llm-exa
LLM plugin for accessing Exa.ai - an AI-powered search engine and data retrieval API.
Installation
Install this plugin in the same environment as LLM.
llm install llm-exa
Configuration
First, set an API key for Exa.ai:
llm keys set exa
# Paste your Exa.ai API key here
You can obtain an API key by signing up at exa.ai and navigating to your dashboard.
Available Models
This plugin provides access to the following Exa.ai capabilities:
exa-search- Search the web using Exa.ai's neural or keyword searchexa-search-contents- Search and retrieve full content of relevant web pagesexa-find-similar- Find web pages similar to a provided URLexa-answer- Get direct answers to questions with citations from the web
You can view all models and their options with:
llm models
llm models --options
Usage Examples
Web Search
Search the web using neural search (semantic understanding):
llm -m exa-search "Latest developments in quantum computing" --option type neural
Search with category filtering:
llm -m exa-search "Recent papers on LLMs" --option category "papers"
Limit search to specific domains:
llm -m exa-search "Climate change impacts" --option include_domains '["nature.com", "science.org"]'
Search with Full Content Retrieval
Search and get full content of web pages:
llm -m exa-search-contents "The future of renewable energy" --option text true --option highlights true
Include summaries of retrieved pages:
llm -m exa-search-contents "AI chip advancements" --option summary true
Finding Similar Content
Find pages similar to a URL:
llm -m exa-find-similar "https://example.com/article-about-agi"
Exclude the original domain:
llm -m exa-find-similar "https://example.com/some-article" --option exclude_domains '["example.com"]'
Getting Direct Answers
Get a direct answer to a question with citations:
llm -m exa-answer "What are the environmental impacts of blockchain?"
Stream the answer in real-time:
llm -m exa-answer "What are the latest developments in fusion energy?" --option stream true
Options
Common options for all models:
num_results- Number of results to return (default: 5)text- Whether to include full text content (default: true)
Search-specific options:
type- Search type: "neural", "keyword", or "auto" (default: "auto")category- Filter by data category (e.g., "company", "research paper", "news")include_domains- List of domains to include in searchexclude_domains- List of domains to exclude from search
Content-specific options:
highlights- Include relevant highlights in response (default: false)summary- Include generated summary in response (default: false)
Answer-specific options:
stream- Stream the response (default: true)
Development
To set up this plugin locally:
git clone https://github.com/nicobailon/llm-exa.git
cd llm-exa
python -m venv venv
source venv/bin/activate
pip install -e '.[test]'
License
This project is licensed under the Apache License 2.0.
Metadata
Release files for llm-exa 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| llm_exa-0.1.0.tar.gz | 9.2 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| llm_exa-0.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 18.8 kB
Release files / llm_exa-0.1.0.tar.gz
| Download URL | llm_exa-0.1.0.tar.gz |
|---|---|
| Size | 9.2 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.13.2
|
Release files / llm_exa-0.1.0-py3-none-any.whl
| Download URL | llm_exa-0.1.0-py3-none-any.whl |
|---|---|
| Size | 9.6 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.13.2
|