diff --git a/api/core/model_runtime/model_providers/oci/__init__.py b/api/core/model_runtime/model_providers/oci/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/api/core/model_runtime/model_providers/oci/_assets/icon_l_en.svg b/api/core/model_runtime/model_providers/oci/_assets/icon_l_en.svg
new file mode 100644
index 0000000000..0981dfcff2
--- /dev/null
+++ b/api/core/model_runtime/model_providers/oci/_assets/icon_l_en.svg
@@ -0,0 +1 @@
+
\ No newline at end of file
diff --git a/api/core/model_runtime/model_providers/oci/_assets/icon_s_en.svg b/api/core/model_runtime/model_providers/oci/_assets/icon_s_en.svg
new file mode 100644
index 0000000000..0981dfcff2
--- /dev/null
+++ b/api/core/model_runtime/model_providers/oci/_assets/icon_s_en.svg
@@ -0,0 +1 @@
+
\ No newline at end of file
diff --git a/api/core/model_runtime/model_providers/oci/llm/cohere.command-r-16k.yaml b/api/core/model_runtime/model_providers/oci/llm/cohere.command-r-16k.yaml
new file mode 100644
index 0000000000..eb60cbcd90
--- /dev/null
+++ b/api/core/model_runtime/model_providers/oci/llm/cohere.command-r-16k.yaml
@@ -0,0 +1,52 @@
+model: cohere.command-r-16k
+label:
+ en_US: cohere.command-r-16k v1.2
+model_type: llm
+features:
+ - multi-tool-call
+ - agent-thought
+ - stream-tool-call
+model_properties:
+ mode: chat
+ context_size: 128000
+parameter_rules:
+ - name: temperature
+ use_template: temperature
+ default: 1
+ max: 1.0
+ - name: topP
+ use_template: top_p
+ default: 0.75
+ min: 0
+ max: 1
+ - name: topK
+ label:
+ zh_Hans: 取样数量
+ en_US: Top k
+ type: int
+ help:
+ zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+ en_US: Only sample from the top K options for each subsequent token.
+ required: false
+ default: 0
+ min: 0
+ max: 500
+ - name: presencePenalty
+ use_template: presence_penalty
+ min: 0
+ max: 1
+ default: 0
+ - name: frequencyPenalty
+ use_template: frequency_penalty
+ min: 0
+ max: 1
+ default: 0
+ - name: maxTokens
+ use_template: max_tokens
+ default: 600
+ max: 4000
+pricing:
+ input: '0.004'
+ output: '0.004'
+ unit: '0.0001'
+ currency: USD
diff --git a/api/core/model_runtime/model_providers/oci/llm/cohere.command-r-plus.yaml b/api/core/model_runtime/model_providers/oci/llm/cohere.command-r-plus.yaml
new file mode 100644
index 0000000000..df31b0d0df
--- /dev/null
+++ b/api/core/model_runtime/model_providers/oci/llm/cohere.command-r-plus.yaml
@@ -0,0 +1,52 @@
+model: cohere.command-r-plus
+label:
+ en_US: cohere.command-r-plus v1.2
+model_type: llm
+features:
+ - multi-tool-call
+ - agent-thought
+ - stream-tool-call
+model_properties:
+ mode: chat
+ context_size: 128000
+parameter_rules:
+ - name: temperature
+ use_template: temperature
+ default: 1
+ max: 1.0
+ - name: topP
+ use_template: top_p
+ default: 0.75
+ min: 0
+ max: 1
+ - name: topK
+ label:
+ zh_Hans: 取样数量
+ en_US: Top k
+ type: int
+ help:
+ zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+ en_US: Only sample from the top K options for each subsequent token.
+ required: false
+ default: 0
+ min: 0
+ max: 500
+ - name: presencePenalty
+ use_template: presence_penalty
+ min: 0
+ max: 1
+ default: 0
+ - name: frequencyPenalty
+ use_template: frequency_penalty
+ min: 0
+ max: 1
+ default: 0
+ - name: maxTokens
+ use_template: max_tokens
+ default: 600
+ max: 4000
+pricing:
+ input: '0.0219'
+ output: '0.0219'
+ unit: '0.0001'
+ currency: USD
diff --git a/api/core/model_runtime/model_providers/oci/llm/llm.py b/api/core/model_runtime/model_providers/oci/llm/llm.py
new file mode 100644
index 0000000000..937513a58b
--- /dev/null
+++ b/api/core/model_runtime/model_providers/oci/llm/llm.py
@@ -0,0 +1,461 @@
+import base64
+import copy
+import json
+import logging
+from collections.abc import Generator
+from typing import Optional, Union
+
+import oci
+from oci.generative_ai_inference.models.base_chat_response import BaseChatResponse
+
+from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta
+from core.model_runtime.entities.message_entities import (
+ AssistantPromptMessage,
+ PromptMessage,
+ PromptMessageContentType,
+ PromptMessageTool,
+ SystemPromptMessage,
+ ToolPromptMessage,
+ UserPromptMessage,
+)
+from core.model_runtime.errors.invoke import (
+ InvokeAuthorizationError,
+ InvokeBadRequestError,
+ InvokeConnectionError,
+ InvokeError,
+ InvokeRateLimitError,
+ InvokeServerUnavailableError,
+)
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
+
+logger = logging.getLogger(__name__)
+
+request_template = {
+ "compartmentId": "",
+ "servingMode": {
+ "modelId": "cohere.command-r-plus",
+ "servingType": "ON_DEMAND"
+ },
+ "chatRequest": {
+ "apiFormat": "COHERE",
+ #"preambleOverride": "You are a helpful assistant.",
+ #"message": "Hello!",
+ #"chatHistory": [],
+ "maxTokens": 600,
+ "isStream": False,
+ "frequencyPenalty": 0,
+ "presencePenalty": 0,
+ "temperature": 1,
+ "topP": 0.75
+ }
+}
+oci_config_template = {
+ "user": "",
+ "fingerprint": "",
+ "tenancy": "",
+ "region": "",
+ "compartment_id": "",
+ "key_content": ""
+ }
+
+class OCILargeLanguageModel(LargeLanguageModel):
+ # https://docs.oracle.com/en-us/iaas/Content/generative-ai/pretrained-models.htm
+ _supported_models = {
+ "meta.llama-3-70b-instruct": {
+ "system": True,
+ "multimodal": False,
+ "tool_call": False,
+ "stream_tool_call": False,
+ },
+ "cohere.command-r-16k": {
+ "system": True,
+ "multimodal": False,
+ "tool_call": True,
+ "stream_tool_call": False,
+ },
+ "cohere.command-r-plus": {
+ "system": True,
+ "multimodal": False,
+ "tool_call": True,
+ "stream_tool_call": False,
+ },
+ }
+
+ def _is_tool_call_supported(self, model_id: str, stream: bool = False) -> bool:
+ feature = self._supported_models.get(model_id)
