92 lines
3.4 KiB
Markdown
92 lines
3.4 KiB
Markdown
|
---
|
|||
|
|
title: Connector Phase
|
|||
|
|
slug: Connector Phase ZH
|
|||
|
|
url: "coagent/connector-phase-zh"
|
|||
|
|
aliases:
|
|||
|
|
- "/coagent/connector-phase-zh"
|
|||
|
|
---
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
## 快速构建一个 agent phase
|
|||
|
|
- 首先增加openai配置，也可以是其它类似于openai接口的模型（通过fastchat启动）
|
|||
|
|
```
|
|||
|
|
from coagent.base_configs.env_config import JUPYTER_WORK_PATH, KB_ROOT_PATH
|
|||
|
|
from coagent.llm_models.llm_config import EmbedConfig, LLMConfig
|
|||
|
|
from coagent.connector.configs import AGETN_CONFIGS
|
|||
|
|
from coagent.connector.phase import BasePhase
|
|||
|
|
from coagent.connector.schema import Message, load_role_configs
|
|||
|
|
|
|||
|
|
|
|||
|
|
os.environ["API_BASE_URL"] = OPENAI_API_BASE
|
|||
|
|
os.environ["OPENAI_API_KEY"] = "sk-xx"
|
|||
|
|
openai.api_key = "sk-xxx"
|
|||
|
|
# os.environ["OPENAI_PROXY"] = "socks5h://127.0.0.1:13659"
|
|||
|
|
os.environ["DUCKDUCKGO_PROXY"] = os.environ.get("DUCKDUCKGO_PROXY") or "socks5://127.0.0.1:13659"
|
|||
|
|
```
|
|||
|
|
|
|||
|
|
|
|||
|
|
- 配置相关 LLM 和 Embedding Model
|
|||
|
|
```
|
|||
|
|
# LLM 和 Embedding Model 配置
|
|||
|
|
llm_config = LLMConfig(
|
|||
|
|
model_name="gpt-3.5-turbo", model_device="cpu",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="D://project/gitlab/llm/external/ant_code/Codefuse-chatbot/embedding_models/text2vec-base-chinese"
|
|||
|
|
)
|
|||
|
|
```
|
|||
|
|
|
|||
|
|
|
|||
|
|
- 这里从已有的 phase 配置中选一个 phase 来做示例
|
|||
|
|
```
|
|||
|
|
# log-level，print prompt和llm predict
|
|||
|
|
os.environ["log_verbose"] = "2"
|
|||
|
|
|
|||
|
|
phase_name = "searchChatPhase"
|
|||
|
|
phase = BasePhase(
|
|||
|
|
phase_name, embed_config=embed_config, llm_config=llm_config,
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
# 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"))
|
|||
|
|
```
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
## Phase 参数配置
|
|||
|
|
|Config Key Name |Type |Description|
|
|||
|
|
| ------------------ | ---------- | ---------- |
|
|||
|
|
|phase_name| String| 场景名称|
|
|||
|
|
|phase_config|CompletePhaseConfig| 默认为None，可直接指定完整的phaseconfig， 暂未实现|
|
|||
|
|
|llm_config |LLMConfig |大语言模型配置|
|
|||
|
|
|embed_config |EmbedConfig |向量模型配置|
|
|||
|
|
|sandbox_server |Dict |沙盒环境即notebook启动配置|
|
|||
|
|
|jupyter_work_path |str |沙盒环境的工作目录|
|
|||
|
|
|kb_root_path |str |memory的存储路径|
|
|||
|
|
|log_verbose |str |agent prompt&predict的日志打印级别|
|
|||
|
|
| base_phase_config | Union[dict, str] | 默认配置：PHASE_CONFIGS，可通过实现对这个变量新增来实现自定义配置 |
|
|||
|
|
| base_chain_config | Union[dict, str] | 默认配置：CHAIN_CONFIGS，可通过实现对这个变量新增来实现自定义配置 |
|
|||
|
|
| base_role_config | Union[dict, str] | 默认配置：AGETN_CONFIGS，可通过实现对这个变量新增来实现自定义配置 |
|