Compare commits

...

67 Commits

Author SHA1 Message Date
Marcus Schiesser 95a578d184 chore: remove cloud package 2025-07-31 12:34:16 +08:00
github-actions[bot] 12892f6dd9 Release 0.11.25 (#2144)
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
2025-07-31 12:21:22 +08:00
Marcus Schiesser d138d87663 chore: deprecate cloud packages (#2143) 2025-07-31 12:21:22 +08:00
github-actions[bot] e524b7b08f Release 0.11.24 (#2141)
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: marcusschiesser <17126+marcusschiesser@users.noreply.github.com>
2025-07-31 12:20:28 +08:00
Clelia (Astra) Bertelli bbecff27f9 chore: deprecate @llamaindex/cloud in favor of llama-cloud-services 2025-07-30 12:26:57 +02:00
Marcus Schiesser f9f1de9516 chore: use Logger for core (#2139) 2025-07-30 11:43:45 +08:00
Twisha Bansal f576812e7a docs: Using MCP Toolbox for Databases with LlamaIndex (#2138) 2025-07-30 11:19:34 +08:00
Adrian Lyjak c3bf3c7178 Adding support for page citations, and refactor the confidence into the field metadata (#2140) 2025-07-30 10:25:19 +08:00
github-actions[bot] 38487da65d Release 0.11.23 (#2136)
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: marcusschiesser <17126+marcusschiesser@users.noreply.github.com>
2025-07-28 14:07:23 +08:00
Marcus Schiesser f29799e385 feat: Add toolcall callbacks to agent workflows (#2137) 2025-07-24 15:37:14 +08:00
Marcus Schiesser 9bca30620b fix: docs build 2025-07-23 12:55:35 +08:00
Marcus Schiesser 7224c06409 feat: Add logger and callbacks to llm.exec (#2135) 2025-07-23 12:37:02 +08:00
github-actions[bot] 29c7cf0989 Release 0.11.22 (#2131)
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
2025-07-23 11:30:04 +08:00
Marcus Schiesser c65a2dc4a7 chore: Deprecate community package and link to AWS package (#2134) 2025-07-23 11:05:50 +08:00
Terence Sim f1c5079290 docs: updated bedrock import and supported models (#2129)
Co-authored-by: Terence Sim <40583743+InTheAxis@users.noreply.github.com>
2025-07-23 10:40:49 +08:00
Terence Sim 9ed31958a7 chore: add logger as param to AgentWorkflow constructor (#2130)
Co-authored-by: Terence Sim <40583743+InTheAxis@users.noreply.github.com>
Co-authored-by: Marcus Schiesser <marcus.schiesser@googlemail.com>
2025-07-22 16:35:28 +08:00
github-actions[bot] e4c7113614 Release 0.11.21 (#2128)
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: marcusschiesser <17126+marcusschiesser@users.noreply.github.com>
2025-07-22 12:23:58 +08:00
Thuc Pham 38da40bc98 feat: VectoryMemoryBlock (#2110)
Co-authored-by: Marcus Schiesser <mail@marcusschiesser.de>
2025-07-22 12:18:09 +08:00
Marcus Schiesser 4d50ca4d84 chore: add streamchat test (#2122) 2025-07-22 11:30:01 +08:00
github-actions[bot] 8b5253a297 Release (#2127) 2025-07-21 15:40:31 -06:00
Logan ea15e75c89 deployment docs nits (#2126) 2025-07-21 15:30:37 -06:00
github-actions[bot] 3be87d4670 Release 0.11.20 (#2121)
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: himself65 <14026360+himself65@users.noreply.github.com>
2025-07-21 09:37:44 -07:00
Terence Sim 94da13db0d fix: azure openai streamchat empty delta throw TypeError (#2118)
Co-authored-by: Terence Sim <40583743+InTheAxis@users.noreply.github.com>
2025-07-21 09:16:09 -07:00
Terence Sim acd50ea99f chore: replaced console.log with logger type from @llamaindex/env (#2123)
Co-authored-by: Terence Sim <40583743+InTheAxis@users.noreply.github.com>
2025-07-21 09:14:06 -07:00
Adrian Lyjak 2967d57ac0 feat: default to _public agent data (#2117) 2025-07-21 09:07:15 -07:00
Thuc Pham a8ec08c682 fix: ensure correct message content in agent workflow (#2114)
Co-authored-by: Marcus Schiesser <mail@marcusschiesser.de>
2025-07-21 15:13:27 +08:00
Terence Sim 678b327051 feat: added apac bedrock models (#2119)
Co-authored-by: Marcus Schiesser <mail@marcusschiesser.de>
2025-07-21 12:13:37 +08:00
Jeremy B. Merrill 650eeb1df3 fix: GeminiEmbedding should send batches of max 100 (#2099)
Co-authored-by: Marcus Schiesser <marcus.schiesser@googlemail.com>
2025-07-21 12:12:42 +08:00
Laurie Voss 50f6747758 Instrumenting with Google Tag Manager (in addition to Google Analytics) (#2116) 2025-07-20 13:18:09 -07:00
github-actions[bot] 12414a6836 Release (#2113)
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: marcusschiesser <17126+marcusschiesser@users.noreply.github.com>
2025-07-18 13:54:38 +08:00
Marcus Schiesser 856dd8cca8 fix: assume new models are function call models (#2112) 2025-07-18 12:52:43 +08:00
Jerry Cheng d8f4f6a859 Update SupabaseVectorStore.ts to fix score calculating error (#2109)
Co-authored-by: Marcus Schiesser <marcus.schiesser@googlemail.com>
2025-07-18 12:48:47 +08:00
Logan f594d7034f revamp getting started flow and main index page (#2079)
Co-authored-by: Thuc Pham <51660321+thucpn@users.noreply.github.com>
Co-authored-by: Marcus Schiesser <mail@marcusschiesser.de>
Co-authored-by: thucpn <thucsh2@gmail.com>
2025-07-17 16:27:28 +08:00
github-actions[bot] c1c58feed2 Release 0.11.19 (#2105)
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: marcusschiesser <17126+marcusschiesser@users.noreply.github.com>
2025-07-17 15:44:22 +08:00
Marcus Schiesser 7ad3411766 feat: add llm.exec (#2078) 2025-07-17 15:36:56 +08:00
Neha Prasad a1fdb07b96 feat: multi-turn image generation support (#2106)
Co-authored-by: Marcus Schiesser <marcus.schiesser@googlemail.com>
2025-07-17 10:30:39 +08:00
Jeremy B. Merrill 5da5b3c89c feat: add progress callback to embeddings (#2098)
Co-authored-by: Marcus Schiesser <mail@marcusschiesser.de>
2025-07-16 13:49:49 +08:00
r3rer3 ddc0eafbaa feat(anthropic): stream partial tool calls (#2100) 2025-07-15 10:06:17 -07:00
github-actions[bot] 1782554488 Release 0.11.18 (#2103)
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
2025-07-14 15:53:20 -07:00
Adrian Lyjak a1b1598bc6 fix(cloud): add generic types into agent data responses (#2102)
Co-authored-by: Alex Yang <himself65@outlook.com>
2025-07-14 12:01:56 -07:00
Terry Zhao b02847ae91 fix(notion): resolve @notionhq/client dependency conflict (#2097) 2025-07-12 11:04:06 -07:00
Alex Yang 50acb4821e feat(cloud): use camelCase (#2096) 2025-07-12 10:59:46 -07:00
github-actions[bot] 47a5b94b0c Release 0.11.17 (#2095)
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
2025-07-11 21:57:02 -07:00
Alex Yang d2be868b93 feat(cloud): missing agent api (#2094) 2025-07-11 20:45:22 -07:00
github-actions[bot] 50d42c4129 Release 0.11.16 (#2093)
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
2025-07-11 20:13:37 -07:00
github-actions[bot] 848b97d4d0 Release 0.11.16 (#2092)
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
2025-07-11 18:19:17 -07:00
Alex Yang c5796b8d2d fix: only allow pnpm (#2091) 2025-07-11 18:17:47 -07:00
Alex Yang 579ca0cf60 chore: bump sdk version (#2090) 2025-07-11 18:10:15 -07:00
Alex Yang f7e670c8d9 fix: sdk type improvement (#2089) 2025-07-11 17:56:41 -07:00
Alex Yang 9ff971435c fix(cloud): agent sdk (#2088) 2025-07-11 17:41:25 -07:00
github-actions[bot] 7c9d0e24c4 Release (#2086)
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: marcusschiesser <17126+marcusschiesser@users.noreply.github.com>
2025-07-11 12:30:04 -07:00
NIEDASEN af3f86694b feat: add supportToolCall getter to DeepSeekLLM class (#2085)
Co-authored-by: Marcus Schiesser <marcus.schiesser@googlemail.com>
2025-07-11 16:11:22 +08:00
github-actions[bot] 5cce681f62 Release 0.11.15 (#2084)
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
2025-07-10 19:08:05 -07:00
Alex Yang 48b0d88941 chore: bump dev deps (#2082) 2025-07-10 19:00:37 -07:00
Alex Yang f18577263a fix(cloud): missing file (#2083) 2025-07-10 18:33:41 -07:00
github-actions[bot] 214e133e92 Release 0.11.14 (#2068)
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: himself65 <14026360+himself65@users.noreply.github.com>
2025-07-10 17:10:02 -07:00
Alex Yang ae58862669 fix: missing agent entry (#2081) 2025-07-10 11:39:07 -07:00
Alex Yang 5a0ed1f990 feat: init agent api on cloud sdk (#2069) 2025-07-10 10:00:53 -07:00
Logan 36773a82b6 fix examples scripts (#2077) 2025-07-09 11:24:07 +08:00
Logan 891562d598 remove workspace from examples package.json (#2075) 2025-07-08 16:36:33 -07:00
Alex Yang 93852e15fd chore: bump zod (#2074) 2025-07-08 13:58:52 -07:00
Clelia (Astra) Bertelli e1320b08a8 fix: adding more details in the contribution guidelines about changesets (#2073) 2025-07-08 13:58:36 -07:00
Logan 8eeac3310f fix memory factory (#2066) 2025-07-08 10:01:19 +07:00
Logan 984a573068 docs: update contributing instructions (#2067) 2025-07-07 16:38:26 -07:00
github-actions[bot] f0160d9646 Release (#2065)
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
2025-07-07 12:15:33 -06:00
Logan 39758ab018 add title to root layout (#2064) 2025-07-07 12:06:13 -06:00
dependabot[bot] f631d4f7d6 chore(deps): bump next from 15.3.0 to 15.3.3 (#2063) 2025-07-07 12:40:42 +07:00
266 changed files with 12965 additions and 51794 deletions
+54 -1
View File
@@ -38,6 +38,7 @@ npm install -g pnpm
```shell
pnpm install
pnpm install -g tsx
```
### Build the packages
@@ -48,6 +49,56 @@ To build all packages, run:
pnpm build
```
### Start Developing
You can launch the package in dev-mode by running:
```shell
pnpm dev
```
This will use turbo to run all packages in watch-mode. This means you can make changes and have them automatically built.
If you want to customize what packages are built/watched, you can run turbo directly and adjust the filter:
```shell
pnpm turbo run dev --filter="./packages/core" --concurrency=100
```
In another terminal, you can write and run any script needed to quickly test your changes. For example:
```typescript
import { createMemory, staticBlock } from "@llamaindex/core/memory";
// Create memory with predefined context
const memory = createMemory({
memoryBlocks: [
staticBlock({
content:
"The user is a software engineer who loves TypeScript and LlamaIndex.",
messageRole: "system",
}),
],
});
async function main() {
const result = await memory.getLLM();
console.log(result);
}
void main().catch(console.error);
```
And run it with:
```shell
pnpm exec tsx my_script.ts
```
This flow allows you to easily test your changes without having to build the entire project.
Once you are happy with your changes, be sure to add tests (and confirm existing tests are passing!).
### Run tests
#### Unit tests
@@ -92,7 +143,7 @@ Before sending a PR, make sure of the following:
3. If you have a new feature, add a new example in the `examples` folder.
4. You have a descriptive changeset for each PR:
### Changesets
### Bumping the versions of packages you've modified
We use [changesets](https://github.com/changesets/changesets) for managing versions and changelogs. To create a new
changeset, run in the root folder:
@@ -101,6 +152,8 @@ changeset, run in the root folder:
pnpm changeset
```
You will be prompted to choose what packages need their versions bumped, and what kind of bump (major, minor or patch) is needed. Once you carry out this operation, the bumping will be automatic after the PR is merged.
## Publishing (maintainers only)
The [Release Github Action](.github/workflows/release.yml) is automatically generating and updating a
+156
View File
@@ -1,5 +1,161 @@
# @llamaindex/doc
## 0.2.48
### Patch Changes
- Updated dependencies [049471b]
- Updated dependencies [049471b]
- @llamaindex/cloud@4.1.0
- llamaindex@0.11.25
## 0.2.47
### Patch Changes
- Updated dependencies [c3bf3c7]
- Updated dependencies [f9f1de9]
- @llamaindex/cloud@4.0.28
- @llamaindex/core@0.6.19
- llamaindex@0.11.24
- @llamaindex/node-parser@2.0.19
- @llamaindex/openai@0.4.14
- @llamaindex/readers@3.1.18
- @llamaindex/workflow@1.1.20
## 0.2.46
### Patch Changes
- Updated dependencies [f29799e]
- Updated dependencies [7224c06]
- @llamaindex/workflow@1.1.19
- @llamaindex/core@0.6.18
- llamaindex@0.11.23
- @llamaindex/cloud@4.0.27
- @llamaindex/node-parser@2.0.18
- @llamaindex/openai@0.4.13
- @llamaindex/readers@3.1.17
## 0.2.45
### Patch Changes
- Updated dependencies [9ed3195]
- @llamaindex/workflow@1.1.18
- llamaindex@0.11.22
## 0.2.44
### Patch Changes
- 38da40b: feat: VectoryMemoryBlock
- Updated dependencies [38da40b]
- @llamaindex/core@0.6.17
- @llamaindex/cloud@4.0.26
- llamaindex@0.11.21
- @llamaindex/node-parser@2.0.17
- @llamaindex/openai@0.4.12
- @llamaindex/readers@3.1.16
- @llamaindex/workflow@1.1.17
## 0.2.43
### Patch Changes
- ea15e75: Minor updates in deployment docs
## 0.2.42
### Patch Changes
- a8ec08c: fix: ensure correct message content in agent workflow
- Updated dependencies [a8ec08c]
- Updated dependencies [2967d57]
- @llamaindex/core@0.6.16
- @llamaindex/workflow@1.1.16
- @llamaindex/cloud@4.0.25
- llamaindex@0.11.20
- @llamaindex/node-parser@2.0.16
- @llamaindex/openai@0.4.11
- @llamaindex/readers@3.1.15
## 0.2.41
### Patch Changes
- Updated dependencies [856dd8c]
- @llamaindex/openai@0.4.10
## 0.2.40
### Patch Changes
- Updated dependencies [7ad3411]
- Updated dependencies [5da5b3c]
- Updated dependencies [a1fdb07]
- @llamaindex/core@0.6.15
- @llamaindex/workflow@1.1.15
- @llamaindex/openai@0.4.9
- @llamaindex/cloud@4.0.24
- llamaindex@0.11.19
- @llamaindex/node-parser@2.0.15
- @llamaindex/readers@3.1.14
## 0.2.39
### Patch Changes
- Updated dependencies [a1b1598]
- @llamaindex/cloud@4.0.23
- llamaindex@0.11.18
## 0.2.38
### Patch Changes
- Updated dependencies [d2be868]
- @llamaindex/cloud@4.0.22
- llamaindex@0.11.17
## 0.2.37
### Patch Changes
- Updated dependencies [579ca0c]
- @llamaindex/cloud@4.0.21
- llamaindex@0.11.16
## 0.2.36
### Patch Changes
- Updated dependencies [48b0d88]
- Updated dependencies [f185772]
- @llamaindex/cloud@4.0.20
- llamaindex@0.11.15
## 0.2.35
### Patch Changes
- Updated dependencies [5a0ed1f]
- Updated dependencies [5a0ed1f]
- Updated dependencies [8eeac33]
- @llamaindex/cloud@4.0.19
- @llamaindex/core@0.6.14
- llamaindex@0.11.14
- @llamaindex/node-parser@2.0.14
- @llamaindex/openai@0.4.8
- @llamaindex/readers@3.1.13
- @llamaindex/workflow@1.1.14
## 0.2.34
### Patch Changes
- 39758ab: Add title to homepage header
## 0.2.33
### Patch Changes
+27
View File
@@ -27,6 +27,33 @@ const config = {
destination: "/docs/workflows/:path*",
permanent: true,
},
{
source: "/docs/llamaindex/getting_started/installation/node.mdx",
destination:
"/docs/llamaindex/getting_started/installation/server-apis.mdx",
permanent: true,
},
{
source: "/docs/llamaindex/getting_started/installation/typescript.mdx",
destination: "/docs/llamaindex/getting_started/installation/index.mdx",
permanent: true,
},
{
source: "/docs/llamaindex/getting_started/installation/next.mdx",
destination: "/docs/llamaindex/getting_started/installation/nextjs.mdx",
permanent: true,
},
{
source: "/docs/llamaindex/getting_started/installation/vite.mdx",
destination: "/docs/llamaindex/getting_started/installation/index.mdx",
permanent: true,
},
{
source: "/docs/llamaindex/getting_started/installation/cloudflare.mdx",
destination:
"/docs/llamaindex/getting_started/installation/serverless.mdx",
permanent: true,
},
];
},
turbopack: {
+11 -12
View File
@@ -1,6 +1,6 @@
{
"name": "@llamaindex/doc",
"version": "0.2.33",
"version": "0.2.48",
"private": true,
"scripts": {
"postinstall": "fumadocs-mdx",
@@ -16,7 +16,6 @@
"@huggingface/transformers": "^3.5.0",
"@icons-pack/react-simple-icons": "^10.1.0",
"@llamaindex/chat-ui-docs": "^0.0.5",
"@llamaindex/cloud": "workspace:*",
"@llamaindex/core": "workspace:*",
"@llamaindex/node-parser": "workspace:*",
"@llamaindex/openai": "workspace:*",
@@ -35,7 +34,7 @@
"@radix-ui/react-tooltip": "^1.1.4",
"@scalar/api-client-react": "^1.1.25",
"@vercel/functions": "^1.5.0",
"ai": "^3.4.33",
"ai": "^4.3.17",
"class-variance-authority": "^0.7.0",
"clsx": "2.1.1",
"foxact": "^0.2.41",
@@ -50,7 +49,7 @@
"hast-util-to-jsx-runtime": "^2.3.2",
"llamaindex": "workspace:*",
"lucide-react": "^0.460.0",
"next": "^15.3.0",
"next": "^15.3.3",
"next-themes": "^0.4.3",
"react": "^19.1.0",
"react-dom": "^19.1.0",
@@ -70,30 +69,30 @@
"twoslash": "^0.3.1",
"use-stick-to-bottom": "^1.0.42",
"web-tree-sitter": "^0.24.4",
"zod": "^3.25.67"
"zod": "^3.25.76"
},
"devDependencies": {
"@next/env": "^15.3.0",
"@tailwindcss/postcss": "^4.0.9",
"@types/mdx": "^2.0.13",
"@types/node": "22.9.0",
"@types/react": "^19.0.10",
"@types/react-dom": "^19.0.4",
"@types/node": "24.0.13",
"@types/react": "^19.1.8",
"@types/react-dom": "^19.1.6",
"autoprefixer": "^10.4.20",
"cross-env": "^7.0.3",
"fast-glob": "^3.3.2",
"gray-matter": "^4.0.3",
"postcss": "^8.5.3",
"postcss": "^8.5.6",
"raw-loader": "^4.0.2",
"remark": "^15.0.1",
"remark-gfm": "^4.0.0",
"remark-mdx": "^3.1.0",
"remark-stringify": "^11.0.0",
"tailwindcss": "^4.0.9",
"tsx": "^4.19.3",
"tailwindcss": "^4.1.11",
"tsx": "^4.20.3",
"typedoc": "0.28.3",
"typedoc-plugin-markdown": "^4.6.2",
"typedoc-plugin-merge-modules": " ^7.0.0",
"typescript": "^5.7.3"
"typescript": "^5.8.3"
}
}
Binary file not shown.

Before

Width:  |  Height:  |  Size: 540 KiB

After

Width:  |  Height:  |  Size: 206 KiB

Binary file not shown.

After

Width:  |  Height:  |  Size: 154 KiB

+1 -1
View File
@@ -1,4 +1,4 @@
import { MockLLM } from "@llamaindex/core/utils";
import { MockLLM } from "@llamaindex/core/llms/mock";
import { LlamaIndexAdapter, type Message } from "ai";
import { Settings, SimpleChatEngine, type ChatMessage } from "llamaindex";
import { NextResponse, type NextRequest } from "next/server";
+5 -1
View File
@@ -1,6 +1,6 @@
import { AIProvider } from "@/actions";
import { TooltipProvider } from "@/components/ui/tooltip";
import { GoogleAnalytics } from "@next/third-parties/google";
import { GoogleAnalytics, GoogleTagManager } from "@next/third-parties/google";
import { RootProvider } from "fumadocs-ui/provider";
import { Inter } from "next/font/google";
import type { ReactNode } from "react";
@@ -32,7 +32,11 @@ export default function Layout({ children }: { children: ReactNode }) {
sizes="16x16"
href="/favicon-16x16.png"
/>
<title>
LlamaIndex.TS - Build LLM-powered document agents and workflows
</title>
</head>
<GoogleTagManager gtmId="GTM-WWRFB36R" />
<body className="flex min-h-screen flex-col">
<TooltipProvider>
<AIProvider>
@@ -19,3 +19,8 @@ npm run dev
to start the development server. You can then visit [http://localhost:3000](http://localhost:3000) to see your app, which should look something like this:
![create-llama interface](/images/create_llama.png)
## Learn more
- [Learn more about `create-llama`](https://github.com/run-llama/create-llama)
- [Want to use the same UI components? You can use our React components](https://ui.llamaindex.ai/)
@@ -17,7 +17,8 @@ npm i
Then you can run any example in the folder with `tsx`, e.g.:
```bash npm2yarn
npx tsx ./vectorIndex.ts
export OPENAI_API_KEY=your-api-key
npx tsx ./agents/agent/openai.ts
```
## Try examples online
@@ -1,70 +0,0 @@
---
title: With Cloudflare Worker
description: In this guide, you'll learn how to use LlamaIndex with CloudFlare Worker
---
Before you start, make sure you have try LlamaIndex.TS in Node.js to make sure you understand the basics.
<Card
title="Getting Started with LlamaIndex.TS in Node.js"
href="/docs/llamaindex/getting_started/installation/node"
/>
Also, you need have the basic understanding of <a href='https://developers.cloudflare.com/workers/'><SiCloudflareworkers className="inline mr-2" color="#F38020" />Cloudflare Worker</a>.
## Adding environment variables
```ts
export default {
async fetch(request: Request, env: Env): Promise<Response> {
const { setEnvs } = await import("@llamaindex/env");
setEnvs(env);
const { OpenAIAgent } = await import("@llamaindex/openai");
// Start your code here
return new Response("Hello, world!");
},
};
```
Then, you need create `.dev.vars` and add LLM api keys for the local development, such as `OPENAI_API_KEY` for OpenAI API key.
<Callout type="warn">Do not commit the api key to git repository.</Callout>
## Integrating with Hono
```ts
import { Hono } from "hono";
type Bindings = {
OPENAI_API_KEY: string;
};
const app = new Hono<{
Bindings: Bindings;
}>();
app.post("/llm", async (c) => {
const { setEnvs } = await import("@llamaindex/env");
setEnvs(c.env);
// ...
return new Response('Hello, world!');
})
export default {
fetch: app.fetch,
};
```
## Difference between Node.js and Cloudflare Worker
In Cloudflare Worker and similar serverless JS environment, you need to be aware of the following differences:
- Some Node.js modules are not available in Cloudflare Worker, such as `node:fs`, `node:child_process`, `node:cluster`...
- You are recommend to design your code using network request, such as use `fetch` API to communicate with database, instead of a long-running process in Node.js.
- Some of LlamaIndex.TS packages are not available in Cloudflare Worker, for example `@llamaindex/readers` and `@llamaindex/huggingface`.
- The main `llamaindex` is designed to work in all JavaScript environment, including Cloudflare Worker. If you find any issue, please report to us.
- `@llamaindex/env` is a JS environment binding module, which polyfill some Node.js/Modern Web API (for example, we have a memory based `fs` module, and Crypto API polyfill). It is designed to work in all JavaScript environment, including Cloudflare Worker.
@@ -1,69 +1,177 @@
---
title: Installation
description: How to install llamaindex packages.
description: How to install and set up LlamaIndex.TS for your project.
---
To install llamaindex, run the following command:
## Quick Start
Install the core package:
```package-install
npm i llamaindex
```
In most cases, you'll also need an LLM package and the Workflow package to use LlamaIndex. For example, to use the OpenAI LLM with agents, you would install the following:
In most cases, you'll also need an LLM provider and the Workflow package:
```package-install
npm i @llamaindex/openai @llamaindex/workflow
```
Go to [LLM APIs](/docs/llamaindex/modules/models/llms) to find out how to use other LLMs.
## Environment Setup
### API Keys
## Frameworks
Most LLM providers require API keys. Set your OpenAI key (or other provider):
LlamaIndex supports a wide range of frameworks and runtimes. Click on the card below to learn more.
```bash
export OPENAI_API_KEY=your-api-key
```
Or use a `.env` file:
```bash
echo "OPENAI_API_KEY=your-api-key" > .env
```
<Callout type="warn">Never commit API keys to your repository.</Callout>
### Loading Environment Variables
For Node.js applications:
```bash
node --env-file .env your-script.js
```
For other environments, see the deployment-specific guides below.
## TypeScript Configuration
LlamaIndex.TS is built with TypeScript and provides excellent type safety. Add these settings to your `tsconfig.json`:
```json5
{
"compilerOptions": {
// Essential for module resolution
"moduleResolution": "bundler", // or "nodenext" | "node16" | "node"
// Required for Web Stream API support
"lib": ["DOM.AsyncIterable"],
// Recommended for better compatibility
"target": "es2020",
"module": "esnext"
}
}
```
## Running your first agent
### Set up
If you don't already have a project, you can create a new one in a new folder:
```package-install
npm init
npm i -D typescript @types/node
npm i @llamaindex/openai @llamaindex/workflow llamaindex zod
```
### Run the agent
Create the file `example.ts`. This code will:
- Create two tools for use by the agent:
- A `sumNumbers` tool that adds two numbers
- A `divideNumbers` tool that divides numbers
- Give an example of the data structure we wish to generate
- Prompt the LLM with instructions and the example, plus a sample transcript
<include cwd>../../examples/agents/agent/openai.ts</include>
To run the code:
```package-install
npx tsx example.ts
```
You should expect output something like:
```
{
result: '5 + 5 is 10. Then, 10 divided by 2 is 5.',
state: {
memory: Memory {
messages: [Array],
tokenLimit: 30000,
shortTermTokenLimitRatio: 0.7,
memoryBlocks: [],
memoryCursor: 0,
adapters: [Object]
},
scratchpad: [],
currentAgentName: 'Agent',
agents: [ 'Agent' ],
nextAgentName: null
}
}
Done
```
## Performance Optimization
### Tokenization Speed
Install `gpt-tokenizer` for 60x faster tokenization (Node.js environments only):
```package-install
npm i gpt-tokenizer
```
LlamaIndex will automatically use this when available.
## Deployment Guides
Choose your deployment target:
<Cards>
<Card title={
<>
<SiNodedotjs className="inline" color="#5FA04E" /> Node.js
</>
} href="/docs/llamaindex/getting_started/installation/node" />
<Card title={
<>
<SiTypescript className="inline" color="#3178C6" /> TypeScript
</>
} href="/docs/llamaindex/getting_started/installation/typescript" />
<Card title={
<>
<SiVite className='inline' color='#646CFF' /> Vite
</>
} href="/docs/llamaindex/getting_started/installation/vite" />
<Card
title={
<>
<SiNextdotjs className='inline' /> Next.js (React Server Component)
</>
}
href="/docs/llamaindex/getting_started/installation/next"
/>
<Card title={
<>
<SiCloudflareworkers className='inline' color='#F38020' /> Cloudflare Workers
</>
} href="/docs/llamaindex/getting_started/installation/cloudflare" />
<Card
title="Server APIs & Backends"
description="Express, Fastify, Koa, standalone Node.js servers"
href="/docs/llamaindex/getting_started/installation/server-apis"
/>
<Card
title="Serverless Functions"
description="Vercel, Netlify, AWS Lambda, Cloudflare Workers"
href="/docs/llamaindex/getting_started/installation/serverless"
/>
<Card
title="Next.js Applications"
description="API routes, server components, edge runtime"
href="/docs/llamaindex/getting_started/installation/nextjs"
/>
<Card
title="Troubleshooting"
description="Common issues, bundle optimization, compatibility"
href="/docs/llamaindex/getting_started/installation/troubleshooting"
/>
</Cards>
## What's next?
## LLM/Embedding Providers
Go to [LLM APIs](/docs/llamaindex/modules/models/llms) and [Embedding APIs](/docs/llamaindex/modules/models/embeddings) to find out how to use different LLM and embedding providers beyond OpenAI.
## What's Next?
<Cards>
<Card
title="Learn LlamaIndex.TS"
description="Learn how to use LlamaIndex.TS by starting with one of our tutorials."
href="/docs/llamaindex/tutorials/rag"
/>
<Card
title="Show me code examples"
description="Explore code examples using LlamaIndex.TS."
href="/docs/llamaindex/getting_started/examples"
/>
<Card
title="Learn LlamaIndex.TS"
description="Learn how to use LlamaIndex.TS by starting with one of our tutorials."
href="/docs/llamaindex/tutorials/basic_agent"
/>
<Card
title="Show me code examples"
description="Explore code examples using LlamaIndex.TS."
href="/docs/llamaindex/getting_started/examples"
/>
</Cards>
@@ -1,4 +1,4 @@
{
"title": "Installation",
"pages": ["node", "typescript", "next", "vite", "cloudflare"]
"pages": ["server-apis", "serverless", "nextjs", "troubleshooting"]
}
@@ -1,41 +0,0 @@
---
title: With Next.js
description: In this guide, you'll learn how to use LlamaIndex with Next.js.
---
Before you start, make sure you have try LlamaIndex.TS in Node.js to make sure you understand the basics.
<Card
title="Getting Started with LlamaIndex.TS in Node.js"
href="/docs/llamaindex/getting_started/installation/node"
/>
## Differences between Node.js and Next.js
Next.js is a React framework that has both server side compatibility and client side compatibility.
This means that you need to be careful when using LlamaIndex.TS in Next.js.
Don't leak the import data like API keys to the client side.
Also, in Next.js, there is build time and runtime. Some computations can be done at build time like Document embedding could be done at build time for better performance.
Where as the `llamaindex` package is working with Next.js, some provider packages like `@llamaindex/huggingface` are not working well with Next.js. This is due to the upstream dependencies used by the provider package.
Make sure to use `withLlamaIndex` to make sure that LlamaIndex.TS works well with Next.js.
```js
// next.config.mjs / next.config.ts
import withLlamaIndex from "llamaindex/next";
/** @type {import('next').NextConfig} */
const nextConfig = {};
export default withLlamaIndex(nextConfig);
```
If you see any dependency issues, you are welcome to open an issue on the GitHub.
## Edge Runtime
[Vercel Edge Runtime](https://edge-runtime.vercel.app/) is a subset of Node.js APIs. Similar to [Cloudflare Workers](/docs/llamaindex/getting_started/installation/cloudflare#difference-between-nodejs-and-cloudflare-worker),
it is a serverless platform that runs your code on the edge.
Not all features of Node.js are supported in Vercel Edge Runtime, so does LlamaIndex.TS, we are working on more compatibility with all JavaScript runtimes.
@@ -0,0 +1,405 @@
---
title: Next.js Applications
description: Deploy LlamaIndex.TS in Next.js applications with API routes, server components, and edge runtime.
---
This guide covers integrating LlamaIndex.TS agents with Next.js applications.