+ if not feature:
+ return False
+ return feature["stream_tool_call"] if stream else feature["tool_call"]
+
+ def _is_multimodal_supported(self, model_id: str) -> bool:
+ feature = self._supported_models.get(model_id)
+ if not feature:
+ return False
+ return feature["multimodal"]
+
+ def _is_system_prompt_supported(self, model_id: str) -> bool:
+ feature = self._supported_models.get(model_id)
+ if not feature:
+ return False
+ return feature["system"]
+
+ def _invoke(self, model: str, credentials: dict,
+ prompt_messages: list[PromptMessage], model_parameters: dict,
+ tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
+ stream: bool = True, user: Optional[str] = None) \
+ -> Union[LLMResult, Generator]:
+ """
+ Invoke large language model
+
+ :param model: model name
+ :param credentials: model credentials
+ :param prompt_messages: prompt messages
+ :param model_parameters: model parameters
+ :param tools: tools for tool calling
+ :param stop: stop words
+ :param stream: is stream response
+ :param user: unique user id
+ :return: full response or stream response chunk generator result
+ """
+ #print("model"+"*"*20)
+ #print(model)
+ #print("credentials"+"*"*20)
+ #print(credentials)
+ #print("model_parameters"+"*"*20)
+ #print(model_parameters)
+ #print("prompt_messages"+"*"*200)
+ #print(prompt_messages)
+ #print("tools"+"*"*20)
+ #print(tools)
+
+ # invoke model
+ return self._generate(model, credentials, prompt_messages, model_parameters, tools, stop, stream, user)
+
+ def get_num_tokens(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
+ tools: Optional[list[PromptMessageTool]] = None) -> int:
+ """
+ Get number of tokens for given prompt messages
+
+ :param model: model name
+ :param credentials: model credentials
+ :param prompt_messages: prompt messages
+ :param tools: tools for tool calling
+ :return:md = genai.GenerativeModel(model)
+ """
+ prompt = self._convert_messages_to_prompt(prompt_messages)
+
+ return self._get_num_tokens_by_gpt2(prompt)
+
+ def get_num_characters(self, model: str, credentials: dict, prompt_messages: list[PromptMessage],
+ tools: Optional[list[PromptMessageTool]] = None) -> int:
+ """
+ Get number of tokens for given prompt messages
+
+ :param model: model name
+ :param credentials: model credentials
+ :param prompt_messages: prompt messages
+ :param tools: tools for tool calling
+ :return:md = genai.GenerativeModel(model)
+ """
+ prompt = self._convert_messages_to_prompt(prompt_messages)
+
+ return len(prompt)
+
+ def _convert_messages_to_prompt(self, messages: list[PromptMessage]) -> str:
+ """
+ :param messages: List of PromptMessage to combine.
+ :return: Combined string with necessary human_prompt and ai_prompt tags.
+ """
+ messages = messages.copy() # don't mutate the original list
+
+ text = "".join(
+ self._convert_one_message_to_text(message)
+ for message in messages
+ )
+
+ return text.rstrip()
+
+ def validate_credentials(self, model: str, credentials: dict) -> None:
+ """
+ Validate model credentials
+
+ :param model: model name
+ :param credentials: model credentials
+ :return:
+ """
+ # Setup basic variables
+ # Auth Config
+ try:
+ ping_message = SystemPromptMessage(content="ping")
+ self._generate(model, credentials, [ping_message], {"maxTokens": 5})
+ except Exception as ex:
+ raise CredentialsValidateFailedError(str(ex))
+
+ def _generate(self, model: str, credentials: dict,
+ prompt_messages: list[PromptMessage], model_parameters: dict,
+ tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
+ stream: bool = True, user: Optional[str] = None
+ ) -> Union[LLMResult, Generator]:
+ """
+ Invoke large language model
+
+ :param model: model name
+ :param credentials: credentials kwargs
+ :param prompt_messages: prompt messages
+ :param model_parameters: model parameters
+ :param stop: stop words
+ :param stream: is stream response
+ :param user: unique user id
+ :return: full response or stream response chunk generator result
+ """
+ # config_kwargs = model_parameters.copy()
+ # config_kwargs['max_output_tokens'] = config_kwargs.pop('max_tokens_to_sample', None)
+ # if stop:
+ # config_kwargs["stop_sequences"] = stop
+
+ # initialize client
+ # ref: https://docs.oracle.com/en-us/iaas/api/#/en/generative-ai-inference/20231130/ChatResult/Chat
+ oci_config = copy.deepcopy(oci_config_template)
+ if "oci_config_content" in credentials:
+ oci_config_content = base64.b64decode(credentials.get('oci_config_content')).decode('utf-8')
+ config_items = oci_config_content.split("/")
+ if len(config_items) != 5:
+ raise CredentialsValidateFailedError("oci_config_content should be base64.b64encode('user_ocid/fingerprint/tenancy_ocid/region/compartment_ocid'.encode('utf-8'))")
+ oci_config["user"] = config_items[0]
+ oci_config["fingerprint"] = config_items[1]
+ oci_config["tenancy"] = config_items[2]
+ oci_config["region"] = config_items[3]
+ oci_config["compartment_id"] = config_items[4]
+ else:
+ raise CredentialsValidateFailedError("need to set oci_config_content in credentials ")
+ if "oci_key_content" in credentials:
+ oci_key_content = base64.b64decode(credentials.get('oci_key_content')).decode('utf-8')
+ oci_config["key_content"] = oci_key_content.encode(encoding="utf-8")
+ else:
+ raise CredentialsValidateFailedError("need to set oci_config_content in credentials ")
+
+ #oci_config = oci.config.from_file('~/.oci/config', credentials.get('oci_api_profile'))
+ compartment_id = oci_config["compartment_id"]
+ client = oci.generative_ai_inference.GenerativeAiInferenceClient(config=oci_config)
+ # call embedding model
+ request_args = copy.deepcopy(request_template)
+ request_args["compartmentId"] = compartment_id
