Developer / API Copilot

from ragrails import RagRails

rag = RagRails(
    collection="devdocs",
    vector_store={"provider": "qdrant", "url": "http://localhost:6333"},
    embedding={"provider": "voyage", "model": "voyage-3"},
    llm={"provider": "openai", "model": "gpt-4o-mini"},
)

rag.ingest(
    api={"url": "https://api.example.com/openapi.json", "title": "API Reference"},
    urls="https://docs.example.com/sdk",
    storage={"collection": "devdocs"},
)

result = rag.chat("How do I create a customer?", history=[])
print(result.answer)

ragrails setup-url --browser chromium

ragrails ingest \
  --api-url https://api.example.com/openapi.json \
  --source-url https://docs.example.com/sdk \
  --vector-db qdrant \
  --collection devdocs \
  --url http://localhost:6333 \
  --provider voyage \
  --model voyage-3

ragrails chat "How do I create a customer?" \
  --vector-db qdrant \
  --collection devdocs \
  --url http://localhost:6333 \
  --embedder-provider voyage \
  --embedder-model voyage-3 \
  --llm-provider openai \
  --llm-model gpt-4o-mini

curl -X POST http://127.0.0.1:8000/v1/pipelines/ingest \
  -H "Content-Type: application/json" \
  -d '{
    "api": {"url": "https://api.example.com/openapi.json", "title": "API Reference"},
    "urls": "https://docs.example.com/sdk",
    "embedding": {"provider": "voyage", "model": "voyage-3"},
    "storage": {"vector_db": "qdrant", "collection": "devdocs", "url": "http://localhost:6333"}
  }'

curl -X POST http://127.0.0.1:8000/v1/chat \
  -H "Content-Type: application/json" \
  -d '{
    "query": "How do I create a customer?",
    "llm_provider": "openai",
    "llm_model": "gpt-4o-mini",
    "embedder_provider": "voyage",
    "embedder_model": "voyage-3",
    "vector_db": "qdrant",
    "collection": "devdocs",
    "url": "http://localhost:6333",
    "history": []
  }'