Name | llama-index-vector-stores-hologres JSON |
Version |
0.1.0
JSON |
| download |
home_page | None |
Summary | llama-index vector stores hologres integration |
upload_time | 2024-05-29 00:49:17 |
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.
|
Travis-CI |
No Travis.
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# LlamaIndex Vector_Stores Integration: Hologres
Please refer to the [notebook](../../../docs/docs/examples/vector_stores/HologresDemo.ipynb) for usage of Hologres as vector store in LlamaIndex.
## Example Usage
```sh
pip install llama-index
pip install llama-index-vector-stores-hologres
```
```python
# Connect to existing instance
from llama_index.core import VectorStoreIndex
from llama_index.vector_stores.hologres import HologresVectorStore
vector_store = HologresVectorStore.from_param(
host="***",
port=80,
user="***",
password="***",
database="***",
table_name="***",
embedding_dimension=1536,
pre_delete_table=True,
)
# Create index from existing stored vectors
index = VectorStoreIndex.from_vector_store(vector_store)
query_engine = index.as_query_engine()
response = query_engine.query(
"What did the author study prior to working on AI?"
)
print(response)
```
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"description": "# LlamaIndex Vector_Stores Integration: Hologres\n\nPlease refer to the [notebook](../../../docs/docs/examples/vector_stores/HologresDemo.ipynb) for usage of Hologres as vector store in LlamaIndex.\n\n## Example Usage\n\n```sh\npip install llama-index\npip install llama-index-vector-stores-hologres\n```\n\n```python\n# Connect to existing instance\nfrom llama_index.core import VectorStoreIndex\nfrom llama_index.vector_stores.hologres import HologresVectorStore\n\nvector_store = HologresVectorStore.from_param(\n host=\"***\",\n port=80,\n user=\"***\",\n password=\"***\",\n database=\"***\",\n table_name=\"***\",\n embedding_dimension=1536,\n pre_delete_table=True,\n)\n\n# Create index from existing stored vectors\nindex = VectorStoreIndex.from_vector_store(vector_store)\nquery_engine = index.as_query_engine()\nresponse = query_engine.query(\n \"What did the author study prior to working on AI?\"\n)\nprint(response)\n```\n",
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