## Essential Configuration
### Next.js Config
Use `withLlamaIndex` to ensure compatibility:
```javascript
// next.config.mjs
import withLlamaIndex from "llamaindex/next";
/** @type {import('next').NextConfig} */
const nextConfig = {
// Your existing config
};
export default withLlamaIndex(nextConfig);
```
## API Routes
### App Router (Recommended)
```typescript
// app/api/chat/route.ts
import { agent } from "@llamaindex/workflow";
import { tool } from "llamaindex";
import { openai } from "@llamaindex/openai";
import { z } from "zod";
import { NextRequest, NextResponse } from "next/server";
// Initialize agent once (consider using a singleton pattern)
let myAgent: any = null;
async function initializeAgent() {
if (myAgent) return myAgent;
try {
const greetTool = tool({
name: "greet",
description: "Greets a user with their name",
parameters: z.object({
name: z.string(),
}),
execute: ({ name }) => `Hello, ${name}! How can I help you today?`,
});
myAgent = agent({
tools: [greetTool],
llm: openai({ model: "gpt-4o-mini" }),
});
return myAgent;
} catch (error) {
console.error("Failed to initialize agent:", error);
throw error;
}
}
export async function POST(request: NextRequest) {
try {
const { message } = await request.json();
if (!message || typeof message !== 'string') {
return NextResponse.json(
{ error: "Message is required and must be a string" },
{ status: 400 }
);
}
const agent = await initializeAgent();
const result = await agent.run(message);
return NextResponse.json({ response: result.data });
} catch (error) {
console.error("Chat error:", error);
return NextResponse.json(
{ error: "Internal server error" },
{ status: 500 }
);
}
}
```
### Pages Router (Legacy)
```typescript
// pages/api/chat.ts
import { agent } from "@llamaindex/workflow";
import { tool } from "llamaindex";
import { openai } from "@llamaindex/openai";
import { z } from "zod";
import type { NextApiRequest, NextApiResponse } from "next";
let myAgent: any = null;
async function initializeAgent() {
if (myAgent) return myAgent;
const timeTool = tool({
name: "getCurrentTime",
description: "Gets the current time",
parameters: z.object({}),
execute: () => new Date().toISOString(),
});
myAgent = agent({
tools: [timeTool],
llm: openai({ model: "gpt-4o-mini" }),
});
return myAgent;
}
export default async function handler(
req: NextApiRequest,
res: NextApiResponse
) {
if (req.method !== "POST") {
return res.status(405).json({ error: "Method not allowed" });
}
try {
const { message } = req.body;
const agent = await initializeAgent();
const result = await agent.run(message);
res.json({ response: result.data });
} catch (error) {
console.error("Chat error:", error);
res.status(500).json({ error: "Internal server error" });
}
}
```
## Server Components
Initialize agents in server components:
```typescript
// app/chat/page.tsx
import { agent } from "@llamaindex/workflow";
import { tool } from "llamaindex";
import { openai } from "@llamaindex/openai";
import { z } from "zod";
async function initializeAgent() {
const helpTool = tool({
name: "getHelp",
description: "Provides help information",
parameters: z.object({
topic: z.string().optional(),
}),
execute: ({ topic }) => {
if (topic) {
return `Here's help for ${topic}: This is a helpful resource about ${topic}.`;
}
return "Available topics: general, troubleshooting, api, deployment";
},
});
return agent({
tools: [helpTool],
llm: openai({ model: "gpt-4o-mini" }),
});
}
export default async function ChatPage() {
const chatAgent = await initializeAgent();
return (
<div>
<h1>Chat Interface</h1>
<p>Agent initialized and ready to help!</p>
{/* Your chat UI components */}
</div>
);
}
```
## Edge Runtime
The Edge Runtime has limited Node.js API access:
```typescript
// app/api/chat-edge/route.ts
import { NextRequest, NextResponse } from "next/server";
export const runtime = "edge";
export async function POST(request: NextRequest) {
const { setEnvs } = await import("@llamaindex/env");
setEnvs(process.env);
try {
const { message } = await request.json();
const { agent } = await import("@llamaindex/workflow");
const { tool } = await import("llamaindex");
const { openai } = await import("@llamaindex/openai");
const { z } = await import("zod");
const timeTool = tool({
name: "time",
description: "Gets current time",
parameters: z.object({}),
execute: () => new Date().toISOString(),
});
const myAgent = agent({
tools: [timeTool],
llm: openai({ model: "gpt-4o-mini" }),
});
const result = await myAgent.run(message);
return NextResponse.json({ response: result.data });
} catch (error) {
return NextResponse.json({ error: error.message }, { status: 500 });
}
}
```
## Streaming Responses
Implement streaming for better user experience:
```typescript
// app/api/chat-stream/route.ts
import { agent } from "@llamaindex/workflow";
import { tool } from "llamaindex";
import { openai } from "@llamaindex/openai";
import { agentStreamEvent } from "@llamaindex/workflow";
import { NextRequest } from "next/server";
import { z } from "zod";
// Initialize agent once (consider using a singleton pattern)
let myAgent: any = null;
async function initializeAgent() {
if (myAgent) return myAgent;
try {
const greetTool = tool({
name: "greet",
description: "Greets a user with their name",
parameters: z.object({
name: z.string(),
}),
execute: ({ name }) => `Hello, ${name}! How can I help you today?`,
});
myAgent = agent({
tools: [greetTool],
llm: openai({ model: "gpt-4o-mini" }),
});
return myAgent;
} catch (error) {
console.error("Failed to initialize agent:", error);
throw error;
}
}
export async function POST(request: NextRequest) {
const { message } = await request.json();
const stream = new ReadableStream({
async start(controller) {
try {
const agent = await initializeAgent();
const events = agent.runStream(message);
for await (const event of events) {
if (agentStreamEvent.include(event)) {
controller.enqueue(new TextEncoder().encode(event.data.delta));
}
}
controller.close();
} catch (error) {
controller.error(error);
}
},
});
return new Response(stream, {
headers: {
"Content-Type": "text/plain",
"Transfer-Encoding": "chunked",
},
});
}
```
## Client-side Integration
### React Hook for API Calls
```typescript
// hooks/useAgentChat.ts
import { useState } from "react";
export function useAgentChat() {
const [loading, setLoading] = useState(false);
const [error, setError] = useState<string | null>(null);
const [response, setResponse] = useState<string | null>(null);
const chat = async (message: string) => {
setLoading(true);
setError(null);
try {
const res = await fetch("/api/chat", {
method: "POST",
headers: { "Content-Type": "application/json" },
body: JSON.stringify({ message }),
});
if (!res.ok) {
throw new Error(`HTTP error! status: ${res.status}`);
}
const data = await res.json();
setResponse(data.response);
} catch (err) {
setError(err instanceof Error ? err.message : "An error occurred");
} finally {
setLoading(false);
}
};
return { chat, loading, error, response };
}
```
### Chat Component
```typescript
// components/ChatInterface.tsx
"use client";
import { useState } from "react";
import { useAgentChat } from "@/hooks/useAgentChat";
export default function ChatInterface() {
const [message, setMessage] = useState("");
const { chat, loading, error, response } = useAgentChat();
const handleSubmit = async (e: React.FormEvent) => {
e.preventDefault();
if (!message.trim()) return;
await chat(message);
setMessage("");
};
return (
<div className="max-w-2xl mx-auto p-4">
<form onSubmit={handleSubmit} className="mb-4">
<input
type="text"
value={message}
onChange={(e) => setMessage(e.target.value)}
placeholder="Send a message..."
className="w-full p-2 border rounded"
disabled={loading}
/>
<button
type="submit"
disabled={loading || !message.trim()}
className="mt-2 px-4 py-2 bg-blue-500 text-white rounded disabled:opacity-50"
>
{loading ? "Thinking..." : "Send"}
</button>
</form>
{error && (
<div className="p-3 mb-4 bg-red-100 border border-red-400 text-red-700 rounded">
Error: {error}
</div>
)}
{response && (
<div className="p-3 bg-gray-100 border rounded">
<strong>Agent:</strong>
<p>{response}</p>
</div>
)}
</div>
);
}
```
## Next Steps
- Learn about [serverless deployment](/docs/llamaindex/getting_started/installation/serverless)
- Explore [server APIs](/docs/llamaindex/getting_started/installation/server-apis)
- Check [troubleshooting guide](/docs/llamaindex/getting_started/installation/troubleshooting) for common issues
@@ -1,40 +0,0 @@
---
title: With Node.js/Bun/Deno
description: In this guide, you'll learn how to use LlamaIndex with Node.js, Bun, and Deno.
---
## Adding environment variables
By default, LlamaIndex uses OpenAI provider, which requires an API key. You can set the `OPENAI_API_KEY` environment variable to authenticate with OpenAI.
```shell
export OPENAI_API_KEY=your-api-key
```
Or you can use a `.env` file:
```shell
echo "OPENAI_API_KEY=your-api-key" > .env
node --env-file .env your-script.js
```
<Callout type="warn">Do not commit the api key to git repository.</Callout>
For more information, see the [How to read environment variables from Node.js](https://nodejs.org/en/learn/command-line/how-to-read-environment-variables-from-nodejs).
## Performance Optimization
By the default, we are using `js-tiktoken` for tokenization. You can install `gpt-tokenizer` which is then automatically used by LlamaIndex to get a 60x speedup for tokenization:
```package-install
npm i gpt-tokenizer
```
**Note**: This only works for Node.js
## TypeScript support
<Card
title="Getting Started with LlamaIndex.TS in TypeScript"
href="/docs/llamaindex/getting_started/installation/typescript"
/>
@@ -0,0 +1,211 @@
---
title: Server APIs & Backends
description: Deploy LlamaIndex.TS in server environments like Express, Fastify, and standalone Node.js applications.
---
This guide covers adding LlamaIndex.TS agents to traditional server environments where you have full Node.js runtime access.
## Supported Runtimes
LlamaIndex.TS works seamlessly with:
- **Node.js** (v18+)
- **Bun** (v1.0+)
- **Deno** (v1.30+)
## Common Server Frameworks
### Express.js
```typescript
import express from 'express';
import { agent } from '@llamaindex/workflow';
import { tool } from 'llamaindex';
import { openai } from '@llamaindex/openai';
import { z } from 'zod';
const app = express();
app.use(express.json());
// Initialize agent once at startup
let myAgent: any;
async function initializeAgent() {
// Create tools for the agent
const sumTool = tool({
name: "sum",
description: "Adds two numbers",
parameters: z.object({
a: z.number(),
b: z.number(),
}),
execute: ({ a, b }) => a + b,
});
const multiplyTool = tool({
name: "multiply",
description: "Multiplies two numbers",
parameters: z.object({
a: z.number(),
b: z.number(),
}),
execute: ({ a, b }) => a * b,
});
// Create the agent
myAgent = agent({
tools: [sumTool, multiplyTool],
llm: openai({ model: "gpt-4o-mini" }),
});
}
app.post('/api/chat', async (req, res) => {
try {
const { message } = req.body;
const result = await myAgent.run(message);
res.json({ response: result.data });
} catch (error) {
res.status(500).json({ error: 'Chat failed' });
}
});
// Initialize and start server
initializeAgent().then(() => {
app.listen(3000, () => {
console.log('Server running on port 3000');
});
});
```
### Fastify
```typescript
import Fastify from 'fastify';
import { agent } from '@llamaindex/workflow';
import { tool } from 'llamaindex';
import { openai } from '@llamaindex/openai';
import { z } from 'zod';
const fastify = Fastify();
let myAgent: any;
async function initializeAgent() {
const sumTool = tool({
name: "sum",
description: "Adds two numbers",
parameters: z.object({
a: z.number(),
b: z.number(),
}),
execute: ({ a, b }) => a + b,
});
myAgent = agent({
tools: [sumTool],
llm: openai({ model: "gpt-4o-mini" }),
});
}
fastify.post('/api/chat', async (request, reply) => {
try {
const { message } = request.body as { message: string };
const result = await myAgent.run(message);
return { response: result.data };
} catch (error) {
reply.status(500).send({ error: 'Chat failed' });
}
});
const start = async () => {
await initializeAgent();
await fastify.listen({ port: 3000 });
console.log('Server running on port 3000');
};
start();
```
### Hono
```typescript
import { Hono } from "hono";
import { agent } from "@llamaindex/workflow";
import { tool } from "llamaindex";
import { openai } from "@llamaindex/openai";
import { z } from "zod";
type Bindings = {
OPENAI_API_KEY: string;
};
const app = new Hono<{ Bindings: Bindings }>();
app.post("/api/chat", async (c) => {
const { setEnvs } = await import("@llamaindex/env");
setEnvs(c.env);
const { message } = await c.req.json();
const greetTool = tool({
name: "greet",
description: "Greets a user",
parameters: z.object({
name: z.string(),
}),
execute: ({ name }) => `Hello, ${name}!`,
});
const myAgent = agent({
tools: [greetTool],
llm: openai({ model: "gpt-4o-mini" }),
});
try {
const result = await myAgent.run(message);
return c.json({ response: result.data });
} catch (error) {
return c.json({ error: error.message }, 500);
}
});
export default app;
```
## Streaming Responses
For real-time agent responses:
```typescript
import { agentStreamEvent } from "@llamaindex/workflow";
app.post('/api/chat-stream', async (req, res) => {
const { message } = req.body;
res.writeHead(200, {
'Content-Type': 'text/plain',
'Transfer-Encoding': 'chunked',
});
try {
const events = myAgent.runStream(message);
for await (const event of events) {
if (agentStreamEvent.include(event)) {
res.write(event.data.delta);
}
}
res.end();
} catch (error) {
res.write('Error: ' + error.message);
res.end();
}
});
```
## Next Steps
- Learn about [serverless deployment](/docs/llamaindex/getting_started/installation/serverless)
- Explore [Next.js integration](/docs/llamaindex/getting_started/installation/nextjs)
- Check [troubleshooting guide](/docs/llamaindex/getting_started/installation/troubleshooting) for common issues
@@ -0,0 +1,240 @@
---
title: Serverless Functions
description: Deploy LlamaIndex.TS in serverless environments like Vercel, Netlify, AWS Lambda, and Cloudflare Workers.
---
This guide covers adding LlamaIndex.TS agents to serverless environments where you have execution time and memory constraints.
## Cloudflare Workers
```typescript
export default {
async fetch(request: Request, env: Env): Promise<Response> {
const { setEnvs } = await import("@llamaindex/env");
setEnvs(env);
const { agent } = await import("@llamaindex/workflow");
const { openai } = await import("@llamaindex/openai");
const { tool } = await import("llamaindex");
const { z } = await import("zod");
const timeTool = tool({
name: "getCurrentTime",
description: "Gets the current time",
parameters: z.object({}),
execute: () => new Date().toISOString(),
});
const myAgent = agent({
tools: [timeTool],
llm: openai({ model: "gpt-4o-mini" }),
});
try {
const { message } = await request.json();
const result = await myAgent.run(message);
return new Response(JSON.stringify({ response: result.data }), {
headers: { "Content-Type": "application/json" },
});
} catch (error) {
return new Response(JSON.stringify({ error: error.message }), {
status: 500,
headers: { "Content-Type": "application/json" },
});
}
},
};
```
## Vercel Functions
### Node.js Runtime
```typescript
// pages/api/chat.ts or app/api/chat/route.ts
import { agent } from "@llamaindex/workflow";
import { tool } from "llamaindex";
import { openai } from "@llamaindex/openai";
import { z } from "zod";
export default async function handler(req, res) {
if (req.method !== 'POST') {
return res.status(405).json({ error: 'Method not allowed' });
}
const { message } = req.body;
const weatherTool = tool({
name: "getWeather",
description: "Get weather information",
parameters: z.object({
city: z.string(),
}),
execute: ({ city }) => `Weather in ${city}: 72°F, sunny`,
});
const myAgent = agent({
tools: [weatherTool],
llm: openai({ model: "gpt-4o-mini" }),
});
try {
const result = await myAgent.run(message);
res.json({ response: result.data });
} catch (error) {
res.status(500).json({ error: error.message });
}
}
```
### Edge Runtime
```typescript
// app/api/chat/route.ts
import { NextRequest, NextResponse } from "next/server";
export const runtime = "edge";
export async function POST(request: NextRequest) {
const { setEnvs } = await import("@llamaindex/env");
setEnvs(process.env);
const { message } = await request.json();
try {
// Use simpler tools for edge runtime
const { agent } = await import("@llamaindex/workflow");
const { tool } = await import("llamaindex");
const { openai } = await import("@llamaindex/openai");
const { z } = await import("zod");
const timeTool = tool({
name: "time",
description: "Gets current time",
parameters: z.object({}),
execute: () => new Date().toISOString(),
});
const myAgent = agent({
tools: [timeTool],
llm: openai({ model: "gpt-4o-mini" }),
});
const result = await myAgent.run(message);
return NextResponse.json({ response: result.data });
} catch (error) {
return NextResponse.json({ error: error.message }, { status: 500 });
}
}
```
## AWS Lambda
```typescript
import { APIGatewayProxyHandler } from "aws-lambda";
import { agent } from "@llamaindex/workflow";
import { tool } from "llamaindex";
import { openai } from "@llamaindex/openai";
import { z } from "zod";
export const handler: APIGatewayProxyHandler = async (event, context) => {
const { message } = JSON.parse(event.body || "{}");
const calculatorTool = tool({
name: "calculate",
description: "Performs basic math",
parameters: z.object({
expression: z.string(),
}),
execute: ({ expression }) => {
// Simple calculator implementation
try {
return `Result: ${eval(expression)}`;
} catch {
return "Invalid expression";
}
},
});
const myAgent = agent({
tools: [calculatorTool],
llm: openai({ model: "gpt-4o-mini" }),
});
try {
const result = await myAgent.run(message);
return {
statusCode: 200,
headers: {
"Content-Type": "application/json",
"Access-Control-Allow-Origin": "*",
},
body: JSON.stringify({ response: result.data }),
};
} catch (error) {
return {
statusCode: 500,
body: JSON.stringify({ error: error.message }),
};
}
};
```
## Netlify Functions
```typescript
// netlify/functions/chat.ts
import { Handler } from "@netlify/functions";
import { agent } from "@llamaindex/workflow";
import { tool } from "llamaindex";
import { openai } from "@llamaindex/openai";
import { z } from "zod";
export const handler: Handler = async (event, context) => {
if (event.httpMethod !== "POST") {
return { statusCode: 405, body: "Method Not Allowed" };
}
const { message } = JSON.parse(event.body || "{}");
const helpTool = tool({
name: "help",
description: "Provides help information",
parameters: z.object({
topic: z.string().optional(),
}),
execute: ({ topic }) => {
return topic ? `Help for ${topic}` : "Available help topics";
},
});
const myAgent = agent({
tools: [helpTool],
llm: openai({ model: "gpt-4o-mini" }),
});
try {
const result = await myAgent.run(message);
return {
statusCode: 200,
body: JSON.stringify({ response: result.data }),
};
} catch (error) {
return {
statusCode: 500,
body: JSON.stringify({ error: error.message }),
};
}
};
```
## Next Steps
- Learn about [Next.js integration](/docs/llamaindex/getting_started/installation/nextjs)
- Explore [server deployment](/docs/llamaindex/getting_started/installation/server-apis)
- Check [troubleshooting guide](/docs/llamaindex/getting_started/installation/troubleshooting) for common issues
@@ -0,0 +1,501 @@
---
title: Troubleshooting
description: Common issues and solutions when installing and deploying LlamaIndex.TS applications.
---
This guide addresses common issues you might encounter when installing and deploying LlamaIndex.TS applications across different environments.
## Installation Issues
### Module Resolution Errors
**Problem:** Import errors or module not found errors
**Solution:** Ensure your `tsconfig.json` is properly configured:
```json5
{
"compilerOptions": {
"moduleResolution": "bundler", // or "nodenext" | "node16" | "node"
"lib": ["DOM.AsyncIterable"],
"target": "es2020",
"module": "esnext"
}
}
```
**Alternative solution:** Try different module resolution strategies:
```bash
# Clear node_modules and reinstall
rm -rf node_modules package-lock.json
npm install
# Or try with different package manager
pnpm install
# or
yarn install
```
### TypeScript Errors
**Problem:** TypeScript compilation errors with LlamaIndex imports
**Solution:** Ensure you have the correct TypeScript configuration:
```json5
{
"compilerOptions": {
"strict": true,
"skipLibCheck": true, // Skip type checking of node_modules
"allowSyntheticDefaultImports": true,
"esModuleInterop": true
}
}
```
### Package Compatibility Issues
**Problem:** Some packages don't work in certain environments
**Common incompatibilities:**
- `@llamaindex/readers` - May not work in serverless environments
- `@llamaindex/huggingface` - Limited browser/edge compatibility
- File system readers - Don't work in browser/edge environments
**Solution:** Use environment-specific alternatives:
```typescript
// Instead of file system readers in serverless
// Use remote data sources
async function loadDocumentsFromAPI() {
const response = await fetch('https://api.example.com/documents');
const data = await response.json();
return data.map(doc => new Document(doc.content));
}
```
## Runtime Issues
### Memory Errors
**Problem:** Out of memory errors during index creation or querying
**Solution:** Optimize memory usage:
```typescript
// Batch process large document sets
async function batchProcessDocuments(documents: Document[], batchSize = 10) {
const results = [];
for (let i = 0; i < documents.length; i += batchSize) {
const batch = documents.slice(i, i + batchSize);
const batchIndex = await VectorStoreIndex.fromDocuments(batch);
results.push(batchIndex);
// Optional: Add delay between batches
await new Promise(resolve => setTimeout(resolve, 100));
}
return results;
}
```
**For serverless environments:**
```typescript
// Use external vector stores instead of in-memory
// TODO: Example with Pinecone, Weaviate, etc.
// const vectorStore = new PineconeVectorStore(/* config */);
// const index = await VectorStoreIndex.fromVectorStore(vectorStore);
```
### API Rate Limiting
**Problem:** Rate limiting errors from LLM providers
**Solution:** Implement retry logic with exponential backoff:
```typescript
async function queryWithRetry(queryEngine: any, question: string, maxRetries = 3) {
for (let i = 0; i < maxRetries; i++) {
try {
return await queryEngine.query(question);
} catch (error) {
if (error.message.includes('rate limit') && i < maxRetries - 1) {
const delay = Math.pow(2, i) * 1000; // Exponential backoff
await new Promise(resolve => setTimeout(resolve, delay));
continue;
}
throw error;
}
}
}
```
### Tokenization Performance
**Problem:** Slow tokenization affecting performance
**Solution:** Install faster tokenizer (Node.js only):
```bash
npm install gpt-tokenizer
```
LlamaIndex will automatically use this for 60x faster tokenization.
## Bundling Issues
### Bundle Size Too Large
**Problem:** Large bundle sizes affecting performance
**Solution:** Use dynamic imports and code splitting:
```typescript
// Lazy load LlamaIndex components
const initializeLlamaIndex = async () => {
const { VectorStoreIndex, SimpleDirectoryReader } = await import("llamaindex");
return { VectorStoreIndex, SimpleDirectoryReader };
};
// In your API route
export async function POST(request: NextRequest) {
const { VectorStoreIndex, SimpleDirectoryReader } = await initializeLlamaIndex();
// Use the imported modules
}
```
### Webpack/Vite Bundling Issues
**Problem:** Bundler compatibility issues
**Solution for Next.js:**
```javascript
// next.config.mjs
import withLlamaIndex from "llamaindex/next";
const nextConfig = {
webpack: (config, { isServer }) => {
// Custom webpack configuration if needed
if (!isServer) {
config.resolve.fallback = {
...config.resolve.fallback,
fs: false,
net: false,
tls: false,
};
}
return config;
},
};
export default withLlamaIndex(nextConfig);
```
**Solution for Vite:**
```typescript
// vite.config.ts
import { defineConfig } from 'vite';
export default defineConfig({
define: {
global: 'globalThis',
},
resolve: {
alias: {
// Add aliases for problematic modules
},
},
optimizeDeps: {
include: ['llamaindex'],
},
});
```
## Environment-Specific Issues
### Node.js Version Compatibility
**Problem:** Node.js version compatibility issues
**Solution:** Use supported Node.js versions:
```json
{
"engines": {
"node": ">=18.0.0"
}
}
```
**Check your Node.js version:**
```bash
node --version
```
### Cloudflare Workers Issues
**Problem:** Module not available in Cloudflare Workers
**Solution:** Use `@llamaindex/env` for environment compatibility:
```typescript
export default {
async fetch(request: Request, env: Env): Promise<Response> {
const { setEnvs } = await import("@llamaindex/env");
setEnvs(env);
// Your LlamaIndex code here
},
};
```
### Vercel Edge Runtime Issues
**Problem:** Limited Node.js API access in Edge Runtime
**Solution:** Use standard runtime or adapt code:
```typescript
// Force standard runtime
export const runtime = "nodejs";
// Or adapt for edge
export const runtime = "edge";
export async function POST(request: NextRequest) {
// Use edge-compatible code only
const { setEnvs } = await import("@llamaindex/env");
setEnvs(process.env);
// Avoid file system operations
// Use remote data sources
}
```
## Performance Issues
### Slow Query Responses
**Problem:** Slow query performance
**Solution:** Implement caching and optimization:
```typescript
import { LRUCache } from 'lru-cache';
const queryCache = new LRUCache<string, string>({
max: 100,
ttl: 1000 * 60 * 10, // 10 minutes
});
export async function optimizedQuery(question: string, queryEngine: any) {
// Check cache first
const cached = queryCache.get(question);
if (cached) return cached;
// Query and cache result
const result = await queryEngine.query(question);
queryCache.set(question, result);
return result;
}
```
### Cold Start Issues
**Problem:** Slow cold starts in serverless environments
**Solution:** Pre-warm your functions:
```typescript
// Pre-initialize outside handler
let cachedQueryEngine: any = null;
export async function handler(event: any) {
if (!cachedQueryEngine) {
cachedQueryEngine = await initializeQueryEngine();
}
// Use cached engine
return await cachedQueryEngine.query(question);
}
```
## Environment Variable Issues
### Missing API Keys
**Problem:** API key not found or invalid
**Solution:** Verify environment variable setup:
```typescript
// Check if API key is available
if (!process.env.OPENAI_API_KEY) {
throw new Error('OPENAI_API_KEY environment variable is required');
}
// For debugging (remove in production)
console.log('API Key present:', !!process.env.OPENAI_API_KEY);
```
### Environment Variable Loading
**Problem:** Environment variables not loading correctly
**Solution:** Use proper loading mechanisms:
```typescript
// For Node.js
import 'dotenv/config';
// For Next.js - use .env.local
// Variables are automatically loaded
// For Cloudflare Workers
export default {
async fetch(request: Request, env: Env): Promise<Response> {
// Use env parameter, not process.env
const apiKey = env.OPENAI_API_KEY;
// ...
},
};
```
## Common Error Messages
### "Cannot find module 'llamaindex'"
**Cause:** Package not installed or module resolution issue
**Solution:**
```bash
npm install llamaindex
```
### "Module not found: Can't resolve 'fs'"
**Cause:** File system modules used in browser/edge environment
**Solution:**
```typescript
// Use dynamic imports with fallbacks
const loadDocuments = async () => {
if (typeof window !== 'undefined') {
// Browser environment - use alternative
return await loadDocumentsFromAPI();
} else {
// Node.js environment - use file system
const { SimpleDirectoryReader } = await import('llamaindex');
return await new SimpleDirectoryReader('data').loadData();
}
};
```
### "ReferenceError: global is not defined"
**Cause:** Global polyfill missing in browser environments
**Solution:**
```typescript
// Add to your app entry point
if (typeof global === 'undefined') {
global = globalThis;
}
```
### "Cannot read properties of undefined (reading 'query')"
**Cause:** Query engine not properly initialized
**Solution:**
```typescript
// Always check initialization
if (!queryEngine) {
throw new Error('Query engine not initialized');
}
// Or use optional chaining
const response = await queryEngine?.query(question);
```
## Debugging Tips
### Enable Debug Logging
```typescript
// Enable debug logging
process.env.DEBUG = "llamaindex:*";
// Or specific modules
process.env.DEBUG = "llamaindex:vector-store";
```
### Check Package Versions
```bash
npm list llamaindex
npm list @llamaindex/openai
```
### Test in Isolation
```typescript
// Create minimal test case
import { VectorStoreIndex } from 'llamaindex';
async function testBasic() {
try {
console.log('Testing basic import...');
const index = new VectorStoreIndex();
console.log('Success!');
} catch (error) {
console.error('Error:', error);
}
}
testBasic();
```
## Getting Help
### Before Asking for Help
1. **Check this troubleshooting guide**
2. **Search existing GitHub issues**
3. **Try minimal reproduction**
4. **Check your environment configuration**
### When Reporting Issues
Include:
- Node.js version (`node --version`)
- Package versions (`npm list llamaindex`)
- Environment (Node.js, Cloudflare Workers, Vercel, etc.)
- Minimal code reproduction
- Full error message and stack trace
### Useful Resources
- [GitHub Issues](https://github.com/run-llama/LlamaIndexTS/issues)
- [Discord Community](https://discord.gg/dGcwcsnxhU)
- [Documentation](https://docs.llamaindex.ai/)
## Next Steps
If you're still experiencing issues:
1. **Check specific deployment guides:**
- [Server APIs](/docs/llamaindex/getting_started/installation/server-apis)
- [Serverless Functions](/docs/llamaindex/getting_started/installation/serverless)
- [Next.js Applications](/docs/llamaindex/getting_started/installation/nextjs)
2. **Open an issue** on GitHub with a minimal reproduction
3. **Join our Discord** for community support
@@ -1,99 +0,0 @@
---
title: With TypeScript
description: In this guide, you'll learn how to use LlamaIndex with TypeScript
---
LlamaIndex.TS is written in TypeScript and designed to be used in TypeScript projects.
We put a lot of work on strong typing to make sure you have a great typing experience with code completion such as:
```ts twoslash
import { PromptTemplate } from 'llamaindex'
const promptTemplate = new PromptTemplate({
template: `Context information from multiple sources is below.
---------------------
{context}
---------------------
Given the information from multiple sources and not prior knowledge.
Answer the query in the style of a Shakespeare play"
Query: {query}
Answer:`,
templateVars: ["context", "query"],
});
// @noErrors
promptTemplate.format({
c
//^|
})
```
## Enable TypeScript
Make sure to set [moduleResolution](https://www.typescriptlang.org/docs/handbook/modules/theory.html#module-resolution) in your `tsconfig.json` file:
```json5
{
compilerOptions: {
// ⬇️ add this line to your tsconfig.json
moduleResolution: "bundler", // or "nodenext" | "node16" | "node"
},
}
```
We recommend using `bundler` or `nodenext`, but due to popularity of `node`, we still added support for it.
## Enable AsyncIterable for `Web Stream` API
Some modules uses `Web Stream` API like `ReadableStream` and `WritableStream`, you need to enable `DOM.AsyncIterable` in your `tsconfig.json`.
```json5
{
compilerOptions: {
// ⬇️ add this lib to your tsconfig.json
lib: ["DOM.AsyncIterable"],
},
}
```
```typescript
import { tool } from 'llamaindex'
import { agent } from "@llamaindex/workflow";
import { openai } from "@llamaindex/openai";
Settings.llm = openai({
model: "gpt-4o-mini",
});
const addTool = tool({
name: "add",
description: "Adds two numbers",
parameters: z.object({x: z.number(), y: z.number()}),
execute: ({ x, y }) => x + y,
});
const myAgent = agent({
tools: [addTool],
});
// Chat with the agent
const context = myAgent.run("Hello, how are you?");
for await (const event of context) {
if (event instanceof AgentStream) {
for (const chunk of event.data.delta) {
process.stdout.write(chunk); // stream response
}
} else {
console.log(event); // other events
}
}
```
## Run TypeScript Script in Node.js
We recommend to use [tsx](https://www.npmjs.com/package/tsx) to run TypeScript script in Node.js.
```shell
node --import tsx ./my-script.ts
```
@@ -1,23 +0,0 @@
---
title: With Vite
description: In this guide, you'll learn how to use LlamaIndex with Vite
---
Before you start, make sure you have try LlamaIndex.TS in Node.js to make sure you understand the basics.
<Card
title="Getting Started with LlamaIndex.TS in Node.js"
href="/docs/llamaindex/getting_started/installation/node"
/>
Also, make sure you have a basic understanding of [Vite](https://vitejs.dev/).
## Why mention Vite?
Vite.js is widely used in building many web applications, like React.js, even for some native app like [Electron](https://www.electronjs.org/).
However, it's not a ready-to-use solution for a Node.js-like application using Vite, as Vite is designed for web applications(run in browser).
There's some plugin/framework based on Vite, like [Waku.gg](https://github.com/dai-shi/waku), or [Electron Vite](https://electron-vite.org/)
For now, we have no clear solution for bundling LlamaIndex.TS with Vite, if you have any idea/solution, please let us know.
+105 -8
View File
@@ -1,21 +1,118 @@
---
title: What is LlamaIndex.TS
description: LlamaIndex is the leading data framework for building LLM applications
title: Welcome to LlamaIndex.TS
description: LlamaIndex.TS is the leading framework for utilizing context engineering to build LLM applications in JavaScript and TypeScript.
---
LlamaIndex is a framework for building context-augmented generative AI applications with LLMs including agents and workflows.
LlamaIndex.TS is a **framework for utilizing context engineering to build generative AI applications** with large language models. From rapid-prototyping RAG chatbots to deploying multi-agent workflows in production, LlamaIndex gives you everything you need — all in idiomatic TypeScript.
The TypeScript implementation is designed for JavaScript server side applications using <SiNodedotjs className="inline" color="#5FA04E" /> Node.js, <SiDeno className="inline" color="#70FFAF" /> Deno, <SiBun className="inline" /> Bun, <SiCloudflareworkers className="inline" color="#F38020" /> Cloudflare Workers, and more.
Built for modern JavaScript runtimes like <SiNodedotjs className="inline" color="#5FA04E" /> **Node.js**, <SiDeno className="inline" color="#70FFAF" /> **Deno**, <SiBun className="inline" /> **Bun**, <SiCloudflareworkers className="inline" color="#F38020" /> **Cloudflare Workers**, and more.
LlamaIndex.TS provides tools for beginners, advanced users, and everyone in between.
