UpstageEmbeddings
This notebook covers how to get started with Upstage embedding models.
Installationโ
Install langchain-upstage
package.
pip install -U langchain-upstage
Environment Setupโ
Make sure to set the following environment variables:
UPSTAGE_API_KEY
: Your Upstage API key from Upstage console.
import os
os.environ["UPSTAGE_API_KEY"] = "YOUR_API_KEY"
Usageโ
Initialize UpstageEmbeddings
class.
from langchain_upstage import UpstageEmbeddings
embeddings = UpstageEmbeddings(model="solar-embedding-1-large")
Use embed_documents
to embed list of texts or documents.
doc_result = embeddings.embed_documents(
["Sung is a professor.", "This is another document"]
)
print(doc_result)
Use embed_query
to embed query string.
query_result = embeddings.embed_query("What does Sung do?")
print(query_result)
Use aembed_documents
and aembed_query
for async operations.
# async embed query
await embeddings.aembed_query("My query to look up")
# async embed documents
await embeddings.aembed_documents(
["This is a content of the document", "This is another document"]
)
Using with vector storeโ
You can use UpstageEmbeddings
with vector store component. The following demonstrates a simple example.
from langchain_community.vectorstores import DocArrayInMemorySearch
vectorstore = DocArrayInMemorySearch.from_texts(
["harrison worked at kensho", "bears like to eat honey"],
embedding=UpstageEmbeddings(model="solar-embedding-1-large"),
)
retriever = vectorstore.as_retriever()
docs = retriever.invoke("Where did Harrison work?")
print(docs)
API Reference:DocArrayInMemorySearch
Relatedโ
- Embedding model conceptual guide
- Embedding model how-to guides