mirror of
https://github.com/RYDE-WORK/Langchain-Chatchat.git
synced 2026-02-09 08:45:44 +08:00
ConfigModelWorkSpace实现
This commit is contained in:
glide-the
parent
5a60f5f149
commit
cd01bb8601
@ -363,108 +363,140 @@ def _import_embedding_keyword_file() -> Any:
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return EMBEDDING_KEYWORD_FILE
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def _import_ConfigModel() -> Any:
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basic_config_load = CONFIG_IMPORTS.get("_model_config.py")
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load_mod = basic_config_load.get("load_mod")
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ConfigModel = load_mod(basic_config_load.get("module"), "ConfigModel")
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return ConfigModel
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def _import_ConfigModelFactory() -> Any:
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basic_config_load = CONFIG_IMPORTS.get("_model_config.py")
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load_mod = basic_config_load.get("load_mod")
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ConfigModelFactory = load_mod(basic_config_load.get("module"), "ConfigModelFactory")
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return ConfigModelFactory
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def _import_ConfigModelWorkSpace() -> Any:
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basic_config_load = CONFIG_IMPORTS.get("_model_config.py")
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load_mod = basic_config_load.get("load_mod")
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ConfigModelWorkSpace = load_mod(basic_config_load.get("module"), "ConfigModelWorkSpace")
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return ConfigModelWorkSpace
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def _import_config_model_workspace() -> Any:
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model_config_load = CONFIG_IMPORTS.get("_model_config.py")
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load_mod = model_config_load.get("load_mod")
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config_model_workspace = load_mod(model_config_load.get("module"), "config_model_workspace")
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return config_model_workspace
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def _import_default_llm_model() -> Any:
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model_config_load = CONFIG_IMPORTS.get("_model_config.py")
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load_mod = model_config_load.get("load_mod")
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DEFAULT_LLM_MODEL = load_mod(model_config_load.get("module"), "DEFAULT_LLM_MODEL")
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config_model_workspace = load_mod(model_config_load.get("module"), "config_model_workspace")
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return DEFAULT_LLM_MODEL
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return config_model_workspace.get_config().DEFAULT_LLM_MODEL
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def _import_default_embedding_model() -> Any:
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model_config_load = CONFIG_IMPORTS.get("_model_config.py")
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load_mod = model_config_load.get("load_mod")
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DEFAULT_EMBEDDING_MODEL = load_mod(model_config_load.get("module"), "DEFAULT_EMBEDDING_MODEL")
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return DEFAULT_EMBEDDING_MODEL
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config_model_workspace = load_mod(model_config_load.get("module"), "config_model_workspace")
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return config_model_workspace.get_config().DEFAULT_EMBEDDING_MODEL
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def _import_agent_model() -> Any:
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model_config_load = CONFIG_IMPORTS.get("_model_config.py")
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load_mod = model_config_load.get("load_mod")
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Agent_MODEL = load_mod(model_config_load.get("module"), "Agent_MODEL")
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config_model_workspace = load_mod(model_config_load.get("module"), "config_model_workspace")
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return Agent_MODEL
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return config_model_workspace.get_config().Agent_MODEL
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def _import_history_len() -> Any:
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model_config_load = CONFIG_IMPORTS.get("_model_config.py")
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load_mod = model_config_load.get("load_mod")
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HISTORY_LEN = load_mod(model_config_load.get("module"), "HISTORY_LEN")
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config_model_workspace = load_mod(model_config_load.get("module"), "config_model_workspace")
