Release 0.7.2 (#1355)

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: himself65 <himself65@users.noreply.github.com>

.github/workflows/preview.yml

@@ -25,4 +25,4 @@ jobs:
         run: pnpm run build
 
       - name: Pre Release
-        run: pnpx pkg-pr-new publish ./packages/*
+        run: pnpx pkg-pr-new publish ./packages/* ./packages/providers/*

.github/workflows/test.yml

@@ -13,8 +13,10 @@ concurrency:
   cancel-in-progress: true
 
 env:
-  POSTGRES_USER: runneradmin
   POSTGRES_HOST_AUTH_METHOD: trust
+  TURBO_TOKEN: ${{ secrets.TURBO_TOKEN }}
+  TURBO_TEAM: ${{ vars.TURBO_TEAM }}
+  TURBO_REMOTE_ONLY: true
 
 jobs:
   e2e:
@@ -87,13 +89,7 @@ jobs:
       - name: Run Type Check
         run: pnpm run type-check
       - name: Run Circular Dependency Check
-        run: pnpm dlx turbo run circular-check
-      - uses: actions/upload-artifact@v3
-        if: failure()
-        with:
-          name: typecheck-build-dist
-          path: ./packages/llamaindex/dist
-          if-no-files-found: error
+        run: pnpm run circular-check
   e2e-llamaindex-examples:
     strategy:
       fail-fast: false
@@ -104,6 +100,7 @@ jobs:
           - nextjs-edge-runtime
           - nextjs-node-runtime
           - waku-query-engine
+          - llama-parse-browser
     runs-on: ubuntu-latest
     name: Build LlamaIndex Example (${{ matrix.packages }})
     steps:
@@ -139,24 +136,36 @@ jobs:
         run: pnpm run build
       - name: Copy examples
         run: rsync -rv --exclude=node_modules ./examples ${{ runner.temp }}
-      - name: Pack @llamaindex/cloud
-        run: pnpm pack --pack-destination ${{ runner.temp }}
-        working-directory: packages/cloud
-      - name: Pack @llamaindex/openai
-        run: pnpm pack --pack-destination ${{ runner.temp }}
-        working-directory: packages/llm/openai
-      - name: Pack @llamaindex/core
-        run: pnpm pack --pack-destination ${{ runner.temp }}
-        working-directory: packages/core
-      - name: Pack @llamaindex/env
-        run: pnpm pack --pack-destination ${{ runner.temp }}
-        working-directory: packages/env
-      - name: Pack llamaindex
-        run: pnpm pack --pack-destination ${{ runner.temp }}
-        working-directory: packages/llamaindex
+      - name: Pack packages
+        run: |
+          for dir in packages/*; do
+            if [ -d "$dir" ] && [ -f "$dir/package.json" ] && [[ ! "$dir" =~ autotool ]]; then
+              echo "Packing $dir"
+              pnpm pack --pack-destination ${{ runner.temp }} -C $dir
+            else
+              echo "Skipping $dir, no package.json found"
+            fi
+          done
+      - name: Pack provider packages
+        run: |
+          for dir in packages/providers/*; do
+            if [ -d "$dir" ] && [ -f "$dir/package.json" ]; then
+              echo "Packing $dir"
+              pnpm pack --pack-destination ${{ runner.temp }} -C $dir
+            else
+              echo "Skipping $dir, no package.json found"
+            fi
+          done
       - name: Install
         run: npm add ${{ runner.temp }}/*.tgz
         working-directory: ${{ runner.temp }}/examples
       - name: Run Type Check
         run: npx tsc --project ./tsconfig.json
         working-directory: ${{ runner.temp }}/examples
+      - uses: actions/upload-artifact@v4
+        if: failure()
+        with:
+          name: build-dist
+          path: |
+            ${{ runner.temp }}/*.tgz
+          if-no-files-found: error

README.md

@@ -7,7 +7,7 @@
 
 LlamaIndex is a data framework for your LLM application.
 
-Use your own data with large language models (LLMs, OpenAI ChatGPT and others) in Typescript and Javascript.
+Use your own data with large language models (LLMs, OpenAI ChatGPT and others) in JS runtime environments with TypeScript support.
 
 Documentation: https://ts.llamaindex.ai/
 
@@ -19,17 +19,36 @@ Try examples online:
 
 LlamaIndex.TS aims to be a lightweight, easy to use set of libraries to help you integrate large language models into your applications with your own data.
 
-## Multiple JS Environment Support
+## Compatibility
+
+### Multiple JS Environment Support
 
 LlamaIndex.TS supports multiple JS environments, including:
 
 - Node.js (18, 20, 22) ✅
 - Deno ✅
 - Bun ✅
-- React Server Components (Next.js) ✅
+- Nitro ✅
+- Vercel Edge Runtime ✅ (with some limitations)
+- Cloudflare Workers ✅ (with some limitations)
 
 For now, browser support is limited due to the lack of support for [AsyncLocalStorage-like APIs](https://github.com/tc39/proposal-async-context)
 
+### Supported LLMs:
+
+- OpenAI LLms
+- Anthropic LLms
+- Groq LLMs
+- Llama2, Llama3, Llama3.1 LLMs
+- MistralAI LLMs
+- Fireworks LLMs
+- DeepSeek LLMs
+- ReplicateAI LLMs
+- TogetherAI LLMs
+- HuggingFace LLms
+- DeepInfra LLMs
+- Gemini LLMs
+
 ## Getting started
 
 ```shell
@@ -77,7 +96,7 @@ See more about [moduleResolution](https://www.typescriptlang.org/docs/handbook/m
 ### Node.js
 
 ```ts
-import fs from "fs/promises";
+import fs from "node:fs/promises";
 import { Document, VectorStoreIndex } from "llamaindex";
 
 async function main() {
@@ -111,9 +130,9 @@ main();
 node --import tsx ./main.ts
 ```
 
-### React Server Component (Next.js, Waku, Redwood.JS...)
+### Next.js
 
-First, you will need to add a llamaindex plugin to your Next.js project.
+You will need to add a llamaindex plugin to your Next.js project.
 
 ```js
 // next.config.js
@@ -124,20 +143,18 @@ module.exports = withLlamaIndex({
 });
 ```
 
-You can combine `ai` with `llamaindex` in Next.js with RSC (React Server Components).
+### React Server Actions
+
+You can combine `ai` with `llamaindex` in Next.js, Waku or Redwood.js with RSC (React Server Components).
 
 ```tsx
-// src/apps/page.tsx
 "use client";
 import { chatWithAgent } from "@/actions";
 import type { JSX } from "react";
-import { useFormState } from "react-dom";
-
-// You can use the Edge runtime in Next.js by adding this line:
-// export const runtime = "edge";
+import { useActionState } from "react";
 
 export default function Home() {
-  const [ui, action] = useFormState<JSX.Element | null>(async () => {
+  const [ui, action] = useActionState<JSX.Element | null>(async () => {
     return chatWithAgent("hello!", []);
   }, null);
   return (
@@ -167,11 +184,13 @@ export async function chatWithAgent(
       // ... adding your tools here
     ],
   });
-  const responseStream = await agent.chat({
-    stream: true,
-    message: question,
-    chatHistory: prevMessages,
-  });
+  const responseStream = await agent.chat(
+    {
+      message: question,
+      chatHistory: prevMessages,
+    },
+    true,
+  );
   const uiStream = createStreamableUI(<div>loading...</div>);
   responseStream
     .pipeTo(
@@ -189,6 +208,48 @@ export async function chatWithAgent(
 }
 ```
 
+### Cloudflare Workers
+
+> [!TIP]
+> Some modules are not supported in Cloudflare Workers which require Node.js APIs.
+
+```ts
+// add `OPENAI_API_KEY` to the `.dev.vars` file
+interface Env {
+  OPENAI_API_KEY: string;
+}
+
+export default {
+  async fetch(
+    request: Request,
+    env: Env,
+    ctx: ExecutionContext,
+  ): Promise<Response> {
+    const { OpenAIAgent, OpenAI } = await import("@llamaindex/openai");
+    const text = await request.text();
+    const agent = new OpenAIAgent({
+      llm: new OpenAI({
+        apiKey: env.OPENAI_API_KEY,
+      }),
+      tools: [],
+    });
+    const responseStream = await agent.chat({
+      stream: true,
+      message: text,
+    });
+    const textEncoder = new TextEncoder();
+    const response = responseStream.pipeThrough<Uint8Array>(
+      new TransformStream({
+        transform: (chunk, controller) => {
+          controller.enqueue(textEncoder.encode(chunk.delta));
+        },
+      }),
+    );
+    return new Response(response);
+  },
+};
+```
+
 ### Vite
 
 We have some wasm dependencies for better performance. You can use `vite-plugin-wasm` to load them.
@@ -204,29 +265,9 @@ export default {
 };
 ```
 
-## Playground
+### Tips when using in non-Node.js environments
 
-Check out our NextJS playground at https://llama-playground.vercel.app/. The source is available at https://github.com/run-llama/ts-playground
-
-## Core concepts for getting started:
-
-- [Document](/packages/llamaindex/src/Node.ts): A document represents a text file, PDF file or other contiguous piece of data.
-
-- [Node](/packages/llamaindex/src/Node.ts): The basic data building block. Most commonly, these are parts of the document split into manageable pieces that are small enough to be fed into an embedding model and LLM.
-
-- [Embedding](/packages/llamaindex/src/embeddings/OpenAIEmbedding.ts): Embeddings are sets of floating point numbers which represent the data in a Node. By comparing the similarity of embeddings, we can derive an understanding of the similarity of two pieces of data. One use case is to compare the embedding of a question with the embeddings of our Nodes to see which Nodes may contain the data needed to answer that question. Because the default service context is OpenAI, the default embedding is `OpenAIEmbedding`. If using different models, say through Ollama, use this [Embedding](/packages/llamaindex/src/embeddings/OllamaEmbedding.ts) (see all [here](/packages/llamaindex/src/embeddings)).
-
-- [Indices](/packages/llamaindex/src/indices/): Indices store the Nodes and the embeddings of those nodes. QueryEngines retrieve Nodes from these Indices using embedding similarity.
-
-- [QueryEngine](/packages/llamaindex/src/engines/query/RetrieverQueryEngine.ts): Query engines are what generate the query you put in and give you back the result. Query engines generally combine a pre-built prompt with selected Nodes from your Index to give the LLM the context it needs to answer your query. To build a query engine from your Index (recommended), use the [`asQueryEngine`](/packages/llamaindex/src/indices/BaseIndex.ts) method on your Index. See all query engines [here](/packages/llamaindex/src/engines/query).
-
-- [ChatEngine](/packages/llamaindex/src/engines/chat/SimpleChatEngine.ts): A ChatEngine helps you build a chatbot that will interact with your Indices. See all chat engines [here](/packages/llamaindex/src/engines/chat).
-
-- [SimplePrompt](/packages/llamaindex/src/Prompt.ts): A simple standardized function call definition that takes in inputs and formats them in a template literal. SimplePrompts can be specialized using currying and combined using other SimplePrompt functions.
-
-## Tips when using in non-Node.js environments
-
-When you are importing `llamaindex` in a non-Node.js environment(such as React Server Components, Cloudflare Workers, etc.)
+When you are importing `llamaindex` in a non-Node.js environment(such as Vercel Edge, Cloudflare Workers, etc.)
 Some classes are not exported from top-level entry file.
 
 The reason is that some classes are only compatible with Node.js runtime,(e.g. `PDFReader`) which uses Node.js specific APIs(like `fs`, `child_process`, `crypto`).
@@ -262,19 +303,31 @@ export async function getDocuments() {
 
 You'll find a complete example with LlamaIndexTS here: https://github.com/run-llama/create_llama_projects/tree/main/nextjs-edge-llamaparse
 
-## Supported LLMs:
+## Playground
 
-- OpenAI GPT-3.5-turbo and GPT-4
-- Anthropic Claude 3 (Opus, Sonnet, and Haiku) and the legacy models (Claude 2 and Instant)
-- Groq LLMs
-- Llama2/3 Chat LLMs (70B, 13B, and 7B parameters)
-- MistralAI Chat LLMs
-- Fireworks Chat LLMs
+Check out our NextJS playground at https://llama-playground.vercel.app/. The source is available at https://github.com/run-llama/ts-playground
+
+## Core concepts for getting started:
+
+- [Document](/packages/llamaindex/src/Node.ts): A document represents a text file, PDF file or other contiguous piece of data.
+
+- [Node](/packages/llamaindex/src/Node.ts): The basic data building block. Most commonly, these are parts of the document split into manageable pieces that are small enough to be fed into an embedding model and LLM.
+
+- [Embedding](/packages/llamaindex/src/embeddings/OpenAIEmbedding.ts): Embeddings are sets of floating point numbers which represent the data in a Node. By comparing the similarity of embeddings, we can derive an understanding of the similarity of two pieces of data. One use case is to compare the embedding of a question with the embeddings of our Nodes to see which Nodes may contain the data needed to answer that question. Because the default service context is OpenAI, the default embedding is `OpenAIEmbedding`. If using different models, say through Ollama, use this [Embedding](/packages/llamaindex/src/embeddings/OllamaEmbedding.ts) (see all [here](/packages/llamaindex/src/embeddings)).
+
+- [Indices](/packages/llamaindex/src/indices/): Indices store the Nodes and the embeddings of those nodes. QueryEngines retrieve Nodes from these Indices using embedding similarity.
+
+- [QueryEngine](/packages/llamaindex/src/engines/query/RetrieverQueryEngine.ts): Query engines are what generate the query you put in and give you back the result. Query engines generally combine a pre-built prompt with selected Nodes from your Index to give the LLM the context it needs to answer your query. To build a query engine from your Index (recommended), use the [`asQueryEngine`](/packages/llamaindex/src/indices/BaseIndex.ts) method on your Index. See all query engines [here](/packages/llamaindex/src/engines/query).
+
+- [ChatEngine](/packages/llamaindex/src/engines/chat/SimpleChatEngine.ts): A ChatEngine helps you build a chatbot that will interact with your Indices. See all chat engines [here](/packages/llamaindex/src/engines/chat).
+
+- [SimplePrompt](/packages/llamaindex/src/Prompt.ts): A simple standardized function call definition that takes in inputs and formats them in a template literal. SimplePrompts can be specialized using currying and combined using other SimplePrompt functions.
 
 ## Contributing:
 
-We are in the very early days of LlamaIndex.TS. If you’re interested in hacking on it with us check out our [contributing guide](/CONTRIBUTING.md)
+Please see our [contributing guide](CONTRIBUTING.md) for more information.
+You are highly encouraged to contribute to LlamaIndex.TS!
 
