class ChromaDBVectorMemory(config: ChromaDBVectorMemoryConfig | None = None)[源]

基类：Memory、Component[ChromaDBVectorMemoryConfig]

使用 ChromaDB 支持的向量相似性搜索存储和检索记忆。

ChromaDBVectorMemory 提供了一种基于向量的记忆实现，它使用 ChromaDB 根据语义相似性存储和检索内容。它通过利用向量嵌入来查找相似内容，增强了智能体在对话中回忆上下文相关信息的能力。

此实现可作为使用向量嵌入的更复杂记忆系统的参考。对于需要检索内容特殊格式的高级用例，用户应扩展此类别并重写 update_context() 方法。

此实现需要安装 ChromaDB 附加组件。安装方式：

pip install "autogen-ext[chromadb]"

参数:

config (ChromaDBVectorMemoryConfig | None) – ChromaDB 记忆的配置。如果为 None，则默认为具有默认值的 PersistentChromaDBVectorMemoryConfig。支持两种配置类型： * PersistentChromaDBVectorMemoryConfig：用于本地存储 * HttpChromaDBVectorMemoryConfig：用于连接远程 ChromaDB 服务器

示例

import os
import asyncio
from pathlib import Path
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.ui import Console
from autogen_core.memory import MemoryContent, MemoryMimeType
from autogen_ext.memory.chromadb import (
    ChromaDBVectorMemory,
    PersistentChromaDBVectorMemoryConfig,
    SentenceTransformerEmbeddingFunctionConfig,
    OpenAIEmbeddingFunctionConfig,
)
from autogen_ext.models.openai import OpenAIChatCompletionClient


def get_weather(city: str) -> str:
    return f"The weather in {city} is sunny with a high of 90°F and a low of 70°F."


def fahrenheit_to_celsius(fahrenheit: float) -> float:
    return (fahrenheit - 32) * 5.0 / 9.0


async def main() -> None:
    # Use default embedding function
    default_memory = ChromaDBVectorMemory(
        config=PersistentChromaDBVectorMemoryConfig(
            collection_name="user_preferences",
            persistence_path=os.path.join(str(Path.home()), ".chromadb_autogen"),
            k=3,  # Return top 3 results
            score_threshold=0.5,  # Minimum similarity score
        )
    )

    # Using a custom SentenceTransformer model
    custom_memory = ChromaDBVectorMemory(
        config=PersistentChromaDBVectorMemoryConfig(
            collection_name="multilingual_memory",
            persistence_path=os.path.join(str(Path.home()), ".chromadb_autogen"),
            embedding_function_config=SentenceTransformerEmbeddingFunctionConfig(
                model_name="paraphrase-multilingual-mpnet-base-v2"
            ),
        )
    )

    # Using OpenAI embeddings
    openai_memory = ChromaDBVectorMemory(
        config=PersistentChromaDBVectorMemoryConfig(
            collection_name="openai_memory",
            persistence_path=os.path.join(str(Path.home()), ".chromadb_autogen"),
            embedding_function_config=OpenAIEmbeddingFunctionConfig(
                api_key=os.environ["OPENAI_API_KEY"], model_name="text-embedding-3-small"
            ),
        )
    )

    # Add user preferences to memory
    await openai_memory.add(
        MemoryContent(
            content="The user prefers weather temperatures in Celsius",
            mime_type=MemoryMimeType.TEXT,
            metadata={"category": "preferences", "type": "units"},
        )
    )

    # Create assistant agent with ChromaDB memory
    assistant = AssistantAgent(
        name="assistant",
        model_client=OpenAIChatCompletionClient(
            model="gpt-4.1",
        ),
        tools=[
            get_weather,
            fahrenheit_to_celsius,
        ],
        max_tool_iterations=10,
        memory=[openai_memory],
    )

    # The memory will automatically retrieve relevant content during conversations
    await Console(assistant.run_stream(task="What's the temperature in New York?"))

