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Parquet-backed embedding store, adapted from the original EmbeddingStore.
Uses QAFD-RAG's async embedding functions (wrapped synchronously) so we
can share models / GPU memory with the rest of the QAFD-RAG system.
"""
import asyncio
import logging
import os
from copy import deepcopy
from typing import List, Dict, Optional, Callable, Any
import numpy as np
import pandas as pd
from .utils import compute_mdhash_id
logger = logging.getLogger(__name__)
class EmbeddingModelWrapper:
"""Thin sync wrapper around a QAFD-RAG *async* embedding function.
The wrapped function must have the signature::
async def embed(texts: list[str], **kwargs) -> np.ndarray
Parameters
----------
embed_func : callable
An async embedding function from ``QAFD-RAG/src/llm.py``.
batch_size : int
Max texts per call.
"""
def __init__(self, embed_func: Callable, batch_size: int = 16):
self._embed_func = embed_func
self.batch_size = batch_size
# ------------------------------------------------------------------
def batch_encode(self, texts, instruction: str = None, norm: bool = True) -> np.ndarray:
"""Synchronously encode *texts* into embeddings."""
if isinstance(texts, str):
texts = [texts]
all_embeddings = []
# Use larger batch for API-based embeddings (OpenAI supports up to 2048)
effective_batch = max(self.batch_size, 512)
for start in range(0, len(texts), effective_batch):
batch = texts[start : start + effective_batch]
if instruction:
batch = [f"{instruction} {t}" for t in batch]
embs = self._run_async(self._embed_func(batch))
if not isinstance(embs, np.ndarray):
embs = np.array(embs)
if norm:
norms = np.linalg.norm(embs, axis=1, keepdims=True)
norms = np.where(norms == 0, 1, norms)
embs = embs / norms
all_embeddings.append(embs)
return np.vstack(all_embeddings)
# ------------------------------------------------------------------
@staticmethod
def _run_async(coro):
"""Run an async coroutine synchronously."""
try:
loop = asyncio.get_running_loop()
except RuntimeError:
loop = None
if loop is not None and loop.is_running():
# We are inside an already-running event loop (e.g. Jupyter).
import concurrent.futures
with concurrent.futures.ThreadPoolExecutor(max_workers=1) as pool:
return pool.submit(asyncio.run, coro).result()
else:
return asyncio.run(coro)
class EmbeddingStore:
"""Parquet-backed vector store.
Mirrors the original EmbeddingStore but uses ``EmbeddingModelWrapper``
(which calls QAFD-RAG's async embedding functions under the hood).
"""
def __init__(
self,
embedding_model: EmbeddingModelWrapper,
db_filename: str,
batch_size: int,
namespace: str,
):
self.embedding_model = embedding_model
self.batch_size = batch_size
self.namespace = namespace
if not os.path.exists(db_filename):
logger.info(f"Creating directory: {db_filename}")
os.makedirs(db_filename, exist_ok=True)
self.filename = os.path.join(db_filename, f"vdb_{self.namespace}.parquet")
self._load_data()
# ------------------------------------------------------------------
# Data persistence
# ------------------------------------------------------------------
def _load_data(self):
if os.path.exists(self.filename):
df = pd.read_parquet(self.filename)
self.hash_ids = df["hash_id"].values.tolist()
self.texts = df["content"].values.tolist()
self.embeddings = df["embedding"].values.tolist()
self._rebuild_indices()
assert len(self.hash_ids) == len(self.texts) == len(self.embeddings)
logger.info(f"Loaded {len(self.hash_ids)} records from {self.filename}")
else:
self.hash_ids, self.texts, self.embeddings = [], [], []
self.hash_id_to_idx: Dict[str, int] = {}