-## Bugs? Questions?
+## Community
 
 Please join our Discord! https://discord.com/invite/eN6D2HQ4aX

apps/docs/CHANGELOG.md

@@ -1,5 +1,197 @@
 # docs
 
+## 0.0.94
+
+### Patch Changes
+
+- llamaindex@0.7.2
+
+## 0.0.93
+
+### Patch Changes
+
+- Updated dependencies [ae49ff4]
+- Updated dependencies [4c38c1b]
+- Updated dependencies [a75af83]
+- Updated dependencies [a75af83]
+  - llamaindex@0.7.1
+
+## 0.0.92
+
+### Patch Changes
+
+- Updated dependencies [1364e8e]
+- Updated dependencies [3b7736f]
+- Updated dependencies [96fc69c]
+  - llamaindex@0.7.0
+  - @llamaindex/examples@0.0.9
+
+## 0.0.91
+
+### Patch Changes
+
+- Updated dependencies [5729bd9]
+  - llamaindex@0.6.22
+
+## 0.0.90
+
+### Patch Changes
+
+- Updated dependencies [6f75306]
+- Updated dependencies [94cb4ad]
+  - llamaindex@0.6.21
+
+## 0.0.89
+
+### Patch Changes
+
+- Updated dependencies [6a9a7b1]
+  - llamaindex@0.6.20
+
+## 0.0.88
+
+### Patch Changes
+
+- Updated dependencies [62cba52]
+- Updated dependencies [d265e96]
+- Updated dependencies [d30bbf7]
+- Updated dependencies [53fd00a]
+  - llamaindex@0.6.19
+
+## 0.0.87
+
+### Patch Changes
+
+- Updated dependencies [5f67820]
+- Updated dependencies [fe08d04]
+  - llamaindex@0.6.18
+
+## 0.0.86
+
+### Patch Changes
+
+- Updated dependencies [ee697fb]
+  - llamaindex@0.6.17
+
+## 0.0.85
+
+### Patch Changes
+
+- Updated dependencies [63e9846]
+- Updated dependencies [6f3a31c]
+  - llamaindex@0.6.16
+
+## 0.0.84
+
+### Patch Changes
+
+- Updated dependencies [2a82413]
+  - llamaindex@0.6.15
+
+## 0.0.83
+
+### Patch Changes
+
+- llamaindex@0.6.14
+
+## 0.0.82
+
+### Patch Changes
+
+- llamaindex@0.6.13
+
+## 0.0.81
+
+### Patch Changes
+
+- Updated dependencies [f7b4e94]
+- Updated dependencies [78037a6]
+- Updated dependencies [1d9e3b1]
+  - llamaindex@0.6.12
+
+## 0.0.80
+
+### Patch Changes
+
+- Updated dependencies [df441e2]
+  - llamaindex@0.6.11
+
+## 0.0.79
+
+### Patch Changes
+
+- Updated dependencies [ebc5105]
+- Updated dependencies [6cce3b1]
+  - llamaindex@0.6.10
+
+## 0.0.78
+
+### Patch Changes
+
+- llamaindex@0.6.9
+
+## 0.0.77
+
+### Patch Changes
+
+- Updated dependencies [8b7fdba]
+  - llamaindex@0.6.8
+
+## 0.0.76
+
+### Patch Changes
+
+- Updated dependencies [23bcc37]
+  - llamaindex@0.6.7
+
+## 0.0.75
+
+### Patch Changes
+
+- Updated dependencies [d902cc3]
+- Updated dependencies [025ffe6]
+- Updated dependencies [a659574]
+  - llamaindex@0.6.6
+
+## 0.0.74
+
+### Patch Changes
+
+- Updated dependencies [e9714db]
+  - llamaindex@0.6.5
+
+## 0.0.73
+
+### Patch Changes
+
+- Updated dependencies [b48bcc3]
+  - llamaindex@0.6.4
+
+## 0.0.72
+
+### Patch Changes
+
+- Updated dependencies [2cd1383]
+- Updated dependencies [5c4badb]
+  - llamaindex@0.6.3
+
+## 0.0.71
+
+### Patch Changes
+
+- Updated dependencies [749b43a]
+  - llamaindex@0.6.2
+
+## 0.0.70
+
+### Patch Changes
+
+- Updated dependencies [fbd5e01]
+- Updated dependencies [6b70c54]
+- Updated dependencies [1a6137b]
+- Updated dependencies [85c2e19]
+  - llamaindex@0.6.1
+
 ## 0.0.69
 
 ### Patch Changes

apps/docs/docs/modules/agent/_category_.yml

@@ -1,2 +1,2 @@
 label: "Agents"
-position: 3
+position: 10

apps/docs/docs/modules/chat_engine.md

@@ -1,5 +1,5 @@
 ---
-sidebar_position: 4
+sidebar_position: 13
 ---
 
 # ChatEngine

apps/docs/docs/modules/data_index.md

@@ -1,5 +1,5 @@
 ---
-sidebar_position: 4
+sidebar_position: 12
 ---
 
 # Index
@@ -8,6 +8,7 @@ An index is the basic container and organization for your data. LlamaIndex.TS su
 
 - `VectorStoreIndex` - will send the top-k `Node`s to the LLM when generating a response. The default top-k is 2.
 - `SummaryIndex` - will send every `Node` in the index to the LLM in order to generate a response
+- `KeywordTableIndex` extracts and provides keywords from `Node`s to the LLM
 
 ```typescript
 import { Document, VectorStoreIndex } from "llamaindex";

apps/docs/docs/modules/data_loaders/index.mdx

@@ -6,6 +6,19 @@ import CodeSource2 from "!raw-loader!../../../../../examples/readers/src/custom-
 
 Before you can start indexing your documents, you need to load them into memory.
 
+All "basic" data loaders can be seen below, mapped to their respective filetypes in `SimpleDirectoryReader`. More loaders are shown in the sidebar on the left.
+Additionally the following loaders exist without separate documentation:
+
+- `AssemblyAIReader` transcribes audio using [AssemblyAI](https://www.assemblyai.com/).
+  - [AudioTranscriptReader](../../api/classes/AudioTranscriptReader.md): loads entire transcript as a single document.
+  - [AudioTranscriptParagraphsReader](../../api/classes/AudioTranscriptParagraphsReader.md): creates a document per paragraph.
+  - [AudioTranscriptSentencesReader](../../api/classes/AudioTranscriptSentencesReader.md): creates a document per sentence.
+  - [AudioSubtitlesReader](../../api/classes/AudioTranscriptParagraphsReader.md): creates a document containing the subtitles of a transcript.
+- [NotionReader](../../api/classes/NotionReader.md) loads [Notion](https://www.notion.so/) pages.
+- [SimpleMongoReader](../../api/classes/SimpleMongoReader) loads data from a [MongoDB](https://www.mongodb.com/).
+
+Check the [LlamaIndexTS Github](https://github.com/run-llama/LlamaIndexTS) for the most up to date overview of integrations.
+
 ## SimpleDirectoryReader
 
 [![Open in StackBlitz](https://developer.stackblitz.com/img/open_in_stackblitz.svg)](https://stackblitz.com/github/run-llama/LlamaIndexTS/tree/main/examples/readers?file=src/simple-directory-reader.ts&title=Simple%20Directory%20Reader)

apps/docs/docs/modules/data_loaders/llama_parse/index.mdx

@@ -13,7 +13,7 @@ Official documentation for LlamaParse can be found [here](https://docs.cloud.lla
 ## Usage
 
 You can then use the `LlamaParseReader` class to load local files and convert them into a parsed document that can be used by LlamaIndex.
-See [LlamaParseReader.ts](https://github.com/run-llama/LlamaIndexTS/blob/main/packages/llamaindex/src/readers/LlamaParseReader.ts) for a list of supported file types:
+See [reader.ts](https://github.com/run-llama/LlamaIndexTS/blob/main/packages/cloud/src/reader.ts) for a list of supported file types:
 
 <CodeBlock language="ts">{CodeSource}</CodeBlock>
 

apps/docs/docs/modules/data_stores/_category_.yml

@@ -0,0 +1,2 @@
+label: "Data Stores"
+position: 2

apps/docs/docs/modules/data_stores/chat_stores/_category_.yml

@@ -0,0 +1 @@
+label: "Chat Stores"

apps/docs/docs/modules/data_stores/chat_stores/index.md

@@ -0,0 +1,13 @@
+# Chat Stores
+
+Chat stores manage chat history by storing sequences of messages in a structured way, ensuring the order of messages is maintained for accurate conversation flow.
+
+## Available Chat Stores
+
+- [SimpleChatStore](../../../api/classes/SimpleChatStore.md): A simple in-memory chat store with support for [persisting](../index.md#local-storage) data to disk.
+
+Check the [LlamaIndexTS Github](https://github.com/run-llama/LlamaIndexTS) for the most up to date overview of integrations.
+
+## API Reference
+
+- [BaseChatStore](../../../api/interfaces/BaseChatStore.md)

apps/docs/docs/modules/data_stores/doc_stores/_category_.yml

@@ -0,0 +1,2 @@
+label: "Document Stores"
+position: 2

apps/docs/docs/modules/data_stores/doc_stores/index.md

@@ -0,0 +1,14 @@
+# Document Stores
+
+Document stores contain ingested document chunks, i.e. [Node](../../documents_and_nodes/index.md)s.
+
+## Available Document Stores
+
+- [SimpleDocumentStore](../../../api/classes/SimpleDocumentStore.md): A simple in-memory document store with support for [persisting](../index.md#local-storage) data to disk.
+- [PostgresDocumentStore](../../../api/classes/PostgresDocumentStore.md): A PostgreSQL document store, see [PostgreSQL Storage](../index.md#postgresql-storage).
+
+Check the [LlamaIndexTS Github](https://github.com/run-llama/LlamaIndexTS) for the most up to date overview of integrations.
+
+## API Reference
+
+- [BaseDocumentStore](../../../api/classes/BaseDocumentStore.md)

apps/docs/docs/modules/data_stores/index.md

@@ -1,7 +1,3 @@
----
-sidebar_position: 7
----
-
 # Storage
 
 Storage in LlamaIndex.TS works automatically once you've configured a
@@ -57,4 +53,4 @@ const index = await VectorStoreIndex.fromDocuments([document], {
 
 ## API Reference
 
-- [StorageContext](../api/interfaces/StorageContext.md)
+- [StorageContext](../../api/interfaces/StorageContext.md)

apps/docs/docs/modules/data_stores/index_stores/_category_.yml

@@ -0,0 +1,2 @@
+label: "Index Stores"
+position: 3

apps/docs/docs/modules/data_stores/index_stores/index.md

@@ -0,0 +1,14 @@
+# Index Stores
+
+Index stores are underlying storage components that contain metadata(i.e. information created when indexing) about the [index](../../data_index.md) itself.
+
+## Available Index Stores
+
+- [SimpleIndexStore](../../../api/classes/SimpleIndexStore.md): A simple in-memory index store with support for [persisting](../index.md#local-storage) data to disk.
+- [PostgresIndexStore](../../../api/classes/PostgresIndexStore.md): A PostgreSQL index store, , see [PostgreSQL Storage](../index.md#postgresql-storage).
+
+Check the [LlamaIndexTS Github](https://github.com/run-llama/LlamaIndexTS) for the most up to date overview of integrations.
+
+## API Reference
+
+- [BaseIndexStore](../../../api/classes/BaseIndexStore.md)

apps/docs/docs/modules/data_stores/kv_stores/_category_.yml

@@ -0,0 +1,2 @@
+label: "Key-Value Stores"
+position: 4

apps/docs/docs/modules/data_stores/kv_stores/index.md

@@ -0,0 +1,14 @@
+# Key-Value Stores
+
+Key-Value Stores represent underlying storage components used in [Document Stores](../doc_stores/index.md) and [Index Stores](../index_stores/index.md)
+
+## Available Key-Value Stores
+
+- [SimpleKVStore](../../../api/classes/SimpleKVStore.md): A simple Key-Value store with support of [persisting](../index.md#local-storage) data to disk.
+- [PostgresKVStore](../../../api/classes/PostgresKVStore.md): A PostgreSQL Key-Value store, see [PostgreSQL Storage](../index.md#postgresql-storage).
+
+Check the [LlamaIndexTS Github](https://github.com/run-llama/LlamaIndexTS) for the most up to date overview of integrations.
+
+## API Reference
+
+- [BaseKVStore](../../../api/classes/BaseKVStore.md)

apps/docs/docs/modules/data_stores/vector_stores/index.md

@@ -0,0 +1,22 @@
+# Vector Stores
+
+Vector stores save embedding vectors of your ingested document chunks.
+
+## Available Vector Stores
+
+Available Vector Stores are shown on the sidebar to the left. Additionally the following integrations exist without separate documentation:
+
+- [SimpleVectorStore](../../../api/classes/SimpleVectorStore.md): A simple in-memory vector store with optional [persistance](../index.md#local-storage) to disk.
+- [AstraDBVectorStore](../../../api/classes/AstraDBVectorStore.md): A cloud-native, scalable Database-as-a-Service built on Apache Cassandra, see [datastax.com](https://www.datastax.com/products/datastax-astra)
+- [ChromaVectorStore](../../../api/classes/ChromaVectorStore.md): An open-source vector database, focused on ease of use and performance, see [trychroma.com](https://www.trychroma.com/)
+- [MilvusVectorStore](../../../api/classes/MilvusVectorStore.md): An open-source, high-performance, highly scalable vector database, see [milvus.io](https://milvus.io/)
+- [MongoDBAtlasVectorSearch](../../../api/classes/MongoDBAtlasVectorSearch.md): A cloud-based vector search solution for MongoDB, see [mongodb.com](https://www.mongodb.com/products/platform/atlas-vector-search)
+- [PGVectorStore](../../../api/classes/PGVectorStore.md): An open-source vector store built on PostgreSQL, see [pgvector Github](https://github.com/pgvector/pgvector)
+- [PineconeVectorStore](../../../api/classes/PineconeVectorStore.md): A managed, cloud-native vector database, see [pinecone.io](https://www.pinecone.io/)
+- [WeaviateVectorStore](../../../api/classes/WeaviateVectorStore.md): An open-source, ai-native vector database, see [weaviate.io](https://weaviate.io/)
+
+Check the [LlamaIndexTS Github](https://github.com/run-llama/LlamaIndexTS) for the most up to date overview of integrations.
+
+## API Reference
+
+- [VectorStoreBase](../../../api/classes/VectorStoreBase.md)

apps/docs/docs/modules/data_stores/vector_stores/qdrant.md

@@ -1,5 +1,7 @@
 # Qdrant Vector Store
 
+[qdrant.tech](https://qdrant.tech/)
+
 To run this example, you need to have a Qdrant instance running. You can run it with Docker:
 
 ```bash
@@ -87,4 +89,4 @@ main().catch(console.error);
 
 ## API Reference
 
-- [QdrantVectorStore](../../api/classes/QdrantVectorStore.md)
+- [QdrantVectorStore](../../../api/classes/QdrantVectorStore.md)

apps/docs/docs/modules/documents_and_nodes/index.md

@@ -1,7 +1,3 @@
----
-sidebar_position: 1
----
-
 # Documents and Nodes
 
 `Document`s and `Node`s are the basic building blocks of any index. While the API for these objects is similar, `Document` objects represent entire files, while `Node`s are smaller pieces of that original document, that are suitable for an LLM and Q&A.

apps/docs/docs/modules/embeddings/_category_.yml

@@ -1,2 +1,2 @@
 label: "Embeddings"
-position: 3
+position: 6

apps/docs/docs/modules/embeddings/available_embeddings/mixedbreadai.md

@@ -7,7 +7,7 @@ To find out more about the latest features, updates, and available models, visit
 ## Table of Contents
 
 1. [Setup](#setup)
-2. [Usage with LlamaIndex](#integration-with-llamaindex)
+2. [Usage with LlamaIndex](#usage-with-llamaindex)
 3. [Embeddings with Custom Parameters](#embeddings-with-custom-parameters)
 
 ## Setup

apps/docs/docs/modules/embeddings/index.md

@@ -16,6 +16,16 @@ Settings.embedModel = new OpenAIEmbedding({
 
 For local embeddings, you can use the [HuggingFace](./available_embeddings/huggingface.md) embedding model.
 
+## Available Embeddings
+
+Most available embeddings are listed in the sidebar on the left.
+Additionally the following integrations exist without separate documentation:
+
+- [ClipEmbedding](../../api/classes/ClipEmbedding.md) using `@xenova/transformers`
+- [FireworksEmbedding](../../api/classes/FireworksEmbedding.md) see [fireworks.ai](https://fireworks.ai/)
+
+Check the [LlamaIndexTS Github](https://github.com/run-llama/LlamaIndexTS) for the most up to date overview of integrations.
+
 ## API Reference
 
 - [OpenAIEmbedding](../../api/classes/OpenAIEmbedding.md)

apps/docs/docs/modules/evaluation/_category_.yml

@@ -1,2 +1,2 @@
 label: "Evaluating"
-position: 3
+position: 9

apps/docs/docs/modules/ingestion_pipeline/_category_.yml

@@ -1,2 +1,2 @@
 label: "Ingestion Pipeline"
-position: 2
+position: 4

apps/docs/docs/modules/llms/_category_.yml

@@ -1,2 +1,2 @@
 label: "LLMs"
-position: 3
+position: 5

apps/docs/docs/modules/llms/available_llms/fireworks.md

@@ -1,6 +1,6 @@
 # Fireworks LLM
 
-Fireworks.ai focus on production use cases for open source LLMs, offering speed and quality.
+[Fireworks.ai](https://fireworks.ai/) focus on production use cases for open source LLMs, offering speed and quality.
 
