Real-time knowledge for LLMs
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Real-time knowledge for LLMs
No credit card required
Unlock the Full Potential of Your LLM with Real-Time Web Knowledge! Say goodbye to outdated responses, misinformation, and hallucinations. Now you can ground your Language Model with the freshest information from the web. NO API NEEDED!
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Created in Jun 2024
Modified 6 months ago
You can access the Real-time knowledge for LLMs programmatically from your own applications by using the Apify API. You can also choose the language preference from below. To use the Apify API, you’ll need an Apify account and your API token, found in Integrations settings in Apify Console.
1# Start Server-Sent Events (SSE) session and keep it running
2curl "https://actors-mcp-server.apify.actor/sse?token=<YOUR_API_TOKEN>&actors=ai-developer/real-time-knowledge-for-llms"
3
4# Session id example output:
5# event: endpoint
6# data: /message?sessionId=9d820491-38d4-4c7d-bb6a-3b7dc542f1faUsing Real-time knowledge for LLMs via Model Context Protocol (MCP) server
MCP server lets you use Real-time knowledge for LLMs within your AI workflows. Send API requests to trigger actions and receive real-time results. Take the received sessionId and use it to communicate with the MCP server. The message starts the Real-time knowledge for LLMs Actor with the provided input.
1curl -X POST "https://actors-mcp-server.apify.actor/message?token=<YOUR_API_TOKEN>&session_id=<SESSION_ID>" -H "Content-Type: application/json" -d '{
2 "jsonrpc": "2.0",
3 "id": 1,
4 "method": "tools/call",
5 "params": {
6 "arguments": {
7 "search": "Elon Musk"
8},
9 "name": "ai-developer/real-time-knowledge-for-llms"
10 }
11}'The response should be: Accepted. You should received response via SSE (JSON) as:
1event: message
2data: {
3 "result": {
4 "content": [
5 {
6 "type": "text",
7 "text": "ACTOR_RESPONSE"
8 }
9 ]
10 }
11}Configure local MCP Server via standard input/output for Real-time knowledge for LLMs
You can connect to the MCP Server using clients like ClaudeDesktop and LibreChat or build your own. The server can run both locally and remotely, giving you full flexibility. Set up the server in the client configuration as follows:
1{
2 "mcpServers": {
3 "actors-mcp-server": {
4 "command": "npx",
5 "args": [
6 "-y",
7 "@apify/actors-mcp-server",
8 "--actors",
9 "ai-developer/real-time-knowledge-for-llms"
10 ],
11 "env": {
12 "APIFY_TOKEN": "<YOUR_API_TOKEN>"
13 }
14 }
15 }
16}You can further access the MCP client through the Tester MCP Client, a chat user interface to interact with the server.
To get started, check out the documentation and example clients. If you are interested in learning more about our MCP server, check out our blog post.