+ request_args["servingMode"]["modelId"] = model
+
+ chathistory = []
+ system_prompts = []
+ #if "meta.llama" in model:
+ # request_args["chatRequest"]["apiFormat"] = "GENERIC"
+ request_args["chatRequest"]["maxTokens"] = model_parameters.pop('maxTokens', 600)
+ request_args["chatRequest"].update(model_parameters)
+ frequency_penalty = model_parameters.get("frequencyPenalty", 0)
+ presence_penalty = model_parameters.get("presencePenalty", 0)
+ if frequency_penalty > 0 and presence_penalty > 0:
+ raise InvokeBadRequestError("Cannot set both frequency penalty and presence penalty")
+
+ # for msg in prompt_messages: # makes message roles strictly alternating
+ # content = self._format_message_to_glm_content(msg)
+ # if history and history[-1]["role"] == content["role"]:
+ # history[-1]["parts"].extend(content["parts"])
+ # else:
+ # history.append(content)
+
+ # temporary not implement the tool call function
+ valid_value = self._is_tool_call_supported(model, stream)
+ if tools is not None and len(tools) > 0:
+ if not valid_value:
+ raise InvokeBadRequestError("Does not support function calling")
+ if model.startswith("cohere"):
+ #print("run cohere " * 10)
+ for message in prompt_messages[:-1]:
+ text = ""
+ if isinstance(message.content, str):
+ text = message.content
+ if isinstance(message, UserPromptMessage):
+ chathistory.append({"role": "USER", "message": text})
+ else:
+ chathistory.append({"role": "CHATBOT", "message": text})
+ if isinstance(message, SystemPromptMessage):
+ if isinstance(message.content, str):
+ system_prompts.append(message.content)
+ args = {"apiFormat": "COHERE",
+ "preambleOverride": ' '.join(system_prompts),
+ "message": prompt_messages[-1].content,
+ "chatHistory": chathistory, }
+ request_args["chatRequest"].update(args)
+ elif model.startswith("meta"):
+ #print("run meta " * 10)
+ meta_messages = []
+ for message in prompt_messages:
+ text = message.content
+ meta_messages.append({"role": message.role.name, "content": [{"type": "TEXT", "text": text}]})
+ args = {"apiFormat": "GENERIC",
+ "messages": meta_messages,
+ "numGenerations": 1,
+ "topK": -1}
+ request_args["chatRequest"].update(args)
+
+ if stream:
+ request_args["chatRequest"]["isStream"] = True
+ #print("final request" + "|" * 20)
+ #print(request_args)
+ response = client.chat(request_args)
+ #print(vars(response))
+
+ if stream:
+ return self._handle_generate_stream_response(model, credentials, response, prompt_messages)
+
+ return self._handle_generate_response(model, credentials, response, prompt_messages)
+
+ def _handle_generate_response(self, model: str, credentials: dict, response: BaseChatResponse,
+ prompt_messages: list[PromptMessage]) -> LLMResult:
+ """
+ Handle llm response
+
+ :param model: model name
+ :param credentials: credentials
+ :param response: response
+ :param prompt_messages: prompt messages
+ :return: llm response
+ """
+ # transform assistant message to prompt message
+ assistant_prompt_message = AssistantPromptMessage(
+ content=response.data.chat_response.text
+ )
+
+ # calculate num tokens
+ prompt_tokens = self.get_num_characters(model, credentials, prompt_messages)
+ completion_tokens = self.get_num_characters(model, credentials, [assistant_prompt_message])
+
+ # transform usage
+ usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)
+
+ # transform response
+ result = LLMResult(
+ model=model,
+ prompt_messages=prompt_messages,
+ message=assistant_prompt_message,
+ usage=usage,
+ )
+
+ return result
+
+ def _handle_generate_stream_response(self, model: str, credentials: dict, response: BaseChatResponse,
+ prompt_messages: list[PromptMessage]) -> Generator:
+ """
+ Handle llm stream response
+
+ :param model: model name
+ :param credentials: credentials
+ :param response: response
+ :param prompt_messages: prompt messages
+ :return: llm response chunk generator result
+ """
+ index = -1
+ events = response.data.events()
+ for stream in events:
+ chunk = json.loads(stream.data)
+ #print(chunk)
+ #chunk: {'apiFormat': 'COHERE', 'text': 'Hello'}
+
+
+
+ #for chunk in response:
+ #for part in chunk.parts:
+ #if part.function_call:
+ # assistant_prompt_message.tool_calls = [
+ # AssistantPromptMessage.ToolCall(
+ # id=part.function_call.name,
+ # type='function',
+ # function=AssistantPromptMessage.ToolCall.ToolCallFunction(
+ # name=part.function_call.name,
+ # arguments=json.dumps(dict(part.function_call.args.items()))
+ # )
+ # )
+ # ]
+
+ if "finishReason" not in chunk:
+ assistant_prompt_message = AssistantPromptMessage(
+ content=''
+ )
+ if model.startswith("cohere"):
+ if chunk["text"]:
+ assistant_prompt_message.content += chunk["text"]
+ elif model.startswith("meta"):
+ assistant_prompt_message.content += chunk["message"]["content"][0]["text"]
+ index += 1
+ # transform assistant message to prompt message
+ yield LLMResultChunk(
+ model=model,
+ prompt_messages=prompt_messages,
+ delta=LLMResultChunkDelta(
+ index=index,
+ message=assistant_prompt_message
+ )
+ )
+ else:
+ # calculate num tokens
+ prompt_tokens = self.get_num_characters(model, credentials, prompt_messages)
+ completion_tokens = self.get_num_characters(model, credentials, [assistant_prompt_message])
+
+ # transform usage
+ usage = self._calc_response_usage(model, credentials, prompt_tokens, completion_tokens)
+
+ yield LLMResultChunk(
+ model=model,
+ prompt_messages=prompt_messages,
+ delta=LLMResultChunkDelta(
+ index=index,
+ message=assistant_prompt_message,
+ finish_reason=str(chunk["finishReason"]),
+ usage=usage
+ )
+ )
+
+ def _convert_one_message_to_text(self, message: PromptMessage) -> str:
+ """
+ Convert a single message to a string.
