LindormVectorStore
This notebook covers how to get started with the Lindorm vector store.
Setup
To access Lindorm vector stores you'll need to create a Lindorm account, get the ak/sk, and install thelangchain-lindorm-integration integration package.
%pip install-qU"langchain-lindorm-integration"
Credentials
Head tohere to sign up to Lindorm and generate the ak/sk.
import os
classConfig:
SEARCH_ENDPOINT= os.environ.get("SEARCH_ENDPOINT","SEARCH_ENDPOINT")
SEARCH_USERNAME= os.environ.get("SEARCH_USERNAME","root")
SEARCH_PWD= os.environ.get("SEARCH_PASSWORD","<PASSWORD>")
AI_LLM_ENDPOINT= os.environ.get("AI_ENDPOINT","<AI_ENDPOINT>")
AI_USERNAME= os.environ.get("AI_USERNAME","root")
AI_PWD= os.environ.get("AI_PASSWORD","<PASSWORD>")
AI_DEFAULT_EMBEDDING_MODEL="bge_m3_model"# set to your model
Initialization
here we use the embedding model deployed on Lindorm AI Service.
from langchain_lindorm_integration.embeddingsimport LindormAIEmbeddings
from langchain_lindorm_integration.vectorstoresimport LindormVectorStore
embeddings= LindormAIEmbeddings(
endpoint=Config.AI_LLM_ENDPOINT,
username=Config.AI_USERNAME,
password=Config.AI_PWD,
model_name=Config.AI_DEFAULT_EMBEDDING_MODEL,
)
index="test_index"
vector= embeddings.embed_query("hello word")
dimension=len(vector)
vector_store= LindormVectorStore(
lindorm_search_url=Config.SEARCH_ENDPOINT,
embedding=embeddings,
http_auth=(Config.SEARCH_USERNAME, Config.SEARCH_PWD),
dimension=dimension,
embeddings=embeddings,
index_name=index,
)
Manage vector store
Add items to vector store
from langchain_core.documentsimport Document
document_1= Document(page_content="foo", metadata={"source":"https://example.com"})
document_2= Document(page_content="bar", metadata={"source":"https://example.com"})
document_3= Document(page_content="baz", metadata={"source":"https://example.com"})
documents=[document_1, document_2, document_3]
vector_store.add_documents(documents=documents, ids=["1","2","3"])
['1', '2', '3']
Delete items from vector store
vector_store.delete(ids=["3"])
{'took': 400,
'timed_out': False,
'total': 1,
'deleted': 1,
'batches': 1,
'version_conflicts': 0,
'noops': 0,
'retries': {'bulk': 0, 'search': 0},
'throttled_millis': 0,
'requests_per_second': -1.0,
'throttled_until_millis': 0,
'failures': []}
Query vector store
Once your vector store has been created and the relevant documents have been added you will most likely wish to query it during the running of your chain or agent.
Query directly
Performing a simple similarity search can be done as follows:
results= vector_store.similarity_search(query="thud", k=1)
for docin results:
print(f"*{doc.page_content} [{doc.metadata}]")
* foo [{'source': 'https://example.com'}]
If you want to execute a similarity search and receive the corresponding scores you can run:
results= vector_store.similarity_search_with_score(query="thud", k=1)
for doc, scorein results:
print(f"* [SIM={score:3f}]{doc.page_content} [{doc.metadata}]")
* [SIM=0.671268] foo [{'source': 'https://example.com'}]
Usage for retrieval-augmented generation
For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:
API reference
For detailed documentation of all LindormVectorStore features and configurations head tothe API reference.
Related
- Vector storeconceptual guide
- Vector storehow-to guides