Skip to content

Vertex

Vertex #

Bases: LLM

顶点LLM。

示例: pip install llama-index-llms-vertex

```python
from llama_index.llms.openai import Vertex

# 设置必要的变量
credentials = {
    "project_id": "插入项目ID",
    "api_key": "插入API密钥",
}

# 创建Vertex类的实例
llm = Vertex(
    model="text-bison",
    project=credentials["project_id"],
    credentials=credentials,
)

# 从实例中访问complete方法
response = llm.complete("你好,世界!")
print(str(response))
```
Source code in llama_index/llms/vertex/base.py
 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
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
class Vertex(LLM):
    """顶点LLM。

    示例:
        `pip install llama-index-llms-vertex`

        ```python
        from llama_index.llms.openai import Vertex

        # 设置必要的变量
        credentials = {
            "project_id": "插入项目ID",
            "api_key": "插入API密钥",
        }

        # 创建Vertex类的实例
        llm = Vertex(
            model="text-bison",
            project=credentials["project_id"],
            credentials=credentials,
        )

        # 从实例中访问complete方法
        response = llm.complete("你好,世界!")
        print(str(response))
        ```"""

    model: str = Field(description="The vertex model to use.")
    temperature: float = Field(description="The temperature to use for sampling.")
    max_tokens: int = Field(description="The maximum number of tokens to generate.")
    examples: Optional[Sequence[ChatMessage]] = Field(
        description="Example messages for the chat model."
    )
    max_retries: int = Field(default=10, description="The maximum number of retries.")
    safety_settings: Optional[SafetySettingsType] = Field(
        default=None, description="Safety settings for the Vertex AI model."
    )
    additional_kwargs: Dict[str, Any] = Field(
        default_factory=dict, description="Additional kwargs for the Vertex."
    )
    iscode: bool = Field(
        default=False, description="Flag to determine if current model is a Code Model"
    )
    _is_gemini: bool = PrivateAttr()
    _is_chat_model: bool = PrivateAttr()
    _client: Any = PrivateAttr()
    _chat_client: Any = PrivateAttr()

    def __init__(
        self,
        model: str = "text-bison",
        project: Optional[str] = None,
        location: Optional[str] = None,
        credentials: Optional[Any] = None,
        examples: Optional[Sequence[ChatMessage]] = None,
        temperature: float = 0.1,
        max_tokens: int = 512,
        max_retries: int = 10,
        iscode: bool = False,
        safety_settings: Optional[SafetySettingsType] = None,
        additional_kwargs: Optional[Dict[str, Any]] = None,
        callback_manager: Optional[CallbackManager] = None,
        system_prompt: Optional[str] = None,
        messages_to_prompt: Optional[Callable[[Sequence[ChatMessage]], str]] = None,
        completion_to_prompt: Optional[Callable[[str], str]] = None,
        pydantic_program_mode: PydanticProgramMode = PydanticProgramMode.DEFAULT,
        output_parser: Optional[BaseOutputParser] = None,
    ) -> None:
        init_vertexai(project=project, location=location, credentials=credentials)

        safety_settings = safety_settings or {}
        additional_kwargs = additional_kwargs or {}
        callback_manager = callback_manager or CallbackManager([])

        self._is_gemini = False
        self._is_chat_model = False
        if model in CHAT_MODELS:
            from vertexai.language_models import ChatModel

            self._chat_client = ChatModel.from_pretrained(model)
            self._is_chat_model = True
        elif model in CODE_CHAT_MODELS:
            from vertexai.language_models import CodeChatModel

            self._chat_client = CodeChatModel.from_pretrained(model)
            iscode = True
            self._is_chat_model = True
        elif model in CODE_MODELS:
            from vertexai.language_models import CodeGenerationModel

            self._client = CodeGenerationModel.from_pretrained(model)
            iscode = True
        elif model in TEXT_MODELS:
            from vertexai.language_models import TextGenerationModel

