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component for RAG: chunk and embed documents, then run semantic search to build LLM context.","maintainers":[{"email":"james@convex.dev","name":"cowling"},{"email":"jordan@convex.dev","name":"jordanhunt"},{"email":"gautam@convex.dev","name":"gautamg795"},{"email":"nipunn1313@gmail.com","name":"nipunn"},{"email":"aritrakh@gmail.com","name":"aritrakh"},{"email":"ian@convex.dev","name":"ianatconvex"},{"email":"christian@convex.dev","name":"dowski-convex"},{"email":"reece@convex.dev","name":"reece-convex"},{"email":"timdoug@convex.dev","name":"timdoug-convex"},{"email":"ayush@convex.dev","name":"ayushdewanconvex"},{"email":"shawn@erquh.art","name":"erquhart"},{"email":"seth@convex.dev","name":"sethconvex"},{"email":"andy@convex.dev","name":"andycai-convex"},{"email":"jamwt@convex.dev","name":"jamwt"},{"email":"nicolas@ettlin.dev","name":"nicolapps"}],"readme":"# Convex RAG Component\n\n[![npm version](https://badge.fury.io/js/@convex-dev%2Frag.svg)](https://badge.fury.io/js/@convex-dev%2Frag)\n\n<!-- START: Include on https://convex.dev/components -->\n\nA component for semantic search, usually used to look up context for LLMs. Use\nwith an Agent for Retrieval-Augmented Generation (RAG).\n\n[![Use AI to search HUGE amounts of text with the RAG Component](https://thumbs.video-to-markdown.com/1ff18153.jpg)](https://youtu.be/dGmtAmdAaFs)\n\n## ✨ Key Features\n\n- **Add Content**: Add or replace content with text chunks and embeddings.\n- **Semantic Search**: Vector-based search using configurable embedding models\n- **Namespaces**: Organize content into namespaces for per-user search.\n- **Custom Filtering**: Filter content with custom indexed fields.\n- **Importance Weighting**: Weight content by providing a 0 to 1 \"importance\".\n- **Chunk Context**: Get surrounding chunks for better context.\n- **Graceful Migrations**: Migrate content or whole namespaces without\n  disruption.\n\nFound a bug? Feature request?\n[File it here](https://github.com/get-convex/rag/issues).\n\n## Installation\n\n```sh\nnpm install @convex-dev/rag ai\n```\n\nThe [AI SDK](https://ai-sdk.dev) (`ai@^7`) is a peer dependency, so your app and\nthe component always share one copy.\n\nCreate a `convex.config.ts` file in your app's `convex/` folder and install the\ncomponent by calling `use`:\n\n```ts\n// convex/convex.config.ts\nimport { defineApp } from \"convex/server\";\nimport rag from \"@convex-dev/rag/convex.config.js\";\n\nconst app = defineApp();\napp.use(rag);\n\nexport default app;\n```\n\nRun `npx convex codegen` if `npx convex dev` isn't already running.\n\n## Basic Setup\n\n```ts\n// convex/example.ts\nimport { components } from \"./_generated/api\";\nimport { RAG } from \"@convex-dev/rag\";\n// Any AI SDK model that supports embeddings will work.\nimport { openai } from \"@ai-sdk/openai\";\n\nconst rag = new RAG(components.rag, {\n  textEmbeddingModel: openai.embedding(\"text-embedding-3-small\"),\n  embeddingDimension: 1536, // Needs to match your embedding model\n});\n```\n\n## Add context to RAG\n\nAdd content with text chunks. Each call to `add` will create a new **entry**. It\nwill embed the chunks automatically if you don't provide them.\n\n```ts\nexport const add = action({\n  args: { text: v.string() },\n  handler: async (ctx, { text }) => {\n    // Add the text to a namespace shared by all users.\n    await rag.add(ctx, {\n      namespace: \"all-users\",\n      text,\n    });\n  },\n});\n```\n\nSee below for how to chunk the text yourself or add content asynchronously, e.g.\nto handle large files.\n\n## Semantic Search\n\nSearch across content with vector similarity\n\n- `text` is a string with the full content of the results, for convenience. It\n  is in order of the entries, with titles at each entry boundary, and separators\n  between non-sequential chunks. See below for more details.\n- `results` is an array of matching chunks with scores and more metadata.\n- `entries` is an array of the entries that matched the query. Each result has a\n  `entryId` referencing one of these source entries.