+
+ :param message: PromptMessage to convert.
+ :return: String representation of the message.
+ """
+ human_prompt = "\n\nuser:"
+ ai_prompt = "\n\nmodel:"
+
+ content = message.content
+ if isinstance(content, list):
+ content = "".join(
+ c.data for c in content if c.type != PromptMessageContentType.IMAGE
+ )
+
+ if isinstance(message, UserPromptMessage):
+ message_text = f"{human_prompt} {content}"
+ elif isinstance(message, AssistantPromptMessage):
+ message_text = f"{ai_prompt} {content}"
+ elif isinstance(message, SystemPromptMessage):
+ message_text = f"{human_prompt} {content}"
+ elif isinstance(message, ToolPromptMessage):
+ message_text = f"{human_prompt} {content}"
+ else:
+ raise ValueError(f"Got unknown type {message}")
+
+ return message_text
+
+ @property
+ def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
+ """
+ Map model invoke error to unified error
+ The key is the error type thrown to the caller
+ The value is the error type thrown by the model,
+ which needs to be converted into a unified error type for the caller.
+
+ :return: Invoke error mapping
+ """
+ return {
+ InvokeConnectionError: [],
+ InvokeServerUnavailableError: [],
+ InvokeRateLimitError: [],
+ InvokeAuthorizationError: [],
+ InvokeBadRequestError: []
+ }
diff --git a/api/core/model_runtime/model_providers/oci/llm/meta.llama-3-70b-instruct.yaml b/api/core/model_runtime/model_providers/oci/llm/meta.llama-3-70b-instruct.yaml
new file mode 100644
index 0000000000..dd5be107c0
--- /dev/null
+++ b/api/core/model_runtime/model_providers/oci/llm/meta.llama-3-70b-instruct.yaml
@@ -0,0 +1,51 @@
+model: meta.llama-3-70b-instruct
+label:
+ zh_Hans: meta.llama-3-70b-instruct
+ en_US: meta.llama-3-70b-instruct
+model_type: llm
+features:
+ - agent-thought
+model_properties:
+ mode: chat
+ context_size: 131072
+parameter_rules:
+ - name: temperature
+ use_template: temperature
+ default: 1
+ max: 2.0
+ - name: topP
+ use_template: top_p
+ default: 0.75
+ min: 0
+ max: 1
+ - name: topK
+ label:
+ zh_Hans: 取样数量
+ en_US: Top k
+ type: int
+ help:
+ zh_Hans: 仅从每个后续标记的前 K 个选项中采样。
+ en_US: Only sample from the top K options for each subsequent token.
+ required: false
+ default: 0
+ min: 0
+ max: 500
+ - name: presencePenalty
+ use_template: presence_penalty
+ min: -2
+ max: 2
+ default: 0
+ - name: frequencyPenalty
+ use_template: frequency_penalty
+ min: -2
+ max: 2
+ default: 0
+ - name: maxTokens
+ use_template: max_tokens
+ default: 600
+ max: 8000
+pricing:
+ input: '0.015'
+ output: '0.015'
+ unit: '0.0001'
+ currency: USD
diff --git a/api/core/model_runtime/model_providers/oci/oci.py b/api/core/model_runtime/model_providers/oci/oci.py
new file mode 100644
index 0000000000..11d67790a0
--- /dev/null
+++ b/api/core/model_runtime/model_providers/oci/oci.py
@@ -0,0 +1,34 @@
+import logging
+
+from core.model_runtime.entities.model_entities import ModelType
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.__base.model_provider import ModelProvider
+
+logger = logging.getLogger(__name__)
+
+
+class OCIGENAIProvider(ModelProvider):
+
+ def validate_provider_credentials(self, credentials: dict) -> None:
+ """
+ Validate provider credentials
+
+ if validate failed, raise exception
+
+ :param credentials: provider credentials, credentials form defined in `provider_credential_schema`.
+ """
+ try:
+ model_instance = self.get_model_instance(ModelType.LLM)
+
+ # Use `cohere.command-r-plus` model for validate,
+ model_instance.validate_credentials(
+ model='cohere.command-r-plus',
+ credentials=credentials
+ )
+ except CredentialsValidateFailedError as ex:
+ raise ex
+ except Exception as ex:
+ logger.exception(f'{self.get_provider_schema().provider} credentials validate failed')
+ raise ex
+
+
diff --git a/api/core/model_runtime/model_providers/oci/oci.yaml b/api/core/model_runtime/model_providers/oci/oci.yaml
new file mode 100644
index 0000000000..f2f23e18f1
--- /dev/null
+++ b/api/core/model_runtime/model_providers/oci/oci.yaml
@@ -0,0 +1,42 @@
+provider: oci
+label:
+ en_US: OCIGenerativeAI
+description:
+ en_US: Models provided by OCI, such as Cohere Command R and Cohere Command R+.
