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Openai like

OpenAILike #

Bases: OpenAI

OpenaAILike LLM.

OpenAILike is a thin wrapper around the OpenAI model that makes it compatible with 3rd party tools that provide an openai-compatible api.

Parameters:

Name Type Description Default
model str

The model to use for the api.

 DEFAULT_OPENAI_MODEL
api_base str

The base url to use for the api. Defaults to "https://api.openai.com/v1".

 None
is_chat_model bool

Whether the model uses the chat or completion endpoint. Defaults to False.

 required
is_function_calling_model bool

Whether the model supports OpenAI function calling/tools over the API. Defaults to False.

 required
api_key str

The api key to use for the api. Set this to some random string if your API does not require an api key.

 None
context_window int

The context window to use for the api. Set this to your model's context window for the best experience. Defaults to 3900.

 required
max_tokens int

The max number of tokens to generate. Defaults to None.

 None
temperature float

The temperature to use for the api. Default is 0.1.

 DEFAULT_TEMPERATURE
additional_kwargs dict

Specify additional parameters to the request body.

 None
max_retries int

How many times to retry the API call if it fails. Defaults to 3.

 3
timeout float

How long to wait, in seconds, for an API call before failing. Defaults to 60.0.

 60.0
reuse_client bool

Reuse the OpenAI client between requests. Defaults to True.

 True
default_headers dict

Override the default headers for API requests. Defaults to None.

 None
http_client Client

Pass in your own httpx.Client instance. Defaults to None.

 None
async_http_client AsyncClient

Pass in your own httpx.AsyncClient instance. Defaults to None.

 None

Examples:

pip install llama-index-llms-openai-like

from llama_index.llms.openai_like import OpenAILike  llm = OpenAILike(     model="my model",     api_base="https://hostname.com/v1",     api_key="fake",     context_window=128000,     is_chat_model=True,     is_function_calling_model=False, )  response = llm.complete("Hello World!") print(str(response))
Source code in llama-index-integrations/llms/llama-index-llms-openai-like/llama_index/llms/openai_like/base.py 
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class OpenAILike(OpenAI):     """     OpenaAILike LLM.      OpenAILike is a thin wrapper around the OpenAI model that makes it compatible with     3rd party tools that provide an openai-compatible api.      Args:         model (str):             The model to use for the api.         api_base (str):             The base url to use for the api.             Defaults to "https://api.openai.com/v1".         is_chat_model (bool):             Whether the model uses the chat or completion endpoint.             Defaults to False.         is_function_calling_model (bool):             Whether the model supports OpenAI function calling/tools over the API.             Defaults to False.         api_key (str):             The api key to use for the api.             Set this to some random string if your API does not require an api key.         context_window (int):             The context window to use for the api. Set this to your model's context window for the best experience.             Defaults to 3900.         max_tokens (int):             The max number of tokens to generate.             Defaults to None.         temperature (float):             The temperature to use for the api.             Default is 0.1.         additional_kwargs (dict):             Specify additional parameters to the request body.         max_retries (int):             How many times to retry the API call if it fails.             Defaults to 3.         timeout (float):             How long to wait, in seconds, for an API call before failing.             Defaults to 60.0.         reuse_client (bool):             Reuse the OpenAI client between requests.             Defaults to True.         default_headers (dict):             Override the default headers for API requests.             Defaults to None.         http_client (httpx.Client):             Pass in your own httpx.Client instance.             Defaults to None.         async_http_client (httpx.AsyncClient):             Pass in your own httpx.AsyncClient instance.             Defaults to None.      Examples:         `pip install llama-index-llms-openai-like`          ```python         from llama_index.llms.openai_like import OpenAILike          llm = OpenAILike(             model="my model",             api_base="https://hostname.com/v1",             api_key="fake",             context_window=128000,             is_chat_model=True,             is_function_calling_model=False,         )          response = llm.complete("Hello World!")         print(str(response))         ```      """      context_window: int = Field(         default=DEFAULT_CONTEXT_WINDOW,         description=LLMMetadata.model_fields["context_window"].description,     )     is_chat_model: bool = Field(         default=False,         description=LLMMetadata.model_fields["is_chat_model"].description,     )     is_function_calling_model: bool = Field(         default=False,         description=LLMMetadata.model_fields["is_function_calling_model"].description,     )     tokenizer: Union[Tokenizer, str, None] = Field(         default=None,         description=(             "An instance of a tokenizer object that has an encode method, or the name"             " of a tokenizer model from Hugging Face. If left as None, then this"             " disables inference of max_tokens."         ),     )      @property     def metadata(self) -> LLMMetadata:         return LLMMetadata(             context_window=self.context_window,             num_output=self.max_tokens or -1,             is_chat_model=self.is_chat_model,             is_function_calling_model=self.is_function_calling_model,             model_name=self.model,         )      @property     def _tokenizer(self) -> Optional[Tokenizer]:         if isinstance(self.tokenizer, str):             return AutoTokenizer.from_pretrained(self.tokenizer)         return self.tokenizer      @classmethod     def class_name(cls) -> str:         return "OpenAILike"      def complete(         self, prompt: str, formatted: bool = False, **kwargs: Any     ) -> CompletionResponse:         """Complete the prompt."""         if not formatted:             prompt = self.completion_to_prompt(prompt)          return super().complete(prompt, **kwargs)      def stream_complete(         self, prompt: str, formatted: bool = False, **kwargs: Any     ) -> CompletionResponseGen:         """Stream complete the prompt."""         if not formatted:             prompt = self.completion_to_prompt(prompt)          return super().stream_complete(prompt, **kwargs)      def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:         """Chat with the model."""         if not self.metadata.is_chat_model:             prompt = self.messages_to_prompt(messages)             completion_response = self.complete(prompt, formatted=True, **kwargs)             return completion_response_to_chat_response(completion_response)          return super().chat(messages, **kwargs)      def stream_chat(         self, messages: Sequence[ChatMessage], **kwargs: Any     ) -> ChatResponseGen:         if not self.metadata.is_chat_model:             prompt = self.messages_to_prompt(messages)             completion_response = self.stream_complete(prompt, formatted=True, **kwargs)             return stream_completion_response_to_chat_response(completion_response)          return super().stream_chat(messages, **kwargs)      # -- Async methods --      async def acomplete(         self, prompt: str, formatted: bool = False, **kwargs: Any     ) -> CompletionResponse:         """Complete the prompt."""         if not formatted:             prompt = self.completion_to_prompt(prompt)          return await super().acomplete(prompt, **kwargs)      async def astream_complete(         self, prompt: str, formatted: bool = False, **kwargs: Any     ) -> CompletionResponseAsyncGen:         """Stream complete the prompt."""         if not formatted:             prompt = self.completion_to_prompt(prompt)          return await super().astream_complete(prompt, **kwargs)      async def achat(         self, messages: Sequence[ChatMessage], **kwargs: Any     ) -> ChatResponse:         """Chat with the model."""         if not self.metadata.is_chat_model:             prompt = self.messages_to_prompt(messages)             completion_response = await self.acomplete(prompt, formatted=True, **kwargs)             return completion_response_to_chat_response(completion_response)          return await super().achat(messages, **kwargs)      async def astream_chat(         self, messages: Sequence[ChatMessage], **kwargs: Any     ) -> ChatResponseAsyncGen:         if not self.metadata.is_chat_model:             prompt = self.messages_to_prompt(messages)             completion_response = await self.astream_complete(                 prompt, formatted=True, **kwargs             )             return async_stream_completion_response_to_chat_response(                 completion_response             )          return await super().astream_chat(messages, **kwargs)