\n- `usage` contains embedding token usage information. Will be `{ tokens: 0 }` if\n  no embedding was performed (e.g. when passing pre-computed embeddings).\n\n```ts\nexport const search = action({\n  args: {\n    query: v.string(),\n  },\n  handler: async (ctx, args) => {\n    const { results, text, entries, usage } = await rag.search(ctx, {\n      namespace: \"global\",\n      query: args.query,\n      limit: 10,\n      vectorScoreThreshold: 0.5, // Only return results with a score >= 0.5\n    });\n\n    return { results, text, entries, usage };\n  },\n});\n```\n\n## Generate a response based on RAG context\n\nOnce you have searched for the context, you can use it with an LLM.\n\nGenerally you'll already be using something to make LLM requests, e.g. the\n[Agent Component](https://www.convex.dev/components/agent), which tracks the\nmessage history for you. See the\n[Agent Component docs](https://docs.convex.dev/agents) for more details on doing\nRAG with the Agent Component.\n\nHowever, if you just want a one-off response, you can use the `generateText`\nfunction as a convenience.\n\nThis will automatically search for relevant entries and use them as context for\nthe LLM, using default formatting.\n\nThe arguments to `generateText` are compatible with all arguments to\n`generateText` from the AI SDK.\n\n```ts\nexport const askQuestion = action({\n  args: {\n    prompt: v.string(),\n  },\n  handler: async (ctx, args) => {\n    const userId = await getAuthUserId(ctx);\n    const { text, context } = await rag.generateText(ctx, {\n      search: { namespace: userId, limit: 10 },\n      prompt: args.prompt,\n      model: openai.chat(\"gpt-4o-mini\"),\n    });\n    return { answer: text, context };\n  },\n```\n\nNote: You can specify any of the search options available on `rag.search`.\n\n## Filtered Search\n\nYou can provide filters when adding content and use them to search. To do this,\nyou'll need to give the RAG component a list of the filter names. You can\noptionally provide a type parameter for type safety (no runtime validation).\n\nNote: these filters can be OR'd together when searching. In order to get an AND,\nyou provide a filter with a more complex value, such as `categoryAndType` below.\n\n```ts\n// convex/example.ts\nimport { components } from \"./_generated/api\";\nimport { RAG } from \"@convex-dev/rag\";\n// Any AI SDK model that supports embeddings will work.\nimport { openai } from \"@ai-sdk/openai\";\n\n// Optional: Add type safety to your filters.\ntype FilterTypes = {\n  category: string;\n  contentType: string;\n  categoryAndType: { category: string; contentType: string };\n};\n\nconst rag = new RAG<FilterTypes>(components.rag, {\n  textEmbeddingModel: openai.embedding(\"text-embedding-3-small\"),\n  embeddingDimension: 1536, // Needs to match your embedding model\n  filterNames: [\"category\", \"contentType\", \"categoryAndType\"],\n});\n```\n\nAdding content with filters:\n\n```ts\nawait rag.add(ctx, {\n  namespace: \"global\",\n  text,\n  filterValues: [\n    { name: \"category\", value: \"news\" },\n    { name: \"contentType\", value: \"article\" },\n    {\n      name: \"categoryAndType\",\n      value: { category: \"news\", contentType: \"article\" },\n    },\n  ],\n});\n```\n\nSearch with metadata filters:\n\n```ts\nexport const searchForNewsOrSports = action({\n  args: {\n    query: v.string(),\n  },\n  handler: async (ctx, args) => {\n    const userId = await getUserId(ctx);\n    if (!userId) throw new Error(\"Unauthorized\");\n\n    const results = await rag.search(ctx, {\n      namespace: userId,\n      query: args.query,\n      filters: [\n        { name: \"category\", value: \"news\" },\n        { name: \"category\", value: \"sports\" },\n      ],\n      limit: 10,\n    });\n\n    return results;\n  },\n});\n```\n\n### Add surrounding chunks to results for context\n\nInstead of getting just the single matching chunk, you can request surrounding\nchunks so there's more context to the result.