+ zh_Hans: OCI 提供的模型，例如 Cohere Command R 和 Cohere Command R+。
+icon_small:
+ en_US: icon_s_en.svg
+icon_large:
+ en_US: icon_l_en.svg
+background: "#FFFFFF"
+help:
+ title:
+ en_US: Get your API Key from OCI
+ zh_Hans: 从 OCI 获取 API Key
+ url:
+ en_US: https://docs.cloud.oracle.com/Content/API/Concepts/sdkconfig.htm
+supported_model_types:
+ - llm
+ - text-embedding
+ #- rerank
+configurate_methods:
+ - predefined-model
+ #- customizable-model
+provider_credential_schema:
+ credential_form_schemas:
+ - variable: oci_config_content
+ label:
+ en_US: oci api key config file's content
+ type: text-input
+ required: true
+ placeholder:
+ zh_Hans: 在此输入您的 oci api key config 文件的内容(base64.b64encode("user_ocid/fingerprint/tenancy_ocid/region/compartment_ocid".encode('utf-8')) )
+ en_US: Enter your oci api key config file's content(base64.b64encode("user_ocid/fingerprint/tenancy_ocid/region/compartment_ocid".encode('utf-8')) )
+ - variable: oci_key_content
+ label:
+ en_US: oci api key file's content
+ type: text-input
+ required: true
+ placeholder:
+ zh_Hans: 在此输入您的 oci api key 文件的内容(base64.b64encode("pem file content".encode('utf-8')))
+ en_US: Enter your oci api key file's content(base64.b64encode("pem file content".encode('utf-8')))
diff --git a/api/core/model_runtime/model_providers/oci/text_embedding/__init__.py b/api/core/model_runtime/model_providers/oci/text_embedding/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/api/core/model_runtime/model_providers/oci/text_embedding/_position.yaml b/api/core/model_runtime/model_providers/oci/text_embedding/_position.yaml
new file mode 100644
index 0000000000..149f1e3797
--- /dev/null
+++ b/api/core/model_runtime/model_providers/oci/text_embedding/_position.yaml
@@ -0,0 +1,5 @@
+- cohere.embed-english-light-v2.0
+- cohere.embed-english-light-v3.0
+- cohere.embed-english-v3.0
+- cohere.embed-multilingual-light-v3.0
+- cohere.embed-multilingual-v3.0
diff --git a/api/core/model_runtime/model_providers/oci/text_embedding/cohere.embed-english-light-v2.0.yaml b/api/core/model_runtime/model_providers/oci/text_embedding/cohere.embed-english-light-v2.0.yaml
new file mode 100644
index 0000000000..259d5b45b7
--- /dev/null
+++ b/api/core/model_runtime/model_providers/oci/text_embedding/cohere.embed-english-light-v2.0.yaml
@@ -0,0 +1,9 @@
+model: cohere.embed-english-light-v2.0
+model_type: text-embedding
+model_properties:
+ context_size: 1024
+ max_chunks: 48
+pricing:
+ input: '0.001'
+ unit: '0.0001'
+ currency: USD
diff --git a/api/core/model_runtime/model_providers/oci/text_embedding/cohere.embed-english-light-v3.0.yaml b/api/core/model_runtime/model_providers/oci/text_embedding/cohere.embed-english-light-v3.0.yaml
new file mode 100644
index 0000000000..065e7474c0
--- /dev/null
+++ b/api/core/model_runtime/model_providers/oci/text_embedding/cohere.embed-english-light-v3.0.yaml
@@ -0,0 +1,9 @@
+model: cohere.embed-english-light-v3.0
+model_type: text-embedding
+model_properties:
+ context_size: 384
+ max_chunks: 48
+pricing:
+ input: '0.001'
+ unit: '0.0001'
+ currency: USD
diff --git a/api/core/model_runtime/model_providers/oci/text_embedding/cohere.embed-english-v3.0.yaml b/api/core/model_runtime/model_providers/oci/text_embedding/cohere.embed-english-v3.0.yaml
new file mode 100644
index 0000000000..3e2deea16a
--- /dev/null
+++ b/api/core/model_runtime/model_providers/oci/text_embedding/cohere.embed-english-v3.0.yaml
@@ -0,0 +1,9 @@
+model: cohere.embed-english-v3.0
+model_type: text-embedding
+model_properties:
+ context_size: 1024
+ max_chunks: 48
+pricing:
+ input: '0.001'
+ unit: '0.0001'
+ currency: USD
diff --git a/api/core/model_runtime/model_providers/oci/text_embedding/cohere.embed-multilingual-light-v3.0.yaml b/api/core/model_runtime/model_providers/oci/text_embedding/cohere.embed-multilingual-light-v3.0.yaml
new file mode 100644
index 0000000000..0d2b892c64
--- /dev/null
+++ b/api/core/model_runtime/model_providers/oci/text_embedding/cohere.embed-multilingual-light-v3.0.yaml
@@ -0,0 +1,9 @@
+model: cohere.embed-multilingual-light-v3.0
+model_type: text-embedding
+model_properties:
+ context_size: 384
+ max_chunks: 48
+pricing:
+ input: '0.001'
+ unit: '0.0001'
+ currency: USD
diff --git a/api/core/model_runtime/model_providers/oci/text_embedding/cohere.embed-multilingual-v3.0.yaml b/api/core/model_runtime/model_providers/oci/text_embedding/cohere.embed-multilingual-v3.0.yaml
new file mode 100644
index 0000000000..9ebe260b32
--- /dev/null
+++ b/api/core/model_runtime/model_providers/oci/text_embedding/cohere.embed-multilingual-v3.0.yaml
@@ -0,0 +1,9 @@
+model: cohere.embed-multilingual-v3.0
+model_type: text-embedding
+model_properties:
+ context_size: 1024
+ max_chunks: 48
+pricing:
+ input: '0.001'
+ unit: '0.0001'
+ currency: USD
diff --git a/api/core/model_runtime/model_providers/oci/text_embedding/text_embedding.py b/api/core/model_runtime/model_providers/oci/text_embedding/text_embedding.py
new file mode 100644
index 0000000000..5e0a85583e
--- /dev/null
+++ b/api/core/model_runtime/model_providers/oci/text_embedding/text_embedding.py
@@ -0,0 +1,242 @@
+import base64
+import copy
+import time
+from typing import Optional
+
+import numpy as np
+import oci
+
+from core.model_runtime.entities.model_entities import PriceType
+from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
+from core.model_runtime.errors.invoke import (
+ InvokeAuthorizationError,
+ InvokeBadRequestError,
+ InvokeConnectionError,
+ InvokeError,
+ InvokeRateLimitError,
+ InvokeServerUnavailableError,
+)
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel
+
+request_template = {
+ "compartmentId": "",
+ "servingMode": {
+ "modelId": "cohere.embed-english-light-v3.0",
+ "servingType": "ON_DEMAND"
+ },
+ "truncate": "NONE",
+ "inputs": [""]
+}
+oci_config_template = {
+ "user": "",
+ "fingerprint": "",
+ "tenancy": "",
+ "region": "",
+ "compartment_id": "",
+ "key_content": ""
+ }
+class OCITextEmbeddingModel(TextEmbeddingModel):
+ """
+ Model class for Cohere text embedding model.
