mirror of
https://github.com/placeholder-soft/chroma.git
synced 2026-01-12 17:02:54 +08:00
896822231e
## Description of changes *Summarize the changes made by this PR.* - New functionality - Adds a basic pulsar producer, consumer and associated tests. As well as a docker compose for the distributed version of chroma. ## Test plan We added bin/cluster-test.sh, which starts pulsar and allows test_producer_consumer to run the pulsar fixture. ## Documentation Changes None required.
430 lines
17 KiB
Python
430 lines
17 KiB
Python
import os
|
|
import shutil
|
|
from overrides import override
|
|
import pickle
|
|
from typing import Dict, List, Optional, Sequence, Set, cast
|
|
from chromadb.config import System
|
|
from chromadb.segment.impl.vector.batch import Batch
|
|
from chromadb.segment.impl.vector.hnsw_params import PersistentHnswParams
|
|
from chromadb.segment.impl.vector.local_hnsw import (
|
|
DEFAULT_CAPACITY,
|
|
LocalHnswSegment,
|
|
)
|
|
from chromadb.segment.impl.vector.brute_force_index import BruteForceIndex
|
|
from chromadb.types import (
|
|
EmbeddingRecord,
|
|
Metadata,
|
|
Operation,
|
|
Segment,
|
|
SeqId,
|
|
Vector,
|
|
VectorEmbeddingRecord,
|
|
VectorQuery,
|
|
VectorQueryResult,
|
|
)
|
|
import hnswlib
|
|
import logging
|
|
|
|
from chromadb.utils.read_write_lock import ReadRWLock, WriteRWLock
|
|
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
class PersistentData:
|
|
"""Stores the data and metadata needed for a PersistentLocalHnswSegment"""
|
|
|
|
dimensionality: Optional[int]
|
|
total_elements_added: int
|
|
max_seq_id: SeqId
|
|
|
|
id_to_label: Dict[str, int]
|
|
label_to_id: Dict[int, str]
|
|
id_to_seq_id: Dict[str, SeqId]
|
|
|
|
def __init__(
|
|
self,
|
|
dimensionality: Optional[int],
|
|
total_elements_added: int,
|
|
max_seq_id: int,
|
|
id_to_label: Dict[str, int],
|
|
label_to_id: Dict[int, str],
|
|
id_to_seq_id: Dict[str, SeqId],
|
|
):
|
|
self.dimensionality = dimensionality
|
|
self.total_elements_added = total_elements_added
|
|
self.max_seq_id = max_seq_id
|
|
self.id_to_label = id_to_label
|
|
self.label_to_id = label_to_id
|
|
self.id_to_seq_id = id_to_seq_id
|
|
|
|
@staticmethod
|
|
def load_from_file(filename: str) -> "PersistentData":
|
|
"""Load persistent data from a file"""
|
|
with open(filename, "rb") as f:
|
|
ret = cast(PersistentData, pickle.load(f))
|
|
return ret
|
|
|
|
|
|
class PersistentLocalHnswSegment(LocalHnswSegment):
|
|
METADATA_FILE: str = "index_metadata.pickle"
|
|
# How many records to add to index at once, we do this because crossing the python/c++ boundary is expensive (for add())
|
|
# When records are not added to the c++ index, they are buffered in memory and served
|
|
# via brute force search.
|
|
_batch_size: int
|
|
_brute_force_index: Optional[BruteForceIndex]
|
|
_index_initialized: bool = False
|
|
_curr_batch: Batch
|
|
# How many records to add to index before syncing to disk
|
|
_sync_threshold: int
|
|
_persist_data: PersistentData
|
|
_persist_directory: str
|
|
_allow_reset: bool
|
|
|
|
def __init__(self, system: System, segment: Segment):
|
|
super().__init__(system, segment)
|
|
|
|
self._params = PersistentHnswParams(segment["metadata"] or {})
|
|
self._batch_size = self._params.batch_size
|
|
self._sync_threshold = self._params.sync_threshold
|
|
self._allow_reset = system.settings.allow_reset
|
|
self._persist_directory = system.settings.require("persist_directory")
|
|
self._curr_batch = Batch()
|
|
self._brute_force_index = None
|
|
if not os.path.exists(self._get_storage_folder()):
|
|
os.makedirs(self._get_storage_folder(), exist_ok=True)
|
|
# Load persist data if it exists already, otherwise create it
|
|
if self._index_exists():
|
|
self._persist_data = PersistentData.load_from_file(
|
|
self._get_metadata_file()
|
|
)
|
|
self._dimensionality = self._persist_data.dimensionality
|
|