 ## Usage
 

apps/docs/docs/modules/llms/index.md

@@ -1,7 +1,3 @@
----
-sidebar_position: 3
----
-
 # Large Language Models (LLMs)
 
 The LLM is responsible for reading text and generating natural language responses to queries. By default, LlamaIndex.TS uses `gpt-3.5-turbo`.
@@ -30,6 +26,15 @@ export AZURE_OPENAI_DEPLOYMENT="gpt-4" # or some other deployment name
 
 For local LLMs, currently we recommend the use of [Ollama](./available_llms/ollama.md) LLM.
 
+## Available LLMs
+
+Most available LLMs are listed in the sidebar on the left. Additionally the following integrations exist without separate documentation:
+
+- [HuggingFaceLLM](../../api/classes/HuggingFaceLLM.md) and [HuggingFaceInferenceAPI](../../api/classes/HuggingFaceInferenceAPI.md).
+- [ReplicateLLM](../../api/classes/ReplicateLLM.md) see [replicate.com](https://replicate.com/)
+
+Check the [LlamaIndexTS Github](https://github.com/run-llama/LlamaIndexTS) for the most up to date overview of integrations.
+
 ## API Reference
 
 - [OpenAI](../../api/classes/OpenAI.md)

apps/docs/docs/modules/node_parser.md

@@ -1,5 +1,5 @@
 ---
-sidebar_position: 4
+sidebar_position: 11
 ---
 
 # NodeParser

apps/docs/docs/modules/node_postprocessors/index.md

@@ -107,3 +107,4 @@ const filteredNodes = processor.postprocessNodes(nodes);
 ## API Reference
 
 - [SimilarityPostprocessor](../../api/classes/SimilarityPostprocessor.md)
+- [MetadataReplacementPostProcessor](../../api/classes/MetadataReplacementPostProcessor.md)

apps/docs/docs/modules/node_postprocessors/mixedbreadiai_reranker.md

@@ -7,7 +7,7 @@ To find out more about the latest features and updates, visit the [mixedbread.ai
 ## Table of Contents
 
 1. [Setup](#setup)
-2. [Usage with LlamaIndex](#integration-with-llamaindex)
+2. [Usage with LlamaIndex](#usage-with-llamaindex)
 3. [Simple Reranking Guide](#simple-reranking-guide)
 4. [Reranking with Objects](#reranking-with-objects)
 

apps/docs/docs/modules/prompt/_category_.yml

@@ -1,2 +1,2 @@
 label: "Prompts"
-position: 0
+position: 7

apps/docs/docs/modules/prompt/index.md

@@ -73,6 +73,5 @@ const response = await queryEngine.query({
 
 ## API Reference
 
-- [TextQaPrompt](../../api/type-aliases/TextQaPrompt.md)
 - [ResponseSynthesizer](../../api/classes/ResponseSynthesizer.md)
 - [CompactAndRefine](../../api/classes/CompactAndRefine.md)

apps/docs/docs/modules/query_engines/_category_.yml

@@ -1,2 +1,2 @@
 label: "Query Engines"
-position: 2
+position: 8

apps/docs/docs/modules/response_synthesizer.md

@@ -1,5 +1,5 @@
 ---
-sidebar_position: 6
+sidebar_position: 15
 ---
 
 # ResponseSynthesizer

apps/docs/docs/modules/retriever.md

@@ -1,5 +1,5 @@
 ---
-sidebar_position: 5
+sidebar_position: 14
 ---
 
 # Retriever

apps/docs/package.json

@@ -1,6 +1,6 @@
 {
   "name": "docs",
-  "version": "0.0.69",
+  "version": "0.0.94",
   "private": true,
   "scripts": {
     "docusaurus": "docusaurus",
@@ -37,7 +37,7 @@
     "docusaurus-plugin-typedoc": "1.0.5",
     "typedoc": "0.26.6",
     "typedoc-plugin-markdown": "4.2.6",
-    "typescript": "^5.5.4"
+    "typescript": "^5.6.2"
   },
   "browserslist": {
     "production": [

apps/docs/turbo.json

@@ -0,0 +1,8 @@
+{
+  "extends": ["//"],
+  "tasks": {
+    "build": {
+      "outputs": ["build/**", ".docusaurus/**"]
+    }
+  }
+}

examples/CHANGELOG.md

@@ -1,5 +1,16 @@
 # examples
 
+## 0.0.9
+
+### Patch Changes
+
+- Updated dependencies [1364e8e]
+- Updated dependencies [96fc69c]
+- Updated dependencies [3b7736f]
+- Updated dependencies [96fc69c]
+  - llamaindex@0.7.0
+  - @llamaindex/core@0.3.0
+
 ## 0.0.8
 
 ### Patch Changes

examples/agent/large_toolcall_with_gpt4o.ts

@@ -13,7 +13,7 @@ import { FunctionTool, OpenAI, ToolCallOptions } from "llamaindex";
   }
 })();
 
-async function callLLM(init: Partial<OpenAI>) {
+async function callLLM(init: { model: string }) {
   const csvData =
     "Country,Average Height (cm)\nNetherlands,156\nDenmark,158\nNorway,160";
 

examples/anthropic/chat_interactive.ts

@@ -1,4 +1,4 @@
-import { Anthropic, SimpleChatEngine, SimpleChatHistory } from "llamaindex";
+import { Anthropic, ChatMemoryBuffer, SimpleChatEngine } from "llamaindex";
 import { stdin as input, stdout as output } from "node:process";
 import readline from "node:readline/promises";
 
@@ -8,8 +8,8 @@ import readline from "node:readline/promises";
     model: "claude-3-opus",
   });
   // chatHistory will store all the messages in the conversation
-  const chatHistory = new SimpleChatHistory({
-    messages: [
+  const chatHistory = new ChatMemoryBuffer({
+    chatHistory: [
       {
         content: "You want to talk in rhymes.",
         role: "system",
@@ -18,7 +18,7 @@ import readline from "node:readline/promises";
   });
   const chatEngine = new SimpleChatEngine({
     llm,
-    chatHistory,
+    memory: chatHistory,
   });
   const rl = readline.createInterface({ input, output });
 

examples/astradb/example.ts

@@ -1,6 +1,7 @@
 import {
   AstraDBVectorStore,
   Document,
+  MetadataFilters,
   storageContextFromDefaults,
   VectorStoreIndex,
 } from "llamaindex";
@@ -42,8 +43,10 @@ async function main() {
     const index = await VectorStoreIndex.fromDocuments(docs, {
       storageContext: ctx,
     });
-
-    const queryEngine = index.asQueryEngine();
+    const preFilters: MetadataFilters = {
+      filters: [{ key: "id", operator: "in", value: [123, 789] }],
+    }; // try changing the filters to see the different results
+    const queryEngine = index.asQueryEngine({ preFilters });
     const response = await queryEngine.query({
       query: "Describe AstraDB.",
     });

examples/chatHistory.ts

@@ -2,10 +2,10 @@ import { stdin as input, stdout as output } from "node:process";
 import readline from "node:readline/promises";
 
 import {
+  ChatSummaryMemoryBuffer,
   OpenAI,
   Settings,
   SimpleChatEngine,
-  SummaryChatHistory,
 } from "llamaindex";
 
 if (process.env.NODE_ENV === "development") {
@@ -18,7 +18,7 @@ async function main() {
   // Set maxTokens to 75% of the context window size of 4096
   // This will trigger the summarizer once the chat history reaches 25% of the context window size (1024 tokens)
   const llm = new OpenAI({ model: "gpt-3.5-turbo", maxTokens: 4096 * 0.75 });
-  const chatHistory = new SummaryChatHistory({ llm });
+  const chatHistory = new ChatSummaryMemoryBuffer({ llm });
   const chatEngine = new SimpleChatEngine({ llm });
   const rl = readline.createInterface({ input, output });
 

examples/chromadb/preFilters.ts

@@ -1,57 +1,83 @@
 import {
   ChromaVectorStore,
   Document,
+  MetadataFilters,
   VectorStoreIndex,
   storageContextFromDefaults,
 } from "llamaindex";
 
-const collectionName = "dog_colors";
+const collectionName = "dogs_with_color";
 
 async function main() {
   try {
-    const docs = [
-      new Document({
-        text: "The dog is brown",
-        metadata: {
-          dogId: "1",
-        },
-      }),
-      new Document({
-        text: "The dog is red",
-        metadata: {
-          dogId: "2",
-        },
-      }),
-    ];
-
-    console.log("Creating ChromaDB vector store");
     const chromaVS = new ChromaVectorStore({ collectionName });
-    const ctx = await storageContextFromDefaults({ vectorStore: chromaVS });
+    const index = await VectorStoreIndex.fromVectorStore(chromaVS);
 
-    console.log("Embedding documents and adding to index");
-    const index = await VectorStoreIndex.fromDocuments(docs, {
-      storageContext: ctx,
-    });
+    const queryFn = async (filters?: MetadataFilters) => {
+      console.log("\nQuerying dogs by filters: ", JSON.stringify(filters));
+      const query = "List all colors of dogs";
+      const queryEngine = index.asQueryEngine({
+        preFilters: filters,
+        similarityTopK: 3,
+      });
+      const response = await queryEngine.query({ query });
+      console.log(response.toString());
+    };
 
-    console.log("Querying index");
-    const queryEngine = index.asQueryEngine({
-      preFilters: {
-        filters: [
-          {
-            key: "dogId",
-            value: "2",
-            operator: "==",
-          },
-        ],
-      },
-    });
-    const response = await queryEngine.query({
-      query: "What is the color of the dog?",
-    });
-    console.log(response.toString());
+    await queryFn(); // red, brown, yellow
+    await queryFn({ filters: [{ key: "dogId", value: "1", operator: "==" }] }); // brown
+    await queryFn({ filters: [{ key: "dogId", value: "1", operator: "!=" }] }); // red, yellow
+    await queryFn({
+      filters: [
+        { key: "dogId", value: "1", operator: "==" },
+        { key: "dogId", value: "3", operator: "==" },
+      ],
+      condition: "or",
+    }); // brown, yellow
+    await queryFn({
+      filters: [{ key: "dogId", value: ["1", "2"], operator: "in" }],
+    }); // red, brown
   } catch (e) {
     console.error(e);
   }
 }
 
-void main();
+async function generate() {
+  const docs = [
+    new Document({
+      id_: "doc1",
+      text: "The dog is brown",
+      metadata: {
+        dogId: "1",
+      },
+    }),
+    new Document({
+      id_: "doc2",
+      text: "The dog is red",
+      metadata: {
+        dogId: "2",
+      },
+    }),
+    new Document({
+      id_: "doc3",
+      text: "The dog is yellow",
+      metadata: {
+        dogId: "3",
+      },
+    }),
+  ];
+
+  console.log("Creating ChromaDB vector store");
+  const chromaVS = new ChromaVectorStore({ collectionName });
+  const ctx = await storageContextFromDefaults({ vectorStore: chromaVS });
+
+  console.log("Embedding documents and adding to index");
+  await VectorStoreIndex.fromDocuments(docs, {
+    storageContext: ctx,
+  });
+}
+
+(async () => {
+  await generate();
+  await main();
+})();

examples/groq.ts

@@ -1,12 +1,23 @@
 import fs from "node:fs/promises";
 
-import { Document, Groq, Settings, VectorStoreIndex } from "llamaindex";
+import {
+  Document,
+  Groq,
+  HuggingFaceEmbedding,
+  Settings,
+  VectorStoreIndex,
+} from "llamaindex";
 
 // Update llm to use Groq
 Settings.llm = new Groq({
   apiKey: process.env.GROQ_API_KEY,
 });
 
+// Use HuggingFace for embeddings
+Settings.embedModel = new HuggingFaceEmbedding({
+  modelType: "Xenova/all-mpnet-base-v2",
+});
+
 async function main() {
   // Load essay from abramov.txt in Node
   const path = "node_modules/llamaindex/examples/abramov.txt";

examples/lowlevel.ts

@@ -1,7 +1,7 @@
 import {
   Document,
+  getResponseSynthesizer,
   NodeWithScore,
-  ResponseSynthesizer,
   SentenceSplitter,
   TextNode,
 } from "llamaindex";
@@ -14,7 +14,7 @@ import {
 
   console.log(nodes);
 
-  const responseSynthesizer = new ResponseSynthesizer();
+  const responseSynthesizer = getResponseSynthesizer("compact");
 
   const nodesWithScore: NodeWithScore[] = [
     {
@@ -30,7 +30,7 @@ import {
   const stream = await responseSynthesizer.synthesize(
     {
       query: "What age am I?",
-      nodesWithScore,
+      nodes: nodesWithScore,
     },
     true,
   );

examples/metadata-filter/preFilters.ts

@@ -0,0 +1,51 @@
+import {
+  Document,
+  MetadataFilters,
+  Settings,
+  SimpleDocumentStore,
+  VectorStoreIndex,
+  storageContextFromDefaults,
+} from "llamaindex";
+
+async function getDataSource() {
+  const docs = [
+    new Document({ text: "The dog is brown", metadata: { dogId: "1" } }),
+    new Document({ text: "The dog is yellow", metadata: { dogId: "2" } }),
+  ];
+  const storageContext = await storageContextFromDefaults({
+    persistDir: "./cache",
+  });
+  const numberOfDocs = Object.keys(
+    (storageContext.docStore as SimpleDocumentStore).toDict(),
+  ).length;
+  if (numberOfDocs === 0) {
+    return await VectorStoreIndex.fromDocuments(docs, { storageContext });
+  }
+  return await VectorStoreIndex.init({
+    storageContext,
+  });
+}
+
+Settings.callbackManager.on("retrieve-end", (event) => {
+  const { nodes, query } = event.detail;
+  console.log(`${query.query} - Number of retrieved nodes:`, nodes.length);
+});
+
+async function main() {
+  const index = await getDataSource();
+  const filters: MetadataFilters = {
+    filters: [{ key: "dogId", value: "2", operator: "==" }],
+  };
+
+  const retriever = index.asRetriever({ similarityTopK: 3, filters });
+  const queryEngine = index.asQueryEngine({
+    similarityTopK: 3,
+    preFilters: filters,
+  });
+
+  console.log("Retriever and query engine should only retrieve 1 node:");
+  await retriever.retrieve({ query: "Retriever: get dog" });
+  await queryEngine.query({ query: "QueryEngine: get dog" });
+}
+
+void main();

examples/multimodal/context.ts

@@ -1,4 +1,5 @@
 // call pnpm tsx multimodal/load.ts first to init the storage
+import { extractText } from "@llamaindex/core/utils";
 import {
   ContextChatEngine,
   NodeWithScore,
@@ -25,8 +26,9 @@ Settings.callbackManager.on("retrieve-end", (event) => {
   const textNodes = nodes.filter(
     (node: NodeWithScore) => node.node.type === ObjectType.TEXT,
   );
+  const text = extractText(query);
   console.log(
-    `Retrieved ${textNodes.length} text nodes and ${imageNodes.length} image nodes for query: ${query}`,
+    `Retrieved ${textNodes.length} text nodes and ${imageNodes.length} image nodes for query: ${text}`,
   );
 });
 

examples/multimodal/rag.ts

@@ -1,5 +1,6 @@
+import { extractText } from "@llamaindex/core/utils";
 import {
-  MultiModalResponseSynthesizer,
+  getResponseSynthesizer,
   OpenAI,
   Settings,
   VectorStoreIndex,
@@ -16,7 +17,8 @@ Settings.llm = new OpenAI({ model: "gpt-4-turbo", maxTokens: 512 });
 // Update callbackManager
 Settings.callbackManager.on("retrieve-end", (event) => {
   const { nodes, query } = event.detail;
-  console.log(`Retrieved ${nodes.length} nodes for query: ${query}`);
+  const text = extractText(query);
+  console.log(`Retrieved ${nodes.length} nodes for query: ${text}`);
 });
 
 async function main() {
@@ -27,7 +29,7 @@ async function main() {
   });
 
   const queryEngine = index.asQueryEngine({
-    responseSynthesizer: new MultiModalResponseSynthesizer(),
+    responseSynthesizer: getResponseSynthesizer("multi_modal"),
     retriever: index.asRetriever({ topK: { TEXT: 3, IMAGE: 1 } }),
   });
   const stream = await queryEngine.query({

examples/package.json

@@ -1,27 +1,29 @@
 {
   "name": "@llamaindex/examples",
   "private": true,
-  "version": "0.0.8",
+  "version": "0.0.9",
   "dependencies": {
     "@aws-crypto/sha256-js": "^5.2.0",
     "@azure/identity": "^4.4.1",
     "@datastax/astra-db-ts": "^1.4.1",
-    "@llamaindex/core": "^0.2.0",
+    "@llamaindex/core": "^0.3.0",
     "@notionhq/client": "^2.2.15",
     "@pinecone-database/pinecone": "^3.0.2",
+    "@vercel/postgres": "^0.10.0",
     "@zilliz/milvus2-sdk-node": "^2.4.6",
     "chromadb": "^1.8.1",
     "commander": "^12.1.0",
     "dotenv": "^16.4.5",
     "js-tiktoken": "^1.0.14",
-    "llamaindex": "^0.6.0",
+    "llamaindex": "^0.7.0",
     "mongodb": "^6.7.0",
-    "pathe": "^1.1.2"
+    "pathe": "^1.1.2",
+    "postgres": "^3.4.4"
   },
   "devDependencies": {
     "@types/node": "^22.5.1",
     "tsx": "^4.19.0",
-    "typescript": "^5.5.4"
+    "typescript": "^5.6.2"
   },
   "scripts": {
     "lint": "eslint ."

