tomevault-io Convex AI Agents
Basic Chat Schema
// convex/schema.ts
import { defineSchema, defineTable } from "convex/server";
import { v } from "convex/values";
export default defineSchema({
threads: defineTable({
userId: v.string(),
title: v.optional(v.string()),
createdAt: v.number(),
updatedAt: v.number(),
}).index("by_user", ["userId"]),
messages: defineTable({
threadId: v.id("threads"),
role: v.union(v.literal("user"), v.literal("assistant"), v.literal("system")),
content: v.string(),
createdAt: v.number(),
}).index("by_thread", ["threadId"]),
});
Thread Management
// convex/threads.ts
import { mutation, query } from "./_generated/server";
import { v } from "convex/values";
import { ConvexError } from "convex/values";
export const create = mutation({
args: {},
returns: v.id("threads"),
handler: async (ctx) => {
const identity = await ctx.auth.getUserIdentity();
if (!identity) {
throw new ConvexError({ code: "UNAUTHENTICATED", message: "Not logged in" });
}
return await ctx.db.insert("threads", {
userId: identity.subject,
createdAt: Date.now(),
updatedAt: Date.now(),
});
},
});
export const list = query({
args: {},
returns: v.array(v.object({
_id: v.id("threads"),
title: v.optional(v.string()),
updatedAt: v.number(),
})),
handler: async (ctx) => {
const identity = await ctx.auth.getUserIdentity();
if (!identity) return [];
return await ctx.db
.query("threads")
.withIndex("by_user", (q) => q.eq("userId", identity.subject))
.order("desc")
.collect();
},
});
export const getMessages = query({
args: { threadId: v.id("threads") },
returns: v.array(v.object({
_id: v.id("messages"),
role: v.union(v.literal("user"), v.literal("assistant"), v.literal("system")),
content: v.string(),
createdAt: v.number(),
})),
handler: async (ctx, args) => {
return await ctx.db
.query("messages")
.withIndex("by_thread", (q) => q.eq("threadId", args.threadId))
.order("asc")
.collect();
},
});
AI Integration with Actions
// convex/ai.ts
"use node";
import { internalAction, internalMutation } from "./_generated/server";
import { internal } from "./_generated/api";
import { v } from "convex/values";
import OpenAI from "openai";
const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });
export const chat = internalAction({
args: {
threadId: v.id("threads"),
userMessage: v.string(),
},
returns: v.null(),
handler: async (ctx, args) => {
// Save user message
await ctx.runMutation(internal.ai.saveMessage, {
threadId: args.threadId,
role: "user",
content: args.userMessage,
});
// Get conversation history
const messages = await ctx.runQuery(internal.ai.getHistory, {
threadId: args.threadId,
});
// Call OpenAI
const response = await openai.chat.completions.create({
model: "gpt-4",
messages: messages.map((m) => ({
role: m.role,
content: m.content,
})),
});
const assistantMessage = response.choices[0]?.message?.content ?? "";
// Save assistant response
await ctx.runMutation(internal.ai.saveMessage, {
threadId: args.threadId,
role: "assistant",
content: assistantMessage,
});
return null;
},
});
export const saveMessage = internalMutation({
args: {
threadId: v.id("threads"),
role: v.union(v.literal("user"), v.literal("assistant"), v.literal("system")),
content: v.string(),
},
returns: v.id("messages"),
handler: async (ctx, args) => {
// Update thread timestamp
await ctx.db.patch(args.threadId, { updatedAt: Date.now() });
return await ctx.db.insert("messages", {
threadId: args.threadId,
role: args.role,
content: args.content,
createdAt: Date.now(),
});
},
});
export const getHistory = internalQuery({
args: { threadId: v.id("threads") },
returns: v.array(v.object({
role: v.union(v.literal("user"), v.literal("assistant"), v.literal("system")),
content: v.string(),
})),
handler: async (ctx, args) => {
const messages = await ctx.db
.query("messages")
.withIndex("by_thread", (q) => q.eq("threadId", args.threadId))
.order("asc")
.collect();
return messages.map((m) => ({ role: m.role, content: m.content }));
},
});
Streaming Responses
// convex/ai.ts
"use node";
export const streamChat = internalAction({
args: {
threadId: v.id("threads"),
userMessage: v.string(),
},
returns: v.null(),
handler: async (ctx, args) => {
// Save user message
await ctx.runMutation(internal.ai.saveMessage, {
threadId: args.threadId,
role: "user",
content: args.userMessage,
});
// Create placeholder for assistant message
const messageId = await ctx.runMutation(internal.ai.saveMessage, {
threadId: args.threadId,
role: "assistant",
content: "",
});
// Get history
const messages = await ctx.runQuery(internal.ai.getHistory, {
threadId: args.threadId,
});
// Stream from OpenAI
const stream = await openai.chat.completions.create({
model: "gpt-4",
messages: messages.slice(0, -1).map((m) => ({
role: m.role,
content: m.content,
})),
stream: true,
});
let fullContent = "";
let lastUpdate = Date.now();
for await (const chunk of stream) {
const content = chunk.choices[0]?.delta?.content ?? "";
fullContent += content;
// Update every 500ms to avoid too many mutations
if (Date.now() - lastUpdate > 500) {
await ctx.runMutation(internal.ai.updateMessage, {
messageId,
content: fullContent,
});
lastUpdate = Date.now();
}
}
// Final update
await ctx.runMutation(internal.ai.updateMessage, {
messageId,
content: fullContent,
});
return null;
},
});
export const updateMessage = internalMutation({
args: { messageId: v.id("messages"), content: v.string() },
returns: v.null(),
handler: async (ctx, args) => {
await ctx.db.patch(args.messageId, { content: args.content });
return null;
},
});
Tool Calling / Function Calling
// convex/ai.ts
"use node";
const tools = [
{
type: "function" as const,
function: {
name: "search_documents",
description: "Search the knowledge base for relevant documents",
parameters: {
type: "object",
properties: {
query: { type: "string", description: "Search query" },
},
required: ["query"],
},
},
},
{
type: "function" as const,
function: {
name: "create_task",
description: "Create a new task for the user",
parameters: {
type: "object",
properties: {
title: { type: "string", description: "Task title" },
dueDate: { type: "string", description: "Due date in ISO format" },
},
required: ["title"],
},
},
},
];
export const chatWithTools = internalAction({
args: { threadId: v.id("threads"), userMessage: v.string() },
returns: v.null(),
handler: async (ctx, args) => {
// Save user message and get history...
