fix: calling tools with large inputs

.changeset/nasty-peas-exercise.md

@@ -0,0 +1,5 @@
+---
+"llamaindex": patch
+---
+
+Show error message if agent tool is called with partial JSON

.eslintrc.cjs

@@ -1,5 +1,3 @@
-const { join } = require("node:path");
-
 module.exports = {
   root: true,
   extends: [
@@ -8,7 +6,7 @@ module.exports = {
     "plugin:@typescript-eslint/recommended-type-checked-only",
   ],
   parserOptions: {
-    project: join(__dirname, "tsconfig.eslint.json"),
+    project: true,
     __tsconfigRootDir: __dirname,
   },
   settings: {
@@ -25,7 +23,6 @@ module.exports = {
         ignoreIIFE: true,
       },
     ],
-    "no-debugger": "error",
     "@typescript-eslint/await-thenable": "off",
     "@typescript-eslint/ban-ts-comment": "off",
     "@typescript-eslint/ban-types": "off",

.github/workflows/publish.yml

@@ -31,6 +31,6 @@ jobs:
 
       - name: Publish @llamaindex/core
         run: npx jsr publish --allow-slow-types
-        working-directory: packages/llamaindex
+        working-directory: packages/core
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/publish_github.yml

@@ -26,12 +26,12 @@ jobs:
       - name: Build tarball
         run: |
           pnpm pack
-        working-directory: packages/llamaindex
+        working-directory: packages/core
 
       - name: Create release
         uses: ncipollo/release-action@v1
         with:
-          artifacts: "packages/llamaindex/llamaindex-*.tgz"
+          artifacts: "packages/core/llamaindex-*.tgz"
           name: Release ${{ github.ref }}
-          bodyFile: "packages/llamaindex/CHANGELOG.md"
+          bodyFile: "packages/core/CHANGELOG.md"
           token: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/test.yml

@@ -71,7 +71,7 @@ jobs:
       - name: Build
         run: pnpm run build
       - name: Use Build For Examples
-        run: pnpm link ../packages/llamaindex/
+        run: pnpm link ../packages/core/
         working-directory: ./examples
       - name: Run Type Check
         run: pnpm run type-check
@@ -81,19 +81,18 @@ jobs:
         if: failure()
         with:
           name: typecheck-build-dist
-          path: ./packages/llamaindex/dist
+          path: ./packages/core/dist
           if-no-files-found: error
-  e2e-llamaindex-examples:
+  e2e-core-examples:
     strategy:
-      fail-fast: false
       matrix:
         packages:
           - cloudflare-worker-agent
           - nextjs-agent
           - nextjs-edge-runtime
-    #          - waku-query-engine
+          - waku-query-engine
     runs-on: ubuntu-latest
-    name: Build LlamaIndex Example (${{ matrix.packages }})
+    name: Build Core Example (${{ matrix.packages }})
     steps:
       - uses: actions/checkout@v4
       - uses: pnpm/action-setup@v3
@@ -108,7 +107,7 @@ jobs:
         run: pnpm run build
       - name: Build ${{ matrix.packages }}
         run: pnpm run build
-        working-directory: packages/llamaindex/e2e/examples/${{ matrix.packages }}
+        working-directory: packages/core/e2e/examples/${{ matrix.packages }}
 
   typecheck-examples:
     runs-on: ubuntu-latest
@@ -132,7 +131,7 @@ jobs:
         working-directory: packages/env
       - name: Pack llamaindex
         run: pnpm pack --pack-destination ${{ runner.temp }}
-        working-directory: packages/llamaindex
+        working-directory: packages/core
       - name: Install
         run: npm add ${{ runner.temp }}/*.tgz
         working-directory: ${{ runner.temp }}/examples