<div className="grid grid-cols-1 gap-4 sm:grid-cols-2 lg:grid-cols-3 my-6">
<a href="#introduction" className="block rounded-lg border border-gray-600/40 p-4 hover:border-gray-400 hover:bg-gray-700/20 no-underline">
<h3 className="mb-1 text-lg font-semibold underline">Introduction</h3>
<p className="text-sm text-gray-400 no-underline">Context engineering, agents &amp; workflows — what do they mean?</p>
</a>
Try it out with a starter example using StackBlitz:
<a href="#use-cases" className="block rounded-lg border border-gray-600/40 p-4 hover:border-gray-400 hover:bg-gray-700/20 no-underline">
<h3 className="mb-1 text-lg font-semibold underline">Use cases</h3>
<p className="text-sm text-gray-400 no-underline">See what you can build with LlamaIndex.TS.</p>
</a>
<a href="#getting-started" className="block rounded-lg border border-gray-600/40 p-4 hover:border-gray-400 hover:bg-gray-700/20 no-underline">
<h3 className="mb-1 text-lg font-semibold underline">Getting started</h3>
<p className="text-sm text-gray-400 no-underline">Your first app in 5 lines of code.</p>
</a>
<a href="https://docs.cloud.llamaindex.ai/" className="block rounded-lg border border-gray-600/40 p-4 hover:border-gray-400 hover:bg-gray-700/20 no-underline" target="_blank" rel="noopener noreferrer">
<h3 className="mb-1 text-lg font-semibold underline">LlamaCloud</h3>
<p className="text-sm text-gray-400 no-underline">Managed parsing, extraction &amp; retrieval pipelines.</p>
</a>
<a href="#community" className="block rounded-lg border border-gray-600/40 p-4 hover:border-gray-400 hover:bg-gray-700/20 no-underline">
<h3 className="mb-1 text-lg font-semibold underline">Community</h3>
<p className="text-sm text-gray-400 no-underline">Join thousands of builders on Discord, Twitter, and more.</p>
</a>
<a href="#related-projects" className="block rounded-lg border border-gray-600/40 p-4 hover:border-gray-400 hover:bg-gray-700/20 no-underline">
<h3 className="mb-1 text-lg font-semibold underline">Related projects</h3>
<p className="text-sm text-gray-400 no-underline">Connectors, demos &amp; starter kits.</p>
</a>
</div>
## Introduction
### What are agents?
[Agents](/docs/llamaindex/tutorials/agents/1_setup) are LLM-powered assistants that can reason, use external tools, and take actions to accomplish tasks such as research, data extraction, and automation.
LlamaIndex.TS provides foundational building blocks for creating and orchestrating these agents.
### What are workflows?
[Workflows](/docs/llamaindex/tutorials/workflows) are multi-step, event-driven processes that combine agents, data connectors, and other tools to solve complex problems.
With LlamaIndex.TS you can chain together retrieval, generation, and tool-calling steps and then deploy the entire pipeline as a microservice.
### What is context engineering?
LLMs come pre-trained on vast public corpora, but not on **your** private or domain-specific data.
Context engineering bridges that gap by injecting the right pieces of your data into the LLM prompt at the right time.
The most popular example is [Retrieval-Augmented Generation (RAG)](/docs/llamaindex/getting_started/concepts), but the same idea powers agent memory, evaluation, extraction, summarisation, and more.
LlamaIndex.TS gives you:
- **Data connectors** to ingest from APIs, files, SQL, and dozens more sources.
- **Indexes & retrievers** to store and retrieve your data for LLM consumption.
- **Agents and Engines** to query and use chat+reasoning interfaces over your data.
- **Workflows** for fine-grained orchestration of your data and LLM-powered agents.
- **Observability** integrations so you can iterate with confidence.
You can learn more about these concepts in our [concepts guide](/docs/llamaindex/getting_started/concepts).
## Use cases
Popular scenarios include:
- [LLM-Powered Agents](/docs/llamaindex/tutorials/agents/1_setup)
- [Indexing and Retrieval](/docs/llamaindex/tutorials/rag)
- [Extracting Structured Data](/docs/llamaindex/tutorials/structured_data_extraction)
- [Custom Orchestration with Workflows](/docs/llamaindex/tutorials/workflows)
## Getting started
The fastest way to get started is in StackBlitz below — no local setup required:
<iframe
className="w-full h-[440px]"
aria-label="LlamaIndex.TS Starter"
aria-description="This is a starter example for LlamaIndex.TS, it shows the basic usage of the library."
aria-description="Interactive starter for LlamaIndex.TS"
src="https://stackblitz.com/github/run-llama/LlamaIndexTS/tree/main/examples?embed=1&file=starter.ts"
/>
You'll need an OpenAI API key to run this example. You can retrieve it from [OpenAI](https://platform.openai.com/api-keys).
Want to learn more? We have several tutorials to get you started:
- [Installation + Runtime Guide](/docs/llamaindex/getting_started/installation)
- [Create your first agent](/docs/llamaindex/tutorials/agents/1_setup)
- [Learn how to index data and chat with it](/docs/llamaindex/tutorials/rag)
- [Learn how to write your own workflows and agents](/docs/llamaindex/tutorials/workflows)
---
## LlamaCloud
Need an end-to-end managed pipeline? Check out **[LlamaCloud](https://cloud.llamaindex.ai/)**: best-in-class document parsing (LlamaParse), extraction (LlamaExtract), and indexing services with generous free tiers.
---
## Community
- [Twitter](https://twitter.com/llama_index)
- [Discord](https://discord.gg/dGcwcsnxhU)
- [LinkedIn](https://www.linkedin.com/company/llamaindex/)
We 💜 contributors! View our [contributing guide](https://github.com/run-llama/LlamaIndexTS/blob/main/CONTRIBUTING.md) to get started.
## Related projects
- [Python framework GitHub](https://github.com/run-llama/llama_index)
- [Python docs](https://docs.llamaindex.ai/)
- [create-llama](https://www.npmjs.com/package/create-llama) — scaffold a new project in seconds!
- [UI Components](https://ui.llamaindex.ai/) — build chat applications with our Next.js components.
@@ -0,0 +1,85 @@
---
title: MCP Toolbox For Databases
description: MCP Toolbox for Databases is an open source MCP server for databases.
---
# MCP Toolbox for Databases
[MCP Toolbox for Databases](https://github.com/googleapis/genai-toolbox) is an open source MCP server for databases. It was designed with enterprise-grade and production-quality in mind. It enables you to develop tools easier, faster, and more securely by handling the complexities such as connection pooling, authentication, and more.
Toolbox Tools can be seemlessly integrated with LlamaIndex applications. For more
information on [getting
started](https://googleapis.github.io/genai-toolbox/getting-started/local_quickstart_js/) or
[configuring](https://googleapis.github.io/genai-toolbox/getting-started/configure/)
Toolbox, see the
[documentation](https://googleapis.github.io/genai-toolbox/getting-started/introduction/).
![architecture](/images/mcp_db_toolbox.png)
### Configure and deploy
Toolbox is an open source server that you deploy and manage yourself. For more
instructions on deploying and configuring, see the official Toolbox
documentation:
* [Installing the Server](https://googleapis.github.io/genai-toolbox/getting-started/introduction/#installing-the-server)
* [Configuring Toolbox](https://googleapis.github.io/genai-toolbox/getting-started/configure/)
### Install client SDK
LlamaIndex relies on the `@toolbox-sdk/core` node package to use Toolbox. Install the
package before getting started:
```shell
npm install @toolbox-sdk/core
```
### Loading Toolbox Tools
Once your Toolbox server is configured and up and running, you can load tools
from your server using the SDK:
```javascript
import { gemini, GEMINI_MODEL } from "@llamaindex/google";
import { agent } from "@llamaindex/workflow";
import { tool } from "llamaindex";
import { ToolboxClient } from "@toolbox-sdk/core";
// Initialize LLM
const llm = gemini({
model: GEMINI_MODEL.GEMINI_2_0_FLASH,
apiKey: process.env.GOOGLE_API_KEY,
});
// Replace with your Toolbox Server URL
const URL = 'https://127.0.0.1:5000';
const client = new ToolboxClient("http://127.0.0.1:5000");
const toolboxTools = await client.loadToolset("my-toolset");
const getTool = (toolboxTool) => tool({
name: toolboxTool.getName(),
description: toolboxTool.getDescription(),
parameters: toolboxTool.getParamSchema(),
execute: toolboxTool
});
const tools = toolboxTools.map(getTool);
const myAgent = agent({
tools: tools,
llm,
memory,
systemPrompt: prompt,
});
const result = await myAgent.run(query);
console.log(result);
```
### Advanced Toolbox Features
Toolbox has a variety of features to make developing Gen AI tools for databases seamless.
For more information, read more about the following:
- [Authenticated Parameters](https://googleapis.github.io/genai-toolbox/resources/tools/#authenticated-parameters): bind tool inputs to values from OIDC tokens automatically, making it easy to run sensitive queries without potentially leaking data
- [Authorized Invocations](https://googleapis.github.io/genai-toolbox/resources/tools/#authorized-invocations): restrict access to use a tool based on the users Auth token
- [OpenTelemetry](https://googleapis.github.io/genai-toolbox/how-to/export_telemetry/): get metrics and tracing from Toolbox with [OpenTelemetry](https://opentelemetry.io/docs/)
@@ -1,5 +1,5 @@
{
"title": "Integration",
"description": "See our integrations",
"pages": ["open-llm-metry", "lang-trace", "vercel"]
"pages": ["open-llm-metry", "lang-trace", "mcp-toolbox", "vercel"]
}
@@ -34,6 +34,7 @@ const jokeAgent = agent({
// Run the workflow
const result = await jokeAgent.run("Tell me something funny");
console.log(result.data.result); // Baby Llama is called cria
console.log(result.data.message); // { role: 'assistant', content: 'Baby Llama is called cria' }
```
### Event Streaming
@@ -0,0 +1,164 @@
---
title: Low-Level LLM Execution
---
Sometimes your need more control over LLM interactions than what high-level agents provide. The `llm.exec` method makes it simple for you to make a single LLM call with tools but hides the complexity of executing the tools and generating the tool messages.
## When to Use `llm.exec`
Use `llm.exec` when you need to:
- Build custom agent logic in [workflow](/docs/llamaindex/modules/agents/workflows) steps
- Have precise control over message handling and tool execution
## Basic Usage
The `llm.exec` method takes messages and tools as parameter and executes one LLM call.
The LLM might either request to call one or more of the tools or generate an assistant message as result.
For each tool call that is requested, `llm.exec` executes it and generates the two tool call messages (call and result). If no tool call is requested, just the assistant message is returned.
```ts
import { openai } from "@llamaindex/openai";
import { ChatMessage, tool } from "llamaindex";
import z from "zod";
const llm = openai({ model: "gpt-4.1-mini" });
const messages = [
{
content: "What's the weather like in San Francisco?",
role: "user",
} as ChatMessage,
];
const { newMessages, toolCalls } = await llm.exec({
messages,
tools: [
tool({
name: "get_weather",
description: "Get the current weather for a location",
parameters: z.object({
address: z.string().describe("The address"),
}),
execute: ({ address }) => {
return `It's sunny in ${address}!`;
},
}),
],
});
// Add the new messages (including tool calls and responses) to your conversation
messages.push(...newMessages);
```
> `newMessages` is an array as each tool call generates two messages: a tool call message and the tool call result message.
## Agent Loop Pattern
A common pattern is to use `llm.exec` in a loop until the LLM stops making tool calls:
```ts
import { openai } from "@llamaindex/openai";
import { ChatMessage, tool } from "llamaindex";
import z from "zod";
async function runAgentLoop() {
const llm = openai({ model: "gpt-4.1-mini" });
const messages = [
{
content: "What's the weather like in San Francisco?",
role: "user",
} as ChatMessage,
];
let exit = false;
do {
const { newMessages, toolCalls } = await llm.exec({
messages,
tools: [
tool({
name: "get_weather",
description: "Get the current weather for a location",
parameters: z.object({
address: z.string().describe("The address"),
}),
execute: ({ address }) => {
return `It's sunny in ${address}!`;
},
}),
],
});
console.log(newMessages);
messages.push(...newMessages);
// Exit when no more tool calls are made
exit = toolCalls.length === 0;
} while (!exit);
}
```
## Streaming Support
For real-time responses, use the `stream` option to get the assistant's response as streamed tokens:
```ts
import { openai } from "@llamaindex/openai";
import { tool } from "llamaindex";
import z from "zod";
async function streamingAgentLoop() {
const llm = openai({ model: "gpt-4o-mini" });
const messages = [
{
content: "What's the weather like in San Francisco?",
role: "user",
} as ChatMessage,
];
let exit = false;
do {
const { stream, newMessages, toolCalls } = await llm.exec({
messages,
tools: [
tool({
name: "get_weather",
description: "Get the current weather for a location",
parameters: z.object({
address: z.string().describe("The address"),
}),
execute: ({ address }) => {
return `It's sunny in ${address}!`;
},
}),
],
stream: true,
});
// Stream the response token by token
for await (const chunk of stream) {
process.stdout.write(chunk.delta);
}
messages.push(...newMessages());
exit = toolCalls.length === 0;
} while (!exit);
}
```
> `newMessages` is a function when streaming. The reason is that the result only is available after streaming. Calling it before, will throw an error.
## Return Values
`llm.exec` returns an object with:
- **`newMessages`**: Array of new chat messages including the LLM response and any tool call messages (call or result). This is a function return the array when streaming.
- **`toolCalls`**: Array of tool calls made by the LLM
- **`stream`**: Async iterable for streaming responses (only when `stream: true`)
## Best Practices
For using `llm.exec` in an agent loop, take care to:
1. **Maintain message history**: Always add `newMessages` to your conversation history
2. **Set exit conditions**: Implement proper logic to avoid infinite loops
@@ -1,4 +1,10 @@
{
"title": "Agents",
"pages": ["tool", "agent_workflow", "workflows", "natural_language_workflow"]
"pages": [
"tool",
"agent_workflow",
"workflows",
"low-level",
"natural_language_workflow"
]
}
@@ -101,6 +101,9 @@ const agent = agent({
});
```
You can also use [MCP Toolbox for
Databases](/docs/llamaindex/integration/mcp-toolbox) to interact with MCP tools.
## Function tool
@@ -106,34 +106,40 @@ const memory = createMemory({
Long-term memory is represented as `Memory Block` objects. These objects contain information that are from previous user sessions or from the beginning of the current conversation. When memory is retrieved (by calling `getLLM`), the short-term and long-term memories are merged together within the given `tokenLimit`.
Currently, there are two predefined memory blocks:
Currently, there are three predefined memory blocks:
- `staticBlock`: A memory block that stores a static piece of information.
- `factExtractionBlock`: A memory block that extracts facts from the chat history.
- `vectorBlock`: A memory block that stores and retrieves chat messages from a vector database using semantic similarity search. Messages are stored individually and retrieved based on their relevance to recent conversation context. Here we've passed in the `vectorStore` to use to store and retrieve the chat messages.
This sounds a bit complicated, but it's actually quite simple. Let's look at an example:
```ts
import { createMemory, factExtractionBlock, staticBlock } from "llamaindex";
import { createMemory, factExtractionBlock, staticBlock, vectorBlock } from "llamaindex";
import { QdrantVectorStore } from "@llamaindex/qdrant";
import { OpenAIEmbedding } from "@llamaindex/openai";
const memoryBlocks= [
staticBlock({
id: "core_info",
content: "My name is Logan, and I live in Saskatoon. I work at LlamaIndex.",
}),
factExtractionBlock({
id: "user-extracted_info",
priority: 1,
llm: llm,
maxFacts: 50,
}),
vectorBlock({
vectorStore: new QdrantVectorStore({ url: "http://localhost:6333" }),
priority: 2,
}),
];
```
Here, we've setup two memory blocks:
Here, we've setup three memory blocks:
- `core_info`: A static memory block that stores some core information about the user. This information will always be inserted into the memory. The type used is `MessageContent` to support multi-modal content.
- `extracted_info`: An extracted memory block that will extract information from the chat history. Here we've passed in the `llm` to use to extract facts from the chat history, and set the `maxFacts` to 50. If the number of extracted facts exceeds this limit, the `maxFacts` will be automatically summarized and reduced to leave room for new information.
- `staticBlock`: A static memory block that stores some core information about the user. This information will always be inserted into the memory. The type used is `MessageContent` to support multi-modal content.
- `factExtractionBlock`: An extracted memory block that will extract information from the chat history. Here we've passed in the `llm` to use to extract facts from the chat history, and set the `maxFacts` to 50. If the number of extracted facts exceeds this limit, the `maxFacts` will be automatically summarized and reduced to leave room for new information.
- `vectorBlock`: A vector memory block that will store in a vector database and retrieve them from there. Messages are stored individually and retrieved based on their relevance to recent conversation context. Here we've passed in the `vectorStore` to use to store and retrieve the chat messages.
You'll also notice that we've set the `priority` for the `factExtractionBlock` block. This is used to determine the handling when the memory blocks content (i.e. long-term memory) + short-term memory exceeds the token limit on the `Memory` object.
@@ -158,6 +164,46 @@ When memory is retrieved (using `getLLM`), the short-term and long-term memories
The amount of short-term memory included is specified by the `shortTermTokenLimitRatio`. If it's set to `0.7`, 70% of the `tokenLimit` is used for short-term memory (not including the static memory block).
#### VectorBlock Configuration Options
The `vectorBlock` offers several configuration options to customize its behavior:
```ts
vectorBlock({
vectorStore: new QdrantVectorStore({ url: "http://localhost:6333" }),
priority: 2,
retrievalContextWindow: 5, // Number of recent messages to use for context when retrieving
formatTemplate: new PromptTemplate({ template: "Context: {{ context }}" }), // Custom formatting template
nodePostprocessors: [/* custom postprocessors */], // Apply processing to retrieved nodes
queryOptions: {
similarityTopK: 3, // Number of top similar results to return (default: 2)
mode: VectorStoreQueryMode.DEFAULT, // Query mode for the vector store
sessionFilterKey: "session_id", // Metadata key for session filtering (default: "session_id")
// Custom filters can be added here - session filter is automatically included
filters: {
filters: [
{ key: "custom_field", value: "custom_value", operator: "==" }
],
condition: "and"
}
}
})
```
**Key Configuration Options:**
- **`retrievalContextWindow`**: Number of recent messages to consider when creating the retrieval query (default: 5). A larger window provides more context but may be less precise.
- **`formatTemplate`**: Template for formatting retrieved information before adding to memory. Defaults to a simple context template.
- **`nodePostprocessors`**: Array of postprocessors to apply to retrieved nodes, useful for filtering or transforming results.
- **`queryOptions.similarityTopK`**: Number of most similar messages to retrieve from the vector store (default: 2).
- **`queryOptions.sessionFilterKey`**: Metadata key used to isolate memory between different sessions (default: "session_id").
- **`queryOptions.filters`**: Additional metadata filters for retrieval. The session filter is automatically added to ensure memory isolation.
**Session Isolation:**
The vectorBlock automatically adds a session filter using the block's ID to ensure that memories from different sessions don't interfere with each other. This filter uses the `sessionFilterKey` (default: "session_id") and can be customized if needed.
## Persistence with Snapshots
Save and restore memory state:
@@ -5,13 +5,13 @@ title: Bedrock
## Installation
```package-install
npm i llamaindex @llamaindex/community
npm i llamaindex @llamaindex/aws
```
## Usage
```ts
import { BEDROCK_MODELS, Bedrock } from "@llamaindex/community";
import { BEDROCK_MODELS, Bedrock } from "@llamaindex/aws";
Settings.llm = new Bedrock({
model: BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_HAIKU,
@@ -23,9 +23,19 @@ Settings.llm = new Bedrock({
});
```
Currently only supports Anthropic and Meta models:
Supported models are listed below (accessible by BEDROCK_MODELS).
```ts
AMAZON_TITAN_TG1_LARGE = "amazon.titan-tg1-large";
AMAZON_TITAN_TEXT_EXPRESS_V1 = "amazon.titan-text-express-v1";
AI21_J2_GRANDE_INSTRUCT = "ai21.j2-grande-instruct";
AI21_J2_JUMBO_INSTRUCT = "ai21.j2-jumbo-instruct";
AI21_J2_MID = "ai21.j2-mid";
AI21_J2_MID_V1 = "ai21.j2-mid-v1";
AI21_J2_ULTRA = "ai21.j2-ultra";
AI21_J2_ULTRA_V1 = "ai21.j2-ultra-v1";
COHERE_COMMAND_TEXT_V14 = "cohere.command-text-v14";
ANTHROPIC_CLAUDE_INSTANT_1 = "anthropic.claude-instant-v1";
ANTHROPIC_CLAUDE_2 = "anthropic.claude-v2";
ANTHROPIC_CLAUDE_2_1 = "anthropic.claude-v2:1";
@@ -33,7 +43,12 @@ ANTHROPIC_CLAUDE_3_SONNET = "anthropic.claude-3-sonnet-20240229-v1:0";
ANTHROPIC_CLAUDE_3_HAIKU = "anthropic.claude-3-haiku-20240307-v1:0";
ANTHROPIC_CLAUDE_3_OPUS = "anthropic.claude-3-opus-20240229-v1:0"; // available on us-west-2
ANTHROPIC_CLAUDE_3_5_SONNET = "anthropic.claude-3-5-sonnet-20240620-v1:0";
ANTHROPIC_CLAUDE_3_5_SONNET_V2 = "anthropic.claude-3-5-sonnet-20241022-v2:0";
ANTHROPIC_CLAUDE_3_5_HAIKU = "anthropic.claude-3-5-haiku-20241022-v1:0";
ANTHROPIC_CLAUDE_3_7_SONNET = "anthropic.claude-3-7-sonnet-20250219-v1:0";
ANTHROPIC_CLAUDE_4_SONNET = "anthropic.claude-sonnet-4-20250514-v1:0";
ANTHROPIC_CLAUDE_4_OPUS = "anthropic.claude-opus-4-20250514-v1:0";
META_LLAMA2_13B_CHAT = "meta.llama2-13b-chat-v1";
META_LLAMA2_70B_CHAT = "meta.llama2-70b-chat-v1";
META_LLAMA3_8B_INSTRUCT = "meta.llama3-8b-instruct-v1:0";
@@ -45,41 +60,66 @@ META_LLAMA3_2_1B_INSTRUCT = "meta.llama3-2-1b-instruct-v1:0"; // only available
META_LLAMA3_2_3B_INSTRUCT = "meta.llama3-2-3b-instruct-v1:0"; // only available via inference endpoints (see below)
META_LLAMA3_2_11B_INSTRUCT = "meta.llama3-2-11b-instruct-v1:0"; // only available via inference endpoints (see below), multimodal and function call supported
META_LLAMA3_2_90B_INSTRUCT = "meta.llama3-2-90b-instruct-v1:0"; // only available via inference endpoints (see below), multimodal and function call supported
META_LLAMA3_3_70B_INSTRUCT = "meta.llama3-3-70b-instruct-v1:0";
MISTRAL_7B_INSTRUCT = "mistral.mistral-7b-instruct-v0:2";
MISTRAL_MIXTRAL_7B_INSTRUCT = "mistral.mixtral-8x7b-instruct-v0:1";
MISTRAL_MIXTRAL_LARGE_2402 = "mistral.mistral-large-2402-v1:0";
AMAZON_NOVA_PREMIER_1 = "amazon.nova-premier-v1:0";
AMAZON_NOVA_PRO_1 = "amazon.nova-pro-v1:0";
AMAZON_NOVA_LITE_1 = "amazon.nova-lite-v1:0";
AMAZON_NOVA_MICRO_1 = "amazon.nova-micro-v1:0";
```
You can also use Bedrock's Inference endpoints by using the model names:
You can also use Bedrock's Inference endpoints by using the model names (accessible by INFERENCE_BEDROCK_MODELS).
Note that the region must be set correctly.
```ts
// US
//US
US_ANTHROPIC_CLAUDE_3_HAIKU = "us.anthropic.claude-3-haiku-20240307-v1:0";
US_ANTHROPIC_CLAUDE_3_5_HAIKU = "us.anthropic.claude-3-5-haiku-20241022-v1:0";
US_ANTHROPIC_CLAUDE_3_OPUS = "us.anthropic.claude-3-opus-20240229-v1:0";
US_ANTHROPIC_CLAUDE_3_SONNET = "us.anthropic.claude-3-sonnet-20240229-v1:0";
US_ANTHROPIC_CLAUDE_3_5_SONNET = "us.anthropic.claude-3-5-sonnet-20240620-v1:0";
US_ANTHROPIC_CLAUDE_3_5_SONNET_V2 =
"us.anthropic.claude-3-5-sonnet-20241022-v2:0";
US_ANTHROPIC_CLAUDE_3_5_SONNET_V2 = "us.anthropic.claude-3-5-sonnet-20241022-v2:0";
US_ANTHROPIC_CLAUDE_3_7_SONNET = "us.anthropic.claude-3-7-sonnet-20250219-v1:0";
US_ANTHROPIC_CLAUDE_4_SONNET = "us.anthropic.claude-sonnet-4-20250514-v1:0";
US_ANTHROPIC_CLAUDE_4_OPUS = "us.anthropic.claude-opus-4-20250514-v1:0";
US_META_LLAMA_3_2_1B_INSTRUCT = "us.meta.llama3-2-1b-instruct-v1:0";
US_META_LLAMA_3_2_3B_INSTRUCT = "us.meta.llama3-2-3b-instruct-v1:0";
US_META_LLAMA_3_2_11B_INSTRUCT = "us.meta.llama3-2-11b-instruct-v1:0";
US_META_LLAMA_3_2_90B_INSTRUCT = "us.meta.llama3-2-90b-instruct-v1:0";
US_AMAZON_NOVA_PRO_1 = "us.amazon.nova-premier-v1:0";
US_META_LLAMA_3_3_70B_INSTRUCT = "us.meta.llama3-3-70b-instruct-v1:0";
US_AMAZON_NOVA_PREMIER_1 = "us.amazon.nova-premier-v1:0";
US_AMAZON_NOVA_PRO_1 = "us.amazon.nova-pro-v1:0";
US_AMAZON_NOVA_LITE_1 = "us.amazon.nova-lite-v1:0";
US_AMAZON_NOVA_MICRO_1 = "us.amazon.nova-micro-v1:0";
// EU
//EU
EU_ANTHROPIC_CLAUDE_3_HAIKU = "eu.anthropic.claude-3-haiku-20240307-v1:0";