            self._client = TextGenerationModel.from_pretrained(model)
        elif is_gemini_model(model):
            self._client = create_gemini_client(model, safety_settings)
            self._chat_client = self._client
            self._is_gemini = True
            self._is_chat_model = True
        else:
            raise (ValueError(f"Model {model} not found, please verify the model name"))

        super().__init__(
            temperature=temperature,
            max_tokens=max_tokens,
            additional_kwargs=additional_kwargs,
            max_retries=max_retries,
            model=model,
            examples=examples,
            iscode=iscode,
            safety_settings=safety_settings,
            callback_manager=callback_manager,
            system_prompt=system_prompt,
            messages_to_prompt=messages_to_prompt,
            completion_to_prompt=completion_to_prompt,
            pydantic_program_mode=pydantic_program_mode,
            output_parser=output_parser,
        )

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

    @property
    def metadata(self) -> LLMMetadata:
        return LLMMetadata(
            is_chat_model=self._is_chat_model,
            model_name=self.model,
            system_role=(
                MessageRole.USER if self._is_gemini else MessageRole.SYSTEM
            ),  # Gemini does not support the default: MessageRole.SYSTEM
        )

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

    def _get_all_kwargs(self, **kwargs: Any) -> Dict[str, Any]:
        return {
            **self._model_kwargs,
            **kwargs,
        }

    @llm_chat_callback()
    def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
        merged_messages = (
            merge_neighboring_same_role_messages(messages)
            if self._is_gemini
            else messages
        )
        question = _parse_message(merged_messages[-1], self._is_gemini)
        chat_history = _parse_chat_history(merged_messages[:-1], self._is_gemini)
        chat_params = {**chat_history}

        kwargs = kwargs if kwargs else {}

        params = {**self._model_kwargs, **kwargs}

        if self.iscode and "candidate_count" in params:
            raise (ValueError("candidate_count is not supported by the codey model's"))
        if self.examples and "examples" not in params:
            chat_params["examples"] = _parse_examples(self.examples)
        elif "examples" in params:
            raise (
                ValueError(
                    "examples are not supported in chat generation pass them as a constructor parameter"
                )
            )

        generation = completion_with_retry(
            client=self._chat_client,
            prompt=question,
            chat=True,
            stream=False,
            is_gemini=self._is_gemini,
            params=chat_params,
            max_retries=self.max_retries,
            **params,
        )

        return ChatResponse(
            message=ChatMessage(role=MessageRole.ASSISTANT, content=generation.text),
            raw=generation.__dict__,
        )

    @llm_completion_callback()
    def complete(
        self, prompt: str, formatted: bool = False, **kwargs: Any
    ) -> CompletionResponse:
        kwargs = kwargs if kwargs else {}
        params = {**self._model_kwargs, **kwargs}
        if self.iscode and "candidate_count" in params:
            raise (ValueError("candidate_count is not supported by the codey model's"))

        completion = completion_with_retry(
            self._client,
            prompt,
            max_retries=self.max_retries,
            is_gemini=self._is_gemini,
            **params,
        )
        return CompletionResponse(text=completion.text, raw=completion.__dict__)

    @llm_chat_callback()
    def stream_chat(
        self, messages: Sequence[ChatMessage], **kwargs: Any
    ) -> ChatResponseGen:
        merged_messages = (
            merge_neighboring_same_role_messages(messages)
            if self._is_gemini
            else messages
        )
        question = _parse_message(merged_messages[-1], self._is_gemini)
        chat_history = _parse_chat_history(merged_messages[:-1], self._is_gemini)
        chat_params = {**chat_history}
        kwargs = kwargs if kwargs else {}
        params = {**self._model_kwargs, **kwargs}
        if self.iscode and "candidate_count" in params:
            raise (ValueError("candidate_count is not supported by the codey model's"))
        if self.examples and "examples" not in params:
            chat_params["examples"] = _parse_examples(self.examples)
        elif "examples" in params:
            raise (
                ValueError(
                    "examples are not supported in chat generation pass them as a constructor parameter"
                )
            )

