A component for semantic search, usually used to look up context for LLMs. Use with an Agent for Retrieval-Augmented Generation (RAG).
- Add Content: Add or replace content with text chunks and embeddings.
- Semantic Search: Vector-based search using configurable embedding models
- Namespaces: Organize content into namespaces for per-user search.
- Custom Filtering: Filter content with custom indexed fields.
- Importance Weighting: Weight content by providing a 0 to 1 "importance".
- Chunk Context: Get surrounding chunks for better context.
- Graceful Migrations: Migrate content or whole namespaces without disruption.
Found a bug? Feature request? File it here.
You'll need an existing Convex project to use the component. Convex is a hosted backend platform, including a database, serverless functions, and a ton more you can learn about here.
Run npm create convex or follow any of the quickstarts to set one up.
Install the component package:
npm install @convex-dev/memoryCreate a convex.config.ts file in your app's convex/ folder and install the component by calling use:
// convex/convex.config.ts
import { defineApp } from "convex/server";
import memory from "@convex-dev/memory/convex.config";
const app = defineApp();
app.use(memory);
export default app;// convex/example.ts
import { components } from "./_generated/api";
import { Memory } from "@convex-dev/memory";
// Any AI SDK model that supports embeddings will work.
import { openai } from "@ai-sdk/openai";
const memory = new Memory<FilterTypes>(components.memory, {
filterNames: ["category", "contentType", "categoryAndType"],
textEmbeddingModel: openai.embedding("text-embedding-3-small"),
embeddingDimension: 1536,
});
// Optional: Add type safety to your filters.
type FilterTypes = {
category: string;
contentType: string;
categoryAndType: { category: string; contentType: string };
};Add content with text chunks. It will embed the chunks automatically if you don't provide them.
export const add = action({
args: {
url: v.string(),
category: v.string(),
},
handler: async (ctx, args) => {
const { url, category } = args;
const response = await fetch(url);
const content = await response.text();
const chunks = await textSplitter.splitText(content);
const contentType = response.headers.get("content-type");
const { entryId } = await memory.add(ctx, {
namespace: "global", // namespace can be any string
key: url,
chunks,
filterValues: [
{ name: "category", value: category },
{ name: "contentType", value: contentType },
// To get an AND filter, use a filter with a more complex value.
{ name: "categoryAndType", value: { category, contentType } },
],
});
return { entryId };
},
});Note: The textSplitter here could be LangChain, Mastra, or otherwise.
See below for more details.
Upload files directly to a Convex action, httpAction, or upload url. See the docs for more details.
export const uploadFile = action({
args: {
filename: v.string(),
contentType: v.string(),
bytes: v.bytes(),
category: v.string(),
},
handler: async (ctx, { filename, contentType, bytes, category }) => {
const userId = await getUserId(ctx);
if (!userId) throw new Error("Unauthorized");
// Extract and chunk text content
const textContent = new TextDecoder().decode(bytes);
const chunks = await textSplitter.splitText(textContent);
const { entryId } = await memory.add(ctx, {
namespace: userId, // per-user namespace
key: filename,
title: filename,
chunks,
filterValues: [
{ name: "category", value: category },
{ name: "contentType", value: contentType },
{ name: "categoryAndType", value: { category, contentType } },
],
});
// Store file in Convex storage
const storageId = await ctx.storage.store(
new Blob([bytes], { type: contentType })
);
// You could then associate it with the key and entryId in your own table,
// for your own bookkeeping.
return { entryId, url: await ctx.storage.getUrl(storageId) };
},
});Search across content with vector similarity
textis a string with the full content of the results, for convenience. It is in order of the entries, with titles at each entry boundary, and separators between non-sequential chunks. See below for more details.resultsis an array of matching chunks with scores and more metadata.entriesis an array of the entries that matched the query. Each result has aentryIdreferencing one of these source entries.
export const search = action({
args: {
query: v.string(),
},
handler: async (ctx, args) => {
const { results, text, entries } = await memory.search(ctx, {
namespace: "global",
query: args.query,
limit: 10
vectorScoreThreshold: 0.5, // Only return results with a score >= 0.5
});
return { results, text, entries };
},
});Search with metadata filters:
export const searchByCategory = action({
args: {
query: v.string(),
category: v.string(),
},
handler: async (ctx, args) => {
const userId = await getUserId(ctx);
if (!userId) throw new Error("Unauthorized");
const results = await memory.search(ctx, {
namespace: userId,
query: args.query,
filters: [{ name: "category", value: args.category }],
limit: 10,
});
return results;
},
});Instead of getting just the single matching chunk, you can request surrounding chunks so there's more context to the result.