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return HISTORY_LEN
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return config_model_workspace.get_config().HISTORY_LEN
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def _import_max_tokens() -> Any:
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model_config_load = CONFIG_IMPORTS.get("_model_config.py")
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load_mod = model_config_load.get("load_mod")
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MAX_TOKENS = load_mod(model_config_load.get("module"), "MAX_TOKENS")
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config_model_workspace = load_mod(model_config_load.get("module"), "config_model_workspace")
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return MAX_TOKENS
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return config_model_workspace.get_config().MAX_TOKENS
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def _import_temperature() -> Any:
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model_config_load = CONFIG_IMPORTS.get("_model_config.py")
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load_mod = model_config_load.get("load_mod")
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TEMPERATURE = load_mod(model_config_load.get("module"), "TEMPERATURE")
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config_model_workspace = load_mod(model_config_load.get("module"), "config_model_workspace")
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return TEMPERATURE
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return config_model_workspace.get_config().TEMPERATURE
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def _import_support_agent_models() -> Any:
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model_config_load = CONFIG_IMPORTS.get("_model_config.py")
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load_mod = model_config_load.get("load_mod")
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SUPPORT_AGENT_MODELS = load_mod(model_config_load.get("module"), "SUPPORT_AGENT_MODELS")
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config_model_workspace = load_mod(model_config_load.get("module"), "config_model_workspace")
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return SUPPORT_AGENT_MODELS
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return config_model_workspace.get_config().SUPPORT_AGENT_MODELS
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def _import_llm_model_config() -> Any:
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model_config_load = CONFIG_IMPORTS.get("_model_config.py")
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load_mod = model_config_load.get("load_mod")
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LLM_MODEL_CONFIG = load_mod(model_config_load.get("module"), "LLM_MODEL_CONFIG")
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config_model_workspace = load_mod(model_config_load.get("module"), "config_model_workspace")
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return LLM_MODEL_CONFIG
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return config_model_workspace.get_config().LLM_MODEL_CONFIG
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def _import_model_platforms() -> Any:
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model_config_load = CONFIG_IMPORTS.get("_model_config.py")
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load_mod = model_config_load.get("load_mod")
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MODEL_PLATFORMS = load_mod(model_config_load.get("module"), "MODEL_PLATFORMS")
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config_model_workspace = load_mod(model_config_load.get("module"), "config_model_workspace")
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return MODEL_PLATFORMS
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return config_model_workspace.get_config().MODEL_PLATFORMS
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def _import_model_providers_cfg_path() -> Any:
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model_config_load = CONFIG_IMPORTS.get("_model_config.py")
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load_mod = model_config_load.get("load_mod")
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MODEL_PROVIDERS_CFG_PATH_CONFIG = load_mod(model_config_load.get("module"), "MODEL_PROVIDERS_CFG_PATH_CONFIG")
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config_model_workspace = load_mod(model_config_load.get("module"), "config_model_workspace")
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return MODEL_PROVIDERS_CFG_PATH_CONFIG
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return config_model_workspace.get_config().MODEL_PROVIDERS_CFG_PATH_CONFIG
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def _import_model_providers_cfg_host() -> Any:
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model_config_load = CONFIG_IMPORTS.get("_model_config.py")
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load_mod = model_config_load.get("load_mod")
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MODEL_PROVIDERS_CFG_HOST = load_mod(model_config_load.get("module"), "MODEL_PROVIDERS_CFG_HOST")