    # Remember to close the memory when finished
    await default_memory.close()
    await custom_memory.close()
    await openai_memory.close()


asyncio.run(main())

输出

---------- TextMessage (user) ----------
What's the temperature in New York?
---------- MemoryQueryEvent (assistant) ----------
[MemoryContent(content='The user prefers weather temperatures in Celsius', mime_type='MemoryMimeType.TEXT', metadata={'type': 'units', 'category': 'preferences', 'mime_type': 'MemoryMimeType.TEXT', 'score': 0.3133561611175537, 'id': 'fb00506c-acf4-4174-93d7-2a942593f3f7'}), MemoryContent(content='The user prefers weather temperatures in Celsius', mime_type='MemoryMimeType.TEXT', metadata={'mime_type': 'MemoryMimeType.TEXT', 'category': 'preferences', 'type': 'units', 'score': 0.3133561611175537, 'id': '34311689-b419-4e1a-8bc4-09143f356c66'})]
---------- ToolCallRequestEvent (assistant) ----------
[FunctionCall(id='call_7TjsFd430J1aKwU5T2w8bvdh', arguments='{"city":"New York"}', name='get_weather')]
---------- ToolCallExecutionEvent (assistant) ----------
[FunctionExecutionResult(content='The weather in New York is sunny with a high of 90°F and a low of 70°F.', name='get_weather', call_id='call_7TjsFd430J1aKwU5T2w8bvdh', is_error=False)]
---------- ToolCallRequestEvent (assistant) ----------
[FunctionCall(id='call_RTjMHEZwDXtjurEYTjDlvq9c', arguments='{"fahrenheit": 90}', name='fahrenheit_to_celsius'), FunctionCall(id='call_3mMuCK1aqtzZPTqIHPoHKxtP', arguments='{"fahrenheit": 70}', name='fahrenheit_to_celsius')]
---------- ToolCallExecutionEvent (assistant) ----------
[FunctionExecutionResult(content='32.22222222222222', name='fahrenheit_to_celsius', call_id='call_RTjMHEZwDXtjurEYTjDlvq9c', is_error=False), FunctionExecutionResult(content='21.11111111111111', name='fahrenheit_to_celsius', call_id='call_3mMuCK1aqtzZPTqIHPoHKxtP', is_error=False)]
---------- TextMessage (assistant) ----------
The temperature in New York today is sunny with a high of about 32°C and a low of about 21°C.

component_config_schema

ChromaDBVectorMemoryConfig 的别名

component_provider_override: ClassVar[str | None] = 'autogen_ext.memory.chromadb.ChromaDBVectorMemory'

覆盖组件的提供者字符串。这应该用于防止内部模块名称成为模块名称的一部分。

property collection_name: str

获取 ChromaDB 集合的名称。

async update_context(model_context: ChatCompletionContext) → UpdateContextResult[源]

使用相关的记忆内容更新提供的模型上下文。

参数:

model_context – 要更新的上下文。

返回:

包含相关记忆的 UpdateContextResult

async add(content: MemoryContent, cancellation_token: CancellationToken | None = None) → None[源]

向记忆中添加新内容。

参数:

• content – 要添加的记忆内容

• cancellation_token – 用于取消操作的可选令牌

async query(query: str | MemoryContent, cancellation_token: CancellationToken | None = None, **kwargs: Any) → MemoryQueryResult[源]

查询记忆存储并返回相关条目。

参数:

• query – 查询内容项

• cancellation_token – 用于取消操作的可选令牌

• **kwargs – 其他特定于实现的参数

返回:

包含具有相关性分数的记忆条目的 MemoryQueryResult

async clear() → None[源]

清除内存中的所有条目。

async close() → None[源]

清理 ChromaDB 客户端和资源。

async reset() → None[源]

pydantic model ChromaDBVectorMemoryConfig[源]

