Aleph Alpha 嵌入¶

如果您在 Colab 上打开此 Notebook，您可能需要安装 LlamaIndex 🦙。

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%pip install llama-index-embeddings-alephalpha

%pip install llama-index-embeddings-alephalpha

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!pip install llama-index

!pip install llama-index

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# Initialise with your AA token
import os

os.environ["AA_TOKEN"] = "your_token_here"

# 用您的 AA token 初始化 import os os.environ["AA_TOKEN"] = "your_token_here"

使用 luminous-base 嵌入。¶

• representation="Document": 用于您想存储在向量数据库中的文本（文档）
• representation="Query": 用于搜索查询，以在向量数据库中找到最相关的文档
• representation="Symmetric": 用于聚类、分类、异常检测或可视化任务。

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from llama_index.embeddings.alephalpha import AlephAlphaEmbedding

# To customize your token, do this
# otherwise it will lookup AA_TOKEN from your env variable
# embed_model = AlephAlpha(token="<aa_token>")

# with representation='query'
embed_model = AlephAlphaEmbedding(
    model="luminous-base",
    representation="Query",
)

embeddings = embed_model.get_text_embedding("Hello Aleph Alpha!")

print(len(embeddings))
print(embeddings[:5])

from llama_index.embeddings.alephalpha import AlephAlphaEmbedding # 要定制您的 token，请这样做 # 否则它将从您的环境变量中查找 AA_TOKEN # embed_model = AlephAlpha(token="") # 使用 representation='query' embed_model = AlephAlphaEmbedding( model="luminous-base", representation="Query", ) embeddings = embed_model.get_text_embedding("Hello Aleph Alpha!") print(len(embeddings)) print(embeddings[:5])

representation_enum: SemanticRepresentation.Query


5120
[0.14257812, 2.59375, 0.33203125, -0.33789062, -0.94140625]

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# with representation='Document'
embed_model = AlephAlphaEmbedding(
    model="luminous-base",
    representation="Document",
)

embeddings = embed_model.get_text_embedding("Hello Aleph Alpha!")

print(len(embeddings))
print(embeddings[:5])

# 使用 representation='Document' embed_model = AlephAlphaEmbedding( model="luminous-base", representation="Document", ) embeddings = embed_model.get_text_embedding("Hello Aleph Alpha!") print(len(embeddings)) print(embeddings[:5])

representation_enum: SemanticRepresentation.Document


5120
[0.14257812, 2.59375, 0.33203125, -0.33789062, -0.94140625]