const response = await openai.chat.completions.create({
model: "gpt-4",
messages,
tools,
});
const choice = response.choices[0];
// Handle tool calls
if (choice?.finish_reason === "tool_calls") {
const toolCalls = choice.message.tool_calls ?? [];
for (const toolCall of toolCalls) {
const { name, arguments: argsJson } = toolCall.function;
const toolArgs = JSON.parse(argsJson);
let result: string;
switch (name) {
case "search_documents":
result = await ctx.runAction(internal.search.query, {
query: toolArgs.query,
});
break;
case "create_task":
await ctx.runMutation(internal.tasks.create, {
title: toolArgs.title,
dueDate: toolArgs.dueDate,
});
result = `Task "${toolArgs.title}" created.`;
break;
default:
result = "Unknown tool";
}
// Continue conversation with tool result
// ... (recursive call or message append)
}
}
return null;
},
});
RAG (Retrieval-Augmented Generation)
Vector Search Schema
// convex/schema.ts
documents: defineTable({
content: v.string(),
embedding: v.array(v.float64()),
metadata: v.object({
source: v.string(),
title: v.optional(v.string()),
}),
}).vectorIndex("by_embedding", {
vectorField: "embedding",
dimensions: 1536, // OpenAI ada-002
}),
Embedding and Search
// convex/search.ts
"use node";
import { internalAction, internalMutation, internalQuery } from "./_generated/server";
import { v } from "convex/values";
import OpenAI from "openai";
const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });
export const embed = internalAction({
args: { text: v.string() },
returns: v.array(v.float64()),
handler: async (ctx, args) => {
const response = await openai.embeddings.create({
model: "text-embedding-ada-002",
input: args.text,
});
return response.data[0].embedding;
},
});
export const indexDocument = internalAction({
args: { content: v.string(), source: v.string(), title: v.optional(v.string()) },
returns: v.id("documents"),
handler: async (ctx, args) => {
const embedding = await ctx.runAction(internal.search.embed, {
text: args.content,
});
return await ctx.runMutation(internal.search.saveDocument, {
content: args.content,
embedding,
metadata: { source: args.source, title: args.title },
});
},
});
export const saveDocument = internalMutation({
args: {
content: v.string(),
embedding: v.array(v.float64()),
metadata: v.object({ source: v.string(), title: v.optional(v.string()) }),
},
returns: v.id("documents"),
handler: async (ctx, args) => {
return await ctx.db.insert("documents", args);
},
});
export const search = internalAction({
args: { query: v.string(), limit: v.optional(v.number()) },
returns: v.array(v.object({
content: v.string(),
score: v.number(),
})),
handler: async (ctx, args) => {
const queryEmbedding = await ctx.runAction(internal.search.embed, {
text: args.query,
});
const results = await ctx.runQuery(internal.search.vectorSearch, {
embedding: queryEmbedding,
limit: args.limit ?? 5,
});
return results;
},
});
export const vectorSearch = internalQuery({
args: { embedding: v.array(v.float64()), limit: v.number() },
returns: v.array(v.object({ content: v.string(), score: v.number() })),
handler: async (ctx, args) => {
const results = await ctx.db
.query("documents")
.withSearchIndex("by_embedding", (q) =>
q.vector(args.embedding).limit(args.limit)
);
return results.map((r) => ({
content: r.content,
score: r._score,
}));
},
});
RAG Chat
export const ragChat = internalAction({
args: { threadId: v.id("threads"), userMessage: v.string() },
returns: v.null(),
handler: async (ctx, args) => {
// Search for relevant documents
const relevantDocs = await ctx.runAction(internal.search.search, {
query: args.userMessage,
limit: 3,
});
const context = relevantDocs.map((d) => d.content).join("\n\n");
// Build system prompt with context
const systemMessage = `You are a helpful assistant. Use the following context to answer questions:
${context}
If the context doesn't contain relevant information, say so.`;
// Continue with chat completion...
},
});
Common Pitfalls
- API keys in client - Always use actions with
"use node"for API calls - Long conversations - Implement context windowing or summarization
- Missing error handling - Handle API rate limits and failures
- No streaming fallback - Have non-streaming backup for reliability
- Unbounded context - Limit message history sent to LLM
References
- Actions: https://docs.convex.dev/functions/actions
- Vector Search: https://docs.convex.dev/search/vector-search
- Environment Variables: https://docs.convex.dev/production/environment-variables
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