self._total_elements_added = self._persist_data.total_elements_added
|
|
self._max_seq_id = self._persist_data.max_seq_id
|
|
self._id_to_label = self._persist_data.id_to_label
|
|
self._label_to_id = self._persist_data.label_to_id
|
|
self._id_to_seq_id = self._persist_data.id_to_seq_id
|
|
# If the index was written to, we need to re-initialize it
|
|
if len(self._id_to_label) > 0:
|
|
self._dimensionality = cast(int, self._dimensionality)
|
|
self._init_index(self._dimensionality)
|
|
else:
|
|
self._persist_data = PersistentData(
|
|
self._dimensionality,
|
|
self._total_elements_added,
|
|
self._max_seq_id,
|
|
self._id_to_label,
|
|
self._label_to_id,
|
|
self._id_to_seq_id,
|
|
)
|
|
|
|
@staticmethod
|
|
@override
|
|
def propagate_collection_metadata(metadata: Metadata) -> Optional[Metadata]:
|
|
# Extract relevant metadata
|
|
segment_metadata = PersistentHnswParams.extract(metadata)
|
|
return segment_metadata
|
|
|
|
def _index_exists(self) -> bool:
|
|
"""Check if the index exists via the metadata file"""
|
|
return os.path.exists(self._get_metadata_file())
|
|
|
|
def _get_metadata_file(self) -> str:
|
|
"""Get the metadata file path"""
|
|
return os.path.join(self._get_storage_folder(), self.METADATA_FILE)
|
|
|
|
def _get_storage_folder(self) -> str:
|
|
"""Get the storage folder path"""
|
|
folder = os.path.join(self._persist_directory, str(self._id))
|
|
return folder
|
|
|
|
@override
|
|
def _init_index(self, dimensionality: int) -> None:
|
|
index = hnswlib.Index(space=self._params.space, dim=dimensionality)
|
|
self._brute_force_index = BruteForceIndex(
|
|
size=self._batch_size,
|
|
dimensionality=dimensionality,
|
|
space=self._params.space,
|
|
)
|
|
|
|
# Check if index exists and load it if it does
|
|
if self._index_exists():
|
|
index.load_index(
|
|
self._get_storage_folder(),
|
|
is_persistent_index=True,
|
|
max_elements=int(
|
|
max(self.count() * self._params.resize_factor, DEFAULT_CAPACITY)
|
|
),
|
|
)
|
|
else:
|
|
index.init_index(
|
|
max_elements=DEFAULT_CAPACITY,
|
|
ef_construction=self._params.construction_ef,
|
|
M=self._params.M,
|
|
is_persistent_index=True,
|
|
persistence_location=self._get_storage_folder(),
|
|
)
|
|
|
|
index.set_ef(self._params.search_ef)
|
|
index.set_num_threads(self._params.num_threads)
|
|
|
|
self._index = index
|
|
self._dimensionality = dimensionality
|
|
self._index_initialized = True
|
|
|
|
def _persist(self) -> None:
|
|
"""Persist the index and data to disk"""
|
|
index = cast(hnswlib.Index, self._index)
|
|
|
|
# Persist the index
|
|
index.persist_dirty()
|
|
|
|
# Persist the metadata
|
|
self._persist_data.dimensionality = self._dimensionality
|
|
self._persist_data.total_elements_added = self._total_elements_added
|
|
self._persist_data.max_seq_id = self._max_seq_id
|
|
|
|
# TODO: This should really be stored in sqlite, the index itself, or a better
|
|
# storage format
|
|
self._persist_data.id_to_label = self._id_to_label
|
|
self._persist_data.label_to_id = self._label_to_id
|
|
self._persist_data.id_to_seq_id = self._id_to_seq_id
|
|
|
|
with open(self._get_metadata_file(), "wb") as metadata_file:
|
|
pickle.dump(self._persist_data, metadata_file, pickle.HIGHEST_PROTOCOL)
|
|
|
|
@override
|
|
def _apply_batch(self, batch: Batch) -> None:
|
|
super()._apply_batch(batch)
|
|
if (
|
|
self._total_elements_added - self._persist_data.total_elements_added
|
|
>= self._sync_threshold
|
|
):
|
|
self._persist()
|
|
|
|
@override
|
|
def _write_records(self, records: Sequence[EmbeddingRecord]) -> None:
|
|
"""Add a batch of embeddings to the index"""
|
|
if not self._running:
|
|
raise RuntimeError("Cannot add embeddings to stopped component")
|
|
with WriteRWLock(self._lock):
|
|
for record in records:
|
|
if record["embedding"] is not None:
|
|
self._ensure_index(len(records), len(record["embedding"]))
|
|
if not self._index_initialized:
