| Name | llama-index-readers-astra-db JSON |
| Version |
0.2.0
JSON |
| download |
| home_page | None |
| Summary | llama-index readers astra_db integration |
| upload_time | 2024-08-22 05:48:06 |
| maintainer | erichare |
| 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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| coveralls test coverage |
No coveralls.
|
# Astra DB Loader
```bash
pip install llama-index-readers-astra-db
```
The Astra DB Loader returns a set of documents retrieved from Astra DB.
The user initializes the loader with an Astra DB index. They then pass in a vector.
## Usage
Here's an example usage of the AstraDBReader.
```python
from openai import OpenAI
# Get the credentials for Astra DB
api_endpoint = "https://324<...>f1c.astra.datastax.com"
token = "AstraCS:<...>"
# EXAMPLE: OpenAI embeddings
client = OpenAI(api_key="sk-<...>")
# Call OpenAI (or generate embeddings another way)
response = client.embeddings.create(
input="Your text string goes here", model="text-embedding-ada-002"
)
# Get the embedding
query_vector = response.data[0].embedding
# Initialize the Reader object
from llama_index.readers.astra_db import AstraDBReader
# Your Astra DB Account will provide you with the endpoint URL and Token
reader = AstraDBReader(
collection_name="astra_v_table",
token=token,
api_endpoint=api_endpoint,
embedding_dimension=len(query_vector),
)
# Fetch data from the reader
documents = reader.load_data(vector=query_vector, limit=5)
```
This loader is designed to be used as a way to load data into [LlamaIndex](https://github.com/run-llama/llama_index/).
> **Note**: Please see the AstraDB documentation [here](https://docs.datastax.com/en/astra/astra-db-vector/clients/python.html).
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"description": "# Astra DB Loader\n\n```bash\npip install llama-index-readers-astra-db\n```\n\nThe Astra DB Loader returns a set of documents retrieved from Astra DB.\nThe user initializes the loader with an Astra DB index. They then pass in a vector.\n\n## Usage\n\nHere's an example usage of the AstraDBReader.\n\n```python\nfrom openai import OpenAI\n\n\n# Get the credentials for Astra DB\napi_endpoint = \"https://324<...>f1c.astra.datastax.com\"\ntoken = \"AstraCS:<...>\"\n\n# EXAMPLE: OpenAI embeddings\nclient = OpenAI(api_key=\"sk-<...>\")\n\n# Call OpenAI (or generate embeddings another way)\nresponse = client.embeddings.create(\n input=\"Your text string goes here\", model=\"text-embedding-ada-002\"\n)\n\n# Get the embedding\nquery_vector = response.data[0].embedding\n\n# Initialize the Reader object\nfrom llama_index.readers.astra_db import AstraDBReader\n\n# Your Astra DB Account will provide you with the endpoint URL and Token\nreader = AstraDBReader(\n collection_name=\"astra_v_table\",\n token=token,\n api_endpoint=api_endpoint,\n embedding_dimension=len(query_vector),\n)\n\n# Fetch data from the reader\ndocuments = reader.load_data(vector=query_vector, limit=5)\n```\n\nThis loader is designed to be used as a way to load data into [LlamaIndex](https://github.com/run-llama/llama_index/).\n\n> **Note**: Please see the AstraDB documentation [here](https://docs.datastax.com/en/astra/astra-db-vector/clients/python.html).\n",
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