complete #

complete(prompt: str, formatted: bool = False, **kwargs: Any) -> CompletionResponse

Complete the prompt.

Source code in llama-index-integrations/llms/llama-index-llms-openai-like/llama_index/llms/openai_like/base.py 
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def complete(     self, prompt: str, formatted: bool = False, **kwargs: Any ) -> CompletionResponse:     """Complete the prompt."""     if not formatted:         prompt = self.completion_to_prompt(prompt)      return super().complete(prompt, **kwargs)

stream_complete #

stream_complete(prompt: str, formatted: bool = False, **kwargs: Any) -> CompletionResponseGen

Stream complete the prompt.

Source code in llama-index-integrations/llms/llama-index-llms-openai-like/llama_index/llms/openai_like/base.py 
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def stream_complete(     self, prompt: str, formatted: bool = False, **kwargs: Any ) -> CompletionResponseGen:     """Stream complete the prompt."""     if not formatted:         prompt = self.completion_to_prompt(prompt)      return super().stream_complete(prompt, **kwargs)

chat #

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

Chat with the model.

Source code in llama-index-integrations/llms/llama-index-llms-openai-like/llama_index/llms/openai_like/base.py 
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def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:     """Chat with the model."""     if not self.metadata.is_chat_model:         prompt = self.messages_to_prompt(messages)         completion_response = self.complete(prompt, formatted=True, **kwargs)         return completion_response_to_chat_response(completion_response)      return super().chat(messages, **kwargs)

acomplete async #

acomplete(prompt: str, formatted: bool = False, **kwargs: Any) -> CompletionResponse

Complete the prompt.

Source code in llama-index-integrations/llms/llama-index-llms-openai-like/llama_index/llms/openai_like/base.py 
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async def acomplete(     self, prompt: str, formatted: bool = False, **kwargs: Any ) -> CompletionResponse:     """Complete the prompt."""     if not formatted:         prompt = self.completion_to_prompt(prompt)      return await super().acomplete(prompt, **kwargs)

astream_complete async #

astream_complete(prompt: str, formatted: bool = False, **kwargs: Any) -> CompletionResponseAsyncGen

Stream complete the prompt.

Source code in llama-index-integrations/llms/llama-index-llms-openai-like/llama_index/llms/openai_like/base.py 
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async def astream_complete(     self, prompt: str, formatted: bool = False, **kwargs: Any ) -> CompletionResponseAsyncGen:     """Stream complete the prompt."""     if not formatted:         prompt = self.completion_to_prompt(prompt)      return await super().astream_complete(prompt, **kwargs)

achat async #

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

Chat with the model.

Source code in llama-index-integrations/llms/llama-index-llms-openai-like/llama_index/llms/openai_like/base.py 
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async def achat(     self, messages: Sequence[ChatMessage], **kwargs: Any ) -> ChatResponse:     """Chat with the model."""     if not self.metadata.is_chat_model:         prompt = self.messages_to_prompt(messages)         completion_response = await self.acomplete(prompt, formatted=True, **kwargs)         return completion_response_to_chat_response(completion_response)      return await super().achat(messages, **kwargs)