|
|
# If the index is not initialized here, it means that we have
|
|
# not yet added any records to the index. So we can just
|
|
# ignore the record since it was a delete.
|
|
continue
|
|
self._brute_force_index = cast(BruteForceIndex, self._brute_force_index)
|
|
|
|
self._max_seq_id = max(self._max_seq_id, record["seq_id"])
|
|
id = record["id"]
|
|
op = record["operation"]
|
|
exists_in_index = self._id_to_label.get(
|
|
id, None
|
|
) is not None or self._brute_force_index.has_id(id)
|
|
exists_in_bf_index = self._brute_force_index.has_id(id)
|
|
|
|
if op == Operation.DELETE:
|
|
if exists_in_index:
|
|
self._curr_batch.apply(record)
|
|
if exists_in_bf_index:
|
|
self._brute_force_index.delete([record])
|
|
else:
|
|
logger.warning(f"Delete of nonexisting embedding ID: {id}")
|
|
|
|
elif op == Operation.UPDATE:
|
|
if record["embedding"] is not None:
|
|
if exists_in_index:
|
|
self._curr_batch.apply(record)
|
|
self._brute_force_index.upsert([record])
|
|
else:
|
|
logger.warning(
|
|
f"Update of nonexisting embedding ID: {record['id']}"
|
|
)
|
|
elif op == Operation.ADD:
|
|
if record["embedding"] is not None:
|
|
if not exists_in_index:
|
|
self._curr_batch.apply(record, not exists_in_index)
|
|
self._brute_force_index.upsert([record])
|
|
else:
|
|
logger.warning(f"Add of existing embedding ID: {id}")
|
|
elif op == Operation.UPSERT:
|
|
if record["embedding"] is not None:
|
|
self._curr_batch.apply(record, exists_in_index)
|
|
self._brute_force_index.upsert([record])
|
|
if len(self._curr_batch) >= self._batch_size:
|
|
self._apply_batch(self._curr_batch)
|
|
self._curr_batch = Batch()
|
|
self._brute_force_index.clear()
|
|
|
|
@override
|
|
def count(self) -> int:
|
|
return (
|
|
len(self._id_to_label)
|
|
+ self._curr_batch.add_count
|
|
- self._curr_batch.delete_count
|
|
)
|
|
|
|
@override
|
|
def get_vectors(
|
|
self, ids: Optional[Sequence[str]] = None
|
|
) -> Sequence[VectorEmbeddingRecord]:
|
|
"""Get the embeddings from the HNSW index and layered brute force
|
|
batch index."""
|
|
|
|
ids_hnsw: Set[str] = set()
|
|
ids_bf: Set[str] = set()
|
|
|
|
if self._index is not None:
|
|
ids_hnsw = set(self._id_to_label.keys())
|
|
if self._brute_force_index is not None:
|
|
ids_bf = set(self._curr_batch.get_written_ids())
|
|
|
|
target_ids = ids or list(ids_hnsw.union(ids_bf))
|
|
self._brute_force_index = cast(BruteForceIndex, self._brute_force_index)
|
|
hnsw_labels = []
|
|
|
|
results: List[Optional[VectorEmbeddingRecord]] = []
|
|
id_to_index: Dict[str, int] = {}
|
|
for i, id in enumerate(target_ids):
|
|
if id in ids_bf:
|
|
results.append(self._brute_force_index.get_vectors([id])[0])
|
|
elif id in ids_hnsw and id not in self._curr_batch._deleted_ids:
|
|
hnsw_labels.append(self._id_to_label[id])
|
|
# Placeholder for hnsw results to be filled in down below so we
|
|
# can batch the hnsw get() call
|
|
results.append(None)
|
|
id_to_index[id] = i
|
|
|
|
if len(hnsw_labels) > 0 and self._index is not None:
|
|
vectors = cast(Sequence[Vector], self._index.get_items(hnsw_labels))
|
|
|
|
for label, vector in zip(hnsw_labels, vectors):
|
|
id = self._label_to_id[label]
|
|
seq_id = self._id_to_seq_id[id]
|
|
results[id_to_index[id]] = VectorEmbeddingRecord(
|
|
id=id, seq_id=seq_id, embedding=vector
|
|
)
|
|
|
|
return results # type: ignore ## Python can't cast List with Optional to List with VectorEmbeddingRecord
|
|
|
|
@override
|
|
def query_vectors(
|
|
self, query: VectorQuery
|
|
) -> Sequence[Sequence[VectorQueryResult]]:
|
|
if self._index is None and self._brute_force_index is None:
|
|
return [[] for _ in range(len(query["vectors"]))]
|
|
|
|
k = query["k"]
|
|
if k > self.count():
|
|
logger.warning(
|
|
f"Number of requested results {k} is greater than number of elements in index {self.count()}, updating n_results = {self.count()}"
|
|
)
|
