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fix: gemini system prompt with variable raise error (#11946)

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非法操作 2024-12-21 23:14:05 +08:00 committed by GitHub
parent 9578246bbb
commit 366857cd26
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1 changed files with 13 additions and 3 deletions
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16
api/core/model_runtime/model_providers/google/llm/llm.py
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@ -21,6 +21,7 @@ from core.model_runtime.entities.message_entities import (
PromptMessageContentType,
PromptMessageTool,
SystemPromptMessage,
TextPromptMessageContent,
ToolPromptMessage,
UserPromptMessage,
)
@ -143,7 +144,7 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
"""
try:
ping_message = SystemPromptMessage(content="ping")
ping_message = UserPromptMessage(content="ping")
self._generate(model, credentials, [ping_message], {"max_output_tokens": 5})
except Exception as ex:
@ -187,17 +188,23 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
config_kwargs["stop_sequences"] = stop
genai.configure(api_key=credentials["google_api_key"])
google_model = genai.GenerativeModel(model_name=model)
history = []
system_instruction = None
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"])
elif content["role"] == "system":
system_instruction = content["parts"][0]
else:
history.append(content)
if not history:
raise InvokeError("The user prompt message is required. You only add a system prompt message.")
google_model = genai.GenerativeModel(model_name=model, system_instruction=system_instruction)
response = google_model.generate_content(
contents=history,
generation_config=genai.types.GenerationConfig(**config_kwargs),
@ -404,7 +411,10 @@ class GoogleLargeLanguageModel(LargeLanguageModel):
)
return glm_content
elif isinstance(message, SystemPromptMessage):
return {"role": "user", "parts": [to_part(message.content)]}
if isinstance(message.content, list):
text_contents = filter(lambda c: isinstance(c, TextPromptMessageContent), message.content)
message.content = "".join(c.data for c in text_contents)
return {"role": "system", "parts": [to_part(message.content)]}
elif isinstance(message, ToolPromptMessage):
return {
"role": "function",