        response = completion_with_retry(
            client=self._chat_client,
            prompt=question,
            chat=True,
            stream=True,
            is_gemini=self._is_gemini,
            params=chat_params,
            max_retries=self.max_retries,
            **params,
        )

        def gen() -> ChatResponseGen:
            content = ""
            role = MessageRole.ASSISTANT
            for r in response:
                content_delta = r.text
                content += content_delta
                yield ChatResponse(
                    message=ChatMessage(role=role, content=content),
                    delta=content_delta,
                    raw=r.__dict__,
                )

        return gen()

    @llm_completion_callback()
    def stream_complete(
        self, prompt: str, formatted: bool = False, **kwargs: Any
    ) -> CompletionResponseGen:
        kwargs = kwargs if kwargs else {}
        params = {**self._model_kwargs, **kwargs}
        if "candidate_count" in params:
            raise (ValueError("candidate_count is not supported by the streaming"))

        completion = completion_with_retry(
            client=self._client,
            prompt=prompt,
            stream=True,
            is_gemini=self._is_gemini,
            max_retries=self.max_retries,
            **params,
        )

        def gen() -> CompletionResponseGen:
            content = ""
            for r in completion:
                content_delta = r.text
                content += content_delta
                yield CompletionResponse(
                    text=content, delta=content_delta, raw=r.__dict__
                )

        return gen()

    @llm_chat_callback()
    async def achat(
        self, messages: Sequence[ChatMessage], **kwargs: Any
    ) -> ChatResponse:
        merged_messages = (
            merge_neighboring_same_role_messages(messages)
            if self._is_gemini
            else messages
        )
        question = _parse_message(merged_messages[-1], self._is_gemini)
        chat_history = _parse_chat_history(merged_messages[:-1], self._is_gemini)
        chat_params = {**chat_history}
        kwargs = kwargs if kwargs else {}
        params = {**self._model_kwargs, **kwargs}
        if self.iscode and "candidate_count" in params:
            raise (ValueError("candidate_count is not supported by the codey model's"))
        if self.examples and "examples" not in params:
            chat_params["examples"] = _parse_examples(self.examples)
        elif "examples" in params:
            raise (
                ValueError(
                    "examples are not supported in chat generation pass them as a constructor parameter"
                )
            )
        generation = await acompletion_with_retry(
            client=self._chat_client,
            prompt=question,
            chat=True,
            is_gemini=self._is_gemini,
            params=chat_params,
            max_retries=self.max_retries,
            **params,
        )
        ##this is due to a bug in vertex AI we have to await twice
        if self.iscode:
            generation = await generation
        return ChatResponse(
            message=ChatMessage(role=MessageRole.ASSISTANT, content=generation.text),
            raw=generation.__dict__,
        )

    @llm_completion_callback()
    async def acomplete(
        self, prompt: str, formatted: bool = False, **kwargs: Any
    ) -> CompletionResponse:
        kwargs = kwargs if kwargs else {}
        params = {**self._model_kwargs, **kwargs}
        if self.iscode and "candidate_count" in params:
            raise (ValueError("candidate_count is not supported by the codey model's"))
        completion = await acompletion_with_retry(
            client=self._client,
            prompt=prompt,
            max_retries=self.max_retries,
            is_gemini=self._is_gemini,
            **params,
        )
        return CompletionResponse(text=completion.text)

    @llm_chat_callback()
    async def astream_chat(
        self, messages: Sequence[ChatMessage], **kwargs: Any
    ) -> ChatResponseAsyncGen:
        raise (ValueError("Not Implemented"))

    @llm_completion_callback()
    async def astream_complete(
        self, prompt: str, formatted: bool = False, **kwargs: Any
    ) -> CompletionResponseAsyncGen:
        raise (ValueError("Not Implemented"))