CONTRIBUTING.md

@@ -41,7 +41,7 @@ To run them, run
 pnpm run test
 ```
 
-To write new test cases write them in [packages/core/src/tests](/packages/llamaindex/src/tests)
+To write new test cases write them in [packages/core/src/tests](/packages/core/src/tests)
 
 We use Jest https://jestjs.io/ to write our test cases. Jest comes with a bunch of built in assertions using the expect function: https://jestjs.io/docs/expect
 

README.md

@@ -194,19 +194,19 @@ Check out our NextJS playground at https://llama-playground.vercel.app/. The sou
 
 ## 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.
+- [Document](/packages/core/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.
+- [Node](/packages/core/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 quesiton. 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)).
+- [Embedding](/packages/core/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 quesiton. Because the default service context is OpenAI, the default embedding is `OpenAIEmbedding`. If using different models, say through Ollama, use this [Embedding](/packages/core/src/embeddings/OllamaEmbedding.ts) (see all [here](/packages/core/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.
+- [Indices](/packages/core/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).
+- [QueryEngine](/packages/core/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/core/src/indices/BaseIndex.ts) method on your Index. See all query engines [here](/packages/core/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).
+- [ChatEngine](/packages/core/src/engines/chat/SimpleChatEngine.ts): A ChatEngine helps you build a chatbot that will interact with your Indices. See all chat engines [here](/packages/core/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.
+- [SimplePrompt](/packages/core/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
 

apps/docs/CHANGELOG.md

@@ -1,54 +1,5 @@
 # docs
 
-## 0.0.27
-
-### Patch Changes
-
-- Updated dependencies [3c47910]
-- Updated dependencies [ed467a9]
-- Updated dependencies [cba5406]
-  - llamaindex@0.4.1
-
-## 0.0.26
-
-### Patch Changes
-
-- b1a4a74: docs: updated Bedrock Opus region and added a basic README
-- Updated dependencies [436bc41]
-- Updated dependencies [a44e54f]
-- Updated dependencies [a51ed8d]
-- Updated dependencies [d3b635b]
-  - llamaindex@0.4.0
-  - @llamaindex/examples@0.0.5
-
-## 0.0.25
-
-### Patch Changes
-
-- Updated dependencies [6bc5bdd]
-- Updated dependencies [bf25ff6]
-- Updated dependencies [e6d6576]
-  - llamaindex@0.3.17
-
-## 0.0.24
-
-### Patch Changes
-
-- 631f000: feat: DeepInfra LLM implementation
-- 8832669: Community bedrock support added
-- a29d835: setDocumentHash should be async
-- Updated dependencies [11ae926]
-- Updated dependencies [631f000]
-- Updated dependencies [1378ec4]
-- Updated dependencies [6b1ded4]
-- Updated dependencies [4d4bd85]
-- Updated dependencies [24a9d1e]
-- Updated dependencies [45952de]
-- Updated dependencies [54230f0]
-- Updated dependencies [a29d835]
-- Updated dependencies [73819bf]
-  - llamaindex@0.3.16
-
 ## 0.0.23
 
 ### Patch Changes

apps/docs/docs/introduction.md

@@ -35,7 +35,7 @@ For more complex applications, our lower-level APIs allow advanced users to cust
 
 `npm install llamaindex`
 
-Our documentation includes [Installation Instructions](./getting_started/installation.mdx) and a [Starter Tutorial](./getting_started/starter_tutorial/retrieval_augmented_generation.mdx) to build your first application.
+Our documentation includes [Installation Instructions](./getting_started/installation.mdx) and a [Starter Tutorial](./getting_started/starter.mdx) to build your first application.
 
 Once you're up and running, [High-Level Concepts](./getting_started/concepts.md) has an overview of LlamaIndex's modular architecture. For more hands-on practical examples, look through our Examples section on the sidebar.
 

apps/docs/docs/modules/data_loader.mdx

@@ -6,7 +6,6 @@ import CodeBlock from "@theme/CodeBlock";
 import CodeSource from "!raw-loader!../../../../examples/readers/src/simple-directory-reader";
 import CodeSource2 from "!raw-loader!../../../../examples/readers/src/custom-simple-directory-reader";
 import CodeSource3 from "!raw-loader!../../../../examples/readers/src/llamaparse";
-import CodeSource4 from "!raw-loader!../../../../examples/readers/src/simple-directory-reader-with-llamaparse.ts";
 
 # Loader
 
@@ -22,13 +21,11 @@ It is a simple reader that reads all files from a directory and its subdirectori
 
 <CodeBlock language="ts">{CodeSource}</CodeBlock>
 
-Currently, it supports reading `.txt`, `.pdf`, `.csv`, `.md`, `.docx`, `.htm`, `.html`, `.jpg`, `.jpeg`, `.png` and `.gif` files, but support for other file types is planned.
+Currently, it supports reading `.csv`, `.docx`, `.html`, `.md` and `.pdf` files,
+but support for other file types is planned.
 
-You can override the default reader for all file types, inlcuding unsupported ones, with the `overrideReader` option.
-Additionally, you can override the default reader for specific file types or add support for additional file types with the `fileExtToReader` option.
-Also, you can provide a `defaultReader` as a fallback for files with unsupported extensions. By default it is `TextFileReader`.
-
-SimpleDirectoryReader supports up to 9 concurrent requests. Use the `numWorkers` option to set the number of concurrent requests. By default it runs in sequential mode, i.e. set to 1.
+Also, you can provide a `defaultReader` as a fallback for files with unsupported extensions.
+Or pass new readers for `fileExtToReader` to support more file types.
 
 <CodeBlock language="ts" showLineNumbers metastring="{8-12,17-21}">
   {CodeSource2}
@@ -38,31 +35,14 @@ SimpleDirectoryReader supports up to 9 concurrent requests. Use the `numWorkers`
 
 LlamaParse is an API created by LlamaIndex to efficiently parse files, e.g. it's great at converting PDF tables into markdown.
 
-To use it, first login and get an API key from https://cloud.llamaindex.ai. Make sure to store the key as `apiKey` parameter or in the environment variable `LLAMA_CLOUD_API_KEY`.
+To use it, first login and get an API key from https://cloud.llamaindex.ai. Make sure to store the key in the environment variable `LLAMA_CLOUD_API_KEY`.
 
-Then, you can use the `LlamaParseReader` class to 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/core/src/readers/LlamaParseReader.ts#L6) for a list of supported file types:
+Then, you can use the `LlamaParseReader` class to read a local PDF file and convert it into a markdown document that can be used by LlamaIndex:
 
 <CodeBlock language="ts">{CodeSource3}</CodeBlock>
 
-Additional options can be set with the `LlamaParseReader` constructor:
-
-- `resultType` can be set to `markdown`, `text` or `.json`. Defaults to `text`
-- `language` primarly helps with OCR recognition. Defaults to `en`. See [../readers/type.ts](https://github.com/run-llama/LlamaIndexTS/blob/main/packages/core/src/readers/type.ts#L20) for a list of supported languages.
-- `parsingInstructions` can help with complicated document structures. See this [LlamaIndex Blog Post](https://www.llamaindex.ai/blog/launching-the-first-genai-native-document-parsing-platform) for an example.
-- `skipDiagonalText` set to true to ignore diagonal text.
-- `invalidateCache` set to true to ignore the LlamaCloud cache. All document are kept in cache for 48hours after the job was completed to avoid processing the same document twice. Can be useful for testing when trying to re-parse the same document with, e.g. different `parsingInstructions`.
-- `gpt4oMode` set to true to use GPT-4o to extract content.
-- `gpt4oApiKey` set the GPT-4o API key. Optional. Lowers the cost of parsing by using your own API key. Your OpenAI account will be charged. Can also be set in the environment variable `LLAMA_CLOUD_GPT4O_API_KEY`.
-- `numWorkers` as in the python version, is set in `SimpleDirectoryReader`. Default is 1.
-
-## LlamaParse with SimpleDirectoryReader
-
-Below a full example of `LlamaParse` integrated in `SimpleDirectoryReader` with additional options.
-
-<CodeBlock language="ts">{CodeSource4}</CodeBlock>
+Alternatively, you can set the [`resultType`](../api/classes/LlamaParseReader.md#resulttype) option to `text` to get the parsed document as a text string.
 
 ## API Reference
 
 - [SimpleDirectoryReader](../api/classes/SimpleDirectoryReader.md)
-- [LlamaParseReader](../api/classes/LlamaParseReader.md)

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

@@ -1,79 +0,0 @@
-# DeepInfra
-
-To use DeepInfra embeddings, you need to import `DeepInfraEmbedding` from llamaindex.
-Check out available embedding models [here](https://deepinfra.com/models/embeddings).
-
-```ts
-import {
-  DeepInfraEmbedding,
-  Settings,
-  Document,
-  VectorStoreIndex,
-} from "llamaindex";
-
-// Update Embed Model
-Settings.embedModel = new DeepInfraEmbedding();
-
-const document = new Document({ text: essay, id_: "essay" });
-
-const index = await VectorStoreIndex.fromDocuments([document]);
-
-const queryEngine = index.asQueryEngine();
-
-const query = "What is the meaning of life?";
-
-const results = await queryEngine.query({
-  query,
-});
-```
-
-By default, DeepInfraEmbedding is using the sentence-transformers/clip-ViT-B-32 model. You can change the model by passing the model parameter to the constructor.
-For example:
-
-```ts
-import { DeepInfraEmbedding } from "llamaindex";
-
-const model = "intfloat/e5-large-v2";
-Settings.embedModel = new DeepInfraEmbedding({
-  model,
-});
-```
-
-You can also set the `maxRetries` and `timeout` parameters when initializing `DeepInfraEmbedding` for better control over the request behavior.
-
-For example:
-
-```ts
-import { DeepInfraEmbedding, Settings } from "llamaindex";
-
-const model = "intfloat/e5-large-v2";
-const maxRetries = 5;
-const timeout = 5000; // 5 seconds
-
-Settings.embedModel = new DeepInfraEmbedding({
-  model,
-  maxRetries,
-  timeout,
-});
-```
-
-Standalone usage:
-
-```ts
-import { DeepInfraEmbedding } from "llamaindex";
-import { config } from "dotenv";
-// For standalone usage, you need to configure DEEPINFRA_API_TOKEN in .env file
-config();
-
-const main = async () => {
-  const model = "intfloat/e5-large-v2";
-  const embeddings = new DeepInfraEmbedding({ model });
-  const text = "What is the meaning of life?";
-  const response = await embeddings.embed([text]);
-  console.log(response);
-};
-
-main();
-```
-
-For questions or feedback, please contact us at [feedback@deepinfra.com](mailto:feedback@deepinfra.com)

apps/docs/docs/modules/evaluation/modules/correctness.md

@@ -21,7 +21,7 @@ export OPENAI_API_KEY=your-api-key
 Import the required modules:
 
 ```ts
-import { CorrectnessEvaluator, OpenAI, Settings, Response } from "llamaindex";
+import { CorrectnessEvaluator, OpenAI, Settings } from "llamaindex";
 ```
 
 Let's setup gpt-4 for better results:
@@ -45,7 +45,7 @@ const evaluator = new CorrectnessEvaluator();
 
 const result = await evaluator.evaluateResponse({
   query,
-  response: new Response(response),
+  response,
 });
 
 console.log(

apps/docs/docs/modules/evaluation/modules/relevancy.md

@@ -21,13 +21,7 @@ export OPENAI_API_KEY=your-api-key
 Import the required modules:
 