examples/prompts/promptMixin.ts

@@ -1,8 +1,7 @@
 import {
   Document,
+  getResponseSynthesizer,
   PromptTemplate,
-  ResponseSynthesizer,
-  TreeSummarize,
   TreeSummarizePrompt,
   VectorStoreIndex,
 } from "llamaindex";
@@ -27,9 +26,7 @@ async function main() {
 
   const query = "The quick brown fox jumps over the lazy dog";
 
-  const responseSynthesizer = new ResponseSynthesizer({
-    responseBuilder: new TreeSummarize(),
-  });
+  const responseSynthesizer = getResponseSynthesizer("tree_summarize");
 
   const queryEngine = index.asQueryEngine({
     responseSynthesizer,

examples/qdrantdb/preFilters.ts

@@ -39,6 +39,12 @@ async function main() {
           dogId: "2",
         },
       }),
+      new Document({
+        text: "The dog is black",
+        metadata: {
+          dogId: "3",
+        },
+      }),
     ];
     console.log("Creating QdrantDB vector store");
     const qdrantVs = new QdrantVectorStore({ url: qdrantUrl, collectionName });
@@ -73,6 +79,42 @@ async function main() {
       query: "What is the color of the dog?",
     });
     console.log("Filter with dogId 2 response:", response.toString());
+
+    console.log("Querying index with dogId !=2: Expected output: Not red");
+    const queryEngineNotDogId2 = index.asQueryEngine({
+      preFilters: {
+        filters: [
+          {
+            key: "dogId",
+            value: "2",
+            operator: "!=",
+          },
+        ],
+      },
+    });
+    const responseNotDogId2 = await queryEngineNotDogId2.query({
+      query: "What is the color of the dog?",
+    });
+    console.log(responseNotDogId2.toString());
+
+    console.log(
+      "Querying index with dogId 2 or 3: Expected output: Red, Black",
+    );
+    const queryEngineIn = index.asQueryEngine({
+      preFilters: {
+        filters: [
+          {
+            key: "dogId",
+            value: ["2", "3"],
+            operator: "in",
+          },
+        ],
+      },
+    });
+    const responseIn = await queryEngineIn.query({
+      query: "List all dogs",
+    });
+    console.log(responseIn.toString());
   } catch (e) {
     console.error(e);
   }

examples/readers/package.json

@@ -23,6 +23,6 @@
   "devDependencies": {
     "@types/node": "^22.5.1",
     "tsx": "^4.19.0",
-    "typescript": "^5.5.4"
+    "typescript": "^5.6.2"
   }
 }

examples/readers/src/csv.ts

@@ -1,8 +1,7 @@
 import {
-  CompactAndRefine,
+  getResponseSynthesizer,
   OpenAI,
   PromptTemplate,
-  ResponseSynthesizer,
   Settings,
   VectorStoreIndex,
 } from "llamaindex";
@@ -29,8 +28,8 @@ Given the CSV file, generate me Typescript code to answer the question: {query}.
 `,
   });
 
-  const responseSynthesizer = new ResponseSynthesizer({
-    responseBuilder: new CompactAndRefine(undefined, csvPrompt),
+  const responseSynthesizer = getResponseSynthesizer("compact", {
+    textQATemplate: csvPrompt,
   });
 
   const queryEngine = index.asQueryEngine({ responseSynthesizer });

examples/readers/src/llamaparse-json.ts

@@ -1,3 +1,4 @@
+import { createMessageContent } from "@llamaindex/core/response-synthesizers";
 import {
   Document,
   ImageNode,
@@ -6,7 +7,6 @@ import {
   PromptTemplate,
   VectorStoreIndex,
 } from "llamaindex";
-import { createMessageContent } from "llamaindex/synthesizers/utils";
 
 const reader = new LlamaParseReader();
 async function main() {

examples/vector-store/pg/.env.template

@@ -0,0 +1,9 @@
+# neon template
+PGHOST=
+PGDATABASE=
+PGUSER=
+PGPASSWORD=
+ENDPOINT_ID=
+
+# vercel template
+POSTGRES_URL=

examples/vector-store/pg/load-docs.ts

@@ -1,11 +1,11 @@
 // load-docs.ts
-import fs from "fs/promises";
 import {
   PGVectorStore,
   SimpleDirectoryReader,
   storageContextFromDefaults,
   VectorStoreIndex,
 } from "llamaindex";
+import fs from "node:fs/promises";
 
 async function getSourceFilenames(sourceDir: string) {
   return await fs
@@ -40,7 +40,11 @@ async function main(args: any) {
     const rdr = new SimpleDirectoryReader(callback);
     const docs = await rdr.loadData({ directoryPath: sourceDir });
 
-    const pgvs = new PGVectorStore();
+    const pgvs = new PGVectorStore({
+      clientConfig: {
+        connectionString: process.env.PG_CONNECTION_STRING,
+      },
+    });
     pgvs.setCollection(sourceDir);
     await pgvs.clearCollection();
 

examples/vector-store/pg/neon.ts

@@ -0,0 +1,45 @@
+/* eslint-disable turbo/no-undeclared-env-vars */
+import dotenv from "dotenv";
+import { Document, PGVectorStore, VectorStoreQueryMode } from "llamaindex";
+import postgres from "postgres";
+
+dotenv.config();
+
+const { PGHOST, PGDATABASE, PGUSER, ENDPOINT_ID } = process.env;
+const PGPASSWORD = decodeURIComponent(process.env.PGPASSWORD!);
+
+const sql = postgres({
+  host: PGHOST,
+  database: PGDATABASE,
+  username: PGUSER,
+  password: PGPASSWORD,
+  port: 5432,
+  ssl: "require",
+  connection: {
+    options: `project=${ENDPOINT_ID}`,
+  },
+});
+
+await sql`CREATE EXTENSION IF NOT EXISTS vector`;
+
+const vectorStore = new PGVectorStore({
+  dimensions: 3,
+  client: sql,
+});
+
+await vectorStore.add([
+  new Document({
+    text: "hello, world",
+    embedding: [1, 2, 3],
+  }),
+]);
+
+const results = await vectorStore.query({
+  mode: VectorStoreQueryMode.DEFAULT,
+  similarityTopK: 1,
+  queryEmbedding: [1, 2, 3],
+});
+
+console.log("result", results);
+
+await sql.end();

examples/vector-store/pg/package.json

@@ -0,0 +1,5 @@
+{
+  "name": "pg-vector-store",
+  "type": "module",
+  "private": true
+}

examples/vector-store/pg/query.ts

@@ -7,7 +7,11 @@ async function main() {
   });
 
   try {
-    const pgvs = new PGVectorStore();
+    const pgvs = new PGVectorStore({
+      clientConfig: {
+        connectionString: process.env.PG_CONNECTION_STRING,
+      },
+    });
     // Optional - set your collection name, default is no filter on this field.
     // pgvs.setCollection();
 

examples/vector-store/pg/tsconfig.json

@@ -0,0 +1,9 @@
+{
+  "extends": "../../tsconfig.json",
+  "compilerOptions": {
+    "outDir": "./dist",
+    "types": ["node"],
+    "skipLibCheck": true
+  },
+  "include": ["./**/*.ts"]
+}

examples/vector-store/pg/vercel.ts

@@ -0,0 +1,30 @@
+// https://vercel.com/docs/storage/vercel-postgres/sdk
+import { sql } from "@vercel/postgres";
+import dotenv from "dotenv";
+import { Document, PGVectorStore, VectorStoreQueryMode } from "llamaindex";
+
+dotenv.config();
+
+await sql`CREATE EXTENSION IF NOT EXISTS vector`;
+
+const vectorStore = new PGVectorStore({
+  dimensions: 3,
+  client: sql,
+});
+
+await vectorStore.add([
+  new Document({
+    text: "hello, world",
+    embedding: [1, 2, 3],
+  }),
+]);
+
+const results = await vectorStore.query({
+  mode: VectorStoreQueryMode.DEFAULT,
+  similarityTopK: 1,
+  queryEmbedding: [1, 2, 3],
+});
+
+console.log("result", results);
+
+await sql.end();

examples/vectorIndexAnthropic.ts

@@ -2,12 +2,10 @@ import fs from "node:fs/promises";
 
 import {
   Anthropic,
-  CompactAndRefine,
   Document,
-  ResponseSynthesizer,
   Settings,
   VectorStoreIndex,
-  anthropicTextQaPrompt,
+  getResponseSynthesizer,
 } from "llamaindex";
 
 // Update llm to use Anthropic
@@ -23,9 +21,7 @@ async function main() {
   const document = new Document({ text: essay, id_: path });
 
   // Split text and create embeddings. Store them in a VectorStoreIndex
-  const responseSynthesizer = new ResponseSynthesizer({
-    responseBuilder: new CompactAndRefine(undefined, anthropicTextQaPrompt),
-  });
+  const responseSynthesizer = getResponseSynthesizer("compact");
 
   const index = await VectorStoreIndex.fromDocuments([document]);
 

examples/vectorIndexCustomize.ts

@@ -25,12 +25,9 @@ async function main() {
     similarityCutoff: 0.7,
   });
   // TODO: cannot pass responseSynthesizer into retriever query engine
-  const queryEngine = new RetrieverQueryEngine(
-    retriever,
-    undefined,
-    undefined,
-    [nodePostprocessor],
-  );
+  const queryEngine = new RetrieverQueryEngine(retriever, undefined, [
+    nodePostprocessor,
+  ]);
 
   const response = await queryEngine.query({
     query: "What did the author do growing up?",

examples/vectorIndexFromVectorStore.ts

@@ -1,13 +1,12 @@
 import {
+  BaseVectorStore,
+  getResponseSynthesizer,
   OpenAI,
   OpenAIEmbedding,
-  ResponseSynthesizer,
   RetrieverQueryEngine,
   Settings,
   TextNode,
-  TreeSummarize,
   VectorIndexRetriever,
-  VectorStore,
   VectorStoreIndex,
   VectorStoreQuery,
   VectorStoreQueryResult,
@@ -25,7 +24,7 @@ Settings.llm = new OpenAI({
  * Please do not use this class in production; it's only for demonstration purposes.
  */
 class PineconeVectorStore<T extends RecordMetadata = RecordMetadata>
-  implements VectorStore
+  implements BaseVectorStore
 {
   storesText = true;
   isEmbeddingQuery = false;
@@ -165,13 +164,8 @@ async function main() {
       similarityTopK: 500,
     });
 
-    const responseSynthesizer = new ResponseSynthesizer({
-      responseBuilder: new TreeSummarize(),
-    });
-
-    return new RetrieverQueryEngine(retriever, responseSynthesizer, {
-      filter,
-    });
+    const responseSynthesizer = getResponseSynthesizer("tree_summarize");
+    return new RetrieverQueryEngine(retriever, responseSynthesizer);
   };
 
   // whatever is a key from your metadata

package.json

@@ -2,9 +2,9 @@
   "name": "@llamaindex/monorepo",
   "private": true,
   "scripts": {
-    "build": "turbo run build --filter=\"!docs\" --filter=\"!*-test\" --filter=\"!*-example\"",
-    "build:release": "turbo run build lint test --filter=\"!docs\" --filter=\"!*-test\" --filter=\"!*-example\"",
-    "dev": "turbo run dev",
+    "build": "turbo run build",
+    "build:release": "turbo run build --filter=\"./packages/*\"",
+    "dev": "turbo run dev --filter=\"./packages/*\"",
     "format": "prettier --ignore-unknown --cache --check .",
     "format:write": "prettier --ignore-unknown --write .",
     "lint": "turbo run lint",
@@ -12,6 +12,7 @@
     "e2e": "turbo run e2e",
     "test": "turbo run test",
     "type-check": "tsc -b --diagnostics",
+    "circular-check": "madge --circular ./packages/**/**/dist/index.js",
     "release": "pnpm run build:release && changeset publish",
     "release-snapshot": "pnpm run build:release && changeset publish --tag snapshot",
     "new-version": "changeset version && pnpm format:write && pnpm run build:release",
@@ -30,8 +31,8 @@
     "madge": "^8.0.0",
     "prettier": "^3.3.3",
     "prettier-plugin-organize-imports": "^4.0.0",
-    "turbo": "^2.1.0",
-    "typescript": "^5.5.4"
+    "turbo": "^2.1.2",
+    "typescript": "^5.6.2"
   },
   "packageManager": "pnpm@9.5.0",
   "pnpm": {

packages/autotool/CHANGELOG.md

@@ -1,5 +1,202 @@
 # @llamaindex/autotool
 
+## 4.0.2
+
+### Patch Changes
+
+- llamaindex@0.7.2
+
+## 4.0.1
+
+### Patch Changes
+
+- a75af83: refactor: move some llm and embedding to single package
+- Updated dependencies [ae49ff4]
+- Updated dependencies [4c38c1b]
+- Updated dependencies [a75af83]
+- Updated dependencies [a75af83]
+  - llamaindex@0.7.1
+
+## 4.0.0
+
+### Patch Changes
+
+- Updated dependencies [1364e8e]
+- Updated dependencies [3b7736f]
+- Updated dependencies [96fc69c]
+  - llamaindex@0.7.0
+
+## 3.0.22
+
+### Patch Changes
+
+- Updated dependencies [5729bd9]
+  - llamaindex@0.6.22
+
+## 3.0.21
+
+### Patch Changes
+
+- Updated dependencies [6f75306]
+- Updated dependencies [94cb4ad]
+  - llamaindex@0.6.21
+
+## 3.0.20
+
+### Patch Changes
+
+- Updated dependencies [6a9a7b1]
+  - llamaindex@0.6.20
+
+## 3.0.19
+
+### Patch Changes
+
+- Updated dependencies [62cba52]
+- Updated dependencies [d265e96]
+- Updated dependencies [d30bbf7]
+- Updated dependencies [53fd00a]
+  - llamaindex@0.6.19
+
+## 3.0.18
+
+### Patch Changes
+
+- Updated dependencies [5f67820]
+- Updated dependencies [fe08d04]
+  - llamaindex@0.6.18
+
+## 3.0.17
+
+### Patch Changes
+
+- Updated dependencies [ee697fb]
+  - llamaindex@0.6.17
+
+## 3.0.16
+
+### Patch Changes
+
+- Updated dependencies [63e9846]
+- Updated dependencies [6f3a31c]
+  - llamaindex@0.6.16
+
+## 3.0.15
+
+### Patch Changes
+
+- Updated dependencies [2a82413]
+  - llamaindex@0.6.15
+
+## 3.0.14
+
+### Patch Changes
+
+- llamaindex@0.6.14
+
+## 3.0.13
+
+### Patch Changes
+
+- llamaindex@0.6.13
+
+## 3.0.12
+
+### Patch Changes
+
+- Updated dependencies [f7b4e94]
+- Updated dependencies [78037a6]
+- Updated dependencies [1d9e3b1]
+  - llamaindex@0.6.12
+
+## 3.0.11
+
+### Patch Changes
+
+- df441e2: fix: consoleLogger is missing from `@llamaindex/env`
+- Updated dependencies [df441e2]
+  - llamaindex@0.6.11
+
+## 3.0.10
+
+### Patch Changes
+
+- Updated dependencies [ebc5105]
+- Updated dependencies [6cce3b1]
+  - llamaindex@0.6.10
+
+## 3.0.9
+
+### Patch Changes
+
+- llamaindex@0.6.9
+
+## 3.0.8
+
+### Patch Changes
+
+- Updated dependencies [8b7fdba]
+  - llamaindex@0.6.8
+
+## 3.0.7
+
+### Patch Changes
+
+- Updated dependencies [23bcc37]
+  - llamaindex@0.6.7
+
+## 3.0.6
+
+### Patch Changes
+
+- Updated dependencies [d902cc3]
+- Updated dependencies [025ffe6]
+- Updated dependencies [a659574]
+  - llamaindex@0.6.6
+
+## 3.0.5
+
+### Patch Changes
+
+- Updated dependencies [e9714db]
+  - llamaindex@0.6.5
+
+## 3.0.4
+
+### Patch Changes
+
+- Updated dependencies [b48bcc3]
+  - llamaindex@0.6.4
+
+## 3.0.3
+
+### Patch Changes
+
+- Updated dependencies [2cd1383]
+- Updated dependencies [5c4badb]
+  - llamaindex@0.6.3
+
+## 3.0.2
+
+### Patch Changes
+
+- Updated dependencies [749b43a]
+  - llamaindex@0.6.2
+
+## 3.0.1
+
+### Patch Changes
+
+- 1a6137b: feat: experimental support for browser
+
+  If you see bundler issue in next.js edge runtime, please bump to `next@14` latest version.
+
+- Updated dependencies [fbd5e01]
+- Updated dependencies [6b70c54]
+- Updated dependencies [1a6137b]
+- Updated dependencies [85c2e19]
+  - llamaindex@0.6.1
+
 ## 3.0.0
 
