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feat: gemini pro function call (#3406)

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pull/3482/head
Yeuoly 2 years ago committed by GitHub
parent 0737e930cb
commit a258a90291
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GPG Key ID: B5690EEEBB952194
4 changed files with 152 additions and 63 deletions
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2
api/core/model_runtime/model_providers/google/llm/gemini-1.5-pro-latest.yaml
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@ -5,6 +5,8 @@ model_type: llm
features:
- agent-thought
- vision
- tool-call
- stream-tool-call
model_properties:
mode: chat
context_size: 1048576

2
api/core/model_runtime/model_providers/google/llm/gemini-pro.yaml
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@ -4,6 +4,8 @@ label:
model_type: llm
features:
- agent-thought
- tool-call
- stream-tool-call
model_properties:
mode: chat
context_size: 30720

200
api/core/model_runtime/model_providers/google/llm/llm.py
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@ -1,7 +1,9 @@
import json
import logging
from collections.abc import Generator
from typing import Optional, Union
import google.ai.generativelanguage as glm
import google.api_core.exceptions as exceptions
import google.generativeai as genai
import google.generativeai.client as client
@ -13,9 +15,9 @@ from core.model_runtime.entities.message_entities import (
AssistantPromptMessage,
PromptMessage,
PromptMessageContentType,
PromptMessageRole,
PromptMessageTool,
SystemPromptMessage,
ToolPromptMessage,
UserPromptMessage,
)
from core.model_runtime.errors.invoke import (
@ -62,7 +64,7 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
:return: full response or stream response chunk generator result
"""
# invoke model
return self._generate(model, credentials, prompt_messages, model_parameters, stop, stream, user)
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:
@ -94,6 +96,32 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
)
return text.rstrip()
def _convert_tools_to_glm_tool(self, tools: list[PromptMessageTool]) -> glm.Tool:
"""
Convert tool messages to glm tools
:param tools: tool messages
:return: glm tools
"""
return glm.Tool(
function_declarations=[
glm.FunctionDeclaration(
name=tool.name,
parameters=glm.Schema(
type=glm.Type.OBJECT,
properties={
key: {
'type_': value.get('type', 'string').upper(),
'description': value.get('description', ''),
'enum': value.get('enum', [])
} for key, value in tool.parameters.get('properties', {}).items()
},
required=tool.parameters.get('required', [])
),
) for tool in tools
]
)
def validate_credentials(self, model: str, credentials: dict) -> None:
"""
@ -105,7 +133,7 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
"""
try:
ping_message = PromptMessage(content="ping", role="system")
ping_message = SystemPromptMessage(content="ping")
self._generate(model, credentials, [ping_message], {"max_tokens_to_sample": 5})
except Exception as ex:
@ -114,8 +142,9 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
def _generate(self, model: str, credentials: dict,
prompt_messages: list[PromptMessage], model_parameters: dict,
stop: Optional[list[str]] = None, stream: bool = True,
user: Optional[str] = None) -> Union[LLMResult, Generator]:
tools: Optional[list[PromptMessageTool]] = None, stop: Optional[list[str]] = None,
stream: bool = True, user: Optional[str] = None
) -> Union[LLMResult, Generator]:
"""
Invoke large language model
@ -153,7 +182,6 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
else:
history.append(content)
# Create a new ClientManager with tenant's API key
new_client_manager = client._ClientManager()
new_client_manager.configure(api_key=credentials["google_api_key"])
@ -167,14 +195,15 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,
HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
}
response = google_model.generate_content(
contents=history,
generation_config=genai.types.GenerationConfig(
**config_kwargs
),
stream=stream,
safety_settings=safety_settings
safety_settings=safety_settings,
tools=self._convert_tools_to_glm_tool(tools) if tools else None,
)
if stream:
@ -228,43 +257,61 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
"""
index = -1
for chunk in response:
content = chunk.text
index += 1
assistant_prompt_message = AssistantPromptMessage(
content=content if content else '',
)
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
)
for part in chunk.parts:
assistant_prompt_message = AssistantPromptMessage(
content=''
)
else:
# calculate num tokens
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=chunk.candidates[0].finish_reason,
usage=usage
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({
key: value
for key, value in 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
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=chunk.candidates[0].finish_reason,
usage=usage
)
)
)
def _convert_one_message_to_text(self, message: PromptMessage) -> str:
"""
@ -288,6 +335,8 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
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}")
@ -300,26 +349,53 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
:param message: one PromptMessage
:return: glm Content representation of message
"""
parts = []
if (isinstance(message.content, str)):
parts.append(to_part(message.content))
if isinstance(message, UserPromptMessage):
glm_content = {
"role": "user",
"parts": []
}
if (isinstance(message.content, str)):
glm_content['parts'].append(to_part(message.content))
else:
for c in message.content:
if c.type == PromptMessageContentType.TEXT:
glm_content['parts'].append(to_part(c.data))
else:
metadata, data = c.data.split(',', 1)
mime_type = metadata.split(';', 1)[0].split(':')[1]
blob = {"inline_data":{"mime_type":mime_type,"data":data}}
glm_content['parts'].append(blob)
return glm_content
elif isinstance(message, AssistantPromptMessage):
glm_content = {
"role": "model",
"parts": []
}
if message.content:
glm_content['parts'].append(to_part(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),
)))
return glm_content
elif isinstance(message, SystemPromptMessage):
return {
"role": "user",
"parts": [to_part(message.content)]
}
elif isinstance(message, ToolPromptMessage):
return {
"role": "function",
"parts": [glm.Part(function_response=glm.FunctionResponse(
name=message.name,
response={
"response": message.content
}
))]
}
else:
for c in message.content:
if c.type == PromptMessageContentType.TEXT:
parts.append(to_part(c.data))
else:
metadata, data = c.data.split(',', 1)
mime_type = metadata.split(';', 1)[0].split(':')[1]
blob = {"inline_data":{"mime_type":mime_type,"data":data}}
parts.append(blob)
glm_content = {
"role": "user" if message.role in (PromptMessageRole.USER, PromptMessageRole.SYSTEM) else "model",
"parts": parts
}
return glm_content
raise ValueError(f"Got unknown type {message}")
@property
def _invoke_error_mapping(self) -> dict[type[InvokeError], list[type[Exception]]]:

11
api/tests/integration_tests/model_runtime/__mock/google.py
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@ -10,6 +10,7 @@ from google.generativeai import GenerativeModel
from google.generativeai.client import _ClientManager, configure
from google.generativeai.types import GenerateContentResponse
from google.generativeai.types.generation_types import BaseGenerateContentResponse
from google.ai.generativelanguage_v1beta.types import content as gag_content
current_api_key = ''
@ -29,7 +30,7 @@ class MockGoogleResponseClass(object):
}),
chunks=[]
)
)
else:
yield GenerateContentResponse(
done=False,
@ -43,6 +44,14 @@ class MockGoogleResponseClass(object):
class MockGoogleResponseCandidateClass(object):
finish_reason = 'stop'
@property
def content(self) -> gag_content.Content:
return gag_content.Content(
parts=[
gag_content.Part(text='it\'s google!')
]
)
class MockGoogleClass(object):
@staticmethod
def generate_content_sync() -> GenerateContentResponse:

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