 ```ts
-import {
-  RelevancyEvaluator,
-  OpenAI,
-  Settings,
-  Document,
-  VectorStoreIndex,
-} from "llamaindex";
+import { RelevancyEvaluator, OpenAI, Settings } from "llamaindex";
 ```
 
 Let's setup gpt-4 for better results:

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

@@ -1,62 +0,0 @@
-# Bedrock
-
-## Usage
-
-```ts
-import { BEDROCK_MODELS, Bedrock } from "@llamaindex/community";
-
-Settings.llm = new Bedrock({
-  model: BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_HAIKU,
-  region: "us-east-1", // can be provided via env AWS_REGION
-  credentials: {
-    accessKeyId: "...", // optional and can be provided via env AWS_ACCESS_KEY_ID
-    secretAccessKey: "...", // optional and can be provided via env AWS_SECRET_ACCESS_KEY
-  },
-});
-```
-
-Currently only supports Anthropic models:
-
-```ts
-ANTHROPIC_CLAUDE_INSTANT_1 = "anthropic.claude-instant-v1";
-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"; // available on us-west-2
-ANTHROPIC_CLAUDE_3_5_SONNET = "anthropic.claude-3-5-sonnet-20240620-v1:0";
-```
-
-Sonnet, Haiku and Opus are multimodal, image_url only supports base64 data url format, e.g. `data:image/jpeg;base64,SGVsbG8sIFdvcmxkIQ==`
-
-## Full Example
-
-```ts
-import { BEDROCK_MODELS, Bedrock } from "llamaindex";
-
-Settings.llm = new Bedrock({
-  model: BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_HAIKU,
-});
-
-async function main() {
-  const document = new Document({ text: essay, id_: "essay" });
-
-  // Load and index documents
-  const index = await VectorStoreIndex.fromDocuments([document]);
-
-  // Create a query engine
-  const queryEngine = index.asQueryEngine({
-    retriever,
-  });
-
-  const query = "What is the meaning of life?";
-
-  // Query
-  const response = await queryEngine.query({
-    query,
-  });
-
-  // Log the response
-  console.log(response.response);
-}
-```

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

@@ -1,83 +0,0 @@
-# DeepInfra
-
-Check out available LLMs [here](https://deepinfra.com/models/text-generation).
-
-```ts
-import { DeepInfra, Settings } from "llamaindex";
-
-// Get the API key from `DEEPINFRA_API_TOKEN` environment variable
-import { config } from "dotenv";
-config();
-Settings.llm = new DeepInfra();
-
-// Set the API key
-apiKey = "YOUR_API_KEY";
-Settings.llm = new DeepInfra({ apiKey });
-```
-
-You can setup the apiKey on the environment variables, like:
-
-```bash
-export DEEPINFRA_API_TOKEN="<YOUR_API_KEY>"
-```
-
-## Load and index documents
-
-For this example, we will use a single document. In a real-world scenario, you would have multiple documents to index.
-
-```ts
-const document = new Document({ text: essay, id_: "essay" });
-
-const index = await VectorStoreIndex.fromDocuments([document]);
-```
-
-## Query
-
-```ts
-const queryEngine = index.asQueryEngine();
-
-const query = "What is the meaning of life?";
-
-const results = await queryEngine.query({
-  query,
-});
-```
-
-## Full Example
-
-```ts
-import { DeepInfra, Document, VectorStoreIndex, Settings } from "llamaindex";
-
-// Use custom LLM
-const model = "meta-llama/Meta-Llama-3-8B-Instruct";
-Settings.llm = new DeepInfra({ model, temperature: 0 });
-
-async function main() {
-  const document = new Document({ text: essay, id_: "essay" });
-
-  // Load and index documents
-  const index = await VectorStoreIndex.fromDocuments([document]);
-
-  // get retriever
-  const retriever = index.asRetriever();
-
-  // Create a query engine
-  const queryEngine = index.asQueryEngine({
-    retriever,
-  });
-
-  const query = "What is the meaning of life?";
-
-  // Query
-  const response = await queryEngine.query({
-    query,
-  });
-
-  // Log the response
-  console.log(response.response);
-}
-```
-
-## Feedback
-
-If you have any feedback, please reach out to us at [feedback@deepinfra.com](mailto:feedback@deepinfra.com)

apps/docs/docusaurus.config.js

@@ -167,7 +167,7 @@ const config = {
     [
       "docusaurus-plugin-typedoc",
       {
-        entryPoints: ["../../packages/llamaindex/src/index.ts"],
+        entryPoints: ["../../packages/core/src/index.ts"],
         tsconfig: "../../tsconfig.json",
         readme: "none",
         sourceLinkTemplate:

apps/docs/i18n/fr/docusaurus-plugin-content-docs/current/api/classes/BaseDocumentStore.md

@@ -271,7 +271,7 @@ custom_edit_url: null
 
 ### setDocumentHash
 
-▸ `Abstract` **setDocumentHash**(`docId`, `docHash`): `Promise`<`void`\>
+▸ `Abstract` **setDocumentHash**(`docId`, `docHash`): `void`
 
 #### Parameters
 

apps/docs/i18n/hr/docusaurus-plugin-content-docs/current/api/classes/BaseDocumentStore.md

@@ -271,7 +271,7 @@ custom_edit_url: null
 
 ### setDocumentHash
 
-▸ `Abstract` **setDocumentHash**(`docId`, `docHash`): `Promise`<`void`\>
+▸ `Abstract` **setDocumentHash**(`docId`, `docHash`): `void`
 
 #### Parameters
 

apps/docs/i18n/zh-Hans/docusaurus-plugin-content-docs/current/api/classes/BaseDocumentStore.md

@@ -271,7 +271,7 @@ custom_edit_url: null
 
 ### setDocumentHash
 
-▸ `Abstract` **setDocumentHash**(`docId`, `docHash`): `Promise`<`void`\>
+▸ `Abstract` **setDocumentHash**(`docId`, `docHash`): `void`
 
 #### Parameters
 

apps/docs/package.json

@@ -1,6 +1,6 @@
 {
   "name": "docs",
-  "version": "0.0.27",
+  "version": "0.0.23",
   "private": true,
   "scripts": {
     "docusaurus": "docusaurus",
@@ -37,7 +37,7 @@
     "docusaurus-plugin-typedoc": "^1.0.1",
     "typedoc": "^0.25.13",
     "typedoc-plugin-markdown": "^4.0.1",
-    "typescript": "^5.5.2"
+    "typescript": "^5.4.5"
   },
   "browserslist": {
     "production": [

examples/CHANGELOG.md

@@ -1,15 +1,5 @@
 # examples
 
-## 0.0.5
-
-### Patch Changes
-
-- Updated dependencies [436bc41]
-- Updated dependencies [a44e54f]
-- Updated dependencies [a51ed8d]
-- Updated dependencies [d3b635b]
-  - llamaindex@0.4.0
-
 ## 0.0.4
 
 ### Patch Changes

examples/agent/large_toolcall_with_gpt4o.ts

@@ -1,74 +0,0 @@
-import { FunctionTool, OpenAI, ToolCallOptions } from "llamaindex";
-
-(async () => {
-  // The tool call will generate a partial JSON for `gpt-4-turbo`
-  // See thread: https://community.openai.com/t/gpt-4o-doesnt-consistently-respect-json-schema-on-tool-use/751125/7
-
-  const models = ["gpt-4o", "gpt-4-turbo"];
-  for (const model of models) {
-    const validJSON = await callLLM({ model });
-    console.log(
-      `LLM call resulting in large tool input with '${model}': LLM generates ${validJSON ? "valid" : "invalid"} JSON.`,
-    );
-  }
-})();
-
-async function callLLM(init: Partial<OpenAI>) {
-  const csvData =
-    "Country,Average Height (cm)\nNetherlands,156\nDenmark,158\nNorway,160";
-
-  const userQuestion = "Describe data in this csv";
-
-  // fake code interpreter tool
-  const interpreterTool = FunctionTool.from(
-    ({ code }: { code: string }) => code,
-    {
-      name: "interpreter",
-      description:
-        "Execute python code in a Jupyter notebook cell and return any result, stdout, stderr, display_data, and error.",
-      parameters: {
-        type: "object",
-        properties: {
-          code: {
-            type: "string",
-            description: "The python code to execute in a single cell.",
-          },
-        },
-        required: ["code"],
-      },
-    },
-  );
-
-  const systemPrompt =
