mirror of
https://gitee.com/dify_ai/dify.git
synced 2024-12-04 04:07:47 +08:00
327 lines
13 KiB
Python
327 lines
13 KiB
Python
|
from typing import Optional, List, Union
|
||
|
|
|
||
|
|
from langchain.callbacks import CallbackManager
|
||
|
|
from langchain.chat_models.base import BaseChatModel
|
||
|
|
from langchain.llms import BaseLLM
|
||
|
|
from langchain.schema import BaseMessage, BaseLanguageModel, HumanMessage
|
||
|
|
from core.constant import llm_constant
|
||
|
|
from core.callback_handler.llm_callback_handler import LLMCallbackHandler
|
||
|
|
from core.callback_handler.std_out_callback_handler import DifyStreamingStdOutCallbackHandler, \
|
||
|
|
DifyStdOutCallbackHandler
|
||
|
|
from core.conversation_message_task import ConversationMessageTask, ConversationTaskStoppedException
|
||
|
|
from core.llm.error import LLMBadRequestError
|
||
|
|
from core.llm.llm_builder import LLMBuilder
|
||
|
|
from core.chain.main_chain_builder import MainChainBuilder
|
||
|
|
from core.llm.streamable_chat_open_ai import StreamableChatOpenAI
|
||
|
|
from core.llm.streamable_open_ai import StreamableOpenAI
|
||
|
|
from core.memory.read_only_conversation_token_db_buffer_shared_memory import \
|
||
|
|
ReadOnlyConversationTokenDBBufferSharedMemory
|
||
|
|
from core.memory.read_only_conversation_token_db_string_buffer_shared_memory import \
|
||
|
|
ReadOnlyConversationTokenDBStringBufferSharedMemory
|
||
|
|
from core.prompt.prompt_builder import PromptBuilder
|
||
|
|
from core.prompt.prompt_template import OutLinePromptTemplate
|
||
|
|
from core.prompt.prompts import MORE_LIKE_THIS_GENERATE_PROMPT
|
||
|
|
from models.model import App, AppModelConfig, Account, Conversation, Message
|
||
|
|
|
||
|
|
|
||
|
|
class Completion:
|
||
|
|
@classmethod
|
||
|
|
def generate(cls, task_id: str, app: App, app_model_config: AppModelConfig, query: str, inputs: dict,
|
||
|
|
user: Account, conversation: Optional[Conversation], streaming: bool, is_override: bool = False):
|
||
|
|
"""
|
||
|
|
errors: ProviderTokenNotInitError
|
||
|
|
"""
|
||
|
|
cls.validate_query_tokens(app.tenant_id, app_model_config, query)
|
||
|
|
|
||
|
|
memory = None
|
||
|
|
if conversation:
|
||
|
|
# get memory of conversation (read-only)
|
||
|
|
memory = cls.get_memory_from_conversation(
|
||
|
|
tenant_id=app.tenant_id,
|
||
|
|
app_model_config=app_model_config,
|
||
|
|
conversation=conversation
|
||
|
|
)
|
||
|
|
|
||
|
|
inputs = conversation.inputs
|
||
|
|
|
||
|
|
conversation_message_task = ConversationMessageTask(
|
||
|
|
task_id=task_id,
|
||
|
|
app=app,
|
||
|
|
app_model_config=app_model_config,
|
||
|
|
user=user,
|
||
|
|
conversation=conversation,
|
||
|
|
is_override=is_override,
|
||
|
|
inputs=inputs,
|
||
|
|
query=query,
|
||
|
|
streaming=streaming
|
||
|
|
)
|
||
|
|
|
||
|
|
# build main chain include agent
|
||
|
|
main_chain = MainChainBuilder.to_langchain_components(
|
||
|
|
tenant_id=app.tenant_id,
|
||
|
|
agent_mode=app_model_config.agent_mode_dict,
|
||
|
|
memory=ReadOnlyConversationTokenDBStringBufferSharedMemory(memory=memory) if memory else None,
|
||
|
|
conversation_message_task=conversation_message_task
|
||
|
|
)
|
||
|
|
|
||
|
|
chain_output = ''
|
||
|
|
if main_chain:
|
||
|
|
chain_output = main_chain.run(query)
|
||
|
|
|
||
|
|
# run the final llm
|
||
|
|
try:
|
||
|
|
cls.run_final_llm(
|
||
|
|
tenant_id=app.tenant_id,
