group-wbl/.venv/lib/python3.13/site-packages/chromadb/db/mixins/embeddings_queue.py

from functools import cached_property
import json
from chromadb.api.configuration import (
    ConfigurationParameter,
    EmbeddingsQueueConfigurationInternal,
)
from chromadb.db.base import SqlDB, ParameterValue, get_sql
from chromadb.errors import BatchSizeExceededError
from chromadb.ingest import (
    Producer,
    Consumer,
    ConsumerCallbackFn,
    decode_vector,
    encode_vector,
)
from chromadb.types import (
    OperationRecord,
    LogRecord,
    ScalarEncoding,
    SeqId,
    Operation,
)
from chromadb.config import System
from chromadb.telemetry.opentelemetry import (
    OpenTelemetryClient,
    OpenTelemetryGranularity,
    trace_method,
)
from overrides import override
from collections import defaultdict
from typing import Sequence, Optional, Dict, Set, Tuple, cast
from uuid import UUID
from pypika import Table, functions
import uuid
import logging
from chromadb.ingest.impl.utils import create_topic_name


logger = logging.getLogger(__name__)

_operation_codes = {
    Operation.ADD: 0,
    Operation.UPDATE: 1,
    Operation.UPSERT: 2,
    Operation.DELETE: 3,
}
_operation_codes_inv = {v: k for k, v in _operation_codes.items()}

# Set in conftest.py to rethrow errors in the "async" path during testing
# https://doc.pytest.org/en/latest/example/simple.html#detect-if-running-from-within-a-pytest-run
_called_from_test = False


class SqlEmbeddingsQueue(SqlDB, Producer, Consumer):
    """A SQL database that stores embeddings, allowing a traditional RDBMS to be used as
    the primary ingest queue and satisfying the top level Producer/Consumer interfaces.

    Note that this class is only suitable for use cases where the producer and consumer
    are in the same process.

    This is because notification of new embeddings happens solely in-process: this
    implementation does not actively listen to the the database for new records added by
    other processes.
    """

    class Subscription:
        id: UUID
        topic_name: str
        start: int
        end: int
        callback: ConsumerCallbackFn

        def __init__(
            self,
            id: UUID,
            topic_name: str,
            start: int,
            end: int,
            callback: ConsumerCallbackFn,
        ):
            self.id = id
            self.topic_name = topic_name
            self.start = start
            self.end = end
            self.callback = callback

    _subscriptions: Dict[str, Set[Subscription]]
    _max_batch_size: Optional[int]
    _tenant: str
    _topic_namespace: str
    # How many variables are in the insert statement for a single record
    VARIABLES_PER_RECORD = 6

    def __init__(self, system: System):
        self._subscriptions = defaultdict(set)
        self._max_batch_size = None
        self._opentelemetry_client = system.require(OpenTelemetryClient)
        self._tenant = system.settings.require("tenant_id")
        self._topic_namespace = system.settings.require("topic_namespace")
        super().__init__(system)

    @trace_method("SqlEmbeddingsQueue.reset_state", OpenTelemetryGranularity.ALL)
    @override
    def reset_state(self) -> None:
        super().reset_state()
        self._subscriptions = defaultdict(set)

        # Invalidate the cached property
        try:
            del self.config
        except AttributeError:
            # Cached property hasn't been accessed yet
            pass

    @trace_method("SqlEmbeddingsQueue.delete_topic", OpenTelemetryGranularity.ALL)
    @override
    def delete_log(self, collection_id: UUID) -> None:
        topic_name = create_topic_name(
            self._tenant, self._topic_namespace, collection_id
        )
        t = Table("embeddings_queue")
        q = (
            self.querybuilder()
            .from_(t)
            .where(t.topic == ParameterValue(topic_name))
            .delete()
        )
        with self.tx() as cur:
            sql, params = get_sql(q, self.parameter_format())
            cur.execute(sql, params)

    @trace_method("SqlEmbeddingsQueue.purge_log", OpenTelemetryGranularity.ALL)
    @override
    def purge_log(self, collection_id: UUID) -> None:
        # (We need to purge on a per topic/collection basis, because the maximum sequence ID is tracked on a per topic/collection basis.)

