Internal Knowledge Base

from ragrails import ChatRetrievalQualityConfig, RagRails

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

rag.ingest(
    docs={"folder": "files/company/"},
    chunking={"chunk_size": 900, "chunk_overlap": 120},
    storage={"collection": "company_kb"},
)

result = rag.chat(
    "How many vacation days do new hires get?",
    history=[],
    retrieval_quality=ChatRetrievalQualityConfig(low_confidence_mode="answer_with_caution"),
)

print(result.answer)
print(result.sources)

ragrails ingest \
  --folder files/company/ \
  --vector-db qdrant \
  --collection company_kb \
  --url http://localhost:6333 \
  --provider voyage \
  --model voyage-3

ragrails chat "How many vacation days do new hires get?" \
  --vector-db qdrant \
  --collection company_kb \
  --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 '{
    "docs": {"folder": "files/company/"},
    "chunking": {"chunk_size": 900, "chunk_overlap": 120},
    "embedding": {"provider": "voyage", "model": "voyage-3"},
    "storage": {"vector_db": "qdrant", "collection": "company_kb", "url": "http://localhost:6333"}
  }'

curl -X POST http://127.0.0.1:8000/v1/chat \
  -H "Content-Type: application/json" \
  -d '{
    "query": "How many vacation days do new hires get?",
    "llm_provider": "openai",
    "llm_model": "gpt-4o-mini",
    "embedder_provider": "voyage",
    "embedder_model": "voyage-3",
    "vector_db": "qdrant",
    "collection": "company_kb",
    "url": "http://localhost:6333",
    "history": []
  }'