基类: BaseModel

ChromaDB 记忆实现的基本配置。

v0.4.1 版本中有所更改：添加了通过 embedding_function_config 支持自定义嵌入函数的功能。

显示 JSON 模式

{
   "title": "ChromaDBVectorMemoryConfig",
   "description": "Base configuration for ChromaDB-based memory implementation.\n\n.. versionchanged:: v0.4.1\n   Added support for custom embedding functions via embedding_function_config.",
   "type": "object",
   "properties": {
      "client_type": {
         "enum": [
            "persistent",
            "http"
         ],
         "title": "Client Type",
         "type": "string"
      },
      "collection_name": {
         "default": "memory_store",
         "description": "Name of the ChromaDB collection",
         "title": "Collection Name",
         "type": "string"
      },
      "distance_metric": {
         "default": "cosine",
         "description": "Distance metric for similarity search",
         "title": "Distance Metric",
         "type": "string"
      },
      "k": {
         "default": 3,
         "description": "Number of results to return in queries",
         "title": "K",
         "type": "integer"
      },
      "score_threshold": {
         "anyOf": [
            {
               "type": "number"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Minimum similarity score threshold",
         "title": "Score Threshold"
      },
      "allow_reset": {
         "default": false,
         "description": "Whether to allow resetting the ChromaDB client",
         "title": "Allow Reset",
         "type": "boolean"
      },
      "tenant": {
         "default": "default_tenant",
         "description": "Tenant to use",
         "title": "Tenant",
         "type": "string"
      },
      "database": {
         "default": "default_database",
         "description": "Database to use",
         "title": "Database",
         "type": "string"
      },
      "embedding_function_config": {
         "description": "Configuration for the embedding function",
         "discriminator": {
            "mapping": {
               "default": "#/$defs/DefaultEmbeddingFunctionConfig",
               "openai": "#/$defs/OpenAIEmbeddingFunctionConfig",
               "sentence_transformer": "#/$defs/SentenceTransformerEmbeddingFunctionConfig"
            },
            "propertyName": "function_type"
         },
         "oneOf": [
            {
               "$ref": "#/$defs/DefaultEmbeddingFunctionConfig"
            },
            {
               "$ref": "#/$defs/SentenceTransformerEmbeddingFunctionConfig"
            },
            {
               "$ref": "#/$defs/OpenAIEmbeddingFunctionConfig"
            }
         ],
         "title": "Embedding Function Config"
      }
   },
   "$defs": {
      "DefaultEmbeddingFunctionConfig": {
         "description": "Configuration for the default ChromaDB embedding function.\n\nUses ChromaDB's default embedding function (Sentence Transformers all-MiniLM-L6-v2).\n\n.. versionadded:: v0.4.1\n   Support for custom embedding functions in ChromaDB memory.",
         "properties": {
            "function_type": {
               "const": "default",
               "default": "default",
               "title": "Function Type",
               "type": "string"
            }
         },
         "title": "DefaultEmbeddingFunctionConfig",
         "type": "object"
      },
      "OpenAIEmbeddingFunctionConfig": {
         "description": "Configuration for OpenAI embedding functions.\n\nUses OpenAI's embedding API for generating embeddings.\n\n.. versionadded:: v0.4.1\n   Support for custom embedding functions in ChromaDB memory.\n\nArgs:\n    api_key (str): OpenAI API key. If empty, will attempt to use environment variable.\n    model_name (str): OpenAI embedding model name. Defaults to \"text-embedding-ada-002\".\n\nExample:\n    .. code-block:: python\n\n        from autogen_ext.memory.chromadb import OpenAIEmbeddingFunctionConfig\n\n        _ = OpenAIEmbeddingFunctionConfig(api_key=\"sk-...\", model_name=\"text-embedding-3-small\")",
         "properties": {
            "function_type": {
               "const": "openai",
               "default": "openai",
               "title": "Function Type",
               "type": "string"
            },
            "api_key": {
               "default": "",
               "description": "OpenAI API key",
               "title": "Api Key",
               "type": "string"
            },
            "model_name": {
               "default": "text-embedding-ada-002",
               "description": "OpenAI embedding model name",
               "title": "Model Name",
               "type": "string"
            }
         },
         "title": "OpenAIEmbeddingFunctionConfig",
         "type": "object"
      },
      "SentenceTransformerEmbeddingFunctionConfig": {
         "description": "Configuration for SentenceTransformer embedding functions.\n\nAllows specifying a custom SentenceTransformer model for embeddings.\n\n.. versionadded:: v0.4.1\n   Support for custom embedding functions in ChromaDB memory.\n\nArgs:\n    model_name (str): Name of the SentenceTransformer model to use.\n        Defaults to \"all-MiniLM-L6-v2\".\n\nExample:\n    .. code-block:: python\n\n        from autogen_ext.memory.chromadb import SentenceTransformerEmbeddingFunctionConfig\n\n        _ = SentenceTransformerEmbeddingFunctionConfig(model_name=\"paraphrase-multilingual-mpnet-base-v2\")",
         "properties": {
            "function_type": {
               "const": "sentence_transformer",
               "default": "sentence_transformer",
               "title": "Function Type",
               "type": "string"
            },
            "model_name": {
               "default": "all-MiniLM-L6-v2",
               "description": "SentenceTransformer model name to use",
               "title": "Model Name",
               "type": "string"
            }
         },
         "title": "SentenceTransformerEmbeddingFunctionConfig",
         "type": "object"
      }
   },
   "required": [
      "client_type"
   ]
}

