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Model Runtime (#1858)

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Co-authored-by: StyleZhang <jasonapring2015@outlook.com>
Co-authored-by: Garfield Dai <dai.hai@foxmail.com>
Co-authored-by: chenhe <guchenhe@gmail.com>
Co-authored-by: jyong <jyong@dify.ai>
Co-authored-by: Joel <iamjoel007@gmail.com>
Co-authored-by: Yeuoly <admin@srmxy.cn>
This commit is contained in:
takatost
2024-01-02 23:42:00 +08:00
committed by GitHub
parent e91dd28a76
commit d069c668f8
807 changed files with 171310 additions and 23806 deletions
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103
api/core/generator/llm_generator.py
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@@ -3,10 +3,10 @@ import logging
from langchain.schema import OutputParserException
from core.model_providers.error import LLMError, ProviderTokenNotInitError
from core.model_providers.model_factory import ModelFactory
from core.model_providers.models.entity.message import PromptMessage, MessageType
from core.model_providers.models.entity.model_params import ModelKwargs
from core.model_manager import ModelManager
from core.model_runtime.entities.message_entities import UserPromptMessage, SystemPromptMessage
from core.model_runtime.entities.model_entities import ModelType
from core.model_runtime.errors.invoke import InvokeAuthorizationError, InvokeError
from core.prompt.output_parser.rule_config_generator import RuleConfigGeneratorOutputParser
from core.prompt.output_parser.suggested_questions_after_answer import SuggestedQuestionsAfterAnswerOutputParser
@@ -26,17 +26,22 @@ class LLMGenerator:
prompt += query + "\n"
model_instance = ModelFactory.get_text_generation_model(
model_manager = ModelManager()
model_instance = model_manager.get_default_model_instance(
tenant_id=tenant_id,
model_kwargs=ModelKwargs(
temperature=1,
max_tokens=100
)
model_type=ModelType.LLM,
)
prompts = [PromptMessage(content=prompt)]
response = model_instance.run(prompts)
answer = response.content
prompts = [UserPromptMessage(content=prompt)]
response = model_instance.invoke_llm(
prompt_messages=prompts,
model_parameters={
"max_tokens": 100,
"temperature": 1
},
stream=False
)
answer = response.message.content
result_dict = json.loads(answer)
answer = result_dict['Your Output']
@@ -62,22 +67,28 @@ class LLMGenerator:
})
try:
model_instance = ModelFactory.get_text_generation_model(
model_manager = ModelManager()
model_instance = model_manager.get_default_model_instance(
tenant_id=tenant_id,
model_kwargs=ModelKwargs(
max_tokens=256,
temperature=0
)
model_type=ModelType.LLM,
)
except ProviderTokenNotInitError:
except InvokeAuthorizationError:
return []
prompt_messages = [PromptMessage(content=prompt)]
prompt_messages = [UserPromptMessage(content=prompt)]
try:
output = model_instance.run(prompt_messages)
questions = output_parser.parse(output.content)
except LLMError:
response = model_instance.invoke_llm(
prompt_messages=prompt_messages,
model_parameters={
"max_tokens": 256,
"temperature": 0
},
stream=False
)
questions = output_parser.parse(response.message.content)
except InvokeError:
questions = []
except Exception as e:
logging.exception(e)
@@ -105,20 +116,26 @@ class LLMGenerator:
remove_template_variables=False
)
model_instance = ModelFactory.get_text_generation_model(
model_manager = ModelManager()
model_instance = model_manager.get_default_model_instance(
tenant_id=tenant_id,
model_kwargs=ModelKwargs(
max_tokens=512,
temperature=0
)
model_type=ModelType.LLM,
)
prompt_messages = [PromptMessage(content=prompt)]
prompt_messages = [UserPromptMessage(content=prompt)]
try:
output = model_instance.run(prompt_messages)
rule_config = output_parser.parse(output.content)
except LLMError as e:
response = model_instance.invoke_llm(
prompt_messages=prompt_messages,
model_parameters={
"max_tokens": 512,
"temperature": 0
},
stream=False
)
rule_config = output_parser.parse(response.message.content)
except InvokeError as e:
raise e
except OutputParserException:
raise ValueError('Please give a valid input for intended audience or hoping to solve problems.')
@@ -136,18 +153,24 @@ class LLMGenerator:
def generate_qa_document(cls, tenant_id: str, query, document_language: str):
prompt = GENERATOR_QA_PROMPT.format(language=document_language)
model_instance = ModelFactory.get_text_generation_model(
model_manager = ModelManager()
model_instance = model_manager.get_default_model_instance(
tenant_id=tenant_id,
model_kwargs=ModelKwargs(
max_tokens=2000
)
model_type=ModelType.LLM,
)
prompts = [
PromptMessage(content=prompt, type=MessageType.SYSTEM),
PromptMessage(content=query)
prompt_messages = [
SystemPromptMessage(content=prompt),
UserPromptMessage(content=query)
]
response = model_instance.run(prompts)
answer = response.content
response = model_instance.invoke_llm(
prompt_messages=prompt_messages,
model_parameters={
"max_tokens": 2000
},
stream=False
)
answer = response.message.content
return answer.strip()