EU_ANTHROPIC_CLAUDE_3_5_HAIKU = "eu.anthropic.claude-3-5-haiku-20240307-v1:0";
EU_ANTHROPIC_CLAUDE_3_SONNET = "eu.anthropic.claude-3-sonnet-20240229-v1:0";
EU_ANTHROPIC_CLAUDE_3_5_SONNET = "eu.anthropic.claude-3-5-sonnet-20240620-v1:0";
EU_ANTHROPIC_CLAUDE_3_7_SONNET = "eu.anthropic.claude-3-7-sonnet-20250219-v1:0";
EU_ANTHROPIC_CLAUDE_4_SONNET = "eu.anthropic.claude-sonnet-4-20250514-v1:0";
EU_ANTHROPIC_CLAUDE_4_OPUS = "eu.anthropic.claude-opus-4-20250514-v1:0";
EU_META_LLAMA_3_2_1B_INSTRUCT = "eu.meta.llama3-2-1b-instruct-v1:0";
EU_META_LLAMA_3_2_3B_INSTRUCT = "eu.meta.llama3-2-3b-instruct-v1:0";
EU_AMAZON_NOVA_PRO_1 = "eu.amazon.nova-premier-v1:0";
EU_AMAZON_NOVA_PREMIER_1 = "eu.amazon.nova-premier-v1:0";
EU_AMAZON_NOVA_PRO_1 = "eu.amazon.nova-pro-v1:0";
EU_AMAZON_NOVA_LITE_1 = "eu.amazon.nova-lite-v1:0";
EU_AMAZON_NOVA_MICRO_1 = "eu.amazon.nova-micro-v1:0";
//APAC
APAC_ANTHROPIC_CLAUDE_3_5_SONNET = "apac.anthropic.claude-3-5-sonnet-20240620-v1:0";
APAC_ANTHROPIC_CLAUDE_3_5_SONNET_V2 = "apac.anthropic.claude-3-5-sonnet-20241022-v2:0";
APAC_ANTHROPIC_CLAUDE_3_7_SONNET = "apac.anthropic.claude-3-7-sonnet-20250219-v1:0";
APAC_ANTHROPIC_CLAUDE_3_HAIKU = "apac.anthropic.claude-3-haiku-20240307-v1:0";
APAC_ANTHROPIC_CLAUDE_3_SONNET = "apac.anthropic.claude-3-sonnet-20240229-v1:0";
APAC_AMAZON_NOVA_PRO_1 = "apac.amazon.nova-pro-v1:0";
APAC_AMAZON_NOVA_LITE_1 = "apac.amazon.nova-lite-v1:0";
APAC_AMAZON_NOVA_MICRO_1 = "apac.amazon.nova-micro-v1:0";
```
Sonnet, Haiku and Opus are multimodal, image_url only supports base64 data url format, e.g. `data:image/jpeg;base64,SGVsbG8sIFdvcmxkIQ==`
@@ -87,10 +127,11 @@ Sonnet, Haiku and Opus are multimodal, image_url only supports base64 data url f
## Full Example
```ts
import { BEDROCK_MODELS, Bedrock } from "llamaindex";
import { INFERENCE_BEDROCK_MODELS, Bedrock } from "@llamaindex/aws";
Settings.llm = new Bedrock({
model: BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_HAIKU,
model: INFERENCE_BEDROCK_MODELS.US_ANTHROPIC_CLAUDE_3_SONNET,
region: "us-east-1",
});
async function main() {
@@ -119,7 +160,7 @@ async function main() {
## Agent Example
```ts
import { BEDROCK_MODELS, Bedrock } from "@llamaindex/community";
import { BEDROCK_MODELS, Bedrock } from "@llamaindex/aws";
import { tool } from "llamaindex";
import { agent } from "@llamaindex/workflow";
import { z } from "zod";
@@ -38,10 +38,13 @@ You should expect output something like:
{
result: '5 + 5 is 10. Then, 10 divided by 2 is 5.',
state: {
memory: ChatMemoryBuffer {
chatStore: SimpleChatStore {},
chatStoreKey: 'chat_history',
tokenLimit: 750000
memory: Memory {
messages: [Array],
tokenLimit: 30000,
shortTermTokenLimitRatio: 0.7,
memoryBlocks: [],
memoryCursor: 0,
adapters: [Object]
},
scratchpad: [],
currentAgentName: 'Agent',
+1 -1
View File
@@ -11,7 +11,7 @@
},
"devDependencies": {
"@cloudflare/workers-types": "^4.20241112.0",
"typescript": "^5.7.3",
"typescript": "^5.8.3",
"wrangler": "^3.89.0"
},
"dependencies": {
@@ -1,5 +1,78 @@
# @llamaindex/cloudflare-worker-agent-test
## 0.0.186
### Patch Changes
- Updated dependencies [049471b]
- llamaindex@0.11.25
## 0.0.185
### Patch Changes
- llamaindex@0.11.24
## 0.0.184
### Patch Changes
- llamaindex@0.11.23
## 0.0.183
### Patch Changes
- llamaindex@0.11.22
## 0.0.182
### Patch Changes
- llamaindex@0.11.21
## 0.0.181
### Patch Changes
- llamaindex@0.11.20
## 0.0.180
### Patch Changes
- llamaindex@0.11.19
## 0.0.179
### Patch Changes
- llamaindex@0.11.18
## 0.0.178
### Patch Changes
- llamaindex@0.11.17
## 0.0.177
### Patch Changes
- llamaindex@0.11.16
## 0.0.176
### Patch Changes
- llamaindex@0.11.15
## 0.0.175
### Patch Changes
- llamaindex@0.11.14
## 0.0.174
### Patch Changes
@@ -1,6 +1,6 @@
{
"name": "@llamaindex/cloudflare-worker-agent-test",
"version": "0.0.174",
"version": "0.0.186",
"type": "module",
"private": true,
"scripts": {
@@ -16,7 +16,7 @@
"@cloudflare/workers-types": "^4.20241112.0",
"@vitest/runner": "2.1.5",
"@vitest/snapshot": "2.1.5",
"typescript": "^5.7.3",
"typescript": "^5.8.3",
"vitest": "2.1.5",
"wrangler": "^3.87.0"
},
@@ -1,5 +1,81 @@
# @llamaindex/llama-parse-browser-test
## 0.0.84
### Patch Changes
- Updated dependencies [049471b]
- @llamaindex/cloud@4.1.0
## 0.0.83
### Patch Changes
- Updated dependencies [c3bf3c7]
- @llamaindex/cloud@4.0.28
## 0.0.82
### Patch Changes
- @llamaindex/cloud@4.0.27
## 0.0.81
### Patch Changes
- @llamaindex/cloud@4.0.26
## 0.0.80
### Patch Changes
- Updated dependencies [2967d57]
- @llamaindex/cloud@4.0.25
## 0.0.79
### Patch Changes
- @llamaindex/cloud@4.0.24
## 0.0.78
### Patch Changes
- Updated dependencies [a1b1598]
- @llamaindex/cloud@4.0.23
## 0.0.77
### Patch Changes
- Updated dependencies [d2be868]
- @llamaindex/cloud@4.0.22
## 0.0.76
### Patch Changes
- Updated dependencies [579ca0c]
- @llamaindex/cloud@4.0.21
## 0.0.75
### Patch Changes
- Updated dependencies [48b0d88]
- Updated dependencies [f185772]
- @llamaindex/cloud@4.0.20
## 0.0.74
### Patch Changes
- Updated dependencies [5a0ed1f]
- Updated dependencies [5a0ed1f]
- @llamaindex/cloud@4.0.19
## 0.0.73
### Patch Changes
@@ -1,7 +1,7 @@
{
"name": "@llamaindex/llama-parse-browser-test",
"private": true,
"version": "0.0.73",
"version": "0.0.84",
"type": "module",
"scripts": {
"dev": "vite",
@@ -9,11 +9,11 @@
"preview": "vite preview"
},
"devDependencies": {
"typescript": "^5.7.3",
"typescript": "^5.8.3",
"vite": "^6.3.3",
"vite-plugin-wasm": "^3.4.1"
},
"dependencies": {
"@llamaindex/cloud": "workspace:*"
"llama-cloud-services": "^0.1.0"
}
}
+73
View File
@@ -1,5 +1,78 @@
# @llamaindex/next-agent-test
## 0.1.186
### Patch Changes
- Updated dependencies [049471b]
- llamaindex@0.11.25
## 0.1.185
### Patch Changes
- llamaindex@0.11.24
## 0.1.184
### Patch Changes
- llamaindex@0.11.23
## 0.1.183
### Patch Changes
- llamaindex@0.11.22
## 0.1.182
### Patch Changes
- llamaindex@0.11.21
## 0.1.181
### Patch Changes
- llamaindex@0.11.20
## 0.1.180
### Patch Changes
- llamaindex@0.11.19
## 0.1.179
### Patch Changes
- llamaindex@0.11.18
## 0.1.178
### Patch Changes
- llamaindex@0.11.17
## 0.1.177
### Patch Changes
- llamaindex@0.11.16
## 0.1.176
### Patch Changes
- llamaindex@0.11.15
## 0.1.175
### Patch Changes
- llamaindex@0.11.14
## 0.1.174
### Patch Changes
+9 -9
View File
@@ -1,6 +1,6 @@
{
"name": "@llamaindex/next-agent-test",
"version": "0.1.174",
"version": "0.1.186",
"private": true,
"scripts": {
"dev": "next dev",
@@ -8,18 +8,18 @@
"start": "next start"
},
"dependencies": {
"ai": "^4.0.0",
"ai": "^4.3.17",
"llamaindex": "workspace:*",
"next": "^15.3.0",
"next": "^15.3.3",
"react": "19.0.0",
"react-dom": "19.0.0"
},
"devDependencies": {
"@types/node": "^22.9.0",
"@types/react": "^19.0.10",
"@types/react-dom": "^19.0.4",
"eslint": "9.16.0",
"eslint-config-next": "15.1.0",
"typescript": "^5.7.3"
"@types/node": "^24.0.13",
"@types/react": "^19.1.8",
"@types/react-dom": "^19.1.6",
"eslint": "9.30.1",
"eslint-config-next": "15.3.5",
"typescript": "^5.8.3"
}
}
@@ -1,5 +1,78 @@
# test-edge-runtime
## 0.1.185
### Patch Changes
- Updated dependencies [049471b]
- llamaindex@0.11.25
## 0.1.184
### Patch Changes
- llamaindex@0.11.24
## 0.1.183
### Patch Changes
- llamaindex@0.11.23
## 0.1.182
### Patch Changes
- llamaindex@0.11.22
## 0.1.181
### Patch Changes
- llamaindex@0.11.21
## 0.1.180
### Patch Changes
- llamaindex@0.11.20
## 0.1.179
### Patch Changes
- llamaindex@0.11.19
## 0.1.178
### Patch Changes
- llamaindex@0.11.18
## 0.1.177
### Patch Changes
- llamaindex@0.11.17
## 0.1.176
### Patch Changes
- llamaindex@0.11.16
## 0.1.175
### Patch Changes
- llamaindex@0.11.15
## 0.1.174
### Patch Changes
- llamaindex@0.11.14
## 0.1.173
### Patch Changes
@@ -1,6 +1,6 @@
{
"name": "@llamaindex/nextjs-edge-runtime-test",
"version": "0.1.173",
"version": "0.1.185",
"private": true,
"scripts": {
"dev": "next dev",
@@ -9,14 +9,14 @@
},
"dependencies": {
"llamaindex": "workspace:*",
"next": "^15.3.0",
"next": "^15.3.3",
"react": "^19.1.0",
"react-dom": "^19.1.0"
},
"devDependencies": {
"@types/node": "^22.9.0",
"@types/react": "^19.0.10",
"@types/react-dom": "^19.0.4",
"typescript": "^5.7.3"
"@types/node": "^24.0.13",
"@types/react": "^19.1.8",
"@types/react-dom": "^19.1.6",
"typescript": "^5.8.3"
}
}
@@ -1,5 +1,96 @@
# @llamaindex/next-node-runtime
## 0.1.55
### Patch Changes
- Updated dependencies [049471b]
- llamaindex@0.11.25
## 0.1.54
### Patch Changes
- llamaindex@0.11.24
- @llamaindex/huggingface@0.1.24
- @llamaindex/readers@3.1.18
## 0.1.53
### Patch Changes
- llamaindex@0.11.23
- @llamaindex/huggingface@0.1.23
- @llamaindex/readers@3.1.17
## 0.1.52
### Patch Changes
- llamaindex@0.11.22
## 0.1.51
### Patch Changes
- llamaindex@0.11.21
- @llamaindex/huggingface@0.1.22
- @llamaindex/readers@3.1.16
## 0.1.50
### Patch Changes
- llamaindex@0.11.20
- @llamaindex/huggingface@0.1.21
- @llamaindex/readers@3.1.15
## 0.1.49
### Patch Changes
- @llamaindex/huggingface@0.1.20
## 0.1.48
### Patch Changes
- llamaindex@0.11.19
- @llamaindex/huggingface@0.1.19
- @llamaindex/readers@3.1.14
## 0.1.47
### Patch Changes
- llamaindex@0.11.18
## 0.1.46
### Patch Changes
- llamaindex@0.11.17
## 0.1.45
### Patch Changes
- llamaindex@0.11.16
## 0.1.44
### Patch Changes
- llamaindex@0.11.15
## 0.1.43
### Patch Changes
- llamaindex@0.11.14
- @llamaindex/huggingface@0.1.18
- @llamaindex/readers@3.1.13
## 0.1.42
### Patch Changes
@@ -1,6 +1,6 @@
{
"name": "@llamaindex/next-node-runtime-test",
"version": "0.1.42",
"version": "0.1.55",
"private": true,
"scripts": {
"dev": "next dev",
@@ -11,16 +11,16 @@
"@llamaindex/huggingface": "workspace:*",
"@llamaindex/readers": "workspace:*",
"llamaindex": "workspace:*",
"next": "^15.3.0",
"next": "^15.3.3",
"react": "19.0.0",
"react-dom": "19.0.0"
},
"devDependencies": {
"@types/node": "^22.9.0",
"@types/react": "^19.0.10",
"@types/react-dom": "^19.0.4",
"eslint": "9.16.0",
"eslint-config-next": "15.1.0",
"typescript": "^5.7.3"
"@types/node": "^24.0.13",
"@types/react": "^19.1.8",
"@types/react-dom": "^19.1.6",
"eslint": "9.30.1",
"eslint-config-next": "15.3.5",
"typescript": "^5.8.3"
}
}
@@ -1,5 +1,78 @@
# vite-import-llamaindex
## 0.0.52
### Patch Changes
- Updated dependencies [049471b]
- llamaindex@0.11.25
## 0.0.51
### Patch Changes
- llamaindex@0.11.24
## 0.0.50
### Patch Changes
- llamaindex@0.11.23
## 0.0.49
### Patch Changes
- llamaindex@0.11.22
## 0.0.48
### Patch Changes
- llamaindex@0.11.21
## 0.0.47
### Patch Changes
- llamaindex@0.11.20
## 0.0.46
### Patch Changes
- llamaindex@0.11.19
## 0.0.45
### Patch Changes
- llamaindex@0.11.18
## 0.0.44
### Patch Changes
- llamaindex@0.11.17
## 0.0.43
### Patch Changes
- llamaindex@0.11.16
## 0.0.42
### Patch Changes
- llamaindex@0.11.15
## 0.0.41
### Patch Changes
- llamaindex@0.11.14
## 0.0.40
### Patch Changes
@@ -1,7 +1,7 @@
{
"name": "vite-import-llamaindex",
"private": true,
"version": "0.0.40",
"version": "0.0.52",
"type": "module",
"scripts": {
"build": "vite build",
@@ -15,7 +15,7 @@
"devDependencies": {
"@size-limit/preset-big-lib": "^11.1.6",
"size-limit": "^11.1.6",
"typescript": "^5.7.3",
"typescript": "^5.8.3",
"vite": "^6.3.3"
},
"dependencies": {
@@ -1,5 +1,78 @@
# @llamaindex/waku-query-engine-test
## 0.0.186
### Patch Changes
- Updated dependencies [049471b]
- llamaindex@0.11.25
## 0.0.185
### Patch Changes
- llamaindex@0.11.24
## 0.0.184
### Patch Changes
- llamaindex@0.11.23
## 0.0.183
### Patch Changes
- llamaindex@0.11.22
## 0.0.182
### Patch Changes
- llamaindex@0.11.21
## 0.0.181
### Patch Changes
- llamaindex@0.11.20
## 0.0.180
### Patch Changes
- llamaindex@0.11.19
## 0.0.179
### Patch Changes
- llamaindex@0.11.18
## 0.0.178
### Patch Changes
- llamaindex@0.11.17
## 0.0.177
### Patch Changes
- llamaindex@0.11.16
## 0.0.176
### Patch Changes
- llamaindex@0.11.15
## 0.0.175
### Patch Changes
- llamaindex@0.11.14
## 0.0.174
### Patch Changes
+5 -5
View File
@@ -1,6 +1,6 @@
{
"name": "@llamaindex/waku-query-engine-test",
"version": "0.0.174",
"version": "0.0.186",
"type": "module",
"private": true,
"scripts": {
@@ -18,10 +18,10 @@
},
"devDependencies": {
"@tailwindcss/postcss": "^4.1.4",
"@types/react": "19.0.10",
"@types/react-dom": "19.0.4",
"@types/react": "19.1.8",
"@types/react-dom": "19.1.6",
"rollup": "4.38.0",
"tailwindcss": "^4.1.4",
"typescript": "5.7.3"
"tailwindcss": "^4.1.11",
"typescript": "5.8.3"
}
}
+1 -1
View File
@@ -23,7 +23,7 @@ await test("pinecone", async (t) => {
});
const vectorStore = new PineconeVectorStore({
embeddingModel: openaiEmbedding,
embedModel: openaiEmbedding,
});
t.after(async () => {
+4 -4
View File
@@ -19,14 +19,14 @@
"@llamaindex/openai": "workspace:*",
"@llamaindex/pinecone": "workspace:*",
"@llamaindex/postgres": "workspace:*",
"@types/node": "^22.9.0",
"@types/node": "^24.0.13",
"@types/pg": "^8.11.8",
"consola": "^3.2.3",
"dotenv": "^16.4.5",
"dotenv": "^17.2.0",
"llamaindex": "workspace:*",
"pg": "^8.12.0",
"pgvector": "0.2.0",
"tsx": "^4.19.3",
"zod": "^3.25.67"
"tsx": "^4.20.3",
"zod": "^3.25.76"
}
}
+351
View File
@@ -1,5 +1,356 @@
# examples
## 0.3.35
### Patch Changes
- Updated dependencies [c3bf3c7]
- Updated dependencies [f9f1de9]
- @llamaindex/cloud@4.0.28
- @llamaindex/core@0.6.19
- llamaindex@0.11.24
- @llamaindex/node-parser@2.0.19
- @llamaindex/anthropic@0.3.21
- @llamaindex/assemblyai@0.1.18
- @llamaindex/clip@0.0.70
- @llamaindex/cohere@0.0.33
- @llamaindex/deepinfra@0.0.70
- @llamaindex/discord@0.1.18
- @llamaindex/google@0.3.18
- @llamaindex/huggingface@0.1.24
- @llamaindex/jinaai@0.0.30
- @llamaindex/mistral@0.1.19
- @llamaindex/mixedbread@0.0.33
- @llamaindex/notion@0.1.18
- @llamaindex/ollama@0.1.19
- @llamaindex/openai@0.4.14
- @llamaindex/perplexity@0.0.27
- @llamaindex/portkey-ai@0.0.61
- @llamaindex/replicate@0.0.61
- @llamaindex/bm25-retriever@0.0.8
- @llamaindex/astra@0.0.33
- @llamaindex/azure@0.1.31
- @llamaindex/chroma@0.0.33
- @llamaindex/elastic-search@0.1.19
- @llamaindex/firestore@1.0.26
- @llamaindex/milvus@0.1.28
- @llamaindex/mongodb@0.0.34
- @llamaindex/pinecone@0.1.19
- @llamaindex/postgres@0.0.62
- @llamaindex/qdrant@0.1.29
- @llamaindex/supabase@0.1.20
- @llamaindex/upstash@0.0.33
- @llamaindex/weaviate@0.0.34
- @llamaindex/vercel@0.1.19
- @llamaindex/voyage-ai@1.0.25
- @llamaindex/readers@3.1.18
- @llamaindex/tools@0.1.9
- @llamaindex/workflow@1.1.20
- @llamaindex/deepseek@0.0.31
- @llamaindex/fireworks@0.0.30
- @llamaindex/groq@0.0.86
- @llamaindex/together@0.0.30
- @llamaindex/vllm@0.0.56
- @llamaindex/xai@0.0.17
## 0.3.34
### Patch Changes
- Updated dependencies [f29799e]
- Updated dependencies [7224c06]
- @llamaindex/workflow@1.1.19
- @llamaindex/core@0.6.18
- llamaindex@0.11.23
- @llamaindex/cloud@4.0.27
- @llamaindex/node-parser@2.0.18
- @llamaindex/anthropic@0.3.20
- @llamaindex/assemblyai@0.1.17
- @llamaindex/clip@0.0.69
- @llamaindex/cohere@0.0.32
- @llamaindex/deepinfra@0.0.69
- @llamaindex/discord@0.1.17
- @llamaindex/google@0.3.17
- @llamaindex/huggingface@0.1.23
- @llamaindex/jinaai@0.0.29
- @llamaindex/mistral@0.1.18
- @llamaindex/mixedbread@0.0.32
- @llamaindex/notion@0.1.17
- @llamaindex/ollama@0.1.18
- @llamaindex/openai@0.4.13
- @llamaindex/perplexity@0.0.26
- @llamaindex/portkey-ai@0.0.60
- @llamaindex/replicate@0.0.60
- @llamaindex/bm25-retriever@0.0.7
- @llamaindex/astra@0.0.32
- @llamaindex/azure@0.1.30
- @llamaindex/chroma@0.0.32
- @llamaindex/elastic-search@0.1.18
- @llamaindex/firestore@1.0.25
- @llamaindex/milvus@0.1.27
- @llamaindex/mongodb@0.0.33
- @llamaindex/pinecone@0.1.18
- @llamaindex/postgres@0.0.61
- @llamaindex/qdrant@0.1.28
- @llamaindex/supabase@0.1.19
- @llamaindex/upstash@0.0.32
- @llamaindex/weaviate@0.0.33
- @llamaindex/vercel@0.1.18
- @llamaindex/voyage-ai@1.0.24
- @llamaindex/readers@3.1.17
- @llamaindex/tools@0.1.8
- @llamaindex/deepseek@0.0.30
- @llamaindex/fireworks@0.0.29
- @llamaindex/groq@0.0.85
- @llamaindex/together@0.0.29
- @llamaindex/vllm@0.0.55
- @llamaindex/xai@0.0.16
## 0.3.33
### Patch Changes
- Updated dependencies [38da40b]
- @llamaindex/core@0.6.17
- @llamaindex/cloud@4.0.26
- llamaindex@0.11.21
- @llamaindex/node-parser@2.0.17
- @llamaindex/anthropic@0.3.19
- @llamaindex/assemblyai@0.1.16
- @llamaindex/clip@0.0.68
- @llamaindex/cohere@0.0.31
- @llamaindex/deepinfra@0.0.68
- @llamaindex/discord@0.1.16
- @llamaindex/google@0.3.16
- @llamaindex/huggingface@0.1.22
- @llamaindex/jinaai@0.0.28
- @llamaindex/mistral@0.1.17
- @llamaindex/mixedbread@0.0.31
- @llamaindex/notion@0.1.16
- @llamaindex/ollama@0.1.17
- @llamaindex/openai@0.4.12
- @llamaindex/perplexity@0.0.25
- @llamaindex/portkey-ai@0.0.59
- @llamaindex/replicate@0.0.59
- @llamaindex/bm25-retriever@0.0.6
- @llamaindex/astra@0.0.31
- @llamaindex/azure@0.1.29
- @llamaindex/chroma@0.0.31
- @llamaindex/elastic-search@0.1.17
- @llamaindex/firestore@1.0.24
- @llamaindex/milvus@0.1.26
- @llamaindex/mongodb@0.0.32
- @llamaindex/pinecone@0.1.17
- @llamaindex/postgres@0.0.60
- @llamaindex/qdrant@0.1.27
- @llamaindex/supabase@0.1.18
- @llamaindex/upstash@0.0.31
- @llamaindex/weaviate@0.0.32
- @llamaindex/vercel@0.1.17
- @llamaindex/voyage-ai@1.0.23
- @llamaindex/readers@3.1.16
- @llamaindex/tools@0.1.7
- @llamaindex/workflow@1.1.17
- @llamaindex/deepseek@0.0.29
- @llamaindex/fireworks@0.0.28
- @llamaindex/groq@0.0.84
- @llamaindex/together@0.0.28
- @llamaindex/vllm@0.0.54
- @llamaindex/xai@0.0.15
## 0.3.32
### Patch Changes
- Updated dependencies [650eeb1]
- Updated dependencies [a8ec08c]
- Updated dependencies [2967d57]
- @llamaindex/google@0.3.15
- @llamaindex/core@0.6.16
- @llamaindex/workflow@1.1.16
- @llamaindex/cloud@4.0.25
- llamaindex@0.11.20
- @llamaindex/node-parser@2.0.16
- @llamaindex/anthropic@0.3.18
- @llamaindex/assemblyai@0.1.15
- @llamaindex/clip@0.0.67
- @llamaindex/cohere@0.0.30
- @llamaindex/deepinfra@0.0.67
- @llamaindex/discord@0.1.15
- @llamaindex/huggingface@0.1.21
- @llamaindex/jinaai@0.0.27
- @llamaindex/mistral@0.1.16
- @llamaindex/mixedbread@0.0.30
- @llamaindex/notion@0.1.15
- @llamaindex/ollama@0.1.16
- @llamaindex/openai@0.4.11
- @llamaindex/perplexity@0.0.24
- @llamaindex/portkey-ai@0.0.58
- @llamaindex/replicate@0.0.58
- @llamaindex/bm25-retriever@0.0.5
- @llamaindex/astra@0.0.30
- @llamaindex/azure@0.1.28
- @llamaindex/chroma@0.0.30
- @llamaindex/elastic-search@0.1.16
- @llamaindex/firestore@1.0.23
- @llamaindex/milvus@0.1.25
- @llamaindex/mongodb@0.0.31
- @llamaindex/pinecone@0.1.16
- @llamaindex/postgres@0.0.59
- @llamaindex/qdrant@0.1.26
- @llamaindex/supabase@0.1.17
- @llamaindex/upstash@0.0.30
- @llamaindex/weaviate@0.0.31
- @llamaindex/vercel@0.1.16
- @llamaindex/voyage-ai@1.0.22
- @llamaindex/readers@3.1.15
- @llamaindex/tools@0.1.6
- @llamaindex/deepseek@0.0.28
- @llamaindex/fireworks@0.0.27
- @llamaindex/groq@0.0.83
- @llamaindex/together@0.0.27
- @llamaindex/vllm@0.0.53
- @llamaindex/xai@0.0.14
## 0.3.31
### Patch Changes
- Updated dependencies [d8f4f6a]
- Updated dependencies [856dd8c]
- @llamaindex/supabase@0.1.16
- @llamaindex/openai@0.4.10
- @llamaindex/clip@0.0.66
- @llamaindex/deepinfra@0.0.66
- @llamaindex/deepseek@0.0.27
- @llamaindex/fireworks@0.0.26
- @llamaindex/groq@0.0.82
- @llamaindex/huggingface@0.1.20
- @llamaindex/jinaai@0.0.26
- @llamaindex/perplexity@0.0.23
- @llamaindex/azure@0.1.27
- @llamaindex/together@0.0.26
- @llamaindex/vllm@0.0.52
- @llamaindex/xai@0.0.13
## 0.3.30
### Patch Changes
- Updated dependencies [7ad3411]
- Updated dependencies [5da5b3c]
- Updated dependencies [a1fdb07]
- Updated dependencies [ddc0eaf]
- @llamaindex/core@0.6.15
- @llamaindex/tools@0.1.5
- @llamaindex/workflow@1.1.15
- @llamaindex/openai@0.4.9
- @llamaindex/anthropic@0.3.17
- @llamaindex/cloud@4.0.24
- llamaindex@0.11.19
- @llamaindex/node-parser@2.0.15
- @llamaindex/assemblyai@0.1.14
- @llamaindex/clip@0.0.65
- @llamaindex/cohere@0.0.29
- @llamaindex/deepinfra@0.0.65
- @llamaindex/discord@0.1.14
- @llamaindex/google@0.3.14
- @llamaindex/huggingface@0.1.19
- @llamaindex/jinaai@0.0.25
- @llamaindex/mistral@0.1.15
- @llamaindex/mixedbread@0.0.29
- @llamaindex/notion@0.1.14
- @llamaindex/ollama@0.1.15
- @llamaindex/perplexity@0.0.22
- @llamaindex/portkey-ai@0.0.57
- @llamaindex/replicate@0.0.57
- @llamaindex/bm25-retriever@0.0.4
- @llamaindex/astra@0.0.29
- @llamaindex/azure@0.1.26
- @llamaindex/chroma@0.0.29
- @llamaindex/elastic-search@0.1.15
- @llamaindex/firestore@1.0.22
- @llamaindex/milvus@0.1.24
- @llamaindex/mongodb@0.0.30
- @llamaindex/pinecone@0.1.15
- @llamaindex/postgres@0.0.58
- @llamaindex/qdrant@0.1.25
- @llamaindex/supabase@0.1.15
- @llamaindex/upstash@0.0.29
- @llamaindex/weaviate@0.0.30
- @llamaindex/vercel@0.1.15
- @llamaindex/voyage-ai@1.0.21
- @llamaindex/readers@3.1.14
- @llamaindex/deepseek@0.0.26
- @llamaindex/fireworks@0.0.25
- @llamaindex/groq@0.0.81
- @llamaindex/together@0.0.25
- @llamaindex/vllm@0.0.51
- @llamaindex/xai@0.0.12
## 0.3.29
### Patch Changes
- Updated dependencies [af3f866]
- @llamaindex/deepseek@0.0.25
## 0.3.28
### Patch Changes
- 36773a8: Fixed start script, reverted some changes to imports
- 891562d: Fix xai dependency in the examples
- Updated dependencies [5a0ed1f]
- Updated dependencies [5a0ed1f]
- Updated dependencies [8eeac33]
- @llamaindex/cloud@4.0.19
- @llamaindex/core@0.6.14
- llamaindex@0.11.14
- @llamaindex/node-parser@2.0.14
- @llamaindex/anthropic@0.3.16
- @llamaindex/assemblyai@0.1.13
- @llamaindex/clip@0.0.64
- @llamaindex/cohere@0.0.28
- @llamaindex/deepinfra@0.0.64
- @llamaindex/discord@0.1.13
- @llamaindex/google@0.3.13
- @llamaindex/huggingface@0.1.18
- @llamaindex/jinaai@0.0.24
- @llamaindex/mistral@0.1.14
- @llamaindex/mixedbread@0.0.28
- @llamaindex/notion@0.1.13
- @llamaindex/ollama@0.1.14
- @llamaindex/openai@0.4.8
- @llamaindex/perplexity@0.0.21
- @llamaindex/portkey-ai@0.0.56
- @llamaindex/replicate@0.0.56
- @llamaindex/bm25-retriever@0.0.3
- @llamaindex/astra@0.0.28
- @llamaindex/azure@0.1.25
- @llamaindex/chroma@0.0.28
- @llamaindex/elastic-search@0.1.14
- @llamaindex/firestore@1.0.21
- @llamaindex/milvus@0.1.23
- @llamaindex/mongodb@0.0.29
- @llamaindex/pinecone@0.1.14
- @llamaindex/postgres@0.0.57
- @llamaindex/qdrant@0.1.24
- @llamaindex/supabase@0.1.14
- @llamaindex/upstash@0.0.28
- @llamaindex/weaviate@0.0.29
- @llamaindex/vercel@0.1.14
- @llamaindex/voyage-ai@1.0.20
- @llamaindex/readers@3.1.13
- @llamaindex/tools@0.1.4
- @llamaindex/workflow@1.1.14
- @llamaindex/deepseek@0.0.24
- @llamaindex/fireworks@0.0.24
- @llamaindex/groq@0.0.80
- @llamaindex/together@0.0.24
- @llamaindex/vllm@0.0.50
- @llamaindex/xai@0.0.11
## 0.3.27
### Patch Changes
+31 -7
View File
@@ -1,9 +1,10 @@
# LlamaIndexTS Examples
Before running any of the code examples,
make sure you have basic knowledge of the [LlamaIndexTS](https://ts.llamaindex.ai/).
This package contains several examples of how to use LlamaIndexTS.
## Usage
Most examples will use OpenAI by default, so be sure to set your API key.
## Running Examples
```shell
# export your API key
@@ -12,8 +13,31 @@ export OPENAI_API_KEY="sk-..."
npx tsx ./rag/chatEngine.ts
```
## Build your own RAG app
## Recommended Starter Examples
```shell
npx create llama
```
Agents:
- [Basic OpenAI Agent with Tools](./agents/agent/openai.ts)
- [Agent with MCP Tools](./agents/agent/mcp-tools.ts)
- [Customizing Memory](./agents/memory/agent-memory.ts)
Workflows:
- [Workflow basics](./agents/workflow/joke.ts)
- [Find more workflow examples in the `workflows-ts` repo!](https://github.com/run-llama/workflows-ts)
Indexing, Retrieval, and Querying:
- [Basic Vector Indexing + Query Engine](./index/vectorIndex.ts)
- [Agent + Query Engine Tool](./agents/agent/query-tool.ts)
Multimodal:
- [Multimodal RAG](./multimodal/rag.ts)
- [Multimodal Chat](./multimodal/context.ts)
Some more general folders that might be useful to explore:
- [storage](./storage/): Examples with various vector stores
- [readers](./readers/): Examples of how to use the various readers
- [models](./models/): Examples of how to use the various LLMs and embedding models from many providers
+2 -1
View File
@@ -3,7 +3,7 @@
*/
import { openai } from "@llamaindex/openai";
import { agent } from "@llamaindex/workflow";
import { getWeatherTool } from "../../deprecated/agents/utils/tools";
import { getWeatherTool } from "../tools/tools";
async function main() {
const weatherAgent = agent({
@@ -24,6 +24,7 @@ async function main() {
state: result.data.state,
});
console.log(`${JSON.stringify(caResult, null, 2)}`);
console.log("assistant message:", result.data.message);
}
main().catch((error) => {
+1 -1
View File
@@ -1,6 +1,6 @@
import { ollama } from "@llamaindex/ollama";
import { agent } from "@llamaindex/workflow";
import { getWeatherTool } from "../../deprecated/agents/utils/tools";
import { getWeatherTool } from "../tools/tools";
async function main() {
const myAgent = agent({
+150
View File
@@ -0,0 +1,150 @@
/**
* Example: Vector Memory Block
*
* This example demonstrates how to use the VectorMemoryBlock to store and retrieve
* conversation history using vector similarity search. The vector memory block
* stores messages in a vector store and can retrieve relevant context based on
* semantic similarity to recent messages.
