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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("---") |