| Name | llama-index-tools-vector-db JSON |
| Version |
0.2.0
JSON |
| download |
| home_page | None |
| Summary | llama-index tools vector_db integration |
| upload_time | 2024-08-22 07:42:11 |
| maintainer | jerryjliu |
| 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.
|
# VectorDB Tool
This tool wraps a VectorStoreIndex and enables a agent to call it with queries and filters to retrieve data.
## Usage
```python
from llama_index.tools.vector_db import VectorDB
from llama_index.agent.openai import OpenAIAgent
from llama_index.core.vector_stores import VectorStoreInfo
from llama_index.core import VectorStoreIndex
index = VectorStoreIndex(nodes=nodes)
tool_spec = VectorDB(index=index)
vector_store_info = VectorStoreInfo(
content_info="brief biography of celebrities",
metadata_info=[
MetadataInfo(
name="category",
type="str",
description="Category of the celebrity, one of [Sports, Entertainment, Business, Music]",
),
MetadataInfo(
name="country",
type="str",
description="Country of the celebrity, one of [United States, Barbados, Portugal]",
),
],
)
agent = OpenAIAgent.from_tools(
tool_spec.to_tool_list(
func_to_metadata_mapping={
"auto_retrieve_fn": ToolMetadata(
name="celebrity_bios",
description=f"""\
Use this tool to look up biographical information about celebrities.
The vector database schema is given below:
{vector_store_info.json()}
{tool_spec.auto_retrieve_fn.__doc__}
""",
fn_schema=create_schema_from_function(
"celebrity_bios", tool_spec.auto_retrieve_fn
),
)
}
),
verbose=True,
)
agent.chat("Tell me about two celebrities from the United States. ")
```
`auto_retrieve_fn`: Retrieves data from the index
This loader is designed to be used as a way to load data as a Tool in a Agent.
Raw data
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"description": "# VectorDB Tool\n\nThis tool wraps a VectorStoreIndex and enables a agent to call it with queries and filters to retrieve data.\n\n## Usage\n\n```python\nfrom llama_index.tools.vector_db import VectorDB\nfrom llama_index.agent.openai import OpenAIAgent\nfrom llama_index.core.vector_stores import VectorStoreInfo\nfrom llama_index.core import VectorStoreIndex\n\nindex = VectorStoreIndex(nodes=nodes)\ntool_spec = VectorDB(index=index)\nvector_store_info = VectorStoreInfo(\n content_info=\"brief biography of celebrities\",\n metadata_info=[\n MetadataInfo(\n name=\"category\",\n type=\"str\",\n description=\"Category of the celebrity, one of [Sports, Entertainment, Business, Music]\",\n ),\n MetadataInfo(\n name=\"country\",\n type=\"str\",\n description=\"Country of the celebrity, one of [United States, Barbados, Portugal]\",\n ),\n ],\n)\n\nagent = OpenAIAgent.from_tools(\n tool_spec.to_tool_list(\n func_to_metadata_mapping={\n \"auto_retrieve_fn\": ToolMetadata(\n name=\"celebrity_bios\",\n description=f\"\"\"\\\n Use this tool to look up biographical information about celebrities.\n The vector database schema is given below:\n\n {vector_store_info.json()}\n\n {tool_spec.auto_retrieve_fn.__doc__}\n \"\"\",\n fn_schema=create_schema_from_function(\n \"celebrity_bios\", tool_spec.auto_retrieve_fn\n ),\n )\n }\n ),\n verbose=True,\n)\n\nagent.chat(\"Tell me about two celebrities from the United States. \")\n```\n\n`auto_retrieve_fn`: Retrieves data from the index\n\nThis loader is designed to be used as a way to load data as a Tool in a Agent.\n",
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