 ### Patch Changes

packages/autotool/examples/01_node/CHANGELOG.md

@@ -1,5 +1,221 @@
 # @llamaindex/autotool-01-node-example
 
+## 0.0.34
+
+### Patch Changes
+
+- llamaindex@0.7.2
+- @llamaindex/autotool@4.0.2
+
+## 0.0.33
+
+### Patch Changes
+
+- Updated dependencies [ae49ff4]
+- Updated dependencies [4c38c1b]
+- Updated dependencies [a75af83]
+- Updated dependencies [a75af83]
+  - llamaindex@0.7.1
+  - @llamaindex/autotool@4.0.1
+
+## 0.0.32
+
+### Patch Changes
+
+- Updated dependencies [1364e8e]
+- Updated dependencies [3b7736f]
+- Updated dependencies [96fc69c]
+  - llamaindex@0.7.0
+  - @llamaindex/autotool@4.0.0
+
+## 0.0.31
+
+### Patch Changes
+
+- Updated dependencies [5729bd9]
+  - llamaindex@0.6.22
+  - @llamaindex/autotool@3.0.22
+
+## 0.0.30
+
+### Patch Changes
+
+- Updated dependencies [6f75306]
+- Updated dependencies [94cb4ad]
+  - llamaindex@0.6.21
+  - @llamaindex/autotool@3.0.21
+
+## 0.0.29
+
+### Patch Changes
+
+- Updated dependencies [6a9a7b1]
+  - llamaindex@0.6.20
+  - @llamaindex/autotool@3.0.20
+
+## 0.0.28
+
+### Patch Changes
+
+- Updated dependencies [62cba52]
+- Updated dependencies [d265e96]
+- Updated dependencies [d30bbf7]
+- Updated dependencies [53fd00a]
+  - llamaindex@0.6.19
+  - @llamaindex/autotool@3.0.19
+
+## 0.0.27
+
+### Patch Changes
+
+- Updated dependencies [5f67820]
+- Updated dependencies [fe08d04]
+  - llamaindex@0.6.18
+  - @llamaindex/autotool@3.0.18
+
+## 0.0.26
+
+### Patch Changes
+
+- Updated dependencies [ee697fb]
+  - llamaindex@0.6.17
+  - @llamaindex/autotool@3.0.17
+
+## 0.0.25
+
+### Patch Changes
+
+- Updated dependencies [63e9846]
+- Updated dependencies [6f3a31c]
+  - llamaindex@0.6.16
+  - @llamaindex/autotool@3.0.16
+
+## 0.0.24
+
+### Patch Changes
+
+- Updated dependencies [2a82413]
+  - llamaindex@0.6.15
+  - @llamaindex/autotool@3.0.15
+
+## 0.0.23
+
+### Patch Changes
+
+- llamaindex@0.6.14
+- @llamaindex/autotool@3.0.14
+
+## 0.0.22
+
+### Patch Changes
+
+- llamaindex@0.6.13
+- @llamaindex/autotool@3.0.13
+
+## 0.0.21
+
+### Patch Changes
+
+- Updated dependencies [f7b4e94]
+- Updated dependencies [78037a6]
+- Updated dependencies [1d9e3b1]
+  - llamaindex@0.6.12
+  - @llamaindex/autotool@3.0.12
+
+## 0.0.20
+
+### Patch Changes
+
+- Updated dependencies [df441e2]
+  - @llamaindex/autotool@3.0.11
+  - llamaindex@0.6.11
+
+## 0.0.19
+
+### Patch Changes
+
+- Updated dependencies [ebc5105]
+- Updated dependencies [6cce3b1]
+  - llamaindex@0.6.10
+  - @llamaindex/autotool@3.0.10
+
+## 0.0.18
+
+### Patch Changes
+
+- llamaindex@0.6.9
+- @llamaindex/autotool@3.0.9
+
+## 0.0.17
+
+### Patch Changes
+
+- Updated dependencies [8b7fdba]
+  - llamaindex@0.6.8
+  - @llamaindex/autotool@3.0.8
+
+## 0.0.16
+
+### Patch Changes
+
+- Updated dependencies [23bcc37]
+  - llamaindex@0.6.7
+  - @llamaindex/autotool@3.0.7
+
+## 0.0.15
+
+### Patch Changes
+
+- Updated dependencies [d902cc3]
+- Updated dependencies [025ffe6]
+- Updated dependencies [a659574]
+  - llamaindex@0.6.6
+  - @llamaindex/autotool@3.0.6
+
+## 0.0.14
+
+### Patch Changes
+
+- Updated dependencies [e9714db]
+  - llamaindex@0.6.5
+  - @llamaindex/autotool@3.0.5
+
+## 0.0.13
+
+### Patch Changes
+
+- Updated dependencies [b48bcc3]
+  - llamaindex@0.6.4
+  - @llamaindex/autotool@3.0.4
+
+## 0.0.12
+
+### Patch Changes
+
+- Updated dependencies [2cd1383]
+- Updated dependencies [5c4badb]
+  - llamaindex@0.6.3
+  - @llamaindex/autotool@3.0.3
+
+## 0.0.11
+
+### Patch Changes
+
+- Updated dependencies [749b43a]
+  - llamaindex@0.6.2
+  - @llamaindex/autotool@3.0.2
+
+## 0.0.10
+
+### Patch Changes
+
+- Updated dependencies [fbd5e01]
+- Updated dependencies [6b70c54]
+- Updated dependencies [1a6137b]
+- Updated dependencies [85c2e19]
+  - llamaindex@0.6.1
+  - @llamaindex/autotool@3.0.1
+
 ## 0.0.9
 
 ### Patch Changes

packages/autotool/examples/01_node/package.json

@@ -13,5 +13,5 @@
   "scripts": {
     "start": "node --import tsx --import @llamaindex/autotool/node ./src/index.ts"
   },
-  "version": "0.0.9"
+  "version": "0.0.34"
 }

packages/autotool/examples/02_nextjs/CHANGELOG.md

@@ -1,5 +1,221 @@
 # @llamaindex/autotool-02-next-example
 
+## 0.1.78
+
+### Patch Changes
+
+- llamaindex@0.7.2
+- @llamaindex/autotool@4.0.2
+
+## 0.1.77
+
+### Patch Changes
+
+- Updated dependencies [ae49ff4]
+- Updated dependencies [4c38c1b]
+- Updated dependencies [a75af83]
+- Updated dependencies [a75af83]
+  - llamaindex@0.7.1
+  - @llamaindex/autotool@4.0.1
+
+## 0.1.76
+
+### Patch Changes
+
+- Updated dependencies [1364e8e]
+- Updated dependencies [3b7736f]
+- Updated dependencies [96fc69c]
+  - llamaindex@0.7.0
+  - @llamaindex/autotool@4.0.0
+
+## 0.1.75
+
+### Patch Changes
+
+- Updated dependencies [5729bd9]
+  - llamaindex@0.6.22
+  - @llamaindex/autotool@3.0.22
+
+## 0.1.74
+
+### Patch Changes
+
+- Updated dependencies [6f75306]
+- Updated dependencies [94cb4ad]
+  - llamaindex@0.6.21
+  - @llamaindex/autotool@3.0.21
+
+## 0.1.73
+
+### Patch Changes
+
+- Updated dependencies [6a9a7b1]
+  - llamaindex@0.6.20
+  - @llamaindex/autotool@3.0.20
+
+## 0.1.72
+
+### Patch Changes
+
+- Updated dependencies [62cba52]
+- Updated dependencies [d265e96]
+- Updated dependencies [d30bbf7]
+- Updated dependencies [53fd00a]
+  - llamaindex@0.6.19
+  - @llamaindex/autotool@3.0.19
+
+## 0.1.71
+
+### Patch Changes
+
+- Updated dependencies [5f67820]
+- Updated dependencies [fe08d04]
+  - llamaindex@0.6.18
+  - @llamaindex/autotool@3.0.18
+
+## 0.1.70
+
+### Patch Changes
+
+- Updated dependencies [ee697fb]
+  - llamaindex@0.6.17
+  - @llamaindex/autotool@3.0.17
+
+## 0.1.69
+
+### Patch Changes
+
+- Updated dependencies [63e9846]
+- Updated dependencies [6f3a31c]
+  - llamaindex@0.6.16
+  - @llamaindex/autotool@3.0.16
+
+## 0.1.68
+
+### Patch Changes
+
+- Updated dependencies [2a82413]
+  - llamaindex@0.6.15
+  - @llamaindex/autotool@3.0.15
+
+## 0.1.67
+
+### Patch Changes
+
+- llamaindex@0.6.14
+- @llamaindex/autotool@3.0.14
+
+## 0.1.66
+
+### Patch Changes
+
+- llamaindex@0.6.13
+- @llamaindex/autotool@3.0.13
+
+## 0.1.65
+
+### Patch Changes
+
+- Updated dependencies [f7b4e94]
+- Updated dependencies [78037a6]
+- Updated dependencies [1d9e3b1]
+  - llamaindex@0.6.12
+  - @llamaindex/autotool@3.0.12
+
+## 0.1.64
+
+### Patch Changes
+
+- Updated dependencies [df441e2]
+  - @llamaindex/autotool@3.0.11
+  - llamaindex@0.6.11
+
+## 0.1.63
+
+### Patch Changes
+
+- Updated dependencies [ebc5105]
+- Updated dependencies [6cce3b1]
+  - llamaindex@0.6.10
+  - @llamaindex/autotool@3.0.10
+
+## 0.1.62
+
+### Patch Changes
+
+- llamaindex@0.6.9
+- @llamaindex/autotool@3.0.9
+
+## 0.1.61
+
+### Patch Changes
+
+- Updated dependencies [8b7fdba]
+  - llamaindex@0.6.8
+  - @llamaindex/autotool@3.0.8
+
+## 0.1.60
+
+### Patch Changes
+
+- Updated dependencies [23bcc37]
+  - llamaindex@0.6.7
+  - @llamaindex/autotool@3.0.7
+
+## 0.1.59
+
+### Patch Changes
+
+- Updated dependencies [d902cc3]
+- Updated dependencies [025ffe6]
+- Updated dependencies [a659574]
+  - llamaindex@0.6.6
+  - @llamaindex/autotool@3.0.6
+
+## 0.1.58
+
+### Patch Changes
+
+- Updated dependencies [e9714db]
+  - llamaindex@0.6.5
+  - @llamaindex/autotool@3.0.5
+
+## 0.1.57
+
+### Patch Changes
+
+- Updated dependencies [b48bcc3]
+  - llamaindex@0.6.4
+  - @llamaindex/autotool@3.0.4
+
+## 0.1.56
+
+### Patch Changes
+
+- Updated dependencies [2cd1383]
+- Updated dependencies [5c4badb]
+  - llamaindex@0.6.3
+  - @llamaindex/autotool@3.0.3
+
+## 0.1.55
+
+### Patch Changes
+
+- Updated dependencies [749b43a]
+  - llamaindex@0.6.2
+  - @llamaindex/autotool@3.0.2
+
+## 0.1.54
+
+### Patch Changes
+
+- Updated dependencies [fbd5e01]
+- Updated dependencies [6b70c54]
+- Updated dependencies [1a6137b]
+- Updated dependencies [85c2e19]
+  - llamaindex@0.6.1
+  - @llamaindex/autotool@3.0.1
+
 ## 0.1.53
 
 ### Patch Changes

packages/autotool/examples/02_nextjs/actions.ts

@@ -5,9 +5,9 @@ import { runWithStreamableUI } from "@/context";
 import "@/tool";
 import { convertTools } from "@llamaindex/autotool";
 import { createStreamableUI } from "ai/rsc";
-import type { JSX } from "react";
+import type { ReactNode } from "react";
 
-export async function chatWithAI(message: string): Promise<JSX.Element> {
+export async function chatWithAI(message: string): Promise<ReactNode> {
   const agent = new OpenAIAgent({
     tools: convertTools("llamaindex"),
   });
@@ -25,7 +25,7 @@ export async function chatWithAI(message: string): Promise<JSX.Element> {
               uiStream.append("\n");
             },
             write: async (message) => {
-              uiStream.append(message.response.delta);
+              uiStream.append(message.response);
             },
             close: () => {
               uiStream.done();

packages/autotool/examples/02_nextjs/package.json

@@ -1,7 +1,7 @@
 {
   "name": "@llamaindex/autotool-02-next-example",
   "private": true,
-  "version": "0.1.53",
+  "version": "0.1.78",
   "scripts": {
     "dev": "next dev",
     "build": "next build",
@@ -32,6 +32,6 @@
     "cross-env": "^7.0.3",
     "postcss": "^8.4.41",
     "tailwindcss": "^3.4.10",
-    "typescript": "^5.5.4"
+    "typescript": "^5.6.2"
   }
 }

packages/autotool/package.json

@@ -1,7 +1,7 @@
 {
   "name": "@llamaindex/autotool",
   "type": "module",
-  "version": "3.0.0",
+  "version": "4.0.2",
   "description": "auto transpile your JS function to LLM Agent compatible",
   "files": [
     "dist",
@@ -51,7 +51,7 @@
     "unplugin": "^1.12.2"
   },
   "peerDependencies": {
-    "llamaindex": "^0.6.0",
+    "llamaindex": "workspace:*",
     "openai": "^4",
     "typescript": "^4"
   },
@@ -70,12 +70,12 @@
     "@swc/types": "^0.1.12",
     "@types/json-schema": "^7.0.15",
     "@types/node": "^22.5.1",
-    "bunchee": "5.3.2",
+    "bunchee": "5.5.1",
     "llamaindex": "workspace:*",
-    "next": "14.2.7",
+    "next": "14.2.11",
     "rollup": "^4.21.2",
     "tsx": "^4.19.0",
-    "typescript": "^5.5.4",
+    "typescript": "^5.6.2",
     "vitest": "^2.0.5",
     "webpack": "^5.94.0"
   }

packages/autotool/src/compiler.ts

@@ -9,7 +9,7 @@ import td from "typedoc";
 import type { SourceMapCompact } from "unplugin";
 import type { InfoString } from "./internal";
 