*/
import { OpenAI, OpenAIEmbedding } from "@llamaindex/openai";
import { QdrantVectorStore } from "@llamaindex/qdrant";
import { createMemory, vectorBlock } from "llamaindex";
// Set up the LLM and embedding model
const llm = new OpenAI({ model: "gpt-4.1-mini" });
const embedModel = new OpenAIEmbedding({ model: "text-embedding-3-small" });
// Simulate a conversation with some context
// This conversation has 8 messages, which is more than the token limit of 100 tokens (set below)
// The last 4 messages are kept in to short term memory block (as their tokens are in the limit)
// Whereas the first 5 messages are added to long term memory block (in here we will use the vector memory block with Qdrant)
const CONVERSATION_TURNS = [
//// This is the first 5 messages that are added to long term memory block (vector memory block)
{
role: "user",
content: "Hi, I'm Sarah and I work as a data scientist at Google.",
},
{
role: "assistant",
content:
"Hello Sarah! It's great to meet you. Data science at Google must be exciting!",
},
{
role: "user",
content:
"Yes, I specialize in machine learning and natural language processing.",
},
{
role: "assistant",
content: "That's impressive! ML and NLP are fascinating fields.",
},
{
role: "user",
content:
"I have a PhD in Computer Science from Stanford, and I love hiking on weekends.",
},
//// This is the last 4 messages that are added to short term memory block
{
role: "assistant",
content:
"Wow, Stanford PhD! And hiking is a great way to unwind from tech work.",
},
{
role: "user",
content: "I also have two cats named Whiskers and Mittens.",
},
{
role: "assistant",
content:
"Cats make wonderful companions! Whiskers and Mittens are cute names.",
},
{
role: "user",
content: "Summary information about Sarah and her cats",
},
];
async function main() {
console.log("=== Vector Memory Block Example ===\n");
/**
* Create a vector store. You can quickly get a local instance of Qdrant running with Docker:
* ```bash
* docker pull qdrant/qdrant
* docker run -p 6333:6333 qdrant/qdrant
* ```
*
* Go to http://localhost:6333/dashboard#/collections to see your data
*/
const vectorStore = new QdrantVectorStore({
url: "http://localhost:6333",
embedModel,
});
// Create a vector memory block using the factory function
const vectorMemoryBlock = vectorBlock({
vectorStore,
priority: 5,
});
// Create a memory store with the vector memory block
const memory = createMemory([], {
llm,
memoryBlocks: [vectorMemoryBlock],
tokenLimit: 100,
shortTermTokenLimitRatio: 0.7,
});
// Store the conversation history in the vector memory
console.log(`Adding ${CONVERSATION_TURNS.length} messages to the memory...`);
for (const message of CONVERSATION_TURNS) {
await memory.add(message);
}
// Retrieve relevant context for the current user request
console.log("Retrieving relevant context...");
const chatHistory = await memory.getLLM();
// You will see there's 1 generated context message from vector memory block, and 4 messages from short term memory block
console.log("Chat memory:", chatHistory);
// Now simulate the assistant responding with context
console.log("\nAssistant response with context:");
const response = await llm.chat({
messages: chatHistory,
});
console.log(response.message.content);
// Try adding more messages to the memory
const newMessages = [
{
role: "user",
content: "Write a long paragraph about weather in Tokyo",
},
{
role: "assistant",
content:
"The weather in Tokyo is sunny and warm. The temperature is around 20 degrees Celsius. The weather is very nice and the people are friendly.",
},
{
role: "user",
content: "What is the weather in Tokyo?",
},
];
// Add the new messages to the memory
for (const message of newMessages) {
await memory.add(message);
}
// Try retrieving the new messages
const newChatHistory = await memory.getLLM();
// You can see now that new chat history will contain the nodes (separated by `\n`) in the
// context message that is generated by the vector memory block
// The number of retrieved nodes is set by `similarityTopK` in `queryOptions` of `vectorBlock`
// (default `similarityTopK` is 2)
console.log("New chat history:", newChatHistory);
}
main().catch(console.error);
@@ -1,7 +1,7 @@
import { OpenAI } from "@llamaindex/openai";
import { openai } from "@llamaindex/openai";
async function main() {
const llm = new OpenAI({ model: "gpt-4-turbo" });
const llm = openai({ model: "gpt-4.1-mini" });
const args: Parameters<typeof llm.chat>[0] = {
additionalChatOptions: {
tool_choice: "auto",
+46
View File
@@ -0,0 +1,46 @@
import { openai } from "@llamaindex/openai";
import { tool } from "llamaindex";
import z from "zod";
import { ChatMessage } from "llamaindex";
async function main() {
const llm = openai({ model: "gpt-4.1-mini" });
const messages = [
{
content: `What's the weather like in San Francisco?`,
role: "user",
} as ChatMessage,
];
let exit = false;
do {
const { stream, newMessages, toolCalls } = await llm.exec({
messages,
tools: [
tool({
name: "get_weather",
description: "Get the current weather for a location",
parameters: z.object({
address: z.string().describe("The address"),
}),
execute: ({ address }) => {
return `It's sunny in ${address}!`;
},
}),
],
stream: true,
});
for await (const chunk of stream) {
process.stdout.write(chunk.delta);
}
messages.push(...newMessages());
// exit condition to stop the agent loop
// here we can also check for specific tool calls or limit the number of llm.exec calls
exit = toolCalls.length === 0;
} while (!exit);
}
(async function () {
await main();
})();
+43
View File
@@ -0,0 +1,43 @@
import { openai } from "@llamaindex/openai";
import { ChatMessage, tool } from "llamaindex";
import z from "zod";
async function main() {
const llm = openai({ model: "gpt-4.1-mini" });
const messages = [
{
content: `What's the weather like in San Francisco?`,
role: "user",
} as ChatMessage,
];
let exit = false;
do {
const { newMessages, toolCalls } = await llm.exec({
messages,
tools: [
tool({
name: "get_weather",
description: "Get the current weather for a location",
parameters: z.object({
address: z.string().describe("The address"),
}),
execute: ({ address }) => {
return `It's sunny in ${address}!`;
},
}),
],
});
console.log(newMessages);
messages.push(...newMessages);
// exit condition to stop the agent loop
// here we can also check for specific tool calls or limit the number of llm.exec calls
exit = toolCalls.length === 0;
} while (!exit);
}
(async function () {
console.log("Starting...");
await main();
console.log("Done");
})();
+1 -1
View File
@@ -4,7 +4,7 @@ import {
getCurrentIDTool,
getUserInfoTool,
getWeatherTool,
} from "./utils/tools";
} from "../../agents/tools/tools";
async function main() {
// Create an OpenAIAgent with the function tools
+1 -1
View File
@@ -3,7 +3,7 @@ import {
getCurrentIDTool,
getUserInfoTool,
getWeatherTool,
} from "./utils/tools";
} from "../../agents/tools/tools";
async function main() {
// Create an OpenAIAgent with the function tools
@@ -15,15 +15,15 @@
},
"devDependencies": {
"@eslint/js": "^9.25.0",
"@types/react": "^19.1.2",
"@types/react-dom": "^19.1.2",
"@types/react": "^19.1.8",
"@types/react-dom": "^19.1.6",
"@vitejs/plugin-react": "^4.5.2",
"eslint": "^9.25.0",
"eslint": "^9.30.1",
"eslint-plugin-react-hooks": "^5.2.0",
"eslint-plugin-react-refresh": "^0.4.19",
"globals": "^16.0.0",
"typescript": "~5.8.3",
"typescript-eslint": "^8.30.1",
"typescript-eslint": "^8.36.0",
"vite": "^6.3.5"
}
}
@@ -30,6 +30,12 @@ async function main() {
);
// and print out the text part
console.log(textPart?.text);
const imageId = response.message.options?.image_id;
if (imageId) {
console.log("Image ID for multi-turn generation:", imageId);
console.log("Use this image_id in subsequent requests to modify the image");
}
}
main().catch(console.error);
@@ -0,0 +1,89 @@
import { openaiResponses } from "@llamaindex/openai";
import fs from "fs";
import { MessageContentDetail } from "llamaindex";
async function main() {
const llm = openaiResponses({
model: "gpt-4.1-mini",
builtInTools: [{ type: "image_generation" }],
});
// First turn: Generate initial image
console.log("=== First Turn: Generate initial image ===");
const firstResponse = await llm.chat({
messages: [
{
role: "user",
content:
"Generate an image of a cute tiny llama wearing a hat playing with a cat on a meadow",
},
],
});
const firstContent = firstResponse.message.content as MessageContentDetail[];
const firstImagePart = firstContent.find((part) => part.type === "image");
const firstTextPart = firstContent.find((part) => part.type === "text");
// Save the first image
if (firstImagePart?.data) {
fs.writeFileSync(
"llama-initial.png",
Buffer.from(firstImagePart.data as string, "base64"),
);
console.log("First image saved as 'llama-initial.png'");
}
if (firstTextPart?.text) {
console.log("First response:", firstTextPart.text);
}
// Get the image_id from the response options for multi-turn
const imageId = firstResponse.message.options?.image_id;
console.log("Image ID for multi-turn:", imageId);
if (imageId) {
// Second turn: Modify the image using the image_id
console.log("\n=== Second Turn: Modify the image ===");
const secondResponse = await llm.chat({
messages: [
{
role: "user",
content:
"Generate an image of a cute tiny llama wearing a hat playing with a cat on a meadow",
},
{
role: "assistant",
content: firstContent,
options: { image_id: imageId },
},
{
role: "user",
content:
"Now add a rainbow in the background and make the llama's hat blue",
},
],
});
const secondContent = secondResponse.message
.content as MessageContentDetail[];
const secondImagePart = secondContent.find((part) => part.type === "image");
const secondTextPart = secondContent.find((part) => part.type === "text");
// Save the modified image
if (secondImagePart?.data) {
fs.writeFileSync(
"llama-modified.png",
Buffer.from(secondImagePart.data as string, "base64"),
);
console.log("Modified image saved as 'llama-modified.png'");
}
if (secondTextPart?.text) {
console.log("Second response:", secondTextPart.text);
}
} else {
console.log("No image_id received, cannot perform multi-turn generation");
}
}
main().catch(console.error);
+55 -55
View File
@@ -1,80 +1,80 @@
{
"name": "@llamaindex/examples",
"version": "0.3.27",
"version": "0.3.35",
"private": true,
"scripts": {
"lint": "eslint .",
"start": "tsx ./starter.ts"
"start": "echo 'To get started, run `npx tsx <path to example>`'"
},
"dependencies": {
"@ai-sdk/openai": "^1.0.5",
"@azure/cosmos": "^4.1.1",
"@azure/identity": "^4.4.1",
"@azure/search-documents": "^12.1.0",
"@llamaindex/anthropic": "^0.3.15",
"@llamaindex/assemblyai": "^0.1.12",
"@llamaindex/astra": "^0.0.27",
"@llamaindex/azure": "^0.1.24",
"@llamaindex/bm25-retriever": "^0.0.2",
"@llamaindex/chroma": "^0.0.27",
"@llamaindex/clip": "^0.0.63",
"@llamaindex/cloud": "^4.0.18",
"@llamaindex/cohere": "^0.0.27",
"@llamaindex/core": "^0.6.13",
"@llamaindex/deepinfra": "^0.0.63",
"@llamaindex/deepseek": "^0.0.23",
"@llamaindex/discord": "^0.1.12",
"@llamaindex/elastic-search": "^0.1.13",
"@llamaindex/anthropic": "^0.3.21",
"@llamaindex/assemblyai": "^0.1.18",
"@llamaindex/astra": "^0.0.33",
"@llamaindex/azure": "^0.1.31",
"@llamaindex/bm25-retriever": "^0.0.8",
"@llamaindex/chroma": "^0.0.33",
"@llamaindex/clip": "^0.0.70",
"llama-cloud-services": "^0.1.0",
"@llamaindex/cohere": "^0.0.33",
"@llamaindex/core": "^0.6.19",
"@llamaindex/deepinfra": "^0.0.70",
"@llamaindex/deepseek": "^0.0.31",
"@llamaindex/discord": "^0.1.18",
"@llamaindex/elastic-search": "^0.1.19",
"@llamaindex/env": "^0.1.30",
"@llamaindex/firestore": "^1.0.20",
"@llamaindex/fireworks": "^0.0.23",
"@llamaindex/google": "^0.3.12",
"@llamaindex/groq": "^0.0.79",
"@llamaindex/huggingface": "^0.1.17",
"@llamaindex/jinaai": "^0.0.23",
"@llamaindex/milvus": "^0.1.22",
"@llamaindex/mistral": "^0.1.13",
"@llamaindex/mixedbread": "^0.0.27",
"@llamaindex/mongodb": "^0.0.28",
"@llamaindex/node-parser": "^2.0.13",
"@llamaindex/notion": "^0.1.12",
"@llamaindex/ollama": "^0.1.13",
"@llamaindex/openai": "^0.4.7",
"@llamaindex/perplexity": "^0.0.20",
"@llamaindex/pinecone": "^0.1.13",
"@llamaindex/portkey-ai": "^0.0.55",
"@llamaindex/postgres": "^0.0.56",
"@llamaindex/qdrant": "^0.1.23",
"@llamaindex/readers": "^3.1.12",
"@llamaindex/replicate": "^0.0.55",
"@llamaindex/supabase": "^0.1.13",
"@llamaindex/together": "^0.0.23",
"@llamaindex/tools": "^0.1.3",
"@llamaindex/upstash": "^0.0.27",
"@llamaindex/vercel": "^0.1.13",
"@llamaindex/vllm": "^0.0.49",
"@llamaindex/voyage-ai": "^1.0.19",
"@llamaindex/weaviate": "^0.0.28",
"@llamaindex/workflow": "^1.1.13",
"@llamaindex/xai": "workspace:^0.0.10",
"@notionhq/client": "^2.2.15",
"@llamaindex/firestore": "^1.0.26",
"@llamaindex/fireworks": "^0.0.30",
"@llamaindex/google": "^0.3.18",
"@llamaindex/groq": "^0.0.86",
"@llamaindex/huggingface": "^0.1.24",
"@llamaindex/jinaai": "^0.0.30",
"@llamaindex/milvus": "^0.1.28",
"@llamaindex/mistral": "^0.1.19",
"@llamaindex/mixedbread": "^0.0.33",
"@llamaindex/mongodb": "^0.0.34",
"@llamaindex/node-parser": "^2.0.19",
"@llamaindex/notion": "^0.1.18",
"@llamaindex/ollama": "^0.1.19",
"@llamaindex/openai": "^0.4.14",
"@llamaindex/perplexity": "^0.0.27",
"@llamaindex/pinecone": "^0.1.19",
"@llamaindex/portkey-ai": "^0.0.61",
"@llamaindex/postgres": "^0.0.62",
"@llamaindex/qdrant": "^0.1.29",
"@llamaindex/readers": "^3.1.18",
"@llamaindex/replicate": "^0.0.61",
"@llamaindex/supabase": "^0.1.20",
"@llamaindex/together": "^0.0.30",
"@llamaindex/tools": "^0.1.9",
"@llamaindex/upstash": "^0.0.33",
"@llamaindex/vercel": "^0.1.19",
"@llamaindex/vllm": "^0.0.56",
"@llamaindex/voyage-ai": "^1.0.25",
"@llamaindex/weaviate": "^0.0.34",
"@llamaindex/workflow": "^1.1.20",
"@llamaindex/xai": "^0.0.17",
"@notionhq/client": "^4.0.0",
"@pinecone-database/pinecone": "^4.0.0",
"@vercel/postgres": "^0.10.0",
"ai": "^4.0.0",
"ai": "^4.3.17",
"ajv": "^8.17.1",
"commander": "^12.1.0",
"dotenv": "^16.4.5",
"dotenv": "^17.2.0",
"js-tiktoken": "^1.0.14",
"llamaindex": "^0.11.13",
"llamaindex": "^0.11.24",
"mongodb": "6.7.0",
"postgres": "^3.4.4",
"wikipedia": "^2.1.2",
"zod": "^3.25.67"
"zod": "^3.25.76"
},
"devDependencies": {
"@types/node": "^22.9.0",
"tsx": "^4.19.3",
"typescript": "^5.7.3"
"@types/node": "^24.0.13",
"tsx": "^4.20.3",
"typescript": "^5.8.3"
},
"stackblitz": {
"startCommand": "npm start"
+2
View File
@@ -0,0 +1,2 @@
packages:
- "**"
+6 -8
View File
@@ -9,10 +9,7 @@
"start:html": "node --import tsx ./src/html.ts",
"start:markdown": "node --import tsx ./src/markdown.ts",
"start:pdf": "node --import tsx ./src/pdf.ts",
"start:llamaparse": "node --import tsx ./src/llamaparse.ts",
"start:notion": "node --import tsx ./src/notion.ts",
"start:llamaparse-dir": "node --import tsx ./src/simple-directory-reader-with-llamaparse.ts",
"start:llamaparse-json": "node --import tsx ./src/llamaparse-json.ts",
"start:discord": "node --import tsx ./src/discord.ts",
"start:json": "node --import tsx ./src/json.ts",
"start:obsidian": "node --import tsx ./src/obsidian.ts",
@@ -20,14 +17,15 @@
"start:excel": "node --import tsx ./src/excel.ts"
},
"dependencies": {
"@llamaindex/cloud": "workspace:* || ^2.0.24",
"@llamaindex/readers": "workspace:* || ^1.0.25",
"llama-cloud-services": "^0.1.0",
"@llamaindex/excel": "workspace:*",
"@llamaindex/readers": "workspace:* || ^1.0.25",
"@notionhq/client": "^4.0.0",
"llamaindex": "workspace:* || ^0.8.37"
},
"devDependencies": {
"@types/node": "^22.9.0",
"tsx": "^4.19.3",
"typescript": "^5.7.3"
"@types/node": "^24.0.13",
"tsx": "^4.20.3",
"typescript": "^5.8.3"
}
}
+1 -1
View File
@@ -15,7 +15,7 @@ async function main() {
const vectorStore = new QdrantVectorStore({
url: process.env.QDRANT_URL,
apiKey: process.env.QDRANT_API_KEY,
embeddingModel: embedding,
embedModel: embedding,
collectionName: "gemini_test",
});
const storageContext = await storageContextFromDefaults({ vectorStore });
+1 -1
View File
@@ -16,7 +16,7 @@ async function main() {
const vectorStore = new QdrantVectorStore({
url: process.env.QDRANT_URL,
apiKey: process.env.QDRANT_API_KEY,
embeddingModel: embedding,
embedModel: embedding,
collectionName: "jina_test",
});
const storageContext = await storageContextFromDefaults({ vectorStore });
-10114
View File
File diff suppressed because it is too large Load Diff
+16 -10
View File
@@ -4,7 +4,7 @@
"scripts": {
"clean": "find . -type d \\( -name .turbo -o -name node_modules -o -name dist -o -name .next -o -name lib \\) -exec rm -rf {} +",
"build": "turbo run build --filter=\"./packages/*\" --filter=\"./packages/providers/**\"",
"dev": "turbo run dev --filter=\"./packages/*\" --filter=\"./packages/providers/**\"",
"dev": "turbo run dev --filter=\"./packages/*\" --filter=\"./packages/providers/**\" --concurrency=100",
"format": "prettier --ignore-unknown --cache --check .",
"format:write": "prettier --ignore-unknown --write .",
"lint": "turbo run lint",
@@ -17,18 +17,19 @@
"release-snapshot": "pnpm run build && changeset publish --tag snapshot",
"new-version": "changeset version && pnpm format:write && pnpm run build",
"new-snapshot": "pnpm run build && changeset version --snapshot",
"lint-staged": "lint-staged"
"lint-staged": "lint-staged",
"preinstall": "npx only-allow pnpm"
},
"devDependencies": {
"@changesets/cli": "^2.27.5",
"@eslint/js": "^9.25.0",
"bunchee": "6.4.0",
"eslint": "9.22.0",
"eslint-config-next": "^15.1.0",
"eslint-config-prettier": "^9.1.0",
"eslint-config-turbo": "^2.3.3",
"eslint-plugin-react": "7.37.2",
"eslint-plugin-turbo": "^2.5.0",
"eslint": "9.30.1",
"eslint-config-next": "^15.3.5",
"eslint-config-prettier": "^10.1.5",
"eslint-config-turbo": "^2.5.4",
"eslint-plugin-react": "7.37.5",
"eslint-plugin-turbo": "^2.5.4",
"globals": "^15.12.0",
"husky": "^9.1.7",
"lint-staged": "^15.2.11",
@@ -37,11 +38,16 @@
"prettier-plugin-organize-imports": "^4.1.0",
"prettier-plugin-tailwindcss": "^0.6.11",
"turbo": "^2.4.4",
"typescript": "^5.7.3",
"typescript-eslint": "^8.18.0",
"typescript": "^5.8.3",
"typescript-eslint": "^8.36.0",
"vitest": "^3.1.1"
},
"packageManager": "pnpm@10.8.1",
"pnpm": {
"overrides": {
"@notionhq/client": "4.0.0"
}
},
"lint-staged": {
"*.{js,jsx,ts,tsx}": [
"eslint --fix",
+73
View File
@@ -1,5 +1,78 @@
# @llamaindex/autotool
## 8.0.25
### Patch Changes
- Updated dependencies [049471b]
- llamaindex@0.11.25
## 8.0.24
### Patch Changes
- llamaindex@0.11.24
## 8.0.23
### Patch Changes
- llamaindex@0.11.23
## 8.0.22
### Patch Changes
- llamaindex@0.11.22
## 8.0.21
### Patch Changes
- llamaindex@0.11.21
## 8.0.20
### Patch Changes
- llamaindex@0.11.20
## 8.0.19
### Patch Changes
- llamaindex@0.11.19
## 8.0.18
### Patch Changes
- llamaindex@0.11.18
## 8.0.17
### Patch Changes
- llamaindex@0.11.17
## 8.0.16
### Patch Changes
- llamaindex@0.11.16
## 8.0.15
### Patch Changes
- llamaindex@0.11.15
## 8.0.14
### Patch Changes
- llamaindex@0.11.14
## 8.0.13
### Patch Changes
@@ -1,5 +1,90 @@
# @llamaindex/autotool-01-node-example
## 0.0.133
### Patch Changes
- Updated dependencies [049471b]
- llamaindex@0.11.25
- @llamaindex/autotool@8.0.25
## 0.0.132
### Patch Changes
- llamaindex@0.11.24
- @llamaindex/autotool@8.0.24
## 0.0.131
### Patch Changes
- llamaindex@0.11.23
- @llamaindex/autotool@8.0.23
## 0.0.130
### Patch Changes
- llamaindex@0.11.22
- @llamaindex/autotool@8.0.22
## 0.0.129
### Patch Changes
- llamaindex@0.11.21
- @llamaindex/autotool@8.0.21
## 0.0.128
### Patch Changes
- llamaindex@0.11.20
- @llamaindex/autotool@8.0.20
## 0.0.127
### Patch Changes
- llamaindex@0.11.19
- @llamaindex/autotool@8.0.19
## 0.0.126
### Patch Changes
- llamaindex@0.11.18
- @llamaindex/autotool@8.0.18
## 0.0.125
### Patch Changes
- llamaindex@0.11.17
- @llamaindex/autotool@8.0.17
## 0.0.124
### Patch Changes
- llamaindex@0.11.16
- @llamaindex/autotool@8.0.16
## 0.0.123
### Patch Changes
- llamaindex@0.11.15
- @llamaindex/autotool@8.0.15
## 0.0.122
### Patch Changes
- llamaindex@0.11.14
- @llamaindex/autotool@8.0.14
## 0.0.121
### Patch Changes
@@ -8,10 +8,10 @@
"openai": "^4.73.1"
},
"devDependencies": {
"tsx": "^4.19.3"
"tsx": "^4.20.3"
},
"scripts": {
"start": "node --import tsx --import @llamaindex/autotool/node ./src/index.ts"
},
"version": "0.0.121"
"version": "0.0.133"
}
+5 -5
View File
@@ -6,7 +6,7 @@
"url": "git+https://github.com/run-llama/LlamaIndexTS.git",
"directory": "packages/autotool"
},
"version": "8.0.13",
"version": "8.0.25",
"description": "auto transpile your JS function to LLM Agent compatible",
"files": [
"dist",
@@ -74,12 +74,12 @@
"devDependencies": {
"@swc/types": "^0.1.12",
"@types/json-schema": "^7.0.15",
"@types/node": "^22.9.0",
"@types/node": "^24.0.13",
"llamaindex": "workspace:*",
"next": "^15.3.0",
"next": "^15.3.3",
"rollup": "^4.28.1",
"tsx": "^4.19.3",
"typescript": "^5.7.3",
"tsx": "^4.20.3",
"typescript": "^5.8.3",
"vitest": "^2.1.5",
"webpack": "^5.97.1"
}
-9
View File
@@ -1,9 +0,0 @@
# @llamaindex/cloud
> LlamaCloud is a new generation of managed parsing, ingestion, and retrieval services, designed to bring production-grade context-augmentation to your LLM and RAG applications.
For more information, see the [API documentation](https://docs.cloud.llamaindex.ai/).
## License
MIT
-8
View File
@@ -1,8 +0,0 @@
{
"type": "module",
"main": "./dist/index.cjs",
"module": "./dist/index.js",
"types": "./dist/index.d.ts",
"exports": "./dist/index.js",
"private": true
}
-24
View File
@@ -1,24 +0,0 @@
import { defaultPlugins, defineConfig } from "@hey-api/openapi-ts";
export default defineConfig({
// you can download this file to get the latest version of the OpenAPI document
// @link https://api.cloud.llamaindex.ai/api/openapi.json
input: "./openapi.json",
output: {
path: "./src/client",
format: "prettier",
lint: "eslint",
},
plugins: [
...defaultPlugins,
"@hey-api/client-fetch",
"zod",
"@hey-api/schemas",
"@hey-api/sdk",
{
enums: "javascript",
identifierCase: "PascalCase",
name: "@hey-api/typescript",
},
],
});
File diff suppressed because it is too large Load Diff
-84
View File
@@ -1,84 +0,0 @@
{
"name": "@llamaindex/cloud",
"version": "4.0.18",
"type": "module",
"license": "MIT",
"scripts": {
"generate": "./node_modules/.bin/openapi-ts",
"build": "pnpm run generate && bunchee",
"dev": "bunchee --watch"
},
"files": [
"openapi.json",
"./api",
"./reader"
],
"exports": {
"./openapi.json": "./openapi.json",
"./api": {
"require": {
"types": "./api/dist/index.d.cts",
"default": "./api/dist/index.cjs"
},
"import": {
"types": "./api/dist/index.d.ts",
"default": "./api/dist/index.js"
},
"default": "./api/dist/index.js"
},
"./reader": {
"require": {
"types": "./reader/dist/index.d.cts",
"default": "./reader/dist/index.cjs"
},
"import": {
"types": "./reader/dist/index.d.ts",
"default": "./reader/dist/index.js"
},
"default": "./reader/dist/index.js"
},
"./parse": {
"require": {
"types": "./parse/dist/index.d.cts",
"default": "./parse/dist/index.cjs"
},
"import": {
"types": "./parse/dist/index.d.ts",
"default": "./parse/dist/index.js"
},
"default": "./parse/dist/index.js"
},
".": {
"require": {
"types": "./reader/dist/index.d.cts",
"default": "./reader/dist/index.cjs"
},
"import": {
"types": "./reader/dist/index.d.ts",
"default": "./reader/dist/index.js"
},
"default": "./reader/dist/index.js"
}
},
"repository": {
"type": "git",
"url": "git+https://github.com/run-llama/LlamaIndexTS.git",
"directory": "packages/cloud"
},
"devDependencies": {
"@hey-api/client-fetch": "^0.10.1",
"@hey-api/openapi-ts": "^0.67.5",
"@llama-flow/core": "^0.4.1",
"@llamaindex/core": "workspace:*",
"@llamaindex/env": "workspace:*"
},
"peerDependencies": {
"@llama-flow/core": "^0.4.1",
"@llamaindex/core": "workspace:*",
"@llamaindex/env": "workspace:*"
},
"dependencies": {
"p-retry": "^6.2.1",
"zod": "^3.25.67"
}
}
-8
View File
@@ -1,8 +0,0 @@
{
"type": "module",
"main": "./dist/index.cjs",
"module": "./dist/index.js",
"types": "./dist/index.d.ts",
"exports": "./dist/index.js",
"private": true
}
-8
View File
@@ -1,8 +0,0 @@
{
"type": "module",
"main": "./dist/index.cjs",
"module": "./dist/index.js",
"types": "./dist/index.d.ts",
"exports": "./dist/index.js",
"private": true
}
-11
View File
@@ -1,11 +0,0 @@
import { client } from "./client/client.gen";
client.setConfig({
baseUrl: "https://api.cloud.llamaindex.ai/",
headers: {
"X-SDK-Name": "llamaindex-ts",
},
});
export * from "./client";
export { client };
-55
View File
@@ -1,55 +0,0 @@
import { workflowEvent } from "@llama-flow/core";
import { zodEvent } from "@llama-flow/core/util/zod";
import { z } from "zod";
import { parseFormSchema } from "./schema";
export const uploadEvent = zodEvent(
parseFormSchema.merge(
z.object({
file: z
.string()
.or(z.instanceof(File))
.or(z.instanceof(Blob))
.or(z.instanceof(Uint8Array))
.optional()
.describe("input"),
}),
),
{
debugLabel: "upload",
uniqueId: "52099967-34a8-419b-8950-c859eab60145",
},
);
export const checkStatusEvent = workflowEvent<string>({
debugLabel: "check-status",
uniqueId: "462157fc-1ded-4ba7-9bc4-3e870395bd20",
});
export const checkStatusSuccessEvent = workflowEvent<string>({
debugLabel: "check-status-success",
uniqueId: "360b7641-30f7-456e-851d-104bb5e3f8d2",
});
export const requestMarkdownEvent = workflowEvent<string>({
debugLabel: "markdown-request",
uniqueId: "aa4c2e3c-0f09-4035-aab6-c72719c877cc",
});
export const requestTextEvent = workflowEvent<string>({
debugLabel: "text-request",
uniqueId: "6976536e-5399-4285-9455-0b70f1dfc92b",
});
export const requestJsonEvent = workflowEvent<string>({
debugLabel: "json-request",
uniqueId: "9fc4a330-52ad-4aac-8092-a650998b7f6f",
});
export const markdownResultEvent = workflowEvent<string>({
debugLabel: "markdown-result",
uniqueId: "2dfb57c8-73d1-4394-bea8-f05246d934d4",
});
export const textResultEvent = workflowEvent<string>({
debugLabel: "text-result",
uniqueId: "a27deec6-b24f-4eda-a5ac-ba2fb2bf37c8",
});
export const jsonResultEvent = workflowEvent<unknown>({
debugLabel: "json-result",
uniqueId: "e086e6bd-a612-4f25-ab41-9b31dcb8af86",
});
-225
View File
@@ -1,225 +0,0 @@
import { createClient, createConfig } from "@hey-api/client-fetch";
import { createWorkflow, type InferWorkflowEventData } from "@llama-flow/core";
import { createStatefulMiddleware } from "@llama-flow/core/middleware/state";
import { withTraceEvents } from "@llama-flow/core/middleware/trace-events";
import { pRetryHandler } from "@llama-flow/core/util/p-retry";
import { fs, getEnv, path } from "@llamaindex/env";
import {
type BodyUploadFileApiV1ParsingUploadPost,
getJobApiV1ParsingJobJobIdGet,
getJobJsonResultApiV1ParsingJobJobIdResultJsonGet,
getJobResultApiV1ParsingJobJobIdResultMarkdownGet,
getJobTextResultApiV1ParsingJobJobIdResultTextGet,
type StatusEnum,
uploadFileApiV1ParsingUploadPost,
} from "./client";
import {
checkStatusEvent,
checkStatusSuccessEvent,
jsonResultEvent,
markdownResultEvent,
requestJsonEvent,
requestMarkdownEvent,
requestTextEvent,
textResultEvent,
uploadEvent,
} from "./events";
export type LlamaParseWorkflowParams = {
region?: "us" | "eu" | "us-staging";
apiKey?: string;
};
const URLS = {
us: "https://api.cloud.llamaindex.ai",
eu: "https://api.cloud.eu.llamaindex.ai",
"us-staging": "https://api.staging.llamaindex.ai",
} as const;
const { withState, getContext } = createStatefulMiddleware(
(params: LlamaParseWorkflowParams) => {
const apiKey = params.apiKey ?? getEnv("LLAMA_CLOUD_API_KEY");
const region = params.region ?? "us";
if (!apiKey) {
throw new Error("LLAMA_CLOUD_API_KEY is not set");
}
return {
cache: {} as Record<string, StatusEnum>,
client: createClient(
createConfig({
baseUrl: URLS[region],
headers: {
Authorization: `Bearer ${apiKey}`,
},
}),
),
};
},
);
const llamaParseWorkflow = withState(withTraceEvents(createWorkflow()));
llamaParseWorkflow.handle([uploadEvent], async ({ data: form }) => {
const { state } = getContext();
const finalForm = { ...form };
if ("file" in form) {
// support loads from the file system
const file = form?.file;
const isFilePath = typeof file === "string";
const data = isFilePath ? await fs.readFile(file) : file;
const filename: string | undefined = isFilePath
? path.basename(file)
: undefined;
finalForm.file = data
? globalThis.File && filename
? new File([data], filename)
: new Blob([data])
: undefined;
}
const {
data: { id, status },
} = await uploadFileApiV1ParsingUploadPost({
throwOnError: true,
body: {
...finalForm,
} as BodyUploadFileApiV1ParsingUploadPost,
client: state.client,
});
state.cache[id] = status;
return checkStatusEvent.with(id);
});
llamaParseWorkflow.handle(
[checkStatusEvent],
pRetryHandler(
async ({ data: uuid }) => {
const { state } = getContext();
if (state.cache[uuid] === "SUCCESS") {
return checkStatusSuccessEvent.with(uuid);
}
const {
data: { status },
} = await getJobApiV1ParsingJobJobIdGet({
throwOnError: true,
path: {
job_id: uuid,
},
client: state.client,
});
state.cache[uuid] = status;
if (status === "SUCCESS") {
return checkStatusSuccessEvent.with(uuid);
}
throw new Error(`LLamaParse status: ${status}`);
},
{
retries: 100,
},
),
);
//#region sub workflow
llamaParseWorkflow.handle([requestMarkdownEvent], async ({ data: job_id }) => {
const { state } = getContext();
const { data } = await getJobResultApiV1ParsingJobJobIdResultMarkdownGet({
throwOnError: true,
path: {
job_id,
},
client: state.client,
});
return markdownResultEvent.with(data.markdown);
});
llamaParseWorkflow.handle([requestTextEvent], async ({ data: job_id }) => {
const { state } = getContext();
const { data } = await getJobTextResultApiV1ParsingJobJobIdResultTextGet({
throwOnError: true,
path: {
job_id,
},
client: state.client,
});
return textResultEvent.with(data.text);
});
llamaParseWorkflow.handle([requestJsonEvent], async ({ data: job_id }) => {
const { state } = getContext();
const { data } = await getJobJsonResultApiV1ParsingJobJobIdResultJsonGet({
throwOnError: true,
path: {
job_id,
},
client: state.client,
});
return jsonResultEvent.with(data.pages);
});
//#endregion
export type ParseJob = {
get jobId(): string;
get signal(): AbortSignal;
get context(): ReturnType<typeof llamaParseWorkflow.createContext>;
get form(): InferWorkflowEventData<typeof uploadEvent>;
markdown(): Promise<string>;
text(): Promise<string>;
//eslint-disable-next-line @typescript-eslint/no-explicit-any
json(): Promise<any[]>;
};
export const upload = async (
params: InferWorkflowEventData<typeof uploadEvent> & LlamaParseWorkflowParams,
): Promise<ParseJob> => {
//#region upload event
const context = llamaParseWorkflow.createContext(params);
const { stream, sendEvent } = context;
const ev = uploadEvent.with(params);
sendEvent(ev);
const uploadThread = await llamaParseWorkflow
.substream(ev, stream)
.until((ev) => checkStatusSuccessEvent.include(ev))
.toArray();
//#region
const jobId: string = uploadThread.at(-1)!.data;
return {
get signal() {
// lazy load
return context.signal;
},
get jobId() {
return jobId;
},
get form() {
return ev.data;
},
get context() {
return context;
},
async markdown(): Promise<string> {
const requestEv = requestMarkdownEvent.with(jobId);
const { sendEvent, stream } = llamaParseWorkflow.createContext(params);
sendEvent(requestEv);
const markdownThread = await stream.until(markdownResultEvent).toArray();
return markdownThread.at(-1)!.data;
},
async text(): Promise<string> {
const requestEv = requestTextEvent.with(jobId);
const { sendEvent, stream } = llamaParseWorkflow.createContext(params);
sendEvent(requestEv);
const textThread = await stream.until(textResultEvent).toArray();
return textThread.at(-1)!.data;
},
//eslint-disable-next-line @typescript-eslint/no-explicit-any
async json(): Promise<any[]> {
const requestEv = requestJsonEvent.with(jobId);
const { sendEvent, stream } = llamaParseWorkflow.createContext(params);
sendEvent(requestEv);
const jsonThread = await stream
.until((ev) => jsonResultEvent.include(ev))
.toArray();
return jsonThread.at(-1)!.data;
},
};
};
-778
View File
@@ -1,778 +0,0 @@
/* eslint-disable @typescript-eslint/no-explicit-any */
import { type Client, createClient, createConfig } from "@hey-api/client-fetch";
import { Document, FileReader } from "@llamaindex/core/schema";
import { fs, getEnv, path } from "@llamaindex/env";
import {
type BodyUploadFileApiParsingUploadPost,
type FailPageMode,
type ParserLanguages,
type ParsingMode,
getJobApiV1ParsingJobJobIdGet,
getJobImageResultApiV1ParsingJobJobIdResultImageNameGet,
getJobJsonResultApiV1ParsingJobJobIdResultJsonGet,
getJobResultApiV1ParsingJobJobIdResultMarkdownGet,
getJobTextResultApiV1ParsingJobJobIdResultTextGet,
uploadFileApiV1ParsingUploadPost,
} from "./api";
import { sleep } from "./utils";
export type Language = ParserLanguages;
export type ResultType = "text" | "markdown" | "json";
// Export the backoff pattern type.
export type BackoffPattern = "constant" | "linear" | "exponential";
// TODO: should move into @llamaindex/env
type WriteStream = {
write: (text: string) => void;
};
// Do not modify this variable or cause type errors
// eslint-disable-next-line no-var
var process: any;
/**
* Represents a reader for parsing files using the LlamaParse API.
