llama-index-vector-stores-hologres


Namellama-index-vector-stores-hologres JSON
Version 0.1.0 PyPI version JSON
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home_pageNone
Summaryllama-index vector stores hologres integration
upload_time2024-05-29 00:49:17
maintainerNone
docs_urlNone
authorYour Name
requires_python<4.0,>=3.8.1
licenseMIT
keywords
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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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