字段:

• allow_reset (bool)

• client_type (Literal['persistent', 'http'])

• collection_name (str)

• database (str)

• distance_metric (str)

• embedding_function_config (autogen_ext.memory.chromadb._chroma_configs.DefaultEmbeddingFunctionConfig | autogen_ext.memory.chromadb._chroma_configs.SentenceTransformerEmbeddingFunctionConfig | autogen_ext.memory.chromadb._chroma_configs.OpenAIEmbeddingFunctionConfig | autogen_ext.memory.chromadb._chroma_configs.CustomEmbeddingFunctionConfig)

• k (int)

• score_threshold (float | None)

• tenant (str)

field client_type: Literal['persistent', 'http'] [必填]

field collection_name: str = 'memory_store'

ChromaDB 集合的名称

field distance_metric: str = 'cosine'

相似性搜索的距离度量

field k: int = 3

查询中返回的结果数量

field score_threshold: float | None = None

最小相似性分数阈值

field allow_reset: bool = False

是否允许重置 ChromaDB 客户端

field tenant: str = 'default_tenant'

要使用的租户

field database: str = 'default_database'

要使用的数据库

field embedding_function_config: Annotated[DefaultEmbeddingFunctionConfig | SentenceTransformerEmbeddingFunctionConfig | OpenAIEmbeddingFunctionConfig | CustomEmbeddingFunctionConfig, FieldInfo(annotation=NoneType, required=True, discriminator='function_type')] [可选]

嵌入函数的配置

pydantic model PersistentChromaDBVectorMemoryConfig[源]

