GoogleGenerativeAIEmbeddings 集成

使用 LangChain Python 集成 Google Gemini API 向量嵌入模型 。

本指南将帮助您开始使用 Google Generative AI 向量嵌入模型 using LangChain. For detailed documentation on GoogleGenerativeAIEmbeddings 功能和配置选项的详细文档，请参阅 API reference.

概述

gemini-embedding-2-preview natively supports text, image, video, audio, and PDF inputs 通过 Google GenAI SDK’s embed_content() API. However, the LangChain Embeddings interface (embed_query / embed_documents) currently only accepts text inputs. Multimodal embedding support in LangChain is planned for a future release. For multimodal use cases today, use the Google GenAI SDK directly.

集成详情

设置

要访问 Google Gemini embedding 模型，您需要创建一个 Google Cloud project, enable the Generative Language API, get an API key, and install the langchain-google-genai 集成包。

凭证

前往 Google AI Studio to sign up 并生成 API 密钥。 请参阅 Gemini API keys documentation 了解更多详情。 完成后设置 GOOGLE_API_KEY 环境变量：

import getpass
import os

if not os.getenv("GOOGLE_API_KEY"):
    os.environ["GOOGLE_API_KEY"] = getpass.getpass("Enter your Google API key: ")

要启用模型调用的自动追踪，请设置您的 LangSmith API key:

os.environ["LANGSMITH_TRACING"] = "true"
os.environ["LANGSMITH_API_KEY"] = getpass.getpass("请输入您的 LangSmith API 密钥: ")

安装

LangChain 的 Google Generative AI 集成位于 langchain-google-genai 包中：

pip install -qU langchain-google-genai

实例化

Now we can instantiate our model object and generate embeddings:

from langchain_google_genai import GoogleGenerativeAIEmbeddings

embeddings = GoogleGenerativeAIEmbeddings(model="gemini-embedding-2-preview")
vector = embeddings.embed_query("hello, world!")
vector[:5]

[-0.024917153641581535,
 0.012005362659692764,
 -0.003886754624545574,
 -0.05774897709488869,
 0.0020742062479257584]

Reduced dimensionality

gemini-embedding-2-preview supports flexible output dimensions via Matryoshka Representation Learning (MRL). You can reduce dimensionality to optimize storage and latency:

embeddings = GoogleGenerativeAIEmbeddings(
    model="gemini-embedding-2-preview",
    output_dimensionality=768,  # Suggested: 768, 1536, or 3072 (default)
)
vector = embeddings.embed_query("hello, world!")
len(vector)

768

Batch

You can also embed multiple strings at once for a processing speedup:

vectors = embeddings.embed_documents(
    [
        "Today is Monday",
        "Today is Tuesday",
        "Today is April Fools day",
    ]
)
len(vectors), len(vectors[0])

(3, 768)

索引与检索

向量嵌入模型常用于检索增强生成 (RAG) 流程中, 既用于索引数据，也用于后续检索数据。 更详细的说明请参阅我们的 RAG tutorials.

下面展示如何使用 embeddings 对象来索引和检索数据。 在此示例中，我们将在 InMemoryVectorStore.

# 使用示例文本创建向量存储
from langchain_core.vectorstores import InMemoryVectorStore

text = "LangChain is the framework for building context-aware reasoning applications"

vectorstore = InMemoryVectorStore.from_texts(
    [text],
    embedding=embeddings,
)

# Use the vectorstore as a retriever
retriever = vectorstore.as_retriever()

# Retrieve the most similar text
retrieved_documents = retriever.invoke("What is LangChain?")

# show the retrieved document's content
retrieved_documents[0].page_content

'LangChain is the framework for building context-aware reasoning applications'

Task type

GoogleGenerativeAIEmbeddings optionally support a task_type, which currently must be one of:

• SEMANTIC_SIMILARITY: Used to generate embeddings that are optimized to assess text similarity.
• CLASSIFICATION: Used to generate embeddings that are optimized to classify texts according to preset labels.
• CLUSTERING: Used to generate embeddings that are optimized to cluster texts based on their similarities.
• RETRIEVAL_DOCUMENT, RETRIEVAL_QUERY, QUESTION_ANSWERING, and FACT_VERIFICATION: Used to generate embeddings that are optimized for document search or information retrieval.
• CODE_RETRIEVAL_QUERY: Used to retrieve a code block based on a natural language query, such as sort an array or reverse a linked list. Embeddings of the code blocks are computed using RETRIEVAL_DOCUMENT.

By default, we use RETRIEVAL_DOCUMENT in the embed_documents method and RETRIEVAL_QUERY in the embed_query method. If you provide a task type, we will use that for all methods.

pip install -qU matplotlib scikit-learn

from langchain_google_genai import GoogleGenerativeAIEmbeddings
from sklearn.metrics.pairwise import cosine_similarity

query_embeddings = GoogleGenerativeAIEmbeddings(
    model="gemini-embedding-2-preview", task_type="RETRIEVAL_QUERY"
)
doc_embeddings = GoogleGenerativeAIEmbeddings(
    model="gemini-embedding-2-preview", task_type="RETRIEVAL_DOCUMENT"
)

q_embed = query_embeddings.embed_query("What is the capital of France?")
d_embed = doc_embeddings.embed_documents(
    ["The capital of France is Paris.", "Philipp likes to eat pizza."]
)

for i, d in enumerate(d_embed):
    print(f"Document {i + 1}:")
    print(f"Cosine similarity with query: {cosine_similarity([q_embed], [d])[0][0]}")
    print("---")

Document 1:
Cosine similarity with query: 0.7892893360164779
---
Document 2:
Cosine similarity with query: 0.5438283285204146
---

Additional configuration

You can pass the following parameters to GoogleGenerativeAIEmbeddings to customize the SDK’s behavior:

• base_url: Custom base URL for the API client (e.g., a custom endpoint)
• output_dimensionality: Reduce the dimensionality of returned embeddings (e.g., output_dimensionality=256)
• request_options: Request options dict (e.g., {"timeout": 10})
• additional_headers: Additional HTTP headers to include in API requests
• client_args: Additional arguments to pass to the underlying HTTP client

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API 参考

For detailed documentation on GoogleGenerativeAIEmbeddings 功能和配置选项的详细文档，请参阅 API reference.

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