|
||
|
|
mode=app.mode,
|
||
|
|
app_model_config=app_model_config,
|
||
|
|
query=query,
|
||
|
|
inputs=inputs,
|
||
|
|
chain_output=chain_output,
|
||
|
|
conversation_message_task=conversation_message_task,
|
||
|
|
memory=memory,
|
||
|
|
streaming=streaming
|
||
|
|
)
|
||
|
|
except ConversationTaskStoppedException:
|
||
|
|
return
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def run_final_llm(cls, tenant_id: str, mode: str, app_model_config: AppModelConfig, query: str, inputs: dict,
|
||
|
|
chain_output: str,
|
||
|
|
conversation_message_task: ConversationMessageTask,
|
||
|
|
memory: Optional[ReadOnlyConversationTokenDBBufferSharedMemory], streaming: bool):
|
||
|
|
final_llm = LLMBuilder.to_llm_from_model(
|
||
|
|
tenant_id=tenant_id,
|
||
|
|
model=app_model_config.model_dict,
|
||
|
|
streaming=streaming
|
||
|
|
)
|
||
|
|
|
||
|
|
# get llm prompt
|
||
|
|
prompt = cls.get_main_llm_prompt(
|
||
|
|
mode=mode,
|
||
|
|
llm=final_llm,
|
||
|
|
pre_prompt=app_model_config.pre_prompt,
|
||
|
|
query=query,
|
||
|
|
inputs=inputs,
|
||
|
|
chain_output=chain_output,
|
||
|
|
memory=memory
|
||
|
|
)
|
||
|
|
|
||
|
|
final_llm.callback_manager = cls.get_llm_callback_manager(final_llm, streaming, conversation_message_task)
|
||
|
|
|
||
|
|
cls.recale_llm_max_tokens(
|
||
|
|
final_llm=final_llm,
|
||
|
|
prompt=prompt,
|
||
|
|
mode=mode
|
||
|
|
)
|
||
|
|
|
||
|
|
response = final_llm.generate([prompt])
|
||
|
|
|
||
|
|
return response
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def get_main_llm_prompt(cls, mode: str, llm: BaseLanguageModel, pre_prompt: str, query: str, inputs: dict, chain_output: Optional[str],
|
||
|
|
memory: Optional[ReadOnlyConversationTokenDBBufferSharedMemory]) -> \
|
||
|
|
Union[str | List[BaseMessage]]:
|
||
|
|
pre_prompt = PromptBuilder.process_template(pre_prompt) if pre_prompt else pre_prompt
|
||
|
|
if mode == 'completion':
|
||
|
|
prompt_template = OutLinePromptTemplate.from_template(
|
||
|
|
template=("Use the following pieces of [CONTEXT] to answer the question at the end. "
|
||
|
|
"If you don't know the answer, "
|
||
|
|
"just say that you don't know, don't try to make up an answer. \n"
|
||
|
|
"```\n"
|
||
|
|
"[CONTEXT]\n"
|
||
|
|
"{context}\n"
|
||
|
|
"```\n" if chain_output else "")
|
||
|
|
+ (pre_prompt + "\n" if pre_prompt else "")
|
||
|
|
+ "{query}\n"
|
||
|
|
)
|
||
|
|
|
||
|
|
if chain_output:
|
||
|
|
inputs['context'] = chain_output
|
||
|
|
|
||
|
|
prompt_inputs = {k: inputs[k] for k in prompt_template.input_variables if k in inputs}
|
||
|
|
prompt_content = prompt_template.format(
|
||
|
|
query=query,
|
||
|
|
**prompt_inputs
|
||
|
|
)
|
||
|
|
|
||
|
|
if isinstance(llm, BaseChatModel):
|
||
|
|
# use chat llm as completion model
|
||
|
|
return [HumanMessage(content=prompt_content)]
|
||
|
|
else:
|
||
|
|
return prompt_content
|
||
|
|
else:
|
||
|
|
messages: List[BaseMessage] = []
|
||
|
|
|
||
|
|
system_message = None
|
||
|
|
if pre_prompt:
|
||
|
|
# append pre prompt as system message
|
||
|
|
system_message = PromptBuilder.to_system_message(pre_prompt, inputs)
|
||
|
|
|
||
|
|
if chain_output:
|
||
|
|
# append context as system message, currently only use simple stuff prompt
|
||
|
|
context_message = PromptBuilder.to_system_message(
|
||
|
|
"""Use the following pieces of [CONTEXT] to answer the users question.