基类：ChromaDBVectorMemoryConfig

持久化 ChromaDB 记忆的配置。

显示 JSON 模式

{
   "title": "PersistentChromaDBVectorMemoryConfig",
   "description": "Configuration for persistent ChromaDB memory.",
   "type": "object",
   "properties": {
      "client_type": {
         "default": "persistent",
         "enum": [
            "persistent",
            "http"
         ],
         "title": "Client Type",
         "type": "string"
      },
      "collection_name": {
         "default": "memory_store",
         "description": "Name of the ChromaDB collection",
         "title": "Collection Name",
         "type": "string"
      },
      "distance_metric": {
         "default": "cosine",
         "description": "Distance metric for similarity search",
         "title": "Distance Metric",
         "type": "string"
      },
      "k": {
         "default": 3,
         "description": "Number of results to return in queries",
         "title": "K",
         "type": "integer"
      },
      "score_threshold": {
         "anyOf": [
            {
               "type": "number"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Minimum similarity score threshold",
         "title": "Score Threshold"
      },
      "allow_reset": {
         "default": false,
         "description": "Whether to allow resetting the ChromaDB client",
         "title": "Allow Reset",
         "type": "boolean"
      },
      "tenant": {
         "default": "default_tenant",
         "description": "Tenant to use",
         "title": "Tenant",
         "type": "string"
      },
      "database": {
         "default": "default_database",
         "description": "Database to use",
         "title": "Database",
         "type": "string"
      },
      "embedding_function_config": {
         "description": "Configuration for the embedding function",
         "discriminator": {
            "mapping": {
               "default": "#/$defs/DefaultEmbeddingFunctionConfig",
               "openai": "#/$defs/OpenAIEmbeddingFunctionConfig",
               "sentence_transformer": "#/$defs/SentenceTransformerEmbeddingFunctionConfig"
            },
            "propertyName": "function_type"
         },
         "oneOf": [
            {
               "$ref": "#/$defs/DefaultEmbeddingFunctionConfig"
            },
            {
               "$ref": "#/$defs/SentenceTransformerEmbeddingFunctionConfig"
            },
            {
               "$ref": "#/$defs/OpenAIEmbeddingFunctionConfig"
            }
         ],
         "title": "Embedding Function Config"
      },
      "persistence_path": {
         "default": "./chroma_db",
         "description": "Path for persistent storage",
         "title": "Persistence Path",
         "type": "string"
      }
   },
   "$defs": {
      "DefaultEmbeddingFunctionConfig": {
         "description": "Configuration for the default ChromaDB embedding function.\n\nUses ChromaDB's default embedding function (Sentence Transformers all-MiniLM-L6-v2).\n\n.. versionadded:: v0.4.1\n   Support for custom embedding functions in ChromaDB memory.",
         "properties": {
            "function_type": {
               "const": "default",
               "default": "default",
               "title": "Function Type",
               "type": "string"
            }
         },
         "title": "DefaultEmbeddingFunctionConfig",
         "type": "object"
      },
      "OpenAIEmbeddingFunctionConfig": {
         "description": "Configuration for OpenAI embedding functions.\n\nUses OpenAI's embedding API for generating embeddings.\n\n.. versionadded:: v0.4.1\n   Support for custom embedding functions in ChromaDB memory.\n\nArgs:\n    api_key (str): OpenAI API key. If empty, will attempt to use environment variable.\n    model_name (str): OpenAI embedding model name. Defaults to \"text-embedding-ada-002\".\n\nExample:\n    .. code-block:: python\n\n        from autogen_ext.memory.chromadb import OpenAIEmbeddingFunctionConfig\n\n        _ = OpenAIEmbeddingFunctionConfig(api_key=\"sk-...\", model_name=\"text-embedding-3-small\")",
         "properties": {
            "function_type": {
               "const": "openai",
               "default": "openai",
               "title": "Function Type",
               "type": "string"
            },
            "api_key": {
               "default": "",
               "description": "OpenAI API key",
               "title": "Api Key",
               "type": "string"
            },
            "model_name": {
               "default": "text-embedding-ada-002",
               "description": "OpenAI embedding model name",
               "title": "Model Name",
               "type": "string"
            }
         },
         "title": "OpenAIEmbeddingFunctionConfig",
         "type": "object"
      },
      "SentenceTransformerEmbeddingFunctionConfig": {
         "description": "Configuration for SentenceTransformer embedding functions.\n\nAllows specifying a custom SentenceTransformer model for embeddings.\n\n.. versionadded:: v0.4.1\n   Support for custom embedding functions in ChromaDB memory.\n\nArgs:\n    model_name (str): Name of the SentenceTransformer model to use.\n        Defaults to \"all-MiniLM-L6-v2\".\n\nExample:\n    .. code-block:: python\n\n        from autogen_ext.memory.chromadb import SentenceTransformerEmbeddingFunctionConfig\n\n        _ = SentenceTransformerEmbeddingFunctionConfig(model_name=\"paraphrase-multilingual-mpnet-base-v2\")",
         "properties": {
            "function_type": {
               "const": "sentence_transformer",
               "default": "sentence_transformer",
               "title": "Function Type",
               "type": "string"
            },
            "model_name": {
               "default": "all-MiniLM-L6-v2",
               "description": "SentenceTransformer model name to use",
               "title": "Model Name",
               "type": "string"
            }
         },
         "title": "SentenceTransformerEmbeddingFunctionConfig",
         "type": "object"
      }
   }
}

字段:

• client_type (Literal['persistent', 'http'])

• persistence_path (str)

field client_type: Literal['persistent', 'http'] = 'persistent'

field persistence_path: str = './chroma_db'

持久化存储路径

pydantic model HttpChromaDBVectorMemoryConfig[源]