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config_model_workspace = load_mod(model_config_load.get("module"), "config_model_workspace")
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return MODEL_PROVIDERS_CFG_HOST
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return config_model_workspace.get_config().MODEL_PROVIDERS_CFG_HOST
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def _import_model_providers_cfg_port() -> Any:
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model_config_load = CONFIG_IMPORTS.get("_model_config.py")
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load_mod = model_config_load.get("load_mod")
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MODEL_PROVIDERS_CFG_PORT = load_mod(model_config_load.get("module"), "MODEL_PROVIDERS_CFG_PORT")
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config_model_workspace = load_mod(model_config_load.get("module"), "config_model_workspace")
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return MODEL_PROVIDERS_CFG_PORT
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return config_model_workspace.get_config().MODEL_PROVIDERS_CFG_PORT
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def _import_tool_config() -> Any:
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model_config_load = CONFIG_IMPORTS.get("_model_config.py")
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load_mod = model_config_load.get("load_mod")
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TOOL_CONFIG = load_mod(model_config_load.get("module"), "TOOL_CONFIG")
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config_model_workspace = load_mod(model_config_load.get("module"), "config_model_workspace")
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return TOOL_CONFIG
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return config_model_workspace.get_config().TOOL_CONFIG
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def _import_prompt_templates() -> Any:
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@ -524,6 +556,14 @@ def __getattr__(name: str) -> Any:
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return _import_ConfigBasicWorkSpace()
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elif name == "config_basic_workspace":
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return _import_config_basic_workspace()
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elif name == "ConfigModel":
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return _import_ConfigModel()
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elif name == "ConfigModelFactory":
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return _import_ConfigModelFactory()
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elif name == "ConfigModelWorkSpace":
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return _import_ConfigModelWorkSpace()
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elif name == "config_model_workspace":
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return _import_config_model_workspace()
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elif name == "log_verbose":
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return _import_log_verbose()
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elif name == "CHATCHAT_ROOT":
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@ -624,7 +664,6 @@ VERSION = "v0.3.0-preview"
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__all__ = [
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"VERSION",
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"config_basic_workspace",
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"log_verbose",
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"CHATCHAT_ROOT",
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"DATA_PATH",
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@ -677,4 +716,12 @@ __all__ = [
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"ConfigBasicFactory",
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"ConfigBasicWorkSpace",
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"config_basic_workspace",
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"ConfigModel",
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"ConfigModelFactory",
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"ConfigModelWorkSpace",
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"config_model_workspace",
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]
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@ -6,7 +6,6 @@ import sys
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import logging
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from typing import Any, Optional
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from chatchat.configs._core_config import CF
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sys.path.append(str(Path(__file__).parent))
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import _core_config as core_config
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@ -128,6 +127,9 @@ class ConfigBasicWorkSpace(core_config.ConfigWorkSpace[ConfigBasicFactory, Confi
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"""
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config_factory_cls = ConfigBasicFactory