-export const isToolFile = (url: string) => /tool\.[jt]sx?$/.test(url);
+export const isToolFile = (url: string) => /\.tool\.[jt]sx?$/.test(url);
 export const isJSorTS = (url: string) => /\.m?[jt]sx?$/.test(url);
 
 async function parseRoot(entryPoint: string) {
@@ -28,7 +28,7 @@ async function parseRoot(entryPoint: string) {
   if (project) {
     return app.serializer.projectToObject(project, process.cwd());
   }
-  throw new Error("Failed to parse root");
+  throw new Error(`Failed to parse root ${entryPoint}`);
 }
 
 export async function transformAutoTool(

packages/cloud/CHANGELOG.md

@@ -1,5 +1,118 @@
 # @llamaindex/cloud
 
+## 1.0.2
+
+### Patch Changes
+
+- Updated dependencies [4ba2cfe]
+  - @llamaindex/env@0.1.15
+  - @llamaindex/core@0.3.2
+
+## 1.0.1
+
+### 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
+
+### Patch Changes
+
+- Updated dependencies [1364e8e]
+- Updated dependencies [96fc69c]
+  - @llamaindex/core@0.3.0
+
+## 0.2.14
+
+### Patch Changes
+
+- Updated dependencies [5f67820]
+  - @llamaindex/core@0.2.12
+
+## 0.2.13
+
+### Patch Changes
+
+- Updated dependencies [ee697fb]
+  - @llamaindex/core@0.2.11
+
+## 0.2.12
+
+### Patch Changes
+
+- Updated dependencies [3489e7d]
+- Updated dependencies [468bda5]
+  - @llamaindex/core@0.2.10
+
+## 0.2.11
+
+### Patch Changes
+
+- 0b20ff9: fix: package.json format
+
+## 0.2.10
+
+### Patch Changes
+
+- 981811e: fix(cloud): llama parse reader save image incorrectly
+
+## 0.2.9
+
+### Patch Changes
+
+- df441e2: fix: consoleLogger is missing from `@llamaindex/env`
+- Updated dependencies [df441e2]
+  - @llamaindex/core@0.2.8
+  - @llamaindex/env@0.1.13
+
+## 0.2.8
+
+### Patch Changes
+
+- ac41ed3: feat: bump cloud sdk version
+
+## 0.2.7
+
+### Patch Changes
+
+- fb36eff: fix: backport for node.js 18
+
+  There could have one missing API in the node.js 18, so we need to backport it to make it work.
+
+- d24d3d1: fix: print warning when llama parse reader has error
+- Updated dependencies [2cd1383]
+  - @llamaindex/core@0.2.3
+
+## 0.2.6
+
+### Patch Changes
+
+- b42adeb: fix: get job result in llama parse reader
+- Updated dependencies [749b43a]
+  - @llamaindex/core@0.2.2
+
+## 0.2.5
+
+### 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]
+- Updated dependencies [ac07e3c]
+  - @llamaindex/core@0.2.1
+  - @llamaindex/env@0.1.11
+
 ## 0.2.4
 
 ### Patch Changes

packages/cloud/api/package.json

@@ -0,0 +1,8 @@
+{
+  "type": "module",
+  "main": "./dist/index.cjs",
+  "module": "./dist/index.js",
+  "types": "./dist/index.d.ts",
+  "exports": "./dist/index.js",
+  "private": true
+}

packages/cloud/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@llamaindex/cloud",
-  "version": "0.2.4",
+  "version": "1.0.2",
   "type": "module",
   "license": "MIT",
   "scripts": {
@@ -9,33 +9,54 @@
   },
   "files": [
     "openapi.json",
-    "dist"
+    "./api",
+    "./reader"
   ],
   "exports": {
     "./openapi.json": "./openapi.json",
     "./api": {
       "require": {
-        "types": "./dist/api.d.cts",
-        "default": "./dist/api.cjs"
+        "types": "./api/dist/index.d.cts",
+        "default": "./api/dist/index.cjs"
       },
       "import": {
-        "types": "./dist/api.d.ts",
-        "default": "./dist/api.js"
+        "types": "./api/dist/index.d.ts",
+        "default": "./api/dist/index.js"
       },
       "default": {
-        "types": "./dist/api.d.ts",
-        "default": "./dist/api.js"
+        "types": "./api/dist/index.d.ts",
+        "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": {
+        "types": "./reader/dist/index.d.ts",
+        "default": "./reader/dist/index.js"
       }
     }
   },
   "repository": {
     "type": "git",
-    "url": "https://github.com/himself65/LlamaIndexTS.git",
+    "url": "https://github.com/run-llama/LlamaIndexTS.git",
     "directory": "packages/cloud"
   },
   "devDependencies": {
     "@hey-api/client-fetch": "^0.2.4",
     "@hey-api/openapi-ts": "^0.53.0",
-    "bunchee": "5.3.2"
+    "@llamaindex/core": "workspace:*",
+    "@llamaindex/env": "workspace:*",
+    "bunchee": "5.5.1"
+  },
+  "peerDependencies": {
+    "@llamaindex/core": "workspace:*",
+    "@llamaindex/env": "workspace:*"
   }
 }

packages/cloud/reader/package.json

@@ -0,0 +1,8 @@
+{
+  "type": "module",
+  "main": "./dist/index.cjs",
+  "module": "./dist/index.js",
+  "types": "./dist/index.d.ts",
+  "exports": "./dist/index.js",
+  "private": true
+}

packages/cloud/src/reader.ts

@@ -1,185 +1,25 @@
+import { type Client, createClient, createConfig } from "@hey-api/client-fetch";
 import { Document, FileReader } from "@llamaindex/core/schema";
-import { fs, getEnv } from "@llamaindex/env";
-import { filetypeinfo } from "magic-bytes.js";
+import { fs, getEnv, path } from "@llamaindex/env";
+import {
+  type Body_upload_file_api_v1_parsing_upload_post,
+  type ParserLanguages,
+  ParsingService,
+} from "./api";
+import { sleep } from "./utils";
+
+export type Language = ParserLanguages;
 
 export type ResultType = "text" | "markdown" | "json";
-export type Language =
-  | "abq"
-  | "ady"
-  | "af"
-  | "ang"
-  | "ar"
-  | "as"
-  | "ava"
-  | "az"
-  | "be"
-  | "bg"
-  | "bh"
-  | "bho"
-  | "bn"
-  | "bs"
-  | "ch_sim"
-  | "ch_tra"
-  | "che"
-  | "cs"
-  | "cy"
-  | "da"
-  | "dar"
-  | "de"
-  | "en"
-  | "es"
-  | "et"
-  | "fa"
-  | "fr"
-  | "ga"
-  | "gom"
-  | "hi"
-  | "hr"
-  | "hu"
-  | "id"
-  | "inh"
-  | "is"
-  | "it"
-  | "ja"
-  | "kbd"
-  | "kn"
-  | "ko"
-  | "ku"
-  | "la"
-  | "lbe"
-  | "lez"
-  | "lt"
-  | "lv"
-  | "mah"
-  | "mai"
-  | "mi"
-  | "mn"
-  | "mr"
-  | "ms"
-  | "mt"
-  | "ne"
-  | "new"
-  | "nl"
-  | "no"
-  | "oc"
-  | "pi"
-  | "pl"
-  | "pt"
-  | "ro"
-  | "ru"
-  | "rs_cyrillic"
-  | "rs_latin"
-  | "sck"
-  | "sk"
-  | "sl"
-  | "sq"
-  | "sv"
-  | "sw"
-  | "ta"
-  | "tab"
-  | "te"
-  | "th"
-  | "tjk"
-  | "tl"
-  | "tr"
-  | "ug"
-  | "uk"
-  | "ur"
-  | "uz"
-  | "vi";
 
-const SUPPORT_FILE_EXT: string[] = [
-  ".pdf",
-  // document and presentations
-  ".602",
-  ".abw",
-  ".cgm",
-  ".cwk",
-  ".doc",
-  ".docx",
-  ".docm",
-  ".dot",
-  ".dotm",
-  ".hwp",
-  ".key",
-  ".lwp",
-  ".mw",
-  ".mcw",
-  ".pages",
-  ".pbd",
-  ".ppt",
-  ".pptm",
-  ".pptx",
-  ".pot",
-  ".potm",
-  ".potx",
-  ".rtf",
-  ".sda",
-  ".sdd",
-  ".sdp",
-  ".sdw",
-  ".sgl",
-  ".sti",
-  ".sxi",
-  ".sxw",
-  ".stw",
-  ".sxg",
-  ".txt",
-  ".uof",
-  ".uop",
-  ".uot",
-  ".vor",
-  ".wpd",
-  ".wps",
-  ".xml",
-  ".zabw",
-  ".epub",
-  // images
-  ".jpg",
-  ".jpeg",
-  ".png",
-  ".gif",
-  ".bmp",
-  ".svg",
-  ".tiff",
-  ".webp",
-  // web
-  ".htm",
-  ".html",
-  // spreadsheets
-  ".xlsx",
-  ".xls",
-  ".xlsm",
-  ".xlsb",
-  ".xlw",
-  ".csv",
-  ".dif",
-  ".sylk",
-  ".slk",
-  ".prn",
-  ".numbers",
-  ".et",
-  ".ods",
-  ".fods",
-  ".uos1",
-  ".uos2",
-  ".dbf",
-  ".wk1",
-  ".wk2",
-  ".wk3",
-  ".wk4",
-  ".wks",
-  ".123",
-  ".wq1",
-  ".wq2",
-  ".wb1",
-  ".wb2",
-  ".wb3",
-  ".qpw",
-  ".xlr",
-  ".eth",
-  ".tsv",
-];
+//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.
@@ -188,8 +28,8 @@ const SUPPORT_FILE_EXT: string[] = [
 export class LlamaParseReader extends FileReader {
   // 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 Parsing API.
-  baseUrl: string = "https://api.cloud.llamaindex.ai/api/parsing";
+  // 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.
@@ -199,7 +39,7 @@ export class LlamaParseReader extends FileReader {
   // Whether to print the progress of the parsing.
   verbose = true;
   // The language of the text to parse.
-  language: Language = "en";
+  language: ParserLanguages[] = ["en"];
   // The parsing instruction for the parser. Backend default is an empty string.
   parsingInstruction?: string | undefined;
   // Wether to ignore diagonal text (when the text rotation in degrees is not 0, 90, 180 or 270, so not a horizontal or vertical text). Backend default is false.
@@ -236,22 +76,48 @@ export class LlamaParseReader extends FileReader {
   vendorMultimodalModelName?: string | undefined;
   // The API key for the multimodal API. Can also be set as an env variable: 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;
+
   // numWorkers is implemented in SimpleDirectoryReader
+  stdout?: WriteStream | undefined;
+
+  readonly #client: Client;
 
   constructor(
-    params: Partial<LlamaParseReader> & {
+    params: Partial<Omit<LlamaParseReader, "language" | "apiKey">> & {
+      language?: ParserLanguages | ParserLanguages[] | undefined;
       apiKey?: string | undefined;
     } = {},
   ) {
     super();
     Object.assign(this, params);
-    params.apiKey = params.apiKey ?? getEnv("LLAMA_CLOUD_API_KEY");
-    if (!params.apiKey) {
+    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 = params.apiKey;
+    this.apiKey = apiKey;
+    if (this.baseUrl.endsWith("/")) {
+      this.baseUrl = this.baseUrl.slice(0, -"/".length);
+    }
+    if (this.baseUrl.endsWith("/api/parsing")) {
+      this.baseUrl = this.baseUrl.slice(0, -"/api/parsing".length);
+    }
 
     if (params.gpt4oMode) {
       params.gpt4oApiKey =
@@ -266,129 +132,148 @@ export class LlamaParseReader extends FileReader {
 
       this.vendorMultimodalApiKey = params.vendorMultimodalApiKey;
     }
+
+    this.#client = createClient(
+      createConfig({
+        headers: {
+          Authorization: `Bearer ${this.apiKey}`,
+        },
+        baseUrl: this.baseUrl,
+      }),
+    );
   }
 
   // Create a job for the LlamaParse API
-  private async createJob(
-    data: Uint8Array,
-    fileName?: string,
-  ): Promise<string> {
-    // Load data, set the mime type
-    const { mime, extension } = await LlamaParseReader.getMimeType(data);
-
+  private async createJob(data: Uint8Array): Promise<string> {
     if (this.verbose) {
-      const name = fileName ? fileName : extension;
-      console.log(`Starting load for ${name} file`);
+      console.log("Started uploading the file");
     }
 
-    const body = new FormData();
-    body.set("file", new Blob([data], { type: mime }), fileName);
-
-    const LlamaParseBodyParams = {
+    const body = {
+      file: new Blob([data]),
       language: this.language,
       parsing_instruction: this.parsingInstruction,
-      skip_diagonal_text: this.skipDiagonalText?.toString(),
-      invalidate_cache: this.invalidateCache?.toString(),
-      do_not_cache: this.doNotCache?.toString(),
-      fast_mode: this.fastMode?.toString(),
-      do_not_unroll_columns: this.doNotUnrollColumns?.toString(),
+      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?.toString(),
+      gpt4o_mode: this.gpt4oMode,
       gpt4o_api_key: this.gpt4oApiKey,
       bounding_box: this.boundingBox,
       target_pages: this.targetPages,
-      use_vendor_multimodal_model: this.useVendorMultimodalModel?.toString(),
+      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,
+      input_s3_path: this.inputS3Path,
+      output_s3_path_prefix: this.outputS3PathPrefix,
+    } satisfies {
+      [Key in keyof Body_upload_file_api_v1_parsing_upload_post]-?:
+        | Body_upload_file_api_v1_parsing_upload_post[Key]
+        | undefined;
+    } as unknown as Body_upload_file_api_v1_parsing_upload_post;
 
-    // Filter out params with invalid values that would cause issues on the backend.
-    const filteredParams = this.filterSpecificParams(LlamaParseBodyParams, [
-      "page_separator",
-      "page_prefix",
-      "page_suffix",
-      "bounding_box",
-      "target_pages",
-    ]);
-
-    // Appends body with any defined LlamaParseBodyParams
-    Object.entries(filteredParams).forEach(([key, value]) => {
-      if (value !== undefined) {
-        body.append(key, value);
-      }
-    });
-
-    const headers = {
-      Authorization: `Bearer ${this.apiKey}`,
-    };
-
-    // Send the request, start job
-    const url = `${this.baseUrl}/upload`;
-    const response = await fetch(url, {
+    const response = await ParsingService.uploadFileApiV1ParsingUploadPost({
+      client: this.#client,
+      throwOnError: true,
       signal: AbortSignal.timeout(this.maxTimeout * 1000),
-      method: "POST",
       body,
-      headers,
     });
-    if (!response.ok) {
-      throw new Error(`Failed to parse the file: ${await response.text()}`);
-    }
-    const jsonResponse = await response.json();
-    return jsonResponse.id;
+
+    return response.data.id;
   }
 
   // Get the result of the job
-  private async getJobResult(jobId: string, resultType: string): Promise<any> {
-    const resultUrl = `${this.baseUrl}/job/${jobId}/result/${resultType}`;
-    const statusUrl = `${this.baseUrl}/job/${jobId}`;
-    const headers = { Authorization: `Bearer ${this.apiKey}` };
-
+  private async getJobResult(
+    jobId: string,
+    resultType: "text" | "json" | "markdown",
+  ): Promise<any> {
     const signal = AbortSignal.timeout(this.maxTimeout * 1000);
     let tries = 0;
     while (true) {
-      await new Promise((resolve) =>
-        setTimeout(resolve, this.checkInterval * 1000),
-      );
+      await sleep(this.checkInterval * 1000);
 
       // Check the job status. If unsuccessful response, checks if maximum timeout has been reached. If reached, throws an error
-      const statusResponse = await fetch(statusUrl, {
-        headers,
+      const result = await ParsingService.getJobApiV1ParsingJobJobIdGet({
+        client: this.#client,
+        throwOnError: true,
+        path: {
+          job_id: jobId,
+        },
         signal,
       });
-      if (!statusResponse.ok) {
-        signal.throwIfAborted();
-        if (this.verbose && tries % 10 === 0) {
-          process.stdout.write(".");
-        }
-        tries++;
-        continue;
-      }
+      const { data } = result;
 
