Integration with Existing Agents
Your Unit can be easily integrated with other AI systems through function calling. Here's how to do it:
const ReiCoreSdk = require('reicore-sdk');
const apiKey = 'your_unit_secret_token';
const reiAgent = new ReiCoreSdk({ agentSecretKey: apiKey });
// Example function to query Rei Agent
async function queryReiAgent(message) {
try {
const response = await reiAgent.chatCompletions(message);
return response;
} catch (error) {
console.error('Error querying Rei Agent:', error);
return null;
}
}
// Example usage in your agent
async function yourAgentFunction() {
// Your agent's logic here
const query = "What are the latest developments in quantum computing?";
const reiResponse = await queryReiAgent(query);
// Process the response
}
Example integration with OpenAI
from openai import OpenAI
from client import Client as ReiClient
# Initialize both clients
openai_client = OpenAI(api_key="your_openai_key")
rei_client = ReiClient(api_key="your_unit_secret_token")
def hybrid_agent_query(query):
# First, get context from OpenAI
openai_response = openai_client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": query}]
)
# Then, enhance with Rei Agent's specialized knowledge
rei_response = rei_client.chat.completions.create(
model="Unit01",
messages=[
{"role": "user", "content": query},
{"role": "assistant", "content": openai_response.choices[0].message.content}
]
)
return rei_response.choices[0].message.content
Integrating a Unit as a counselor for common LLMs models allows the seamless integration of memories: simply passing the query and asking for more details unlocks memory without having to code message loops.
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