+ """
+
+ def _invoke(self, model: str, credentials: dict,
+ texts: list[str], user: Optional[str] = None) \
+ -> TextEmbeddingResult:
+ """
+ Invoke text embedding model
+
+ :param model: model name
+ :param credentials: model credentials
+ :param texts: texts to embed
+ :param user: unique user id
+ :return: embeddings result
+ """
+ # get model properties
+ context_size = self._get_context_size(model, credentials)
+ max_chunks = self._get_max_chunks(model, credentials)
+
+ inputs = []
+ indices = []
+ used_tokens = 0
+
+ for i, text in enumerate(texts):
+
+ # Here token count is only an approximation based on the GPT2 tokenizer
+ num_tokens = self._get_num_tokens_by_gpt2(text)
+
+ if num_tokens >= context_size:
+ cutoff = int(len(text) * (np.floor(context_size / num_tokens)))
+ # if num tokens is larger than context length, only use the start
+ inputs.append(text[0: cutoff])
+ else:
+ inputs.append(text)
+ indices += [i]
+
+ batched_embeddings = []
+ _iter = range(0, len(inputs), max_chunks)
+
+ for i in _iter:
+ # call embedding model
+ embeddings_batch, embedding_used_tokens = self._embedding_invoke(
+ model=model,
+ credentials=credentials,
+ texts=inputs[i: i + max_chunks]
+ )
+
+ used_tokens += embedding_used_tokens
+ batched_embeddings += embeddings_batch
+
+ # calc usage
+ usage = self._calc_response_usage(
+ model=model,
+ credentials=credentials,
+ tokens=used_tokens
+ )
+
+ return TextEmbeddingResult(
+ embeddings=batched_embeddings,
+ usage=usage,
+ model=model
+ )
+
+ def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int:
+ """
+ Get number of tokens for given prompt messages
+
+ :param model: model name
+ :param credentials: model credentials
+ :param texts: texts to embed
+ :return:
+ """
+ return sum(self._get_num_tokens_by_gpt2(text) for text in texts)
+
+ def get_num_characters(self, model: str, credentials: dict, texts: list[str]) -> int:
+ """
+ Get number of tokens for given prompt messages
+
+ :param model: model name
+ :param credentials: model credentials
+ :param texts: texts to embed
+ :return:
+ """
+ characters = 0
+ for text in texts:
+ characters += len(text)
+ return characters
+ def validate_credentials(self, model: str, credentials: dict) -> None:
+ """
+ Validate model credentials
+
+ :param model: model name
+ :param credentials: model credentials
+ :return:
+ """
+ try:
+ # call embedding model
+ self._embedding_invoke(
+ model=model,
+ credentials=credentials,
+ texts=['ping']
+ )
+ except Exception as ex:
+ raise CredentialsValidateFailedError(str(ex))
+
+ def _embedding_invoke(self, model: str, credentials: dict, texts: list[str]) -> tuple[list[list[float]], int]:
+ """
+ Invoke embedding model
+
+ :param model: model name
+ :param credentials: model credentials
+ :param texts: texts to embed
+ :return: embeddings and used tokens
+ """
+
+ # oci
+ # initialize client
+ oci_config = copy.deepcopy(oci_config_template)
+ if "oci_config_content" in credentials:
+ oci_config_content = base64.b64decode(credentials.get('oci_config_content')).decode('utf-8')
+ config_items = oci_config_content.split("/")
+ if len(config_items) != 5:
+ raise CredentialsValidateFailedError("oci_config_content should be base64.b64encode('user_ocid/fingerprint/tenancy_ocid/region/compartment_ocid'.encode('utf-8'))")
+ oci_config["user"] = config_items[0]
+ oci_config["fingerprint"] = config_items[1]
+ oci_config["tenancy"] = config_items[2]
+ oci_config["region"] = config_items[3]
+ oci_config["compartment_id"] = config_items[4]
+ else:
+ raise CredentialsValidateFailedError("need to set oci_config_content in credentials ")
+ if "oci_key_content" in credentials:
+ oci_key_content = base64.b64decode(credentials.get('oci_key_content')).decode('utf-8')
+ oci_config["key_content"] = oci_key_content.encode(encoding="utf-8")
+ else:
+ raise CredentialsValidateFailedError("need to set oci_config_content in credentials ")
+ # oci_config = oci.config.from_file('~/.oci/config', credentials.get('oci_api_profile'))
+ compartment_id = oci_config["compartment_id"]
+ client = oci.generative_ai_inference.GenerativeAiInferenceClient(config=oci_config)
+ # call embedding model
+ request_args = copy.deepcopy(request_template)
+ request_args["compartmentId"] = compartment_id
+ request_args["servingMode"]["modelId"] = model
+ request_args["inputs"] = texts
+ response = client.embed_text(request_args)
+ return response.data.embeddings, self.get_num_characters(model=model, credentials=credentials, texts=texts)
+
+ def _calc_response_usage(self, model: str, credentials: dict, tokens: int) -> EmbeddingUsage:
+ """
+ Calculate response usage
+
+ :param model: model name
+ :param credentials: model credentials
+ :param tokens: input tokens
+ :return: usage
+ """
+ # get input price info
+ input_price_info = self.get_price(
+ model=model,
+ credentials=credentials,
+ price_type=PriceType.INPUT,
+ tokens=tokens
+ )
+
+ # transform usage
+ usage = EmbeddingUsage(
+ tokens=tokens,
+ total_tokens=tokens,
+ unit_price=input_price_info.unit_price,
+ price_unit=input_price_info.unit,
+ total_price=input_price_info.total_amount,
+ currency=input_price_info.currency,
+ latency=time.perf_counter() - self.started_at
+ )
+
+ return usage
+
+ @property
+ def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:
+ """
+ Map model invoke error to unified error
+ The key is the error type thrown to the caller
+ The value is the error type thrown by the model,
+ which needs to be converted into a unified error type for the caller.
