67 lines
2.2 KiB
Python
67 lines
2.2 KiB
Python
import os, sys, requests
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src_dir = os.path.join(
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os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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)
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sys.path.append(src_dir)
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from dev_opsgpt.tools import (
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toLangchainTools, get_tool_schema, DDGSTool, DocRetrieval,
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TOOL_DICT, TOOL_SETS
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)
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from configs.model_config import *
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from dev_opsgpt.connector.phase import BasePhase
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from dev_opsgpt.connector.agents import BaseAgent
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from dev_opsgpt.connector.chains import BaseChain
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from dev_opsgpt.connector.schema import (
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Message, Memory, load_role_configs, load_phase_configs, load_chain_configs
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)
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from dev_opsgpt.connector.configs import AGETN_CONFIGS, CHAIN_CONFIGS, PHASE_CONFIGS
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import importlib
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tools = toLangchainTools([TOOL_DICT[i] for i in TOOL_SETS if i in TOOL_DICT])
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role_configs = load_role_configs(AGETN_CONFIGS)
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chain_configs = load_chain_configs(CHAIN_CONFIGS)
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phase_configs = load_phase_configs(PHASE_CONFIGS)
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agent_module = importlib.import_module("dev_opsgpt.connector.agents")
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phase_name = "docChatPhase"
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phase = BasePhase(phase_name,
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task = None,
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phase_config = PHASE_CONFIGS,
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chain_config = CHAIN_CONFIGS,
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role_config = AGETN_CONFIGS,
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do_summary=False,
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do_code_retrieval=False,
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do_doc_retrieval=True,
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do_search=False,
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)
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# round-1
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query_content = "langchain有哪些模块"
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query = Message(
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role_name="user", role_type="human",
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role_content=query_content, input_query=query_content, origin_query=query_content,
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doc_engine_name="DSADSAD", score_threshold=1.0, top_k=3
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)
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output_message, _ = phase.step(query)
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# round-2
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history = Memory(messages=[
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Message(role_name="user", role_type="human", role_content="langchain有哪些模块"),
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Message(role_name="ai", role_type="assistant", role_content=output_message.step_content),
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])
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query_content = "提示（prompts）有什么用？"
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query = Message(
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role_name="human", role_type="user",
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role_content=query_content, input_query=query_content, origin_query=query_content,
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doc_engine_name="DSADSAD", score_threshold=1.0, top_k=3
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)
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output_message, _ = phase.step(query) |