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Update the LLM API examples to support multiple APIs (#2629)

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* Update the LLM API examples to support multiple APIs

* Update docs/core/llm/index.md

Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>

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Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>
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docs/core/llm/index.md
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@ -13,11 +13,11 @@ The Assist API is equivalent to the capabilities and exposed entities that are a
## Supporting LLM APIs ## Supporting LLM APIs
The LLM API needs to be integrated in two places in your integration. Users need to be able to configure which API should be used, and the tools offered by the API should be passed to the LLM when interacting with it. The LLM API needs to be integrated in two places in your integration. Users need to be able to configure which APIs should be used, and the tools offered by the APIs should be passed to the LLM when interacting with it.
### Options flow ### Options flow
The chosen API should be stored in the config entry options. It should hold a string reference to the API ID. If no API is selected, the key must be omitted. The chosen API should be stored in the config entry options. It should hold a string or list of selected API IDs, if any. If no API is selected, the key must be omitted.
In your options flow, you should offer a selector to the user to pick which API should be used. In your options flow, you should offer a selector to the user to pick which API should be used.
@ -41,41 +41,27 @@ def async_get_options_schema(
) -> vol.Schema: ) -> vol.Schema:
"""Return the options schema.""" """Return the options schema."""
apis: list[SelectOptionDict] = [ apis: list[SelectOptionDict] = [
SelectOptionDict(
label="No control",
value="none",
)
]
apis.extend(
SelectOptionDict( SelectOptionDict(
label=api.name, label=api.name,
value=api.id, value=api.id,
) )
for api in llm.async_get_apis(hass) for api in llm.async_get_apis(hass)
) ]
return vol.Schema( return vol.Schema(
{ {
vol.Optional( vol.Optional(
CONF_LLM_HASS_API, CONF_LLM_HASS_API,
description={"suggested_value": options.get(CONF_LLM_HASS_API)}, description={"suggested_value": options.get(CONF_LLM_HASS_API)},
default="none", ): SelectSelector(SelectSelectorConfig(options=apis, multiple=True)),
): SelectSelector(SelectSelectorConfig(options=apis)),
} }
) )
``` ```
When processing the options, make sure to remove the key if the user selected `none` before storing the options.
```python
if user_input[CONF_LLM_HASS_API] == "none":
user_input.pop(CONF_LLM_HASS_API)
return self.async_create_entry(title="", data=user_input)
```
### Fetching tools ### Fetching tools
When interacting with the LLM, you should fetch the tools from the selected API and pass them to the LLM together with the extra prompt provided by the API. When interacting with the LLM, the provided `ChatLog` will make any selected tools available from the selected API and the conversation entity should pass them to the LLM together with the extra prompt provided by the API.
```python ```python
from homeassistant.const import CONF_LLM_HASS_API from homeassistant.const import CONF_LLM_HASS_API
@ -92,82 +78,63 @@ class MyConversationEntity(conversation.ConversationEntity):
... ...
async def async_process( async def _async_handle_message(
self, user_input: conversation.ConversationInput self,
user_input: conversation.ConversationInput,
chat_log: conversation.ChatLog,
) -> conversation.ConversationResult: ) -> conversation.ConversationResult:
"""Process the user input.""" """Call the API."""
intent_response = intent.IntentResponse(language=user_input.language)
llm_api: llm.API | None = None
tools: list[dict[str, Any]] | None = None
if self.entry.options.get(CONF_LLM_HASS_API): try:
try: await chat_log.async_update_llm_data(
llm_api = await llm.async_get_api( DOMAIN,
self.hass, user_input,
self.entry.options[CONF_LLM_HASS_API], self.entry.options.get(CONF_LLM_HASS_API),
llm.LLMContext( self.entry.options.get(CONF_PROMPT),
platform=DOMAIN, )
context=user_input.context, except conversation.ConverseError as err:
user_prompt=user_input.text, return err.as_conversation_result()
language=user_input.language,
assistant=conversation.DOMAIN, tools: list[dict[str, Any]] | None = None
device_id=user_input.device_id, if chat_log.llm_api:
),
)
except HomeAssistantError as err:
LOGGER.error("Error getting LLM API: %s", err)
intent_response.async_set_error(
intent.IntentResponseErrorCode.UNKNOWN,
f"Error preparing LLM API: {err}",
)
return conversation.ConversationResult(
response=intent_response, conversation_id=user_input.conversation_id
)
tools = [ tools = [
_format_tool(tool) # TODO format the tools as your LLM expects _format_tool(tool) # TODO format the tools as your LLM expects
for tool in llm_api.tools for tool in chat_log.llm_api.tools
] ]
if llm_api: messages = [
api_prompt = llm_api.api_prompt m
for content in chat_log.content
else: for m in _convert_content(content) # TODO format messages
api_prompt = llm.async_render_no_api_prompt(self.hass) ]
prompt = "\n".join((user_prompt, api_prompt))
# Interact with LLM and pass tools # Interact with LLM and pass tools
request = user_input.text request = user_input.text
for _iteration in range(10): for _iteration in range(10):
response = ... # Send request to LLM and get response, include tools response = ... # Send request to LLM and get streaming response
if not response.tool_call: messages.extend(
[
_convert_content(content) # TODO format messages
async for content in chat_log.async_add_delta_content_stream(
user_input.agent_id, _transform_stream(response) # TODO call tools and stream responses
)
]
)
if not chat_log.unresponded_tool_results:
break break
LOGGER.debug( # Send the final response to the user
"Tool call: %s(%s)", intent_response = intent.IntentResponse(language=user_input.language)
response.tool_call.function.name, intent_response.async_set_speech(chat_log.content[-1].content or "")
response.tool_call.function.arguments, return conversation.ConversationResult(
) response=intent_response,
tool_input = llm.ToolInput( conversation_id=chat_log.conversation_id,
tool_name=response.tool_call.function.name, continue_conversation=chat_log.continue_conversation,
tool_args=json.loads(response.tool_call.function.arguments), )
)
try:
tool_response = await llm_api.async_call_tool(tool_input)
except (HomeAssistantError, vol.Invalid) as e:
tool_response = {"error": type(e).__name__}
if str(e):
tool_response["error_text"] = str(e)
LOGGER.debug("Tool response: %s", tool_response)
response = tool_response
``` ```
## Best practices
If your conversation entity allows the user to maintain conversation history using the `conversation_id`, make sure to re-generate the prompt for each interaction and override it in the history that is passed for the follow-up command. This allows the user to always be able to query the latest state of the home.
## Creating your own API ## Creating your own API
To create your own API, you need to create a class that inherits from `API` and implement the `async_get_tools` method. The `async_get_tools` method should return a list of `Tool` objects that represent the functionality that you want to expose to the LLM. To create your own API, you need to create a class that inherits from `API` and implement the `async_get_tools` method. The `async_get_tools` method should return a list of `Tool` objects that represent the functionality that you want to expose to the LLM.