Skip to content

Oci genai

OCIGenAI #

Bases: LLM

OCI large language models.

To authenticate, the OCI client uses the methods described in https://docs.oracle.com/en-us/iaas/Content/API/Concepts/sdk_authentication_methods.htm

The authentifcation method is passed through auth_type and should be one of: API_KEY (default), SECURITY_TOKEN, INSTANCE_PRINCIPAL, RESOURCE_PRINCIPAL

Make sure you have the required policies (profile/roles) to access the OCI Generative AI service. If a specific config profile is used, you must pass the name of the profile (from ~/.oci/config) through auth_profile.

To use, you must provide the compartment id along with the endpoint url, and model id as named parameters to the constructor.

Example

.. code-block:: python

from llama_index.llms.oci_genai import OCIGenAI

llm = OCIGenAI(
        model="MY_MODEL_ID",
        service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",
        compartment_id="MY_OCID"
    )
Source code in llama-index-integrations/llms/llama-index-llms-oci-genai/llama_index/llms/oci_genai/base.py
 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
class OCIGenAI(LLM):
    """OCI large language models.

    To authenticate, the OCI client uses the methods described in
    https://docs.oracle.com/en-us/iaas/Content/API/Concepts/sdk_authentication_methods.htm

    The authentifcation method is passed through auth_type and should be one of:
    API_KEY (default), SECURITY_TOKEN, INSTANCE_PRINCIPAL, RESOURCE_PRINCIPAL

    Make sure you have the required policies (profile/roles) to
    access the OCI Generative AI service.
    If a specific config profile is used, you must pass
    the name of the profile (from ~/.oci/config) through auth_profile.

    To use, you must provide the compartment id
    along with the endpoint url, and model id
    as named parameters to the constructor.

    Example:
        .. code-block:: python

            from llama_index.llms.oci_genai import OCIGenAI

            llm = OCIGenAI(
                    model="MY_MODEL_ID",
                    service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",
                    compartment_id="MY_OCID"
                )
    """

    model: str = Field(description="Id of the OCI Generative AI 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.")
    context_size: int = Field("The maximum number of tokens available for input.")

    service_endpoint: Optional[str] = Field(
        default=None,
        description="service endpoint url.",
    )

    compartment_id: Optional[str] = Field(
        default=None,
        description="OCID of compartment.",
    )

    auth_type: Optional[str] = Field(
        description="Authentication type, can be: API_KEY, SECURITY_TOKEN, INSTANCE_PRINCIPAL, RESOURCE_PRINCIPAL. If not specified, API_KEY will be used",
        default="API_KEY",
    )

    auth_profile: Optional[str] = Field(
        description="The name of the profile in ~/.oci/config. If not specified , DEFAULT will be used",
        default="DEFAULT",
    )

    additional_kwargs: Dict[str, Any] = Field(
        default_factory=dict,
        description="Additional kwargs for the OCI Generative AI request.",
    )

    _client: Any = PrivateAttr()
    _provider: str = PrivateAttr()
    _serving_mode: str = PrivateAttr()
    _completion_generator: str = PrivateAttr()
    _chat_generator: str = PrivateAttr()

    def __init__(
        self,
        model: str,
        temperature: Optional[float] = DEFAULT_TEMPERATURE,
        max_tokens: Optional[int] = 512,
        context_size: Optional[int] = None,
        service_endpoint: Optional[str] = None,
        compartment_id: Optional[str] = None,
        auth_type: Optional[str] = "API_KEY",
        auth_profile: Optional[str] = "DEFAULT",
        client: Optional[Any] = None,
        provider: Optional[str] = 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:
        """
        Initializes the OCIGenAI class.

        Args:
            model (str): The Id of the model to be used for generating embeddings, e.g., "meta.llama-2-70b-chat".

            temperature (Optional[float]): The temperature to use for sampling. Default specified in lama_index.core.constants.DEFAULT_TEMPERATURE.

            max_tokens (Optional[int]): The maximum number of tokens to generate. Default is 512.

            context_size (Optional[int]): The maximum number of tokens available for input. If not specified, the default context size for the model will be used.

            service_endpoint (str): service endpoint url, e.g., "https://inference.generativeai.us-chicago-1.oci.oraclecloud.com"

            compartment_id (str): OCID of the compartment.

            auth_type (Optional[str]): Authentication type, can be: API_KEY (default), SECURITY_TOKEN, INSTANCEAL, RESOURCE_PRINCIPAL.
                                    If not specified, API_KEY will be used

            auth_profile (Optional[str]): The name of the profile in ~/.oci/config. If not specified , DEFAULT will be used

            client (Optional[Any]): An optional OCI client object. If not provided, the client will be created using the
                                    provided service endpoint and authentifcation method.

            provider (Optional[str]): Provider name of the model. If not specified, the provider will be derived from the model name.

            additional_kwargs (Optional[Dict[str, Any]]): Additional kwargs for the the LLM.
        """
        context_size = get_context_size(model, context_size)