* See https://github.com/run-llama/llama_parse
*/
export class LlamaParseReader extends FileReader {
project_id?: string | undefined;
organization_id?: string | undefined;
// The API key for the LlamaParse API. Can be set as an environment variable: LLAMA_CLOUD_API_KEY
apiKey: string;
// The base URL of the Llama Cloud Platform.
baseUrl: string = "https://api.cloud.llamaindex.ai";
// The result type for the parser.
resultType: ResultType = "text";
// The interval in seconds to check if the parsing is done.
checkInterval: number = 1;
// The maximum timeout in seconds to wait for the parsing to finish.
maxTimeout = 2000;
// Whether to print the progress of the parsing.
verbose = true;
// The language to parse the file in.
language: ParserLanguages[] = ["en"];
// New polling options:
// Controls the backoff mode: "constant", "linear", or "exponential".
backoffPattern: BackoffPattern = "linear";
// Maximum interval in seconds between polls.
maxCheckInterval: number = 5;
// Maximum number of retryable errors before giving up.
maxErrorCount: number = 4;
// The parsing instruction for the parser. Backend default is an empty string.
parsingInstruction?: string | undefined;
// Whether to ignore diagonal text (when the text rotation in degrees is not 0, 90, 180, or 270). Backend default is false.
skipDiagonalText?: boolean | undefined;
// Whether to ignore the cache and re-process the document. Documents are cached for 48 hours after job completion. Backend default is false.
invalidateCache?: boolean | undefined;
// Whether the document should not be cached. Backend default is false.
doNotCache?: boolean | undefined;
// Whether to use a faster mode to extract text (skipping OCR and table/heading reconstruction). Not compatible with gpt4oMode. Backend default is false.
fastMode?: boolean | undefined;
// Whether to keep columns in the text according to document layout. May reduce reconstruction accuracy and LLM/embedings performance.
doNotUnrollColumns?: boolean | undefined;
// A templated page separator for splitting text. If not set, default is "\n---\n".
pageSeparator?: string | undefined;
// A templated prefix to add at the beginning of each page.
pagePrefix?: string | undefined;
// A templated suffix to add at the end of each page.
pageSuffix?: string | undefined;
// Deprecated. Use vendorMultimodal params. Whether to use gpt-4o to extract text.
gpt4oMode: boolean = false;
// Deprecated. Use vendorMultimodal params. The API key for GPT-4o. Can be set via LLAMA_CLOUD_GPT4O_API_KEY.
gpt4oApiKey?: string | undefined;
// The bounding box margins as a string.
boundingBox?: string | undefined;
// The target pages (comma separated list, starting at 0).
targetPages?: string | undefined;
// Whether to ignore errors during parsing.
ignoreErrors: boolean = true;
// Whether to split by page using the pageSeparator (or "\n---\n" as default).
splitByPage: boolean = true;
// Whether to use the vendor multimodal API.
useVendorMultimodalModel: boolean = false;
// The model name for the vendor multimodal API.
vendorMultimodalModelName?: string | undefined;
// The API key for the multimodal API. Can be set via LLAMA_CLOUD_VENDOR_MULTIMODAL_API_KEY.
vendorMultimodalApiKey?: string | undefined;
webhookUrl?: string | undefined;
premiumMode?: boolean | undefined;
takeScreenshot?: boolean | undefined;
disableOcr?: boolean | undefined;
disableReconstruction?: boolean | undefined;
inputS3Path?: string | undefined;
outputS3PathPrefix?: string | undefined;
continuousMode?: boolean | undefined;
isFormattingInstruction?: boolean | undefined;
annotateLinks?: boolean | undefined;
azureOpenaiDeploymentName?: string | undefined;
azureOpenaiEndpoint?: string | undefined;
azureOpenaiApiVersion?: string | undefined;
azureOpenaiKey?: string | undefined;
auto_mode?: boolean | undefined;
auto_mode_trigger_on_image_in_page?: boolean | undefined;
auto_mode_trigger_on_table_in_page?: boolean | undefined;
auto_mode_trigger_on_text_in_page?: string | undefined;
auto_mode_trigger_on_regexp_in_page?: string | undefined;
bbox_bottom?: number | undefined;
bbox_left?: number | undefined;
bbox_right?: number | undefined;
bbox_top?: number | undefined;
disable_image_extraction?: boolean | undefined;
extract_charts?: boolean | undefined;
guess_xlsx_sheet_name?: boolean | undefined;
html_make_all_elements_visible?: boolean | undefined;
html_remove_fixed_elements?: boolean | undefined;
html_remove_navigation_elements?: boolean | undefined;
http_proxy?: string | undefined;
input_url?: string | undefined;
max_pages?: number | undefined;
output_pdf_of_document?: boolean | undefined;
structured_output?: boolean | undefined;
structured_output_json_schema?: string | undefined;
structured_output_json_schema_name?: string | undefined;
extract_layout?: boolean | undefined;
// numWorkers is implemented in SimpleDirectoryReader
stdout?: WriteStream | undefined;
readonly #client: Client;
output_tables_as_HTML: boolean = false;
input_s3_region?: string | undefined;
output_s3_region?: string | undefined;
preserve_layout_alignment_across_pages?: boolean | undefined;
spreadsheet_extract_sub_tables?: boolean | undefined;
formatting_instruction?: string | undefined;
parse_mode?: ParsingMode | undefined;
system_prompt?: string | undefined;
system_prompt_append?: string | undefined;
user_prompt?: string | undefined;
job_timeout_in_seconds?: number | undefined;
job_timeout_extra_time_per_page_in_seconds?: number | undefined;
strict_mode_image_extraction?: boolean | undefined;
strict_mode_image_ocr?: boolean | undefined;
strict_mode_reconstruction?: boolean | undefined;
strict_mode_buggy_font?: boolean | undefined;
ignore_document_elements_for_layout_detection?: boolean | undefined;
complemental_formatting_instruction?: string | undefined;
content_guideline_instruction?: string | undefined;
adaptive_long_table?: boolean | undefined;
model?: string | undefined;
auto_mode_configuration_json?: string | undefined;
compact_markdown_table?: boolean | undefined;
markdown_table_multiline_header_separator?: string | undefined;
page_error_tolerance?: number | undefined;
replace_failed_page_mode?: FailPageMode | undefined;
replace_failed_page_with_error_message_prefix?: string | undefined;
replace_failed_page_with_error_message_suffix?: string | undefined;
save_images?: boolean | undefined;
preset?: string | undefined;
high_res_ocr?: boolean | undefined;
outlined_table_extraction?: boolean | undefined;
hide_headers?: boolean | undefined;
hide_footers?: boolean | undefined;
page_header_prefix?: string | undefined;
page_header_suffix?: string | undefined;
page_footer_prefix?: string | undefined;
page_footer_suffix?: string | undefined;
constructor(
params: Partial<Omit<LlamaParseReader, "language" | "apiKey">> & {
language?: ParserLanguages | ParserLanguages[] | undefined;
apiKey?: string | undefined;
} = {},
) {
super();
Object.assign(this, params);
this.language = Array.isArray(this.language)
? this.language
: [this.language];
this.stdout =
(params.stdout ?? typeof process !== "undefined")
? process!.stdout
: undefined;
const apiKey = params.apiKey ?? getEnv("LLAMA_CLOUD_API_KEY");
if (!apiKey) {
throw new Error(
"API Key is required for LlamaParseReader. Please pass the apiKey parameter or set the LLAMA_CLOUD_API_KEY environment variable.",
);
}
this.apiKey = apiKey;
if (this.baseUrl.endsWith("/")) {
this.baseUrl = this.baseUrl.slice(0, -1);
}
if (this.baseUrl.endsWith("/api/parsing")) {
this.baseUrl = this.baseUrl.slice(0, -"/api/parsing".length);
}
if (params.gpt4oMode) {
params.gpt4oApiKey =
params.gpt4oApiKey ?? getEnv("LLAMA_CLOUD_GPT4O_API_KEY");
this.gpt4oApiKey = params.gpt4oApiKey;
}
if (params.useVendorMultimodalModel) {
params.vendorMultimodalApiKey =
params.vendorMultimodalApiKey ??
getEnv("LLAMA_CLOUD_VENDOR_MULTIMODAL_API_KEY");
this.vendorMultimodalApiKey = params.vendorMultimodalApiKey;
}
this.#client = createClient(
createConfig({
headers: {
Authorization: `Bearer ${this.apiKey}`,
},
baseUrl: this.baseUrl,
}),
);
}
/**
* Creates a job for the LlamaParse API.
*
* @param data - The file data as a Uint8Array.
* @param filename - Optional filename for the file.
* @returns A Promise resolving to the job ID as a string.
*/
async #createJob(
data: Uint8Array | string,
filename?: string,
): Promise<string> {
if (this.verbose) {
console.log("Started uploading the file");
}
let file: File | Blob | null = null;
let input_s3_path: string | undefined = this.inputS3Path;
let input_url: string | undefined = this.input_url;
if (typeof data !== "string") {
// TODO: remove Blob usage when we drop Node.js 18 support
file =
globalThis.File && filename
? new File([data], filename)
: new Blob([data]);
} else if (data.startsWith("s3://")) {
input_s3_path = data;
} else if (data.startsWith("http://") || data.startsWith("https://")) {
input_url = data;
}
const body = {
file,
input_s3_path,
input_url,
language: this.language,
parsing_instruction: this.parsingInstruction,
skip_diagonal_text: this.skipDiagonalText,
invalidate_cache: this.invalidateCache,
do_not_cache: this.doNotCache,
fast_mode: this.fastMode,
do_not_unroll_columns: this.doNotUnrollColumns,
page_separator: this.pageSeparator,
page_prefix: this.pagePrefix,
page_suffix: this.pageSuffix,
gpt4o_mode: this.gpt4oMode,
gpt4o_api_key: this.gpt4oApiKey,
bounding_box: this.boundingBox,
target_pages: this.targetPages,
use_vendor_multimodal_model: this.useVendorMultimodalModel,
vendor_multimodal_model_name: this.vendorMultimodalModelName,
vendor_multimodal_api_key: this.vendorMultimodalApiKey,
premium_mode: this.premiumMode,
webhook_url: this.webhookUrl,
take_screenshot: this.takeScreenshot,
disable_ocr: this.disableOcr,
disable_reconstruction: this.disableReconstruction,
output_s3_path_prefix: this.outputS3PathPrefix,
continuous_mode: this.continuousMode,
is_formatting_instruction: this.isFormattingInstruction,
annotate_links: this.annotateLinks,
azure_openai_deployment_name: this.azureOpenaiDeploymentName,
azure_openai_endpoint: this.azureOpenaiEndpoint,
azure_openai_api_version: this.azureOpenaiApiVersion,
azure_openai_key: this.azureOpenaiKey,
auto_mode: this.auto_mode,
auto_mode_trigger_on_image_in_page:
this.auto_mode_trigger_on_image_in_page,
auto_mode_trigger_on_table_in_page:
this.auto_mode_trigger_on_table_in_page,
auto_mode_trigger_on_text_in_page: this.auto_mode_trigger_on_text_in_page,
auto_mode_trigger_on_regexp_in_page:
this.auto_mode_trigger_on_regexp_in_page,
bbox_bottom: this.bbox_bottom,
bbox_left: this.bbox_left,
bbox_right: this.bbox_right,
bbox_top: this.bbox_top,
disable_image_extraction: this.disable_image_extraction,
extract_charts: this.extract_charts,
guess_xlsx_sheet_name: this.guess_xlsx_sheet_name,
html_make_all_elements_visible: this.html_make_all_elements_visible,
html_remove_fixed_elements: this.html_remove_fixed_elements,
html_remove_navigation_elements: this.html_remove_navigation_elements,
http_proxy: this.http_proxy,
max_pages: this.max_pages,
output_pdf_of_document: this.output_pdf_of_document,
structured_output: this.structured_output,
structured_output_json_schema: this.structured_output_json_schema,
structured_output_json_schema_name:
this.structured_output_json_schema_name,
extract_layout: this.extract_layout,
output_tables_as_HTML: this.output_tables_as_HTML,
input_s3_region: this.input_s3_region,
output_s3_region: this.output_s3_region,
preserve_layout_alignment_across_pages:
this.preserve_layout_alignment_across_pages,
spreadsheet_extract_sub_tables: this.spreadsheet_extract_sub_tables,
formatting_instruction: this.formatting_instruction,
parse_mode: this.parse_mode,
system_prompt: this.system_prompt,
system_prompt_append: this.system_prompt_append,
user_prompt: this.user_prompt,
job_timeout_in_seconds: this.job_timeout_in_seconds,
job_timeout_extra_time_per_page_in_seconds:
this.job_timeout_extra_time_per_page_in_seconds,
strict_mode_image_extraction: this.strict_mode_image_extraction,
strict_mode_image_ocr: this.strict_mode_image_ocr,
strict_mode_reconstruction: this.strict_mode_reconstruction,
strict_mode_buggy_font: this.strict_mode_buggy_font,
ignore_document_elements_for_layout_detection:
this.ignore_document_elements_for_layout_detection,
complemental_formatting_instruction:
this.complemental_formatting_instruction,
content_guideline_instruction: this.content_guideline_instruction,
adaptive_long_table: this.adaptive_long_table,
model: this.model,
auto_mode_configuration_json: this.auto_mode_configuration_json,
compact_markdown_table: this.compact_markdown_table,
markdown_table_multiline_header_separator:
this.markdown_table_multiline_header_separator,
page_error_tolerance: this.page_error_tolerance,
replace_failed_page_mode: this.replace_failed_page_mode,
replace_failed_page_with_error_message_prefix:
this.replace_failed_page_with_error_message_prefix,
replace_failed_page_with_error_message_suffix:
this.replace_failed_page_with_error_message_suffix,
save_images: this.save_images,
preset: this.preset,
high_res_ocr: this.high_res_ocr,
outlined_table_extraction: this.outlined_table_extraction,
hide_headers: this.hide_headers,
hide_footers: this.hide_footers,
page_header_prefix: this.page_header_prefix,
page_header_suffix: this.page_header_suffix,
page_footer_prefix: this.page_footer_prefix,
page_footer_suffix: this.page_footer_suffix,
} satisfies {
[Key in keyof BodyUploadFileApiParsingUploadPost]-?:
| BodyUploadFileApiParsingUploadPost[Key]
| undefined;
} as unknown as BodyUploadFileApiParsingUploadPost;
const response = await uploadFileApiV1ParsingUploadPost({
client: this.#client,
throwOnError: true,
query: {
project_id: this.project_id ?? null,
organization_id: this.organization_id ?? null,
},
signal: AbortSignal.timeout(this.maxTimeout * 1000),
body,
});
return response.data.id;
}
/**
* Retrieves the result of a parsing job.
*
* Uses a polling loop with retry logic. Each API call is retried
* up to maxErrorCount times if it fails with a 5XX or socket error.
* The delay between polls increases according to the specified backoffPattern ("constant", "linear", or "exponential"),
* capped by maxCheckInterval.
*
* @param jobId - The job ID.
* @param resultType - The type of result to fetch ("text", "json", or "markdown").
* @returns A Promise resolving to the job result.
*/
private async getJobResult(
jobId: string,
resultType: "text" | "json" | "markdown",
): Promise<any> {
let tries = 0;
let currentInterval = this.checkInterval;
const { default: pRetry } = await import("p-retry");
while (true) {
await sleep(currentInterval * 1000);
// Wraps the API call in a retry
let result;
try {
result = await pRetry(
() =>
getJobApiV1ParsingJobJobIdGet({
client: this.#client,
throwOnError: true,
path: { job_id: jobId },
signal: AbortSignal.timeout(this.maxTimeout * 1000),
}),
{
retries: this.maxErrorCount,
onFailedAttempt: (error) => {
// Retry only on 5XX or socket errors.
const status = (error.cause as any)?.response?.status;
if (
!(
(status && status >= 500 && status < 600) ||
((error.cause as any)?.code &&
((error.cause as any).code === "ECONNRESET" ||
(error.cause as any).code === "ETIMEDOUT" ||
(error.cause as any).code === "ECONNREFUSED"))
)
) {
throw error;
}
if (this.verbose) {
console.warn(
`Attempting to get job ${jobId} result (attempt ${error.attemptNumber}) failed. Retrying...`,
);
}
},
},
);
} catch (e: any) {
throw new Error(
`Max error count reached for job ${jobId}: ${e.message}`,
);
}
const { data } = result;
const status = (data as Record<string, unknown>)["status"];
if (status === "SUCCESS") {
let resultData;
switch (resultType) {
case "json": {
resultData =
await getJobJsonResultApiV1ParsingJobJobIdResultJsonGet({
client: this.#client,
throwOnError: true,
path: { job_id: jobId },
query: {
organization_id: this.organization_id ?? null,
},
signal: AbortSignal.timeout(this.maxTimeout * 1000),
});
break;
}
case "markdown": {
resultData =
await getJobResultApiV1ParsingJobJobIdResultMarkdownGet({
client: this.#client,
throwOnError: true,
path: { job_id: jobId },
query: {
organization_id: this.organization_id ?? null,
},
signal: AbortSignal.timeout(this.maxTimeout * 1000),
});
break;
}
case "text": {
resultData =
await getJobTextResultApiV1ParsingJobJobIdResultTextGet({
client: this.#client,
throwOnError: true,
path: { job_id: jobId },
query: {
organization_id: this.organization_id ?? null,
},
signal: AbortSignal.timeout(this.maxTimeout * 1000),
});
break;
}
}
return resultData.data;
} else if (status === "PENDING") {
if (this.verbose && tries % 10 === 0) {
this.stdout?.write(".");
}
tries++;
} else {
if (this.verbose) {
console.error(
`Received error response ${status} for job ${jobId}. Got Error Code: ${data.error_code} and Error Message: ${data.error_message}`,
);
}
throw new Error(
`Failed to parse the file: ${jobId}, status: ${status}`,
);
}
// Adjust the polling interval based on the backoff pattern.
if (this.backoffPattern === "exponential") {
currentInterval = Math.min(currentInterval * 2, this.maxCheckInterval);
} else if (this.backoffPattern === "linear") {
currentInterval = Math.min(
currentInterval + this.checkInterval,
this.maxCheckInterval,
);
} else if (this.backoffPattern === "constant") {
currentInterval = this.checkInterval;
}
}
}
override async loadData(filePath?: string): Promise<Document[]> {
if (!filePath) {
if (this.input_url) {
return this.loadDataAsContent(this.input_url, this.input_url);
} else if (this.inputS3Path) {
return this.loadDataAsContent(this.inputS3Path, this.inputS3Path);
} else {
throw new TypeError("File path is required");
}
} else {
const data =
filePath.startsWith("s3://") ||
filePath.startsWith("http://") ||
filePath.startsWith("https://")
? filePath
: await fs.readFile(filePath);
return this.loadDataAsContent(data, filePath);
}
}
/**
* Loads data from a file and returns an array of Document objects.
* To be used with resultType "text" or "markdown".
*
* @param fileContent - The content of the file as a Uint8Array.
* @param filename - Optional filename for the file.
* @returns A Promise that resolves to an array of Document objects.
*/
async loadDataAsContent(
fileContent: Uint8Array | string,
filename?: string,
): Promise<Document[]> {
return this.#createJob(fileContent, filename)
.then(async (jobId) => {
if (this.verbose) {
console.log(`Started parsing the file under job id ${jobId}`);
}
// Return results as Document objects.
const jobResults = await this.getJobResult(jobId, this.resultType);
const resultText = jobResults[this.resultType];
// Split the text by separator if splitByPage is true.
if (this.splitByPage) {
return this.splitTextBySeparator(resultText);
}
return [new Document({ text: resultText })];
})
.catch((error) => {
console.warn(
`Error while parsing the file with: ${error.message ?? error.detail}`,
);
if (this.ignoreErrors) {
return [];
} else {
throw error;
}
});
}
/**
* Loads data from a file and returns an array of JSON objects.
* To be used with resultType "json".
*
* @param filePathOrContent - The file path or the file content as a Uint8Array.
* @returns A Promise that resolves to an array of JSON objects.
*/
async loadJson(
filePathOrContent: string | Uint8Array,
): Promise<Record<string, any>[]> {
let jobId;
const isFilePath =
typeof filePathOrContent === "string" &&
!(
filePathOrContent.startsWith("s3://") ||
filePathOrContent.startsWith("http://") ||
filePathOrContent.startsWith("https://")
);
try {
const data = isFilePath
? await fs.readFile(filePathOrContent)
: filePathOrContent;
// Create a job for the file.
jobId = await this.#createJob(
data,
isFilePath ? path.basename(filePathOrContent) : undefined,
);
if (this.verbose) {
console.log(`Started parsing the file under job id ${jobId}`);
}
// Return results as an array of JSON objects.
const resultJson = await this.getJobResult(jobId, "json");
resultJson.job_id = jobId;
resultJson.file_path = isFilePath ? filePathOrContent : undefined;
return [resultJson];
} catch (e) {
console.error(`Error while parsing the file under job id ${jobId}`, e);
if (this.ignoreErrors) {
return [];
} else {
throw e;
}
}
}
/**
* Downloads and saves images from a given JSON result to a specified download path.
* Currently only supports resultType "json".
*
* @param jsonResult - The JSON result containing image information.
* @param downloadPath - The path where the downloaded images will be saved.
* @returns A Promise that resolves to an array of image objects.
*/
async getImages(
jsonResult: Record<string, any>[],
downloadPath: string,
): Promise<Record<string, any>[]> {
try {
// Create download directory if it doesn't exist (checks for write access).
try {
await fs.access(downloadPath);
} catch {
await fs.mkdir(downloadPath, { recursive: true });
}
const images: Record<string, any>[] = [];
for (const result of jsonResult) {
const jobId = result.job_id;
for (const page of result.pages) {
if (this.verbose) {
console.log(`> Image for page ${page.page}: ${page.images}`);
}
for (const image of page.images) {
const imageName = image.name;
const imagePath = await this.getImagePath(
downloadPath,
jobId,
imageName,
);
await this.fetchAndSaveImage(imageName, imagePath, jobId);
// Assign metadata to the image.
image.path = imagePath;
image.job_id = jobId;
image.original_pdf_path = result.file_path;
image.page_number = page.page;
images.push(image);
}
}
}
return images;
} catch (e) {
console.error(`Error while downloading images from the parsed result`, e);
if (this.ignoreErrors) {
return [];
} else {
throw e;
}
}
}
/**
* Constructs the file path for an image.
*
* @param downloadPath - The base download directory.
* @param jobId - The job ID.
* @param imageName - The image name.
* @returns A Promise that resolves to the full image path.
*/
private async getImagePath(
downloadPath: string,
jobId: string,
imageName: string,
): Promise<string> {
return path.join(downloadPath, `${jobId}-${imageName}`);
}
/**
* Fetches an image from the API and saves it to the specified path.
*
* @param imageName - The name of the image.
* @param imagePath - The local path to save the image.
* @param jobId - The associated job ID.
*/
private async fetchAndSaveImage(
imageName: string,
imagePath: string,
jobId: string,
): Promise<void> {
const response =
await getJobImageResultApiV1ParsingJobJobIdResultImageNameGet({
client: this.#client,
path: {
job_id: jobId,
name: imageName,
},
});
if (response.error) {
throw new Error(`Failed to download image: ${response.error.detail}`);
}
const blob = (await response.data) as Blob;
// Write the image buffer to the specified imagePath.
await fs.writeFile(imagePath, new Uint8Array(await blob.arrayBuffer()));
}
/**
* Filters out invalid values (null, undefined, empty string) for specific parameters.
*
* @param params - The parameters object.
* @param keysToCheck - The keys to check for valid values.
* @returns A new object with filtered parameters.
*/
private filterSpecificParams(
params: Record<string, any>,
keysToCheck: string[],
): Record<string, any> {
const filteredParams: Record<string, any> = {};
for (const [key, value] of Object.entries(params)) {
if (keysToCheck.includes(key)) {
if (value !== null && value !== undefined && value !== "") {
filteredParams[key] = value;
}
} else {
filteredParams[key] = value;
}
}
return filteredParams;
}
/**
* Splits text into Document objects using the page separator.
*
* @param text - The text to be split.
* @returns An array of Document objects.
*/
private splitTextBySeparator(text: string): Document[] {
const separator = this.pageSeparator ?? "\n---\n";
const textChunks = text.split(separator);
return textChunks.map(
(docChunk: string) =>
new Document({
text: docChunk,
}),
);
}
}
-131
View File
@@ -1,131 +0,0 @@
import { FailPageMode, ParserLanguages, ParsingMode } from "./client";
import { z } from "zod";
type Language = ParserLanguages;
const VALUES: [Language, ...Language[]] = [
ParserLanguages.EN,
...Object.values(ParserLanguages),
];
const languageSchema = z.enum(VALUES);
const PARSE_PRESETS = [
"fast",
"balanced",
"premium",
"structured",
"auto",
"scientific",
"invoice",
"slides",
"_carlyle",
] as const;
export const parsePresetSchema = z.enum(PARSE_PRESETS);
export const parseFormSchema = z.object({
adaptive_long_table: z.boolean().optional(),
annotate_links: z.boolean().optional(),
auto_mode: z.boolean().optional(),
auto_mode_trigger_on_image_in_page: z.boolean().optional(),
auto_mode_trigger_on_table_in_page: z.boolean().optional(),
auto_mode_trigger_on_text_in_page: z.string().optional(),
auto_mode_trigger_on_regexp_in_page: z.string().optional(),
auto_mode_configuration_json: z.string().optional(),
azure_openai_api_version: z.string().optional(),
azure_openai_deployment_name: z.string().optional(),
azure_openai_endpoint: z.string().optional(),
azure_openai_key: z.string().optional(),
bbox_bottom: z.number().min(0).max(1).optional(),
bbox_left: z.number().min(0).max(1).optional(),
bbox_right: z.number().min(0).max(1).optional(),
bbox_top: z.number().min(0).max(1).optional(),
disable_ocr: z.boolean().optional(),
disable_reconstruction: z.boolean().optional(),
disable_image_extraction: z.boolean().optional(),
do_not_cache: z.coerce.boolean().optional(),
do_not_unroll_columns: z.coerce.boolean().optional(),
extract_charts: z.boolean().optional(),
guess_xlsx_sheet_name: z.boolean().optional(),
html_make_all_elements_visible: z.boolean().optional(),
html_remove_fixed_elements: z.boolean().optional(),
html_remove_navigation_elements: z.boolean().optional(),
http_proxy: z
.string()
.url(
'Set a valid URL for the HTTP proxy, e.g., "http://proxy.example.com:8080"',
)
.refine(
(url) => {
try {
const parsedUrl = new URL(url);
return (
parsedUrl.protocol === "http:" || parsedUrl.protocol === "https:"
);
} catch {
return false;
}
},
{
message: "Invalid HTTP proxy URL",
},
)
.optional(),
input_s3_path: z.string().optional(),
input_s3_region: z.string().optional(),
input_url: z.string().optional(),
invalidate_cache: z.boolean().optional(),
language: z.array(languageSchema).optional(),
extract_layout: z.boolean().optional(),
max_pages: z.number().nullable().optional(),
output_pdf_of_document: z.boolean().optional(),
output_s3_path_prefix: z.string().optional(),
output_s3_region: z.string().optional(),
page_prefix: z.string().optional(),
page_separator: z.string().optional(),
page_suffix: z.string().optional(),
preserve_layout_alignment_across_pages: z.boolean().optional(),
skip_diagonal_text: z.boolean().optional(),
spreadsheet_extract_sub_tables: z.boolean().optional(),
structured_output: z.boolean().optional(),
structured_output_json_schema: z.string().optional(),
structured_output_json_schema_name: z.string().optional(),
take_screenshot: z.boolean().optional(),
target_pages: z.string().optional(),
vendor_multimodal_api_key: z.string().optional(),
vendor_multimodal_model_name: z.string().optional(),
model: z.string().optional(),
webhook_url: z.string().url().optional(),
parse_mode: z.nativeEnum(ParsingMode).nullable().optional(),
system_prompt: z.string().optional(),
system_prompt_append: z.string().optional(),
user_prompt: z.string().optional(),
job_timeout_in_seconds: z.number().optional(),
job_timeout_extra_time_per_page_in_seconds: z.number().optional(),
strict_mode_image_extraction: z.boolean().optional(),
strict_mode_image_ocr: z.boolean().optional(),
strict_mode_reconstruction: z.boolean().optional(),
strict_mode_buggy_font: z.boolean().optional(),
save_images: z.boolean().optional(),
ignore_document_elements_for_layout_detection: z.boolean().optional(),
output_tables_as_HTML: z.boolean().optional(),
use_vendor_multimodal_model: z.boolean().optional(),
bounding_box: z.string().optional(),
gpt4o_mode: z.boolean().optional(),
gpt4o_api_key: z.string().optional(),
complemental_formatting_instruction: z.string().optional(),
content_guideline_instruction: z.string().optional(),
premium_mode: z.boolean().optional(),
is_formatting_instruction: z.boolean().optional(),
continuous_mode: z.boolean().optional(),
parsing_instruction: z.string().optional(),
fast_mode: z.boolean().optional(),
formatting_instruction: z.string().optional(),
preset: parsePresetSchema.optional(),
compact_markdown_table: z.boolean().optional(),
markdown_table_multiline_header_separator: z.string().optional(),
page_error_tolerance: z.number().min(0).max(1).optional(),
replace_failed_page_mode: z.nativeEnum(FailPageMode).nullable().optional(),
replace_failed_page_with_error_message_prefix: z.string().optional(),
replace_failed_page_with_error_message_suffix: z.string().optional(),
});
-3
View File
@@ -1,3 +0,0 @@
export async function sleep(ms: number): Promise<void> {
return new Promise((resolve) => setTimeout(resolve, ms));
}
-9
View File
@@ -1,9 +0,0 @@
{
"$schema": "https://turbo.build/schema.json",
"extends": ["//"],
"tasks": {
"build": {
"outputs": ["**/dist/**", "src/client/**"]
}
}
}
@@ -1,95 +1,27 @@
# @llamaindex/cloud
# @llamaindex/community
## 4.0.18
## 0.0.101
### Patch Changes
- 47a7555: chore: bump sdk version
- Updated dependencies [f9f1de9]
- @llamaindex/core@0.6.19
## 4.0.17
## 0.0.100
### Patch Changes
- Updated dependencies [d578889]
- Updated dependencies [0fcc92f]
- Updated dependencies [515a8b9]
- @llamaindex/core@0.6.13
- Updated dependencies [f29799e]
- Updated dependencies [7224c06]
- @llamaindex/core@0.6.18
## 4.0.16
## 0.0.99
### Patch Changes
- Updated dependencies [7039e1a]
- Updated dependencies [7039e1a]
- @llamaindex/core@0.6.12
- c65a2dc: Deprecate community package and link to AWS package
## 4.0.15
### Patch Changes
- Updated dependencies [a89e187]
- Updated dependencies [62699b7]
- Updated dependencies [c5b2691]
- Updated dependencies [d8ac8d3]
- @llamaindex/core@0.6.11
## 4.0.14
### Patch Changes
- Updated dependencies [1b5af14]
- @llamaindex/core@0.6.10
## 4.0.13
### Patch Changes
- Updated dependencies [71598f8]
- @llamaindex/core@0.6.9
## 4.0.12
### Patch Changes
- Updated dependencies [c927457]
- @llamaindex/core@0.6.8
## 4.0.11
### Patch Changes
- 76ff23d: Fix pRetry not working with CommonJS
## 4.0.10
### Patch Changes
- Updated dependencies [59601dd]
- @llamaindex/core@0.6.7
## 4.0.9
### Patch Changes
- 3703f90: feat(parse): add upload API
## 4.0.8
### Patch Changes
- Updated dependencies [680b529]
- Updated dependencies [361a685]
- @llamaindex/core@0.6.6
## 4.0.7
### Patch Changes
- 40f5f41: Improve the loadJson function in LlamaParseReader to align with loadData by allowing URL inputs. Ensures s3://, http://, and https:// paths are not treated as local file paths.
