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feat: add zhipuai (#1188)

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This commit is contained in:
takatost
2023-09-18 17:32:31 +08:00
committed by GitHub
parent c8bd76cd66
commit 827c97f0d3
36 changed files with 1087 additions and 111 deletions
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81
api/core/completion.py
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@@ -7,6 +7,7 @@ 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.chain.sensitive_word_avoidance_chain import SensitiveWordAvoidanceError
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 \
@@ -76,28 +77,53 @@ class Completion:
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(final_model_instance, [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:
# parse sensitive_word_avoidance_chain
chain_callback = MainChainGatherCallbackHandler(conversation_message_task)
sensitive_word_avoidance_chain = orchestrator_rule_parser.to_sensitive_word_avoidance_chain(
final_model_instance, [chain_callback])
if sensitive_word_avoidance_chain:
try:
query = sensitive_word_avoidance_chain.run(query)
except SensitiveWordAvoidanceError as ex:
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=None,
conversation_message_task=conversation_message_task,
memory=memory,
fake_response=ex.message
)
return
# 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,
retriever_from=retriever_from
)
# 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)
# 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
# run the final llm
cls.run_final_llm(
model_instance=final_model_instance,
mode=app.mode,
@@ -106,7 +132,8 @@ class Completion:
inputs=inputs,
agent_execute_result=agent_execute_result,
conversation_message_task=conversation_message_task,
memory=memory
memory=memory,
fake_response=fake_response
)
except ConversationTaskStoppedException:
return
@@ -121,14 +148,8 @@ class Completion:
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
memory: Optional[ReadOnlyConversationTokenDBBufferSharedMemory],
fake_response: Optional[str]):
# get llm prompt
prompt_messages, stop_words = model_instance.get_prompt(
mode=mode,