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

        super().__init__(
            model=model,
            temperature=temperature,
            max_tokens=max_tokens,
            context_size=context_size,
            service_endpoint=service_endpoint,
            compartment_id=compartment_id,
            auth_type=auth_type,
            auth_profile=auth_profile,
            additional_kwargs=additional_kwargs,
            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,
        )

        self._client = client or create_client(
            auth_type, auth_profile, service_endpoint
        )

        self._provider = get_provider(model, provider)

        self._serving_mode = get_serving_mode(model)

        self._completion_generator = get_completion_generator()

        self._chat_generator = get_chat_generator()

    @classmethod
    def class_name(cls) -> str:
        """Get class name."""
        return "OCIGenAI_LLM"

    @property
    def metadata(self) -> LLMMetadata:
        return LLMMetadata(
            context_window=self.context_size,
            num_output=self.max_tokens,
            is_chat_model=self.model in CHAT_MODELS,
            model_name=self.model,
        )

    @property
    def _model_kwargs(self) -> Dict[str, Any]:
        base_kwargs = {
            "temperature": self.temperature,
            "max_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_completion_callback()
    def complete(
        self, prompt: str, formatted: bool = False, **kwargs: Any
    ) -> CompletionResponse:
        inference_params = self._get_all_kwargs(**kwargs)
        inference_params["is_stream"] = False
        inference_params["prompt"] = prompt

        request = self._completion_generator(
            compartment_id=self.compartment_id,
            serving_mode=self._serving_mode,
            inference_request=self._provider.oci_completion_request(**inference_params),
        )

        response = self._client.generate_text(request)
        return CompletionResponse(
            text=self._provider.completion_response_to_text(response),
            raw=response.__dict__,
        )

    @llm_completion_callback()
    def stream_complete(
        self, prompt: str, formatted: bool = False, **kwargs: Any
    ) -> CompletionResponseGen:
        inference_params = self._get_all_kwargs(**kwargs)
        inference_params["is_stream"] = True
        inference_params["prompt"] = prompt

        request = self._completion_generator(
            compartment_id=self.compartment_id,
            serving_mode=self._serving_mode,
            inference_request=self._provider.oci_completion_request(**inference_params),
        )

        response = self._client.generate_text(request)

        def gen() -> CompletionResponseGen:
            content = ""
            for event in response.data.events():
                content_delta = self._provider.completion_stream_to_text(
                    json.loads(event.data)
                )
                content += content_delta
                yield CompletionResponse(
                    text=content, delta=content_delta, raw=event.__dict__
                )

        return gen()

    @llm_chat_callback()
    def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
        oci_params = self._provider.messages_to_oci_params(messages)
        oci_params["is_stream"] = False
        all_kwargs = self._get_all_kwargs(**kwargs)
        chat_params = {**all_kwargs, **oci_params}

        request = self._chat_generator(
            compartment_id=self.compartment_id,
            serving_mode=self._serving_mode,
            chat_request=self._provider.oci_chat_request(**chat_params),
        )

        response = self._client.chat(request)

        return ChatResponse(
            message=ChatMessage(
                role=MessageRole.ASSISTANT,
                content=self._provider.chat_response_to_text(response),
            ),
            raw=response.__dict__,
        )

    def stream_chat(
        self, messages: Sequence[ChatMessage], **kwargs: Any
    ) -> ChatResponseGen:
        oci_params = self._provider.messages_to_oci_params(messages)
        oci_params["is_stream"] = True
        all_kwargs = self._get_all_kwargs(**kwargs)
        chat_params = {**all_kwargs, **oci_params}

        request = self._chat_generator(
            compartment_id=self.compartment_id,
            serving_mode=self._serving_mode,
            chat_request=self._provider.oci_chat_request(**chat_params),
        )

        response = self._client.chat(request)

        def gen() -> ChatResponseGen:
            content = ""
            for event in response.data.events():
                content_delta = self._provider.chat_stream_to_text(
                    json.loads(event.data)
                )
                content += content_delta
                yield ChatResponse(
                    message=ChatMessage(role=MessageRole.ASSISTANT, content=content),
                    delta=content_delta,
                    raw=event.__dict__,
                )

        return gen()

    async def acomplete(
        self, prompt: str, formatted: bool = False, **kwargs: Any
    ) -> CompletionResponse:
        # do synchronous complete for now
        return self.complete(prompt, formatted=formatted, **kwargs)

    async def achat(
        self, messages: Sequence[ChatMessage], **kwargs: Any
    ) -> ChatResponse:
        # do synchronous chat for now
        return self.chat(messages, **kwargs)

    async def astream_chat(
        self, messages: Sequence[ChatMessage], **kwargs: Any
    ) -> ChatResponseAsyncGen:
        # do synchronous stream chat for now
        return self.stream_chat(messages, **kwargs)

    async def astream_complete(
        self, prompt: str, formatted: bool = False, **kwargs: Any
    ) -> CompletionResponseAsyncGen:
        # do synchronous stream complete for now
        return self.stream_complete(prompt, formatted, **kwargs)

class_name classmethod #

class_name() -> str

Get class name.

Source code in llama-index-integrations/llms/llama-index-llms-oci-genai/llama_index/llms/oci_genai/base.py
191
192
193
194
@classmethod
def class_name(cls) -> str:
    """Get class name."""
    return "OCIGenAI_LLM"