\n\nNote: If there are results that have overlapping ranges, it will not return\nduplicate chunks, but instead give priority to adding the \"before\" context to\neach chunk. For example if you requested 2 before and 1 after, and your results\nwere for the same entryId indexes 1, 4, and 7, the results would be:\n\n```ts\n[\n  // Only one before chunk available, and leaves chunk2 for the next result.\n  { order: 1, content: [chunk0, chunk1], startOrder: 0, ... },\n  // 2 before chunks available, but leaves chunk5 for the next result.\n  { order: 4, content: [chunk2, chunk3, chunk4], startOrder: 2, ... },\n  // 2 before chunks available, and includes one after chunk.\n  { order: 7, content: [chunk5, chunk6, chunk7, chunk8], startOrder: 5, ... },\n]\n```\n\n```ts\nexport const searchWithContext = action({\n  args: {\n    query: v.string(),\n    userId: v.string(),\n  },\n  handler: async (ctx, args) => {\n    const { results, text, entries, usage } = await rag.search(ctx, {\n      namespace: args.userId,\n      query: args.query,\n      chunkContext: { before: 2, after: 1 }, // Include 2 chunks before, 1 after\n      limit: 5,\n    });\n\n    return { results, text, entries, usage };\n  },\n});\n```\n\n<!-- END: Include on https://convex.dev/components -->\n\n## Formatting results\n\nFormatting the results for use in a prompt depends a bit on the use case. By\ndefault, the results will be sorted by score, not necessarily in the order they\nappear in the original text. You may want to sort them by the order they appear\nin the original text so they follow the flow of the original document.\n\nFor convenience, the `text` field of the search results is a string formatted\nwith `...` separating non-sequential chunks, `---` separating entries, and\n`# Title:` at each entry boundary (if titles are available).\n\n```ts\nconst { text } = await rag.search(ctx, { ... });\nconsole.log(text);\n```\n\n```txt\n## Title 1:\nChunk 1 contents\nChunk 2 contents\n\n...\n\nChunk 8 contents\nChunk 9 contents\n\n---\n\n## Title 2:\nChunk 4 contents\nChunk 5 contents\n```\n\nThere is also a `text` field on each entry that is the full text of the entry,\nsimilarly formatted with `...` separating non-sequential chunks, if you want to\nformat each entry differently.\n\nFor a fully custom format, you can use the `results` field and entries directly:\n\n```ts\nconst { results, text, entries } = await rag.search(ctx, {\n  namespace: args.userId,\n  query: args.query,\n  chunkContext: { before: 2, after: 1 }, // Include 2 chunks before, 1 after\n  limit: 5,\n  vectorScoreThreshold: 0.5, // Only return results with a score >= 0.5\n});\n\n// Get results in the order of the entries (highest score first)\nconst contexts = entries\n  .map((e) => {\n    const ranges = results\n      .filter((r) => r.entryId === e.entryId)\n      .sort((a, b) => a.startOrder - b.startOrder);\n    let text = (e.title ?? \"\") + \":\\n\\n\";\n    let previousEnd = 0;\n    for (const range of ranges) {\n      if (range.startOrder !== previousEnd) {\n        text += \"\\n...\\n\";\n      }\n      text += range.content.map((c) => c.text).join(\"\\n\");\n      previousEnd = range.startOrder + range.content.length;\n    }\n    return {\n      ...e,\n      entryId: e.entryId as EntryId,\n      filterValues: e.filterValues as EntryFilterValues<FitlerSchemas>[],\n      text,\n    };\n  })\n  .map((e) => (e.title ? `# ${e.title}:\\n${e.text}` : e.text));\n\nawait generateText({\n  model: openai.chat(\"gpt-4o-mini\"),\n  prompt:\n    \"Use the following context:\\n\\n\" +\n    contexts.join(\"\\n---\\n\") +\n    \"\\n\\n---\\n\\n Based on the context, answer the question:\\n\\n\" +\n    args.query,\n});\n```\n\n## Using keys to gracefully replace content\n\nWhen you add content to a namespace, you can provide a `key` to uniquely\nidentify the content. If you add content with the same key, it will make a new\nentry to replace the old one.