Note: If there are results that have overlapping ranges, it will not return duplicate chunks, but instead give priority to adding the "before" context to each chunk. For example if you requested 2 before and 1 after, and your results were for the same entryId indexes 1, 4, and 7, the results would be:
[
// Only one before chunk available, and leaves chunk2 for the next result.
{ order: 1, content: [chunk0, chunk1], startOrder: 0, ... },
// 2 before chunks available, but leaves chunk5 for the next result.
{ order: 4, content: [chunk2, chunk3, chunk4], startOrder: 2, ... },
// 2 before chunks available, and includes one after chunk.
{ order: 7, content: [chunk5, chunk6, chunk7, chunk8], startOrder: 5, ... },
]export const searchWithContext = action({
args: {
query: v.string(),
userId: v.string(),
},
handler: async (ctx, args) => {
const { results, text, entries } = await memory.search(ctx, {
namespace: args.userId,
query: args.query,
chunkContext: { before: 2, after: 1 }, // Include 2 chunks before, 1 after
limit: 5,
});
return { results, text, entries };
},
});Formatting the results for use in a prompt depends a bit on the use case. By default, the results will be sorted by score, not necessarily in the order they appear in the original text. You may want to sort them by the order they appear in the original text so they follow the flow of the original document.
For convenienct, the text field of the search results is a string formatted
with ... separating non-sequential chunks, --- separating entries, and
# Title: at each entry boundary (if titles are available).
# Title 1:
Chunk 1 contents
Chunk 2 contents
...
Chunk 8 contents
Chunk 9 contents
---
# Title 2:
Chunk 4 contents
Chunk 5 contentsThere is also a text field on each entry that is the full text of the entry,
similarly formatted with ... separating non-sequential chunks, if you want
to format each entry differently.
For a fully custom format, you can use the results field and entries directly:
const { results, text, entries } = await memory.search(ctx, {
namespace: args.userId,
query: args.query,
chunkContext: { before: 2, after: 1 }, // Include 2 chunks before, 1 after
limit: 5,
vectorScoreThreshold: 0.5, // Only return results with a score >= 0.5
});
// Get results in the order of the entries (highest score first)
const contexts = entries.map((e) => {
const ranges = results
.filter((r) => r.entryId === e.entryId)
.sort((a, b) => a.startOrder - b.startOrder);
let text = (e.title ?? "") + ":\n\n";
let previousEnd = 0;
for (const range of ranges) {
if (range.startOrder !== previousEnd) {
text += "\n...\n";
}
text += range.content.map((c) => c.text).join("\n");
previousEnd = range.startOrder + range.content.length;
}
return {
...e,
entryId: e.entryId as EntryId,
filterValues: e.filterValues as EntryFilterValues<FitlerSchemas>[],
text,
};
}).map((e) => (e.title ? `# ${e.title}:\n${e.text}` : e.text));
await generateText({
model: openai.chat("gpt-4o-mini"),
prompt: "Use the following context:\n\n" + contexts.join("\n---\n") +
"\n\n---\n\n Based on the context, answer the question:\n\n" + args.query,
});Delete an entry:
export const delete = mutation({
args: { entryId: vEntry },
handler: async (ctx, args) => {
await memory.delete(ctx, {
entryId: args.entryId,
});
},
});For large files, use async processing:
export const chunkerAction = memory.defineChunkerAction(
async (ctx, args) => {
// Custom chunking logic for large files
// This can be an async iterator if you can't fit it all in memory at once.
const chunks = await processLargeFile(args.key);
return { chunks };
}
);
export const uploadLargeFile = action({
args: {
filename: v.string(),
url: v.string(),
},
handler: async (ctx, args) => {
const userId = await getUserId(ctx);
if (!userId) throw new Error("Unauthorized");
const { entryId } = await memory.addAsync(ctx, {
namespace: userId,
key: args.filename,
source: { kind: "url", url: args.url },
chunkerAction: internal.example.chunkerAction,
});
return { entryId };
},
});See more example usage in example.ts.
Run the example with npm i && npm run example.