GoogleGenerativeAIEmbeddings
class, found in the langchain-google-genai package.
This will help you get started with Google’s Generative AI embedding models (like Gemini) using LangChain. For detailed documentation on GoogleGenerativeAIEmbeddings
features and configuration options, please refer to the API reference.
Overview
Integration details
Setup
To access Google Generative AI embedding models you’ll need to create a Google Cloud project, enable the Generative Language API, get an API key, and install thelangchain-google-genai
integration package.
Credentials
To use Google Generative AI models, you must have an API key. You can create one in Google AI Studio. See the Google documentation for instructions. Once you have a key, set it as an environment variableGOOGLE_API_KEY
:
Installation
Usage
Batch
You can also embed multiple strings at once for a processing speedup:Indexing and Retrieval
Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our RAG tutorials. Below, see how to index and retrieve data using theembeddings
object we initialized above. In this example, we will index and retrieve a sample document in the InMemoryVectorStore
.
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
, andFACT_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 usingRETRIEVAL_DOCUMENT
.
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.
API Reference
For detailed documentation onGoogleGenerativeAIEmbeddings
features and configuration options, please refer to the API reference.
Additional Configuration
You can pass the following parameters to ChatGoogleGenerativeAI in order to customize the SDK’s behavior:client_options
: Client Options to pass to the Google API Client, such as a customclient_options["api_endpoint"]
transport
: The transport method to use, such asrest
,grpc
, orgrpc_asyncio
.