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(components.rag, {
textEmbeddingModel: openai.embedding("text-embedding-3-small"),
embeddingDimension: 1536, // Needs to match your embedding model
});Add content with text chunks. Each call to add will create a new entry.
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",
text,
});
},
});See below for how to chunk the text yourself or add content asynchronously, e.g. to handle large files.
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 };
},
});Once you have searched for the context, you can use it with an LLM.
Generally you'll already be using something to make LLM requests, e.g. the Agent Component, which tracks the message history for you. See the Agent Component docs for more details on doing RAG with the Agent Component.
However, if you just want a one-off response, you can use the generateText
function as a convenience.
This will automatically search for relevant entries and use them as context for the LLM, using default formatting.
The arguments to generateText are compatible with all arguments to
generateText from the AI SDK.
export const askQuestion = action({
args: {
prompt: v.string(),
},
handler: async (ctx, args) => {
const userId = await getAuthUserId(ctx);
const { text, context } = await rag.generateText(ctx, {
search: { namespace: userId, limit: 10 },
prompt: args.prompt,
model: openai.chat("gpt-4o-mini"),
});
return { answer: text, context };
},Note: You can specify any of the search options available on rag.search.
You can provide filters when adding content and use them to search. To do this, you'll need to give the RAG component a list of the filter names. You can optionally provide a type parameter for type safety (no runtime validation).
Note: these filters can be OR'd together when searching. In order to get an AND,
you provide a filter with a more complex value, such as categoryAndType below.
// 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";
// Optional: Add type safety to your filters.
type FilterTypes = {
category: string;
contentType: string;
categoryAndType: { category: string; contentType: string };
};
const rag = new RAG<FilterTypes>(components.rag, {
textEmbeddingModel: openai.embedding("text-embedding-3-small"),
embeddingDimension: 1536, // Needs to match your embedding model
filterNames: ["category", "contentType", "categoryAndType"],
});Adding content with filters:
await rag.add(ctx, {
namespace: "global",
text,
filterValues: [
{ name: "category", value: "news" },
{ name: "contentType", value: "article" },
{ name: "categoryAndType", value: { category: "news", contentType: "article" } },
],
});Search with metadata filters:
export const searchForNewsOrSports = action({
args: {
query: 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: "news" },
{ name: "category", value: "sports" },
],
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 convenience, 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,
});When you add content to a namespace, you can provide a key to uniquely identify the content.
If you add content with the same key, it will make a new entry to replace the old one.
await rag.add(ctx, { namespace: userId, key: "my-file.txt", text });When a new document is added, it will start with a status of "pending" while it chunks, embeds, and inserts the data into the database. Once all data is inserted, it will iterate over the chunks and swap the old content embeddings with the new ones, and then update the status to "ready", marking the previous version as "replaced".
The old content is kept around by default, so in-flight searches will get results for old vector search results. See below for more details on deleting.
This means that if searches are happening while the document is being added, they will see the old content results This is useful if you want to add content to a namespace and then immediately search for it, or if you want to add content to a namespace and then immediately add more content to the same namespace.
By default, the component uses the defaultChunker to split the content into chunks.
You can pass in your own content chunks to the add or addAsync functions.
const chunks = await textSplitter.split(content);
await rag.add(ctx, { namespace: "global", chunks });Note: The textSplitter here could be LangChain, Mastra, or something custom.
The simplest version makes an array of strings like content.split("\n").
Note: you can pass in an async iterator instead of an array to handle large content.
Or use the addAsync function (see below).
In addition to the text, you can provide your own embeddings for each chunk.
This can be beneficial if you want to embed something other than the chunk contents, e.g. a summary of each chunk.
const chunks = await textSplitter.split(content);
const chunksWithEmbeddings = await Promise.all(chunks.map(async chunk => {
return {
...chunk,
embedding: await embedSummary(chunk)
}
}));
await rag.add(ctx, { namespace: "global", chunks });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.foo.docComplete, // See next section
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 default cors.http;You can upload files directly to a Convex action, httpAction, or upload url. See the docs for details.
You can register an onComplete handler when adding content that will be called
when the entry is ready, or if there was an error or it was replaced before it
finished.
// in an action
await rag.add(ctx, { namespace, text, onComplete: internal.foo.docComplete });
// in convex/foo.ts
export const docComplete = rag.defineOnComplete<DataModel>(
async (ctx, { replacedEntry, entry, namespace, error }) => {
if (error) {
await rag.delete(ctx, { entryId: entry.entryId });
return;
}
if (replacedEntry) {
await rag.delete(ctx, { entryId: replacedEntry.entryId });
}
// You can associate the entry with your own data here. This will commit
// in the same transaction as the entry becoming ready.
}
);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 };
},
});You can delete the old content by calling rag.delete with the entryId of the
old version.