self.hash_id_to_row: Dict[str, dict] = {}
self.hash_id_to_text: Dict[str, str] = {}
self.text_to_hash_id: Dict[str, str] = {}
def _rebuild_indices(self):
self.hash_id_to_idx = {h: idx for idx, h in enumerate(self.hash_ids)}
self.hash_id_to_row = {
h: {"hash_id": h, "content": t} for h, t in zip(self.hash_ids, self.texts)
}
self.hash_id_to_text = {h: self.texts[idx] for idx, h in enumerate(self.hash_ids)}
self.text_to_hash_id = {self.texts[idx]: h for idx, h in enumerate(self.hash_ids)}
def _save_data(self):
data = pd.DataFrame({
"hash_id": self.hash_ids,
"content": self.texts,
"embedding": self.embeddings,
})
data.to_parquet(self.filename, index=False)
self._rebuild_indices()
logger.info(f"Saved {len(self.hash_ids)} records to {self.filename}")
def _upsert(self, hash_ids, texts, embeddings):
self.embeddings.extend(embeddings)
self.hash_ids.extend(hash_ids)
self.texts.extend(texts)
self._save_data()
# ------------------------------------------------------------------
# Public API
# ------------------------------------------------------------------
def get_missing_string_hash_ids(self, texts: List[str]) -> Dict[str, dict]:
nodes_dict = {}
for text in texts:
hid = compute_mdhash_id(text, prefix=self.namespace + "-")
nodes_dict[hid] = {"content": text}
if not nodes_dict:
return {}
existing = set(self.hash_id_to_row.keys())
missing = {h: {"hash_id": h, "content": v["content"]}
for h, v in nodes_dict.items() if h not in existing}
return missing
def insert_strings(self, texts: List[str]):
nodes_dict = {}
for text in texts:
if not text or not text.strip():
continue
hid = compute_mdhash_id(text, prefix=self.namespace + "-")
nodes_dict[hid] = {"content": text}
all_ids = list(nodes_dict.keys())
if not all_ids:
return
existing = set(self.hash_id_to_row.keys())
missing_ids = [h for h in all_ids if h not in existing]
logger.info(
f"Inserting {len(missing_ids)} new records, "
f"{len(all_ids) - len(missing_ids)} already exist."
)
if not missing_ids:
return
texts_to_encode = [nodes_dict[h]["content"] for h in missing_ids]
missing_embeddings = self.embedding_model.batch_encode(texts_to_encode)
# Convert ndarray rows to list of lists for parquet storage
if isinstance(missing_embeddings, np.ndarray):
missing_embeddings = missing_embeddings.tolist()
self._upsert(missing_ids, texts_to_encode, missing_embeddings)
def delete(self, hash_ids):
indices = sorted(
[self.hash_id_to_idx[h] for h in hash_ids], reverse=True
)
for idx in indices:
self.hash_ids.pop(idx)
self.texts.pop(idx)
self.embeddings.pop(idx)
self._save_data()
# Lookups
def get_row(self, hash_id: str) -> dict:
return self.hash_id_to_row[hash_id]
def get_hash_id(self, text: str) -> str:
return self.text_to_hash_id[text]
def get_rows(self, hash_ids: List[str], dtype=np.float32) -> Dict[str, dict]:
if not hash_ids:
return {}
return {hid: self.hash_id_to_row[hid] for hid in hash_ids}
def get_all_ids(self) -> List[str]:
return deepcopy(self.hash_ids)
def get_all_id_to_rows(self) -> Dict[str, dict]:
return deepcopy(self.hash_id_to_row)
def get_all_texts(self) -> set:
return set(row["content"] for row in self.hash_id_to_row.values())
def get_embedding(self, hash_id: str, dtype=np.float32) -> np.ndarray:
return np.array(self.embeddings[self.hash_id_to_idx[hash_id]], dtype=dtype)
def get_embeddings(self, hash_ids: List[str], dtype=np.float32) -> np.ndarray:
if not hash_ids:
return np.array([])
indices = np.array([self.hash_id_to_idx[h] for h in hash_ids], dtype=np.intp)
all_embs = np.array(self.embeddings, dtype=dtype)
return all_embs[indices]
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