基类：ChromaDBVectorMemoryConfig

HTTP ChromaDB 记忆的配置。

显示 JSON 模式

{
   "title": "HttpChromaDBVectorMemoryConfig",
   "description": "Configuration for HTTP ChromaDB memory.",
   "type": "object",
   "properties": {
      "client_type": {
         "default": "http",
         "enum": [
            "persistent",
            "http"
         ],
         "title": "Client Type",
         "type": "string"
      },
      "collection_name": {
         "default": "memory_store",
         "description": "Name of the ChromaDB collection",
         "title": "Collection Name",
         "type": "string"
      },
      "distance_metric": {
         "default": "cosine",
         "description": "Distance metric for similarity search",
         "title": "Distance Metric",
         "type": "string"
      },
      "k": {
         "default": 3,
         "description": "Number of results to return in queries",
         "title": "K",
         "type": "integer"
      },
      "score_threshold": {
         "anyOf": [
            {
               "type": "number"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Minimum similarity score threshold",
         "title": "Score Threshold"
      },
      "allow_reset": {
         "default": false,
         "description": "Whether to allow resetting the ChromaDB client",
         "title": "Allow Reset",
         "type": "boolean"
      },
      "tenant": {
         "default": "default_tenant",
         "description": "Tenant to use",
         "title": "Tenant",
         "type": "string"
      },
      "database": {
         "default": "default_database",
         "description": "Database to use",
         "title": "Database",
         "type": "string"
      },
      "embedding_function_config": {
         "description": "Configuration for the embedding function",
         "discriminator": {
            "mapping": {
               "default": "#/$defs/DefaultEmbeddingFunctionConfig",
               "openai": "#/$defs/OpenAIEmbeddingFunctionConfig",
               "sentence_transformer": "#/$defs/SentenceTransformerEmbeddingFunctionConfig"
            },
            "propertyName": "function_type"
         },
         "oneOf": [
            {
               "$ref": "#/$defs/DefaultEmbeddingFunctionConfig"
            },
            {
               "$ref": "#/$defs/SentenceTransformerEmbeddingFunctionConfig"
            },
            {
               "$ref": "#/$defs/OpenAIEmbeddingFunctionConfig"
            }
         ],
         "title": "Embedding Function Config"
      },
      "host": {
         "default": "localhost",
         "description": "Host of the remote server",
         "title": "Host",
         "type": "string"
      },
      "port": {
         "default": 8000,
         "description": "Port of the remote server",
         "title": "Port",
         "type": "integer"
      },
      "ssl": {
         "default": false,
         "description": "Whether to use HTTPS",
         "title": "Ssl",
         "type": "boolean"
      },
      "headers": {
         "anyOf": [
            {
               "additionalProperties": {
                  "type": "string"
               },
               "type": "object"
            },
            {
               "type": "null"
            }
         ],
         "default": null,
         "description": "Headers to send to the server",
         "title": "Headers"
      }
   },
   "$defs": {
      "DefaultEmbeddingFunctionConfig": {
         "description": "Configuration for the default ChromaDB embedding function.\n\nUses ChromaDB's default embedding function (Sentence Transformers all-MiniLM-L6-v2).\n\n.. versionadded:: v0.4.1\n   Support for custom embedding functions in ChromaDB memory.",
         "properties": {
            "function_type": {
               "const": "default",
               "default": "default",
               "title": "Function Type",
               "type": "string"
            }
         },
         "title": "DefaultEmbeddingFunctionConfig",
         "type": "object"
      },
      "OpenAIEmbeddingFunctionConfig": {
         "description": "Configuration for OpenAI embedding functions.\n\nUses OpenAI's embedding API for generating embeddings.\n\n.. versionadded:: v0.4.1\n   Support for custom embedding functions in ChromaDB memory.\n\nArgs:\n    api_key (str): OpenAI API key. If empty, will attempt to use environment variable.\n    model_name (str): OpenAI embedding model name. Defaults to \"text-embedding-ada-002\".\n\nExample:\n    .. code-block:: python\n\n        from autogen_ext.memory.chromadb import OpenAIEmbeddingFunctionConfig\n\n        _ = OpenAIEmbeddingFunctionConfig(api_key=\"sk-...\", model_name=\"text-embedding-3-small\")",
         "properties": {
            "function_type": {
               "const": "openai",
               "default": "openai",
               "title": "Function Type",
               "type": "string"
            },
            "api_key": {
               "default": "",
               "description": "OpenAI API key",
               "title": "Api Key",
               "type": "string"
            },
            "model_name": {
               "default": "text-embedding-ada-002",
               "description": "OpenAI embedding model name",
               "title": "Model Name",
               "type": "string"
            }
         },
         "title": "OpenAIEmbeddingFunctionConfig",
         "type": "object"
      },
      "SentenceTransformerEmbeddingFunctionConfig": {
         "description": "Configuration for SentenceTransformer embedding functions.\n\nAllows specifying a custom SentenceTransformer model for embeddings.\n\n.. versionadded:: v0.4.1\n   Support for custom embedding functions in ChromaDB memory.\n\nArgs:\n    model_name (str): Name of the SentenceTransformer model to use.\n        Defaults to \"all-MiniLM-L6-v2\".\n\nExample:\n    .. code-block:: python\n\n        from autogen_ext.memory.chromadb import SentenceTransformerEmbeddingFunctionConfig\n\n        _ = SentenceTransformerEmbeddingFunctionConfig(model_name=\"paraphrase-multilingual-mpnet-base-v2\")",
         "properties": {
            "function_type": {
               "const": "sentence_transformer",
               "default": "sentence_transformer",
               "title": "Function Type",
               "type": "string"
            },
            "model_name": {
               "default": "all-MiniLM-L6-v2",
               "description": "SentenceTransformer model name to use",
               "title": "Model Name",
               "type": "string"
            }
         },
         "title": "SentenceTransformerEmbeddingFunctionConfig",
         "type": "object"
      }
   }
}

