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import faiss
import numpy as np
import pickle
from pathlib import Path
from langchain_openai import OpenAIEmbeddings
from threading import Lock
from typing import List, Dict, Any
import logging
logger = logging.getLogger(__name__)
logger.addHandler(logging.NullHandler())
DocumentChunk = Dict[str, Any]
class FAISSVectorStore:
def __init__(
self,
dimension: int = 3072,
index_path: str = "faiss_index",
embedding_model: str = "text-embedding-3-large", #3072-dim vectors
):
if OpenAIEmbeddings is None:
raise ImportError(
"Could not import OpenAIEmbeddings from langchain. "
"Install langchain or adapt the import to your environment."
)
self.dimension = dimension
self.index_path = Path(index_path)
self._lock = Lock()
self.index_path.mkdir(parents=True, exist_ok=True)
# Instantiate embeddings (may make API calls later when embedding)
self.embeddings = OpenAIEmbeddings(model=embedding_model)
# in-memory structures
self.documents: List[DocumentChunk] = []
# Create a new FAISS index (will be replaced by load if a saved index exists)
self.index = faiss.IndexFlatIP(self.dimension) # All vectors must be this length
# If there's a saved index, load it (overwrites the index created above).
self.load_index() # safe: will return False if nothing to load
def _ensure_index_dim(self, d: int):
"""Ensure FAISS index has dimension d."""
# If current index has no vectors, and d != self.dimension, recreate.
# Using getattr for defensive programming
if getattr(self.index, "ntotal", 0) == 0 and getattr(self.index, "d", None) != d:
logger.info("Recreating an empty index with dimension %d", d)
self.dimension = d
self.index = faiss.IndexFlatIP(self.dimension)
elif getattr(self.index, "d", None) is not None and self.index.d != d:
raise ValueError(f"Embedding dimension ({d}) does not match existing index dimension ({self.index.d}).")
def add_documents(self, chunks: List[DocumentChunk], save: bool = True):
"""
Add list of chunks to the FAISS index. Each chunk MUST contain 'text'.
If index is empty and embedding dimension differs, the index will be re-created.
"""
with self._lock:
if not chunks:
logger.debug("No chunks to add.")
return
texts = []
for i, chunk in enumerate(chunks):
if not isinstance(chunk, dict):
raise ValueError(f"Chunk {i} is not a dictionary")
if "text" not in chunk:
raise ValueError(f"Chunk {i} missing required 'text' field")
if not isinstance(chunk["text"], str):
raise ValueError(f"Chunk {i} 'text' field must be a string")
if not chunk["text"].strip():
logger.warning(f"Chunk {i} has empty text content")
continue
texts.append(chunk["text"])
# Get embeddings from the embedding provider (call to a model)
embeddings = self.embeddings.embed_documents(texts)
embeddings_np = np.asarray(embeddings, dtype=np.float32)
# Embedding shape checks
if embeddings_np.ndim == 1:
# single vector returned as 1D array -> reshape to (1, d)
embeddings_np = embeddings_np.reshape(1, -1)
emb_d = embeddings_np.shape[1]
# If needed, recreate the index dimension (only possible if index currently empty)
self._ensure_index_dim(emb_d)
if emb_d != self.index.d:
raise ValueError(f"Embedding dim {emb_d} != index dim {self.index.d}")
# L2-normalize rows (in place) so inner product == cosine similarity
faiss.normalize_L2(embeddings_np)
# Add to index
self.index.add(embeddings_np)
# The documentation of "add" suggests we have to put the number of vectors,
# as a first argument, but Python does it for us.
# Append documents (simple positional mapping: index position -> documents list)
self.documents.extend(chunks)
if save:
self.save_index()
def search(self, query: str, k: int = 5) -> List[Dict[str, Any]]:
"""
Search similar documents for `query`. Returns up to k results.
Each result: { "content": <text>, "metadata": <metadata>, "similarity_score": <float> }
similarity_score is the inner product of normalized vectors => cosine similarity in [-1,1].
"""
with self._lock:
# guard: no vectors at all
if getattr(self.index, "ntotal", 0) == 0:
logger.debug("Search called but index is empty.")
return []
# embed query
q_emb = self.embeddings.embed_query(query)
q_np = np.asarray([q_emb], dtype=np.float32)
if q_np.ndim == 1:
q_np = q_np.reshape(1, -1)
if q_np.shape[1] != self.index.d:
# if index is empty we could recreate; but at this point we know index has vectors.
raise ValueError(f"Query embedding dim {q_np.shape[1]} does not match index dimension {self.index.d}")
faiss.normalize_L2(q_np)
# clamp k
k = min(k, int(self.index.ntotal))
distances, indices = self.index.search(q_np, k) # distances shape (1,k) ; indices shape (1,k)
results = []
for score, idx in zip(distances[0], indices[0]):
if idx < 0:
# FAISS returns -1 for "empty" slots sometimes; skip
continue
if idx >= len(self.documents):
logger.warning("Index returned idx %d but documents list has length %d", idx, len(self.documents))
continue
doc = self.documents[idx]
results.append({
"content": doc.get("text"),
"metadata": doc.get("metadata", {}),
"similarity_score": float(score) # already cosine because of normalization
})
return results
def save_index(self):
"""Persist index and documents to disk."""
self.index_path.mkdir(parents=True, exist_ok=True)
faiss.write_index(self.index, str(self.index_path / "index.faiss"))
with open(self.index_path / "documents.pkl", "wb") as f:
pickle.dump(self.documents, f)
logger.debug("FAISS index and documents saved to %s", self.index_path)
def load_index(self) -> bool:
"""Load index and documents from disk. Returns True if loaded."""
index_file = self.index_path / "index.faiss"
docs_file = self.index_path / "documents.pkl"
if index_file.exists() and docs_file.exists():
self.index = faiss.read_index(str(index_file))
with open(docs_file, "rb") as f:
self.documents = pickle.load(f)
# update dimension to match loaded index
if getattr(self.index, "d", None) is not None:
self.dimension = int(self.index.d)
if self.index.d == 0 or len(self.documents) != self.index.ntotal:
logger.error("Corrupted index detected, deleting...")
index_file.unlink()
docs_file.unlink()
return False
# warn if counts differ
if len(self.documents) != self.index.ntotal:
logger.warning(
"Loaded documents list length (%d) differs from index.ntotal (%d). "
"This can lead to mismatches. Using what's available.",
len(self.documents),
self.index.ntotal,
)
logger.info("Loaded FAISS index from %s (ntotal=%d, dim=%d)",
index_file, int(self.index.ntotal), int(self.index.d))
return True
return False
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