Module livekit.plugins.anthropic
Anthropic plugin for LiveKit Agents
See https://docs.livekit.io/agents/integrations/llm/anthropic/ for more information.
Classes
class LLM (*,
model: str | ChatModels = 'claude-3-5-sonnet-20241022',
api_key: NotGivenOr[str] = NOT_GIVEN,
base_url: NotGivenOr[str] = NOT_GIVEN,
user: NotGivenOr[str] = NOT_GIVEN,
client: anthropic.AsyncClient | None = None,
top_k: NotGivenOr[int] = NOT_GIVEN,
max_tokens: NotGivenOr[int] = NOT_GIVEN,
temperature: NotGivenOr[float] = NOT_GIVEN,
parallel_tool_calls: NotGivenOr[bool] = NOT_GIVEN,
tool_choice: NotGivenOr[ToolChoice] = NOT_GIVEN,
caching: "NotGivenOr[Literal['ephemeral']]" = NOT_GIVEN)-
Expand source code
class LLM(llm.LLM): def __init__( self, *, model: str | ChatModels = "claude-3-5-sonnet-20241022", api_key: NotGivenOr[str] = NOT_GIVEN, base_url: NotGivenOr[str] = NOT_GIVEN, user: NotGivenOr[str] = NOT_GIVEN, client: anthropic.AsyncClient | None = None, top_k: NotGivenOr[int] = NOT_GIVEN, max_tokens: NotGivenOr[int] = NOT_GIVEN, temperature: NotGivenOr[float] = NOT_GIVEN, parallel_tool_calls: NotGivenOr[bool] = NOT_GIVEN, tool_choice: NotGivenOr[ToolChoice] = NOT_GIVEN, caching: NotGivenOr[Literal["ephemeral"]] = NOT_GIVEN, ) -> None: """ Create a new instance of Anthropic LLM. ``api_key`` must be set to your Anthropic API key, either using the argument or by setting the ``ANTHROPIC_API_KEY`` environmental variable. model (str | ChatModels): The model to use. Defaults to "claude-3-5-sonnet-20241022". api_key (str, optional): The Anthropic API key. Defaults to the ANTHROPIC_API_KEY environment variable. base_url (str, optional): The base URL for the Anthropic API. Defaults to None. user (str, optional): The user for the Anthropic API. Defaults to None. client (anthropic.AsyncClient | None): The Anthropic client to use. Defaults to None. temperature (float, optional): The temperature for the Anthropic API. Defaults to None. parallel_tool_calls (bool, optional): Whether to parallelize tool calls. Defaults to None. tool_choice (ToolChoice, optional): The tool choice for the Anthropic API. Defaults to "auto". caching (Literal["ephemeral"], optional): If set to "ephemeral", caching will be enabled for the system prompt, tools, and chat history. """ # noqa: E501 super().__init__() self._opts = _LLMOptions( model=model, user=user, temperature=temperature, parallel_tool_calls=parallel_tool_calls, tool_choice=tool_choice, caching=caching, top_k=top_k, max_tokens=max_tokens, ) anthropic_api_key = api_key if is_given(api_key) else os.environ.get("ANTHROPIC_API_KEY") if not anthropic_api_key: raise ValueError("Anthropic API key is required") self._client = anthropic.AsyncClient( api_key=anthropic_api_key, base_url=base_url if is_given(base_url) else None, http_client=httpx.AsyncClient( timeout=5.0, follow_redirects=True, limits=httpx.Limits( max_connections=1000, max_keepalive_connections=100, keepalive_expiry=120, ), ), ) def chat( self, *, chat_ctx: ChatContext, tools: list[FunctionTool] | None = None, conn_options: APIConnectOptions = DEFAULT_API_CONNECT_OPTIONS, parallel_tool_calls: NotGivenOr[bool] = NOT_GIVEN, tool_choice: NotGivenOr[ToolChoice] = NOT_GIVEN, extra_kwargs: NotGivenOr[dict[str, Any]] = NOT_GIVEN, ) -> LLMStream: extra = {} if is_given(extra_kwargs): extra.update(extra_kwargs) if is_given(self._opts.user): extra["user"] = self._opts.user if is_given(self._opts.temperature): extra["temperature"] = self._opts.temperature if is_given(self._opts.top_k): extra["top_k"] = self._opts.top_k extra["max_tokens"] = self._opts.max_tokens if is_given(self._opts.max_tokens) else 1024 if tools: extra["tools"] = to_fnc_ctx(tools, self._opts.caching) tool_choice = tool_choice if is_given(tool_choice) else self._opts.tool_choice if is_given(tool_choice): anthropic_tool_choice: dict[str, Any] | None = {"type": "auto"} if isinstance(tool_choice, dict) and tool_choice.get("type") == "function": anthropic_tool_choice = { "type": "tool", "name": tool_choice["function"]["name"], } elif isinstance(tool_choice, str): if tool_choice == "required": anthropic_tool_choice = {"type": "any"} elif tool_choice == "none": extra["tools"] = [] anthropic_tool_choice = None if anthropic_tool_choice is not None: parallel_tool_calls = ( parallel_tool_calls if is_given(parallel_tool_calls) else self._opts.parallel_tool_calls ) if is_given(parallel_tool_calls): anthropic_tool_choice["disable_parallel_tool_use"] = not parallel_tool_calls extra["tool_choice"] = anthropic_tool_choice anthropic_ctx, system_message = to_chat_ctx(chat_ctx, id(self), caching=self._opts.caching) if system_message: extra["system"] = [system_message] stream = self._client.messages.create( messages=anthropic_ctx, model=self._opts.model, stream=True, **extra, ) return LLMStream( self, anthropic_stream=stream, chat_ctx=chat_ctx, tools=tools or [], conn_options=conn_options, )Helper class that provides a standard way to create an ABC using inheritance.
