备注
Go to the end to download the full example code.
嵌入(Embedding)¶
AgentScope 中，嵌入模块提供了用于向量生成的统一接口，具有以下特性：
支持 缓存 embedding 以避免冗余的 API 调用
支持 不同 embedding API 提供商 并提供一致的接口
AgentScope 内置支持以下 API：
API 提供商 |
类 |
|---|---|
OpenAI |
|
Gemini |
|
DashScope |
|
Ollama |
|
所有类都继承自 EmbeddingModelBase，实现了 __call__ 方法并生成包含嵌入和使用信息的 EmbeddingResponse 对象。
以 DashScope 嵌入类为例，您可以按如下方式使用：
import asyncio
import os
import tempfile
from agentscope.embedding import DashScopeTextEmbedding, FileEmbeddingCache
async def example_dashscope_embedding() -> None:
"""DashScope 文本嵌入的使用示例。"""
texts = [
"法国的首都是什么？",
"巴黎是法国的首都城市。",
]
# 初始化 DashScope 文本嵌入实例
embedding_model = DashScopeTextEmbedding(
model_name="text-embedding-v2",
api_key=os.getenv("DASHSCOPE_API_KEY"),
)
# 从模型获取嵌入
response = await embedding_model(texts)
print("嵌入 ID: ", response.id)
print("嵌入创建时间: ", response.created_at)
print("嵌入使用情况: ", response.usage)
print("嵌入向量:")
print(response.embeddings)
asyncio.run(example_dashscope_embedding())
嵌入 ID: 2025-08-27 03:47:01.407_9b75d7
嵌入创建时间: 2025-08-27 03:47:01.407
嵌入使用情况: EmbeddingUsage(time=1.194958, tokens=11, type='embedding')
嵌入向量:
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可以通过继承 EmbeddingModelBase 并实现 __call__ 方法来自定义 embedding 模型。
Embedding 缓存¶
AgentScope 提供了用于缓存 embedding 的基类 EmbeddingCacheBase，以及基于文件的实现 FileEmbeddingCache。
它在 embedding 模块中的工作方式如下：
要使用缓存，只需将 FileEmbeddingCache 实例（或自定义缓存）传给模型的构造函数，如下所示：
async def example_embedding_cache() -> None:
"""演示带有缓存功能的 embedding。"""
# 示例文本
texts = [
"法国的首都是什么？",
"巴黎是法国的首都城市。",
]
# 为缓存演示创建临时目录
# 在实际应用中，建议使用持久目录以最大发挥缓存效果
cache_dir = tempfile.mkdtemp(prefix="embedding_cache_")
print(f"使用缓存目录: {cache_dir}")
# 使用缓存初始化嵌入模型
# 为演示目的，我们将缓存限制为 100 个文件和 10MB
embedder = DashScopeTextEmbedding(
model_name="text-embedding-v3",
api_key=os.getenv("DASHSCOPE_API_KEY"),
embedding_cache=FileEmbeddingCache(
cache_dir=cache_dir,
max_file_number=100,
max_cache_size=10, # 最大缓存大小（MB）
),
)
# 第一次调用 - 将从 API 获取并存储在缓存中
print("\n=== 第一次 API 调用（无缓存命中）===")
start_time = asyncio.get_event_loop().time()
response1 = await embedder(texts)
elapsed_time1 = asyncio.get_event_loop().time() - start_time
print(f"来源: {response1.source}") # 应该是 'api'
print(f"耗时: {elapsed_time1:.4f} 秒")
print(f"使用的 token: {response1.usage.tokens}")
# 使用相同文本的第二次调用 - 应该使用缓存
print("\n=== 第二次 API 调用（预期缓存命中）===")
start_time = asyncio.get_event_loop().time()
response2 = await embedder(texts)
elapsed_time2 = asyncio.get_event_loop().time() - start_time
print(f"来源: {response2.source}") # 应该是 'cache'
print(f"耗时: {elapsed_time2:.4f} 秒")
print(
f"使用的 token: {response2.usage.tokens}",
) # 缓存结果应该为 0
print(
f"速度提升: 使用缓存快 {elapsed_time1 / elapsed_time2:.1f} 倍",
)
asyncio.run(example_embedding_cache())
使用缓存目录: /tmp/embedding_cache_6utnlglm
=== 第一次 API 调用（无缓存命中）===
来源: api
耗时: 1.3020 秒
使用的 token: 12
=== 第二次 API 调用（预期缓存命中）===
来源: cache
耗时: 0.0003 秒
使用的 token: 0
速度提升: 使用缓存快 4213.5 倍
Total running time of the script: (0 minutes 2.502 seconds)