Skip to content

Openai like

OpenAILike #

Bases: OpenAI

OpenAILike LLM。

OpenAILike是对OpenAI模型的轻量封装，使其与提供openai兼容API的第三方工具兼容。

目前，llama_index阻止使用自定义模型与其OpenAI类，因为它们需要能够从模型名称推断出一些元数据。

注意：您仍然需要设置OPENAI_BASE_API和OPENAI_API_KEY环境变量，或者api_key和api_base构造函数参数。 OPENAI_API_KEY/api_key在这种情况下通常可以设置为任何值，但将取决于您使用的工具。

示例： 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")

response = llm.complete("Hello World!")
print(str(response))
```
Source code in llama_index/llms/openai_like/base.py 
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
class OpenAILike(OpenAI):
    """OpenAILike LLM。

    OpenAILike是对OpenAI模型的轻量封装，使其与提供openai兼容API的第三方工具兼容。

    目前，llama_index阻止使用自定义模型与其OpenAI类，因为它们需要能够从模型名称推断出一些元数据。

    注意：您仍然需要设置OPENAI_BASE_API和OPENAI_API_KEY环境变量，或者api_key和api_base构造函数参数。
    OPENAI_API_KEY/api_key在这种情况下通常可以设置为任何值，但将取决于您使用的工具。

    示例：
        `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")

        response = llm.complete("Hello World!")
        print(str(response))
        ```
"""

    context_window: int = Field(
        default=DEFAULT_CONTEXT_WINDOW,
        description=LLMMetadata.__fields__["context_window"].field_info.description,
    )
    is_chat_model: bool = Field(
        default=False,
        description=LLMMetadata.__fields__["is_chat_model"].field_info.description,
    )
    is_function_calling_model: bool = Field(
        default=False,
        description=LLMMetadata.__fields__[
            "is_function_calling_model"
        ].field_info.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:
        """完成提示。"""
        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:
        """完成了提示的翻译。"""
        if not formatted:
            prompt = self.completion_to_prompt(prompt)

        return super().stream_complete(prompt, **kwargs)

    def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
        """与模型进行交流。"""
        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:
        """完成提示。"""
        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:
        """完成了提示的翻译。"""
        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:
        """与模型进行交流。"""
        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

完成提示。

Source code in llama_index/llms/openai_like/base.py 
90
91
92
93
94
95
96
97
def complete(
    self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
    """完成提示。"""
    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

完成了提示的翻译。

Source code in llama_index/llms/openai_like/base.py 
 99
100
101
102
103
104
105
106
def stream_complete(
    self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseGen:
    """完成了提示的翻译。"""
    if not formatted:
        prompt = self.completion_to_prompt(prompt)

    return super().stream_complete(prompt, **kwargs)

chat #

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

与模型进行交流。

Source code in llama_index/llms/openai_like/base.py 
108
109
110
111
112
113
114
115
def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
    """与模型进行交流。"""
    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

完成提示。

Source code in llama_index/llms/openai_like/base.py 
129
130
131
132
133
134
135
136
async def acomplete(
    self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponse:
    """完成提示。"""
    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

完成了提示的翻译。

Source code in llama_index/llms/openai_like/base.py 
138
139
140
141
142
143
144
145
async def astream_complete(
    self, prompt: str, formatted: bool = False, **kwargs: Any
) -> CompletionResponseAsyncGen:
    """完成了提示的翻译。"""
    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

与模型进行交流。

Source code in llama_index/llms/openai_like/base.py 
147
148
149
150
151
152
153
154
155
156
async def achat(
    self, messages: Sequence[ChatMessage], **kwargs: Any
) -> ChatResponse:
    """与模型进行交流。"""
    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)