-    "You are a Python interpreter.\n- You are given tasks to complete and you run python code to solve them.\n- The python code runs in a Jupyter notebook. Every time you call $(interpreter) tool, the python code is executed in a separate cell. It's okay to make multiple calls to $(interpreter).\n- Display visualizations using matplotlib or any other visualization library directly in the notebook. Shouldn't save the visualizations to a file, just return the base64 encoded data.\n- You can install any pip package (if it exists) if you need to but the usual packages for data analysis are already preinstalled.\n- You can run any python code you want in a secure environment.";
-
-  const llm = new OpenAI(init);
-  const response = await llm.chat({
-    tools: [interpreterTool],
-    messages: [
-      { role: "system", content: systemPrompt },
-      {
-        role: "user",
-        content: [
-          {
-            type: "text",
-            text: userQuestion,
-          },
-          {
-            type: "text",
-            text: `Use data from following CSV raw contents:\n${csvData}`,
-          },
-        ],
-      },
-    ],
-  });
-
-  const options = response.message?.options as ToolCallOptions;
-  const input = options.toolCall[0].input as string;
-  try {
-    JSON.parse(input);
-    return true;
-  } catch {
-    return false;
-  }
-}

examples/agent/openai.ts

@@ -53,7 +53,7 @@ async function main() {
     message: "How much is 5 + 5? then divide by 2",
   });
 
-  console.log(response.message);
+  console.log(response.response.message);
 }
 
 void main().then(() => {

examples/agent/react_agent.ts

@@ -68,7 +68,7 @@ async function main() {
   });
 
   // Chat with the agent
-  const response = await agent.chat({
+  const { response } = await agent.chat({
     message: "Divide 16 by 2 then add 20",
   });
 

examples/agent/step_wise_query_tool.ts

@@ -31,7 +31,7 @@ async function main() {
     tools: [queryEngineTool],
   });
 
-  const response = await agent.chat({
+  const { response } = await agent.chat({
     message: "What was his salary?",
   });
 

examples/agent/stream_openai_agent.ts

@@ -68,7 +68,9 @@ async function main() {
 
   console.log("Response:");
 
-  for await (const { delta } of stream) {
+  for await (const {
+    response: { delta },
+  } of stream) {
     process.stdout.write(delta);
   }
 }

examples/agent/wiki.ts

@@ -16,7 +16,9 @@ async function main() {
     stream: true,
   });
 
-  for await (const { delta } of response) {
+  for await (const {
+    response: { delta },
+  } of response) {
     process.stdout.write(delta);
   }
 }

examples/deepinfra/chat.ts

@@ -1,19 +0,0 @@
-import { DeepInfra } from "llamaindex";
-
-(async () => {
-  if (!process.env.DEEPINFRA_API_TOKEN) {
-    throw new Error("Please set the DEEPINFRA_API_TOKEN environment variable.");
-  }
-  const deepinfra = new DeepInfra({});
-  const result = await deepinfra.chat({
-    messages: [
-      { content: "You want to talk in rhymes.", role: "system" },
-      {
-        content:
-          "How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
-        role: "user",
-      },
-    ],
-  });
-  console.log(result);
-})();

examples/deepinfra/embedding.ts

@@ -1,17 +0,0 @@
-import { DeepInfraEmbedding } from "llamaindex";
-
-async function main() {
-  // API token can be provided as an environment variable too
-  // using DEEPINFRA_API_TOKEN variable
-  const apiToken = "YOUR_API_TOKEN" ?? process.env.DEEPINFRA_API_TOKEN;
-  const model = "BAAI/bge-large-en-v1.5";
-  const embedModel = new DeepInfraEmbedding({
-    model,
-    apiToken,
-  });
-  const texts = ["hello", "world"];
-  const embeddings = await embedModel.getTextEmbeddingsBatch(texts);
-  console.log(`\nWe have ${embeddings.length} embeddings`);
-}
-
-main().catch(console.error);

examples/groq.ts

@@ -35,4 +35,4 @@ async function main() {
   console.log(response.response);
 }
 
-main().catch(console.error);
+await main();

examples/package.json

@@ -1,26 +1,26 @@
 {
   "name": "@llamaindex/examples",
   "private": true,
-  "version": "0.0.5",
+  "version": "0.0.4",
   "dependencies": {
     "@aws-crypto/sha256-js": "^5.2.0",
-    "@datastax/astra-db-ts": "^1.2.1",
+    "@datastax/astra-db-ts": "^1.1.0",
     "@notionhq/client": "^2.2.15",
-    "@pinecone-database/pinecone": "^2.2.2",
+    "@pinecone-database/pinecone": "^2.2.0",
     "@zilliz/milvus2-sdk-node": "^2.4.2",
-    "chromadb": "^1.8.1",
-    "commander": "^12.1.0",
+    "chromadb": "^1.7.3",
+    "commander": "^12.0.0",
     "dotenv": "^16.4.5",
-    "js-tiktoken": "^1.0.12",
-    "llamaindex": "^0.4.0",
-    "mongodb": "^6.7.0",
+    "js-tiktoken": "^1.0.11",
+    "llamaindex": "*",
+    "mongodb": "^6.6.1",
     "pathe": "^1.1.2"
   },
   "devDependencies": {
-    "@types/node": "^20.14.1",
+    "@types/node": "^20.12.11",
     "ts-node": "^10.9.2",
-    "tsx": "^4.15.6",
-    "typescript": "^5.5.2"
+    "tsx": "^4.9.3",
+    "typescript": "^5.4.5"
   },
   "scripts": {
     "lint": "eslint ."

examples/readers/package.json

@@ -11,15 +11,14 @@
     "start:pdf": "node --import tsx ./src/pdf.ts",
     "start:llamaparse": "node --import tsx ./src/llamaparse.ts",
     "start:notion": "node --import tsx ./src/notion.ts",
-    "start:llamaparse-dir": "node --import tsx ./src/simple-directory-reader-with-llamaparse.ts",
-    "start:llamaparse-json": "node --import tsx ./src/llamaparse-json.ts"
+    "start:llamaparse2": "node --import tsx ./src/llamaparse_2.ts"
   },
   "dependencies": {
     "llamaindex": "*"
   },
   "devDependencies": {
     "@types/node": "^20.12.11",
-    "tsx": "^4.15.6",
-    "typescript": "^5.5.2"
+    "tsx": "^4.9.3",
+    "typescript": "^5.4.5"
   }
 }

examples/readers/src/custom-simple-directory-reader.ts

@@ -1,13 +1,12 @@
-import type { Document, Metadata } from "llamaindex";
-import { FileReader } from "llamaindex";
+import type { BaseReader, Document, Metadata } from "llamaindex";
 import {
   FILE_EXT_TO_READER,
   SimpleDirectoryReader,
 } from "llamaindex/readers/SimpleDirectoryReader";
 import { TextFileReader } from "llamaindex/readers/TextFileReader";
 
-class ZipReader extends FileReader {
-  loadDataAsContent(fileContent: Buffer): Promise<Document<Metadata>[]> {
+class ZipReader implements BaseReader {
+  loadData(...args: any[]): Promise<Document<Metadata>[]> {
     throw new Error("Implement me");
   }
 }

examples/readers/src/llamaparse-json.ts

@@ -1,30 +0,0 @@
-import fs from "fs/promises";
-import { LlamaParseReader } from "llamaindex";
-
-async function main() {
-  // Load PDF using LlamaParse json mode
-  const reader = new LlamaParseReader({ resultType: "json" });
-  const jsonObjs = await reader.loadJson("../data/uber_10q_march_2022.pdf");
-
-  // Write the JSON objects to a file
-  try {
-    await fs.writeFile("jsonObjs.json", JSON.stringify(jsonObjs, null, 4));
-    console.log("Array of JSON objects has been written to jsonObjs.json");
-  } catch (e) {
-    console.error("Error writing jsonObjs.json", e);
-  }
-
-  const jsonList = jsonObjs[0]["pages"];
-
-  // Write the list of JSON objects as a single array to a file
-  try {
-    await fs.writeFile("jsonList.json", JSON.stringify(jsonList, null, 4));
-    console.log(
-      "List of JSON objects as single array has been written to jsonList.json",
-    );
-  } catch (e) {
-    console.error("Error writing jsonList.json", e);
-  }
-}
-
-main().catch(console.error);

examples/readers/src/llamaparse_2.ts

@@ -0,0 +1,26 @@
+import fs from "fs/promises";
+import { LlamaParseReader } from "llamaindex";
+
+async function main() {
+  // Load PDF using LlamaParse. set apiKey here or in environment variable LLAMA_CLOUD_API_KEY
+  const reader = new LlamaParseReader({
+    resultType: "markdown",
+    language: "en",