- Updated dependencies [d671ed6]
- @llamaindex/core@0.6.5
## 4.0.6
## 0.0.98
### Patch Changes
@@ -97,33 +29,33 @@
- @llamaindex/core@0.6.4
- @llamaindex/env@0.1.30
## 4.0.5
### Patch Changes
- 2225ffd: feat: bump llama cloud sdk
## 4.0.4
## 0.0.97
### Patch Changes
- Updated dependencies [3ee8c83]
- @llamaindex/core@0.6.3
## 4.0.3
## 0.0.96
### Patch Changes
- 41191d0: fix(parse): file input
- e9bf442: fix: update the tool call schema for nova
## 4.0.2
## 0.0.95
### Patch Changes
- 411dcea: Add Nova Premier to AWS Nova models. Add EU endpoints
## 0.0.94
### Patch Changes
- Updated dependencies [9c63f3f]
- @llamaindex/core@0.6.2
## 4.0.1
## 0.0.93
### Patch Changes
@@ -131,75 +63,77 @@
- Updated dependencies [eaf326e]
- @llamaindex/core@0.6.1
## 4.0.0
## 0.0.92
### Patch Changes
- 1325178: fix: stringify all tool results for anthropic on bedrock
## 0.0.91
### Patch Changes
- bf56fc0: chore: bump sdk openapi.json
- 5189b44: fix: add retry handling logic to parser reader and fix lint issues
- 3fd4cc3: feat: use google's new gen ai library to support multimodal output
- Updated dependencies [21bebfc]
- Updated dependencies [93bc0ff]
- Updated dependencies [91a18e7]
- Updated dependencies [5189b44]
- @llamaindex/core@0.6.0
## 3.0.9
## 0.0.90
### Patch Changes
- Updated dependencies [40ee761]
- @llamaindex/core@0.5.8
## 3.0.8
## 0.0.89
### Patch Changes
- Updated dependencies [4bac71d]
- @llamaindex/core@0.5.7
## 3.0.7
## 0.0.88
### Patch Changes
- e28c29d: Added Llama 3.3 70B Instruct support
- Updated dependencies [beb922b]
- @llamaindex/env@0.1.29
- @llamaindex/core@0.5.6
## 3.0.6
## 0.0.87
### Patch Changes
- Updated dependencies [5668970]
- @llamaindex/core@0.5.5
## 3.0.5
## 0.0.86
### Patch Changes
- Updated dependencies [ad3c7f1]
- @llamaindex/core@0.5.4
## 3.0.4
## 0.0.85
### Patch Changes
- 1914b52: Added Claude 3.7 Sonnet support
- Updated dependencies [cb021e7]
- @llamaindex/core@0.5.3
## 3.0.3
## 0.0.84
### Patch Changes
- Updated dependencies [d952e68]
- @llamaindex/core@0.5.2
## 3.0.2
### Patch Changes
- c902fcb: chore: bump llamacloud openapi
## 3.0.1
## 0.0.83
### Patch Changes
@@ -207,7 +141,7 @@
- @llamaindex/env@0.1.28
- @llamaindex/core@0.5.1
## 3.0.0
## 0.0.82
### Patch Changes
@@ -215,7 +149,7 @@
- Updated dependencies [d924c63]
- @llamaindex/core@0.5.0
## 2.0.24
## 0.0.81
### Patch Changes
@@ -224,7 +158,7 @@
- @llamaindex/core@0.4.23
- @llamaindex/env@0.1.27
## 2.0.23
## 0.0.80
### Patch Changes
@@ -233,54 +167,47 @@
- @llamaindex/core@0.4.22
- @llamaindex/env@0.1.26
## 2.0.22
## 0.0.79
### Patch Changes
- d6c270e: feat: support pass project and org id to llama parse reader
- Updated dependencies [9456616]
- Updated dependencies [1931bbc]
- @llamaindex/core@0.4.21
## 2.0.21
## 0.0.78
### Patch Changes
- 5dec9f9: chore: bump sdk deps version
- fd9c829: chore: bump llamacloud openapi
- Updated dependencies [d211b7a]
- @llamaindex/core@0.4.20
## 2.0.20
### Patch Changes
- 012495b: chore: bump llamacloud sdk
## 2.0.19
## 0.0.77
### Patch Changes
- 24caf93: fix: added inference profile mapping for nova models"
- Updated dependencies [a9b5b99]
- @llamaindex/core@0.4.19
## 2.0.18
## 0.0.76
### Patch Changes
- c1850ee: feat: Amazon Nova support via Bedrock
- Updated dependencies [b504303]
- Updated dependencies [e0f6cc3]
- @llamaindex/env@0.1.25
- @llamaindex/core@0.4.18
## 2.0.17
## 0.0.75
### Patch Changes
- Updated dependencies [3d1808b]
- @llamaindex/core@0.4.17
## 2.0.16
## 0.0.74
### Patch Changes
@@ -289,7 +216,7 @@
- @llamaindex/core@0.4.16
- @llamaindex/env@0.1.24
## 2.0.15
## 0.0.73
### Patch Changes
@@ -297,7 +224,7 @@
- @llamaindex/env@0.1.23
- @llamaindex/core@0.4.15
## 2.0.14
## 0.0.72
### Patch Changes
@@ -305,7 +232,7 @@
- @llamaindex/env@0.1.22
- @llamaindex/core@0.4.14
## 2.0.13
## 0.0.71
### Patch Changes
@@ -314,21 +241,21 @@
- @llamaindex/core@0.4.13
- @llamaindex/env@0.1.21
## 2.0.12
## 0.0.70
### Patch Changes
- Updated dependencies [ef4f63d]
- @llamaindex/core@0.4.12
## 2.0.11
## 0.0.69
### Patch Changes
- Updated dependencies [6d22fa2]
- @llamaindex/core@0.4.11
## 2.0.10
## 0.0.68
### Patch Changes
@@ -336,21 +263,21 @@
- Updated dependencies [c69605f]
- @llamaindex/core@0.4.10
## 2.0.9
## 0.0.67
### Patch Changes
- Updated dependencies [7ae6eaa]
- @llamaindex/core@0.4.9
## 2.0.8
## 0.0.66
### Patch Changes
- Updated dependencies [f865c98]
- @llamaindex/core@0.4.8
## 2.0.7
## 0.0.65
### Patch Changes
@@ -358,7 +285,7 @@
- Updated dependencies [fd8c882]
- @llamaindex/core@0.4.7
## 2.0.6
## 0.0.64
### Patch Changes
@@ -366,7 +293,7 @@
- @llamaindex/env@0.1.20
- @llamaindex/core@0.4.6
## 2.0.5
## 0.0.63
### Patch Changes
@@ -374,7 +301,7 @@
- @llamaindex/core@0.4.5
- @llamaindex/env@0.1.19
## 2.0.4
## 0.0.62
### Patch Changes
@@ -382,14 +309,15 @@
- @llamaindex/env@0.1.18
- @llamaindex/core@0.4.4
## 2.0.3
## 0.0.61
### Patch Changes
- 487782c: Add missing inference endpoints for Haiku 3.5
- Updated dependencies [95a5cc6]
- @llamaindex/core@0.4.3
## 2.0.2
## 0.0.60
### Patch Changes
@@ -397,14 +325,20 @@
- @llamaindex/env@0.1.17
- @llamaindex/core@0.4.2
## 2.0.1
## 0.0.59
### Patch Changes
- 47a7c3e: feat: added support for Haiku 3.5 via Bedrock
## 0.0.58
### Patch Changes
- Updated dependencies [9c73f0a]
- @llamaindex/core@0.4.1
## 2.0.0
## 0.0.57
### Patch Changes
@@ -414,21 +348,21 @@
- Updated dependencies [620c63c]
- @llamaindex/core@0.4.0
## 1.0.8
## 0.0.56
### Patch Changes
- Updated dependencies [60b185f]
- @llamaindex/core@0.3.7
## 1.0.7
## 0.0.55
### Patch Changes
- Updated dependencies [691c5bc]
- @llamaindex/core@0.3.6
## 1.0.6
## 0.0.54
### Patch Changes
@@ -436,27 +370,27 @@
- @llamaindex/env@0.1.16
- @llamaindex/core@0.3.5
## 1.0.5
## 0.0.53
### Patch Changes
- Updated dependencies [e2a0876]
- @llamaindex/core@0.3.4
## 1.0.4
## 0.0.52
### Patch Changes
- 06f632b: fix(cloud): allow filename in llama parse
- a5a75f6: feat: added sonnet 3.5 v2
## 1.0.3
## 0.0.51
### Patch Changes
- Updated dependencies [0493f67]
- @llamaindex/core@0.3.3
## 1.0.2
## 0.0.50
### Patch Changes
@@ -464,19 +398,17 @@
- @llamaindex/env@0.1.15
- @llamaindex/core@0.3.2
## 1.0.1
## 0.0.49
### Patch Changes
- 4c38c1b: fix(cloud): do not detect file type in llama parse
- 24d065f: Log Parse Job Errors when verbose is enabled
- a75af83: refactor: move some llm and embedding to single package
- Updated dependencies [ae49ff4]
- Updated dependencies [a75af83]
- @llamaindex/env@0.1.14
- @llamaindex/core@0.3.1
## 1.0.0
## 0.0.48
### Patch Changes
@@ -484,21 +416,21 @@
- Updated dependencies [96fc69c]
- @llamaindex/core@0.3.0
## 0.2.14
## 0.0.47
### Patch Changes
- Updated dependencies [5f67820]
- @llamaindex/core@0.2.12
## 0.2.13
## 0.0.46
### Patch Changes
- Updated dependencies [ee697fb]
- @llamaindex/core@0.2.11
## 0.2.12
## 0.0.45
### Patch Changes
@@ -506,19 +438,20 @@
- Updated dependencies [468bda5]
- @llamaindex/core@0.2.10
## 0.2.11
## 0.0.44
### Patch Changes
- 0b20ff9: fix: package.json format
- Updated dependencies [b17d439]
- @llamaindex/core@0.2.9
## 0.2.10
## 0.0.43
### Patch Changes
- 981811e: fix(cloud): llama parse reader save image incorrectly
- 2774e80: feat: added meta3.2 support via Bedrock including vision, tool call and inference region support
## 0.2.9
## 0.0.42
### Patch Changes
@@ -527,41 +460,54 @@
- @llamaindex/core@0.2.8
- @llamaindex/env@0.1.13
## 0.2.8
## 0.0.41
### Patch Changes
- ac41ed3: feat: bump cloud sdk version
- Updated dependencies [6cce3b1]
- @llamaindex/core@0.2.7
## 0.2.7
## 0.0.40
### Patch Changes
- fb36eff: fix: backport for node.js 18
- 50e6b57: feat: add Amazon Bedrock Retriever
- Updated dependencies [8b7fdba]
- @llamaindex/core@0.2.6
There could have one missing API in the node.js 18, so we need to backport it to make it work.
## 0.0.39
### Patch Changes
- Updated dependencies [d902cc3]
- @llamaindex/core@0.2.5
## 0.0.38
### Patch Changes
- Updated dependencies [b48bcc3]
- @llamaindex/core@0.2.4
- @llamaindex/env@0.1.12
## 0.0.37
### Patch Changes
- d24d3d1: fix: print warning when llama parse reader has error
- Updated dependencies [2cd1383]
- @llamaindex/core@0.2.3
## 0.2.6
## 0.0.36
### Patch Changes
- b42adeb: fix: get job result in llama parse reader
- Updated dependencies [749b43a]
- @llamaindex/core@0.2.2
## 0.2.5
## 0.0.35
### Patch Changes
- 85c2e19: feat: `@llamaindex/cloud` package update
- Bump to latest openapi schema
- Move LlamaParse class from llamaindex, this will allow you use llamaparse in more non-node.js environment
- Updated dependencies [ac07e3c]
- Updated dependencies [70ccb4a]
- Updated dependencies [1a6137b]
@@ -569,56 +515,263 @@
- @llamaindex/core@0.2.1
- @llamaindex/env@0.1.11
## 0.2.4
## 0.0.34
### Patch Changes
- 4810364: fix: bump version
- Updated dependencies [11feef8]
- @llamaindex/core@0.2.0
## 0.2.3
## 0.0.33
### Patch Changes
- 0bf8d80: fix: bump version
- Updated dependencies [711c814]
- @llamaindex/core@0.1.12
## 0.2.2
## 0.0.32
### Patch Changes
- Updated dependencies [4648da6]
- @llamaindex/env@0.1.10
- @llamaindex/core@0.1.11
## 0.0.31
### Patch Changes
- Updated dependencies [0148354]
- @llamaindex/core@0.1.10
## 0.0.30
### Patch Changes
- Updated dependencies [e27e7dd]
- @llamaindex/core@0.1.9
## 0.0.29
### Patch Changes
- 58abc57: fix: align version
- Updated dependencies [58abc57]
- @llamaindex/core@0.1.8
- @llamaindex/env@0.1.9
## 0.2.1
## 0.0.28
### Patch Changes
- 1f680d7: chore: bump llamacloud api
- Updated dependencies [04b2f8e]
- @llamaindex/core@0.1.7
## 0.2.0
### Minor Changes
- 3ed6acc: feat: cloud api change
## 0.1.4
## 0.0.27
### Patch Changes
- 36ddec4: fix: typo in custom page separator parameter for LlamaParse
- Updated dependencies [0452af9]
- @llamaindex/core@0.1.6
## 0.1.3
## 0.0.26
### Patch Changes
- 1c444d5: feat(cloud): update openapi.json
- 224d507: fix: prevent tool calling getting mixed with conversation
- 376d29a: feat: added tool calling and agent support for llama3.1 504B
## 0.1.2
## 0.0.25
### Patch Changes
- Updated dependencies [91d02a4]
- @llamaindex/core@0.1.5
## 0.0.24
### Patch Changes
- 3d9a802: feat: added llama 3.1
- Updated dependencies [15962b3]
- @llamaindex/core@0.1.4
## 0.0.23
### Patch Changes
- Updated dependencies [6cf6ae6]
- @llamaindex/core@0.1.3
## 0.0.22
### Patch Changes
- Updated dependencies [b974eea]
- @llamaindex/core@0.1.2
## 0.0.21
### Patch Changes
- Updated dependencies [b3681bf]
- @llamaindex/core@0.1.1
## 0.0.20
### Patch Changes
- 56746c2: fix: llama3 patched to handle empty content (can happen with system) and added max tokens export
## 0.0.19
### Patch Changes
- 16ef5dd: refactor: depends on core pacakge instead of llamaindex
- Updated dependencies [16ef5dd]
- Updated dependencies [16ef5dd]
- @llamaindex/core@0.1.0
## 0.0.18
### Patch Changes
- llamaindex@0.4.14
## 0.0.17
### Patch Changes
- Updated dependencies [e8f8bea]
- Updated dependencies [304484b]
- llamaindex@0.4.13
## 0.0.16
### Patch Changes
- f326ab8: chore: bump version
- Updated dependencies [f326ab8]
- llamaindex@0.4.12
## 0.1.1
## 0.0.15
### Patch Changes
- 321c39d: fix: generate api as class
- Updated dependencies [8bf5b4a]
- llamaindex@0.4.11
## 0.0.14
### Patch Changes
- Updated dependencies [7dce3d2]
- llamaindex@0.4.10
## 0.0.13
### Patch Changes
- Updated dependencies [3a96a48]
- llamaindex@0.4.9
## 0.0.12
### Patch Changes
- Updated dependencies [83ebdfb]
- llamaindex@0.4.8
## 0.0.11
### Patch Changes
- Updated dependencies [41fe871]
- Updated dependencies [321c39d]
- Updated dependencies [f7f1af0]
- llamaindex@0.4.7
## 0.0.10
### Patch Changes
- Updated dependencies [1feb23b]
- Updated dependencies [08c55ec]
- llamaindex@0.4.6
## 0.0.9
### Patch Changes
- Updated dependencies [6c3e5d0]
- llamaindex@0.4.5
## 0.0.8
### Patch Changes
- Updated dependencies [42eb73a]
- llamaindex@0.4.4
## 0.0.7
### Patch Changes
- Updated dependencies [2ef62a9]
- llamaindex@0.4.3
## 0.0.6
### Patch Changes
- a87a4d1: feat: added tool support calling for Bedrock's Calude and general llm support for agents
- Updated dependencies [a87a4d1]
- Updated dependencies [0730140]
- llamaindex@0.4.2
## 0.0.5
### Patch Changes
- ed467a9: Add model ids for Anthropic Claude 3.5 Sonnet model on Anthropic and Bedrock
- Updated dependencies [3c47910]
- Updated dependencies [ed467a9]
- Updated dependencies [cba5406]
- llamaindex@0.4.1
## 0.0.4
### Patch Changes
- b1a4a74: docs: updated Bedrock Opus region and added a basic README
- Updated dependencies [436bc41]
- Updated dependencies [a44e54f]
- Updated dependencies [a51ed8d]
- Updated dependencies [d3b635b]
- llamaindex@0.4.0
## 0.0.3
### Patch Changes
- Updated dependencies [6bc5bdd]
- Updated dependencies [bf25ff6]
- Updated dependencies [e6d6576]
- llamaindex@0.3.17
## 0.0.2
### Patch Changes
- 8832669: Community bedrock support added
- Updated dependencies [11ae926]
- Updated dependencies [631f000]
- Updated dependencies [1378ec4]
- Updated dependencies [6b1ded4]
- Updated dependencies [4d4bd85]
- Updated dependencies [24a9d1e]
- Updated dependencies [45952de]
- Updated dependencies [54230f0]
- Updated dependencies [a29d835]
- Updated dependencies [73819bf]
- llamaindex@0.3.16
+17
View File
@@ -0,0 +1,17 @@
# @llamaindex/community
AWS package for LlamaIndexTS, deprecated, use [@llamaindex/aws](https://www.npmjs.com/package/@llamaindex/aws) instead.
## Current Features:
- Bedrock support for Amazon Nova models Pro, Lite and Micro
- Bedrock support for the Anthropic Claude Models [usage](https://ts.llamaindex.ai/docs/llamaindex/modules/llms/bedrock) including the latest Sonnet 3.5 v2 and Haiku 3.5
- Bedrock support for the Meta LLama 2, 3, 3.1 and 3.2 Models [usage](https://ts.llamaindex.ai/docs/llamaindex/modules/llms/bedrock)
- Meta LLama3.1 405b and Llama3.2 tool call support
- Meta 3.2 11B and 90B vision support
- Bedrock support for querying Knowledge Base
- Bedrock: [Supported Regions and models for cross-region inference](https://docs.aws.amazon.com/bedrock/latest/userguide/cross-region-inference-support.html)
## LICENSE
MIT
+53
View File
@@ -0,0 +1,53 @@
{
"name": "@llamaindex/community",
"description": "Community package for LlamaIndexTS",
"version": "0.0.101",
"type": "module",
"types": "dist/type/index.d.ts",
"main": "dist/cjs/index.js",
"exports": {
".": {
"import": {
"types": "./dist/type/index.d.ts",
"default": "./dist/index.js"
},
"require": {
"types": "./dist/type/index.d.ts",
"default": "./dist/index.cjs"
}
},
"./llm/bedrock": {
"import": {
"types": "./dist/type/llm/bedrock.d.ts",
"default": "./dist/llm/bedrock/index.js"
},
"require": {
"types": "./dist/type/llm/bedrock.d.ts",
"default": "./dist/llm/bedrock/index.cjs"
}
}
},
"files": [
"dist",
"CHANGELOG.md",
"!**/*.tsbuildinfo"
],
"repository": {
"type": "git",
"url": "git+https://github.com/run-llama/LlamaIndexTS.git",
"directory": "packages/community"
},
"scripts": {
"build": "bunchee",
"dev": "bunchee --watch"
},
"devDependencies": {
"@types/node": "^22.9.0"
},
"dependencies": {
"@aws-sdk/client-bedrock-agent-runtime": "^3.706.0",
"@aws-sdk/client-bedrock-runtime": "^3.706.0",
"@llamaindex/core": "workspace:*",
"@llamaindex/env": "workspace:*"
}
}
+8
View File
@@ -0,0 +1,8 @@
export {
BEDROCK_MODELS,
BEDROCK_MODEL_MAX_TOKENS,
Bedrock,
INFERENCE_BEDROCK_MODELS,
INFERENCE_TO_BEDROCK_MAP,
} from "./llm/bedrock/index.js";
export { AmazonKnowledgeBaseRetriever } from "./retrievers/bedrock.js";
@@ -0,0 +1,134 @@
import type {
ContentBlockDelta,
ConverseOutput,
ConverseRequest,
ConverseResponse,
ConverseStreamOutput,
InvokeModelCommandInput,
InvokeModelWithResponseStreamCommandInput,
ResponseStream,
} from "@aws-sdk/client-bedrock-runtime";
import type {
BaseTool,
ChatMessage,
LLMMetadata,
ToolCall,
ToolCallLLMMessageOptions,
} from "@llamaindex/core/llms";
import { toUtf8 } from "../utils";
import { Provider, type BedrockChatStreamResponse } from "../provider";
import {
mapBaseToolsToAmazonTools,
mapChatMessagesToAmazonMessages,
} from "./utils";
export class AmazonProvider extends Provider<ConverseStreamOutput> {
// eslint-disable-next-line @typescript-eslint/no-explicit-any
getResultFromResponse(response: Record<string, any>): ConverseResponse {
return JSON.parse(toUtf8(response.body));
}
getToolsFromResponse<ToolContent>(response: ConverseOutput): ToolContent[] {
return (
response.message?.content
?.filter((item) => item.toolUse)
.map(
(item) =>
({
id: item.toolUse!.toolUseId,
name: item.toolUse!.name,
input: item.toolUse!.input
? JSON.parse(item.toolUse!.input as string)
: "",
}) as ToolContent,
) ?? []
);
}
getTextFromResponse(response: ConverseResponse): string {
const result = this.getResultFromResponse(response);
const content = result.output?.message?.content ?? [];
return content.map((item) => item.text).join(" ");
}
getTextFromStreamResponse(response: ResponseStream): string {
const event: ConverseStreamOutput | undefined =
this.getStreamingEventResponse(response);
if (!event || !event.contentBlockDelta) return "";
const delta: ContentBlockDelta | undefined = event.contentBlockDelta.delta;
return delta?.text || "";
}
async *reduceStream(
stream: AsyncIterable<ResponseStream>,
): BedrockChatStreamResponse {
let toolId: string | undefined = undefined;
let toolName: string | undefined = undefined;
for await (const response of stream) {
const event = this.getStreamingEventResponse(response);
const delta = this.getTextFromStreamResponse(response);
let options: undefined | ToolCallLLMMessageOptions = undefined;
if (event?.contentBlockStart && event.contentBlockStart.start?.toolUse) {
toolId = event.contentBlockStart.start?.toolUse.toolUseId;
toolName = event.contentBlockStart.start?.toolUse.name;
continue;
}
if (
toolId &&
toolName &&
event?.contentBlockDelta?.delta?.toolUse?.input
) {
options = {
toolCall: [
{
id: toolId,
name: toolName,
input: JSON.parse(event?.contentBlockDelta?.delta?.toolUse.input),
} as ToolCall,
],
};
toolId = undefined;
toolName = undefined;
}
if (!delta && !options) continue;
yield {
delta: options ? "" : delta,
options,
raw: response,
};
}
}
getRequestBody<T extends ChatMessage>(
metadata: LLMMetadata,
messages: T[],
tools: BaseTool[] = [],
options: Omit<ConverseRequest, "modelId" | "messages" | "inferenceConfig">,
): InvokeModelCommandInput | InvokeModelWithResponseStreamCommandInput {
const request: Omit<ConverseRequest, "modelId"> = {
...options,
messages: mapChatMessagesToAmazonMessages(messages),
inferenceConfig: {
maxTokens: metadata.maxTokens,
temperature: metadata.temperature,
topP: metadata.topP,
},
};
if (tools.length) {
request.toolConfig = {
tools: mapBaseToolsToAmazonTools(tools),
};
}
return {
modelId: metadata.model,
contentType: "application/json",
accept: "application/json",
body: JSON.stringify(request),
};
}
}
@@ -0,0 +1,5 @@
import type { ConverseRequest, Message } from "@aws-sdk/client-bedrock-runtime";
export type AmazonMessages = ConverseRequest["messages"];
export type AmazonMessage = Message;
@@ -0,0 +1,141 @@
import type {
ImageBlock,
ImageFormat,
Message,
Tool,
} from "@aws-sdk/client-bedrock-runtime";
import type {
BaseTool,
ChatMessage,
MessageContentDetail,
ToolCallLLMMessageOptions,
} from "@llamaindex/core/llms";
import { extractDataUrlComponents } from "../utils";
import type { JSONObject } from "@llamaindex/core/global";
import { mapMessageContentToMessageContentDetails } from "../../utils";
import type { AmazonMessage, AmazonMessages } from "./types";
const ACCEPTED_IMAGE_MIME_TYPES = [
"image/jpeg",
"image/png",
"image/webp",
"image/gif",
] as const;
const ACCEPTED_IMAGE_MIME_TYPE_FORMAT_MAP: Record<
(typeof ACCEPTED_IMAGE_MIME_TYPES)[number],
ImageFormat
> = {
"image/jpeg": "jpeg",
"image/png": "png",
"image/webp": "webp",
"image/gif": "gif",
};
export const mapImageContent = (imageUrl: string): ImageBlock => {
if (!imageUrl.startsWith("data:"))
throw new Error(
"For Amazon please only use base64 data url, e.g.: data:image/jpeg;base64,SGVsbG8sIFdvcmxkIQ==",
);
const { mimeType, base64: data } = extractDataUrlComponents(imageUrl);
if (
!ACCEPTED_IMAGE_MIME_TYPES.includes(
mimeType as keyof typeof ACCEPTED_IMAGE_MIME_TYPE_FORMAT_MAP,
)
)
throw new Error(
`Amazon only accepts the following mimeTypes: ${ACCEPTED_IMAGE_MIME_TYPES.join("\n")}`,
);
return {
format:
ACCEPTED_IMAGE_MIME_TYPE_FORMAT_MAP[
mimeType as keyof typeof ACCEPTED_IMAGE_MIME_TYPE_FORMAT_MAP
],
// @ts-expect-error: there's a mistake in the "@aws-sdk/client-bedrock-runtime" compared to the actual api
source: { bytes: data },
};
};
export const mapMessageContentDetailToAmazonContent = <
T extends MessageContentDetail,
>(
detail: T,
): Message["content"] => {
let content: Message["content"] = [];
if (detail.type === "text") {
content = [{ text: detail.text }];
} else if (detail.type === "image_url") {
content = [{ image: mapImageContent(detail.image_url.url) }];
} else {
throw new Error("Unsupported content detail type");
}
return content;
};
export const mapChatMessagesToAmazonMessages = <
T extends ChatMessage<ToolCallLLMMessageOptions>,
>(
messages: T[],
): AmazonMessages => {
return messages.flatMap((msg: T): AmazonMessage[] => {
return mapMessageContentToMessageContentDetails(msg.content).map(
(detail: MessageContentDetail): AmazonMessage => {
if (msg.options && "toolCall" in msg.options) {
return {
role: "assistant",
content: msg.options.toolCall.map((call) => ({
toolUse: {
toolUseId: call.id,
name: call.name,
input: call.input as JSONObject,
},
})),
};
}
if (msg.options && "toolResult" in msg.options) {
return {
role: "user",
content: [
{
toolResult: {
toolUseId: msg.options.toolResult.id,
content: [
{
text: msg.options.toolResult.result,
},
],
},
},
],
};
}
return {
role: msg.role === "assistant" ? "assistant" : "user",
content: mapMessageContentDetailToAmazonContent(detail),
};
},
);
});
};
export const mapBaseToolsToAmazonTools = (tools?: BaseTool[]): Tool[] => {
if (!tools) return [];
return tools.map((tool: BaseTool) => {
const {
metadata: { parameters, ...options },
} = tool;
return {
toolSpec: {
...options,
inputSchema: {
json: parameters,
},
},
} as Tool;
});
};
@@ -0,0 +1,156 @@
import {
type InvokeModelCommandInput,
type InvokeModelWithResponseStreamCommandInput,
ResponseStream,
} from "@aws-sdk/client-bedrock-runtime";
import type {
BaseTool,
ChatMessage,
LLMMetadata,
PartialToolCall,
ToolCall,
ToolCallLLMMessageOptions,
} from "@llamaindex/core/llms";
import { type BedrockChatStreamResponse, Provider } from "../provider";
import { toUtf8 } from "../utils";
import type {
AnthropicAdditionalChatOptions,
AnthropicNoneStreamingResponse,
AnthropicStreamEvent,
AnthropicTextContent,
ToolBlock,
} from "./types";
import {
mapBaseToolsToAnthropicTools,
mapChatMessagesToAnthropicMessages,
} from "./utils";
export class AnthropicProvider extends Provider<AnthropicStreamEvent> {
getResultFromResponse(
// eslint-disable-next-line @typescript-eslint/no-explicit-any
response: Record<string, any>,
): AnthropicNoneStreamingResponse {
return JSON.parse(toUtf8(response.body));
}
getToolsFromResponse<AnthropicToolContent>(
// eslint-disable-next-line @typescript-eslint/no-explicit-any
response: Record<string, any>,
): AnthropicToolContent[] {
const result = this.getResultFromResponse(response);
return result.content
.filter((item) => item.type === "tool_use")
.map((item) => item as AnthropicToolContent);
}
// eslint-disable-next-line @typescript-eslint/no-explicit-any
getTextFromResponse(response: Record<string, any>): string {
const result = this.getResultFromResponse(response);
return result.content
.filter((item) => item.type === "text")
.map((item) => (item as AnthropicTextContent).text)
.join(" ");
}
// eslint-disable-next-line @typescript-eslint/no-explicit-any
getTextFromStreamResponse(response: Record<string, any>): string {
const event = this.getStreamingEventResponse(response);
if (event?.type === "content_block_delta") {
if (event.delta.type === "text_delta") return event.delta.text;
if (event.delta.type === "input_json_delta")
return event.delta.partial_json;
}
return "";
}
async *reduceStream(
stream: AsyncIterable<ResponseStream>,
): BedrockChatStreamResponse {
let collecting = [];
let tool: ToolBlock | undefined = undefined;
// #TODO this should be broken down into a separate consumer
for await (const response of stream) {
const delta = this.getTextFromStreamResponse(response);
const event = this.getStreamingEventResponse(response);
if (
event?.type === "content_block_start" &&
event.content_block.type === "tool_use"
) {
tool = event.content_block;
continue;
}
if (
event?.type === "content_block_delta" &&
event.delta.type === "input_json_delta"
) {
collecting.push(event.delta.partial_json);
}
let options: undefined | ToolCallLLMMessageOptions = undefined;
if (tool && collecting.length) {
const input = collecting.filter((item) => item).join("");
// We have all we need to parse the tool_use json
if (event?.type === "content_block_stop") {
options = {
toolCall: [
{
id: tool.id,
name: tool.name,
input: JSON.parse(input),
} as ToolCall,
],
};
// reset the collection/tool
collecting = [];
tool = undefined;
} else {
options = {
toolCall: [
{
id: tool.id,
name: tool.name,
input,
} as PartialToolCall,
],
};
}
}
if (!delta && !options) continue;
yield {
delta: options ? "" : delta,
options,
raw: response,
};
}
}
getRequestBody<T extends ChatMessage<ToolCallLLMMessageOptions>>(
metadata: LLMMetadata,
messages: T[],
tools?: BaseTool[],
options?: AnthropicAdditionalChatOptions,
): InvokeModelCommandInput | InvokeModelWithResponseStreamCommandInput {
const extra: Record<string, unknown> = {};
if (options?.toolChoice) {
extra["tool_choice"] = options?.toolChoice;
}
const mapped = mapChatMessagesToAnthropicMessages(messages);
return {
modelId: metadata.model,
contentType: "application/json",
accept: "application/json",
body: JSON.stringify({
anthropic_version: "bedrock-2023-05-31",
messages: mapped,
tools: mapBaseToolsToAnthropicTools(tools),
max_tokens: metadata.maxTokens,
temperature: metadata.temperature,
top_p: metadata.topP,
...extra,
}),
};
}
}
@@ -0,0 +1,161 @@
import type { ToolMetadata } from "@llamaindex/core/llms";
import type { InvocationMetrics } from "../types";
export type ToolChoice =
| { type: "any" }
| { type: "auto" }
| { type: "tool"; name: string };
export interface ThinkingConfigDisabled {
type: "disabled";
}
export interface ThinkingConfigEnabled {
budget_tokens: number;
type: "enabled";
}
export type AnthropicAdditionalChatOptions = {
toolChoice: ToolChoice;
thinking?: ThinkingConfigDisabled | ThinkingConfigEnabled;
};
type Usage = {
input_tokens: number;
output_tokens: number;
};
type Message = {
id: string;
type: string;
role: string;
content: string[];
model: string;
stop_reason: string | null;
stop_sequence: string | null;
usage: Usage;
};
export type ToolBlock = {
id: string;
input: unknown;
name: string;
type: "tool_use";
};
export type TextBlock = {
type: "text";
text: string;
};
type ContentBlockStart = {
type: "content_block_start";
index: number;
content_block: ToolBlock | TextBlock;
};
type Delta =
| {
type: "text_delta";
text: string;
}
| {
type: "input_json_delta";
partial_json: string;
};
type ContentBlockDelta = {
type: "content_block_delta";
index: number;
delta: Delta;
};
type ContentBlockStop = {
type: "content_block_stop";
index: number;
};
type MessageDelta = {
type: "message_delta";
delta: {
stop_reason: string;
stop_sequence: string | null;
};
usage: Usage;
};
export type MessageStop = {
type: "message_stop";
"amazon-bedrock-invocationMetrics": InvocationMetrics;
};
export type AnthropicStreamEvent =
| { type: "message_start"; message: Message }
| ContentBlockStart
| ContentBlockDelta
| ContentBlockStop
| MessageDelta
| MessageStop;
export type AnthropicContent =
| AnthropicTextContent
| AnthropicImageContent
| AnthropicToolContent
| AnthropicToolResultContent;
export type AnthropicTextContent = {
type: "text";
text: string;
};
export type AnthropicToolContent = {
type: "tool_use";
id: string;
name: string;
input: Record<string, unknown>;
};
export type AnthropicToolResultContent = {
type: "tool_result";
tool_use_id: string;
content: string;
};
export type AnthropicMediaTypes =
| "image/jpeg"
| "image/png"
| "image/webp"
| "image/gif";
export type AnthropicImageSource = {
type: "base64";
media_type: AnthropicMediaTypes;