Create a new instance of Anthropic LLM.
api_keymust be set to your Anthropic API key, either using the argument or by setting theANTHROPIC_API_KEYenvironmental variable.model (str | ChatModels): The model to use. Defaults to "claude-3-5-sonnet-20241022". api_key (str, optional): The Anthropic API key. Defaults to the ANTHROPIC_API_KEY environment variable. base_url (str, optional): The base URL for the Anthropic API. Defaults to None. user (str, optional): The user for the Anthropic API. Defaults to None. client (anthropic.AsyncClient | None): The Anthropic client to use. Defaults to None. temperature (float, optional): The temperature for the Anthropic API. Defaults to None. parallel_tool_calls (bool, optional): Whether to parallelize tool calls. Defaults to None. tool_choice (ToolChoice, optional): The tool choice for the Anthropic API. Defaults to "auto". caching (Literal["ephemeral"], optional): If set to "ephemeral", caching will be enabled for the system prompt, tools, and chat history.
Ancestors
- livekit.agents.llm.llm.LLM
- abc.ABC
- EventEmitter
- typing.Generic
Methods
def chat(self,
*,
chat_ctx: ChatContext,
tools: list[FunctionTool] | None = None,
conn_options: APIConnectOptions = APIConnectOptions(max_retry=3, retry_interval=2.0, timeout=10.0),
parallel_tool_calls: NotGivenOr[bool] = NOT_GIVEN,
tool_choice: NotGivenOr[ToolChoice] = NOT_GIVEN,
extra_kwargs: NotGivenOr[dict[str, Any]] = NOT_GIVEN) ‑> livekit.plugins.anthropic.llm.LLMStream-
Expand source code
def chat( self, *, chat_ctx: ChatContext, tools: list[FunctionTool] | None = None, conn_options: APIConnectOptions = DEFAULT_API_CONNECT_OPTIONS, parallel_tool_calls: NotGivenOr[bool] = NOT_GIVEN, tool_choice: NotGivenOr[ToolChoice] = NOT_GIVEN, extra_kwargs: NotGivenOr[dict[str, Any]] = NOT_GIVEN, ) -> LLMStream: extra = {} if is_given(extra_kwargs): extra.update(extra_kwargs) if is_given(self._opts.user): extra["user"] = self._opts.user if is_given(self._opts.temperature): extra["temperature"] = self._opts.temperature if is_given(self._opts.top_k): extra["top_k"] = self._opts.top_k extra["max_tokens"] = self._opts.max_tokens if is_given(self._opts.max_tokens) else 1024 if tools: extra["tools"] = to_fnc_ctx(tools, self._opts.caching) tool_choice = tool_choice if is_given(tool_choice) else self._opts.tool_choice if is_given(tool_choice): anthropic_tool_choice: dict[str, Any] | None = {"type": "auto"} if isinstance(tool_choice, dict) and tool_choice.get("type") == "function": anthropic_tool_choice = { "type": "tool", "name": tool_choice["function"]["name"], } elif isinstance(tool_choice, str): if tool_choice == "required": anthropic_tool_choice = {"type": "any"} elif tool_choice == "none": extra["tools"] = [] anthropic_tool_choice = None if anthropic_tool_choice is not None: parallel_tool_calls = ( parallel_tool_calls if is_given(parallel_tool_calls) else self._opts.parallel_tool_calls ) if is_given(parallel_tool_calls): anthropic_tool_choice["disable_parallel_tool_use"] = not parallel_tool_calls extra["tool_choice"] = anthropic_tool_choice anthropic_ctx, system_message = to_chat_ctx(chat_ctx, id(self), caching=self._opts.caching) if system_message: extra["system"] = [system_message] stream = self._client.messages.create( messages=anthropic_ctx, model=self._opts.model, stream=True, **extra, ) return LLMStream( self, anthropic_stream=stream, chat_ctx=chat_ctx, tools=tools or [], conn_options=conn_options, )
Inherited members
class LLMStream (llm: LLM,
*,
anthropic_stream: Awaitable[anthropic.AsyncStream[anthropic.types.RawMessageStreamEvent]],
chat_ctx: llm.ChatContext,
tools: list[FunctionTool],
conn_options: APIConnectOptions)-
Expand source code