\n\n```ts\nawait rag.add(ctx, { namespace: userId, key: \"my-file.txt\", text });\n```\n\nWhen a new document is added, it will start with a status of \"pending\" while it\nchunks, embeds, and inserts the data into the database. Once all data is\ninserted, it will iterate over the chunks and swap the old content embeddings\nwith the new ones, and then update the status to \"ready\", marking the previous\nversion as \"replaced\".\n\nThe old content is kept around by default, so in-flight searches will get\nresults for old vector search results. See below for more details on deleting.\n\nThis means that if searches are happening while the document is being added,\nthey will see the old content results This is useful if you want to add content\nto a namespace and then immediately search for it, or if you want to add content\nto a namespace and then immediately add more content to the same namespace.\n\n## Using your own content splitter\n\nBy default, the component uses the `defaultChunker` to split the content into\nchunks. You can pass in your own content chunks to the `add` or `addAsync`\nfunctions.\n\n```ts\nconst chunks = await textSplitter.split(content);\nawait rag.add(ctx, { namespace: \"global\", chunks });\n```\n\nNote: The `textSplitter` here could be LangChain, Mastra, or something custom.\nThe simplest version makes an array of strings like `content.split(\"\\n\")`.\n\nNote: you can pass in an async iterator instead of an array to handle large\ncontent. Or use the `addAsync` function (see below).\n\n## Providing custom embeddings per-chunk\n\nIn addition to the text, you can provide your own embeddings for each chunk.\n\nThis can be beneficial if you want to embed something other than the chunk\ncontents, e.g. a summary of each chunk.\n\n```ts\nconst chunks = await textSplitter.split(content);\nconst chunksWithEmbeddings = await Promise.all(\n  chunks.map(async (chunk) => {\n    return {\n      ...chunk,\n      embedding: await embedSummary(chunk),\n    };\n  }),\n);\nawait rag.add(ctx, { namespace: \"global\", chunks });\n```\n\n## Add Entries Asynchronously using File Storage\n\nFor large files, you can upload them to file storage, then provide a chunker\naction to split them into chunks.\n\nIn `convex/http.ts`:\n\n```ts\nimport { corsRouter } from \"convex-helpers/server/cors\";\nimport { httpRouter } from \"convex/server\";\nimport { internal } from \"./_generated/api.js\";\nimport { DataModel } from \"./_generated/dataModel.js\";\nimport { httpAction } from \"./_generated/server.js\";\nimport { rag } from \"./example.js\";\n\nconst cors = corsRouter(httpRouter());\n\ncors.route({\n  path: \"/upload\",\n  method: \"POST\",\n  handler: httpAction(async (ctx, request) => {\n    const storageId = await ctx.storage.store(await request.blob());\n    await rag.addAsync(ctx, {\n      namespace: \"all-files\",\n      chunkerAction: internal.http.chunkerAction,\n      onComplete: internal.foo.docComplete, // See next section\n      metadata: { storageId },\n    });\n    return new Response();\n  }),\n});\n\nexport const chunkerAction = rag.defineChunkerAction(async (ctx, args) => {\n  const storageId = args.entry.metadata!.storageId;\n  const file = await ctx.storage.get(storageId);\n  const text = await new TextDecoder().decode(await file!.arrayBuffer());\n  return { chunks: text.split(\"\\n\\n\") };\n});\n\nexport default cors.http;\n```\n\nYou can upload files directly to a Convex action, httpAction, or upload url. See\nthe [docs](https://docs.convex.dev/file-storage/upload-files) for details.\n\n### OnComplete Handling\n\nYou can register an `onComplete` handler when adding content that will be called\nwhen the entry was created and is ready, or if there was an error or it was\nreplaced before it finished.\n\n```ts\n// in an action\nawait rag.add(ctx, { namespace, text, onComplete: internal.foo.docComplete });\n\n// in convex/foo.ts\nexport const docComplete = rag.defineOnComplete<DataModel>(\n  async (ctx, { replacedEntry, entry, namespace, error }) => {\n    if (error) {\n      await rag.delete(ctx, { entryId: entry.entryId });\n      return;\n    }\n    if (replacedEntry) {\n      await rag.delete(ctx, { entryId: replacedEntry.entryId });\n    }\n    // You can associate the entry with your own data here. This will commit\n    // in the same transaction as the entry becoming ready.