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/ragCreate 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 rag from "@convex-dev/rag/convex.config";
const app = defineApp();
app.use(rag);
export default app;// convex/example.ts
import { components } from "./_generated/api";
import { RAG } from "@convex-dev/rag";
// Any AI SDK model that supports embeddings will work.
import { openai } from "@ai-sdk/openai";
const rag = new RAG<FilterTypes>(components.rag, {
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: { text: v.string() },
handler: async (ctx, { text }) => {
// Add the text to a namespace shared by all users.
await rag.add(ctx, {
namespace: "all-users",
chunks: text.split("\n\n"),
});
},
});Here's a simple example fetching content from a URL to add.
It also adds filters to the entry, so you can search for it later by category, contentType, or both.
export const add = action({
args: { url: v.string(), category: v.string() },
handler: async (ctx, { url, category }) => {
const response = await fetch(url);
const content = await response.text();
const contentType = response.headers.get("content-type");
const { entryId } = await rag.add(ctx, {
namespace: "global", // namespace can be any string
key: url,
chunks: content.split("\n\n"),
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.
For large files, you can upload them to file storage, then provide a chunker action to split them into chunks.
In convex/http.ts:
import { corsRouter } from "convex-helpers/server/cors";
import { httpRouter } from "convex/server";
import { internal } from "./_generated/api.js";
import { DataModel } from "./_generated/dataModel.js";
import { httpAction } from "./_generated/server.js";
import { rag } from "./example.js";
const cors = corsRouter(httpRouter());
cors.route({
path: "/upload",
method: "POST",
handler: httpAction(async (ctx, request) => {
const storageId = await ctx.storage.store(await request.blob());
await rag.addAsync(ctx, {
namespace: "all-files",
chunkerAction: internal.http.chunkerAction,
onComplete: internal.http.handleEntryComplete,
metadata: { storageId },
});
return new Response();
}),
});
export const chunkerAction = rag.defineChunkerAction(async (ctx, args) => {
const storageId = args.entry.metadata!.storageId;
const file = await ctx.storage.get(storageId);
const text = await new TextDecoder().decode(await file!.arrayBuffer());
return { chunks: text.split("\n\n") };
});
export const handleEntryComplete = rag.defineOnComplete<DataModel>(
async (ctx, { replacedEntry, entry, namespace, error }) => {
if (error) {
await rag.delete(ctx, { entryId: entry.entryId });
return;
}
// You can associate the entry with your own data here. This will commit
// in the same transaction as the entry becoming ready.
}
);
export default cors.http;You can upload files directly to a Convex action, httpAction, or upload url. See the docs for details.
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 rag.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 rag.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 rag.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).
const { text } = await rag.search(ctx, { ... });
console.log(text);# 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 rag.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 rag.delete(ctx, {
entryId: args.entryId,
});
},
});See more example usage in example.ts.
Run the example with npm i && npm run example.