wehub-resource-sync
142 lines
6.5 KiB
Plaintext
142 lines
6.5 KiB
Plaintext
---
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title: "ClickHouse chat agent"
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sidebarTitle: "ClickHouse chat agent"
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description: "Build a chat agent that answers questions about your data by writing and running SQL against ClickHouse Cloud, using chat.agent() and the ClickHouse Node.js client."
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---
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## Overview
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This example is a [chat agent](/ai-chat/overview) that answers natural-language questions about the data in a [ClickHouse Cloud](https://clickhouse.com/cloud) database. The agent discovers the schema, writes ClickHouse SQL, runs it through the official [ClickHouse Node.js client](https://clickhouse.com/docs/integrations/javascript), and streams back answers with markdown tables. Trigger.dev handles the chat session, turn loop, streaming, and resumability — the whole agent is one `chat.agent()` call and three tools.
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**Tech stack:**
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- **[Trigger.dev AI chat](/ai-chat/overview)** for the agent session, turn loop, and streaming
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- **[ClickHouse Node.js client](https://clickhouse.com/docs/integrations/javascript)** (`@clickhouse/client`) for queries over HTTPS
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- **[AI SDK](https://ai-sdk.dev/)** with Anthropic Claude for the model and tool calling
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**Features:**
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- **Schema discovery tools**: `listTables` reads table names, engines, and row counts from `system.tables`; `describeTable` returns column names and types using a bound `Identifier` query param, so table names are never interpolated into SQL strings
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- **Read-only query tool**: `runQuery` accepts SELECT-style statements only, enforced in code and backed by ClickHouse settings — `readonly=2`, a 1,000-row result cap, and a 30 second execution timeout
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- **Self-correcting SQL**: query errors are returned to the model as tool output, so the agent reads the ClickHouse error, fixes its SQL, and retries
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- **Single environment variable**: the ClickHouse connection is one `CLICKHOUSE_URL` with the credentials embedded, set in the Trigger.dev dashboard
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## GitHub repo
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<Card
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title="View the ClickHouse chat agent repo"
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icon="GitHub"
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href="https://github.com/triggerdotdev/examples/tree/main/clickhouse-chat-agent"
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>
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Click here to view the full code for this project in our examples repository on GitHub. You can
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fork it and use it as a starting point for your own project.
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</Card>
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## How it works
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### The agent
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The agent is defined with [`chat.agent()`](/ai-chat/overview). Tools are declared on the config so tool results survive history re-conversion across turns, and the `run` function returns a `streamText()` call:
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```ts trigger/clickhouse-agent.ts
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import { chat } from "@trigger.dev/sdk/ai";
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import { anthropic } from "@ai-sdk/anthropic";
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import { stepCountIs, streamText } from "ai";
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export const clickhouseAgent = chat.agent({
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id: "clickhouse-agent",
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idleTimeoutInSeconds: 300,
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tools: { listTables, describeTable, runQuery },
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run: async ({ messages, tools, signal }) => {
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return streamText({
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// Spread chat.toStreamTextOptions() FIRST — it wires up
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// prepareStep (compaction, steering, background injection),
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// the system prompt set via chat.prompt(), and telemetry.
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...chat.toStreamTextOptions(),
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model: anthropic("claude-opus-4-8"),
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system: SYSTEM_PROMPT,
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messages,
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tools,
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stopWhen: stepCountIs(15),
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abortSignal: signal,
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});
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},
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});
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```
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The system prompt tells the agent to explore the schema before querying, write ClickHouse SQL (not Postgres dialect), prefer aggregations, and present results as markdown tables.
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### The query tool
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`runQuery` guards against writes twice: a statement allowlist in code, and ClickHouse settings on the request itself. Errors are returned to the model instead of thrown, which is what makes the agent self-correct:
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```ts trigger/clickhouse-agent.ts
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const READ_ONLY_STATEMENTS = /^\s*(select|with|show|describe|desc|explain|exists)\b/i;
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const runQuery = tool({
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description:
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"Run a read-only SQL query against ClickHouse and get the results as JSON rows.",
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inputSchema: z.object({
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query: z.string().describe("The ClickHouse SQL query to run"),
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}),
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execute: async ({ query }) => {
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if (!READ_ONLY_STATEMENTS.test(query)) {
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return { error: "Only read-only statements are allowed." };
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}
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try {
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const result = await getClickHouse().query({
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query,
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format: "JSONEachRow",
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clickhouse_settings: {
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// readonly=2: reads only (no writes/DDL), but per-query settings
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// like the limits below are still allowed.
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readonly: "2",
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max_result_rows: "1000",
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result_overflow_mode: "break",
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max_execution_time: 30,
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},
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});
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const rows = await result.json();
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return { rowCount: rows.length, rows };
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} catch (error) {
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// Return ClickHouse errors to the model so it can fix the query and retry.
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return { error: error instanceof Error ? error.message : String(error) };
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}
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},
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});
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```
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### Connecting to ClickHouse
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The client reads a single `CLICKHOUSE_URL` environment variable — the HTTPS endpoint with credentials embedded — set in the Trigger.dev dashboard on the [Environment Variables page](/deploy-environment-variables):
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```bash
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CLICKHOUSE_URL=https://default:YOUR_PASSWORD@YOUR_SERVICE.clickhouse.cloud:8443
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```
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```ts trigger/clickhouse-agent.ts
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import { createClient } from "@clickhouse/client";
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const clickhouse = createClient({ url: process.env.CLICKHOUSE_URL });
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```
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### Chatting with the agent
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Run `npx trigger.dev@latest dev`, then open the **AI agents** page in the dashboard and chat with `clickhouse-agent` in the playground. With a dataset like [NYC Taxi](https://clickhouse.com/docs/getting-started/example-datasets/nyc-taxi) loaded, asking "What were the top 5 busiest pickup days?" produces a `listTables` call, a `describeTable` call, a SQL aggregation, and a streamed markdown table of results.
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## Relevant code
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- **Agent + tools**: [trigger/clickhouse-agent.ts](https://github.com/triggerdotdev/examples/blob/main/clickhouse-chat-agent/trigger/clickhouse-agent.ts): the `chat.agent()` definition, the three tools, the read-only guards, and the ClickHouse client
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- **Trigger config**: [trigger.config.ts](https://github.com/triggerdotdev/examples/blob/main/clickhouse-chat-agent/trigger.config.ts): project config pointing at the `trigger/` directory
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## Learn more
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<CardGroup cols={2}>
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<Card title="AI chat overview" icon="message-bot" href="/ai-chat/overview">
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How chat agents, sessions, and the turn loop work.
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</Card>
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<Card title="Tools" icon="wrench" href="/ai-chat/tools">
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Declaring tools on your agent and how they persist across turns.
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</Card>
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</CardGroup>
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