Index
MultiModalLLMMetadata #
Bases: BaseModel
Parameters:
Name | Type | Description | Default |
---|---|---|---|
context_window
|
int | None
|
Total number of tokens the model can be input when generating a response. |
3900
|
num_output
|
int | None
|
Number of tokens the model can output when generating a response. |
256
|
num_input_files
|
int | None
|
Number of input files the model can take when generating a response. |
10
|
is_function_calling_model
|
bool | None
|
Set True if the model supports function calling messages, similar to OpenAI's function calling API. For example, converting 'Email Anya to see if she wants to get coffee next Friday' to a function call like |
False
|
model_name
|
str
|
The model's name used for logging, testing, and sanity checking. For some models this can be automatically discerned. For other models, like locally loaded models, this must be manually specified. |
'unknown'
|
is_chat_model
|
bool
|
Set True if the model exposes a chat interface (i.e. can be passed a sequence of messages, rather than text), like OpenAI's /v1/chat/completions endpoint. |
False
|
Source code in llama-index-core/llama_index/core/multi_modal_llms/base.py
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|
MultiModalLLM #
Bases: ChainableMixin
, BaseComponent
, DispatcherSpanMixin
Multi-Modal LLM interface.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
callback_manager
|
CallbackManager
|
Callback manager that handles callbacks for events within LlamaIndex. The callback manager provides a way to call handlers on event starts/ends. Additionally, the callback manager traces the current stack of events. It does this by using a few key attributes. - trace_stack - The current stack of events that have not ended yet. When an event ends, it's removed from the stack. Since this is a contextvar, it is unique to each thread/task. - trace_map - A mapping of event ids to their children events. On the start of events, the bottom of the trace stack is used as the current parent event for the trace map. - trace_id - A simple name for the current trace, usually denoting the entrypoint (query, index_construction, insert, etc.) Args: handlers (List[BaseCallbackHandler]): list of handlers to use. Usage: with callback_manager.event(CBEventType.QUERY) as event: event.on_start(payload={key, val}) ... event.on_end(payload={key, val}) |
<dynamic>
|
Source code in llama-index-core/llama_index/core/multi_modal_llms/base.py
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|
complete
abstractmethod
#
complete(prompt: str, image_documents: List[Union[ImageNode, ImageBlock]], **kwargs: Any) -> CompletionResponse
Completion endpoint for Multi-Modal LLM.
Source code in llama-index-core/llama_index/core/multi_modal_llms/base.py
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|
stream_complete
abstractmethod
#
stream_complete(prompt: str, image_documents: List[Union[ImageNode, ImageBlock]], **kwargs: Any) -> CompletionResponseGen
Streaming completion endpoint for Multi-Modal LLM.
Source code in llama-index-core/llama_index/core/multi_modal_llms/base.py
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|
chat
abstractmethod
#
chat(messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse
Chat endpoint for Multi-Modal LLM.
Source code in llama-index-core/llama_index/core/multi_modal_llms/base.py
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|
stream_chat
abstractmethod
#
stream_chat(messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponseGen
Stream chat endpoint for Multi-Modal LLM.
Source code in llama-index-core/llama_index/core/multi_modal_llms/base.py
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|
acomplete
abstractmethod
async
#
acomplete(prompt: str, image_documents: List[Union[ImageNode, ImageBlock]], **kwargs: Any) -> CompletionResponse
Async completion endpoint for Multi-Modal LLM.
Source code in llama-index-core/llama_index/core/multi_modal_llms/base.py
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|
astream_complete
abstractmethod
async
#
astream_complete(prompt: str, image_documents: List[Union[ImageNode, ImageBlock]], **kwargs: Any) -> CompletionResponseAsyncGen
Async streaming completion endpoint for Multi-Modal LLM.
Source code in llama-index-core/llama_index/core/multi_modal_llms/base.py
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|
achat
abstractmethod
async
#
achat(messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse
Async chat endpoint for Multi-Modal LLM.
Source code in llama-index-core/llama_index/core/multi_modal_llms/base.py
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|
astream_chat
abstractmethod
async
#
astream_chat(messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponseAsyncGen
Async streaming chat endpoint for Multi-Modal LLM.
Source code in llama-index-core/llama_index/core/multi_modal_llms/base.py
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|
BaseMultiModalComponent #
Bases: QueryComponent
Base LLM component.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
multi_modal_llm
|
MultiModalLLM
|
LLM |
required |
streaming
|
bool
|
Streaming mode |
False
|
Source code in llama-index-core/llama_index/core/multi_modal_llms/base.py
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|
set_callback_manager #
set_callback_manager(callback_manager: Any) -> None
Set callback manager.
Source code in llama-index-core/llama_index/core/multi_modal_llms/base.py
207 208 |
|
MultiModalCompleteComponent #
Bases: BaseMultiModalComponent
Multi-modal completion component.
Source code in llama-index-core/llama_index/core/multi_modal_llms/base.py
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|