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def __init__(self):
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super().__init__()
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def _build_config_factory(self, config_json: Any) -> ConfigBasicFactory:
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_config_factory = self.config_factory_cls()
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@ -145,9 +147,6 @@ class ConfigBasicWorkSpace(core_config.ConfigWorkSpace[ConfigBasicFactory, Confi
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def get_type(cls) -> str:
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return ConfigBasic.class_name()
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def __init__(self):
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super().__init__()
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def get_config(self) -> ConfigBasic:
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return self._config_factory.get_config()
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@ -163,9 +162,5 @@ class ConfigBasicWorkSpace(core_config.ConfigWorkSpace[ConfigBasicFactory, Confi
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self._config_factory.log_format(log_format)
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self.store_config()
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def clear(self):
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logger.info("Clear workspace config.")
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os.remove(self.workspace_config)
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config_basic_workspace: ConfigBasicWorkSpace = ConfigBasicWorkSpace()
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@ -1,38 +1,100 @@
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import os
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import logging
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import sys
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from pathlib import Path
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from typing import Any, Optional, List, Dict
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# 默认选用的 LLM 名称
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DEFAULT_LLM_MODEL = "chatglm3-6b"
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from dataclasses import dataclass
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# 默认选用的 Embedding 名称
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DEFAULT_EMBEDDING_MODEL = "bge-large-zh-v1.5"
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sys.path.append(str(Path(__file__).parent))
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import _core_config as core_config
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logger = logging.getLogger()
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# AgentLM模型的名称 (可以不指定，指定之后就锁定进入Agent之后的Chain的模型，不指定就是LLM_MODELS[0])
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Agent_MODEL = None
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class ConfigModel(core_config.Config):
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DEFAULT_LLM_MODEL: Optional[str] = None
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"""默认选用的 LLM 名称"""
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DEFAULT_EMBEDDING_MODEL: Optional[str] = None
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"""默认选用的 Embedding 名称"""
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Agent_MODEL: Optional[str] = None
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"""AgentLM模型的名称 (可以不指定，指定之后就锁定进入Agent之后的Chain的模型，不指定就是LLM_MODELS[0])"""
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HISTORY_LEN: Optional[int] = None
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"""历史对话轮数"""
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MAX_TOKENS: Optional[int] = None
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"""大模型最长支持的长度，如果不填写，则使用模型默认的最大长度，如果填写，则为用户设定的最大长度"""
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TEMPERATURE: Optional[float] = None
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"""LLM通用对话参数"""
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SUPPORT_AGENT_MODELS: Optional[List[str]] = None
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"""支持的Agent模型"""
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LLM_MODEL_CONFIG: Optional[Dict[str, Dict[str, Any]]] = None
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"""LLM模型配置，包括了不同模态初始化参数"""
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MODEL_PLATFORMS: Optional[List[Dict[str, Any]]] = None
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"""模型平台配置"""
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MODEL_PROVIDERS_CFG_PATH_CONFIG: Optional[str] = None
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"""模型平台配置文件路径"""
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MODEL_PROVIDERS_CFG_HOST: Optional[str] = None
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"""模型平台配置文件host"""
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MODEL_PROVIDERS_CFG_PORT: Optional[int] = None
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"""模型平台配置文件port"""
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TOOL_CONFIG: Optional[Dict[str, Any]] = None
