谷歌Gemini嵌入¶

如果您在colab上打开这个笔记本，您可能需要安装LlamaIndex 🦙。

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%pip install llama-index-embeddings-gemini

%pip install llama-index-embeddings-gemini

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!pip install llama-index 'google-generativeai>=0.3.0' matplotlib

!pip install llama-index 'google-generativeai>=0.3.0' matplotlib

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import os

GOOGLE_API_KEY = ""  # 在这里添加你的GOOGLE API密钥
os.environ["GOOGLE_API_KEY"] = GOOGLE_API_KEY

import os GOOGLE_API_KEY = "" # 在这里添加你的GOOGLE API密钥 os.environ["GOOGLE_API_KEY"] = GOOGLE_API_KEY

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# 导入
来自llama_index.embeddings.gemini的GeminiEmbedding

# 导入 来自llama_index.embeddings.gemini的GeminiEmbedding

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# 获取API密钥并创建嵌入

model_name = "models/embedding-001"

embed_model = GeminiEmbedding(
    model_name=model_name, api_key=GOOGLE_API_KEY, title="this is a document"
)

embeddings = embed_model.get_text_embedding("Google Gemini Embeddings.")

# 获取API密钥并创建嵌入 model_name = "models/embedding-001" embed_model = GeminiEmbedding( model_name=model_name, api_key=GOOGLE_API_KEY, title="this is a document" ) embeddings = embed_model.get_text_embedding("Google Gemini Embeddings.")

/Users/haotianzhang/llama_index/venv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

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print(f"Dimension of embeddings: {len(embeddings)}")

print(f"Dimension of embeddings: {len(embeddings)}")

Dimension of embeddings: 768

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embeddings[:5]

embeddings[:5]

Out[ ]:

[0.028174246, -0.0290093, -0.013280814, 0.008629, 0.025442218]

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embeddings = embed_model.get_query_embedding("Google Gemini Embeddings.")
embeddings[:5]

embeddings = embed_model.get_query_embedding("Google Gemini Embeddings.") embeddings[:5]

Out[ ]:

[0.028174246, -0.0290093, -0.013280814, 0.008629, 0.025442218]

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embeddings = embed_model.get_text_embedding(
    ["Google Gemini Embeddings.", "Google is awesome."]
)

embeddings = embed_model.get_text_embedding( ["Google Gemini Embeddings.", "Google is awesome."] )

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print(f"Dimension of embeddings: {len(embeddings)}")
print(embeddings[0][:5])
print(embeddings[1][:5])

print(f"Dimension of embeddings: {len(embeddings)}") print(embeddings[0][:5]) print(embeddings[1][:5])

Dimension of embeddings: 2
[0.028174246, -0.0290093, -0.013280814, 0.008629, 0.025442218]
[0.009427786, -0.009968997, -0.03341217, -0.025396815, 0.03210592]

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embedding = await embed_model.aget_text_embedding("Google Gemini Embeddings.")
print(embedding[:5])

embedding = await embed_model.aget_text_embedding("Google Gemini Embeddings.") print(embedding[:5])

[0.028174246, -0.0290093, -0.013280814, 0.008629, 0.025442218]

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embeddings = await embed_model.aget_text_embedding_batch(
    [
        "Google Gemini Embeddings.",
        "Google is awesome.",
        "Llamaindex is awesome.",
    ]
)
print(embeddings[0][:5])
print(embeddings[1][:5])
print(embeddings[2][:5])

embeddings = await embed_model.aget_text_embedding_batch( [ "Google Gemini Embeddings.", "Google is awesome.", "Llamaindex is awesome.", ] ) print(embeddings[0][:5]) print(embeddings[1][:5]) print(embeddings[2][:5])

[0.028174246, -0.0290093, -0.013280814, 0.008629, 0.025442218]
[0.009427786, -0.009968997, -0.03341217, -0.025396815, 0.03210592]
[0.013159992, -0.021570021, -0.060150445, -0.042500723, 0.041159637]

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embedding = await embed_model.aget_query_embedding("Google Gemini Embeddings.")
print(embedding[:5])

embedding = await embed_model.aget_query_embedding("Google Gemini Embeddings.") print(embedding[:5])

[0.028174246, -0.0290093, -0.013280814, 0.008629, 0.025442218]