        segments_t = Table("segments")
        segment_ids_q = (
            self.querybuilder()
            .from_(segments_t)
            # This coalesce prevents a correctness bug when > 1 segments exist and:
            # - > 1 has written to the max_seq_id table
            # - > 1 has not never written to the max_seq_id table
            # In that case, we should not delete any WAL entries as we can't be sure that the all segments are caught up.
            .select(functions.Coalesce(Table("max_seq_id").seq_id, -1))
            .where(
                segments_t.collection == ParameterValue(self.uuid_to_db(collection_id))
            )
            .left_join(Table("max_seq_id"))
            .on(segments_t.id == Table("max_seq_id").segment_id)
        )

        topic_name = create_topic_name(
            self._tenant, self._topic_namespace, collection_id
        )
        with self.tx() as cur:
            sql, params = get_sql(segment_ids_q, self.parameter_format())
            cur.execute(sql, params)
            results = cur.fetchall()
            if results:
                min_seq_id = min(row[0] for row in results)
            else:
                return

            t = Table("embeddings_queue")
            q = (
                self.querybuilder()
                .from_(t)
                .where(t.seq_id < ParameterValue(min_seq_id))
                .where(t.topic == ParameterValue(topic_name))
                .delete()
            )

            sql, params = get_sql(q, self.parameter_format())
            cur.execute(sql, params)

    @trace_method("SqlEmbeddingsQueue.submit_embedding", OpenTelemetryGranularity.ALL)
    @override
    def submit_embedding(
        self, collection_id: UUID, embedding: OperationRecord
    ) -> SeqId:
        if not self._running:
            raise RuntimeError("Component not running")

        return self.submit_embeddings(collection_id, [embedding])[0]

    @trace_method("SqlEmbeddingsQueue.submit_embeddings", OpenTelemetryGranularity.ALL)
    @override
    def submit_embeddings(
        self, collection_id: UUID, embeddings: Sequence[OperationRecord]
    ) -> Sequence[SeqId]:
        if not self._running:
            raise RuntimeError("Component not running")

        if len(embeddings) == 0:
            return []

        if len(embeddings) > self.max_batch_size:
            raise BatchSizeExceededError(
                f"""
                Cannot submit more than {self.max_batch_size:,} embeddings at once.
                Please submit your embeddings in batches of size
                {self.max_batch_size:,} or less.
                """
            )

        # This creates the persisted configuration if it doesn't exist.
        # It should be run as soon as possible (before any WAL mutations) since the default configuration depends on the WAL size.
        # (We can't run this in __init__()/start() because the migrations have not been run at that point and the table may not be available.)
        _ = self.config

        topic_name = create_topic_name(
            self._tenant, self._topic_namespace, collection_id
        )

        t = Table("embeddings_queue")
        insert = (
            self.querybuilder()
            .into(t)
            .columns(t.operation, t.topic, t.id, t.vector, t.encoding, t.metadata)
        )
        id_to_idx: Dict[str, int] = {}
        for embedding in embeddings:
            (
                embedding_bytes,
                encoding,
                metadata,
            ) = self._prepare_vector_encoding_metadata(embedding)
            insert = insert.insert(
                ParameterValue(_operation_codes[embedding["operation"]]),
                ParameterValue(topic_name),
                ParameterValue(embedding["id"]),
                ParameterValue(embedding_bytes),
                ParameterValue(encoding),
                ParameterValue(metadata),
            )
            id_to_idx[embedding["id"]] = len(id_to_idx)
        with self.tx() as cur:
            sql, params = get_sql(insert, self.parameter_format())
            # The returning clause does not guarantee order, so we need to do reorder
            # the results. https://www.sqlite.org/lang_returning.html
            sql = f"{sql} RETURNING seq_id, id"  # Pypika doesn't support RETURNING
            results = cur.execute(sql, params).fetchall()
            # Reorder the results
            seq_ids = [cast(SeqId, None)] * len(
                results
            )  # Lie to mypy: https://stackoverflow.com/questions/76694215/python-type-casting-when-preallocating-list
            embedding_records = []
            for seq_id, id in results:
                seq_ids[id_to_idx[id]] = seq_id
                submit_embedding_record = embeddings[id_to_idx[id]]
                # We allow notifying consumers out of order relative to one call to
                # submit_embeddings so we do not reorder the records before submitting them
                embedding_record = LogRecord(
                    log_offset=seq_id,
                    record=OperationRecord(
                        id=id,
                        embedding=submit_embedding_record["embedding"],
                        encoding=submit_embedding_record["encoding"],
                        metadata=submit_embedding_record["metadata"],
                        operation=submit_embedding_record["operation"],
                    ),
                )
                embedding_records.append(embedding_record)
            self._notify_all(topic_name, embedding_records)

