Spaces:
Sleeping
Sleeping
File size: 15,486 Bytes
43cd37c |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 |
import numpy as np
from typing import List, Tuple, Dict
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from sentence_transformers import SentenceTransformer
import math
from functools import lru_cache
from concurrent.futures import ThreadPoolExecutor
import openai
from transformers import T5ForConditionalGeneration, T5Tokenizer
import torch
import re
import psycopg2
from psycopg2.extras import execute_values
import sqlite3
import logging
########################################################################################################################################################################################################################################
#
# RAG Chunking
# To fully integrate this chunking system, you'd need to:
#
# Create the UnvectorizedMediaChunks table in your SQLite database.
# Modify your document ingestion process to use chunk_and_store_unvectorized.
# Implement a background process that periodically calls vectorize_all_documents to process unvectorized chunks.
# This chunking is pretty weak and needs improvement
# See notes for improvements #FIXME
import json
from typing import List, Dict, Any
from datetime import datetime
def chunk_and_store_unvectorized(
db_connection,
media_id: int,
text: str,
chunk_size: int = 1000,
overlap: int = 100,
chunk_type: str = 'fixed-length'
) -> List[int]:
chunks = create_chunks(text, chunk_size, overlap)
return store_unvectorized_chunks(db_connection, media_id, chunks, chunk_type)
def create_chunks(text: str, chunk_size: int, overlap: int) -> List[Dict[str, Any]]:
words = text.split()
chunks = []
for i in range(0, len(words), chunk_size - overlap):
chunk_text = ' '.join(words[i:i + chunk_size])
start_char = text.index(words[i])
end_char = start_char + len(chunk_text)
chunks.append({
'text': chunk_text,
'start_char': start_char,
'end_char': end_char,
'index': len(chunks)
})
return chunks
def store_unvectorized_chunks(
db_connection,
media_id: int,
chunks: List[Dict[str, Any]],
chunk_type: str
) -> List[int]:
cursor = db_connection.cursor()
chunk_ids = []
for chunk in chunks:
cursor.execute("""
INSERT INTO UnvectorizedMediaChunks
(media_id, chunk_text, chunk_index, start_char, end_char, chunk_type, metadata)
VALUES (?, ?, ?, ?, ?, ?, ?)
""", (
media_id,
chunk['text'],
chunk['index'],
chunk['start_char'],
chunk['end_char'],
chunk_type,
json.dumps({'length': len(chunk['text'])}) # Example metadata
))
chunk_ids.append(cursor.lastrowid)
db_connection.commit()
return chunk_ids
def get_unvectorized_chunks(
db_connection,
media_id: int,
limit: int = 100,
offset: int = 0
) -> List[Dict[str, Any]]:
cursor = db_connection.cursor()
cursor.execute("""
SELECT id, chunk_text, chunk_index, start_char, end_char, chunk_type, metadata
FROM UnvectorizedMediaChunks
WHERE media_id = ? AND is_processed = FALSE
ORDER BY chunk_index
LIMIT ? OFFSET ?
""", (media_id, limit, offset))
return [
{
'id': row[0],
'text': row[1],
'index': row[2],
'start_char': row[3],
'end_char': row[4],
'type': row[5],
'metadata': json.loads(row[6])
}
for row in cursor.fetchall()
]
def mark_chunks_as_processed(db_connection, chunk_ids: List[int]):
cursor = db_connection.cursor()
cursor.executemany("""
UPDATE UnvectorizedMediaChunks
SET is_processed = TRUE, last_modified = ?
WHERE id = ?
""", [(datetime.now(), chunk_id) for chunk_id in chunk_ids])
db_connection.commit()
# Usage example
def process_media_chunks(db_connection, media_id: int, text: str):
chunk_ids = chunk_and_store_unvectorized(db_connection, media_id, text)
print(f"Stored {len(chunk_ids)} unvectorized chunks for media_id {media_id}")
# Later, when you want to process these chunks:
unprocessed_chunks = get_unvectorized_chunks(db_connection, media_id)
# Process chunks (e.g., vectorize them)
# ...
# After processing, mark them as processed
mark_chunks_as_processed(db_connection, [chunk['id'] for chunk in unprocessed_chunks])
###########################################################################################################################################################################################################