|
||
|
|
If you don't know the answer, just say that you don't know, don't try to make up an answer.
|
||
|
|
```
|
||
|
|
[CONTEXT]
|
||
|
|
{context}
|
||
|
|
```""",
|
||
|
|
{'context': chain_output}
|
||
|
|
)
|
||
|
|
|
||
|
|
if not system_message:
|
||
|
|
system_message = context_message
|
||
|
|
else:
|
||
|
|
system_message.content = context_message.content + "\n\n" + system_message.content
|
||
|
|
|
||
|
|
if system_message:
|
||
|
|
messages.append(system_message)
|
||
|
|
|
||
|
|
human_inputs = {
|
||
|
|
"query": query
|
||
|
|
}
|
||
|
|
|
||
|
|
# construct main prompt
|
||
|
|
human_message = PromptBuilder.to_human_message(
|
||
|
|
prompt_content="{query}",
|
||
|
|
inputs=human_inputs
|
||
|
|
)
|
||
|
|
|
||
|
|
if memory:
|
||
|
|
# append chat histories
|
||
|
|
tmp_messages = messages.copy() + [human_message]
|
||
|
|
curr_message_tokens = memory.llm.get_messages_tokens(tmp_messages)
|
||
|
|
rest_tokens = llm_constant.max_context_token_length[
|
||
|
|
memory.llm.model_name] - memory.llm.max_tokens - curr_message_tokens
|
||
|
|
rest_tokens = max(rest_tokens, 0)
|
||
|
|
history_messages = cls.get_history_messages_from_memory(memory, rest_tokens)
|
||
|
|
messages += history_messages
|
||
|
|
|
||
|
|
messages.append(human_message)
|
||
|
|
|
||
|
|
return messages
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def get_llm_callback_manager(cls, llm: Union[StreamableOpenAI, StreamableChatOpenAI],
|
||
|
|
streaming: bool, conversation_message_task: ConversationMessageTask) -> CallbackManager:
|
||
|
|
llm_callback_handler = LLMCallbackHandler(llm, conversation_message_task)
|
||
|
|
if streaming:
|
||
|
|
callback_handlers = [llm_callback_handler, DifyStreamingStdOutCallbackHandler()]
|
||
|
|
else:
|
||
|
|
callback_handlers = [llm_callback_handler, DifyStdOutCallbackHandler()]
|
||
|
|
|
||
|
|
return CallbackManager(callback_handlers)
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def get_history_messages_from_memory(cls, memory: ReadOnlyConversationTokenDBBufferSharedMemory,
|
||
|
|
max_token_limit: int) -> \
|
||
|
|
List[BaseMessage]:
|
||
|
|
"""Get memory messages."""