+    parsingInstruction:
+      "The provided document is a manga comic book. Most pages do NOT have title. It does not contain tables. Try to reconstruct the dialogue happening in a cohesive way. Output any math equation in LATEX markdown (between $$)",
+  });
+  const documents = await reader.loadData("../data/manga.pdf"); // The manga.pdf in the data folder is just a copy of the TOS, due to copyright laws. You have to place your own. I used "The Manga Guide to Calculus" by Hiroyuki Kojima
+
+  // Assuming documents contain an array of pages or sections
+  const parsedManga = documents.map((page) => page.text).join("\n---\n");
+
+  // Output the parsed manga to .md file. Will be placed in ../example/readers/
+  try {
+    await fs.writeFile("./parsedManga.md", parsedManga);
+    console.log("Output successfully written to parsedManga.md");
+  } catch (err) {
+    console.error("Error writing to file:", err);
+  }
+}
+
+main().catch(console.error);

examples/readers/src/simple-directory-reader-with-llamaparse.ts

@@ -1,35 +0,0 @@
-import {
-  LlamaParseReader,
-  SimpleDirectoryReader,
-  VectorStoreIndex,
-} from "llamaindex";
-
-async function main() {
-  const reader = new SimpleDirectoryReader();
-
-  const docs = await reader.loadData({
-    directoryPath: "../data/parallel", // brk-2022.pdf split into 6 parts
-    numWorkers: 2,
-    // set LlamaParse as the default reader for all file types. Set apiKey here or in environment variable LLAMA_CLOUD_API_KEY
-    overrideReader: new LlamaParseReader({
-      language: "en",
-      resultType: "markdown",
-      parsingInstruction:
-        "The provided files is Berkshire Hathaway's 2022 Annual Report. They contain figures, tables and raw data. Capture the data in a structured format. Mathematical equation should be put out as LATEX markdown (between $$).",
-    }),
-  });
-
-  const index = await VectorStoreIndex.fromDocuments(docs);
-
-  // Query the index
-  const queryEngine = index.asQueryEngine();
-  const response = await queryEngine.query({
-    query:
-      "What is the general strategy for shareholder safety outlined in the report? Use a concrete example with numbers",
-  });
-
-  // Output response
-  console.log(response.toString());
-}
-
-main().catch(console.error);

package.json

@@ -14,25 +14,25 @@
     "type-check": "tsc -b --diagnostics",
     "release": "pnpm run check-minor-version && pnpm run build:release && changeset publish",
     "release-snapshot": "pnpm run check-minor-version && pnpm run build:release && changeset publish --tag snapshot",
-    "check-minor-version": "node ./scripts/check-minor-version.mjs",
-    "new-version": "changeset version && pnpm run check-minor-version && pnpm format:write && pnpm install && pnpm run build:release",
+    "check-minor-version": "node ./scripts/check-minor-version",
+    "new-version": "changeset version && pnpm run check-minor-version && pnpm format:write && pnpm run build:release",
     "new-snapshot": "pnpm run build:release && changeset version --snapshot"
   },
   "devDependencies": {
-    "@changesets/cli": "^2.27.5",
-    "@typescript-eslint/eslint-plugin": "^7.13.1",
+    "@changesets/cli": "^2.27.1",
+    "@typescript-eslint/eslint-plugin": "^7.8.0",
     "eslint": "^8.57.0",
-    "eslint-config-next": "^14.2.4",
+    "eslint-config-next": "^14.2.3",
     "eslint-config-prettier": "^9.1.0",
-    "eslint-config-turbo": "^1.13.4",
+    "eslint-config-turbo": "^1.13.3",
     "eslint-plugin-react": "7.34.1",
     "husky": "^9.0.11",
-    "lint-staged": "^15.2.7",
+    "lint-staged": "^15.2.2",
     "madge": "^7.0.0",
-    "prettier": "^3.3.2",
+    "prettier": "^3.2.5",
     "prettier-plugin-organize-imports": "^3.2.4",
-    "turbo": "^1.13.4",
-    "typescript": "^5.5.2"
+    "turbo": "^1.13.3",
+    "typescript": "^5.4.5"
   },
   "packageManager": "pnpm@9.0.5",
   "pnpm": {

packages/autotool/CHANGELOG.md

@@ -1,11 +0,0 @@
-# @llamaindex/autotool
-
-## 1.0.0
-
-### Patch Changes
-
-- Updated dependencies [436bc41]
-- Updated dependencies [a44e54f]
-- Updated dependencies [a51ed8d]
-- Updated dependencies [d3b635b]
-  - llamaindex@0.4.0

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

@@ -5,10 +5,10 @@
   "dependencies": {
     "@llamaindex/autotool": "workspace:*",
     "llamaindex": "workspace:*",
-    "openai": "^4.52.0"
+    "openai": "^4.43.0"
   },
   "devDependencies": {
-    "tsx": "^4.15.6"
+    "tsx": "^4.9.3"
   },
   "scripts": {
     "start": "node --import tsx --import @llamaindex/autotool/node ./src/index.ts"

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

@@ -1,53 +1,5 @@
 # @llamaindex/autotool-02-next-example
 
-## 0.1.11
-
-### Patch Changes
-
-- Updated dependencies [3c47910]
-- Updated dependencies [ed467a9]
-- Updated dependencies [cba5406]
-  - llamaindex@0.4.1
-  - @llamaindex/autotool@1.0.0
-
-## 0.1.10
-
-### Patch Changes
-
-- Updated dependencies [436bc41]
-- Updated dependencies [a44e54f]
-- Updated dependencies [a51ed8d]
-- Updated dependencies [d3b635b]
-  - llamaindex@0.4.0
-  - @llamaindex/autotool@1.0.0
-
-## 0.1.9
-
-### Patch Changes
-
-- Updated dependencies [6bc5bdd]
-- Updated dependencies [bf25ff6]
-- Updated dependencies [e6d6576]
-  - llamaindex@0.3.17
-  - @llamaindex/autotool@0.0.1
-
-## 0.1.8
-
-### Patch Changes
-
-- Updated dependencies [11ae926]
-- Updated dependencies [631f000]
-- Updated dependencies [1378ec4]
-- Updated dependencies [6b1ded4]
-- Updated dependencies [4d4bd85]
-- Updated dependencies [24a9d1e]
-- Updated dependencies [45952de]
-- Updated dependencies [54230f0]
-- Updated dependencies [a29d835]
-- Updated dependencies [73819bf]
-  - llamaindex@0.3.16
-  - @llamaindex/autotool@0.0.1
-
 ## 0.1.7
 
 ### Patch Changes

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

@@ -1,7 +1,7 @@
 {
   "name": "@llamaindex/autotool-02-next-example",
   "private": true,
-  "version": "0.1.11",
+  "version": "0.1.7",
   "scripts": {
     "dev": "next dev",
     "build": "next build",
@@ -9,8 +9,8 @@
   },
   "dependencies": {
     "@llamaindex/autotool": "workspace:*",
-    "@radix-ui/react-slot": "^1.1.0",
-    "ai": "^3.2.1",
+    "@radix-ui/react-slot": "^1.0.2",
+    "ai": "^3.1.3",
     "class-variance-authority": "^0.7.0",
     "dotenv": "^16.3.1",
     "llamaindex": "workspace:*",
@@ -20,18 +20,18 @@
     "react-dom": "^18.3.1",
     "react-markdown": "^9.0.1",
     "react-syntax-highlighter": "^15.5.0",
-    "sonner": "^1.5.0",
+    "sonner": "^1.4.41",
     "tailwind-merge": "^2.1.0"
   },
   "devDependencies": {
     "@types/node": "^20.12.11",
-    "@types/react": "^18.3.3",
+    "@types/react": "^18.3.1",
     "@types/react-dom": "^18.3.0",
     "@types/react-syntax-highlighter": "^15.5.11",
     "autoprefixer": "^10.4.16",
     "cross-env": "^7.0.3",
     "postcss": "^8.4.32",
-    "tailwindcss": "^3.4.4",
-    "typescript": "^5.5.2"
+    "tailwindcss": "^3.3.6",
+    "typescript": "^5.4.5"
   }
 }

packages/autotool/package.json

@@ -1,7 +1,7 @@
 {
   "name": "@llamaindex/autotool",
   "type": "module",
-  "version": "1.0.0",
+  "version": "0.0.1",
   "description": "auto transpile your JS function to LLM Agent compatible",
   "files": [
     "dist",
@@ -45,13 +45,13 @@
     "dev": "bunchee --watch"
   },
   "dependencies": {
-    "@swc/core": "^1.6.3",
-    "jotai": "^2.8.3",
+    "@swc/core": "^1.5.5",
+    "jotai": "^2.8.0",
     "typedoc": "^0.25.13",
     "unplugin": "^1.10.1"
   },
   "peerDependencies": {
-    "llamaindex": "^0.4.1",
+    "llamaindex": "^0.3.15",
     "openai": "^4",
