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111 lines
4.0 KiB
Python
111 lines
4.0 KiB
Python
from typing import Optional, List, cast
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from langchain.chains import SequentialChain
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from langchain.chains.base import Chain
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from langchain.memory.chat_memory import BaseChatMemory
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from core.callback_handler.main_chain_gather_callback_handler import MainChainGatherCallbackHandler
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from core.callback_handler.std_out_callback_handler import DifyStdOutCallbackHandler
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from core.chain.chain_builder import ChainBuilder
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from core.chain.multi_dataset_router_chain import MultiDatasetRouterChain
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from core.conversation_message_task import ConversationMessageTask
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from extensions.ext_database import db
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from models.dataset import Dataset
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class MainChainBuilder:
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@classmethod
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def to_langchain_components(cls, tenant_id: str, agent_mode: dict, memory: Optional[BaseChatMemory],
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rest_tokens: int,
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conversation_message_task: ConversationMessageTask):
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first_input_key = "input"
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final_output_key = "output"
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chains = []
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chain_callback_handler = MainChainGatherCallbackHandler(conversation_message_task)
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# agent mode
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tool_chains, chains_output_key = cls.get_agent_chains(
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tenant_id=tenant_id,
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agent_mode=agent_mode,
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rest_tokens=rest_tokens,
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memory=memory,
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conversation_message_task=conversation_message_task
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)
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chains += tool_chains
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if chains_output_key:
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final_output_key = chains_output_key
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if len(chains) == 0:
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return None
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for chain in chains:
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chain = cast(Chain, chain)
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chain.callbacks.append(chain_callback_handler)
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# build main chain
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overall_chain = SequentialChain(
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chains=chains,
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input_variables=[first_input_key],
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output_variables=[final_output_key],
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memory=memory, # only for use the memory prompt input key
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)
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return overall_chain
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@classmethod
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def get_agent_chains(cls, tenant_id: str, agent_mode: dict,
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rest_tokens: int,
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memory: Optional[BaseChatMemory],
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conversation_message_task: ConversationMessageTask):
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# agent mode
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chains = []
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if agent_mode and agent_mode.get('enabled'):
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tools = agent_mode.get('tools', [])
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pre_fixed_chains = []
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# agent_tools = []
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datasets = []
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for tool in tools:
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tool_type = list(tool.keys())[0]
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tool_config = list(tool.values())[0]
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if tool_type == 'sensitive-word-avoidance':
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chain = ChainBuilder.to_sensitive_word_avoidance_chain(tool_config)
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if chain:
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pre_fixed_chains.append(chain)
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elif tool_type == "dataset":
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# get dataset from dataset id
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dataset = db.session.query(Dataset).filter(
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Dataset.tenant_id == tenant_id,
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Dataset.id == tool_config.get("id")
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).first()
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if dataset:
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datasets.append(dataset)
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# add pre-fixed chains
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chains += pre_fixed_chains
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if len(datasets) > 0:
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# tool to chain
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multi_dataset_router_chain = MultiDatasetRouterChain.from_datasets(
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tenant_id=tenant_id,
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datasets=datasets,
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conversation_message_task=conversation_message_task,
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rest_tokens=rest_tokens,
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callbacks=[DifyStdOutCallbackHandler()]
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)
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chains.append(multi_dataset_router_chain)
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final_output_key = cls.get_chains_output_key(chains)
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return chains, final_output_key
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@classmethod
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def get_chains_output_key(cls, chains: List[Chain]):
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if len(chains) > 0:
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return chains[-1].output_keys[0]
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return None
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