字段:

• client_type (Literal['persistent', 'http'])

• headers (Dict[str, str] | None)

• host (str)

• port (int)

• ssl (bool)

field client_type: Literal['persistent', 'http'] = 'http'

field host: str = 'localhost'

远程服务器的主机

field port: int = 8000

远程服务器的端口

field ssl: bool = False

是否使用 HTTPS

field headers: Dict[str, str] | None = None

发送到服务器的标头

pydantic model DefaultEmbeddingFunctionConfig[源]

基类: BaseModel

默认 ChromaDB 嵌入函数的配置。

使用 ChromaDB 的默认嵌入函数（Sentence Transformers all-MiniLM-L6-v2）。

v0.4.1 版本中新增：ChromaDB 记忆中对自定义嵌入函数的支持。

显示 JSON 模式

{
   "title": "DefaultEmbeddingFunctionConfig",
   "description": "Configuration for the default ChromaDB embedding function.\n\nUses ChromaDB's default embedding function (Sentence Transformers all-MiniLM-L6-v2).\n\n.. versionadded:: v0.4.1\n   Support for custom embedding functions in ChromaDB memory.",
   "type": "object",
   "properties": {
      "function_type": {
         "const": "default",
         "default": "default",
         "title": "Function Type",
         "type": "string"
      }
   }
}

字段:

• function_type (Literal['default'])

field function_type: Literal['default'] = 'default'

pydantic model SentenceTransformerEmbeddingFunctionConfig[源]

基类: BaseModel

SentenceTransformer 嵌入函数的配置。

允许为嵌入指定自定义 SentenceTransformer 模型。

v0.4.1 版本中新增：ChromaDB 记忆中对自定义嵌入函数的支持。

参数:

model_name (str) – 要使用的 SentenceTransformer 模型的名称。默认为“all-MiniLM-L6-v2”。

示例

from autogen_ext.memory.chromadb import SentenceTransformerEmbeddingFunctionConfig

_ = SentenceTransformerEmbeddingFunctionConfig(model_name="paraphrase-multilingual-mpnet-base-v2")

显示 JSON 模式

{
   "title": "SentenceTransformerEmbeddingFunctionConfig",
   "description": "Configuration for SentenceTransformer embedding functions.\n\nAllows specifying a custom SentenceTransformer model for embeddings.\n\n.. versionadded:: v0.4.1\n   Support for custom embedding functions in ChromaDB memory.\n\nArgs:\n    model_name (str): Name of the SentenceTransformer model to use.\n        Defaults to \"all-MiniLM-L6-v2\".\n\nExample:\n    .. code-block:: python\n\n        from autogen_ext.memory.chromadb import SentenceTransformerEmbeddingFunctionConfig\n\n        _ = SentenceTransformerEmbeddingFunctionConfig(model_name=\"paraphrase-multilingual-mpnet-base-v2\")",
   "type": "object",
   "properties": {
      "function_type": {
         "const": "sentence_transformer",
         "default": "sentence_transformer",
         "title": "Function Type",
         "type": "string"
      },
      "model_name": {
         "default": "all-MiniLM-L6-v2",
         "description": "SentenceTransformer model name to use",
         "title": "Model Name",
         "type": "string"
      }
   }
}