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"""工具配置项"""
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# 历史对话轮数
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HISTORY_LEN = 3
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@classmethod
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def class_name(cls) -> str:
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return cls.__name__
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# 大模型最长支持的长度，如果不填写，则使用模型默认的最大长度，如果填写，则为用户设定的最大长度
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MAX_TOKENS = None
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def __str__(self):
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return self.to_json()
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# LLM通用对话参数
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TEMPERATURE = 0.7
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# TOP_P = 0.95 # ChatOpenAI暂不支持该参数
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SUPPORT_AGENT_MODELS = [
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@dataclass
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class ConfigModelFactory(core_config.ConfigFactory[ConfigModel]):
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"""ConfigModel工厂类"""
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def __init__(self):
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# 默认选用的 LLM 名称
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self.DEFAULT_LLM_MODEL = "chatglm3-6b"
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# 默认选用的 Embedding 名称
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self.DEFAULT_EMBEDDING_MODEL = "bge-large-zh-v1.5"
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# AgentLM模型的名称 (可以不指定，指定之后就锁定进入Agent之后的Chain的模型，不指定就是LLM_MODELS[0])
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self.Agent_MODEL = None
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# 历史对话轮数
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self.HISTORY_LEN = 3
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# 大模型最长支持的长度，如果不填写，则使用模型默认的最大长度，如果填写，则为用户设定的最大长度
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self.MAX_TOKENS = None
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# LLM通用对话参数
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self.TEMPERATURE = 0.7
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# TOP_P = 0.95 # ChatOpenAI暂不支持该参数
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self.SUPPORT_AGENT_MODELS = [
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"chatglm3-6b",
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"openai-api",
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"Qwen-14B-Chat",
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"Qwen-7B-Chat",
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"qwen-turbo",
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]
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]
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self.MODEL_PROVIDERS_CFG_PATH_CONFIG = os.path.join(os.path.dirname(os.path.abspath(__file__)),
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"model_providers.yaml")
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self.MODEL_PROVIDERS_CFG_HOST = "127.0.0.1"
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LLM_MODEL_CONFIG = {
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self.MODEL_PROVIDERS_CFG_PORT = 20000
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self._init_llm_work_config()
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def _init_llm_work_config(self):
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"""初始化知识库runtime的一些配置"""
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self.LLM_MODEL_CONFIG = {
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# 意图识别不需要输出，模型后台知道就行
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"preprocess_model": {
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DEFAULT_LLM_MODEL: {
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self.DEFAULT_LLM_MODEL: {
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"temperature": 0.05,
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"max_tokens": 4096,
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"history_len": 100,
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@ -41,7 +103,7 @@ LLM_MODEL_CONFIG = {
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},
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},
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"llm_model": {
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DEFAULT_LLM_MODEL: {
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self.DEFAULT_LLM_MODEL: {
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"temperature": 0.9,
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"max_tokens": 4096,
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"history_len": 10,
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@ -50,7 +112,7 @@ LLM_MODEL_CONFIG = {
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},
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},
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"action_model": {
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DEFAULT_LLM_MODEL: {