-      // If response is succesful, check status of job. Allowed values "PENDING", "SUCCESS", "ERROR", "CANCELED"
-      const statusJson = await statusResponse.json();
-      const status = statusJson.status;
+      const status = (data as Record<string, unknown>)["status"];
       // If job has completed, return the result
       if (status === "SUCCESS") {
-        const resultResponse = await fetch(resultUrl, {
-          headers,
-          signal,
-        });
-        if (!resultResponse.ok) {
-          throw new Error(
-            `Failed to fetch result: ${await resultResponse.text()}`,
-          );
+        let result;
+        switch (resultType) {
+          case "json": {
+            result =
+              await ParsingService.getJobJsonResultApiV1ParsingJobJobIdResultJsonGet(
+                {
+                  client: this.#client,
+                  throwOnError: true,
+                  path: {
+                    job_id: jobId,
+                  },
+                  signal,
+                },
+              );
+            break;
+          }
+          case "markdown": {
+            result =
+              await ParsingService.getJobResultApiV1ParsingJobJobIdResultMarkdownGet(
+                {
+                  client: this.#client,
+                  throwOnError: true,
+                  path: {
+                    job_id: jobId,
+                  },
+                  signal,
+                },
+              );
+            break;
+          }
+          case "text": {
+            result =
+              await ParsingService.getJobTextResultApiV1ParsingJobJobIdResultTextGet(
+                {
+                  client: this.#client,
+                  throwOnError: true,
+                  path: {
+                    job_id: jobId,
+                  },
+                  signal,
+                },
+              );
+            break;
+          }
         }
-        return resultResponse.json();
+        return result.data;
         // If job is still pending, check if maximum timeout has been reached. If reached, throws an error
       } else if (status === "PENDING") {
         signal.throwIfAborted();
         if (this.verbose && tries % 10 === 0) {
-          process.stdout.write(".");
+          this.stdout?.write(".");
         }
         tries++;
       } else {
+        if (this.verbose) {
+          console.error(
+            `Recieved 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}`,
         );
@@ -401,43 +286,38 @@ export class LlamaParseReader extends FileReader {
    * To be used with resultType = "text" and "markdown"
    *
    * @param {Uint8Array} fileContent - The content of the file to be loaded.
-   * @param {string} [fileName] - The optional name of the file to be loaded.
    * @return {Promise<Document[]>} A Promise object that resolves to an array of Document objects.
    */
-  async loadDataAsContent(
-    fileContent: Uint8Array,
-    fileName?: string,
-  ): Promise<Document[]> {
-    let jobId;
-    try {
-      // Creates a job for the file
-      jobId = await this.createJob(fileContent, fileName);
-      if (this.verbose) {
-        console.log(`Started parsing the file under job id ${jobId}`);
-      }
+  async loadDataAsContent(fileContent: Uint8Array): Promise<Document[]> {
+    return this.createJob(fileContent)
+      .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];
+        // 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);
-      }
+        // Split the text by separator if splitByPage is true
+        if (this.splitByPage) {
+          return this.splitTextBySeparator(resultText);
+        }
 
-      return [
-        new Document({
-          text: resultText,
-        }),
-      ];
-    } catch (e) {
-      console.error(`Error while parsing file under job id ${jobId}`, e);
-      if (this.ignoreErrors) {
-        return [];
-      } else {
-        throw e;
-      }
-    }
+        return [
+          new Document({
+            text: resultText,
+          }),
+        ];
+      })
+      .catch((error) => {
+        if (this.ignoreErrors) {
+          console.warn(`Error while parsing the file: ${error.message}`);
+          return [];
+        } else {
+          throw error;
+        }
+      });
   }
   /**
    * Loads data from a file and returns an array of JSON objects.
@@ -467,8 +347,8 @@ export class LlamaParseReader extends FileReader {
       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) {
+        console.error(`Error while parsing the file under job id ${jobId}`, e);
         return [];
       } else {
         throw e;
@@ -536,14 +416,7 @@ export class LlamaParseReader extends FileReader {
     jobId: string,
     imageName: string,
   ): Promise<string> {
-    // Get the full path
-    let imagePath = `${downloadPath}/${jobId}-${imageName}`;
-    // Get a valid image path
-    if (!imagePath.endsWith(".png") && !imagePath.endsWith(".jpg")) {
-      imagePath += ".png";
-    }
-
-    return imagePath;
+    return path.join(downloadPath, `${jobId}-${imageName}`);
   }
 
   private async fetchAndSaveImage(
@@ -551,18 +424,22 @@ export class LlamaParseReader extends FileReader {
     imagePath: string,
     jobId: string,
   ): Promise<void> {
-    const headers = { Authorization: `Bearer ${this.apiKey}` };
-    // Construct the image URL
-    const imageUrl = `${this.baseUrl}/job/${jobId}/result/image/${imageName}`;
-    const response = await fetch(imageUrl, { headers });
-    if (!response.ok) {
-      throw new Error(`Failed to download image: ${await response.text()}`);
+    const response =
+      await ParsingService.getJobImageResultApiV1ParsingJobJobIdResultImageNameGet(
+        {
+          client: this.#client,
+          path: {
+            job_id: jobId,
+            name: imageName,
+          },
+        },
+      );
+    if (response.error) {
+      throw new Error(`Failed to download image: ${response.error.detail}`);
     }
-    // Convert the response to an ArrayBuffer and then to a Buffer
-    const arrayBuffer = await response.arrayBuffer();
-    const buffer = new Uint8Array(arrayBuffer);
+    const blob = (await response.data) as Blob;
     // Write the image buffer to the specified imagePath
-    await fs.writeFile(imagePath, buffer);
+    await fs.writeFile(imagePath, new Uint8Array(await blob.arrayBuffer()));
   }
 
   // Filters out invalid values (null, undefined, empty string) of specific params.
@@ -593,24 +470,4 @@ export class LlamaParseReader extends FileReader {
         }),
     );
   }
-
-  static async getMimeType(
-    data: Uint8Array,
-  ): Promise<{ mime: string; extension: string }> {
-    const typeinfos = filetypeinfo(data);
-    // find the first type info that matches the supported MIME types
-    // It could be happened that docx file is recognized as zip file, so we need to check the mime type
-    const info = typeinfos.find((info) => {
-      if (info.extension && SUPPORT_FILE_EXT.includes(`.${info.extension}`)) {
-        return info;
-      }
-    });
-    if (!info || !info.mime || !info.extension) {
-      const ext = SUPPORT_FILE_EXT.join(", ");
-      throw new Error(
-        `File has type which does not match supported MIME Types. Supported formats include: ${ext}`,
-      );
-    }
-    return { mime: info.mime, extension: info.extension };
-  }
 }

packages/cloud/src/utils.ts

@@ -0,0 +1,3 @@
+export async function sleep(ms: number): Promise<void> {
+  return new Promise((resolve) => setTimeout(resolve, ms));
+}

packages/cloud/tsconfig.json

@@ -8,8 +8,17 @@
     "moduleResolution": "Bundler",
     "skipLibCheck": true,
     "strict": true,
-    "lib": ["DOM", "ESNext"]
+    "lib": ["DOM", "ESNext"],
+    "types": []
   },
   "include": ["./src"],
-  "exclude": ["node_modules"]
+  "exclude": ["node_modules"],
+  "references": [
+    {
+      "path": "../core/tsconfig.json"
+    },
+    {
+      "path": "../env/tsconfig.json"
+    }
+  ]
 }

packages/cloud/turbo.json

@@ -0,0 +1,8 @@
+{
+  "extends": ["//"],
+  "tasks": {
+    "build": {
+      "outputs": ["**/dist/**", "src/client/**"]
+    }
+  }
+}

packages/community/CHANGELOG.md

@@ -1,5 +1,130 @@
 # @llamaindex/community
 
+## 0.0.50
+
+### Patch Changes
+
+- Updated dependencies [4ba2cfe]
+  - @llamaindex/env@0.1.15
+  - @llamaindex/core@0.3.2
+
+## 0.0.49
+
+### Patch Changes
+
+- 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
+
+## 0.0.48
+
+### Patch Changes
+
+- Updated dependencies [1364e8e]
+- Updated dependencies [96fc69c]
+  - @llamaindex/core@0.3.0
+
+## 0.0.47
+
+### Patch Changes
+
+- Updated dependencies [5f67820]
+  - @llamaindex/core@0.2.12
+
+## 0.0.46
+
+### Patch Changes
+
+- Updated dependencies [ee697fb]
+  - @llamaindex/core@0.2.11
+
+## 0.0.45
+
+### Patch Changes
+
+- Updated dependencies [3489e7d]
+- Updated dependencies [468bda5]
+  - @llamaindex/core@0.2.10
+
+## 0.0.44
+
+### Patch Changes
+
+- Updated dependencies [b17d439]
+  - @llamaindex/core@0.2.9
+
+## 0.0.43
+
+### Patch Changes
+
+- 2774e80: feat: added meta3.2 support via Bedrock including vision, tool call and inference region support
+
+## 0.0.42
+
+### Patch Changes
+
+- df441e2: fix: consoleLogger is missing from `@llamaindex/env`
+- Updated dependencies [df441e2]
+  - @llamaindex/core@0.2.8
+  - @llamaindex/env@0.1.13
+
+## 0.0.41
+
+### Patch Changes
+
+- Updated dependencies [6cce3b1]
+  - @llamaindex/core@0.2.7
+
+## 0.0.40
+
+### Patch Changes
+
+- 50e6b57: feat: add Amazon Bedrock Retriever
+- Updated dependencies [8b7fdba]
+  - @llamaindex/core@0.2.6
+
+## 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
+
+- Updated dependencies [2cd1383]
+  - @llamaindex/core@0.2.3
+
+## 0.0.36
+
+### Patch Changes
+
+- Updated dependencies [749b43a]
+  - @llamaindex/core@0.2.2
+
+## 0.0.35
+
+### Patch Changes
+
+- Updated dependencies [ac07e3c]
+- Updated dependencies [70ccb4a]
+- Updated dependencies [1a6137b]
+- Updated dependencies [ac07e3c]
+  - @llamaindex/core@0.2.1
+  - @llamaindex/env@0.1.11
+
 ## 0.0.34
 
 ### Patch Changes

packages/community/README.md

@@ -5,8 +5,11 @@
 ## Current Features:
 
 - Bedrock support for the Anthropic Claude Models [usage](https://ts.llamaindex.ai/modules/llms/available_llms/bedrock)
-- Bedrock support for the Meta LLama 2, 3 and 3.1 Models [usage](https://ts.llamaindex.ai/modules/llms/available_llms/bedrock)
-- Meta LLama3.1 405b tool call support
+- Bedrock support for the Meta LLama 2, 3, 3.1 and 3.2 Models [usage](https://ts.llamaindex.ai/modules/llms/available_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
 

packages/community/package.json

@@ -1,7 +1,7 @@
 {
   "name": "@llamaindex/community",
   "description": "Community package for LlamaIndexTS",
-  "version": "0.0.34",
+  "version": "0.0.50",
   "type": "module",
   "types": "dist/type/index.d.ts",
   "main": "dist/cjs/index.js",
@@ -43,9 +43,10 @@
   },
   "devDependencies": {
     "@types/node": "^22.5.1",
-    "bunchee": "5.3.2"
+    "bunchee": "5.5.1"
   },
   "dependencies": {
+    "@aws-sdk/client-bedrock-agent-runtime": "^3.642.0",
     "@aws-sdk/client-bedrock-runtime": "^3.642.0",
     "@llamaindex/core": "workspace:*",
     "@llamaindex/env": "workspace:*"

packages/community/src/index.ts

@@ -2,4 +2,7 @@ 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";

packages/community/src/llm/bedrock/anthropic/utils.ts

@@ -6,7 +6,10 @@ import type {
   MessageContentDetail,
   ToolCallLLMMessageOptions,
 } from "@llamaindex/core/llms";
-import { mapMessageContentToMessageContentDetails } from "../utils";
+import {
+  extractDataUrlComponents,
+  mapMessageContentToMessageContentDetails,
+} from "../utils";
 import type {
   AnthropicContent,
   AnthropicImageContent,
@@ -143,27 +146,6 @@ export const mapTextContent = (text: string): AnthropicTextContent => {
   return { type: "text", text };
 };
 
-export const extractDataUrlComponents = (
-  dataUrl: string,
-): {
-  mimeType: string;
-  base64: string;
-} => {
-  const parts = dataUrl.split(";base64,");
-
-  if (parts.length !== 2 || !parts[0]!.startsWith("data:")) {
-    throw new Error("Invalid data URL");
-  }
-
-  const mimeType = parts[0]!.slice(5);
-  const base64 = parts[1]!;
-
-  return {
-    mimeType,
-    base64,
-  };
-};
-
 export const mapImageContent = (imageUrl: string): AnthropicImageContent => {
   if (!imageUrl.startsWith("data:"))
     throw new Error(

packages/community/src/llm/bedrock/index.ts

@@ -16,7 +16,7 @@ import {
   ToolCallLLM,
   type ToolCallLLMMessageOptions,
 } from "@llamaindex/core/llms";
-import { streamConverter, wrapLLMEvent } from "@llamaindex/core/utils";
+import { streamConverter } from "@llamaindex/core/utils";
 import {
   type BedrockAdditionalChatOptions,
   type BedrockChatStreamResponse,
@@ -24,6 +24,7 @@ import {
 } from "./provider";
 import { mapMessageContentToMessageContentDetails } from "./utils";
 
+import { wrapLLMEvent } from "@llamaindex/core/decorator";
 import { AnthropicProvider } from "./anthropic/provider";
 import { MetaProvider } from "./meta/provider";
 
@@ -46,35 +47,96 @@ export type BedrockChatParamsNonStreaming = LLMChatParamsNonStreaming<
 export type BedrockChatNonStreamResponse =
   ChatResponse<ToolCallLLMMessageOptions>;
 
-export enum 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",
-  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",
-  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",
-}
+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",
+  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",
+  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",
+};
+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_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_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",
+
+  EU_ANTHROPIC_CLAUDE_3_HAIKU: "eu.anthropic.claude-3-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_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",
+};
+
+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_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.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_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,
+};
 
 /*
  * Values taken from https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters.html#model-parameters-claude
@@ -108,6 +170,10 @@ const CHAT_ONLY_MODELS = {
   [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.MISTRAL_7B_INSTRUCT]: 32000,
   [BEDROCK_MODELS.MISTRAL_MIXTRAL_7B_INSTRUCT]: 32000,
   [BEDROCK_MODELS.MISTRAL_MIXTRAL_LARGE_2402]: 32000,
@@ -138,17 +204,25 @@ export const STREAMING_MODELS = new Set([
   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.MISTRAL_7B_INSTRUCT,
   BEDROCK_MODELS.MISTRAL_MIXTRAL_7B_INSTRUCT,
   BEDROCK_MODELS.MISTRAL_MIXTRAL_LARGE_2402,
 ]);
 
-export const TOOL_CALL_MODELS = [
+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.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,
 ];
 
 const getProvider = (model: string): Provider => {
@@ -165,7 +239,7 @@ const getProvider = (model: string): Provider => {
 };
 
 export type BedrockModelParams = {
-  model: keyof typeof BEDROCK_FOUNDATION_LLMS;
+  model: BEDROCK_MODELS | INFERENCE_BEDROCK_MODELS;
   temperature?: number;
   topP?: number;
   maxTokens?: number;
@@ -184,6 +258,10 @@ export const BEDROCK_MODEL_MAX_TOKENS: Partial<Record<BEDROCK_MODELS, number>> =
     [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,
   };
 
 const DEFAULT_BEDROCK_PARAMS = {
@@ -192,14 +270,15 @@ const DEFAULT_BEDROCK_PARAMS = {
   maxTokens: 1024, // required by anthropic
 };
 
-export type BedrockParams = BedrockModelParams & BedrockRuntimeClientConfig;
+export type BedrockParams = BedrockRuntimeClientConfig & BedrockModelParams;
 
 /**
  * ToolCallLLM for Bedrock
  */
 export class Bedrock extends ToolCallLLM<BedrockAdditionalChatOptions> {
   private client: BedrockRuntimeClient;
-  model: keyof typeof BEDROCK_FOUNDATION_LLMS;
+  protected actualModel: BEDROCK_MODELS | INFERENCE_BEDROCK_MODELS;
+  model: BEDROCK_MODELS;
   temperature: number;
   topP: number;
   maxTokens?: number;
@@ -216,8 +295,8 @@ export class Bedrock extends ToolCallLLM<BedrockAdditionalChatOptions> {
     ...params
   }: BedrockParams) {
     super();
-
-    this.model = model;
+    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;
@@ -240,7 +319,7 @@ export class Bedrock extends ToolCallLLM<BedrockAdditionalChatOptions> {
       temperature: this.temperature,
       topP: this.topP,
       maxTokens: this.maxTokens,
-      contextWindow: BEDROCK_FOUNDATION_LLMS[this.model],
+      contextWindow: BEDROCK_FOUNDATION_LLMS[this.model] ?? 128000,
       tokenizer: undefined,
     };
   }
@@ -255,6 +334,8 @@ export class Bedrock extends ToolCallLLM<BedrockAdditionalChatOptions> {
       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) {
@@ -286,6 +367,8 @@ export class Bedrock extends ToolCallLLM<BedrockAdditionalChatOptions> {
       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);

packages/community/src/llm/bedrock/meta/provider.ts

@@ -67,21 +67,26 @@ export class MetaProvider extends Provider<MetaStreamEvent> {
     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
+        event?.prompt_token_count !== null
       )
         continue;
 
