Google Generative AI Embeddings
Connect to Google's generative AI embeddings service using the GoogleGenerativeAIEmbeddings
class, found in the langchain-google-genai package.
Installationโ
%pip install --upgrade --quiet langchain-google-genai
Credentialsโ
import getpass
import os
if "GOOGLE_API_KEY" not in os.environ:
os.environ["GOOGLE_API_KEY"] = getpass("Provide your Google API key here")
Usageโ
from langchain_google_genai import GoogleGenerativeAIEmbeddings
embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
vector = embeddings.embed_query("hello, world!")
vector[:5]
[0.05636945, 0.0048285457, -0.0762591, -0.023642512, 0.05329321]
Batchโ
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)
Task typeโ
GoogleGenerativeAIEmbeddings
optionally support a task_type
, which currently must be one of:
- task_type_unspecified
- retrieval_query
- retrieval_document
- semantic_similarity
- classification
- clustering
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 --upgrade --quiet matplotlib scikit-learn
Note: you may need to restart the kernel to use updated packages.
query_embeddings = GoogleGenerativeAIEmbeddings(
model="models/embedding-001", task_type="retrieval_query"
)
doc_embeddings = GoogleGenerativeAIEmbeddings(
model="models/embedding-001", task_type="retrieval_document"
)
All of these will be embedded with the 'retrieval_query' task set
query_vecs = [query_embeddings.embed_query(q) for q in [query, query_2, answer_1]]
All of these will be embedded with the 'retrieval_document' task set
doc_vecs = [doc_embeddings.embed_query(q) for q in [query, query_2, answer_1]]
In retrieval, relative distance matters. In the image above, you can see the difference in similarity scores between the "relevant doc" and "simil stronger delta between the similar query and relevant doc on the latter case.
Additional Configurationโ
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
.