Generally you'd do this:
- When using
rag.addwith a key returns areplacedEntry. - When your
onCompletehandler provides a non-nullreplacedEntryargument. - Periodically by querying:
// in convex/crons.ts
import { cronJobs } from "convex/server";
import { internal } from "./_generated/api.js";
import { internalMutation } from "./_generated/server.js";
import { v } from "convex/values";
import { rag } from "./example.js";
import { assert } from "convex-helpers";
const WEEK = 7 * 24 * 60 * 60 * 1000;
export const deleteOldContent = internalMutation({
args: { cursor: v.optional(v.string()) },
handler: async (ctx, args) => {
const toDelete = await rag.list(ctx, {
status: "replaced",
paginationOpts: { cursor: args.cursor ?? null, numItems: 100 },
});
for (const entry of toDelete.page) {
assert(entry.status === "replaced");
if (entry.replacedAt >= Date.now() - WEEK) {
return; // we're done when we catch up to a week ago
}
await rag.delete(ctx, { entryId: entry.entryId });
}
if (!toDelete.isDone) {
await ctx.scheduler.runAfter(0, internal.example.deleteOldContent, {
cursor: toDelete.continueCursor,
});
}
},
});
// See example/convex/crons.ts for a complete example.
const crons = cronJobs();
crons.interval("deleteOldContent", { hours: 1 }, internal.crons.deleteOldContent, {});
export default crons;You can use the provided types to validate and store data.
import { ... } from "@convex-dev/rag";
Types for the various elements:
Entry, EntryFilter, SearchEntry, SearchResult
SearchEntryis anEntrywith atextfield including the combined search results for that entry, whereas aSearchResultis a specific chunk result, along with surrounding chunks.
EntryId, NamespaceId
- While the
EntryIdandNamespaceIdare strings under the hood, they are given more specific types to make it easier to use them correctly.
Validators can be used in args and schema table definitions:
vEntry, vEntryId, vNamespaceId, vSearchEntry, vSearchResult
e.g. defineTable({ myDocTitle: v.string(), entryId: vEntryId })
The validators for the branded IDs will only validate they are strings, but will have the more specific types, to provide type safety.
In addition to the function on the rag instance, there are other utilities
provided:
This is the default chunker used by the add and addAsync functions.
It is customizable, but by default:
- It tries to break up the text into paragraphs between 100-1k characters.
- It will combine paragraphs to meet the minimum character count (100).
- It will break up paragraphs into separate lines to keep it under 1k.
- It will not split up a single line unless it's longer than 10k characters.
import { defaultChunker } from "@convex-dev/rag";
const chunks = defaultChunker(text, {
// these are the defaults
minLines: 1,
minCharsSoftLimit: 100,
maxCharsSoftLimit: 1000,
maxCharsHardLimit: 10000,
delimiter: "\n\n",
});This is an implementation of "Reciprocal Rank Fusion" for ranking search results based on multiple scoring arrays. The premise is that if both arrays of results are sorted by score, the best results show up near the top of both arrays and should be preferred over results higher in one but much lower in the other.
import { hybridRank } from "@convex-dev/rag";
const textSearchResults = [id1, id2, id3];
const vectorSearchResults = [id2, id3, id1];
const results = hybridRank([
textSearchResults,
vectorSearchResults,
]);
// results = [id2, id1, id3]It can take more than two arrays, and you can provide weights for each array.
const recentSearchResults = [id5, id4, id3];
const results = hybridRank([
textSearchResults,
vectorSearchResults,
recentSearchResults,
], {
weights: [2, 1, 3], // prefer recent results more than text or vector
});
// results = [ id3, id5, id1, id2, id4 ]To have it more biased towards the top few results, you can set the k value
to a lower number (10 by default).
const results = hybridRank([
textSearchResults,
vectorSearchResults,
recentSearchResults,
], { k: 1 });
// results = [ id5, id1, id3, id2, id4 ]This generates the hash of a file's contents, which can be used to avoid adding the same file twice.
Note: doing blob.arrayBuffer() will consume the blob's data, so you'll need
to make a new blob to use it after calling this function.
import { contentHashFromArrayBuffer } from "@convex-dev/rag";
export const addFile = action({
args: { bytes: v.bytes() },
handler: async (ctx, { bytes }) => {
const hash = await contentHashFromArrayBuffer(bytes);
const existing = await rag.findEntryByContentHash(ctx, {
namespace: "global",
key: "my-file.txt",
contentHash: hash,
});
if (existing) {
console.log("File contents are the same, skipping");
return;
}
const blob = new Blob([bytes], { type: "text/plain" });
//...
},
});This guesses the mime type of a file from its extension.
import { guessMimeTypeFromExtension } from "@convex-dev/rag";
const mimeType = guessMimeTypeFromExtension("my-file.mjs");
console.log(mimeType); // "text/javascript"This guesses the mime type of a file from the first few bytes of its contents.
import { guessMimeTypeFromContents } from "@convex-dev/rag";
const mimeType = guessMimeTypeFromContents(await file.arrayBuffer());See more example usage in example.ts.
Run the example with npm i && npm run setup && npm run example.