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Prerequisites
- Create a service and a route
- Start a Redis-Stack instance in your environment
You can now create the AI RAG Injector plugin at the global, service, or route level, using the following examples.
Examples
The following examples show how to configure the AI RAG Injector plugin, and the expected behavior when making requests.
1. Configure the AI RAG Injector plugin
Configure the AI RAG Injector plugin with the AI Proxy Advanced plugin:
_format_version: '3.0'
services:
- name: ai-proxy
url: http://localhost:65535
routes:
- name: openai-chat
paths:
- /
plugins:
- name: ai-proxy-advanced
config:
targets:
- logging:
log_statistics: true
route_type: llm/v1/chat
model:
name: gpt-4o
provider: openai
options:
max_tokens: 512
temperature: 1.0
- name: ai-rag-injector
config:
inject_template: |
Only use the following information surrounded by <CONTEXT></CONTEXT>to and your existing knowledge to provide the best possible answer to the user.
<CONTEXT><RAG RESPONSE></CONTEXT>
User's question: <PROMPT>
embeddings:
auth:
header_name: Authorization
header_value: Bearer <openai_key>
model:
provider: openai
name: text-embedding-3-large
vectordb:
strategy: redis
redis:
host: <redis_host>
port: <redis_port>
distance_metric: cosine
dimensions: 768
2. Make a AI request to the AI Proxy Advanced plugin
The AI RAG Injector plugin uses the OpenAI text-embedding-3-large model to generate embeddings for the content and stores them in Redis.
Once vector database has ingested data and built a knowledge base, you can make requests to it. For example:
curl --http1.1 localhost:8000/chat \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-d '{
"messages": [{"role": "user", "content": "What is kong"}]
}' | jq