class LLMStream(llm.LLMStream): def __init__( self, llm: LLM, *, anthropic_stream: Awaitable[anthropic.AsyncStream[anthropic.types.RawMessageStreamEvent]], chat_ctx: llm.ChatContext, tools: list[FunctionTool], conn_options: APIConnectOptions, ) -> None: super().__init__(llm, chat_ctx=chat_ctx, tools=tools, conn_options=conn_options) self._awaitable_anthropic_stream = anthropic_stream self._anthropic_stream: ( anthropic.AsyncStream[anthropic.types.RawMessageStreamEvent] | None ) = None # current function call that we're waiting for full completion (args are streamed) self._tool_call_id: str | None = None self._fnc_name: str | None = None self._fnc_raw_arguments: str | None = None self._request_id: str = "" self._ignoring_cot = False # ignore chain of thought self._input_tokens = 0 self._cache_creation_tokens = 0 self._cache_read_tokens = 0 self._output_tokens = 0 async def _run(self) -> None: retryable = True try: if not self._anthropic_stream: self._anthropic_stream = await self._awaitable_anthropic_stream async with self._anthropic_stream as stream: async for event in stream: chat_chunk = self._parse_event(event) if chat_chunk is not None: self._event_ch.send_nowait(chat_chunk) retryable = False self._event_ch.send_nowait( llm.ChatChunk( id=self._request_id, usage=llm.CompletionUsage( completion_tokens=self._output_tokens, prompt_tokens=self._input_tokens, total_tokens=self._input_tokens + self._output_tokens + self._cache_creation_tokens + self._cache_read_tokens, cache_creation_input_tokens=self._cache_creation_tokens, cache_read_input_tokens=self._cache_read_tokens, ), ) ) except anthropic.APITimeoutError as e: raise APITimeoutError(retryable=retryable) from e except anthropic.APIStatusError as e: raise APIStatusError( e.message, status_code=e.status_code, request_id=e.request_id, body=e.body, ) from e except Exception as e: raise APIConnectionError(retryable=retryable) from e def _parse_event(self, event: anthropic.types.RawMessageStreamEvent) -> llm.ChatChunk | None: if event.type == "message_start": self._request_id = event.message.id self._input_tokens = event.message.usage.input_tokens self._output_tokens = event.message.usage.output_tokens if event.message.usage.cache_creation_input_tokens: self._cache_creation_tokens = event.message.usage.cache_creation_input_tokens if event.message.usage.cache_read_input_tokens: self._cache_read_tokens = event.message.usage.cache_read_input_tokens elif event.type == "message_delta": self._output_tokens += event.usage.output_tokens elif event.type == "content_block_start": if event.content_block.type == "tool_use": self._tool_call_id = event.content_block.id self._fnc_name = event.content_block.name self._fnc_raw_arguments = "" elif event.type == "content_block_delta": delta = event.delta if delta.type == "text_delta": text = delta.text if self._tools is not None: # anthropic may inject COC when using functions if text.startswith("<thinking>"): self._ignoring_cot = True elif self._ignoring_cot and "</thinking>" in text: text = text.split("</thinking>")[-1] self._ignoring_cot = False if self._ignoring_cot: return None return llm.ChatChunk( id=self._request_id, delta=llm.ChoiceDelta(content=text, role="assistant"), ) elif delta.type == "input_json_delta": assert self._fnc_raw_arguments is not None self._fnc_raw_arguments += delta.partial_json elif event.type == "content_block_stop": if self._tool_call_id is not None: assert self._fnc_name is not None assert self._fnc_raw_arguments is not None chat_chunk = llm.ChatChunk( id=self._request_id, delta=llm.ChoiceDelta( role="assistant", tool_calls=[ llm.FunctionToolCall( arguments=self._fnc_raw_arguments or "", name=self._fnc_name or "", call_id=self._tool_call_id or "", ) ], ), ) self._tool_call_id = self._fnc_raw_arguments = self._fnc_name = None return chat_chunk return NoneHelper class that provides a standard way to create an ABC using inheritance.
Ancestors
- livekit.agents.llm.llm.LLMStream
- abc.ABC