-      if (delta === TOKENS.TOOL_CALL) {
+      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: [
@@ -110,11 +115,18 @@ export class MetaProvider extends Provider<MetaStreamEvent> {
   getRequestBody<T extends ChatMessage>(
     metadata: LLMMetadata,
     messages: T[],
-    tools?: BaseTool[],
+    tools: BaseTool[] = [],
   ): InvokeModelCommandInput | InvokeModelWithResponseStreamCommandInput {
     let prompt: string = "";
+    let images: string[] = [];
     if (metadata.model.startsWith("meta.llama3")) {
-      prompt = mapChatMessagesToMetaLlama3Messages(messages, tools);
+      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 {
@@ -127,6 +139,7 @@ export class MetaProvider extends Provider<MetaStreamEvent> {
       accept: "application/json",
       body: JSON.stringify({
         prompt,
+        images: images.length ? images : undefined,
         max_gen_len: metadata.maxTokens,
         temperature: metadata.temperature,
         top_p: metadata.topP,

packages/community/src/llm/bedrock/meta/utils.ts

@@ -1,9 +1,12 @@
 import type {
   BaseTool,
   ChatMessage,
+  LLMMetadata,
   MessageContentTextDetail,
   ToolCallLLMMessageOptions,
 } from "@llamaindex/core/llms";
+import { extractDataUrlComponents } from "../utils";
+import { TOKENS } from "./constants";
 import type { MetaMessage } from "./types";
 
 const getToolCallInstructionString = (tool: BaseTool): string => {
@@ -24,7 +27,7 @@ const getToolCallParametersString = (tool: BaseTool): string => {
 
 // ported from https://github.com/meta-llama/llama-agentic-system/blob/main/llama_agentic_system/system_prompt.py
 // NOTE: using json instead of the above xml style tool calling works more reliability
-export const getToolsPrompt = (tools?: BaseTool[]) => {
+export const getToolsPrompt_3_1 = (tools?: BaseTool[]) => {
   if (!tools?.length) return "";
 
   const customToolParams = tools.map((tool) => {
@@ -77,6 +80,46 @@ Reminder:
   `;
 };
 
+export const getToolsPrompt_3_2 = (tools?: BaseTool[]) => {
+  if (!tools?.length) return "";
+  return `
+You are an expert in composing functions. You are given a question and a set of possible functions.
+Based on the question, you will need to make one or more function/tool calls to achieve the purpose.
+If none of the function can be used, point it out. If the given question lacks the parameters required by the function,
+also point it out. You should only return the function call in tools call sections.
+
+If you decide to invoke any of the function(s), you MUST put it in the format of and start with the token: ${TOKENS.TOOL_CALL}:
+{
+  "name": function_name,
+  "parameters": parameters,
+}
+where
+
+{
+  "name": function_name,
+  "parameters": parameters, => a JSON dict with the function argument name as key and function argument value as value.
+}
+
+Here is an example,
+
+{
+  "name": "example_function_name",
+  "parameters": {"example_name": "example_value"}
+}
+
+Reminder:
+- Function calls MUST follow the specified format
+- Required parameters MUST be specified
+- Only call one function at a time
+- You SHOULD NOT include any other text in the response
+- Put the entire function call reply on one line
+
+Here is a list of functions in JSON format that you can invoke.
+
+${JSON.stringify(tools)}
+`;
+};
+
 export const mapChatRoleToMetaRole = (
   role: ChatMessage["role"],
 ): MetaMessage["role"] => {
@@ -125,16 +168,46 @@ export const mapChatMessagesToMetaMessages = <
 /**
  * Documentation at https://llama.meta.com/docs/model-cards-and-prompt-formats/meta-llama-3
  */
-export const mapChatMessagesToMetaLlama3Messages = <T extends ChatMessage>(
-  messages: T[],
-  tools?: BaseTool[],
-): string => {
+export const mapChatMessagesToMetaLlama3Messages = <T extends ChatMessage>({
+  messages,
+  model,
+  tools,
+}: {
+  messages: T[];
+  model: LLMMetadata["model"];
+  tools?: BaseTool[];
+}): { prompt: string; images: string[] } => {
+  const images: string[] = [];
+  const textMessages: T[] = [];
+
+  messages.forEach((message) => {
+    if (Array.isArray(message.content)) {
+      message.content.forEach((content) => {
+        if (content.type === "image_url") {
+          const { base64 } = extractDataUrlComponents(content.image_url.url);
+          images.push(base64);
+        } else {
+          textMessages.push(message);
+        }
+      });
+    } else {
+      textMessages.push(message);
+    }
+  });
+
   const parts: string[] = [];
-  if (tools?.length) {
+
+  let toolsPrompt = "";
+  if (model.startsWith("meta.llama3-2")) {
+    toolsPrompt = getToolsPrompt_3_2(tools);
+  } else if (model.startsWith("meta.llama3-1")) {
+    toolsPrompt = getToolsPrompt_3_1(tools);
+  }
+  if (toolsPrompt) {
     parts.push(
       "<|begin_of_text|>",
       "<|start_header_id|>system<|end_header_id|>",
-      getToolsPrompt(tools),
+      toolsPrompt,
       "<|eot_id|>",
     );
   }
@@ -154,7 +227,9 @@ export const mapChatMessagesToMetaLlama3Messages = <T extends ChatMessage>(
     ...mapped,
     "<|start_header_id|>assistant<|end_header_id|>",
   );
-  return parts.join("\n");
+
+  const prompt = parts.join("\n");
+  return { prompt, images };
 };
 
 /**

packages/community/src/llm/bedrock/utils.ts

@@ -11,3 +11,24 @@ export const mapMessageContentToMessageContentDetails = (
 
 export const toUtf8 = (input: Uint8Array): string =>
   new TextDecoder("utf-8").decode(input);
+
+export const extractDataUrlComponents = (
+  dataUrl: string,
+): {
+  mimeType: string;
+  base64: string;
+} => {
+  const parts = dataUrl.split(";base64,");
+
+  if (parts.length !== 2 || !parts[0]!.startsWith("data:")) {
+    throw new Error("Invalid data URL");
+  }
+
+  const mimeType = parts[0]!.slice(5);
+  const base64 = parts[1]!;
+
+  return {
+    mimeType,
+    base64,
+  };
+};

packages/community/src/retrievers/bedrock.ts

@@ -0,0 +1,165 @@
+import type { KnowledgeBaseVectorSearchConfiguration } from "@aws-sdk/client-bedrock-agent-runtime";
+import {
+  BedrockAgentRuntimeClient,
+  type BedrockAgentRuntimeClientConfig,
+  type RetrievalFilter,
+  RetrieveCommand,
+  type SearchType,
+} from "@aws-sdk/client-bedrock-agent-runtime";
+import type { QueryBundle } from "@llamaindex/core/query-engine";
+import { BaseRetriever } from "@llamaindex/core/retriever";
+import { Document, type NodeWithScore } from "@llamaindex/core/schema";
+import { extractText } from "@llamaindex/core/utils";
+
+/**
+ * Interface for the arguments required to initialize an
+ * AmazonKnowledgeBaseRetriever instance.
+ */
+export interface AmazonKnowledgeBaseRetrieverArgs {
+  knowledgeBaseId: string;
+  topK: number;
+  region: string;
+  clientOptions?: BedrockAgentRuntimeClientConfig;
+  filter?: RetrievalFilter;
+  overrideSearchType?: SearchType;
+}
+
+/**
+ * Class for interacting with Amazon Bedrock Knowledge Bases, a RAG workflow oriented service
+ * Extends the BaseRetriever class.
+ * @example
+ * ```typescript
+ * const retriever = new AmazonKnowledgeBaseRetriever({
+ *   topK: 10,
+ *   knowledgeBaseId: "YOUR_KNOWLEDGE_BASE_ID",
+ *   region: "us-east-2",
+ *   clientOptions: {
+ *     credentials: {
+ *       accessKeyId: "YOUR_ACCESS_KEY_ID",
+ *       secretAccessKey: "YOUR_SECRET_ACCESS_KEY",
+ *     },
+ *   },
+ * });
+ *
+ * const docs = await retriever.retrieve({query: "How are clouds formed?"});
+ * ```
+ */
+export class AmazonKnowledgeBaseRetriever extends BaseRetriever {
+  static lc_name() {
+    return "AmazonKnowledgeBaseRetriever";
+  }
+
+  lc_namespace = ["llamaindex", "retrievers", "amazon_bedrock_knowledge_base"];
+
+  knowledgeBaseId: string;
+
+  topK: number;
+
+  bedrockAgentRuntimeClient: BedrockAgentRuntimeClient;
+
+  filter: RetrievalFilter | undefined;
+
+  overrideSearchType: SearchType | undefined;
+
+  constructor({
+    knowledgeBaseId,
+    topK = 10,
+    clientOptions,
+    region,
+    filter,
+    overrideSearchType,
+  }: AmazonKnowledgeBaseRetrieverArgs) {
+    super();
+
+    this.topK = topK;
+    this.filter = filter;
+    this.overrideSearchType = overrideSearchType;
+    this.bedrockAgentRuntimeClient = new BedrockAgentRuntimeClient({
+      region,
+      ...clientOptions,
+    });
+    this.knowledgeBaseId = knowledgeBaseId;
+  }
+
+  /**
+   * Cleans the result text by replacing sequences of whitespace with a
+   * single space and removing ellipses.
+   * @param resText The result text to clean.
+   * @returns The cleaned result text.
+   */
+  cleanResult(resText: string) {
+    const res = resText.replace(/\s+/g, " ").replace(/\.\.\./g, "");
+    return res;
+  }
+
+  async queryKnowledgeBase(
+    query: QueryBundle,
+    topK: number,
+    filter?: RetrievalFilter,
+    overrideSearchType?: SearchType,
+  ): Promise<NodeWithScore[]> {
+    const retrieveCommand = new RetrieveCommand({
+      knowledgeBaseId: this.knowledgeBaseId,
+      retrievalQuery: {
+        text: extractText(query),
+      },
+      retrievalConfiguration: {
+        vectorSearchConfiguration: {
+          numberOfResults: topK,
+          overrideSearchType,
+          filter,
+        } as KnowledgeBaseVectorSearchConfiguration,
+      },
+    });
+
+    const retrieveResponse =
+      await this.bedrockAgentRuntimeClient.send(retrieveCommand);
+
+    return (
+      retrieveResponse.retrievalResults?.map((result) => {
+        let source;
+        switch (result.location?.type) {
+          case "CONFLUENCE":
+            source = result.location?.confluenceLocation?.url;
+            break;
+          case "S3":
+            source = result.location?.s3Location?.uri;
+            break;
+          case "SALESFORCE":
+            source = result.location?.salesforceLocation?.url;
+            break;
+          case "SHAREPOINT":
+            source = result.location?.sharePointLocation?.url;
+            break;
+          case "WEB":
+            source = result.location?.webLocation?.url;
+            break;
+          default:
+            source = result.location?.s3Location?.uri;
+            break;
+        }
+
+        return {
+          node: new Document({
+            text: this.cleanResult(result.content?.text || ""),
+            metadata: {
+              source,
+              score: result.score,
+              ...result.metadata,
+            },
+          }),
+          score: result.score ?? 1.0,
+        };
+      }) ?? []
+    );
+  }
+
+  async _retrieve(query: QueryBundle): Promise<NodeWithScore[]> {
+    return await this.queryKnowledgeBase(
+      query,
+      this.topK,
+      this.filter,
+      this.overrideSearchType,
+    );
+  }
+}

packages/core/CHANGELOG.md

@@ -1,5 +1,123 @@
 # @llamaindex/core
 
+## 0.3.2
+
+### Patch Changes
+
+- Updated dependencies [4ba2cfe]
+  - @llamaindex/env@0.1.15
+
+## 0.3.1
+
+### Patch Changes
+
+- a75af83: refactor: move some llm and embedding to single package
+- Updated dependencies [ae49ff4]
+- Updated dependencies [a75af83]
+  - @llamaindex/env@0.1.14
+
+## 0.3.0
+
+### Minor Changes
+
+- 1364e8e: update metadata extractors to use PromptTemplate
+- 96fc69c: add defaultQuestionExtractPrompt
+
+## 0.2.12
+
+### Patch Changes
+
+- 5f67820: Fix that node parsers generate nodes with UUIDs
+
+## 0.2.11
+
+### Patch Changes
+
+- ee697fb: fix: generate uuid when inserting to Qdrant
+
+## 0.2.10
+
+### Patch Changes
+
+- 3489e7d: fix: num output incorrect in prompt helper
+- 468bda5: fix: correct warning when chunk size smaller than 0
+
+## 0.2.9
+
+### Patch Changes
+
+- b17d439: Fix #1278: resolved issue where the id\_ was not correctly passed as the id when creating a TextNode. As a result, the upsert operation to the vector database was using a generated ID instead of the provided document ID, if available.
+
+## 0.2.8
+
+### Patch Changes
+
+- df441e2: fix: consoleLogger is missing from `@llamaindex/env`
+- Updated dependencies [df441e2]
+  - @llamaindex/env@0.1.13
+
+## 0.2.7
+
+### Patch Changes
+
+- 6cce3b1: feat: support `npm:postgres`
+
+## 0.2.6
+
+### Patch Changes
+
+- 8b7fdba: refactor: move chat engine & retriever into core.
+
+  - `chatHistory` in BaseChatEngine now returns `ChatMessage[] | Promise<ChatMessage[]>`, instead of `BaseMemory`
+  - update `retrieve-end` type
+
+## 0.2.5
+
+### Patch Changes
+
+- d902cc3: Fix context not being sent using ContextChatEngine
+
+## 0.2.4
+
+### Patch Changes
+
+- b48bcc3: feat: add `load-transformers` event type when loading `@xenova/transformers` module
+
+  This would benefit user who want to customize the transformer env.
+
+- Updated dependencies [b48bcc3]
+  - @llamaindex/env@0.1.12
+
+## 0.2.3
+
+### Patch Changes
+
+- 2cd1383: refactor: align `response-synthesizers` & `chat-engine` module
+
+  - builtin event system
+  - correct class extends
+  - aligin APIs, naming with llama-index python
+  - move stream out of first parameter to second parameter for the better tyep checking
+  - remove JSONQueryEngine in `@llamaindex/experimental`, as the code quality is not satisify and we will bring it back later
+
+## 0.2.2
+
+### Patch Changes
+
+- 749b43a: fix: clip embedding transform function
+
+## 0.2.1
+
+### Patch Changes
+
+- ac07e3c: fix: replace instanceof check with `.type` check
+- 70ccb4a: Allow arbitrary types in workflow's StartEvent and StopEvent
+- ac07e3c: fix: add `console.warn` when import dual module
+- Updated dependencies [ac07e3c]
+- Updated dependencies [1a6137b]
+- Updated dependencies [ac07e3c]
+  - @llamaindex/env@0.1.11
+
 ## 0.2.0
 
 ### Minor Changes

packages/core/agent/package.json

@@ -0,0 +1,8 @@
+{
+  "type": "module",
+  "main": "./dist/index.cjs",
+  "module": "./dist/index.js",
+  "types": "./dist/index.d.ts",
+  "exports": "./dist/index.js",
+  "private": true
+}

packages/core/chat-engine/package.json

@@ -0,0 +1,8 @@
+{
+  "type": "module",
+  "main": "./dist/index.cjs",
+  "module": "./dist/index.js",
+  "types": "./dist/index.d.ts",
+  "exports": "./dist/index.js",
+  "private": true
+}