init

__all__ = ['MessageMetadata', 'MessageSearchResult', 'MessageSearchResults', 'MessageSearchUnitOfWork', 'add_all_user_messages', 'add_messages', 'delete_all_user_messages', 'search_messages', 'stored_message_stats'] module-attribute

MessageMetadata

Bases: AbstractQdrantMetadata

conversation_uid instance-attribute

embed_type = MessageEmbeddingType.FULL_MESSAGE class-attribute instance-attribute

message_creation_time instance-attribute

message_type instance-attribute

message_uid instance-attribute

model_config = ConfigDict(extra='ignore') class-attribute instance-attribute

pk_id instance-attribute

snippet_index = None class-attribute instance-attribute

thread_uid instance-attribute

tool_call_ids instance-attribute

tool_names instance-attribute

user_clerk_id instance-attribute

as_content()

from_message(message, user_id, conversation_uid, thread_uid) classmethod

from_messages(messages, user_id, conversation_uid, thread_uid) classmethod

MessageSearchResult dataclass

content instance-attribute

metadata instance-attribute

qdrant_id instance-attribute

score instance-attribute

__init__(qdrant_id, content, metadata, score)

as_artifact()

as_content()

MessageSearchResults dataclass

results instance-attribute

__init__(results)

__post_init__()

as_artifact()

as_content()

MessageSearchUnitOfWork

collection_name = COLLECTION_NAME class-attribute

qdrant = qdrant instance-attribute

read_only_uow property

__init__(qdrant, read_only_uow)

add_all_user_messages(uow, user_id, wait=False, updatable=None) async

Add users messages to qdrant (loading from db).

RETURNS	DESCRIPTION
int	Number of messages added.

add_messages(uow, messages, user_id, conversation_uid, thread_uid, wait=False, text_splitter=None) async

Add new messages to the vectorstore.

PARAMETER	DESCRIPTION
uow	The unit of work for the message search service. TYPE: MessageSearchUnitOfWork
messages	The messages to add. TYPE: list[MessageSchema]
user_id	The user the message belongs to. TYPE: UserID | None
conversation_uid	The conversation the message belongs to. TYPE: UUID | None
thread_uid	The thread that the message belongs to. TYPE: UUID | None
wait	Whether to wait for the operation to complete (defaults to False). TYPE: bool DEFAULT: False
text_splitter	The text splitter to use for splitting long messages into snippets. TYPE: TextSplitter | None DEFAULT: None

delete_all_user_messages(uow, user_id, wait=True) async

Delete all messages for a user from the vectorstore.

search_messages(uow, query_text, user_id, conversation_uid=None, thread_uid=None, embed_type=None, max_results=None, max_message_age=None, message_types=None, result_index_offset=None, include_long_content=False) async

Search for message contents/snippets in the vectorstore.

Optionally, filter or customize the search.

PARAMETER	DESCRIPTION
uow	Search unit of work. TYPE: MessageSearchUnitOfWork
query_text	Text to search for (by embedding and doing similarity search). TYPE: str
user_id	User id to filter by. TYPE: UserID | None
conversation_uid	Conversation uid to filter by. TYPE: list[UUID] | UUID | None DEFAULT: None
thread_uid	Thread uid to filter by. (Assistant run) TYPE: UUID | None DEFAULT: None
embed_type	Type of embedding to search for (default None means all types). TYPE: MessageEmbeddingType | None DEFAULT: None
max_results	Max number of results to return. TYPE: int | None DEFAULT: None
max_message_age	Maximum age of messages to search. TYPE: timedelta | None DEFAULT: None
message_types	Types of messages to search. TYPE: list[MessageRoles] | None DEFAULT: None
result_index_offset	Offset of the first result to return. TYPE: int | None DEFAULT: None
include_long_content	Whether to also search messages with long content (EmbeddedContentLocation). TYPE: bool DEFAULT: False

RETURNS	DESCRIPTION
MessageSearchResults	MessageSearchResults (has list of MessageSearchResult with methods to transform)

stored_message_stats(uow, user_id, conversation_uid=None, thread_uid=None) async

Get the number of messages stored for a user.