> For the complete documentation index, see [llms.txt](/llms.txt).
> A full single-fetch corpus is available at [llms-full.txt](/llms-full.txt).
---
title: Build a RAG chatbot with memory
description: Retrieve knowledge, preserve user context, isolate tenants, and trace each answer back to evidence.
tags: ["RAG", "Memory", "Tenancy"]
date: 2026-05-13
last_verified: 2026-05-13
audience: both
---

This cookbook builds a RAG chatbot that behaves like a production workflow:
tenant-aware retrieval, durable memory updates, traceable evidence, and
recoverable answer generation.

## Scenario

A SaaS user asks a product question. The chatbot retrieves relevant docs,
combines them with user memory, generates an answer, and records useful context
for the next turn.

## What you build

- Tenant-scoped retrieval.
- Session memory lookup.
- Evidence-grounded answer generation.
- A memory update step.
- Trace evidence for every answer.

## Workflow shape

```python
@workflow
async def answer_chat_turn(ctx: WorkflowContext, request: ChatRequest) -> ChatAnswer:
    memory = await ctx.step(load_session_memory, request.session_id)
    passages = await ctx.step(retrieve_docs, request.tenant_id, request.message)
    answer = await ctx.step(generate_grounded_answer, request.message, memory, passages)
    await ctx.step(update_memory_once, request.session_id, request.message, answer)
    return answer
```

The retrieval step must receive the tenant ID. Do not rely on global vector
indexes without tenant filters.

## Evidence model

Return citations as structured data.

```python
class ChatAnswer(BaseModel):
    answer: str
    citations: list[DocumentCitation]
    memory_updates: list[str]
```

This lets the UI show citations and lets the trace explain the answer.

## Production checks

- Direct HTTP calls include `X-TENANT-ID` and `X-DEPLOYMENT-ID`.
- Retrieval filters by tenant.
- The answer stores citations.
- Memory updates are idempotent per turn.
- A bad answer can be replayed with the same retrieved passages.

## Next steps

- [Build a customer support agent](/cookbooks/customer-support-agent.md)
- [Build a data extraction workflow](/cookbooks/data-extraction.md)
- [Turn a failed production AI run into an eval](/cookbooks/production-run-to-eval.md)