|
k = self.count()
|
|
|
|
# Overquery by updated and deleted elements layered on the index because they may
|
|
# hide the real nearest neighbors in the hnsw index
|
|
hnsw_k = k + self._curr_batch.update_count + self._curr_batch.delete_count
|
|
if hnsw_k > len(self._id_to_label):
|
|
hnsw_k = len(self._id_to_label)
|
|
hnsw_query = VectorQuery(
|
|
vectors=query["vectors"],
|
|
k=hnsw_k,
|
|
allowed_ids=query["allowed_ids"],
|
|
include_embeddings=query["include_embeddings"],
|
|
options=query["options"],
|
|
)
|
|
|
|
# For each query vector, we want to take the top k results from the
|
|
# combined results of the brute force and hnsw index
|
|
results: List[List[VectorQueryResult]] = []
|
|
self._brute_force_index = cast(BruteForceIndex, self._brute_force_index)
|
|
with ReadRWLock(self._lock):
|
|
bf_results = self._brute_force_index.query(query)
|
|
hnsw_results = super().query_vectors(hnsw_query)
|
|
for i in range(len(query["vectors"])):
|
|
# Merge results into a single list of size k
|
|
bf_pointer: int = 0
|
|
hnsw_pointer: int = 0
|
|
curr_bf_result: Sequence[VectorQueryResult] = bf_results[i]
|
|
curr_hnsw_result: Sequence[VectorQueryResult] = hnsw_results[i]
|
|
curr_results: List[VectorQueryResult] = []
|
|
# In the case where filters cause the number of results to be less than k,
|
|
# we set k to be the number of results
|
|
total_results = len(curr_bf_result) + len(curr_hnsw_result)
|
|
if total_results == 0:
|
|
results.append([])
|
|
else:
|
|
while len(curr_results) < min(k, total_results):
|
|
if bf_pointer < len(curr_bf_result) and hnsw_pointer < len(
|
|
curr_hnsw_result
|
|
):
|
|
bf_dist = curr_bf_result[bf_pointer]["distance"]
|
|
hnsw_dist = curr_hnsw_result[hnsw_pointer]["distance"]
|
|
if bf_dist <= hnsw_dist:
|
|
curr_results.append(curr_bf_result[bf_pointer])
|
|
bf_pointer += 1
|
|
else:
|
|
id = curr_hnsw_result[hnsw_pointer]["id"]
|
|
# Only add the hnsw result if it is not in the brute force index
|
|
# as updated or deleted
|
|
if not self._brute_force_index.has_id(
|
|
id
|
|
) and not self._curr_batch.is_deleted(id):
|
|
curr_results.append(curr_hnsw_result[hnsw_pointer])
|
|
hnsw_pointer += 1
|
|
else:
|
|
break
|
|
remaining = min(k, total_results) - len(curr_results)
|
|
if remaining > 0 and hnsw_pointer < len(curr_hnsw_result):
|
|
for i in range(
|
|
hnsw_pointer,
|
|
min(len(curr_hnsw_result), hnsw_pointer + remaining + 1),
|
|
):
|
|
id = curr_hnsw_result[i]["id"]
|
|
if not self._brute_force_index.has_id(
|
|
id
|
|
) and not self._curr_batch.is_deleted(id):
|
|
curr_results.append(curr_hnsw_result[i])
|
|
elif remaining > 0 and bf_pointer < len(curr_bf_result):
|
|
curr_results.extend(
|
|
curr_bf_result[bf_pointer : bf_pointer + remaining]
|
|
)
|
|
results.append(curr_results)
|
|
return results
|
|
|
|
@override
|
|
def reset_state(self) -> None:
|
|
if self._allow_reset:
|
|
data_path = self._get_storage_folder()
|
|
if os.path.exists(data_path):
|
|
self.close_persistent_index()
|
|
shutil.rmtree(data_path, ignore_errors=True)
|
|
|
|
@override
|
|
def delete(self) -> None:
|
|
data_path = self._get_storage_folder()
|
|
if os.path.exists(data_path):
|
|
self.close_persistent_index()
|
|
shutil.rmtree(data_path, ignore_errors=False)
|
|
|
|
@staticmethod
|
|
def get_file_handle_count() -> int:
|
|
"""Return how many file handles are used by the index"""
|
|
hnswlib_count = hnswlib.Index.file_handle_count
|
|
hnswlib_count = cast(int, hnswlib_count)
|
|
# One extra for the metadata file
|
|
return hnswlib_count + 1 # type: ignore
|
|
|
|
def open_persistent_index(self) -> None:
|
|
"""Open the persistent index"""
|
|
if self._index is not None:
|
|
self._index.open_file_handles()
|
|
|
|
def close_persistent_index(self) -> None:
|
|
"""Close the persistent index"""
|
|
if self._index is not None:
|
|
self._index.close_file_handles()
|