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"""
Knowledge base management using FAISS and HuggingFace embeddings
"""
import os
import json
import pickle
from typing import List, Dict, Tuple, Optional
import numpy as np
import faiss
from sentence_transformers import SentenceTransformer
from transformers import AutoTokenizer
import hashlib
from datetime import datetime
from pathlib import Path
class KnowledgeBase:
"""Manages the vector store for knowledge retrieval"""
def __init__(self, config):
self.config = config
self.embedding_model = SentenceTransformer(config.models.embedding_model)
self.dimension = config.vector_store.dimension
self.index = None
self.metadata = []
self.chunks = []
self.index_path = config.INDEX_DIR
self.books_path = config.BOOKS_DIR
# Initialize tokenizer for chunk splitting
#self.tokenizer = AutoTokenizer.from_pretrained(config.models.mistral_model)
self.tokenizer = AutoTokenizer.from_pretrained(config.models.tinygpt2_model)
# Load or create index
self._initialize_index()
def _initialize_index(self):
"""Initialize or load existing FAISS index"""
index_file = os.path.join(self.index_path, "knowledge.index")
metadata_file = os.path.join(self.index_path, "metadata.pkl")
chunks_file = os.path.join(self.index_path, "chunks.pkl")
if os.path.exists(index_file) and os.path.exists(metadata_file):
# Load existing index
self.index = faiss.read_index(index_file)
with open(metadata_file, 'rb') as f:
self.metadata = pickle.load(f)
with open(chunks_file, 'rb') as f:
self.chunks = pickle.load(f)
print(f"Loaded existing index with {self.index.ntotal} vectors")
else:
# Create new index
if self.config.vector_store.metric == "cosine":
# Use IndexFlatIP with normalized vectors for cosine similarity
self.index = faiss.IndexFlatIP(self.dimension)
else:
# Use IndexFlatL2 for Euclidean distance
self.index = faiss.IndexFlatL2(self.dimension)
print("Created new index")
def process_books(self, force_rebuild: bool = False):
"""Process all books in the books directory"""
if self.index.ntotal > 0 and not force_rebuild:
print(f"Index already contains {self.index.ntotal} vectors. Use force_rebuild=True to rebuild.")
return
# Clear existing data if rebuilding
if force_rebuild:
self.index = faiss.IndexFlatIP(self.dimension) if self.config.vector_store.metric == "cosine" else faiss.IndexFlatL2(self.dimension)
self.metadata = []
self.chunks = []
# Process each book
book_files = list(Path(self.books_path).glob("*.txt"))
print(f"Found {len(book_files)} books to process")
for book_file in book_files:
print(f"Processing {book_file.name}...")
self._process_single_book(book_file)
# Save index
self._save_index()
print(f"Processing complete. Index contains {self.index.ntotal} vectors")
def _process_single_book(self, book_path: Path):
"""Process a single book file"""
try:
# Read book content
with open(book_path, 'r', encoding='utf-8') as f:
content = f.read()
# Extract book name
book_name = book_path.stem.replace('_', ' ').title()
# Split into chunks
chunks = self._create_chunks(content)
# Process each chunk
for i, chunk in enumerate(chunks):
# Skip empty chunks
if not chunk.strip():
continue
# Create embedding
embedding = self._create_embedding(chunk)
# Normalize for cosine similarity
if self.config.vector_store.metric == "cosine":
embedding = embedding / np.linalg.norm(embedding)
# Add to index
self.index.add(np.array([embedding]))
# Store metadata
metadata = {
"book": book_name,
"chunk_id": i,
"timestamp": datetime.now().isoformat(),
"char_count": len(chunk),
"checksum": hashlib.md5(chunk.encode()).hexdigest()
}
self.metadata.append(metadata)
self.chunks.append(chunk)
except Exception as e:
print(f"Error processing {book_path}: {str(e)}")
def _create_chunks(self, text: str) -> List[str]:
"""Split text into chunks using sliding window"""
# Clean text
text = text.strip()
if not text:
return []
# Tokenize
tokens = self.tokenizer.encode(text, add_special_tokens=False)
chunks = []
chunk_size = self.config.vector_store.chunk_size
overlap = self.config.vector_store.chunk_overlap
# Create chunks with overlap
for i in range(0, len(tokens), chunk_size - overlap):
chunk_tokens = tokens[i:i + chunk_size]
chunk_text = self.tokenizer.decode(chunk_tokens, skip_special_tokens=True)
chunks.append(chunk_text)
return chunks
def _create_embedding(self, text: str) -> np.ndarray:
"""Create embedding for text"""
embedding = self.embedding_model.encode(text, convert_to_numpy=True)
return embedding.astype('float32')
def search(self, query: str, k: int = None, filter_books: List[str] = None) -> List[Dict]:
"""Search for similar chunks in the knowledge base"""
if self.index.ntotal == 0:
return []