            if self.config.get_parameter("automatically_purge").value:
                self.purge_log(collection_id)

            return seq_ids

    @trace_method("SqlEmbeddingsQueue.subscribe", OpenTelemetryGranularity.ALL)
    @override
    def subscribe(
        self,
        collection_id: UUID,
        consume_fn: ConsumerCallbackFn,
        start: Optional[SeqId] = None,
        end: Optional[SeqId] = None,
        id: Optional[UUID] = None,
    ) -> UUID:
        if not self._running:
            raise RuntimeError("Component not running")

        topic_name = create_topic_name(
            self._tenant, self._topic_namespace, collection_id
        )

        subscription_id = id or uuid.uuid4()
        start, end = self._validate_range(start, end)

        subscription = self.Subscription(
            subscription_id, topic_name, start, end, consume_fn
        )

        # Backfill first, so if it errors we do not add the subscription
        self._backfill(subscription)
        self._subscriptions[topic_name].add(subscription)

        return subscription_id

    @trace_method("SqlEmbeddingsQueue.unsubscribe", OpenTelemetryGranularity.ALL)
    @override
    def unsubscribe(self, subscription_id: UUID) -> None:
        for topic_name, subscriptions in self._subscriptions.items():
            for subscription in subscriptions:
                if subscription.id == subscription_id:
                    subscriptions.remove(subscription)
                    if len(subscriptions) == 0:
                        del self._subscriptions[topic_name]
                    return

    @override
    def min_seqid(self) -> SeqId:
        return -1

    @override
    def max_seqid(self) -> SeqId:
        return 2**63 - 1

    @property
    @trace_method("SqlEmbeddingsQueue.max_batch_size", OpenTelemetryGranularity.ALL)
    @override
    def max_batch_size(self) -> int:
        if self._max_batch_size is None:
            with self.tx() as cur:
                cur.execute("PRAGMA compile_options;")
                compile_options = cur.fetchall()

                for option in compile_options:
                    if "MAX_VARIABLE_NUMBER" in option[0]:
                        # The pragma returns a string like 'MAX_VARIABLE_NUMBER=999'
                        self._max_batch_size = int(option[0].split("=")[1]) // (
                            self.VARIABLES_PER_RECORD
                        )

                if self._max_batch_size is None:
                    # This value is the default for sqlite3 versions < 3.32.0
                    # It is the safest value to use if we can't find the pragma for some
                    # reason
                    self._max_batch_size = 999 // self.VARIABLES_PER_RECORD
        return self._max_batch_size

    @trace_method(
        "SqlEmbeddingsQueue._prepare_vector_encoding_metadata",
        OpenTelemetryGranularity.ALL,
    )
    def _prepare_vector_encoding_metadata(
        self, embedding: OperationRecord
    ) -> Tuple[Optional[bytes], Optional[str], Optional[str]]:
        if embedding["embedding"] is not None:
            encoding_type = cast(ScalarEncoding, embedding["encoding"])
            encoding = encoding_type.value
            embedding_bytes = encode_vector(embedding["embedding"], encoding_type)
        else:
            embedding_bytes = None
            encoding = None
        metadata = json.dumps(embedding["metadata"]) if embedding["metadata"] else None
        return embedding_bytes, encoding, metadata

    @trace_method("SqlEmbeddingsQueue._backfill", OpenTelemetryGranularity.ALL)
    def _backfill(self, subscription: Subscription) -> None:
        """Backfill the given subscription with any currently matching records in the
        DB"""
        t = Table("embeddings_queue")
        q = (
            self.querybuilder()
            .from_(t)
            .where(t.topic == ParameterValue(subscription.topic_name))
            .where(t.seq_id > ParameterValue(subscription.start))
            .where(t.seq_id <= ParameterValue(subscription.end))
            .select(t.seq_id, t.operation, t.id, t.vector, t.encoding, t.metadata)
            .orderby(t.seq_id)
        )
        with self.tx() as cur:
            sql, params = get_sql(q, self.parameter_format())
            cur.execute(sql, params)
            rows = cur.fetchall()
            for row in rows:
                if row[3]:
                    encoding = ScalarEncoding(row[4])
                    vector = decode_vector(row[3], encoding)
                else:
                    encoding = None
                    vector = None
                self._notify_one(
                    subscription,
                    [
                        LogRecord(
                            log_offset=row[0],
                            record=OperationRecord(
                                operation=_operation_codes_inv[row[1]],
                                id=row[2],
                                embedding=vector,
                                encoding=encoding,
                                metadata=json.loads(row[5]) if row[5] else None,
                            ),
                        )
                    ],
                )