\n  },\n);\n```\n\nNote: The `onComplete` callback is only triggered when new content is processed.\nIf you add content that already exists (`contentHash` did not change for the\nsame `key`), `onComplete` will not be called. To handle this case, you can check\nthe return value of `rag.add()`:\n\n```ts\nconst { status, created } = await rag.add(ctx, {\n  namespace,\n  text,\n  key: \"my-key\",\n  contentHash: \"...\",\n  onComplete: internal.foo.docComplete,\n});\n\nif (status === \"ready\" && !created) {\n  // Entry already existed - onComplete will not be called\n  // Handle this case if needed\n}\n```\n\n### Add Entries with filters from a URL\n\nHere's a simple example fetching content from a URL to add.\n\nIt also adds filters to the entry, so you can search for it later by category,\ncontentType, or both.\n\n```ts\nexport const add = action({\n  args: { url: v.string(), category: v.string() },\n  handler: async (ctx, { url, category }) => {\n    const response = await fetch(url);\n    const content = await response.text();\n    const contentType = response.headers.get(\"content-type\");\n\n    const { entryId } = await rag.add(ctx, {\n      namespace: \"global\", // namespace can be any string\n      key: url,\n      chunks: content.split(\"\\n\\n\"),\n      filterValues: [\n        { name: \"category\", value: category },\n        { name: \"contentType\", value: contentType },\n        // To get an AND filter, use a filter with a more complex value.\n        { name: \"categoryAndType\", value: { category, contentType } },\n      ],\n    });\n\n    return { entryId };\n  },\n});\n```\n\n### Lifecycle Management\n\nYou can delete the old content by calling `rag.delete` with the entryId of the\nold version.\n\nGenerally you'd do this:\n\n1. When using `rag.add` with a key returns a `replacedEntry`.\n1. When your `onComplete` handler provides a non-null `replacedEntry` argument.\n1. Periodically by querying:\n\n```ts\n// in convex/crons.ts\nimport { cronJobs } from \"convex/server\";\nimport { internal } from \"./_generated/api.js\";\nimport { internalMutation } from \"./_generated/server.js\";\nimport { v } from \"convex/values\";\nimport { rag } from \"./example.js\";\nimport { assert } from \"convex-helpers\";\n\nconst WEEK = 7 * 24 * 60 * 60 * 1000;\n\nexport const deleteOldContent = internalMutation({\n  args: { cursor: v.optional(v.string()) },\n  handler: async (ctx, args) => {\n    const toDelete = await rag.list(ctx, {\n      status: \"replaced\",\n      paginationOpts: { cursor: args.cursor ?? null, numItems: 100 },\n    });\n\n    for (const entry of toDelete.page) {\n      assert(entry.status === \"replaced\");\n      if (entry.replacedAt >= Date.now() - WEEK) {\n        return; // we're done when we catch up to a week ago\n      }\n      await rag.delete(ctx, { entryId: entry.entryId });\n    }\n    if (!toDelete.isDone) {\n      await ctx.scheduler.runAfter(0, internal.example.deleteOldContent, {\n        cursor: toDelete.continueCursor,\n      });\n    }\n  },\n});\n\n// See example/convex/crons.ts for a complete example.\nconst crons = cronJobs();\ncrons.interval(\n  \"deleteOldContent\",\n  { hours: 1 },\n  internal.crons.deleteOldContent,\n  {},\n);\nexport default crons;\n```\n\n## Working with types\n\nYou can use the provided types to validate and store data.\n`import { ... } from \"@convex-dev/rag\";`\n\nTypes for the various elements:\n\n`Entry`, `EntryFilter`, `SearchEntry`, `SearchResult`\n\n- `SearchEntry` is an `Entry` with a `text` field including the combined search\n  results for that entry, whereas a `SearchResult` is a specific chunk result,\n  along with surrounding chunks.\n\n`EntryId`, `NamespaceId`\n\n- While the `EntryId` and `NamespaceId` are strings under the hood, they are\n  given more specific types to make it easier to use them correctly.