded_file, file_type, self.data_dir)
if processed_data is None:
return False
# create document record
file_id = str(uuid.uuid4())
timestamp = datetime.now().isoformat()
document = {
'id': file_id,
'type': file_type if file_type not in ['png', 'jpg', 'jpeg'] else 'image',
'filename': uploaded_file.name,
'content': processed_data['content'],
'timestamp': timestamp,
'embedding_type': processed_data['embedding_type']
}
# handle file-specific storage (images and audio)
if file_type in ['png', 'jpg', 'jpeg', 'mp3', 'wav', 'ogg', 'm4a']:
# save the actual file
file_save_path = self.data_dir / f"{file_id}.{file_type}"
uploaded_file.seek(0) # Reset file pointer
with open(file_save_path, 'wb') as f:
f.write(uploaded_file.read())
document[f"{document['type']}_path"] = str(file_save_path)
# add embedding to collection
embedding = processed_data['embedding']
if self.embeddings is None:
self.embeddings = embedding.reshape(1, -1)
else:
# handle dimension mismatch (for mixed embedding types)
current_dim = self.embeddings.shape[1]
if embedding.shape[0] != current_dim:
if embedding.shape[0] > current_dim:
embedding = embedding[:current_dim]
else:
padding = np.zeros(current_dim - embedding.shape[0])
embedding = np.concatenate([embedding, padding])
self.embeddings = np.vstack([self.embeddings, embedding.reshape(1, -1)])
# Add to collections
self.documents.append(document)
self.metadata.append({
'id': file_id,
'type': document['type'],
'filename': uploaded_file.name,
'timestamp': timestamp,
'content_preview': processed_data['content'][:200] + "..." if len(processed_data['content']) > 200 else processed_data['content']
})
# Rebuild search index
self.build_faiss_index()
self.save_data()
clear_gpu_memory()
return True
except Exception as e:
st.error(f"Error processing file: {e}")
return False
def build_faiss_index(self):
"""Build or rebuild FAISS index for similarity search"""
try:
if self.embeddings is not None and len(self.embeddings) > 0:
dimension = self.embeddings.shape[1]
if self.has_gpu:
try:
# Create GPU resources with limited temp memory
res = faiss.StandardGpuResources()
res.setTempMemory(FAISS_TEMP_MEMORY)
cpu_index = faiss.IndexFlatIP(dimension)
self.index = faiss.index_cpu_to_gpu(res, 0, cpu_index)
except RuntimeError as e:
st.warning(f"GPU FAISS failed: {e}. Using CPU indexing.")
self.index = faiss.IndexFlatIP(dimension)
else:
self.index = faiss.IndexFlatIP(dimension)
# Normalize embeddings and add to index
embeddings_normalized = self.embeddings.copy().astype('float32')
faiss.normalize_L2(embeddings_normalized)
self.index.add(embeddings_normalized)
except Exception as e:
st.error(f"Error building FAISS index: {e}")
def search(self, query: str, top_k: int = DEFAULT_TOP_K) -> List[Dict]:
"""Search for relevant documents"""
try:
if self.index is None or len(self.documents) == 0:
return []
# get query embedding
query_embedding = self.processor.get_text_embedding(query)
query_embedding = query_embedding.reshape(1, -1).astype('float32')
faiss.normalize_L2(query_embedding)
# search
scores, indices = self.index.search(query_embedding, min(top_k, len(self.documents)))
# build results
results = []
for score, idx in zip(scores[0], indices[0]):
if idx < len(self.documents):
result = self.documents[idx].copy()
result['score'] = float(score)
results.append(result)
return results
except Exception as e:
st.error(f"Error searching: {e}")
return []
def generate_answer(self, query: str, retrieved_docs: List[Dict]) -> str:
"""Generate answer using local LLM"""
try:
if not retrieved_docs:
return "No relevant documents found to answer your query."
# build context from retrieved documents
context = "Context from retrieved documents:\n\n"
citations = []
for i, doc in enumerate(retrieved_docs[:3]): # using top 3 documents
context += f"[{i+1}] {doc['filename']} ({doc['type']}): {doc['content'][:MAX_CONTEXT_LENGTH]}...\n\n"
citations.append(f"[{i+1}] {doc['filename']}")
# Create prompt
prompt = f"""Based on the following context, answer the user's question clearly and concisely. Use citations [1], [2], etc.
Context:
{context}
Question: {query}
Answer:"""
# generate response
response = self.llm.generate(prompt, temp=LLM_TEMPERATURE)
response += "\n\nSources:\n" + "\n".join(citations)
return response
except Exception as e:
st.error(f"Error generating answer: {e}")
return "Error generating response."
def get_stats(self) -> Dict:
"""to get knowledge base stats"""
if not self.documents:
return {"total": 0}
stats = {"total": len(self.documents)}
doc_types = {}
for doc in self.documents:
doc_type = doc['type']
doc_types[doc_type] = doc_types.get(doc_type, 0) + 1
stats.update(doc_types)
return stats |