From 2268126a3b7cc132e774c905eb6e593f765ab4dc Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E9=9D=9E=E6=B3=95=E6=93=8D=E4=BD=9C?= Date: Fri, 13 Dec 2024 14:16:44 +0800 Subject: [PATCH 1/6] cherry pick --- api/pyproject.toml | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/api/pyproject.toml b/api/pyproject.toml index da9eabecf55ccf..e1f4e59f1d0c67 100644 --- a/api/pyproject.toml +++ b/api/pyproject.toml @@ -37,13 +37,12 @@ flask-restful = "~0.3.10" flask-sqlalchemy = "~3.1.1" gevent = "~24.11.1" gmpy2 = "~2.2.1" -google-ai-generativelanguage = "0.6.9" google-api-core = "2.18.0" google-api-python-client = "2.90.0" google-auth = "2.29.0" google-auth-httplib2 = "0.2.0" google-cloud-aiplatform = "1.49.0" -google-generativeai = "0.8.1" +google-genai = "0.2.2" googleapis-common-protos = "1.63.0" gunicorn = "~23.0.0" httpx = { version = "~0.27.0", extras = ["socks"] } @@ -89,7 +88,7 @@ validators = "0.21.0" volcengine-python-sdk = {extras = ["ark"], version = "~1.0.98"} websocket-client = "~1.7.0" xinference-client = "0.15.2" -yarl = "~1.9.4" +yarl = "~1.12.0" youtube-transcript-api = "~0.6.2" zhipuai = "~2.1.5" # Before adding new dependency, consider place it in alphabet order (a-z) and suitable group. From 2815ef7c0075a82ff34abbe8c200e67e692424dc Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E9=9D=9E=E6=B3=95=E6=93=8D=E4=BD=9C?= Date: Fri, 13 Dec 2024 14:16:49 +0800 Subject: [PATCH 2/6] cherry pick --- .../model_providers/google/llm/llm.py | 295 +++++++++--------- 1 file changed, 139 insertions(+), 156 deletions(-) diff --git a/api/core/model_runtime/model_providers/google/llm/llm.py b/api/core/model_runtime/model_providers/google/llm/llm.py index b54668a12d3207..ae1ef3a20825f1 100644 --- a/api/core/model_runtime/model_providers/google/llm/llm.py +++ b/api/core/model_runtime/model_providers/google/llm/llm.py @@ -4,14 +4,11 @@ import tempfile import time from collections.abc import Generator -from typing import Optional, Union +from typing import Iterator, Optional, Union -import google.ai.generativelanguage as glm -import google.generativeai as genai import requests -from google.api_core import exceptions -from google.generativeai.types import ContentType, File, GenerateContentResponse -from google.generativeai.types.content_types import to_part +from google import genai +from google.genai import errors, types from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta from core.model_runtime.entities.message_entities import ( @@ -39,15 +36,15 @@ class GoogleLargeLanguageModel(LargeLanguageModel): 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, + 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 @@ -66,11 +63,11 @@ def _invoke( 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, + self, + model: str, + credentials: dict, + prompt_messages: list[PromptMessage], + tools: Optional[list[PromptMessageTool]] = None, ) -> int: """ Get number of tokens for given prompt messages @@ -98,7 +95,7 @@ def _convert_messages_to_prompt(self, messages: list[PromptMessage]) -> str: return text.rstrip() - def _convert_tools_to_glm_tool(self, tools: list[PromptMessageTool]) -> glm.Tool: + def _convert_tools_to_glm_tool(self, tools: list[PromptMessageTool]) -> types.Tool: """ Convert tool messages to glm tools @@ -109,29 +106,31 @@ def _convert_tools_to_glm_tool(self, tools: list[PromptMessageTool]) -> glm.Tool for tool in tools: properties = {} for key, value in tool.parameters.get("properties", {}).items(): - properties[key] = { - "type_": glm.Type.STRING, + property_def = { + "type": "STRING", "description": value.get("description", ""), - "enum": value.get("enum", []), } + if "enum" in value: + property_def["enum"] = value["enum"] + properties[key] = property_def if properties: - parameters = glm.Schema( - type=glm.Type.OBJECT, + parameters = types.Schema( + type="OBJECT", properties=properties, required=tool.parameters.get("required", []), ) else: parameters = None - function_declaration = glm.FunctionDeclaration( + functions = types.FunctionDeclaration( name=tool.name, parameters=parameters, description=tool.description, ) - function_declarations.append(function_declaration) + function_declarations.append(functions) - return glm.Tool(function_declarations=function_declarations) + return types.Tool(function_declarations=function_declarations) def validate_credentials(self, model: str, credentials: dict) -> None: """ @@ -150,15 +149,15 @@ def validate_credentials(self, model: str, credentials: dict) -> None: 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, + 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 @@ -177,42 +176,51 @@ def _generate( try: schema = json.loads(schema) except: - raise exceptions.InvalidArgument("Invalid JSON Schema") + raise errors.FunctionInvocationError("Invalid JSON Schema") if tools: - raise exceptions.InvalidArgument("gemini not support use Tools and JSON Schema at same time") + raise errors.FunctionInvocationError("gemini not support use Tools and JSON Schema at same time") config_kwargs["response_schema"] = schema config_kwargs["response_mime_type"] = "application/json" if stop: config_kwargs["stop_sequences"] = stop - genai.configure(api_key=credentials["google_api_key"]) - google_model = genai.GenerativeModel(model_name=model) + config_kwargs["tools"] = [] + if tools: + config_kwargs["tools"].append(self._convert_tools_to_glm_tool(tools)) + + self.client = genai.Client(api_key=credentials["google_api_key"]) history = [] 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"]) + if history and history[-1].role == content.role: + history[-1].parts.extend(content.parts) else: history.append(content) - response = google_model.generate_content( - contents=history, - generation_config=genai.types.GenerationConfig(**config_kwargs), - stream=stream, - tools=self._convert_tools_to_glm_tool(tools) if tools else None, - request_options={"timeout": 600}, - ) - if stream: + response = self.client.models.generate_content_stream( + model=model, + contents=history, + config=types.GenerateContentConfig(**config_kwargs), + ) return self._handle_generate_stream_response(model, credentials, response, prompt_messages) + response = self.client.models.generate_content( + model=model, + contents=history, + config=types.GenerateContentConfig(**config_kwargs), + ) return self._handle_generate_response(model, credentials, response, prompt_messages) def _handle_generate_response( - self, model: str, credentials: dict, response: GenerateContentResponse, prompt_messages: list[PromptMessage] + self, + model: str, + credentials: dict, + response: types.GenerateContentResponse, + prompt_messages: list[PromptMessage], ) -> LLMResult: """ Handle llm response @@ -248,7 +256,11 @@ def _handle_generate_response( return result def _handle_generate_stream_response( - self, model: str, credentials: dict, response: GenerateContentResponse, prompt_messages: list[PromptMessage] + self, + model: str, + credentials: dict, + response: Iterator[types.GenerateContentResponse], + prompt_messages: list[PromptMessage], ) -> Generator: """ Handle llm stream response @@ -260,56 +272,52 @@ def _handle_generate_stream_response( :return: llm response chunk generator result """ index = -1 - for chunk in response: - for part in chunk.parts: - assistant_prompt_message = AssistantPromptMessage(content="") - - if part.text: - assistant_prompt_message.content += part.text - - 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())), - ), - ) - ] - - index += 1 - - if not response._done: - # 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 - if hasattr(response, "usage_metadata") and response.usage_metadata: - prompt_tokens = response.usage_metadata.prompt_token_count - completion_tokens = response.usage_metadata.candidates_token_count - else: - prompt_tokens = self.get_num_tokens(model, credentials, prompt_messages) - completion_tokens = self.get_num_tokens(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.candidates[0].finish_reason), - usage=usage, - ), - ) + for r in response: + assistant_prompt_message = AssistantPromptMessage(content="") + parts = r.candidates[0].content.parts + index += 1 + if parts is None: + # calculate num tokens + prompt_tokens = r.usage_metadata.prompt_token_count or self.get_num_tokens( + model, credentials, prompt_messages + ) + completion_tokens = r.usage_metadata.candidates_token_count or self.get_num_tokens( + 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(r.candidates[0].finish_reason), + usage=usage, + ), + ) + + else: + for part in parts: + if part.text: + assistant_prompt_message.content += part.text + elif 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())), + ), + ) + ] + # transform assistant message to prompt message + yield LLMResultChunk( + model=model, + prompt_messages=prompt_messages, + delta=LLMResultChunkDelta(index=index, message=assistant_prompt_message), + ) def _convert_one_message_to_text(self, message: PromptMessage) -> str: """ @@ -336,11 +344,11 @@ def _convert_one_message_to_text(self, message: PromptMessage) -> str: return message_text - def _upload_file_content_to_google(self, message_content: PromptMessageContent) -> File: + def _upload_file_content_to_google(self, message_content: PromptMessageContent) -> types.File: key = f"{message_content.type.value}:{hash(message_content.data)}" if redis_client.exists(key): try: - return genai.get_file(redis_client.get(key).decode()) + return self.client.files.get(name=redis_client.get(key).decode()) except: pass with tempfile.NamedTemporaryFile(delete=False) as temp_file: @@ -370,7 +378,7 @@ def _upload_file_content_to_google(self, message_content: PromptMessageContent) pass return file - def _format_message_to_glm_content(self, message: PromptMessage) -> ContentType: + def _format_message_to_glm_content(self, message: PromptMessage) -> types.Content: """ Format a single message into glm.Content for Google API @@ -378,44 +386,37 @@ def _format_message_to_glm_content(self, message: PromptMessage) -> ContentType: :return: glm Content representation of message """ if isinstance(message, UserPromptMessage): - glm_content = {"role": "user", "parts": []} + glm_content = types.Content(role="user", parts=[]) if isinstance(message.content, str): - glm_content["parts"].append(to_part(message.content)) + glm_content.parts.append(types.Part.from_text(message.content)) else: for c in message.content: if c.type == PromptMessageContentType.TEXT: - glm_content["parts"].append(to_part(c.data)) + glm_content.parts.append(types.Part.from_text(c.data)) else: - glm_content["parts"].append(self._upload_file_content_to_google(c)) + f = self._upload_file_content_to_google(c) + glm_content.parts.append(types.Part.from_uri(file_uri=f.uri, mime_type=f.mime_type)) return glm_content elif isinstance(message, AssistantPromptMessage): - glm_content = {"role": "model", "parts": []} + glm_content = types.Content(role="model", parts=[]) if message.content: - glm_content["parts"].append(to_part(message.content)) + glm_content.parts.append(types.Part.from_text(message.content)) if message.tool_calls: - glm_content["parts"].append( - to_part( - glm.FunctionCall( - name=message.tool_calls[0].function.name, - args=json.loads(message.tool_calls[0].function.arguments), - ) + glm_content.parts.append( + types.Part.from_function_call( + name=message.tool_calls[0].function.name, + args=json.loads(message.tool_calls[0].function.arguments), ) ) return glm_content elif isinstance(message, SystemPromptMessage): - return {"role": "user", "parts": [to_part(message.content)]} + return types.Content(role="user", parts=[types.Part.from_text(message.content)]) elif isinstance(message, ToolPromptMessage): - return { - "role": "function", - "parts": [ - glm.Part( - function_response=glm.FunctionResponse( - name=message.name, response={"response": message.content} - ) - ) - ], - } + return types.Content( + role="function", + parts=[types.Part.from_function_response(name=message.name, response={"response": message.content})], + ) else: raise ValueError(f"Got unknown type {message}") @@ -430,34 +431,16 @@ def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]] :return: Invoke emd = genai.GenerativeModel(model) error