data: string; // base64 encoded image bytes
};
export type AnthropicImageContent = {
type: "image";
source: AnthropicImageSource;
};
export type AnthropicMessage = {
role: "user" | "assistant";
content: AnthropicContent[];
};
export type AnthropicNoneStreamingResponse = {
id: string;
type: "message";
role: "assistant";
content: AnthropicContent[];
model: string;
stop_reason: "end_turn" | "max_tokens" | "stop_sequence";
stop_sequence?: string;
usage: { input_tokens: number; output_tokens: number };
};
export type AnthropicTool = {
name: string;
description: string;
input_schema: ToolMetadata["parameters"];
};
@@ -0,0 +1,166 @@
import type { JSONObject } from "@llamaindex/core/global";
import type {
BaseTool,
ChatMessage,
MessageContent,
MessageContentDetail,
ToolCallLLMMessageOptions,
} from "@llamaindex/core/llms";
import { mapMessageContentToMessageContentDetails } from "../../utils";
import { extractDataUrlComponents } from "../utils";
import type {
AnthropicContent,
AnthropicImageContent,
AnthropicMediaTypes,
AnthropicMessage,
AnthropicTextContent,
AnthropicTool,
} from "./types.js";
const ACCEPTED_IMAGE_MIME_TYPES = [
"image/jpeg",
"image/png",
"image/webp",
"image/gif",
];
export const mergeNeighboringSameRoleMessages = (
messages: AnthropicMessage[],
): AnthropicMessage[] => {
return messages.reduce(
(result: AnthropicMessage[], current: AnthropicMessage, index: number) => {
if (index > 0 && messages[index - 1]!.role === current.role) {
result[result.length - 1]!.content = [
...result[result.length - 1]!.content,
...current.content,
];
} else {
result.push(current);
}
return result;
},
[],
);
};
export const mapMessageContentDetailToAnthropicContent = <
T extends MessageContentDetail,
>(
detail: T,
): AnthropicContent => {
let content: AnthropicContent;
if (detail.type === "text") {
content = mapTextContent(detail.text);
} else if (detail.type === "image_url") {
content = mapImageContent(detail.image_url.url);
} else {
throw new Error("Unsupported content detail type");
}
return content;
};
export const mapMessageContentToAnthropicContent = <T extends MessageContent>(
content: T,
): AnthropicContent[] => {
return mapMessageContentToMessageContentDetails(content).map(
mapMessageContentDetailToAnthropicContent,
);
};
export const mapBaseToolsToAnthropicTools = (
tools?: BaseTool[],
): AnthropicTool[] => {
if (!tools) return [];
return tools.map((tool: BaseTool) => {
const {
metadata: { parameters, ...options },
} = tool;
return {
...options,
input_schema: parameters,
};
});
};
export const mapChatMessagesToAnthropicMessages = <
T extends ChatMessage<ToolCallLLMMessageOptions>,
>(
messages: T[],
): AnthropicMessage[] => {
const mapped = messages
.flatMap((msg: T): AnthropicMessage[] => {
if (msg.options && "toolCall" in msg.options) {
return [
{
role: "assistant",
content: msg.options.toolCall.map((call) => ({
type: "tool_use",
id: call.id,
name: call.name,
input: call.input as JSONObject,
})),
},
];
}
if (msg.options && "toolResult" in msg.options) {
return [
{
role: "user",
content: [
{
type: "tool_result",
tool_use_id: msg.options.toolResult.id,
content: JSON.stringify(msg.options.toolResult.result),
},
],
},
];
}
return mapMessageContentToMessageContentDetails(msg.content).map(
(detail: MessageContentDetail): AnthropicMessage => {
const content = mapMessageContentDetailToAnthropicContent(detail);
return {
role: msg.role === "assistant" ? "assistant" : "user",
content: [content],
};
},
);
})
.filter((message: AnthropicMessage) => {
const content = message.content[0]!;
if (content.type === "text" && !content.text) return false;
if (content.type === "image" && !content.source.data) return false;
if (content.type === "image" && message.role === "assistant")
return false;
return true;
});
return mergeNeighboringSameRoleMessages(mapped);
};
export const mapTextContent = (text: string): AnthropicTextContent => {
return { type: "text", text };
};
export const mapImageContent = (imageUrl: string): AnthropicImageContent => {
if (!imageUrl.startsWith("data:"))
throw new Error(
"For Anthropic please only use base64 data url, e.g.: data:image/jpeg;base64,SGVsbG8sIFdvcmxkIQ==",
);
const { mimeType, base64: data } = extractDataUrlComponents(imageUrl);
if (!ACCEPTED_IMAGE_MIME_TYPES.includes(mimeType))
throw new Error(
`Anthropic only accepts the following mimeTypes: ${ACCEPTED_IMAGE_MIME_TYPES.join("\n")}`,
);
return {
type: "image",
source: {
type: "base64",
media_type: mimeType as AnthropicMediaTypes,
data,
},
};
};
+513
View File
@@ -0,0 +1,513 @@
import {
BedrockRuntimeClient,
type BedrockRuntimeClientConfig,
InvokeModelCommand,
InvokeModelWithResponseStreamCommand,
} from "@aws-sdk/client-bedrock-runtime";
import {
type ChatMessage,
type ChatResponse,
type CompletionResponse,
type LLMChatParamsNonStreaming,
type LLMChatParamsStreaming,
type LLMCompletionParamsNonStreaming,
type LLMCompletionParamsStreaming,
type LLMMetadata,
ToolCallLLM,
type ToolCallLLMMessageOptions,
} from "@llamaindex/core/llms";
import { streamConverter } from "@llamaindex/core/utils";
import {
type BedrockAdditionalChatOptions,
type BedrockChatStreamResponse,
Provider,
} from "./provider";
import { wrapLLMEvent } from "@llamaindex/core/decorator";
import { mapMessageContentToMessageContentDetails } from "../utils";
import { AmazonProvider } from "./amazon/provider";
import { AnthropicProvider } from "./anthropic/provider";
import { MetaProvider } from "./meta/provider";
// Other providers should go here
export const PROVIDERS: { [key: string]: Provider } = {
anthropic: new AnthropicProvider(),
meta: new MetaProvider(),
amazon: new AmazonProvider(),
};
export type BedrockChatParamsStreaming = LLMChatParamsStreaming<
BedrockAdditionalChatOptions,
ToolCallLLMMessageOptions
>;
export type BedrockChatParamsNonStreaming = LLMChatParamsNonStreaming<
BedrockAdditionalChatOptions,
ToolCallLLMMessageOptions
>;
export type BedrockChatNonStreamResponse =
ChatResponse<ToolCallLLMMessageOptions>;
export const BEDROCK_MODELS = {
AMAZON_TITAN_TG1_LARGE: "amazon.titan-tg1-large",
AMAZON_TITAN_TEXT_EXPRESS_V1: "amazon.titan-text-express-v1",
AI21_J2_GRANDE_INSTRUCT: "ai21.j2-grande-instruct",
AI21_J2_JUMBO_INSTRUCT: "ai21.j2-jumbo-instruct",
AI21_J2_MID: "ai21.j2-mid",
AI21_J2_MID_V1: "ai21.j2-mid-v1",
AI21_J2_ULTRA: "ai21.j2-ultra",
AI21_J2_ULTRA_V1: "ai21.j2-ultra-v1",
COHERE_COMMAND_TEXT_V14: "cohere.command-text-v14",
ANTHROPIC_CLAUDE_INSTANT_1: "anthropic.claude-instant-v1",
ANTHROPIC_CLAUDE_1: "anthropic.claude-v1", // EOF: No longer supported
ANTHROPIC_CLAUDE_2: "anthropic.claude-v2",
ANTHROPIC_CLAUDE_2_1: "anthropic.claude-v2:1",
ANTHROPIC_CLAUDE_3_SONNET: "anthropic.claude-3-sonnet-20240229-v1:0",
ANTHROPIC_CLAUDE_3_HAIKU: "anthropic.claude-3-haiku-20240307-v1:0",
ANTHROPIC_CLAUDE_3_OPUS: "anthropic.claude-3-opus-20240229-v1:0",
ANTHROPIC_CLAUDE_3_5_SONNET: "anthropic.claude-3-5-sonnet-20240620-v1:0",
ANTHROPIC_CLAUDE_3_5_SONNET_V2: "anthropic.claude-3-5-sonnet-20241022-v2:0",
ANTHROPIC_CLAUDE_3_5_HAIKU: "anthropic.claude-3-5-haiku-20241022-v1:0",
ANTHROPIC_CLAUDE_3_7_SONNET: "anthropic.claude-3-7-sonnet-20250219-v1:0",
META_LLAMA2_13B_CHAT: "meta.llama2-13b-chat-v1",
META_LLAMA2_70B_CHAT: "meta.llama2-70b-chat-v1",
META_LLAMA3_8B_INSTRUCT: "meta.llama3-8b-instruct-v1:0",
META_LLAMA3_70B_INSTRUCT: "meta.llama3-70b-instruct-v1:0",
META_LLAMA3_1_8B_INSTRUCT: "meta.llama3-1-8b-instruct-v1:0",
META_LLAMA3_1_70B_INSTRUCT: "meta.llama3-1-70b-instruct-v1:0",
META_LLAMA3_1_405B_INSTRUCT: "meta.llama3-1-405b-instruct-v1:0",
META_LLAMA3_2_1B_INSTRUCT: "meta.llama3-2-1b-instruct-v1:0",
META_LLAMA3_2_3B_INSTRUCT: "meta.llama3-2-3b-instruct-v1:0",
META_LLAMA3_2_11B_INSTRUCT: "meta.llama3-2-11b-instruct-v1:0",
META_LLAMA3_2_90B_INSTRUCT: "meta.llama3-2-90b-instruct-v1:0",
META_LLAMA3_3_70B_INSTRUCT: "meta.llama3-3-70b-instruct-v1:0",
MISTRAL_7B_INSTRUCT: "mistral.mistral-7b-instruct-v0:2",
MISTRAL_MIXTRAL_7B_INSTRUCT: "mistral.mixtral-8x7b-instruct-v0:1",
MISTRAL_MIXTRAL_LARGE_2402: "mistral.mistral-large-2402-v1:0",
AMAZON_NOVA_PREMIER_1: "amazon.nova-premier-v1:0",
AMAZON_NOVA_PRO_1: "amazon.nova-pro-v1:0",
AMAZON_NOVA_LITE_1: "amazon.nova-lite-v1:0",
AMAZON_NOVA_MICRO_1: "amazon.nova-micro-v1:0",
};
export type BEDROCK_MODELS =
(typeof BEDROCK_MODELS)[keyof typeof BEDROCK_MODELS];
export const INFERENCE_BEDROCK_MODELS = {
US_ANTHROPIC_CLAUDE_3_HAIKU: "us.anthropic.claude-3-haiku-20240307-v1:0",
US_ANTHROPIC_CLAUDE_3_5_HAIKU: "us.anthropic.claude-3-5-haiku-20241022-v1:0",
US_ANTHROPIC_CLAUDE_3_OPUS: "us.anthropic.claude-3-opus-20240229-v1:0",
US_ANTHROPIC_CLAUDE_3_SONNET: "us.anthropic.claude-3-sonnet-20240229-v1:0",
US_ANTHROPIC_CLAUDE_3_5_SONNET:
"us.anthropic.claude-3-5-sonnet-20240620-v1:0",
US_ANTHROPIC_CLAUDE_3_5_SONNET_V2:
"us.anthropic.claude-3-5-sonnet-20241022-v2:0",
US_ANTHROPIC_CLAUDE_3_7_SONNET:
"us.anthropic.claude-3-7-sonnet-20250219-v1:0",
US_META_LLAMA_3_2_1B_INSTRUCT: "us.meta.llama3-2-1b-instruct-v1:0",
US_META_LLAMA_3_2_3B_INSTRUCT: "us.meta.llama3-2-3b-instruct-v1:0",
US_META_LLAMA_3_2_11B_INSTRUCT: "us.meta.llama3-2-11b-instruct-v1:0",
US_META_LLAMA_3_2_90B_INSTRUCT: "us.meta.llama3-2-90b-instruct-v1:0",
US_META_LLAMA_3_3_70B_INSTRUCT: "us.meta.llama3-3-70b-instruct-v1:0",
US_AMAZON_NOVA_PREMIER_1: "us.amazon.nova-premier-v1:0",
US_AMAZON_NOVA_PRO_1: "us.amazon.nova-pro-v1:0",
US_AMAZON_NOVA_LITE_1: "us.amazon.nova-lite-v1:0",
US_AMAZON_NOVA_MICRO_1: "us.amazon.nova-micro-v1:0",
EU_ANTHROPIC_CLAUDE_3_HAIKU: "eu.anthropic.claude-3-haiku-20240307-v1:0",
EU_ANTHROPIC_CLAUDE_3_5_HAIKU: "eu.anthropic.claude-3-5-haiku-20240307-v1:0",
EU_ANTHROPIC_CLAUDE_3_SONNET: "eu.anthropic.claude-3-sonnet-20240229-v1:0",
EU_ANTHROPIC_CLAUDE_3_5_SONNET:
"eu.anthropic.claude-3-5-sonnet-20240620-v1:0",
EU_ANTHROPIC_CLAUDE_3_7_SONNET:
"eu.anthropic.claude-3-7-sonnet-20250219-v1:0",
EU_META_LLAMA_3_2_1B_INSTRUCT: "eu.meta.llama3-2-1b-instruct-v1:0",
EU_META_LLAMA_3_2_3B_INSTRUCT: "eu.meta.llama3-2-3b-instruct-v1:0",
EU_AMAZON_NOVA_PREMIER_1: "eu.amazon.nova-premier-v1:0",
EU_AMAZON_NOVA_PRO_1: "eu.amazon.nova-pro-v1:0",
EU_AMAZON_NOVA_LITE_1: "eu.amazon.nova-lite-v1:0",
EU_AMAZON_NOVA_MICRO_1: "eu.amazon.nova-micro-v1:0",
};
export type INFERENCE_BEDROCK_MODELS =
(typeof INFERENCE_BEDROCK_MODELS)[keyof typeof INFERENCE_BEDROCK_MODELS];
export const INFERENCE_TO_BEDROCK_MAP: Record<
INFERENCE_BEDROCK_MODELS,
BEDROCK_MODELS
> = {
[INFERENCE_BEDROCK_MODELS.US_ANTHROPIC_CLAUDE_3_HAIKU]:
BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_HAIKU,
[INFERENCE_BEDROCK_MODELS.US_ANTHROPIC_CLAUDE_3_OPUS]:
BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_OPUS,
[INFERENCE_BEDROCK_MODELS.US_ANTHROPIC_CLAUDE_3_SONNET]:
BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_SONNET,
[INFERENCE_BEDROCK_MODELS.US_ANTHROPIC_CLAUDE_3_5_SONNET]:
BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_5_SONNET,
[INFERENCE_BEDROCK_MODELS.US_ANTHROPIC_CLAUDE_3_7_SONNET]:
BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_7_SONNET,
[INFERENCE_BEDROCK_MODELS.US_ANTHROPIC_CLAUDE_3_5_SONNET_V2]:
BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_5_SONNET_V2,
[INFERENCE_BEDROCK_MODELS.US_ANTHROPIC_CLAUDE_3_5_HAIKU]:
BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_5_HAIKU,
[INFERENCE_BEDROCK_MODELS.US_META_LLAMA_3_2_1B_INSTRUCT]:
BEDROCK_MODELS.META_LLAMA3_2_1B_INSTRUCT,
[INFERENCE_BEDROCK_MODELS.US_META_LLAMA_3_2_3B_INSTRUCT]:
BEDROCK_MODELS.META_LLAMA3_2_3B_INSTRUCT,
[INFERENCE_BEDROCK_MODELS.US_META_LLAMA_3_2_11B_INSTRUCT]:
BEDROCK_MODELS.META_LLAMA3_2_11B_INSTRUCT,
[INFERENCE_BEDROCK_MODELS.US_META_LLAMA_3_2_90B_INSTRUCT]:
BEDROCK_MODELS.META_LLAMA3_2_90B_INSTRUCT,
[INFERENCE_BEDROCK_MODELS.US_META_LLAMA_3_3_70B_INSTRUCT]:
BEDROCK_MODELS.META_LLAMA3_3_70B_INSTRUCT,
[INFERENCE_BEDROCK_MODELS.US_AMAZON_NOVA_PREMIER_1]:
BEDROCK_MODELS.AMAZON_NOVA_PREMIER_1,
[INFERENCE_BEDROCK_MODELS.US_AMAZON_NOVA_PRO_1]:
BEDROCK_MODELS.AMAZON_NOVA_PRO_1,
[INFERENCE_BEDROCK_MODELS.US_AMAZON_NOVA_LITE_1]:
BEDROCK_MODELS.AMAZON_NOVA_LITE_1,
[INFERENCE_BEDROCK_MODELS.US_AMAZON_NOVA_MICRO_1]:
BEDROCK_MODELS.AMAZON_NOVA_MICRO_1,
[INFERENCE_BEDROCK_MODELS.EU_ANTHROPIC_CLAUDE_3_HAIKU]:
BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_HAIKU,
[INFERENCE_BEDROCK_MODELS.EU_ANTHROPIC_CLAUDE_3_SONNET]:
BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_SONNET,
[INFERENCE_BEDROCK_MODELS.EU_ANTHROPIC_CLAUDE_3_5_SONNET]:
BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_5_SONNET,
[INFERENCE_BEDROCK_MODELS.EU_ANTHROPIC_CLAUDE_3_7_SONNET]:
BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_7_SONNET,
[INFERENCE_BEDROCK_MODELS.EU_META_LLAMA_3_2_1B_INSTRUCT]:
BEDROCK_MODELS.META_LLAMA3_2_1B_INSTRUCT,
[INFERENCE_BEDROCK_MODELS.EU_META_LLAMA_3_2_3B_INSTRUCT]:
BEDROCK_MODELS.META_LLAMA3_2_3B_INSTRUCT,
[INFERENCE_BEDROCK_MODELS.EU_AMAZON_NOVA_PREMIER_1]:
BEDROCK_MODELS.AMAZON_NOVA_PREMIER_1,
[INFERENCE_BEDROCK_MODELS.EU_AMAZON_NOVA_PRO_1]:
BEDROCK_MODELS.AMAZON_NOVA_PRO_1,
[INFERENCE_BEDROCK_MODELS.EU_AMAZON_NOVA_LITE_1]:
BEDROCK_MODELS.AMAZON_NOVA_LITE_1,
[INFERENCE_BEDROCK_MODELS.EU_AMAZON_NOVA_MICRO_1]:
BEDROCK_MODELS.AMAZON_NOVA_MICRO_1,
};
/*
* Values taken from https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters.html#model-parameters-claude
*/
const COMPLETION_MODELS = {
[BEDROCK_MODELS.AMAZON_TITAN_TG1_LARGE]: 8000,
[BEDROCK_MODELS.AMAZON_TITAN_TEXT_EXPRESS_V1]: 8000,
[BEDROCK_MODELS.AI21_J2_GRANDE_INSTRUCT]: 8000,
[BEDROCK_MODELS.AI21_J2_JUMBO_INSTRUCT]: 8000,
[BEDROCK_MODELS.AI21_J2_MID]: 8000,
[BEDROCK_MODELS.AI21_J2_MID_V1]: 8000,
[BEDROCK_MODELS.AI21_J2_ULTRA]: 8000,
[BEDROCK_MODELS.AI21_J2_ULTRA_V1]: 8000,
[BEDROCK_MODELS.COHERE_COMMAND_TEXT_V14]: 4096,
};
const CHAT_ONLY_MODELS = {
[BEDROCK_MODELS.ANTHROPIC_CLAUDE_INSTANT_1]: 100000,
[BEDROCK_MODELS.ANTHROPIC_CLAUDE_1]: 100000,
[BEDROCK_MODELS.ANTHROPIC_CLAUDE_2]: 100000,
[BEDROCK_MODELS.ANTHROPIC_CLAUDE_2_1]: 200000,
[BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_SONNET]: 200000,
[BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_HAIKU]: 200000,
[BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_OPUS]: 200000,
[BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_5_SONNET]: 200000,
[BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_5_SONNET_V2]: 200000,
[BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_5_HAIKU]: 200000,
[BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_7_SONNET]: 200000,
[BEDROCK_MODELS.META_LLAMA2_13B_CHAT]: 2048,
[BEDROCK_MODELS.META_LLAMA2_70B_CHAT]: 4096,
[BEDROCK_MODELS.META_LLAMA3_8B_INSTRUCT]: 8192,
[BEDROCK_MODELS.META_LLAMA3_70B_INSTRUCT]: 8192,
[BEDROCK_MODELS.META_LLAMA3_1_8B_INSTRUCT]: 128000,
[BEDROCK_MODELS.META_LLAMA3_1_70B_INSTRUCT]: 128000,
[BEDROCK_MODELS.META_LLAMA3_1_405B_INSTRUCT]: 128000,
[BEDROCK_MODELS.META_LLAMA3_2_1B_INSTRUCT]: 131000,
[BEDROCK_MODELS.META_LLAMA3_2_3B_INSTRUCT]: 131000,
[BEDROCK_MODELS.META_LLAMA3_2_11B_INSTRUCT]: 128000,
[BEDROCK_MODELS.META_LLAMA3_2_90B_INSTRUCT]: 128000,
[BEDROCK_MODELS.META_LLAMA3_3_70B_INSTRUCT]: 128000,
[BEDROCK_MODELS.MISTRAL_7B_INSTRUCT]: 32000,
[BEDROCK_MODELS.MISTRAL_MIXTRAL_7B_INSTRUCT]: 32000,
[BEDROCK_MODELS.MISTRAL_MIXTRAL_LARGE_2402]: 32000,
[BEDROCK_MODELS.AMAZON_NOVA_PREMIER_1]: 300000,
[BEDROCK_MODELS.AMAZON_NOVA_PRO_1]: 300000,
[BEDROCK_MODELS.AMAZON_NOVA_LITE_1]: 300000,
[BEDROCK_MODELS.AMAZON_NOVA_MICRO_1]: 130000,
};
const BEDROCK_FOUNDATION_LLMS = { ...COMPLETION_MODELS, ...CHAT_ONLY_MODELS };
/*
* Only the following models support streaming as
* per result of Bedrock.Client.list_foundation_models
* https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/bedrock/client/list_foundation_models.html
*/
export const STREAMING_MODELS = new Set([
BEDROCK_MODELS.AMAZON_TITAN_TG1_LARGE,
BEDROCK_MODELS.AMAZON_TITAN_TEXT_EXPRESS_V1,
BEDROCK_MODELS.ANTHROPIC_CLAUDE_INSTANT_1,
BEDROCK_MODELS.ANTHROPIC_CLAUDE_1,
BEDROCK_MODELS.ANTHROPIC_CLAUDE_2,
BEDROCK_MODELS.ANTHROPIC_CLAUDE_2_1,
BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_SONNET,
BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_HAIKU,
BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_OPUS,
BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_5_SONNET,
BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_5_SONNET_V2,
BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_5_HAIKU,
BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_7_SONNET,
BEDROCK_MODELS.META_LLAMA2_13B_CHAT,
BEDROCK_MODELS.META_LLAMA2_70B_CHAT,
BEDROCK_MODELS.META_LLAMA3_8B_INSTRUCT,
BEDROCK_MODELS.META_LLAMA3_70B_INSTRUCT,
BEDROCK_MODELS.META_LLAMA3_1_8B_INSTRUCT,
BEDROCK_MODELS.META_LLAMA3_1_70B_INSTRUCT,
BEDROCK_MODELS.META_LLAMA3_1_405B_INSTRUCT,
BEDROCK_MODELS.META_LLAMA3_2_1B_INSTRUCT,
BEDROCK_MODELS.META_LLAMA3_2_3B_INSTRUCT,
BEDROCK_MODELS.META_LLAMA3_2_11B_INSTRUCT,
BEDROCK_MODELS.META_LLAMA3_2_90B_INSTRUCT,
BEDROCK_MODELS.META_LLAMA3_3_70B_INSTRUCT,
BEDROCK_MODELS.MISTRAL_7B_INSTRUCT,
BEDROCK_MODELS.MISTRAL_MIXTRAL_7B_INSTRUCT,
BEDROCK_MODELS.MISTRAL_MIXTRAL_LARGE_2402,
BEDROCK_MODELS.AMAZON_NOVA_PREMIER_1,
BEDROCK_MODELS.AMAZON_NOVA_PRO_1,
BEDROCK_MODELS.AMAZON_NOVA_LITE_1,
BEDROCK_MODELS.AMAZON_NOVA_MICRO_1,
]);
export const TOOL_CALL_MODELS: BEDROCK_MODELS[] = [
BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_SONNET,
BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_HAIKU,
BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_OPUS,
BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_5_SONNET,
BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_5_SONNET_V2,
BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_5_HAIKU,
BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_7_SONNET,
BEDROCK_MODELS.META_LLAMA3_1_405B_INSTRUCT,
BEDROCK_MODELS.META_LLAMA3_2_1B_INSTRUCT,
BEDROCK_MODELS.META_LLAMA3_2_3B_INSTRUCT,
BEDROCK_MODELS.META_LLAMA3_2_11B_INSTRUCT,
BEDROCK_MODELS.META_LLAMA3_2_90B_INSTRUCT,
BEDROCK_MODELS.META_LLAMA3_3_70B_INSTRUCT,
BEDROCK_MODELS.AMAZON_NOVA_PREMIER_1,
BEDROCK_MODELS.AMAZON_NOVA_PRO_1,
BEDROCK_MODELS.AMAZON_NOVA_LITE_1,
BEDROCK_MODELS.AMAZON_NOVA_MICRO_1,
];
const getProvider = (model: string): Provider => {
const providerName = model.split(".")[0];
if (!providerName) {
throw new Error(`Model ${model} is not supported`);
}
if (!(providerName in PROVIDERS)) {
throw new Error(
`Provider ${providerName} for model ${model} is not supported`,
);
}
return PROVIDERS[providerName]!;
};
export type BedrockModelParams = {
model: BEDROCK_MODELS | INFERENCE_BEDROCK_MODELS;
temperature?: number;
topP?: number;
maxTokens?: number;
};
export const BEDROCK_MODEL_MAX_TOKENS: Partial<Record<BEDROCK_MODELS, number>> =
{
[BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_SONNET]: 4096,
[BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_HAIKU]: 4096,
[BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_OPUS]: 4096,
[BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_5_SONNET]: 4096,
[BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_5_SONNET_V2]: 8192,
[BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_5_HAIKU]: 8192,
[BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_7_SONNET]: 8192,
[BEDROCK_MODELS.META_LLAMA2_13B_CHAT]: 2048,
[BEDROCK_MODELS.META_LLAMA2_70B_CHAT]: 2048,
[BEDROCK_MODELS.META_LLAMA3_8B_INSTRUCT]: 2048,
[BEDROCK_MODELS.META_LLAMA3_70B_INSTRUCT]: 2048,
[BEDROCK_MODELS.META_LLAMA3_1_8B_INSTRUCT]: 2048,
[BEDROCK_MODELS.META_LLAMA3_1_70B_INSTRUCT]: 2048,
[BEDROCK_MODELS.META_LLAMA3_1_405B_INSTRUCT]: 2048,
[BEDROCK_MODELS.META_LLAMA3_2_1B_INSTRUCT]: 2048,
[BEDROCK_MODELS.META_LLAMA3_2_3B_INSTRUCT]: 2048,
[BEDROCK_MODELS.META_LLAMA3_2_11B_INSTRUCT]: 2048,
[BEDROCK_MODELS.META_LLAMA3_2_90B_INSTRUCT]: 2048,
[BEDROCK_MODELS.META_LLAMA3_3_70B_INSTRUCT]: 2048,
};
const DEFAULT_BEDROCK_PARAMS = {
temperature: 0.1,
topP: 1,
maxTokens: 1024, // required by anthropic
};
export type BedrockParams = BedrockRuntimeClientConfig & BedrockModelParams;
/**
* ToolCallLLM for Bedrock
*/
export class Bedrock extends ToolCallLLM<BedrockAdditionalChatOptions> {
private client: BedrockRuntimeClient;
protected actualModel: BEDROCK_MODELS | INFERENCE_BEDROCK_MODELS;
model: BEDROCK_MODELS;
temperature: number;
topP: number;
maxTokens?: number;
provider: Provider;
topK?: number;
// there should be no check for env variables. Bedrock can be authenticated in various ways
// AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY and AWS_REGION are the env variables used directly by the sdk
constructor({
temperature,
topP,
maxTokens,
model,
...params
}: BedrockParams) {
super();
this.actualModel = model;
this.model = INFERENCE_TO_BEDROCK_MAP[model] ?? model;
this.provider = getProvider(this.model);
this.maxTokens = maxTokens ?? DEFAULT_BEDROCK_PARAMS.maxTokens;
this.temperature = temperature ?? DEFAULT_BEDROCK_PARAMS.temperature;
this.topP = topP ?? DEFAULT_BEDROCK_PARAMS.topP;
this.client = new BedrockRuntimeClient(params);
}
get supportToolCall(): boolean {
return TOOL_CALL_MODELS.includes(this.model);
}
get metadata(): LLMMetadata {
// NOTE, Anthropic supports top_k but LLMMetadata does not
return {
model: this.model,
temperature: this.temperature,
topP: this.topP,
maxTokens: this.maxTokens,
contextWindow: BEDROCK_FOUNDATION_LLMS[this.model] ?? 128000,
tokenizer: undefined,
structuredOutput: false,
};
}
protected async nonStreamChat(
params: BedrockChatParamsNonStreaming,
): Promise<BedrockChatNonStreamResponse> {
if (!this.supportToolCall && params.tools?.length) {
console.warn(`The model "${this.model}" doesn't support ToolCall`);
}
const input = this.provider.getRequestBody(
this.metadata,
params.messages,
params.tools,
params.additionalChatOptions,
);
const command = new InvokeModelCommand(input);
command.input.modelId = this.actualModel;
const response = await this.client.send(command);
let options: ToolCallLLMMessageOptions = {};
if (this.supportToolCall) {
const tools = this.provider.getToolsFromResponse(response);
if (tools.length) {
options = { toolCall: tools };
}
}
return {
raw: response,
message: {
role: "assistant",
content: this.provider.getTextFromResponse(response),
options,
},
};
}
protected async *streamChat(
params: BedrockChatParamsStreaming,
): BedrockChatStreamResponse {
if (!STREAMING_MODELS.has(this.model))
throw new Error(`The model: ${this.model} does not support streaming`);
if (!this.supportToolCall && params.tools?.length) {
console.warn(`The model "${this.model}" doesn't support ToolCall`);
}
const input = this.provider.getRequestBody(
this.metadata,
params.messages,
params.tools,
params.additionalChatOptions,
);
const command = new InvokeModelWithResponseStreamCommand(input);
command.input.modelId = this.actualModel;
const response = await this.client.send(command);
if (response.body) yield* this.provider.reduceStream(response.body);
}
chat(params: BedrockChatParamsStreaming): Promise<BedrockChatStreamResponse>;
chat(
params: BedrockChatParamsNonStreaming,
): Promise<BedrockChatNonStreamResponse>;
@wrapLLMEvent
async chat(
params: BedrockChatParamsStreaming | BedrockChatParamsNonStreaming,
): Promise<BedrockChatStreamResponse | BedrockChatNonStreamResponse> {
if (params.stream) {
return this.streamChat(params);
}
return this.nonStreamChat(params);
}
complete(
params: LLMCompletionParamsStreaming,
): Promise<AsyncIterable<CompletionResponse>>;
complete(
params: LLMCompletionParamsNonStreaming,
): Promise<CompletionResponse>;
async complete(
params: LLMCompletionParamsStreaming | LLMCompletionParamsNonStreaming,
): Promise<CompletionResponse | AsyncIterable<CompletionResponse>> {
const message: ChatMessage = {
role: "user",
content: mapMessageContentToMessageContentDetails(params.prompt),
};
const input = this.provider.getRequestBody(this.metadata, [message]);
if (params.stream) {
const command = new InvokeModelWithResponseStreamCommand(input);
const response = await this.client.send(command);
if (response.body)
return streamConverter(response.body, (response) => {
return {
text: this.provider.getTextFromStreamResponse(response),
raw: response,
};
});
}
const command = new InvokeModelCommand(input);
const response = await this.client.send(command);
return {
text: this.provider.getTextFromResponse(response),
raw: response,
};
}
}
@@ -0,0 +1,3 @@
export const TOKENS = {
TOOL_CALL: "<|python_tag|>",
};
@@ -0,0 +1,153 @@
import type {
InvokeModelCommandInput,
InvokeModelWithResponseStreamCommandInput,
ResponseStream,
} from "@aws-sdk/client-bedrock-runtime";
import type {
BaseTool,
ChatMessage,
LLMMetadata,
ToolCall,
ToolCallLLMMessageOptions,
} from "@llamaindex/core/llms";
import { toUtf8 } from "../utils";
import type { MetaNoneStreamingResponse, MetaStreamEvent } from "./types";
import { randomUUID } from "@llamaindex/env";
import { Provider, type BedrockChatStreamResponse } from "../provider";
import { TOKENS } from "./constants";
import {
mapChatMessagesToMetaLlama2Messages,
mapChatMessagesToMetaLlama3Messages,
} from "./utils";
export class MetaProvider extends Provider<MetaStreamEvent> {
getResultFromResponse(
// eslint-disable-next-line @typescript-eslint/no-explicit-any
response: Record<string, any>,
): MetaNoneStreamingResponse {
return JSON.parse(toUtf8(response.body));
}
getToolsFromResponse<ToolContent>(
// eslint-disable-next-line @typescript-eslint/no-explicit-any
response: Record<string, any>,
): ToolContent[] {
const result = this.getResultFromResponse(response);
if (!result.generation.trim().startsWith(TOKENS.TOOL_CALL)) return [];
const tool = JSON.parse(
result.generation.trim().split(TOKENS.TOOL_CALL)[1]!,
);
return [
{
id: randomUUID(),
name: tool.name,
input: tool.parameters,
} as ToolContent,
];
}
// eslint-disable-next-line @typescript-eslint/no-explicit-any
getTextFromResponse(response: Record<string, any>): string {
const result = this.getResultFromResponse(response);
if (result.generation.trim().startsWith(TOKENS.TOOL_CALL)) return "";
return result.generation;
}
// eslint-disable-next-line @typescript-eslint/no-explicit-any
getTextFromStreamResponse(response: Record<string, any>): string {
const event = this.getStreamingEventResponse(response);
if (event?.generation) {
return event.generation;
}
return "";
}
async *reduceStream(
stream: AsyncIterable<ResponseStream>,
): BedrockChatStreamResponse {
const collecting: string[] = [];
let toolId: string | undefined = undefined;
for await (const response of stream) {
const event = this.getStreamingEventResponse(response);
const delta = this.getTextFromStreamResponse(response);
// odd quirk of llama3.1, start token is \n\n
if (
!toolId &&
!event?.generation.trim() &&
event?.generation_token_count === 1 &&
event?.prompt_token_count !== null
)
continue;
if (delta.startsWith(TOKENS.TOOL_CALL)) {
toolId = randomUUID();
const parts = delta.split(TOKENS.TOOL_CALL).filter((part) => part);
collecting.push(...parts);
continue;
}
let options: undefined | ToolCallLLMMessageOptions = undefined;
if (toolId && event?.stop_reason === "stop") {
if (delta) collecting.push(delta);
const tool = JSON.parse(collecting.join(""));
options = {
toolCall: [
{
id: toolId,
name: tool.name,
input: tool.parameters,
} as ToolCall,
],
};
} else if (toolId && !event?.stop_reason) {
collecting.push(delta);
continue;
}
if (!delta && !options) continue;
yield {
delta: options ? "" : delta,
options,
raw: response,
};
}
}
getRequestBody<T extends ChatMessage>(
metadata: LLMMetadata,
messages: T[],
tools: BaseTool[] = [],
): InvokeModelCommandInput | InvokeModelWithResponseStreamCommandInput {
let prompt: string = "";
let images: string[] = [];
if (metadata.model.startsWith("meta.llama3")) {
const mapped = mapChatMessagesToMetaLlama3Messages({
messages,
tools,
model: metadata.model,
});
prompt = mapped.prompt;
images = mapped.images;
} else if (metadata.model.startsWith("meta.llama2")) {
prompt = mapChatMessagesToMetaLlama2Messages(messages);
} else {
throw new Error(`Meta model ${metadata.model} is not supported`);
}
return {
modelId: metadata.model,
contentType: "application/json",
accept: "application/json",
body: JSON.stringify({
prompt,
images: images.length ? images : undefined,
max_gen_len: metadata.maxTokens,
temperature: metadata.temperature,
top_p: metadata.topP,
}),
};
}
}

Some files were not shown because too many files have changed in this diff Show More