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self.DEFAULT_LLM_MODEL: {
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"temperature": 0.01,
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"max_tokens": 4096,
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"prompt_name": "ChatGLM3",
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@ -58,7 +120,7 @@ LLM_MODEL_CONFIG = {
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},
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},
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"postprocess_model": {
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DEFAULT_LLM_MODEL: {
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self.DEFAULT_LLM_MODEL: {
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"temperature": 0.01,
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"max_tokens": 4096,
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"prompt_name": "default",
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@ -70,17 +132,16 @@ LLM_MODEL_CONFIG = {
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"size": "256*256",
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}
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}
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}
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}
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# 可以通过 model_providers 提供转换不同平台的接口为openai endpoint的能力，启动后下面变量会自动增加相应的平台
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# ### 如果您已经有了一个openai endpoint的能力的地址，可以在这里直接配置
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# - platform_name 可以任意填写，不要重复即可
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# - platform_type 以后可能根据平台类型做一些功能区分,与platform_name一致即可
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# - 将框架部署的模型填写到对应列表即可。不同框架可以加载同名模型，项目会自动做负载均衡。
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# 可以通过 model_providers 提供转换不同平台的接口为openai endpoint的能力，启动后下面变量会自动增加相应的平台
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# ### 如果您已经有了一个openai endpoint的能力的地址，可以在这里直接配置
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# - platform_name 可以任意填写，不要重复即可
|
||||
# - platform_type 以后可能根据平台类型做一些功能区分,与platform_name一致即可
|
||||
# - 将框架部署的模型填写到对应列表即可。不同框架可以加载同名模型，项目会自动做负载均衡。
|
||||
|
||||
|
||||
# 创建一个全局的共享字典
|
||||
MODEL_PLATFORMS = [
|
||||
# 创建一个全局的共享字典
|
||||
self.MODEL_PLATFORMS = [
|
||||
|
||||
{
|
||||
"platform_name": "oneapi",
|
||||
@ -138,14 +199,9 @@ MODEL_PLATFORMS = [
|
||||
"tts_models": [],
|
||||
},
|
||||
|
||||
]
|
||||
|
||||
MODEL_PROVIDERS_CFG_PATH_CONFIG = os.path.join(os.path.dirname(os.path.abspath(__file__)), "model_providers.yaml")
|
||||
MODEL_PROVIDERS_CFG_HOST = "127.0.0.1"
|
||||
|
||||
MODEL_PROVIDERS_CFG_PORT = 20000
|
||||
# 工具配置项
|
||||
TOOL_CONFIG = {
|
||||
]
|
||||
# 工具配置项
|
||||
self.TOOL_CONFIG = {
|
||||
"search_local_knowledgebase": {
|
||||
"use": False,
|
||||
"top_k": 3,
|
||||
@ -244,17 +300,151 @@ TOOL_CONFIG = {
|
||||
# 务必评估是否需要开启read_only,开启后会对sql语句进行检查，请确认text2sql.py中的intercept_sql拦截器是否满足你使用的数据库只读要求
|
||||
# 优先推荐从数据库层面对用户权限进行限制
|
||||
"read_only": False,
|
||||
#限定返回的行数
|
||||
"top_k":50,
|
||||
#是否返回中间步骤
|
||||
# 限定返回的行数
|
||||
"top_k": 50,
|
||||
# 是否返回中间步骤
|
||||
"return_intermediate_steps": True,
|
||||
#如果想指定特定表，请填写表名称，如["sys_user","sys_dept"]，不填写走智能判断应该使用哪些表
|
||||
"table_names":[],
|
||||
#对表名进行额外说明，辅助大模型更好的判断应该使用哪些表，尤其是SQLDatabaseSequentialChain模式下,是根据表名做的预测，很容易误判。
|
||||
"table_comments":{
|
||||
# 如果想指定特定表，请填写表名称，如["sys_user","sys_dept"]，不填写走智能判断应该使用哪些表
|
||||
"table_names": [],
|
||||
# 对表名进行额外说明，辅助大模型更好的判断应该使用哪些表，尤其是SQLDatabaseSequentialChain模式下,是根据表名做的预测，很容易误判。
|
||||
"table_comments": {
|
||||
# 如果出现大模型选错表的情况，可尝试根据实际情况填写表名和说明
|
||||
# "tableA":"这是一个用户表，存储了用户的基本信息",
|
||||
# "tanleB":"角色表",
|
||||
}
|
||||
},
|
||||
}
|
||||
}
|
||||
|
||||
def default_llm_model(self, llm_model: str):
|
||||
self.DEFAULT_LLM_MODEL = llm_model
|
||||
|
||||
def default_embedding_model(self, embedding_model: str):
|
||||
self.DEFAULT_EMBEDDING_MODEL = embedding_model
|
||||
|
||||
def agent_model(self, agent_model: str):
|
||||
self.Agent_MODEL = agent_model
|
||||
|
||||
def history_len(self, history_len: int):
|
||||
self.HISTORY_LEN = history_len
|
||||
|
||||
def max_tokens(self, max_tokens: int):
|
||||
self.MAX_TOKENS = max_tokens
|
||||
|
||||
def temperature(self, temperature: float):
|
||||
self.TEMPERATURE = temperature
|
||||
|
||||
def support_agent_models(self, support_agent_models: List[str]):
|
||||
self.SUPPORT_AGENT_MODELS = support_agent_models
|
||||
|
||||
def model_providers_cfg_path_config(self, model_providers_cfg_path_config: str):
|
||||
self.MODEL_PROVIDERS_CFG_PATH_CONFIG = model_providers_cfg_path_config
|
||||
|
||||
def model_providers_cfg_host(self, model_providers_cfg_host: str):
|
||||
self.MODEL_PROVIDERS_CFG_HOST = model_providers_cfg_host
|
||||
|
||||
def model_providers_cfg_port(self, model_providers_cfg_port: int):
|
||||
self.MODEL_PROVIDERS_CFG_PORT = model_providers_cfg_port
|
||||
|
||||
def get_config(self) -> ConfigModel:
|
||||
config = ConfigModel()
|
||||
config.DEFAULT_LLM_MODEL = self.DEFAULT_LLM_MODEL
|
||||
config.DEFAULT_EMBEDDING_MODEL = self.DEFAULT_EMBEDDING_MODEL
|
||||
config.Agent_MODEL = self.Agent_MODEL
|
||||