     "typescript": "^4"
   },
@@ -67,16 +67,16 @@
     }
   },
   "devDependencies": {
-    "@swc/types": "^0.1.8",
+    "@swc/types": "^0.1.6",
     "@types/json-schema": "^7.0.15",
     "@types/node": "^20.12.11",
-    "bunchee": "^5.2.1",
+    "bunchee": "^5.1.5",
     "llamaindex": "workspace:*",
     "next": "14.2.3",
-    "rollup": "^4.18.0",
-    "tsx": "^4.15.6",
-    "typescript": "^5.5.2",
+    "rollup": "^4.17.2",
+    "tsx": "^4.9.3",
+    "typescript": "^5.4.5",
     "vitest": "^1.6.0",
-    "webpack": "^5.92.1"
+    "webpack": "^5.91.0"
   }
 }

packages/autotool/tsconfig.json

@@ -10,7 +10,7 @@
   "include": ["./src"],
   "references": [
     {
-      "path": "../llamaindex/tsconfig.json"
+      "path": "../core/tsconfig.json"
     },
     {
       "path": "../env/tsconfig.json"

packages/community/CHANGELOG.md

@@ -1,48 +0,0 @@
-# @llamaindex/community
-
-## 0.0.5
-
-### Patch Changes
-
-- ed467a9: Add model ids for Anthropic Claude 3.5 Sonnet model on Anthropic and Bedrock
-- Updated dependencies [3c47910]
-- Updated dependencies [ed467a9]
-- Updated dependencies [cba5406]
-  - llamaindex@0.4.1
-
-## 0.0.4
-
-### Patch Changes
-
-- b1a4a74: docs: updated Bedrock Opus region and added a basic README
-- Updated dependencies [436bc41]
-- Updated dependencies [a44e54f]
-- Updated dependencies [a51ed8d]
-- Updated dependencies [d3b635b]
-  - llamaindex@0.4.0
-
-## 0.0.3
-
-### Patch Changes
-
-- Updated dependencies [6bc5bdd]
-- Updated dependencies [bf25ff6]
-- Updated dependencies [e6d6576]
-  - llamaindex@0.3.17
-
-## 0.0.2
-
-### Patch Changes
-
-- 8832669: Community bedrock support added
-- Updated dependencies [11ae926]
-- Updated dependencies [631f000]
-- Updated dependencies [1378ec4]
-- Updated dependencies [6b1ded4]
-- Updated dependencies [4d4bd85]
-- Updated dependencies [24a9d1e]
-- Updated dependencies [45952de]
-- Updated dependencies [54230f0]
-- Updated dependencies [a29d835]
-- Updated dependencies [73819bf]
-  - llamaindex@0.3.16

packages/community/README.md

@@ -1,11 +0,0 @@
-# @llamaindex/community
-
-> Tools written by the community for llamaindex
-
-## Current Features:
-
-- Bedrock support for the Anthropic Claude Models [usage](https://ts.llamaindex.ai/modules/llms/available_llms/bedrock)
-
-## LICENSE
-
-MIT

packages/community/jsr.json

@@ -1,8 +0,0 @@
-{
-  "name": "@llamaindex/community",
-  "version": "0.0.5",
-  "exports": {
-    ".": "./src/index.ts",
-    "./type": "./src/type.ts"
-  }
-}

packages/community/package.json

@@ -1,57 +0,0 @@
-{
-  "name": "@llamaindex/community",
-  "description": "Community package for LlamaIndexTS",
-  "version": "0.0.5",
-  "type": "module",
-  "types": "dist/type/index.d.ts",
-  "main": "dist/cjs/index.js",
-  "exports": {
-    ".": {
-      "import": {
-        "types": "./dist/type/index.d.ts",
-        "default": "./dist/index.js"
-      },
-      "require": {
-        "types": "./dist/type/index.d.ts",
-        "default": "./dist/index.cjs"
-      }
-    },
-    "./llm/bedrock": {
-      "import": {
-        "types": "./dist/type/llm/bedrock.d.ts",
-        "default": "./dist/llm/bedrock/base.js"
-      },
-      "require": {
-        "types": "./dist/type/llm/bedrock.d.ts",
-        "default": "./dist/llm/bedrock/base.cjs"
-      }
-    }
-  },
-  "files": [
-    "dist",
-    "CHANGELOG.md",
-    "!**/*.tsbuildinfo"
-  ],
-  "repository": {
-    "type": "git",
-    "url": "https://github.com/run-llama/LlamaIndexTS.git",
-    "directory": "packages/community"
-  },
-  "scripts": {
-    "build": "rm -rf ./dist && pnpm run build:code && pnpm run build:type",
-    "build:code": "tsup",
-    "build:type": "tsc -p tsconfig.json",
-    "dev": "concurrently \"pnpm run build:esm --watch\" \"pnpm run build:cjs --watch\" \"pnpm run build:type --watch\""
-  },
-  "devDependencies": {
-    "@swc/cli": "^0.3.12",
-    "@swc/core": "^1.6.3",
-    "concurrently": "^8.2.2",
-    "tsup": "^8.1.0"
-  },
-  "dependencies": {
-    "@aws-sdk/client-bedrock-runtime": "^3.600.0",
-    "@types/node": "^20.14.2",
-    "llamaindex": "workspace:*"
-  }
-}

packages/community/src/index.ts

@@ -1 +0,0 @@
-export { BEDROCK_MODELS, Bedrock } from "./llm/bedrock/base.js";

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

@@ -1,355 +0,0 @@
-import {
-  BedrockRuntimeClient,
-  InvokeModelCommand,
-  InvokeModelWithResponseStreamCommand,
-  type BedrockRuntimeClientConfig,
-  type InvokeModelCommandInput,
-  type InvokeModelWithResponseStreamCommandInput,
-} from "@aws-sdk/client-bedrock-runtime";
-
-import type {
-  ChatMessage,
-  ChatResponse,
-  ChatResponseChunk,
-  CompletionResponse,
-  LLMChatParamsNonStreaming,
-  LLMChatParamsStreaming,
-  LLMCompletionParamsNonStreaming,
-  LLMCompletionParamsStreaming,
-  LLMMetadata,
-  ToolCallLLMMessageOptions,
-} from "llamaindex";
-import { ToolCallLLM, streamConverter, wrapLLMEvent } from "llamaindex";
-import type {
-  AnthropicNoneStreamingResponse,
-  AnthropicTextContent,
-  StreamEvent,
-} from "./types.js";
-import {
-  mapChatMessagesToAnthropicMessages,
-  mapMessageContentToMessageContentDetails,
-  toUtf8,
-} from "./utils.js";
-
-export type BedrockAdditionalChatOptions = {};
-
-export type BedrockChatParamsStreaming = LLMChatParamsStreaming<
-  BedrockAdditionalChatOptions,
-  ToolCallLLMMessageOptions
->;
-
-export type BedrockChatStreamResponse = AsyncIterable<
-  ChatResponseChunk<ToolCallLLMMessageOptions>
->;
-
-export type BedrockChatParamsNonStreaming = LLMChatParamsNonStreaming<
-  BedrockAdditionalChatOptions,
-  ToolCallLLMMessageOptions
->;
-
-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",
-  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",
-}
-
-/*
- * Values taken from https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters.html#model-parameters-claude
- */
-
-const COMPLETION_MODELS = {
-  [BEDROCK_MODELS.AMAZON_TITAN_TG1_LARGE]: 8000,
-  [BEDROCK_MODELS.AMAZON_TITAN_TEXT_EXPRESS_V1]: 8000,
-  [BEDROCK_MODELS.AI21_J2_GRANDE_INSTRUCT]: 8000,
-  [BEDROCK_MODELS.AI21_J2_JUMBO_INSTRUCT]: 8000,
-  [BEDROCK_MODELS.AI21_J2_MID]: 8000,
-  [BEDROCK_MODELS.AI21_J2_MID_V1]: 8000,
-  [BEDROCK_MODELS.AI21_J2_ULTRA]: 8000,
-  [BEDROCK_MODELS.AI21_J2_ULTRA_V1]: 8000,
-  [BEDROCK_MODELS.COHERE_COMMAND_TEXT_V14]: 4096,
-};
-
-const CHAT_ONLY_MODELS = {
-  [BEDROCK_MODELS.ANTHROPIC_CLAUDE_INSTANT_1]: 100000,
-  [BEDROCK_MODELS.ANTHROPIC_CLAUDE_1]: 100000,
-  [BEDROCK_MODELS.ANTHROPIC_CLAUDE_2]: 100000,
-  [BEDROCK_MODELS.ANTHROPIC_CLAUDE_2_1]: 200000,
-  [BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_SONNET]: 200000,
-  [BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_HAIKU]: 200000,
-  [BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_OPUS]: 200000,
-  [BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_5_SONNET]: 200000,
-  [BEDROCK_MODELS.META_LLAMA2_13B_CHAT]: 2048,
-  [BEDROCK_MODELS.META_LLAMA2_70B_CHAT]: 4096,
-  [BEDROCK_MODELS.META_LLAMA3_8B_INSTRUCT]: 8192,
-  [BEDROCK_MODELS.META_LLAMA3_70B_INSTRUCT]: 8192,
-  [BEDROCK_MODELS.MISTRAL_7B_INSTRUCT]: 32000,
-  [BEDROCK_MODELS.MISTRAL_MIXTRAL_7B_INSTRUCT]: 32000,
-  [BEDROCK_MODELS.MISTRAL_MIXTRAL_LARGE_2402]: 32000,
-};
-
-const BEDROCK_FOUNDATION_LLMS = { ...COMPLETION_MODELS, ...CHAT_ONLY_MODELS };
-
-/*
- * Only the following models support streaming as
- * per result of Bedrock.Client.list_foundation_models
- * https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/bedrock/client/list_foundation_models.html
- */
-export const STREAMING_MODELS = new Set([
