Client Wrapper

defineDatabaseChat creates a DatabaseChatClient that wraps the component endpoints with a consistent API.

import { defineDatabaseChat } from "./components/databaseChat/client";

const chat = defineDatabaseChat(components.databaseChat, {
  model: "openai/gpt-4o",
  systemPrompt: "You are a helpful assistant.",
  toolGuidance: "auto",
  tools,
  maxMessagesForDisplay: 100,
  maxMessagesForLLM: 50,
});

Configuration options

• model: default model for chat.send (default: openai/gpt-4o).
• systemPrompt: default prompt for chat.send.
• toolGuidance: standard tool-result guidance. Use "auto" or omit for generated guidance, "disabled" to opt out, or a custom string to append instead.
• tools: explicit tool definitions with already-created handler strings.
• autoTools: generate tools from schema-like definitions.
• maxMessagesForDisplay: default message limit for getMessages (default: 100).
• maxMessagesForLLM: default message limit for LLM context (default: 50).

autoTools shape

autoTools: {
  tables: TableInfo[];
  handlers: {
    query: string;
    count: string;
    aggregate?: string;
    search?: string;
    getById?: string;
  };
  allowedTables: string[];
  excludeFields?: Record<string, string[]>;
  tableDescriptions?: Record<string, string>;
  fieldDescriptions?: Record<string, string>;
}

Common methods

• createConversation(ctx, { externalId, title? })
• getConversation(ctx, conversationId)
• listConversations(ctx, externalId)
• getMessages(ctx, conversationId)
• getStreamState(ctx, conversationId)
• getStreamDeltas(ctx, streamId, cursor)
• abortStream(ctx, conversationId, reason?)
• send(ctx, { conversationId, message, apiKey, model?, systemPrompt?, toolGuidance?, toolContext? })

Advanced methods

• addMessage(ctx, conversationId, role, content, { toolCalls?, toolResults? })
• getMessagesForLLM(ctx, conversationId, { systemPrompt?, includeTools?, toolGuidance? })
• getTools() and getToolsForLLM()
• getSystemPromptWithTools(basePrompt?, toolGuidance?)