55 lines
2.0 KiB
Python
55 lines
2.0 KiB
Python
import os, sys, requests
|
||
|
||
src_dir = os.path.join(
|
||
os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
|
||
)
|
||
sys.path.append(src_dir)
|
||
|
||
from configs.model_config import KB_ROOT_PATH, JUPYTER_WORK_PATH
|
||
from configs.server_config import SANDBOX_SERVER
|
||
from coagent.tools import toLangchainTools, TOOL_DICT, TOOL_SETS
|
||
from coagent.llm_models.llm_config import EmbedConfig, LLMConfig
|
||
|
||
from coagent.connector.phase import BasePhase
|
||
from coagent.connector.schema import Message, Memory
|
||
|
||
|
||
|
||
# log-level，print prompt和llm predict
|
||
os.environ["log_verbose"] = "2"
|
||
|
||
phase_name = "searchChatPhase"
|
||
llm_config = LLMConfig(
|
||
model_name="gpt-3.5-turbo", api_key=os.environ["OPENAI_API_KEY"],
|
||
api_base_url=os.environ["API_BASE_URL"], temperature=0.3
|
||
)
|
||
embed_config = EmbedConfig(
|
||
embed_engine="model", embed_model="text2vec-base-chinese",
|
||
embed_model_path=os.path.join(src_dir, "embedding_models/text2vec-base-chinese")
|
||
)
|
||
phase = BasePhase(
|
||
phase_name, sandbox_server=SANDBOX_SERVER, jupyter_work_path=JUPYTER_WORK_PATH,
|
||
embed_config=embed_config, llm_config=llm_config, kb_root_path=KB_ROOT_PATH,
|
||
)
|
||
|
||
# round-1
|
||
query_content1 = "美国当前总统是谁？"
|
||
query = Message(
|
||
role_name="human", role_type="user",
|
||
role_content=query_content1, input_query=query_content1, origin_query=query_content1,
|
||
search_engine_name="duckduckgo", score_threshold=1.0, top_k=3
|
||
)
|
||
|
||
output_message, output_memory = phase.step(query)
|
||
|
||
print(output_memory.to_str_messages(return_all=True, content_key="parsed_output_list"))
|
||
|
||
# round-2
|
||
query_content2 = "美国上一任总统是谁，两个人有什么关系没？"
|
||
query = Message(
|
||
role_name="human", role_type="user",
|
||
role_content=query_content2, input_query=query_content2, origin_query=query_content2,
|
||
search_engine_name="duckduckgo", score_threshold=1.0, top_k=3
|
||
)
|
||
output_message, output_memory = phase.step(query)
|
||
print(output_memory.to_str_messages(return_all=True, content_key="parsed_output_list")) |