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Ollama

OllamaMultiModal #

Bases: MultiModalLLM

Source code in llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-ollama/llama_index/multi_modal_llms/ollama/base.py 
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class OllamaMultiModal(MultiModalLLM):
    base_url: str = Field(
        default="http://localhost:11434",
        description="Base url the model is hosted under.",
    )
    model: str = Field(description="The MultiModal Ollama model to use.")
    temperature: float = Field(
        default=0.75,
        description="The temperature to use for sampling.",
        gte=0.0,
        lte=1.0,
    )
    context_window: int = Field(
        default=DEFAULT_CONTEXT_WINDOW,
        description="The maximum number of context tokens for the model.",
        gt=0,
    )
    request_timeout: Optional[float] = Field(
        description="The timeout for making http request to Ollama API server",
    )
    additional_kwargs: Dict[str, Any] = Field(
        default_factory=dict,
        description="Additional model parameters for the Ollama API.",
    )
    _client: Client = PrivateAttr()

    def __init__(self, **kwargs: Any) -> None:
        """Init params and ollama client."""
        super().__init__(**kwargs)
        self._client = Client(host=self.base_url, timeout=self.request_timeout)

    @classmethod
    def class_name(cls) -> str:
        return "Ollama_multi_modal_llm"

    @property
    def metadata(self) -> MultiModalLLMMetadata:
        """LLM metadata."""
        return MultiModalLLMMetadata(
            context_window=self.context_window,
            num_output=DEFAULT_NUM_OUTPUTS,
            model_name=self.model,
            is_chat_model=True,  # Ollama supports chat API for all models
        )

    @property
    def _model_kwargs(self) -> Dict[str, Any]:
        base_kwargs = {
            "temperature": self.temperature,
            "num_ctx": self.context_window,
        }
        return {
            **base_kwargs,
            **self.additional_kwargs,
        }

    def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
        """Chat."""
        ollama_messages = _messages_to_dicts(messages)
        response = self._client.chat(
            model=self.model, messages=ollama_messages, stream=False, **kwargs
        )
        return ChatResponse(
            message=ChatMessage(
                content=response["message"]["content"],
                role=MessageRole(response["message"]["role"]),
                additional_kwargs=get_additional_kwargs(response, ("message",)),
            ),
            raw=response["message"],
            additional_kwargs=get_additional_kwargs(response, ("message",)),
        )

    def stream_chat(
        self, messages: Sequence[ChatMessage], **kwargs: Any
    ) -> ChatResponseGen:
        """Stream chat."""
        ollama_messages = _messages_to_dicts(messages)
        response = self._client.chat(
            model=self.model, messages=ollama_messages, stream=True, **kwargs
        )
        text = ""
        for chunk in response:
            if "done" in chunk and chunk["done"]:
                break
            message = chunk["message"]
            delta = message.get("content")
            text += delta
            yield ChatResponse(
                message=ChatMessage(
                    content=text,
                    role=MessageRole(message["role"]),
                    additional_kwargs=get_additional_kwargs(
                        message, ("content", "role")
                    ),
                ),
                delta=delta,
                raw=message,
                additional_kwargs=get_additional_kwargs(chunk, ("message",)),
            )

    def complete(
        self,
        prompt: str,
        image_documents: Sequence[ImageDocument],
        formatted: bool = False,
        **kwargs: Any,
    ) -> CompletionResponse:
        """Complete."""
        response = self._client.generate(
            model=self.model,
            prompt=prompt,
            images=image_documents_to_base64(image_documents),
            stream=False,
            options=self._model_kwargs,
            **kwargs,
        )
        return CompletionResponse(
            text=response["response"],
            raw=response,
            additional_kwargs=get_additional_kwargs(response, ("response",)),
        )

    def stream_complete(
        self,
        prompt: str,
        image_documents: Sequence[ImageDocument],
        formatted: bool = False,
        **kwargs: Any,
    ) -> CompletionResponseGen:
        """Stream complete."""
        response = self._client.generate(
            model=self.model,
            prompt=prompt,
            images=image_documents_to_base64(image_documents),
            stream=True,
            options=self._model_kwargs,
            **kwargs,
        )
        text = ""
        for chunk in response:
            if "done" in chunk and chunk["done"]:
                break
            delta = chunk.get("response")
            text += delta
            yield CompletionResponse(
                text=str(chunk["response"]),
                delta=delta,
                raw=chunk,
                additional_kwargs=get_additional_kwargs(chunk, ("response",)),
            )

    async def acomplete(
        self, prompt: str, image_documents: Sequence[ImageDocument], **kwargs: Any
    ) -> CompletionResponse:
        raise NotImplementedError("Ollama does not support async completion.")

