| Name | llama-index-tools-vectara-query JSON |
| Version |
0.4.1
JSON |
| download |
| home_page | None |
| Summary | llama-index tools vectara query integration |
| upload_time | 2025-09-08 20:49:24 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | <4.0,>=3.9 |
| license | None |
| keywords |
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| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
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| Travis-CI |
No Travis.
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No coveralls.
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## Vectara Query Tool
This tool connects to a Vectara corpus and allows agents to make semantic search or retrieval augmented generation (RAG) queries.
## Usage
Please note that this usage example relies on version >=0.3.0.
This tool has a more extensive example usage documented in a Jupyter notebok [here](https://github.com/run-llama/llama_index/blob/main/llama-index-integrations/tools/llama-index-tools-vectara-query/examples/vectara_query.ipynb)
To use this tool, you'll need a Vectara account (If you don't have an account, you can create one [here](https://vectara.com/integrations/llamaindex)) and the following information in your environment:
- `VECTARA_CORPUS_KEY`: The corpus key for the Vectara corpus that you want your tool to search for information. If you need help creating a corpus with your data, follow this [Quick Start](https://docs.vectara.com/docs/quickstart) guide.
- `VECTARA_API_KEY`: An API key that can perform queries on this corpus.
Here's an example usage of the VectaraQueryToolSpec.
```python
from llama_index.tools.vectara_query import VectaraQueryToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
# Connecting to a Vectara corpus about Electric Vehicles
tool_spec = VectaraQueryToolSpec()
agent = FunctionAgent(
tools=tool_spec.to_tool_list(),
llm=OpenAI(model="gpt-4.1"),
)
print(await agent.run("What are the different types of electric vehicles?"))
```
The available tools are:
`semantic_search`: A tool that accepts a query and uses semantic search to obtain the top search results.
`rag_query`: A tool that accepts a query and uses RAG to obtain a generative response grounded in the search results.
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"description": "## Vectara Query Tool\n\nThis tool connects to a Vectara corpus and allows agents to make semantic search or retrieval augmented generation (RAG) queries.\n\n## Usage\n\nPlease note that this usage example relies on version >=0.3.0.\n\nThis tool has a more extensive example usage documented in a Jupyter notebok [here](https://github.com/run-llama/llama_index/blob/main/llama-index-integrations/tools/llama-index-tools-vectara-query/examples/vectara_query.ipynb)\n\nTo use this tool, you'll need a Vectara account (If you don't have an account, you can create one [here](https://vectara.com/integrations/llamaindex)) and the following information in your environment:\n\n- `VECTARA_CORPUS_KEY`: The corpus key for the Vectara corpus that you want your tool to search for information. If you need help creating a corpus with your data, follow this [Quick Start](https://docs.vectara.com/docs/quickstart) guide.\n- `VECTARA_API_KEY`: An API key that can perform queries on this corpus.\n\nHere's an example usage of the VectaraQueryToolSpec.\n\n```python\nfrom llama_index.tools.vectara_query import VectaraQueryToolSpec\nfrom llama_index.core.agent.workflow import FunctionAgent\nfrom llama_index.llms.openai import OpenAI\n\n# Connecting to a Vectara corpus about Electric Vehicles\ntool_spec = VectaraQueryToolSpec()\n\nagent = FunctionAgent(\n tools=tool_spec.to_tool_list(),\n llm=OpenAI(model=\"gpt-4.1\"),\n)\n\nprint(await agent.run(\"What are the different types of electric vehicles?\"))\n```\n\nThe available tools are:\n\n`semantic_search`: A tool that accepts a query and uses semantic search to obtain the top search results.\n\n`rag_query`: A tool that accepts a query and uses RAG to obtain a generative response grounded in the search results.\n",
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