mapping """ return { - InvokeConnectionError: [exceptions.RetryError], + InvokeConnectionError: [errors.APIError], InvokeServerUnavailableError: [ - exceptions.ServiceUnavailable, - exceptions.InternalServerError, - exceptions.BadGateway, - exceptions.GatewayTimeout, - exceptions.DeadlineExceeded, - ], - InvokeRateLimitError: [exceptions.ResourceExhausted, exceptions.TooManyRequests], - InvokeAuthorizationError: [ - exceptions.Unauthenticated, - exceptions.PermissionDenied, - exceptions.Unauthenticated, - exceptions.Forbidden, + errors.ServerError, ], + InvokeRateLimitError: [], + InvokeAuthorizationError: [], InvokeBadRequestError: [ - exceptions.BadRequest, - exceptions.InvalidArgument, - exceptions.FailedPrecondition, - exceptions.OutOfRange, - exceptions.NotFound, - exceptions.MethodNotAllowed, - exceptions.Conflict, - exceptions.AlreadyExists, - exceptions.Aborted, - exceptions.LengthRequired, - exceptions.PreconditionFailed, - exceptions.RequestRangeNotSatisfiable, - exceptions.Cancelled, + errors.ClientError, + errors.UnkownFunctionCallArgumentError, + errors.UnsupportedFunctionError, + errors.FunctionInvocationError, ], } From 5102995af1a128838c20c983754f726658582878 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E9=9D=9E=E6=B3=95=E6=93=8D=E4=BD=9C?= Date: Fri, 13 Dec 2024 17:39:03 +0800 Subject: [PATCH 3/6] make gemini tests use new sdk --- .../model_runtime/__mock/google.py | 91 +++++++------------ 1 file changed, 31 insertions(+), 60 deletions(-) diff --git a/api/tests/integration_tests/model_runtime/__mock/google.py b/api/tests/integration_tests/model_runtime/__mock/google.py index 5ea86baa83dd4b..b6ae725a9e8f09 100644 --- a/api/tests/integration_tests/model_runtime/__mock/google.py +++ b/api/tests/integration_tests/model_runtime/__mock/google.py @@ -1,79 +1,53 @@ -from collections.abc import Generator from unittest.mock import MagicMock -import google.generativeai.types.generation_types as generation_config_types import pytest from _pytest.monkeypatch import MonkeyPatch -from google.ai import generativelanguage as glm -from google.ai.generativelanguage_v1beta.types import content as gag_content -from google.generativeai import GenerativeModel -from google.generativeai.types import GenerateContentResponse, content_types, safety_types -from google.generativeai.types.generation_types import BaseGenerateContentResponse +from google.genai import errors, types from extensions import ext_redis -class MockGoogleResponseClass: - _done = False - - def __iter__(self): +class MockGoogleStreamResponse: + def __iter__(self) -> types.GenerateContentResponse: full_response_text = "it's google!" for i in range(0, len(full_response_text) + 1, 1): if i == len(full_response_text): - self._done = True - yield GenerateContentResponse( - done=True, iterator=None, result=glm.GenerateContentResponse({}), chunks=[] + yield types.GenerateContentResponse( + candidates=[types.Candidate(content=types.Content(parts=None))], + usage_metadata=types.GenerateContentResponseUsageMetadata(prompt_token_count=0), ) else: - yield GenerateContentResponse( - done=False, iterator=None, result=glm.GenerateContentResponse({}), chunks=[] + yield types.GenerateContentResponse( + candidates=[types.Candidate(content=types.Content(parts=[types.Part(text=str(i))]))] ) -class MockGoogleResponseCandidateClass: - finish_reason = "stop" +class MockGoogleSyncResponse: + def __init__(self): + self._text = "it's google!" + self.usage_metadata = None @property - def content(self) -> gag_content.Content: - return gag_content.Content(parts=[gag_content.Part(text="it's google!")]) - - -class MockGoogleClass: - @staticmethod - def generate_content_sync() -> GenerateContentResponse: - return GenerateContentResponse(done=True, iterator=None, result=glm.GenerateContentResponse({}), chunks=[]) - - @staticmethod - def generate_content_stream() -> Generator[GenerateContentResponse, None, None]: - return MockGoogleResponseClass() + def text(self) -> str: + return self._text - def generate_content( - self: GenerativeModel, - contents: content_types.ContentsType, - *, - generation_config: generation_config_types.GenerationConfigType | None = None, - safety_settings: safety_types.SafetySettingOptions | None = None, - stream: bool = False, - **kwargs, - ) -> GenerateContentResponse: - if stream: - return MockGoogleClass.generate_content_stream() - return MockGoogleClass.generate_content_sync() - - @property - def generative_response_text(self) -> str: - return "it's google!" - - @property - def generative_response_candidates(self) -> list[MockGoogleResponseCandidateClass]: - return [MockGoogleResponseCandidateClass()] +class MockGoogleClient: + def __init__(self, api_key=None): + if len(api_key) < 16: + raise errors.ClientError("Invalid API key") + self.models = self.MockModels() + self.files = MagicMock() + self.files.get = mock_get_file + self.files.upload = mock_upload_file + class MockModels: + def generate_content(self, *args, **kwargs) -> types.GenerateContentResponse: + return MockGoogleSyncResponse() -def mock_configure(api_key: str): - if len(api_key) < 16: - raise Exception("Invalid API key") + def generate_content_stream(self, *args, **kwargs) -> types.GenerateContentResponse: + return MockGoogleStreamResponse() class MockFileState: @@ -85,24 +59,21 @@ class MockGoogleFile: def __init__(self, name: str = "mock_file_name"): self.name = name self.state = MockFileState() + self.uri = "http://example.com" + self.mime_type = "image/png" def mock_get_file(name: str) -> MockGoogleFile: return MockGoogleFile(name) -def mock_upload_file(path: str, mime_type: str) -> MockGoogleFile: +def mock_upload_file(path: str, config: dict) -> MockGoogleFile: return MockGoogleFile() @pytest.fixture def setup_google_mock(request, monkeypatch: MonkeyPatch): - monkeypatch.setattr(BaseGenerateContentResponse, "text", MockGoogleClass.generative_response_text) - monkeypatch.setattr(BaseGenerateContentResponse, "candidates", MockGoogleClass.generative_response_candidates) - monkeypatch.setattr(GenerativeModel, "generate_content", MockGoogleClass.generate_content) - monkeypatch.setattr("google.generativeai.configure", mock_configure) - monkeypatch.setattr("google.generativeai.get_file", mock_get_file) - monkeypatch.setattr("google.generativeai.upload_file", mock_upload_file) + monkeypatch.setattr("google.genai.Client", MockGoogleClient) yield From 1c253651f4724793eafd1a2d8ac0c6e9395063bc Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E9=9D=9E=E6=B3=95=E6=93=8D=E4=BD=9C?= Date: Wed, 18 Dec 2024 18:00:43 +0800 Subject: [PATCH 4/6] update to google-genai 0.3.0 --- .../model_providers/google/llm/llm.py | 11 +- api/poetry.lock | 1211 ++++++++--------- api/pyproject.toml | 2 +- 3 files changed, 607 insertions(+), 617 deletions(-) diff --git a/api/core/model_runtime/model_providers/google/llm/llm.py b/api/core/model_runtime/model_providers/google/llm/llm.py index ae1ef3a20825f1..851b937a19d6be 100644 --- a/api/core/model_runtime/model_providers/google/llm/llm.py +++ b/api/core/model_runtime/model_providers/google/llm/llm.py @@ -364,10 +364,10 @@ def _upload_file_content_to_google(self, message_content: PromptMessageContent) raise ValueError(f"Failed to fetch data from url {message_content.url}, {ex}") temp_file.flush() - file = genai.upload_file(path=temp_file.name, mime_type=message_content.mime_type) - while file.state.name == "PROCESSING": + file = self.client.files.upload(path=temp_file.name, config={"mime_type": message_content.mime_type}) + while file.state == "PROCESSING": time.sleep(5) - file = genai.get_file(file.name) + file = self.client.files.get(name=file.name) # google will delete your upload files in 2 days. redis_client.setex(key, 47 * 60 * 60, file.name) @@ -424,11 +424,6 @@ def _format_message_to_glm_content(self, message: PromptMessage) -> types.Conten def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]: """ Map model invoke error to unified error - The key is the ermd = genai.GenerativeModel(model) error type thrown to the caller - The value is the md = genai.GenerativeModel(model) error type thrown by the model, - which