https://github.com/relevanceai/relevanceai
Home of the AI workforce - Multi-agent system, AI agents & tools
https://github.com/relevanceai/relevanceai
clustering computer-vision embeddings natural-language-processing nlp python search search-engine unstructured-data vector-database vector-search
Last synced: 8 months ago
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Home of the AI workforce - Multi-agent system, AI agents & tools
- Host: GitHub
- URL: https://github.com/relevanceai/relevanceai
- Owner: RelevanceAI
- License: apache-2.0
- Created: 2021-07-05T11:45:10.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2025-02-25T22:36:08.000Z (11 months ago)
- Last Synced: 2025-04-11T23:14:28.146Z (9 months ago)
- Topics: clustering, computer-vision, embeddings, natural-language-processing, nlp, python, search, search-engine, unstructured-data, vector-database, vector-search
- Language: Python
- Homepage: https://sdk.relevanceai.com
- Size: 68.9 MB
- Stars: 223
- Watchers: 14
- Forks: 33
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## Relevance AI - The home of your AI Workforce
🔥 Use Relevance to build AI agents for your AI workforce
[Sign up for a free account ->](https://app.relevanceai.com)
## 🧠Documentation
| Type | Link |
| ------------- | ----------- |
| Home Page | [Home Page](https://relevanceai.com/) |
| Platform | [Platform](https://app.relevanceai.com/) |
| Developer Documentation | [Documentation](https://sdk.relevanceai.com/) |
# Relevance AI SDK
Welcome to the Relevance AI SDK! This guide will help you set up and start using the SDK to interact with your AI agents, tools, and knowledge.
## Installation
To get started, you'll need to install the RelevanceAI library in a Python 3 environment. Run the following command in your terminal:
```bash
pip install relevanceai
```
## Create an Account
Before using the SDK, ensure you have an account with Relevance AI.
1. Sign up for a free account at [Relevance AI](https://app.relevanceai.com) and log in.
2. Create a new secret key at [SDK Login](https://app.relevanceai.com/login/sdk). Scroll to the bottom of the integrations page, click on "+ Create new secret key," and select "Admin" permissions.
## Set Up Your Client
To interact with Relevance AI, you'll need to set up a client. Start by importing the library:
```python
from relevanceai import RelevanceAI
client = RelevanceAI()
```
### Validate Client Credentials
You can validate your client credentials by storing them as environment variables and loading them into your project using `python-dotenv` or the `os` library:
```env
RAI_API_KEY=
RAI_REGION=
RAI_PROJECT=
```
```python
from dotenv import load_dotenv
load_dotenv()
from relevanceai import RelevanceAI
client = RelevanceAI()
```
Alternatively, pass the credentials directly to the client:
```python
from relevanceai import RelevanceAI
client = RelevanceAI(
api_key="your_api_key",
region="your_region",
project="your_project"
)
```
You are now ready to start using Relevance AI via the Python SDK.
## Quickstart
### Using Agents & Tasks
List all the agents in your project:
```python
from relevanceai import RelevanceAI
client = RelevanceAI()
agents = client.agents.list_agents()
print(agents)
# Example output: [Agent(agent_id="xxxxxxxx", name="Sales Qualifier"), ...]
```
Retrieve and interact with a specific agent:
```python
my_agent = client.agents.retrieve_agent(agent_id="xxxxxxxx")
message = "Let's qualify this lead:\n\nName: Ethan Trang\n\nCompany: Relevance AI\n\nEmail: ethan@relevanceai.com"
# Trigger a task with the agent
task = my_agent.trigger_task(message=message)
print(f"Task started with ID: {task.conversation_id}")
# View task progress
steps = my_agent.view_task_steps(conversation_id=task.conversation_id)
```
### Using Tools
List all the tools in your project:
```python
tools = client.tools.list_tools()
print(tools)
# Example output: [Tool(tool_id="xxxxxxxx", title="Search Website"), ...]
```
Retrieve and interact with a specific tool:
```python
my_tool = client.tools.retrieve_tool(tool_id="xxxxxxxx")
# Check tool parameters schema
params_schema = my_tool.get_params_schema()
# Trigger the tool
result = my_tool.trigger(params={"search_query": "AI automation"})
```
### Managing Knowledge Sets
Work with knowledge sets to store and retrieve data:
```python
# List knowledge sets
knowledge_sets = client.knowledge.list_knowledge()
# Retrieve data from a knowledge set
data = client.knowledge.retrieve_knowledge(knowledge_set="my_dataset")
```
### Managing Tasks
Track and manage ongoing tasks:
```python
# Get task metadata
metadata = client.tasks.get_metadata(conversation_id="xxxxxxxx")
# Delete a completed task
client.tasks.delete_task(conversation_id="xxxxxxxx")
```
## Explore More
Explore all the methods available for agents, tasks, tools, and knowledge with the [documentation](https://sdk.relevanceai.com/)