| Name | llama-index-readers-myscale JSON |
| Version |
0.2.0
JSON |
| download |
| home_page | None |
| Summary | llama-index readers myscale integration |
| upload_time | 2024-08-22 03:14:06 |
| 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.
|
| coveralls test coverage |
No coveralls.
|
# LlamaIndex Readers Integration: Myscale
## Overview
MyScale Reader allows loading data from a MyScale backend. It constructs a query to retrieve documents based on a given query vector and additional search parameters.
### Installation
You can install Myscale Reader via pip:
```bash
pip install llama-index-readers-myscale
```
### Usage
```python
from llama_index.readers.myscale import MyScaleReader
# Initialize MyScaleReader
reader = MyScaleReader(
myscale_host="<MyScale Host>", # MyScale host address
username="<Username>", # Username to login
password="<Password>", # Password to login
database="<Database Name>", # Database name (default: 'default')
table="<Table Name>", # Table name (default: 'llama_index')
index_type="<Index Type>", # Index type (default: "IVFLAT")
metric="<Metric>", # Metric to compute distance (default: 'cosine')
batch_size=32, # Batch size for inserting documents (default: 32)
index_params=None, # Index parameters for MyScale (default: None)
search_params=None, # Search parameters for MyScale query (default: None)
)
# Load data from MyScale
documents = reader.load_data(
query_vector=[0.1, 0.2, 0.3], # Query vector
where_str="<Where Condition>", # Where condition string (default: None)
limit=10, # Number of results to return (default: 10)
)
```
This loader is designed to be used as a way to load data into
[LlamaIndex](https://github.com/run-llama/llama_index/tree/main/llama_index) and/or subsequently
used as a Tool in a [LangChain](https://github.com/hwchase17/langchain) Agent.
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"description": "# LlamaIndex Readers Integration: Myscale\n\n## Overview\n\nMyScale Reader allows loading data from a MyScale backend. It constructs a query to retrieve documents based on a given query vector and additional search parameters.\n\n### Installation\n\nYou can install Myscale Reader via pip:\n\n```bash\npip install llama-index-readers-myscale\n```\n\n### Usage\n\n```python\nfrom llama_index.readers.myscale import MyScaleReader\n\n# Initialize MyScaleReader\nreader = MyScaleReader(\n myscale_host=\"<MyScale Host>\", # MyScale host address\n username=\"<Username>\", # Username to login\n password=\"<Password>\", # Password to login\n database=\"<Database Name>\", # Database name (default: 'default')\n table=\"<Table Name>\", # Table name (default: 'llama_index')\n index_type=\"<Index Type>\", # Index type (default: \"IVFLAT\")\n metric=\"<Metric>\", # Metric to compute distance (default: 'cosine')\n batch_size=32, # Batch size for inserting documents (default: 32)\n index_params=None, # Index parameters for MyScale (default: None)\n search_params=None, # Search parameters for MyScale query (default: None)\n)\n\n# Load data from MyScale\ndocuments = reader.load_data(\n query_vector=[0.1, 0.2, 0.3], # Query vector\n where_str=\"<Where Condition>\", # Where condition string (default: None)\n limit=10, # Number of results to return (default: 10)\n)\n```\n\nThis loader is designed to be used as a way to load data into\n[LlamaIndex](https://github.com/run-llama/llama_index/tree/main/llama_index) and/or subsequently\nused as a Tool in a [LangChain](https://github.com/hwchase17/langchain) Agent.\n",
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