import logging
import re
from typing import Optional, List, Union, Tuple
from langchain.schema import BaseMessage
from requests.exceptions import ChunkedEncodingError
from core.agent.agent_executor import AgentExecuteResult, PlanningStrategy
from core.callback_handler.main_chain_gather_callback_handler import MainChainGatherCallbackHandler
from core.callback_handler.llm_callback_handler import LLMCallbackHandler
from core.conversation_message_task import ConversationMessageTask, ConversationTaskStoppedException
from core.model_providers.error import LLMBadRequestError
from core.memory.read_only_conversation_token_db_buffer_shared_memory import \
ReadOnlyConversationTokenDBBufferSharedMemory
from core.model_providers.model_factory import ModelFactory
from core.model_providers.models.entity.message import PromptMessage, to_prompt_messages
from core.model_providers.models.llm.base import BaseLLM
from core.orchestrator_rule_parser import OrchestratorRuleParser
from core.prompt.prompt_builder import PromptBuilder
from core.prompt.prompt_template import JinjaPromptTemplate
from core.prompt.prompts import MORE_LIKE_THIS_GENERATE_PROMPT
from models.model import App, AppModelConfig, Account, Conversation, Message, EndUser
class Completion:
@classmethod
def generate(cls, task_id: str, app: App, app_model_config: AppModelConfig, query: str, inputs: dict,
user: Union[Account, EndUser], conversation: Optional[Conversation], streaming: bool, is_override: bool = False):
"""
errors: ProviderTokenNotInitError
"""
query = PromptBuilder.process_template(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,
return_messages=False
)
inputs = conversation.inputs
final_model_instance = ModelFactory.get_text_generation_model_from_model_config(
tenant_id=app.tenant_id,
model_config=app_model_config.model_dict,
streaming=streaming
)
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,
model_instance=final_model_instance
)
rest_tokens_for_context_and_memory = cls.get_validate_rest_tokens(
mode=app.mode,
model_instance=final_model_instance,
app_model_config=app_model_config,
query=query,
inputs=inputs
)
# init orchestrator rule parser
orchestrator_rule_parser = OrchestratorRuleParser(
tenant_id=app.tenant_id,
app_model_config=app_model_config
)
# parse sensitive_word_avoidance_chain
chain_callback = MainChainGatherCallbackHandler(conversation_message_task)
sensitive_word_avoidance_chain = orchestrator_rule_parser.to_sensitive_word_avoidance_chain([chain_callback])
if sensitive_word_avoidance_chain:
query = sensitive_word_avoidance_chain.run(query)
# get agent executor
agent_executor = orchestrator_rule_parser.to_agent_executor(
conversation_message_task=conversation_message_task,
memory=memory,
rest_tokens=rest_tokens_for_context_and_memory,
chain_callback=chain_callback
)
# run agent executor
agent_execute_result = None
if agent_executor:
should_use_agent = agent_executor.should_use_agent(query)
if should_use_agent:
agent_execute_result = agent_executor.run(query)
# run the final llm
try:
cls.run_final_llm(
model_instance=final_model_instance,
mode=app.mode,
app_model_config=app_model_config,
query=query,
inputs=inputs,
agent_execute_result=agent_execute_result,
conversation_message_task=conversation_message_task,
memory=memory
)
except ConversationTaskStoppedException:
return
except ChunkedEncodingError as e:
# Interrupt by LLM (like OpenAI), handle it.
logging.warning(f'ChunkedEncodingError: {e}')
conversation_message_task.end()
return
@classmethod
def run_final_llm(cls, model_instance: BaseLLM, mode: str, app_model_config: AppModelConfig, query: str, inputs: dict,
agent_execute_result: Optional[AgentExecuteResult],
conversation_message_task: ConversationMessageTask,
memory: Optional[ReadOnlyConversationTokenDBBufferSharedMemory]):
# When no extra pre prompt is specified,
# the output of the agent can be used directly as the main output content without calling LLM again
fake_response = None
if not app_model_config.pre_prompt and agent_execute_result and agent_execute_result.output \
and agent_execute_result.strategy not in [PlanningStrategy.ROUTER, PlanningStrategy.REACT_ROUTER]:
fake_response = agent_execute_result.output
# get llm prompt
prompt_messages, stop_words = cls.get_main_llm_prompt(
mode=mode,
model=app_model_config.model_dict,
pre_prompt=app_model_config.pre_prompt,
query=query,
inputs=inputs,
agent_execute_result=agent_execute_result,
memory=memory
)
cls.recale_llm_max_tokens(
model_instance=model_instance,
prompt_messages=prompt_messages,
)
response = model_instance.run(
messages=prompt_messages,
stop=stop_words,
callbacks=[LLMCallbackHandler(model_instance, conversation_message_task)],
fake_response=fake_response
)
return response
@classmethod
def get_main_llm_prompt(cls, mode: str, model: dict,
pre_prompt: str, query: str, inputs: dict,
agent_execute_result: Optional[AgentExecuteResult],
memory: Optional[ReadOnlyConversationTokenDBBufferSharedMemory]) -> \
Tuple[List[PromptMessage], Optional[List[str]]]:
if mode == 'completion':
prompt_template = JinjaPromptTemplate.from_template(
template=("""Use the following context as your learned knowledge, inside <context></context> XML tags.
<context>
{{context}}
</context>
When answer to user:
- If you don't know, just say that you don't know.
- If you don't know when you are not sure, ask for clarification.
Avoid mentioning that you obtained the information from the context.
And answer according to the language of the user's question.
