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.
langchain-google-genai
integration package.
GOOGLE_API_KEY
:
embeddings
object we initialized above. In this example, we will index and retrieve a sample document in the InMemoryVectorStore
.
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
.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.
GoogleGenerativeAIEmbeddings
features and configuration options, please refer to the API reference.
client_options
: Client Options to pass to the Google API Client, such as a custom client_options["api_endpoint"]
transport
: The transport method to use, such as rest
, grpc
, or grpc_asyncio
.