字段:

• function_type (Literal['sentence_transformer'])

• model_name (str)

field function_type: Literal['sentence_transformer'] = 'sentence_transformer'

field model_name: str = 'all-MiniLM-L6-v2'

要使用的 SentenceTransformer 模型名称

pydantic model OpenAIEmbeddingFunctionConfig[源]

基类: BaseModel

OpenAI 嵌入函数的配置。

使用 OpenAI 的嵌入 API 生成嵌入。

v0.4.1 版本中新增：ChromaDB 记忆中对自定义嵌入函数的支持。

参数:

• api_key (str) – OpenAI API 密钥。如果为空，将尝试使用环境变量。

• model_name (str) – OpenAI 嵌入模型名称。默认为“text-embedding-ada-002”。

示例

from autogen_ext.memory.chromadb import OpenAIEmbeddingFunctionConfig

_ = OpenAIEmbeddingFunctionConfig(api_key="sk-...", model_name="text-embedding-3-small")

显示 JSON 模式

{
   "title": "OpenAIEmbeddingFunctionConfig",
   "description": "Configuration for OpenAI embedding functions.\n\nUses OpenAI's embedding API for generating embeddings.\n\n.. versionadded:: v0.4.1\n   Support for custom embedding functions in ChromaDB memory.\n\nArgs:\n    api_key (str): OpenAI API key. If empty, will attempt to use environment variable.\n    model_name (str): OpenAI embedding model name. Defaults to \"text-embedding-ada-002\".\n\nExample:\n    .. code-block:: python\n\n        from autogen_ext.memory.chromadb import OpenAIEmbeddingFunctionConfig\n\n        _ = OpenAIEmbeddingFunctionConfig(api_key=\"sk-...\", model_name=\"text-embedding-3-small\")",
   "type": "object",
   "properties": {
      "function_type": {
         "const": "openai",
         "default": "openai",
         "title": "Function Type",
         "type": "string"
      },
      "api_key": {
         "default": "",
         "description": "OpenAI API key",
         "title": "Api Key",
         "type": "string"
      },
      "model_name": {
         "default": "text-embedding-ada-002",
         "description": "OpenAI embedding model name",
         "title": "Model Name",
         "type": "string"
      }
   }
}

字段:

• api_key (str)

• function_type (Literal['openai'])

• model_name (str)

field function_type: Literal['openai'] = 'openai'

field api_key: str = ''

OpenAI API 密钥

field model_name: str = 'text-embedding-ada-002'

OpenAI 嵌入模型名称

pydantic model CustomEmbeddingFunctionConfig[源]

基类: BaseModel

自定义嵌入函数的配置。

允许使用返回 ChromaDB 兼容嵌入函数的自定义函数。

v0.4.1 版本中新增：ChromaDB 记忆中对自定义嵌入函数的支持。

警告

包含自定义函数的配置不可序列化。

参数:

• function (Callable) – 返回 ChromaDB 兼容嵌入函数的函数。

• params (Dict[str, Any]) – 传递给函数的参数。

显示 JSON 模式

{
   "title": "CustomEmbeddingFunctionConfig",
   "type": "object",
   "properties": {
      "function_type": {
         "const": "custom",
         "default": "custom",
         "title": "Function Type",
         "type": "string"
      },
      "function": {
         "default": null,
         "title": "Function"
      },
      "params": {
         "description": "Parameters to pass to the function",
         "title": "Params",
         "type": "object"
      }
   }
}

字段:

• function (Callable[[...], Any])

• function_type (Literal['custom'])

• params (Dict[str, Any])

field function_type: Literal['custom'] = 'custom'

field function: Callable[[...], Any] [必填]

返回嵌入函数的函数

field params: Dict[str, Any] [可选]

传递给函数的参数

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