    async def achat(
        self, messages: Sequence[ChatMessage], **kwargs: Any
    ) -> ChatResponse:
        raise NotImplementedError("Ollama does not support async chat.")

    async def astream_complete(
        self, prompt: str, image_documents: Sequence[ImageDocument], **kwargs: Any
    ) -> CompletionResponseAsyncGen:
        raise NotImplementedError("Ollama does not support async streaming completion.")

    async def astream_chat(
        self, messages: Sequence[ChatMessage], **kwargs: Any
    ) -> ChatResponseAsyncGen:
        raise NotImplementedError("Ollama does not support async streaming chat.")

metadata property #

metadata: MultiModalLLMMetadata

LLM metadata.

chat #

chat(messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse

Chat.

Source code in llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-ollama/llama_index/multi_modal_llms/ollama/base.py 
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def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
    """Chat."""
    ollama_messages = _messages_to_dicts(messages)
    response = self._client.chat(
        model=self.model, messages=ollama_messages, stream=False, **kwargs
    )
    return ChatResponse(
        message=ChatMessage(
            content=response["message"]["content"],
            role=MessageRole(response["message"]["role"]),
            additional_kwargs=get_additional_kwargs(response, ("message",)),
        ),
        raw=response["message"],
        additional_kwargs=get_additional_kwargs(response, ("message",)),
    )

stream_chat #

stream_chat(messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponseGen

Stream chat.

Source code in llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-ollama/llama_index/multi_modal_llms/ollama/base.py 
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def stream_chat(
    self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponseGen:
    """Stream chat."""
    ollama_messages = _messages_to_dicts(messages)
    response = self._client.chat(
        model=self.model, messages=ollama_messages, stream=True, **kwargs
    )
    text = ""
    for chunk in response:
        if "done" in chunk and chunk["done"]:
            break
        message = chunk["message"]
        delta = message.get("content")
        text += delta
        yield ChatResponse(
            message=ChatMessage(
                content=text,
                role=MessageRole(message["role"]),
                additional_kwargs=get_additional_kwargs(
                    message, ("content", "role")
                ),
            ),
            delta=delta,
            raw=message,
            additional_kwargs=get_additional_kwargs(chunk, ("message",)),
        )

complete #

complete(prompt: str, image_documents: Sequence[ImageDocument], formatted: bool = False, **kwargs: Any) -> CompletionResponse

Complete.

Source code in llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-ollama/llama_index/multi_modal_llms/ollama/base.py 
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def complete(
    self,
    prompt: str,
    image_documents: Sequence[ImageDocument],
    formatted: bool = False,
    **kwargs: Any,
) -> CompletionResponse:
    """Complete."""
    response = self._client.generate(
        model=self.model,
        prompt=prompt,
        images=image_documents_to_base64(image_documents),
        stream=False,
        options=self._model_kwargs,
        **kwargs,
    )
    return CompletionResponse(
        text=response["response"],
        raw=response,
        additional_kwargs=get_additional_kwargs(response, ("response",)),
    )

stream_complete #

stream_complete(prompt: str, image_documents: Sequence[ImageDocument], formatted: bool = False, **kwargs: Any) -> CompletionResponseGen

Stream complete.

Source code in llama-index-integrations/multi_modal_llms/llama-index-multi-modal-llms-ollama/llama_index/multi_modal_llms/ollama/base.py 
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def stream_complete(
    self,
    prompt: str,
    image_documents: Sequence[ImageDocument],
    formatted: bool = False,
    **kwargs: Any,
) -> CompletionResponseGen:
    """Stream complete."""
    response = self._client.generate(
        model=self.model,
        prompt=prompt,
        images=image_documents_to_base64(image_documents),
        stream=True,
        options=self._model_kwargs,
        **kwargs,
    )
    text = ""
    for chunk in response:
        if "done" in chunk and chunk["done"]:
            break
        delta = chunk.get("response")
        text += delta
        yield CompletionResponse(
            text=str(chunk["response"]),
            delta=delta,
            raw=chunk,
            additional_kwargs=get_additional_kwargs(chunk, ("response",)),
        )