-  BEDROCK_MODELS.AMAZON_TITAN_TG1_LARGE,
-  BEDROCK_MODELS.AMAZON_TITAN_TEXT_EXPRESS_V1,
-  BEDROCK_MODELS.ANTHROPIC_CLAUDE_INSTANT_1,
-  BEDROCK_MODELS.ANTHROPIC_CLAUDE_1,
-  BEDROCK_MODELS.ANTHROPIC_CLAUDE_2,
-  BEDROCK_MODELS.ANTHROPIC_CLAUDE_2_1,
-  BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_SONNET,
-  BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_HAIKU,
-  BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_OPUS,
-  BEDROCK_MODELS.ANTHROPIC_CLAUDE_3_5_SONNET,
-  BEDROCK_MODELS.META_LLAMA2_13B_CHAT,
-  BEDROCK_MODELS.META_LLAMA2_70B_CHAT,
-  BEDROCK_MODELS.META_LLAMA3_8B_INSTRUCT,
-  BEDROCK_MODELS.META_LLAMA3_70B_INSTRUCT,
-  BEDROCK_MODELS.MISTRAL_7B_INSTRUCT,
-  BEDROCK_MODELS.MISTRAL_MIXTRAL_7B_INSTRUCT,
-  BEDROCK_MODELS.MISTRAL_MIXTRAL_LARGE_2402,
-]);
-
-abstract class Provider {
-  abstract getTextFromResponse(response: Record<string, any>): string;
-
-  getTextFromStreamResponse(response: Record<string, any>): string {
-    return this.getTextFromResponse(response);
-  }
-
-  abstract getRequestBody<T extends ChatMessage>(
-    metadata: LLMMetadata,
-    messages: T[],
-  ): InvokeModelCommandInput | InvokeModelWithResponseStreamCommandInput;
-}
-
-class AnthropicProvider extends Provider {
-  getResultFromResponse(
-    response: Record<string, any>,
-  ): AnthropicNoneStreamingResponse {
-    return JSON.parse(toUtf8(response.body));
-  }
-
-  getTextFromResponse(response: Record<string, any>): string {
-    const result = this.getResultFromResponse(response);
-    return result.content
-      .filter((item) => item.type === "text")
-      .map((item) => (item as AnthropicTextContent).text)
-      .join(" ");
-  }
-
-  getTextFromStreamResponse(response: Record<string, any>): string {
-    const event: StreamEvent | undefined = response.chunk?.bytes
-      ? JSON.parse(toUtf8(response.chunk?.bytes))
-      : undefined;
-
-    if (event?.type === "content_block_delta") return event.delta.text;
-    return "";
-  }
-
-  getRequestBody<T extends ChatMessage>(
-    metadata: LLMMetadata,
-    messages: T[],
-  ): InvokeModelCommandInput | InvokeModelWithResponseStreamCommandInput {
-    return {
-      modelId: metadata.model,
-      contentType: "application/json",
-      accept: "application/json",
-      body: JSON.stringify({
-        anthropic_version: "bedrock-2023-05-31",
-        messages: mapChatMessagesToAnthropicMessages(messages),
-        max_tokens: metadata.maxTokens,
-        temperature: metadata.temperature,
-        top_p: metadata.topP,
-      }),
-    };
-  }
-}
-
-// Other providers could go here
-const PROVIDERS: { [key: string]: Provider } = {
-  anthropic: new AnthropicProvider(),
-};
-
-const getProvider = (model: string): Provider => {
-  const providerName = model.split(".")[0];
-  if (!(providerName in PROVIDERS)) {
-    throw new Error(
-      `Provider ${providerName} for model ${model} is not supported`,
-    );
-  }
-  return PROVIDERS[providerName];
-};
-
-export type BedrockModelParams = {
-  model: keyof typeof BEDROCK_FOUNDATION_LLMS;
-  temperature?: number;
-  topP?: number;
-  maxTokens?: number;
-};
-
-const DEFAULT_BEDROCK_PARAMS = {
-  temperature: 0.1,
-  topP: 1,
-  maxTokens: 1024, // required by anthropic
-};
-
-export type BedrockParams = BedrockModelParams & BedrockRuntimeClientConfig;
-
-/**
- * ToolCallLLM for Bedrock
- */
-export class Bedrock extends ToolCallLLM<BedrockAdditionalChatOptions> {
-  private client: BedrockRuntimeClient;
-  model: keyof typeof BEDROCK_FOUNDATION_LLMS;
-  temperature: number;
-  topP: number;
-  maxTokens?: number;
-  provider: Provider;
-  topK?: number;
-
-  // there should be no check for env variables. Bedrock can be authenticated in various ways
-  // AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY and AWS_REGION are the env variables used directly by the sdk
-  constructor({
-    temperature,
-    topP,
-    maxTokens,
-    model,
-    ...params
-  }: BedrockParams) {
-    super();
-
-    this.model = model;
-    this.provider = getProvider(this.model);
-    this.maxTokens = maxTokens ?? DEFAULT_BEDROCK_PARAMS.maxTokens;
-    this.temperature = temperature ?? DEFAULT_BEDROCK_PARAMS.temperature;
-    this.topP = topP ?? DEFAULT_BEDROCK_PARAMS.topP;
-    this.client = new BedrockRuntimeClient(params);
-  }
-
-  get supportToolCall(): boolean {
-    return false;
-  }
-
-  get metadata(): LLMMetadata {
-    // NOTE, Anthropic supports top_k but LLMMetadata does not
-    return {
-      model: this.model,
-      temperature: this.temperature,
-      topP: this.topP,
-      maxTokens: this.maxTokens,
-      contextWindow: BEDROCK_FOUNDATION_LLMS[this.model],
-      tokenizer: undefined,
-    };
-  }
-
-  protected async nonStreamChat(
-    params: BedrockChatParamsNonStreaming,
-  ): Promise<BedrockChatNonStreamResponse> {
-    const input = this.provider.getRequestBody(this.metadata, params.messages);
-    const command = new InvokeModelCommand(input);
-    const response = await this.client.send(command);
-    return {
-      raw: response,
-      message: {
-        content: this.provider.getTextFromResponse(response),
-        role: "assistant",
-      },
-    };
-  }
-
-  protected async *streamChat(
-    params: BedrockChatParamsStreaming,
-  ): BedrockChatStreamResponse {
-    if (!STREAMING_MODELS.has(this.model))
-      throw new Error(`The model: ${this.model} does not support streaming`);
-    const input = this.provider.getRequestBody(this.metadata, params.messages);
-    const command = new InvokeModelWithResponseStreamCommand(input);
-    const response = await this.client.send(command);
-
-    if (response.body)
-      yield* streamConverter(response.body, (response) => {
-        return {
-          delta: this.provider.getTextFromStreamResponse(response),
-          raw: response,
-        };
-      });
-  }
-
-  chat(params: BedrockChatParamsStreaming): Promise<BedrockChatStreamResponse>;
-  chat(
-    params: BedrockChatParamsNonStreaming,
-  ): Promise<BedrockChatNonStreamResponse>;
-
-  @wrapLLMEvent
-  async chat(
-    params: BedrockChatParamsStreaming | BedrockChatParamsNonStreaming,
-  ): Promise<BedrockChatStreamResponse | BedrockChatNonStreamResponse> {
-    if (params.stream) return this.streamChat(params);
-    return this.nonStreamChat(params);
-  }
-
-  complete(
-    params: LLMCompletionParamsStreaming,
-  ): Promise<AsyncIterable<CompletionResponse>>;
-  complete(
-    params: LLMCompletionParamsNonStreaming,
-  ): Promise<CompletionResponse>;
-  async complete(
-    params: LLMCompletionParamsStreaming | LLMCompletionParamsNonStreaming,
-  ): Promise<CompletionResponse | AsyncIterable<CompletionResponse>> {
-    const message: ChatMessage = {
-      role: "user",
-      content: mapMessageContentToMessageContentDetails(params.prompt),
-    };
-
-    const input = this.provider.getRequestBody(this.metadata, [message]);
-
-    if (params.stream) {
-      const command = new InvokeModelWithResponseStreamCommand(input);
-      const response = await this.client.send(command);
-      if (response.body)
-        return streamConverter(response.body, (response) => {
-          return {
-            text: this.provider.getTextFromStreamResponse(response),
-            raw: response,
-          };
-        });
-    }
-
-    const command = new InvokeModelCommand(input);
-    const response = await this.client.send(command);
-    return {
-      text: this.provider.getTextFromResponse(response),
-      raw: response,
-    };
-  }
-}

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