needs to be converted into a unified error type for the caller. - - :return: Invoke emd = genai.GenerativeModel(model) error mapping """ return { InvokeConnectionError: [errors.APIError], diff --git a/api/poetry.lock b/api/poetry.lock index 35fda9b36fa42a..20095a46da19af 100644 --- a/api/poetry.lock +++ b/api/poetry.lock @@ -24,102 +24,102 @@ files = [ [[package]] name = "aiohttp" -version = "3.10.5" +version = "3.10.11" description = "Async http client/server framework (asyncio)" optional = false python-versions = ">=3.8" files = [ - {file = "aiohttp-3.10.5-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:18a01eba2574fb9edd5f6e5fb25f66e6ce061da5dab5db75e13fe1558142e0a3"}, - {file = "aiohttp-3.10.5-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:94fac7c6e77ccb1ca91e9eb4cb0ac0270b9fb9b289738654120ba8cebb1189c6"}, - {file = "aiohttp-3.10.5-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:2f1f1c75c395991ce9c94d3e4aa96e5c59c8356a15b1c9231e783865e2772699"}, - {file = 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8043caa5595d6013025eb83899d7a2614625258b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E9=9D=9E=E6=B3=95=E6=93=8D=E4=BD=9C?= Date: Wed, 18 Dec 2024 20:27:31 +0800 Subject: [PATCH 5/6] fix CI --- api/core/model_runtime/model_providers/google/llm/llm.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/api/core/model_runtime/model_providers/google/llm/llm.py b/api/core/model_runtime/model_providers/google/llm/llm.py index 851b937a19d6be..39add4b3c7808b 100644 --- a/api/core/model_runtime/model_providers/google/llm/llm.py +++ b/api/core/model_runtime/model_providers/google/llm/llm.py @@ -3,8 +3,8 @@ import os import tempfile import time -from collections.abc import Generator -from typing import Iterator, Optional, Union +from collections.abc import Generator, Iterator +from typing import Optional, Union import requests from google import genai From 219309e61ebcca46f8e2db1bc71543291fae2151 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E9=9D=9E=E6=B3=95=E6=93=8D=E4=BD=9C?= Date: Wed, 18 Dec 2024 20:30:23 +0800 Subject: [PATCH 6/6] fix CI --- .../model_providers/google/llm/llm.py | 66 +++++++++---------- 1 file changed, 33 insertions(+), 33 deletions(-) diff --git a/api/core/model_runtime/model_providers/google/llm/llm.py b/api/core/model_runtime/model_providers/google/llm/llm.py index 39add4b3c7808b..124d1de03b7059 100644 --- a/api/core/model_runtime/model_providers/google/llm/llm.py +++ b/api/core/model_runtime/model_providers/google/llm/llm.py @@ -36,15 +36,15 @@ class GoogleLargeLanguageModel(LargeLanguageModel): 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, + 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 @@ -63,11 +63,11 @@ def _invoke( 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, + self, + model: str, + credentials: dict, + prompt_messages: list[PromptMessage], + tools: Optional[list[PromptMessageTool]] = None, ) -> int: """ Get number of tokens for given prompt messages @@ -149,15 +149,15 @@ def validate_credentials(self, model: str, credentials: dict) -> None: 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, + 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 @@ -216,11 +216,11 @@ def _generate( return self._handle_generate_response(model, credentials, response, prompt_messages) def _handle_generate_response( - self, - model: str, - credentials: dict, - response: types.GenerateContentResponse, - prompt_messages: list[PromptMessage], + self, + model: str, + credentials: dict, + response: types.GenerateContentResponse, + prompt_messages: list[PromptMessage], ) -> LLMResult: """ Handle llm response @@ -256,11 +256,11 @@ def _handle_generate_response( return result def _handle_generate_stream_response( - self, - model: str, - credentials: dict, - response: Iterator[types.GenerateContentResponse], - prompt_messages: list[PromptMessage], + self, + model: str, + credentials: dict, + response: Iterator[types.GenerateContentResponse], + prompt_messages: list[PromptMessage], ) -> Generator: """ Handle llm stream response