+ :return: Invoke error mapping
+ """
+ return {
+ InvokeConnectionError: [
+ InvokeConnectionError
+ ],
+ InvokeServerUnavailableError: [
+ InvokeServerUnavailableError
+ ],
+ InvokeRateLimitError: [
+ InvokeRateLimitError
+ ],
+ InvokeAuthorizationError: [
+ InvokeAuthorizationError
+ ],
+ InvokeBadRequestError: [
+ KeyError
+ ]
+ }
diff --git a/api/poetry.lock b/api/poetry.lock
index 290468d123..93dd4c71bb 100644
--- a/api/poetry.lock
+++ b/api/poetry.lock
@@ -1151,78 +1151,78 @@ files = [
[[package]]
name = "cffi"
-version = "1.17.0"
+version = "1.17.1"
description = "Foreign Function Interface for Python calling C code."
optional = false
python-versions = ">=3.8"
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sdist = ["build"]
ssh = ["bcrypt (>=3.1.5)"]
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test-randomorder = ["pytest-randomly"]
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]
[package.dependencies]
@@ -10073,4 +10126,4 @@ cffi = ["cffi (>=1.11)"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.10,<3.13"
-content-hash = "71f8ba1097b6b0008782c652228f7361ff5251e45bf7081f7e812571665bfdca"
+content-hash = "78c7db0bf525a72f4c8309e3363304d1a0a23cf0a6836bfb974a38a1fcde9158"
diff --git a/api/pyproject.toml b/api/pyproject.toml
index 12726dbc6e..52e6147345 100644
--- a/api/pyproject.toml
+++ b/api/pyproject.toml
@@ -190,6 +190,7 @@ zhipuai = "1.0.7"
azure-ai-ml = "^1.19.0"
azure-ai-inference = "^1.0.0b3"
volcengine-python-sdk = {extras = ["ark"], version = "^1.0.98"}
+oci = "^2.133.0"
[tool.poetry.group.indriect.dependencies]
kaleido = "0.2.1"
rank-bm25 = "~0.2.2"
diff --git a/api/tests/integration_tests/model_runtime/oci/__init__.py b/api/tests/integration_tests/model_runtime/oci/__init__.py
new file mode 100644
index 0000000000..e69de29bb2
diff --git a/api/tests/integration_tests/model_runtime/oci/test_llm.py b/api/tests/integration_tests/model_runtime/oci/test_llm.py
new file mode 100644
index 0000000000..531f26a32e
--- /dev/null
+++ b/api/tests/integration_tests/model_runtime/oci/test_llm.py
@@ -0,0 +1,130 @@
+import os
+from collections.abc import Generator
+
+import pytest
+
+from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta
+from core.model_runtime.entities.message_entities import (
+ AssistantPromptMessage,
+ PromptMessageTool,
+ SystemPromptMessage,
+ TextPromptMessageContent,
+ UserPromptMessage,
+)
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.oci.llm.llm import OCILargeLanguageModel
+
+
+def test_validate_credentials():
+ model = OCILargeLanguageModel()
+
+ with pytest.raises(CredentialsValidateFailedError):
+ model.validate_credentials(
+ model="cohere.command-r-plus",
+ credentials={"oci_config_content": "invalid_key", "oci_key_content": "invalid_key"},
+ )
+
+ model.validate_credentials(
+ model="cohere.command-r-plus",
+ credentials={
+ "oci_config_content": os.environ.get("OCI_CONFIG_CONTENT"),
+ "oci_key_content": os.environ.get("OCI_KEY_CONTENT"),
+ },
+ )
+
+
+def test_invoke_model():
+ model = OCILargeLanguageModel()
+
+ response = model.invoke(
+ model="cohere.command-r-plus",
+ credentials={
+ "oci_config_content": os.environ.get("OCI_CONFIG_CONTENT"),
+ "oci_key_content": os.environ.get("OCI_KEY_CONTENT"),
+ },
+ prompt_messages=[UserPromptMessage(content="Hi")],
+ model_parameters={"temperature": 0.5, "max_tokens": 10},
+ stop=["How"],
+ stream=False,
+ user="abc-123",
+ )
+
+ assert isinstance(response, LLMResult)
+ assert len(response.message.content) > 0
+
+
+def test_invoke_stream_model():
+ model = OCILargeLanguageModel()
+
+ response = model.invoke(
+ model="meta.llama-3-70b-instruct",
+ credentials={
+ "oci_config_content": os.environ.get("OCI_CONFIG_CONTENT"),
+ "oci_key_content": os.environ.get("OCI_KEY_CONTENT"),
+ },
+ prompt_messages=[UserPromptMessage(content="Hi")],
+ model_parameters={"temperature": 0.5, "max_tokens": 100, "seed": 1234},
+ stream=True,
+ user="abc-123",
+ )
+
+ assert isinstance(response, Generator)
+
+ for chunk in response:
+ assert isinstance(chunk, LLMResultChunk)
+ assert isinstance(chunk.delta, LLMResultChunkDelta)
+ assert isinstance(chunk.delta.message, AssistantPromptMessage)
