> ## Documentation Index
> Fetch the complete documentation index at: https://docs.ragrails.com/llms.txt
> Use this file to discover all available pages before exploring further.

# AI Support Agent

> A support bot that answers from your help docs and refuses when it should not guess.

## The problem

Support teams answer the same questions repeatedly. Generic chatbots make the problem worse when they invent refund rules, pricing terms, or policy exceptions that are not in your docs.

## Why Ragrails fits

Ragrails gives you a grounded support workflow: ingest help content, retrieve the right chunks, rewrite follow-ups, and tune low-confidence behavior so the bot can answer carefully or refuse.

| Need                                       | Ragrails capability                                                                     |
| ------------------------------------------ | --------------------------------------------------------------------------------------- |
| Help center plus policy files in one index | [Extraction](/features/extraction) and [Pipeline Overview](/features/pipeline-overview) |
| Multi-turn support questions               | [Query Rewriting](/capabilities/query-rewriting)                                        |
| Avoid hallucinated policy answers          | [Chat Tuning](/capabilities/chat-tuning)                                                |
| Stream a responsive support UI             | [Streaming](/capabilities/streaming)                                                    |
| Keep policies current                      | [Knowledge Base Maintenance](/features/knowledge-base-maintenance)                      |

## Sample

<CodeGroup>
  ```python SDK theme={null}
  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)
  ```

  ```bash CLI theme={null}
  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
  ```

  ```bash REST API theme={null}
  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": []
    }'
  ```
</CodeGroup>

## Production notes

* Persist `result.history` per customer session.
* Display `result.sources` so agents and customers can inspect the policy source.
* Use `answer_confidence` and `retrieval_quality` to route weak answers to a human.
* Refresh changed policies with [Knowledge Base Maintenance](/features/knowledge-base-maintenance).