AI Support Agent

from ragrails import ChatRetrievalQualityConfig, QueryRewriteConfig, RagRails

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

rag.ingest(
    urls="https://help.example.com",
    docs={"folder": "files/policies/"},
    ingestion={"urls": {"mode": "full", "max_pages": 200}},
    storage={"collection": "support"},
)

result = rag.chat(
    "Can I get a refund after 30 days?",
    history=[],
    query_rewrite=QueryRewriteConfig(enabled=True),
    retrieval_quality=ChatRetrievalQualityConfig(
        low_confidence_mode="refuse_grounded_answer",
        min_retrieval_score=0.35,
    ),
)

print(result.answer)
print(result.answer_confidence)

ragrails setup-url --browser chromium

ragrails ingest \
  --source-url https://help.example.com \
  --folder files/policies/ \
  --vector-db qdrant \
  --collection support \
  --url http://localhost:6333 \
  --provider voyage \
  --model voyage-3

ragrails chat "Can I get a refund after 30 days?" \
  --rewrite-query \
  --vector-db qdrant \
  --collection support \
  --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 '{
    "urls": "https://help.example.com",
    "docs": {"folder": "files/policies/"},
    "ingestion": {"urls": {"mode": "full", "max_pages": 200}},
    "embedding": {"provider": "voyage", "model": "voyage-3"},
    "storage": {"vector_db": "qdrant", "collection": "support", "url": "http://localhost:6333"}
  }'

curl -X POST http://127.0.0.1:8000/v1/chat \
  -H "Content-Type: application/json" \
  -d '{
    "query": "Can I get a refund after 30 days?",
    "query_rewrite": {"enabled": true},
    "retrieval_quality": {"low_confidence_mode": "refuse_grounded_answer", "min_retrieval_score": 0.35},
    "llm_provider": "openai",
    "llm_model": "gpt-4o-mini",
    "embedder_provider": "voyage",
    "embedder_model": "voyage-3",
    "vector_db": "qdrant",
    "collection": "support",
    "url": "http://localhost:6333",
    "history": []
  }'