+ assert len(chunk.delta.message.content) > 0 if chunk.delta.finish_reason is None else True
+
+
+def test_invoke_model_with_function():
+ model = OCILargeLanguageModel()
+
+ response = model.invoke(
+ model="cohere.command-r-plus",
+ credentials={
+ "oci_config_content": os.environ.get("OCI_CONFIG_CONTENT"),
+ "oci_key_content": os.environ.get("OCI_KEY_CONTENT"),
+ },
+ prompt_messages=[UserPromptMessage(content="Hi")],
+ model_parameters={"temperature": 0.5, "max_tokens": 100, "seed": 1234},
+ stream=False,
+ user="abc-123",
+ tools=[
+ PromptMessageTool(
+ name="get_current_weather",
+ description="Get the current weather in a given location",
+ parameters={
+ "type": "object",
+ "properties": {
+ "location": {"type": "string", "description": "The city and state e.g. San Francisco, CA"},
+ "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
+ },
+ "required": ["location"],
+ },
+ )
+ ],
+ )
+
+ assert isinstance(response, LLMResult)
+ assert len(response.message.content) > 0
+
+
+def test_get_num_tokens():
+ model = OCILargeLanguageModel()
+
+ num_tokens = model.get_num_tokens(
+ model="cohere.command-r-plus",
+ credentials={
+ "oci_config_content": os.environ.get("OCI_CONFIG_CONTENT"),
+ "oci_key_content": os.environ.get("OCI_KEY_CONTENT"),
+ },
+ prompt_messages=[
+ SystemPromptMessage(
+ content="You are a helpful AI assistant.",
+ ),
+ UserPromptMessage(content="Hello World!"),
+ ],
+ )
+
+ assert num_tokens == 18
diff --git a/api/tests/integration_tests/model_runtime/oci/test_provider.py b/api/tests/integration_tests/model_runtime/oci/test_provider.py
new file mode 100644
index 0000000000..2c7107c7cc
--- /dev/null
+++ b/api/tests/integration_tests/model_runtime/oci/test_provider.py
@@ -0,0 +1,20 @@
+import os
+
+import pytest
+
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.oci.oci import OCIGENAIProvider
+
+
+def test_validate_provider_credentials():
+ provider = OCIGENAIProvider()
+
+ with pytest.raises(CredentialsValidateFailedError):
+ provider.validate_provider_credentials(credentials={})
+
+ provider.validate_provider_credentials(
+ credentials={
+ "oci_config_content": os.environ.get("OCI_CONFIG_CONTENT"),
+ "oci_key_content": os.environ.get("OCI_KEY_CONTENT"),
+ }
+ )
diff --git a/api/tests/integration_tests/model_runtime/oci/test_text_embedding.py b/api/tests/integration_tests/model_runtime/oci/test_text_embedding.py
new file mode 100644
index 0000000000..032c5c681a
--- /dev/null
+++ b/api/tests/integration_tests/model_runtime/oci/test_text_embedding.py
@@ -0,0 +1,58 @@
+import os
+
+import pytest
+
+from core.model_runtime.entities.text_embedding_entities import TextEmbeddingResult
+from core.model_runtime.errors.validate import CredentialsValidateFailedError
+from core.model_runtime.model_providers.oci.text_embedding.text_embedding import OCITextEmbeddingModel
+
+
+def test_validate_credentials():
+ model = OCITextEmbeddingModel()
+
+ with pytest.raises(CredentialsValidateFailedError):
+ model.validate_credentials(
+ model="cohere.embed-multilingual-v3.0",
+ credentials={"oci_config_content": "invalid_key", "oci_key_content": "invalid_key"},
+ )
+
+ model.validate_credentials(
+ model="cohere.embed-multilingual-v3.0",
+ credentials={
+ "oci_config_content": os.environ.get("OCI_CONFIG_CONTENT"),
+ "oci_key_content": os.environ.get("OCI_KEY_CONTENT"),
+ },
+ )
+
+
+def test_invoke_model():
+ model = OCITextEmbeddingModel()
+
+ result = model.invoke(
+ model="cohere.embed-multilingual-v3.0",
+ credentials={
+ "oci_config_content": os.environ.get("OCI_CONFIG_CONTENT"),
+ "oci_key_content": os.environ.get("OCI_KEY_CONTENT"),
+ },
+ texts=["hello", "world", " ".join(["long_text"] * 100), " ".join(["another_long_text"] * 100)],
+ user="abc-123",
+ )
+
+ assert isinstance(result, TextEmbeddingResult)
+ assert len(result.embeddings) == 4
+ # assert result.usage.total_tokens == 811
+
+
+def test_get_num_tokens():
+ model = OCITextEmbeddingModel()
+
+ num_tokens = model.get_num_tokens(
+ model="cohere.embed-multilingual-v3.0",
+ credentials={
+ "oci_config_content": os.environ.get("OCI_CONFIG_CONTENT"),
+ "oci_key_content": os.environ.get("OCI_KEY_CONTENT"),
+ },
+ texts=["hello", "world"],
+ )
+
+ assert num_tokens == 2