#
# RAG System
# To use this updated RAG system in your existing application:
#
# Install required packages:
# pip install sentence-transformers psycopg2-binary scikit-learn transformers torch
# Set up PostgreSQL with pgvector:
#
# Install PostgreSQL and the pgvector extension.
# Create a new database for vector storage.
#
# Update your main application to use the RAG system:
#
# Import the RAGSystem class from this new file.
# Initialize the RAG system with your SQLite and PostgreSQL configurations.
# Use the vectorize_all_documents method to initially vectorize your existing documents.
#
#
# Modify your existing PDF_Ingestion_Lib.py and Book_Ingestion_Lib.py:
#
# After successfully ingesting a document into SQLite, call the vectorization method from the RAG system.
# Example modification for ingest_text_file in Book_Ingestion_Lib.py:
# from RAG_Library import RAGSystem
#
# # Initialize RAG system (do this once in your main application)
# rag_system = RAGSystem(sqlite_path, pg_config)
#
# def ingest_text_file(file_path, title=None, author=None, keywords=None):
# try:
# # ... (existing code)
#
# # Add the text file to the database
# doc_id = add_media_with_keywords(
# url=file_path,
# title=title,
# media_type='document',
# content=content,
# keywords=keywords,
# prompt='No prompt for text files',
# summary='No summary for text files',
# transcription_model='None',
# author=author,
# ingestion_date=datetime.now().strftime('%Y-%m-%d')
# )
#
# # Vectorize the newly added document
# rag_system.vectorize_document(doc_id, content)
#
# return f"Text file '{title}' by {author} ingested and vectorized successfully."
# except Exception as e:
# logging.error(f"Error ingesting text file: {str(e)}")
# return f"Error ingesting text file: {str(e)}"
# Setup logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
# Constants
EMBEDDING_MODEL = 'all-MiniLM-L6-v2'
VECTOR_DIM = 384 # Dimension of the chosen embedding model
class RAGSystem:
def __init__(self, sqlite_path: str, pg_config: Dict[str, str], cache_size: int = 100):
self.sqlite_path = sqlite_path
self.pg_config = pg_config
self.model = SentenceTransformer(EMBEDDING_MODEL)
self.cache_size = cache_size
self._init_postgres()
def _init_postgres(self):
with psycopg2.connect(**self.pg_config) as conn:
with conn.cursor() as cur:
cur.execute("""
CREATE TABLE IF NOT EXISTS document_vectors (
id SERIAL PRIMARY KEY,
document_id INTEGER UNIQUE,
vector vector(384)
)
""")
conn.commit()
@lru_cache(maxsize=100)
def _get_embedding(self, text: str) -> np.ndarray:
return self.model.encode([text])[0]
def vectorize_document(self, doc_id: int, content: str):
chunks = create_chunks(content, chunk_size=1000, overlap=100)
for chunk in chunks:
vector = self._get_embedding(chunk['text'])
with psycopg2.connect(**self.pg_config) as conn:
with conn.cursor() as cur:
cur.execute("""
INSERT INTO document_vectors (document_id, chunk_index, vector, metadata)
VALUES (%s, %s, %s, %s)
ON CONFLICT (document_id, chunk_index) DO UPDATE SET vector = EXCLUDED.vector
""", (doc_id, chunk['index'], vector.tolist(), json.dumps(chunk)))
conn.commit()
def vectorize_all_documents(self):
with sqlite3.connect(self.sqlite_path) as sqlite_conn:
unprocessed_chunks = get_unvectorized_chunks(sqlite_conn, limit=1000)
for chunk in unprocessed_chunks:
self.vectorize_document(chunk['id'], chunk['text'])
mark_chunks_as_processed(sqlite_conn, [chunk['id'] for chunk in unprocessed_chunks])
def semantic_search(self, query: str, top_k: int = 5) -> List[Tuple[int, int, float]]:
query_vector = self._get_embedding(query)
with psycopg2.connect(**self.pg_config) as conn:
with conn.cursor() as cur:
cur.execute("""
SELECT document_id, chunk_index, 1 - (vector <-> %s) AS similarity
FROM document_vectors
ORDER BY vector <-> %s ASC
LIMIT %s
""", (query_vector.tolist(), query_vector.tolist(), top_k))
results = cur.fetchall()
return results
def get_document_content(self, doc_id: int) -> str:
with sqlite3.connect(self.sqlite_path) as conn:
cur = conn.cursor()
cur.execute("SELECT content FROM media WHERE id = ?", (doc_id,))
result = cur.fetchone()
return result[0] if result else ""
