kiarina-agi-text 2.7.0

Chat and text embedding abstractions for AI agents

kiarina-agi-text

English | 日本語

[!NOTE] What is this? Provides interchangeable chat models, chat logging, and text embeddings for AI agents.

Dependencies

Required Dependencies

Package	Version	License
kiarina-agi-base	>=2.6.0	MIT
kiarina-agi-data	>=2.6.0	MIT
kiarina-agi-file	>=2.6.0	MIT
kiarina-utils-app	>=2.4.0	MIT
kiarina-utils-common	>=2.3.0	MIT
kiarina-utils-file	>=2.3.1	MIT
LangChain	>=1.0.0,<2	MIT
langchain-core	>=1.0.0,<2	MIT
Pydantic	>=2.11.7	MIT
pydantic-settings	>=2.10.1	MIT
pydantic-settings-manager	>=3.2.0	MIT

Optional Dependencies

Package	Version	License	Extras
anthropic	>=0.84.0,<1	MIT	chat-provider-lc-anthropic chat-provider-lc-anthropic-vertex
google-genai	>=1.65.0,<3	Apache-2.0	chat-provider-lc-google-genai text-embedding-provider-google
kiarina-lib-google	>=2.3.1	MIT	chat-provider-lc-anthropic-vertex chat-provider-lc-google-genai text-embedding-provider-google
kiarina-lib-openai	>=2.3.1	MIT	chat-provider-lc-openai text-embedding-provider-openai
langchain-anthropic	>=1.0.0,<2	MIT	chat-provider-lc-anthropic chat-provider-lc-anthropic-vertex
langchain-google-genai	>=4.0.0,<5	MIT	chat-provider-lc-google-genai
langchain-google-vertexai	>=3.0.0,<4	MIT	chat-provider-lc-anthropic-vertex
langchain-openai	>=1.0.0,<2	MIT	chat-provider-lc-openai
NumPy	>=2.0	BSD-3-Clause	text-embedding-provider-google text-embedding-provider-mock text-embedding-provider-openai
openai	>=2.0.1,<3	Apache-2.0	chat-provider-lc-openai text-embedding-provider-openai
tiktoken	>=0.13.0	MIT	text-embedding-provider-openai
ulid-py	>=1.1.0	Apache-2.0	chat-provider-mock

The all Extra installs every optional dependency listed above.

Installation

pip install "kiarina-agi-text[all]"

Features

• Chat Models Select a model by name or alias and receive one response or a stream. Tool calls and image, audio, video, and PDF inputs and outputs are also supported.
• Text Embeddings Select a registered model and vectorize text through one API.

API Reference

kiarina.agi.chat_model

from kiarina.agi.chat_model import (
    ChatModel,
    ChatModelAlias,
    ChatModelConfig,
    ChatModelName,
    ChatModelSettings,
    ChatModelSpecifier,
    ChatOptions,
    chat_model_registry,
    invoke_chat,
    run_chat,
    settings_manager,
    stream_chat,
)

async def invoke_chat(
    messages: list[Message],
    *,
    tool_infos: list[ToolInfo] | None = None,
    chat_options: ChatOptions | None = None,
    cost_recorder: CostRecorder | None = None,
    run_context: RunContext,
) -> AIMessage: ...

async def run_chat(
    messages: list[Message],
    *,
    tool_infos: list[ToolInfo] | None = None,
    chat_options: ChatOptions | None = None,
    cost_recorder: CostRecorder | None = None,
    run_context: RunContext,
) -> AsyncIterator[AIMessageChunk | AIMessage]: ...

async def stream_chat(
    messages: list[Message],
    *,
    tool_infos: list[ToolInfo] | None = None,
    chat_options: ChatOptions | None = None,
    cost_recorder: CostRecorder | None = None,
    run_context: RunContext,
) -> AsyncIterator[AIMessageChunk | AIMessage]: ...

class ChatModel:
    def __init__(self, name: ChatModelName, config: ChatModelConfig) -> None: ...
    @property
    def provider_name(self) -> ChatProviderName: ...
    @property
    def provider_config(self) -> dict[str, Any]: ...
    @property
    def token_scale_factor(self) -> float: ...
    @property
    def provider(self) -> ChatProvider: ...
    def get_capabilities(self) -> ChatCapabilities: ...
    async def run(
        self,
        messages: list[Message],
        *,
        tool_infos: list[ToolInfo] | None = None,
        tool_choice: ToolChoice | None = None,
        parallel_tool_calls: bool | None = None,
        streaming: bool | None = None,
        cost_recorder: CostRecorder | None = None,
        run_context: RunContext,
    ) -> AsyncIterator[AIMessageChunk | AIMessage]: ...

class ChatModelConfig(BaseModel):
    provider_name: ChatProviderName
    provider_config: dict[str, Any] = {}
    token_scale_factor: float = 1.0
    visible: bool = True

class ChatOptions(TypedDict, total=False):
    chat_model: ChatModel | ChatModelSpecifier | None
    tool_choice: ToolChoice | None
    parallel_tool_calls: bool | None
    streaming: bool | None

ChatModelName: TypeAlias = str
ChatModelAlias: TypeAlias = str
ChatModelSpecifier: TypeAlias = ChatModelName | ChatModelAlias | str

chat_model_registry: ObjectRegistry[ChatModel, ChatModelConfig]
settings_manager: SettingsManager[ChatModelSettings]

ChatModelSettings is a Pydantic settings class with default, aliases, presets, and customs fields.

kiarina.agi.text_embedding_model

from kiarina.agi.text_embedding_model import (
    TextEmbeddingModel,
    TextEmbeddingModelAlias,
    TextEmbeddingModelConfig,
    TextEmbeddingModelName,
    TextEmbeddingModelSettings,
    TextEmbeddingModelSpecifier,
    TextEmbeddingOptions,
    embed_text,
    settings_manager,
    text_embedding_model_registry,
)

async def embed_text(
    text: str,
    *,
    text_embedding_options: TextEmbeddingOptions | None = None,
    cost_recorder: CostRecorder | None = None,
    run_context: RunContext,
) -> Embedding: ...

class TextEmbeddingModel:
    def __init__(
        self, name: TextEmbeddingModelName, config: TextEmbeddingModelConfig
    ) -> None: ...
    @property
    def provider_name(self) -> TextEmbeddingProviderName: ...
    @property
    def provider_config(self) -> dict[str, Any]: ...
    @property
    def provider(self) -> TextEmbeddingProvider: ...
    def get_space(self) -> EmbeddingSpace: ...
    async def embed(
        self,
        text: str,
        *,
        cost_recorder: CostRecorder | None = None,
        run_context: RunContext,
    ) -> Embedding: ...

class TextEmbeddingModelConfig(BaseModel):
    provider_name: TextEmbeddingProviderName
    provider_config: dict[str, Any] = {}
    visible: bool = True

class TextEmbeddingOptions(TypedDict, total=False):
    text_embedding_model: TextEmbeddingModel | TextEmbeddingModelSpecifier | None

TextEmbeddingModelName: TypeAlias = str
TextEmbeddingModelAlias: TypeAlias = str
TextEmbeddingModelSpecifier: TypeAlias = (
    TextEmbeddingModelName | TextEmbeddingModelAlias | str
)
text_embedding_model_registry: ObjectRegistry[
    TextEmbeddingModel, TextEmbeddingModelConfig
]
settings_manager: SettingsManager[TextEmbeddingModelSettings]

TextEmbeddingModelSettings has default, aliases, presets, and customs fields.