|
||
|
|
memory.max_token_limit = max_token_limit
|
||
|
|
memory_key = memory.memory_variables[0]
|
||
|
|
external_context = memory.load_memory_variables({})
|
||
|
|
return external_context[memory_key]
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def get_memory_from_conversation(cls, tenant_id: str, app_model_config: AppModelConfig,
|
||
|
|
conversation: Conversation,
|
||
|
|
**kwargs) -> ReadOnlyConversationTokenDBBufferSharedMemory:
|
||
|
|
# only for calc token in memory
|
||
|
|
memory_llm = LLMBuilder.to_llm_from_model(
|
||
|
|
tenant_id=tenant_id,
|
||
|
|
model=app_model_config.model_dict
|
||
|
|
)
|
||
|
|
|
||
|
|
# use llm config from conversation
|
||
|
|
memory = ReadOnlyConversationTokenDBBufferSharedMemory(
|
||
|
|
conversation=conversation,
|
||
|
|
llm=memory_llm,
|
||
|
|
max_token_limit=kwargs.get("max_token_limit", 2048),
|
||
|
|
memory_key=kwargs.get("memory_key", "chat_history"),
|
||
|
|
return_messages=kwargs.get("return_messages", True),
|
||
|
|
input_key=kwargs.get("input_key", "input"),
|
||
|
|
output_key=kwargs.get("output_key", "output"),
|
||
|
|
message_limit=kwargs.get("message_limit", 10),
|
||
|
|
)
|
||
|
|
|
||
|
|
return memory
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def validate_query_tokens(cls, tenant_id: str, app_model_config: AppModelConfig, query: str):
|
||
|
|
llm = LLMBuilder.to_llm_from_model(
|
||
|
|
tenant_id=tenant_id,
|
||
|
|
model=app_model_config.model_dict
|
||
|
|
)
|
||
|
|
|
||
|
|
model_limited_tokens = llm_constant.max_context_token_length[llm.model_name]
|
||
|
|
max_tokens = llm.max_tokens
|
||
|
|
|
||
|
|
if model_limited_tokens - max_tokens - llm.get_num_tokens(query) < 0:
|
||
|
|
raise LLMBadRequestError("Query is too long")
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def recale_llm_max_tokens(cls, final_llm: Union[StreamableOpenAI, StreamableChatOpenAI],
|
||
|
|
prompt: Union[str, List[BaseMessage]], mode: str):
|
||
|
|
# recalc max_tokens if sum(prompt_token + max_tokens) over model token limit
|
||
|
|
model_limited_tokens = llm_constant.max_context_token_length[final_llm.model_name]
|
||
|
|
max_tokens = final_llm.max_tokens
|
||
|
|
|
||
|
|
if mode == 'completion' and isinstance(final_llm, BaseLLM):
|
||
|
|
prompt_tokens = final_llm.get_num_tokens(prompt)
|
||
|
|
else:
|
||
|
|
prompt_tokens = final_llm.get_messages_tokens(prompt)
|
||
|
|
|
||
|
|
if prompt_tokens + max_tokens > model_limited_tokens:
|
||
|
|
max_tokens = max(model_limited_tokens - prompt_tokens, 16)
|
||
|
|
final_llm.max_tokens = max_tokens
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def generate_more_like_this(cls, task_id: str, app: App, message: Message, pre_prompt: str,
|
||
|
|
app_model_config: AppModelConfig, user: Account, streaming: bool):
|
||
|
|
llm: StreamableOpenAI = LLMBuilder.to_llm(
|
||
|
|
tenant_id=app.tenant_id,
|
||
|
|
model_name='gpt-3.5-turbo',
|
||
|
|
streaming=streaming
|
||
|
|
)
|
||
|
|
|
||
|
|
# get llm prompt
|
||
|
|
original_prompt = cls.get_main_llm_prompt(
|
||
|
|
mode="completion",
|
||
|
|
llm=llm,
|
||
|
|
pre_prompt=pre_prompt,
|
||
|
|
query=message.query,
|
||
|
|
inputs=message.inputs,
|
||
|
|
chain_output=None,
|
||
|
|
memory=None
|
||
|
|
)
|
||
|
|
|
||
|
|
original_completion = message.answer.strip()
|
||
|
|
|
||
|
|
prompt = MORE_LIKE_THIS_GENERATE_PROMPT
|
||
|
|
prompt = prompt.format(prompt=original_prompt, original_completion=original_completion)
|
||
|
|
|
||
|
|
if isinstance(llm, BaseChatModel):
|
||
|
|
prompt = [HumanMessage(content=prompt)]
|
||
|
|
|
||
|
|
conversation_message_task = ConversationMessageTask(
|
||
|
|
task_id=task_id,
|
||
|
|
app=app,
|
||
|
|
app_model_config=app_model_config,
|
||
|
|
user=user,
|
||
|
|
inputs=message.inputs,
|
||
|
|
query=message.query,
|
||
|
|
is_override=True if message.override_model_configs else False,
|
||
|
|
streaming=streaming
|
||
|
|
)
|
||
|
|
|
||
|
|
llm.callback_manager = cls.get_llm_callback_manager(llm, streaming, conversation_message_task)
|
||
|
|
|
||
|
|
cls.recale_llm_max_tokens(
|
||
|
|
final_llm=llm,
|
||
|
|
prompt=prompt,
|
||
|
|
mode='completion'
|
||
|
|
)
|
||
|
|
|
||
|
|
llm.generate([prompt])
|