\n\nValidators can be used in `args` and schema table definitions: `vEntry`,\n`vEntryId`, `vNamespaceId`, `vSearchEntry`, `vSearchResult`\n\ne.g. `defineTable({ myDocTitle: v.string(), entryId: vEntryId })`\n\nThe validators for the branded IDs will only validate they are strings, but will\nhave the more specific types, to provide type safety.\n\n## Utility Functions\n\nIn addition to the function on the `rag` instance, there are other utilities\nprovided:\n\n### `defaultChunker`\n\nThis is the default chunker used by the `add` and `addAsync` functions.\n\nIt is customizable, but by default:\n\n- It tries to break up the text into paragraphs between 100-1k characters.\n- It will combine paragraphs to meet the minimum character count (100).\n- It will break up paragraphs into separate lines to keep it under 1k.\n- It will not split up a single line unless it's longer than 10k characters.\n\n```ts\nimport { defaultChunker } from \"@convex-dev/rag\";\n\nconst chunks = defaultChunker(text, {\n  // these are the defaults\n  minLines: 1,\n  minCharsSoftLimit: 100,\n  maxCharsSoftLimit: 1000,\n  maxCharsHardLimit: 10000,\n  delimiter: \"\\n\\n\",\n});\n```\n\n### `hybridRank`\n\nThis is an implementation of \"Reciprocal Rank Fusion\" for ranking search results\nbased on multiple scoring arrays. The premise is that if both arrays of results\nare sorted by score, the best results show up near the top of both arrays and\nshould be preferred over results higher in one but much lower in the other.\n\n```ts\nimport { hybridRank } from \"@convex-dev/rag\";\n\nconst textSearchResults = [id1, id2, id3];\nconst vectorSearchResults = [id2, id3, id1];\nconst results = hybridRank([textSearchResults, vectorSearchResults]);\n// results = [id2, id1, id3]\n```\n\nIt can take more than two arrays, and you can provide weights for each array.\n\n```ts\nconst recentSearchResults = [id5, id4, id3];\nconst results = hybridRank(\n  [textSearchResults, vectorSearchResults, recentSearchResults],\n  {\n    weights: [2, 1, 3], // prefer recent results more than text or vector\n  },\n);\n// results = [ id3, id5, id1, id2, id4 ]\n```\n\nTo have it more biased towards the top few results, you can set the `k` value to\na lower number (10 by default).\n\n```ts\nconst results = hybridRank(\n  [textSearchResults, vectorSearchResults, recentSearchResults],\n  { k: 1 },\n);\n// results = [ id5, id1, id3, id2, id4 ]\n```\n\n### `contentHashFromArrayBuffer`\n\nThis generates the hash of a file's contents, which can be used to avoid adding\nthe same file twice.\n\nNote: doing `blob.arrayBuffer()` will consume the blob's data, so you'll need to\nmake a new blob to use it after calling this function.\n\n```ts\nimport { contentHashFromArrayBuffer } from \"@convex-dev/rag\";\n\nexport const addFile = action({\n  args: { bytes: v.bytes() },\n  handler: async (ctx, { bytes }) => {\n    const hash = await contentHashFromArrayBuffer(bytes);\n\n    const existing = await rag.findEntryByContentHash(ctx, {\n      namespace: \"global\",\n      key: \"my-file.txt\",\n      contentHash: hash,\n    });\n    if (existing) {\n      console.log(\"File contents are the same, skipping\");\n      return;\n    }\n    const blob = new Blob([bytes], { type: \"text/plain\" });\n    //...\n  },\n});\n```\n\n### `guessMimeTypeFromExtension`\n\nThis guesses the mime type of a file from its extension.\n\n```ts\nimport { guessMimeTypeFromExtension } from \"@convex-dev/rag\";\n\nconst mimeType = guessMimeTypeFromExtension(\"my-file.mjs\");\nconsole.log(mimeType); // \"text/javascript\"\n```\n\n### `guessMimeTypeFromContents`\n\nThis guesses the mime type of a file from the first few bytes of its contents.\n\n```ts\nimport { guessMimeTypeFromContents } from \"@convex-dev/rag\";\n\nconst mimeType = guessMimeTypeFromContents(await file.arrayBuffer());\n```\n\n### Example Usage\n\nSee more example usage in [example.ts](./example/convex/example.ts).\n\n### Running the example\n\nRun the example with `npm run setup && npm run dev`.\n","readmeFilename":"README.md"}