114 lines
4.3 KiB
Markdown
114 lines
4.3 KiB
Markdown
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---
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title: Connector Agent
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slug: Connector Agent ZH
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url: "coagent/connector-agent-zh"
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aliases:
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- "/coagent/connector-agent-zh"
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---
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## 快速构建一个Agent
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- 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）
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```
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from coagent.base_configs.env_config import JUPYTER_WORK_PATH, KB_ROOT_PATH
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from coagent.llm_models.llm_config import EmbedConfig, LLMConfig
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from coagent.connector.configs import AGETN_CONFIGS
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from coagent.connector.agents import BaseAgent
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from coagent.connector.schema import Message, load_role_configs
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os.environ["API_BASE_URL"] = OPENAI_API_BASE
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os.environ["OPENAI_API_KEY"] = "sk-xx"
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openai.api_key = "sk-xxx"
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# os.environ["OPENAI_PROXY"] = "socks5h://127.0.0.1:13659"
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os.environ["DUCKDUCKGO_PROXY"] = os.environ.get("DUCKDUCKGO_PROXY") or "socks5://127.0.0.1:13659"
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```
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- 配置相关 LLM 和 Embedding Model
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```
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# LLM 和 Embedding Model 配置
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llm_config = LLMConfig(
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model_name="gpt-3.5-turbo", model_device="cpu",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="D://project/gitlab/llm/external/ant_code/Codefuse-chatbot/embedding_models/text2vec-base-chinese"
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)
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```
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- 这里从已有的agent配置选一个role来做示例
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```
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# 从已有的配置中选择一个config，具体参数细节见下面
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role_configs = load_role_configs(AGETN_CONFIGS)
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agent_config = role_configs["general_planner"]
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# 生成agent实例
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base_agent = BaseAgent(
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role=agent_config.role,
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prompt_config = agent_config.prompt_config,
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prompt_manager_type=agent_config.prompt_manager_type,
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chat_turn=agent_config.chat_turn,
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focus_agents=[],
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focus_message_keys=[],
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llm_config=llm_config,
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embed_config=embed_config,
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jupyter_work_path=JUPYTER_WORK_PATH,
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kb_root_path=KB_ROOT_PATH,
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)
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# round-1
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query_content = "确认本地是否存在employee_data.csv，并查看它有哪些列和数据类型;然后画柱状图"
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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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)
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output_message = base_agent.step(query)
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print(output_message.to_str_content(content_key="parsed_output_list"))
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```
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## Agent 参数配置
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```
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# 配置结构在这个目录
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from coagent.connector.schema import Role, PromptField
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```
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### Agent Config
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|Config Key Name| Type| Description|
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| ------------------ | ---------- | ---------- |
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|role| Role |角色描述|
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|prompt_config |List[PromptField] |Enum：PromptManager 也可以继承以上几种Agent然后去构造相关的Agent|
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|prompt_manager_type |String |Enum：PromptManager 也可以继承以上几种Agent然后去构造自定义的Enum：PromptManager|
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|focus_agents |List[String] |metagpt的逻辑，关注哪些agent生成的message，可选值范围为：role_name
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|focus_message_keys |List[String]| 额外增加的逻辑，关注message里面具体的 key 信息可选值范围为：agent 的 output_keys|
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|chat_turn |int |只针对ReactAgent有效|
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|llm_config |LLMConfig |大语言模型配置|
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|embed_config |EmbedConfig |向量模型配置|
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|sandbox_server |Dict |沙盒环境即notebook启动配置|
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|jupyter_work_path |str |沙盒环境的工作目录|
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|kb_root_path |str |memory的存储路径|
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|log_verbose |str |agent prompt&predict的日志打印级别|
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### Role
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| Config Key Name | Type | Description |
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|------------------|------|--------------------|
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| role_type | str | 角色类型, Enum: system、user、assistant、function、observation、summary |
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| role_name | str | 角色名称 |
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| role_desc | str | 角色描述 |
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| agent_type | str | 代理类型 |
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| role_prompt | str | 角色提示 |
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| template_prompt | str | 模板提示 |
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### PromptField
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| Config Key Name | Type | Description |
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|-----------------|------|-------------|
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| field_name | str | |
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| function_name | str | |
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| title | str | |
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| description | str | |
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| is_context | bool | |
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| omit_if_empty | bool | |
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