""" if agent_execute_result else "")
+ (pre_prompt + "\n" if pre_prompt else "")
+ "{{query}}\n"
)
if agent_execute_result:
inputs['context'] = agent_execute_result.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
)
return [PromptMessage(content=prompt_content)], None
else:
messages: List[BaseMessage] = []
human_inputs = {
"query": query
}
human_message_prompt = ""
if pre_prompt:
pre_prompt_inputs = {k: inputs[k] for k in
JinjaPromptTemplate.from_template(template=pre_prompt).input_variables
if k in inputs}
if pre_prompt_inputs:
human_inputs.update(pre_prompt_inputs)
if agent_execute_result:
human_inputs['context'] = agent_execute_result.output
human_message_prompt += """Use the following context as your learned knowledge, inside <context></context> XML tags.
<context>
{{context}}
</context>
When answer to user:
- If you don't know, just say that you don't know.
- If you don't know when you are not sure, ask for clarification.
Avoid mentioning that you obtained the information from the context.
And answer according to the language of the user's question.
"""
if pre_prompt:
human_message_prompt += pre_prompt
query_prompt = "\n\nHuman: {{query}}\n\nAssistant: "
if memory:
# append chat histories
tmp_human_message = PromptBuilder.to_human_message(
prompt_content=human_message_prompt + query_prompt,
inputs=human_inputs
)
if memory.model_instance.model_rules.max_tokens.max:
curr_message_tokens = memory.model_instance.get_num_tokens(to_prompt_messages([tmp_human_message]))
max_tokens = model.get("completion_params").get('max_tokens')
rest_tokens = memory.model_instance.model_rules.max_tokens.max - max_tokens - curr_message_tokens
rest_tokens = max(rest_tokens, 0)
else:
rest_tokens = 2000
histories = cls.get_history_messages_from_memory(memory, rest_tokens)
human_message_prompt += "\n\n" if human_message_prompt else ""
human_message_prompt += "Here is the chat histories between human and assistant, " \
"inside <histories></histories> XML tags.\n\n<histories>\n"
human_message_prompt += histories + "\n</histories>"
human_message_prompt += query_prompt
# construct main prompt
human_message = PromptBuilder.to_human_message(
prompt_content=human_message_prompt,
inputs=human_inputs
)
messages.append(human_message)
for message in messages:
message.content = re.sub(r'<\|.*?\|>', '', message.content)
return to_prompt_messages(messages), ['\nHuman:', '</histories>']
@classmethod
def get_history_messages_from_memory(cls, memory: ReadOnlyConversationTokenDBBufferSharedMemory,
max_token_limit: int) -> str:
"""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_model_instance = ModelFactory.get_text_generation_model_from_model_config(
tenant_id=tenant_id,
model_config=app_model_config.model_dict
)
# use llm config from conversation
memory = ReadOnlyConversationTokenDBBufferSharedMemory(
conversation=conversation,
model_instance=memory_model_instance,
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 get_validate_rest_tokens(cls, mode: str, model_instance: BaseLLM, app_model_config: AppModelConfig,
query: str, inputs: dict) -> int:
model_limited_tokens = model_instance.model_rules.max_tokens.max
max_tokens = model_instance.get_model_kwargs().max_tokens
if model_limited_tokens is None:
return -1
if max_tokens is None:
max_tokens = 0
# get prompt without memory and context
prompt_messages, _ = cls.get_main_llm_prompt(
mode=mode,
model=app_model_config.model_dict,
pre_prompt=app_model_config.pre_prompt,
query=query,
inputs=inputs,
agent_execute_result=None,
memory=None
)
prompt_tokens = model_instance.get_num_tokens(prompt_messages)
rest_tokens = model_limited_tokens - max_tokens - prompt_tokens
if rest_tokens < 0:
raise LLMBadRequestError("Query or prefix prompt is too long, you can reduce the prefix prompt, "
"or shrink the max token, or switch to a llm with a larger token limit size.")
return rest_tokens
@classmethod
def recale_llm_max_tokens(cls, model_instance: BaseLLM, prompt_messages: List[PromptMessage]):
# recalc max_tokens if sum(prompt_token + max_tokens) over model token limit
model_limited_tokens = model_instance.model_rules.max_tokens.max
max_tokens = model_instance.get_model_kwargs().max_tokens
if model_limited_tokens is None:
return
if max_tokens is None:
max_tokens = 0
prompt_tokens = model_instance.get_num_tokens(prompt_messages)
if prompt_tokens + max_tokens > model_limited_tokens:
max_tokens = max(model_limited_tokens - prompt_tokens, 16)
# update model instance max tokens
model_kwargs = model_instance.get_model_kwargs()
model_kwargs.max_tokens = max_tokens
model_instance.set_model_kwargs(model_kwargs)
@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):
final_model_instance = ModelFactory.get_text_generation_model_from_model_config(
tenant_id=app.tenant_id,
model_config=app_model_config.model_dict,
streaming=streaming
)
# get llm prompt
old_prompt_messages, _ = cls.get_main_llm_prompt(
mode="completion",
model=app_model_config.model_dict,
pre_prompt=pre_prompt,
query=message.query,
inputs=message.inputs,
agent_execute_result=None,
memory=None
)
original_completion = message.answer.strip()
prompt = MORE_LIKE_THIS_GENERATE_PROMPT
prompt = prompt.format(prompt=old_prompt_messages[0].content, original_completion=original_completion)
prompt_messages = [PromptMessage(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,
model_instance=final_model_instance
)
cls.recale_llm_max_tokens(
model_instance=final_model_instance,
prompt_messages=prompt_messages
)
final_model_instance.run(
messages=prompt_messages,
callbacks=[LLMCallbackHandler(final_model_instance, conversation_message_task)]
)