def bm25_search(self, query: str, top_k: int = 5) -> List[Tuple[int, float]]:
with sqlite3.connect(self.sqlite_path) as conn:
cur = conn.cursor()
cur.execute("SELECT id, content FROM media")
documents = cur.fetchall()
vectorizer = TfidfVectorizer(use_idf=True)
tfidf_matrix = vectorizer.fit_transform([doc[1] for doc in documents])
query_vector = vectorizer.transform([query])
doc_lengths = tfidf_matrix.sum(axis=1).A1
avg_doc_length = np.mean(doc_lengths)
k1, b = 1.5, 0.75
scores = []
for i, doc_vector in enumerate(tfidf_matrix):
score = np.sum(
((k1 + 1) * query_vector.multiply(doc_vector)).A1 /
(k1 * (1 - b + b * doc_lengths[i] / avg_doc_length) + query_vector.multiply(doc_vector).A1)
)
scores.append((documents[i][0], score))
return sorted(scores, key=lambda x: x[1], reverse=True)[:top_k]
def combine_search_results(self, bm25_results: List[Tuple[int, float]], vector_results: List[Tuple[int, float]],
alpha: float = 0.5) -> List[Tuple[int, float]]:
combined_scores = {}
for idx, score in bm25_results + vector_results:
if idx in combined_scores:
combined_scores[idx] += score * (alpha if idx in dict(bm25_results) else (1 - alpha))
else:
combined_scores[idx] = score * (alpha if idx in dict(bm25_results) else (1 - alpha))
return sorted(combined_scores.items(), key=lambda x: x[1], reverse=True)
def expand_query(self, query: str) -> str:
model = T5ForConditionalGeneration.from_pretrained("t5-small")
tokenizer = T5Tokenizer.from_pretrained("t5-small")
input_text = f"expand query: {query}"
input_ids = tokenizer.encode(input_text, return_tensors="pt")
outputs = model.generate(input_ids, max_length=50, num_return_sequences=1)
expanded_query = tokenizer.decode(outputs[0], skip_special_tokens=True)
return f"{query} {expanded_query}"
def cross_encoder_rerank(self, query: str, initial_results: List[Tuple[int, float]], top_k: int = 5) -> List[
Tuple[int, float]]:
from sentence_transformers import CrossEncoder
model = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
candidate_docs = [self.get_document_content(doc_id) for doc_id, _ in initial_results[:top_k * 2]]
pairs = [[query, doc] for doc in candidate_docs]
scores = model.predict(pairs)
reranked = sorted(zip(initial_results[:top_k * 2], scores), key=lambda x: x[1], reverse=True)
return [(idx, score) for (idx, _), score in reranked[:top_k]]
def rag_query(self, query: str, search_type: str = 'combined', top_k: int = 5, use_hyde: bool = False,
rerank: bool = False, expand: bool = False) -> List[Dict[str, any]]:
try:
if expand:
query = self.expand_query(query)
if use_hyde:
# Implement HyDE if needed
pass
elif search_type == 'vector':
results = self.semantic_search(query, top_k)
elif search_type == 'bm25':
results = self.bm25_search(query, top_k)
elif search_type == 'combined':
bm25_results = self.bm25_search(query, top_k)
vector_results = self.semantic_search(query, top_k)
results = self.combine_search_results(bm25_results, vector_results)
else:
raise ValueError("Invalid search type. Choose 'vector', 'bm25', or 'combined'.")
if rerank:
results = self.cross_encoder_rerank(query, results, top_k)
enriched_results = []
for doc_id, score in results:
content = self.get_document_content(doc_id)
enriched_results.append({
"document_id": doc_id,
"score": score,
"content": content[:500] # Truncate content for brevity
})
return enriched_results
except Exception as e:
logger.error(f"An error occurred during RAG query: {str(e)}")
return []
# Example usage
if __name__ == "__main__":
sqlite_path = "path/to/your/sqlite/database.db"
pg_config = {
"dbname": "your_db_name",
"user": "your_username",
"password": "your_password",
"host": "localhost"
}
rag_system = RAGSystem(sqlite_path, pg_config)
# Vectorize all documents (run this once or periodically)
rag_system.vectorize_all_documents()
# Example query
query = "programming concepts for beginners"
results = rag_system.rag_query(query, search_type='combined', expand=True, rerank=True)
print(f"Search results for query: '{query}'\n")
for i, result in enumerate(results, 1):
print(f"Result {i}:")
print(f"Document ID: {result['document_id']}")
print(f"Score: {result['score']:.4f}")
print(f"Content snippet: {result['content']}")
print("---") |