    @trace_method("SqlEmbeddingsQueue._validate_range", OpenTelemetryGranularity.ALL)
    def _validate_range(
        self, start: Optional[SeqId], end: Optional[SeqId]
    ) -> Tuple[int, int]:
        """Validate and normalize the start and end SeqIDs for a subscription using this
        impl."""
        start = start or self._next_seq_id()
        end = end or self.max_seqid()
        if not isinstance(start, int) or not isinstance(end, int):
            raise TypeError("SeqIDs must be integers for sql-based EmbeddingsDB")
        if start >= end:
            raise ValueError(f"Invalid SeqID range: {start} to {end}")
        return start, end

    @trace_method("SqlEmbeddingsQueue._next_seq_id", OpenTelemetryGranularity.ALL)
    def _next_seq_id(self) -> int:
        """Get the next SeqID for this database."""
        t = Table("embeddings_queue")
        q = self.querybuilder().from_(t).select(functions.Max(t.seq_id))
        with self.tx() as cur:
            cur.execute(q.get_sql())
            return int(cur.fetchone()[0]) + 1

    @trace_method("SqlEmbeddingsQueue._notify_all", OpenTelemetryGranularity.ALL)
    def _notify_all(self, topic: str, embeddings: Sequence[LogRecord]) -> None:
        """Send a notification to each subscriber of the given topic."""
        if self._running:
            for sub in self._subscriptions[topic]:
                self._notify_one(sub, embeddings)

    @trace_method("SqlEmbeddingsQueue._notify_one", OpenTelemetryGranularity.ALL)
    def _notify_one(self, sub: Subscription, embeddings: Sequence[LogRecord]) -> None:
        """Send a notification to a single subscriber."""
        # Filter out any embeddings that are not in the subscription range
        should_unsubscribe = False
        filtered_embeddings = []
        for embedding in embeddings:
            if embedding["log_offset"] <= sub.start:
                continue
            if embedding["log_offset"] > sub.end:
                should_unsubscribe = True
                break
            filtered_embeddings.append(embedding)

        # Log errors instead of throwing them to preserve async semantics
        # for consistency between local and distributed configurations
        try:
            if len(filtered_embeddings) > 0:
                sub.callback(filtered_embeddings)
            if should_unsubscribe:
                self.unsubscribe(sub.id)
        except BaseException as e:
            logger.error(
                f"Exception occurred invoking consumer for subscription {sub.id.hex}"
                + f"to topic {sub.topic_name} %s",
                str(e),
            )
            if _called_from_test:
                raise e

    @cached_property
    def config(self) -> EmbeddingsQueueConfigurationInternal:
        t = Table("embeddings_queue_config")
        q = self.querybuilder().from_(t).select(t.config_json_str).limit(1)

        with self.tx() as cur:
            cur.execute(q.get_sql())
            result = cur.fetchone()

        if result is None:
            is_fresh_system = self._get_wal_size() == 0
            config = EmbeddingsQueueConfigurationInternal(
                [ConfigurationParameter("automatically_purge", is_fresh_system)]
            )
            self.set_config(config)
            return config

        return EmbeddingsQueueConfigurationInternal.from_json_str(result[0])

    def set_config(self, config: EmbeddingsQueueConfigurationInternal) -> None:
        with self.tx() as cur:
            cur.execute(
                """
                INSERT OR REPLACE INTO embeddings_queue_config (id, config_json_str)
                VALUES (?, ?)
            """,
                (
                    1,
                    config.to_json_str(),
                ),
            )

        # Invalidate the cached property
        try:
            del self.config
        except AttributeError:
            # Cached property hasn't been accessed yet
            pass

    def _get_wal_size(self) -> int:
        t = Table("embeddings_queue")
        q = self.querybuilder().from_(t).select(functions.Count("*"))

        with self.tx() as cur:
            cur.execute(q.get_sql())
            return int(cur.fetchone()[0])