112 lines
3.8 KiB
Python
112 lines
3.8 KiB
Python
import os, sys
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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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sys.path.append(os.path.join(src_dir, "examples"))
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from configs.model_config import EMBEDDING_MODEL, CB_ROOT_PATH
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from configs.model_config import KB_ROOT_PATH, JUPYTER_WORK_PATH
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from configs.server_config import SANDBOX_SERVER
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from coagent.tools import toLangchainTools, TOOL_DICT, TOOL_SETS
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from coagent.llm_models.llm_config import EmbedConfig, LLMConfig
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from coagent.connector.phase import BasePhase
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from coagent.connector.schema import Message, Memory
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tools = toLangchainTools([TOOL_DICT[i] for i in TOOL_SETS if i in TOOL_DICT])
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llm_config = LLMConfig(
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model_name="gpt-3.5-turbo",api_key=os.environ["OPENAI_API_KEY"],
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api_base_url=os.environ["API_BASE_URL"], temperature=0.3
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)
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embed_config = EmbedConfig(
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embed_engine="model", embed_model="text2vec-base-chinese",
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embed_model_path=os.path.join(src_dir, "embedding_models/text2vec-base-chinese")
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)
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# create your knowledge base
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from io import BytesIO
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from pathlib import Path
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from coagent.service.kb_api import create_kb, upload_doc
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from coagent.service.service_factory import get_kb_details
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from coagent.utils.server_utils import run_async
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kb_list = {x["kb_name"]: x for x in get_kb_details(KB_ROOT_PATH)}
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# create a knowledge base
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kb_name = "example_test"
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data = {
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"knowledge_base_name": kb_name,
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"vector_store_type": "faiss", # default
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"kb_root_path": KB_ROOT_PATH,
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"embed_model": embed_config.embed_model,
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"embed_engine": embed_config.embed_engine,
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"embed_model_path": embed_config.embed_model_path,
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"model_device": embed_config.model_device,
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}
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run_async(create_kb(**data))
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# add doc to knowledge base
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file = os.path.join("D://project/gitlab/llm/external/ant_code/Codefuse-chatbot/sources/docs/langchain_text_10.jsonl")
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files = [file]
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# if embedding init failed, you can use override = True
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data = [{"override": True, "file": f,
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"knowledge_base_name": kb_name, "not_refresh_vs_cache": False,
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"kb_root_path": KB_ROOT_PATH, "embed_model": embed_config.embed_model,
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"embed_engine": embed_config.embed_engine, "embed_model_path": embed_config.embed_model_path,
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"model_device": embed_config.model_device,
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}
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for f in files]
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for k in data:
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file = Path(file).absolute().open("rb")
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filename = file.name
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from fastapi import UploadFile
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from tempfile import SpooledTemporaryFile
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temp_file = SpooledTemporaryFile(max_size=10 * 1024 * 1024)
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temp_file.write(file.read())
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temp_file.seek(0)
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k.update({"file": UploadFile(file=temp_file, filename=filename),})
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run_async(upload_doc(**k))
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## start to chat with knowledge base
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# log-level，print prompt和llm predict
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os.environ["log_verbose"] = "2"
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# set chat phase
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phase_name = "docChatPhase"
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phase = BasePhase(
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phase_name, sandbox_server=SANDBOX_SERVER, jupyter_work_path=JUPYTER_WORK_PATH,
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embed_config=embed_config, llm_config=llm_config, kb_root_path=KB_ROOT_PATH,
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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="human", role_type="user",
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origin_query=query_content,
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doc_engine_name=kb_name, score_threshold=1.0, top_k=3
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)
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output_message, output_memory = phase.step(query)
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print(output_memory.to_str_messages(return_all=True, content_key="parsed_output_list"))
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# round-2
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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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origin_query=query_content,
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doc_engine_name=kb_name, score_threshold=1.0, top_k=3
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)
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output_message, output_memory = phase.step(query)
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print(output_memory.to_str_messages(return_all=True, content_key="parsed_output_list")) |