| Name | llama-index-vector-stores-lindorm JSON |
| Version |
0.2.0
JSON |
| download |
| home_page | None |
| Summary | llama-index vector_stores lindorm integration |
| upload_time | 2024-08-22 08:08:15 |
| maintainer | None |
| docs_url | None |
| author | Your Name |
| requires_python | <4.0,>=3.8.1 |
| license | MIT |
| keywords |
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
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# LlamaIndex Vector_Stores Integration: Lindorm
- LindormVectorStore support pure vector search, search with metadata filtering, hybrid search, async, etc.
- Please refer to the [notebook](../../../docs/docs/examples/vector_stores/LindormDemo.ipynb) for usage of Lindorm as vector store in LlamaIndex.
# Example Usage
```sh
pip install llama-index
pip install opensearch-py
pip install llama-index-vector-stores-lindorm
```
```python
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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"description": "# LlamaIndex Vector_Stores Integration: Lindorm\n\n- LindormVectorStore support pure vector search, search with metadata filtering, hybrid search, async, etc.\n- Please refer to the [notebook](../../../docs/docs/examples/vector_stores/LindormDemo.ipynb) for usage of Lindorm as vector store in LlamaIndex.\n\n# Example Usage\n\n```sh\npip install llama-index\npip install opensearch-py\npip install llama-index-vector-stores-lindorm\n```\n\n```python\nfrom llama_index.vector_stores.lindorm import (\n LindormVectorStore,\n LindormVectorClient,\n)\n\n# how to obtain an lindorm search instance:\n# https://alibabacloud.com/help/en/lindorm/latest/create-an-instance\n\n# how to access your lindorm search instance:\n# https://www.alibabacloud.com/help/en/lindorm/latest/view-endpoints\n\n# run curl commands to connect to and use LindormSearch:\n# https://www.alibabacloud.com/help/en/lindorm/latest/connect-and-use-the-search-engine-with-the-curl-command\n\n# lindorm instance info\nhost = \"ld-bp******jm*******-proxy-search-pub.lindorm.aliyuncs.com\"\nport = 30070\nusername = \"your_username\"\npassword = \"your_password\"\n\n# index to demonstrate the VectorStore impl\nindex_name = \"lindorm_test_index\"\n\n# extension param of lindorm search, number of cluster units to query; between 1 and method.parameters.nlist.\nnprobe = \"a number(string type)\"\n\n# extension param of lindorm search, usually used to improve recall accuracy, but it increases performance overhead;\n# between 1 and 200; default: 10.\nreorder_factor = \"a number(string type)\"\n\n# LindormVectorClient encapsulates logic for a single index with vector search enabled\nclient = LindormVectorClient(\n host=host,\n port=port,\n username=username,\n password=password,\n index=index_name,\n dimension=1536, # match with your embedding model\n nprobe=nprobe,\n reorder_factor=reorder_factor,\n # filter_type=\"pre_filter/post_filter(default)\"\n)\n\n# initialize vector store\nvector_store = LindormVectorStore(client)\n```\n",
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