llama-index vector_stores lindorm integration
Project description
LlamaIndex Vector_Stores Integration: Lindorm
- LindormVectorStore support pure vector search, search with metadata filtering, hybrid search, async, etc.
- Please refer to the notebook for usage of Lindorm as vector store in LlamaIndex.
Example Usage
pip install llama-index
pip install opensearch-py
pip install llama-index-vector-stores-lindorm
from llama_index.vector_stores.lindorm import (
LindormVectorStore,
LindormVectorClient,
)
# how to obtain an lindorm search instance:
# https://alibabacloud.com/help/en/lindorm/latest/create-an-instance
# how to access your lindorm search instance:
# https://www.alibabacloud.com/help/en/lindorm/latest/view-endpoints
# run curl commands to connect to and use LindormSearch:
# https://www.alibabacloud.com/help/en/lindorm/latest/connect-and-use-the-search-engine-with-the-curl-command
# lindorm instance info
host = "ld-bp******jm*******-proxy-search-pub.lindorm.aliyuncs.com"
port = 30070
username = "your_username"
password = "your_password"
# index to demonstrate the VectorStore impl
index_name = "lindorm_test_index"
# extension param of lindorm search, number of cluster units to query; between 1 and method.parameters.nlist.
nprobe = "a number(string type)"
# extension param of lindorm search, usually used to improve recall accuracy, but it increases performance overhead;
# between 1 and 200; default: 10.
reorder_factor = "a number(string type)"
# LindormVectorClient encapsulates logic for a single index with vector search enabled
client = LindormVectorClient(
host=host,
port=port,
username=username,
password=password,
index=index_name,
dimension=1536, # match with your embedding model
nprobe=nprobe,
reorder_factor=reorder_factor,
# filter_type="pre_filter/post_filter(default)"
)
# initialize vector store
vector_store = LindormVectorStore(client)
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