k = k or self.config.vector_store.n_results
# Create query embedding
query_embedding = self._create_embedding(query)
# Normalize for cosine similarity
if self.config.vector_store.metric == "cosine":
query_embedding = query_embedding / np.linalg.norm(query_embedding)
# Search
distances, indices = self.index.search(
np.array([query_embedding]),
min(k, self.index.ntotal)
)
# Compile results
results = []
for i, (dist, idx) in enumerate(zip(distances[0], indices[0])):
if idx < 0: # Invalid index
continue
metadata = self.metadata[idx]
# Apply book filter if specified
if filter_books and metadata["book"] not in filter_books:
continue
result = {
"text": self.chunks[idx],
"book": metadata["book"],
"score": float(dist),
"rank": i + 1,
"metadata": metadata
}
results.append(result)
# Sort by score (higher is better for cosine similarity)
results.sort(key=lambda x: x["score"], reverse=True)
return results[:k]
def search_with_context(self, query: str, k: int = None, context_window: int = 1) -> List[Dict]:
"""Search and include surrounding context chunks"""
results = self.search(query, k)
# Expand each result with context
expanded_results = []
for result in results:
chunk_idx = result["metadata"]["chunk_id"]
book = result["book"]
# Get surrounding chunks from the same book
context_chunks = []
# Get previous chunks
for i in range(context_window, 0, -1):
prev_idx = self._find_chunk_index(book, chunk_idx - i)
if prev_idx is not None:
context_chunks.append(self.chunks[prev_idx])
# Add main chunk
context_chunks.append(result["text"])
# Get next chunks
for i in range(1, context_window + 1):
next_idx = self._find_chunk_index(book, chunk_idx + i)
if next_idx is not None:
context_chunks.append(self.chunks[next_idx])
# Create expanded result
expanded_result = result.copy()
expanded_result["context"] = "\n\n".join(context_chunks)
expanded_result["context_size"] = len(context_chunks)
expanded_results.append(expanded_result)
return expanded_results
def _find_chunk_index(self, book: str, chunk_id: int) -> Optional[int]:
"""Find index of a specific chunk"""
for i, metadata in enumerate(self.metadata):
if metadata["book"] == book and metadata["chunk_id"] == chunk_id:
return i
return None
def add_text(self, text: str, source: str, metadata: Dict = None):
"""Add a single text to the knowledge base"""
# Create chunks
chunks = self._create_chunks(text)
# Process each chunk
for i, chunk in enumerate(chunks):
if not chunk.strip():
continue
# Create embedding
embedding = self._create_embedding(chunk)
# Normalize if needed
if self.config.vector_store.metric == "cosine":
embedding = embedding / np.linalg.norm(embedding)
# Add to index
self.index.add(np.array([embedding]))
# Create metadata
chunk_metadata = {
"book": source,
"chunk_id": i,
"timestamp": datetime.now().isoformat(),
"char_count": len(chunk),
"checksum": hashlib.md5(chunk.encode()).hexdigest()
}
# Add custom metadata if provided
if metadata:
chunk_metadata.update(metadata)
self.metadata.append(chunk_metadata)
self.chunks.append(chunk)
# Save changes
self._save_index()
def _save_index(self):
"""Save index and metadata to disk"""
os.makedirs(self.index_path, exist_ok=True)
# Save FAISS index
index_file = os.path.join(self.index_path, "knowledge.index")
faiss.write_index(self.index, index_file)
# Save metadata
metadata_file = os.path.join(self.index_path, "metadata.pkl")
with open(metadata_file, 'wb') as f:
pickle.dump(self.metadata, f)
# Save chunks
chunks_file = os.path.join(self.index_path, "chunks.pkl")
with open(chunks_file, 'wb') as f:
pickle.dump(self.chunks, f)
# Save config
config_file = os.path.join(self.index_path, "config.json")
with open(config_file, 'w') as f:
json.dump({
"dimension": self.dimension,
"metric": self.config.vector_store.metric,
"total_chunks": len(self.chunks),
"books": list(set(m["book"] for m in self.metadata)),
"last_updated": datetime.now().isoformat()
}, f, indent=2)
def get_stats(self) -> Dict:
"""Get statistics about the knowledge base"""
if not self.metadata:
return {"status": "empty"}
books = {}
for metadata in self.metadata:
book = metadata["book"]
if book not in books:
books[book] = {"chunks": 0, "chars": 0}
books[book]["chunks"] += 1
books[book]["chars"] += metadata["char_count"]
return {
"total_chunks": len(self.chunks),
"total_books": len(books),
"books": books,
"index_size": self.index.ntotal,
"dimension": self.dimension,
"metric": self.config.vector_store.metric
}
def clear(self):
"""Clear the entire knowledge base"""
self.index = faiss.IndexFlatIP(self.dimension) if self.config.vector_store.metric == "cosine" else faiss.IndexFlatL2(self.dimension)
self.metadata = []
self.chunks = []
self._save_index()
print("Knowledge base cleared") |