config.HISTORY_LEN = self.HISTORY_LEN
|
||||
config.MAX_TOKENS = self.MAX_TOKENS
|
||||
config.TEMPERATURE = self.TEMPERATURE
|
||||
config.SUPPORT_AGENT_MODELS = self.SUPPORT_AGENT_MODELS
|
||||
config.LLM_MODEL_CONFIG = self.LLM_MODEL_CONFIG
|
||||
config.MODEL_PLATFORMS = self.MODEL_PLATFORMS
|
||||
config.MODEL_PROVIDERS_CFG_PATH_CONFIG = self.MODEL_PROVIDERS_CFG_PATH_CONFIG
|
||||
config.MODEL_PROVIDERS_CFG_HOST = self.MODEL_PROVIDERS_CFG_HOST
|
||||
config.MODEL_PROVIDERS_CFG_PORT = self.MODEL_PROVIDERS_CFG_PORT
|
||||
config.TOOL_CONFIG = self.TOOL_CONFIG
|
||||
|
||||
return config
|
||||
|
||||
|
||||
class ConfigModelWorkSpace(core_config.ConfigWorkSpace[ConfigModelFactory, ConfigModel]):
|
||||
"""
|
||||
工作空间的配置预设, 提供ConfigModel建造方法产生实例。
|
||||
"""
|
||||
config_factory_cls = ConfigModelFactory
|
||||
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
|
||||
def _build_config_factory(self, config_json: Any) -> ConfigModelFactory:
|
||||
|
||||
_config_factory = self.config_factory_cls()
|
||||
if config_json.get("DEFAULT_LLM_MODEL"):
|
||||
_config_factory.default_llm_model(config_json.get("DEFAULT_LLM_MODEL"))
|
||||
if config_json.get("DEFAULT_EMBEDDING_MODEL"):
|
||||
_config_factory.default_embedding_model(config_json.get("DEFAULT_EMBEDDING_MODEL"))
|
||||
if config_json.get("Agent_MODEL"):
|
||||
_config_factory.agent_model(config_json.get("Agent_MODEL"))
|
||||
if config_json.get("HISTORY_LEN"):
|
||||
_config_factory.history_len(config_json.get("HISTORY_LEN"))
|
||||
if config_json.get("MAX_TOKENS"):
|
||||
_config_factory.max_tokens(config_json.get("MAX_TOKENS"))
|
||||
if config_json.get("TEMPERATURE"):
|
||||
_config_factory.temperature(config_json.get("TEMPERATURE"))
|
||||
if config_json.get("SUPPORT_AGENT_MODELS"):
|
||||
_config_factory.support_agent_models(config_json.get("SUPPORT_AGENT_MODELS"))
|
||||
if config_json.get("MODEL_PROVIDERS_CFG_PATH_CONFIG"):
|
||||
_config_factory.model_providers_cfg_path_config(config_json.get("MODEL_PROVIDERS_CFG_PATH_CONFIG"))
|
||||
if config_json.get("MODEL_PROVIDERS_CFG_HOST"):
|
||||
_config_factory.model_providers_cfg_host(config_json.get("MODEL_PROVIDERS_CFG_HOST"))
|
||||
if config_json.get("MODEL_PROVIDERS_CFG_PORT"):
|
||||
_config_factory.model_providers_cfg_port(config_json.get("MODEL_PROVIDERS_CFG_PORT"))
|
||||
|
||||
return _config_factory
|
||||
|
||||
@classmethod
|
||||
def get_type(cls) -> str:
|
||||
return ConfigModel.class_name()
|
||||
|
||||
def get_config(self) -> ConfigModel:
|
||||
return self._config_factory.get_config()
|
||||
|
||||
def set_default_llm_model(self, llm_model: str):
|
||||
self._config_factory.default_llm_model(llm_model)
|
||||
self.store_config()
|
||||
|
||||
def set_default_embedding_model(self, embedding_model: str):
|
||||
self._config_factory.default_embedding_model(embedding_model)
|
||||
self.store_config()
|
||||
|
||||
def set_agent_model(self, agent_model: str):
|
||||
self._config_factory.agent_model(agent_model)
|
||||
self.store_config()
|
||||
|
||||
def set_history_len(self, history_len: int):
|
||||
self._config_factory.history_len(history_len)
|
||||
self.store_config()
|
||||
|
||||
def set_max_tokens(self, max_tokens: int):
|
||||
self._config_factory.max_tokens(max_tokens)
|
||||
self.store_config()
|
||||
|
||||
def set_temperature(self, temperature: float):
|
||||
self._config_factory.temperature(temperature)
|
||||
self.store_config()
|
||||
|
||||
def set_support_agent_models(self, support_agent_models: List[str]):
|
||||
self._config_factory.support_agent_models(support_agent_models)
|
||||
self.store_config()
|
||||
|
||||
def set_model_providers_cfg_path_config(self, model_providers_cfg_path_config: str):
|
||||
self._config_factory.model_providers_cfg_path_config(model_providers_cfg_path_config)
|
||||
self.store_config()
|
||||
|
||||
def set_model_providers_cfg_host(self, model_providers_cfg_host: str):
|
||||
self._config_factory.model_providers_cfg_host(model_providers_cfg_host)
|
||||
self.store_config()
|
||||
|
||||
def set_model_providers_cfg_port(self, model_providers_cfg_port: int):
|
||||
self._config_factory.model_providers_cfg_port(model_providers_cfg_port)
|
||||
self.store_config()
|
||||
|
||||
|
||||
config_model_workspace: ConfigModelWorkSpace = ConfigModelWorkSpace()
|
||||
@ -1,6 +1,6 @@
|
||||
[tool.poetry]
|
||||
name = "langchain-chatchat"
|
||||
version = "0.3.0.20240606"
|
||||
version = "0.3.0.20240611"
|
||||
description = ""
|
||||
authors = ["chatchat"]
|
||||
readme = "README.md"
|
||||
@ -121,7 +121,6 @@ extended_testing = [
|
||||
"xmltodict",
|
||||
"faiss-cpu",
|
||||
"openapi-pydantic",
|
||||
"markdownify",
|
||||
"arxiv",
|
||||
"sqlite-vss",
|
||||
"rapidocr-onnxruntime",
|
||||
|
||||
@ -1,6 +1,10 @@
|
||||
from pathlib import Path
|
||||
|
||||
from chatchat.configs import ConfigBasicFactory, ConfigBasic, ConfigBasicWorkSpace
|
||||
from chatchat.configs import (
|
||||
ConfigBasicFactory,
|
||||
ConfigBasic,
|
||||
ConfigBasicWorkSpace
|
||||
)
|
||||
import os
|
||||
|
||||
|
||||
@ -36,3 +40,6 @@ def test_workspace_default():
|
||||
assert LOG_FORMAT is not None
|
||||
assert LOG_PATH is not None
|
||||
assert MEDIA_PATH is not None
|
||||
|
||||
|
||||
def test_config_model_workspace():
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user