@@ -1,109 +0,0 @@
-type Usage = {
-  input_tokens: number;
-  output_tokens: number;
-};
-
-type Message = {
-  id: string;
-  type: string;
-  role: string;
-  content: string[];
-  model: string;
-  stop_reason: string | null;
-  stop_sequence: string | null;
-  usage: Usage;
-};
-
-type ContentBlockStart = {
-  type: "content_block_start";
-  index: number;
-  content_block: {
-    type: string;
-    text: string;
-  };
-};
-
-type Delta = {
-  type: string;
-  text: string;
-};
-
-type ContentBlockDelta = {
-  type: "content_block_delta";
-  index: number;
-  delta: Delta;
-};
-
-type ContentBlockStop = {
-  type: "content_block_stop";
-  index: number;
-};
-
-type MessageDelta = {
-  type: "message_delta";
-  delta: {
-    stop_reason: string;
-    stop_sequence: string | null;
-  };
-  usage: Usage;
-};
-
-type InvocationMetrics = {
-  inputTokenCount: number;
-  outputTokenCount: number;
-  invocationLatency: number;
-  firstByteLatency: number;
-};
-
-type MessageStop = {
-  type: "message_stop";
-  "amazon-bedrock-invocationMetrics": InvocationMetrics;
-};
-
-export type StreamEvent =
-  | { type: "message_start"; message: Message }
-  | ContentBlockStart
-  | ContentBlockDelta
-  | ContentBlockStop
-  | MessageDelta
-  | MessageStop;
-
-export type AnthropicContent = AnthropicTextContent | AnthropicImageContent;
-
-export type AnthropicTextContent = {
-  type: "text";
-  text: string;
-};
-
-export type AnthropicMediaTypes =
-  | "image/jpeg"
-  | "image/png"
-  | "image/webp"
-  | "image/gif";
-
-export type AnthropicImageSource = {
-  type: "base64";
-  media_type: AnthropicMediaTypes;
-  data: string; // base64 encoded image bytes
-};
-
-export type AnthropicImageContent = {
-  type: "image";
-  source: AnthropicImageSource;
-};
-
-export type AnthropicMessage = {
-  role: "user" | "assistant";
-  content: AnthropicContent[];
-};
-
-export type AnthropicNoneStreamingResponse = {
-  id: string;
-  type: "message";
-  role: "assistant";
-  content: AnthropicContent[];
-  model: string;
-  stop_reason: "end_turn" | "max_tokens" | "stop_sequence";
-  stop_sequence?: string;
-  usage: { input_tokens: number; output_tokens: number };
-};

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

@@ -1,144 +0,0 @@
-import type {
-  ChatMessage,
-  MessageContent,
-  MessageContentDetail,
-} from "llamaindex";
-import type {
-  AnthropicContent,
-  AnthropicImageContent,
-  AnthropicMediaTypes,
-  AnthropicMessage,
-  AnthropicTextContent,
-} from "./types.js";
-
-const ACCEPTED_IMAGE_MIME_TYPES = [
-  "image/jpeg",
-  "image/png",
-  "image/webp",
-  "image/gif",
-];
-
-export const mapMessageContentToMessageContentDetails = (
-  content: MessageContent,
-): MessageContentDetail[] => {
-  return Array.isArray(content) ? content : [{ type: "text", text: content }];
-};
-
-export const mergeNeighboringSameRoleMessages = (
-  messages: AnthropicMessage[],
-): AnthropicMessage[] => {
-  return messages.reduce(
-    (result: AnthropicMessage[], current: AnthropicMessage, index: number) => {
-      if (index > 0 && messages[index - 1].role === current.role) {
-        result[result.length - 1].content = [
-          ...result[result.length - 1].content,
-          ...current.content,
-        ];
-      } else {
-        result.push(current);
-      }
-      return result;
-    },
-    [],
-  );
-};
-
-export const mapMessageContentDetailToAnthropicContent = <
-  T extends MessageContentDetail,
->(
-  detail: T,
-): AnthropicContent => {
-  let content: AnthropicContent;
-
-  if (detail.type === "text") {
-    content = mapTextContent(detail.text);
-  } else if (detail.type === "image_url") {
-    content = mapImageContent(detail.image_url.url);
-  } else {
-    throw new Error("Unsupported content detail type");
-  }
-  return content;
-};
-
-export const mapMessageContentToAnthropicContent = <T extends MessageContent>(
-  content: T,
-): AnthropicContent[] => {
-  return mapMessageContentToMessageContentDetails(content).map(
-    mapMessageContentDetailToAnthropicContent,
-  );
-};
-
-export const mapChatMessagesToAnthropicMessages = <T extends ChatMessage>(
-  messages: T[],
-): AnthropicMessage[] => {
-  const mapped = messages
-    .flatMap((msg: T): AnthropicMessage[] => {
-      return mapMessageContentToMessageContentDetails(msg.content).map(
-        (detail: MessageContentDetail): AnthropicMessage => {
-          const content = mapMessageContentDetailToAnthropicContent(detail);
-
-          return {
-            role: msg.role === "assistant" ? "assistant" : "user",
-            content: [content],
-          };
-        },
-      );
-    })
-    .filter((message: AnthropicMessage) => {
-      const content = message.content[0];
-      if (content.type === "text" && !content.text) return false;
-      if (content.type === "image" && !content.source.data) return false;
-      return true;
-    });
-
-  return mergeNeighboringSameRoleMessages(mapped);
-};
-
-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(
-      "For Anthropic please only use base64 data url, e.g.: data:image/jpeg;base64,SGVsbG8sIFdvcmxkIQ==",
-    );
-  const { mimeType, base64: data } = extractDataUrlComponents(imageUrl);
-  if (!ACCEPTED_IMAGE_MIME_TYPES.includes(mimeType))
-    throw new Error(
-      `Anthropic only accepts the following mimeTypes: ${ACCEPTED_IMAGE_MIME_TYPES.join("\n")}`,
-    );
-
-  return {
-    type: "image",
-    source: {
-      type: "base64",
-      media_type: mimeType as AnthropicMediaTypes,
-      data,
-    },
-  };
-};
-
-export const toUtf8 = (input: Uint8Array): string =>
-  new TextDecoder("utf-8").decode(input);

packages/community/tsconfig.json

@@ -1,19 +0,0 @@
-{
-  "extends": "../../tsconfig.json",
-  "compilerOptions": {
-    "rootDir": "./src",
-    "outDir": "./dist/type",
-    "tsBuildInfoFile": "./dist/.tsbuildinfo",
-    "emitDeclarationOnly": true,
-    "module": "ESNext",
-    "moduleResolution": "bundler",
-    "types": ["node"]
-  },
-  "include": ["./src"],
-  "exclude": ["node_modules"],
-  "references": [
-    {
-      "path": "../llamaindex/tsconfig.json"
-    }
-  ]
-}

packages/community/tsconfig.script.json

@@ -1,9 +0,0 @@
-{
-  "extends": "../../tsconfig.json",
-  "compilerOptions": {
-    "outDir": "./dist/script/type",
-    "tsBuildInfoFile": "./dist/script/.tsbuildinfo",
-    "emitDeclarationOnly": true
-  },
-  "include": ["./tsup.config.ts"]
-}

packages/community/tsup.config.ts

@@ -1,9 +0,0 @@
-import { defineConfig } from "tsup";
-
-export default defineConfig([
-  {
-    entry: ["src/index.ts", "src/llm/bedrock/base.ts"],
-    format: ["cjs", "esm"],
-    sourcemap: true,
-  },
-]);

packages/core/CHANGELOG.md

@@ -1,50 +1,5 @@
 # llamaindex
 
-## 0.4.1
-
-### Patch Changes
-
-- 3c47910: fix: groq llm
-- ed467a9: Add model ids for Anthropic Claude 3.5 Sonnet model on Anthropic and Bedrock
-- cba5406: fix: every Llama Parse job being called "blob"
-- Updated dependencies [56fabbb]
-  - @llamaindex/env@0.1.4
-
-## 0.4.0
-
-### Minor Changes
-
-- 436bc41: Unify chat engine response and agent response
-
-### Patch Changes
-
-- a44e54f: Truncate text to embed for OpenAI if it exceeds maxTokens
-- a51ed8d: feat: add support for managed identity for Azure OpenAI
-- d3b635b: fix: agents to use chat history
-
-## 0.3.17
-
-### Patch Changes
-
-- 6bc5bdd: feat: add cache disabling, fast mode, do not unroll columns mode and custom page seperator to LlamaParseReader
-- bf25ff6: fix: polyfill for cloudflare worker
-- e6d6576: chore: use `unpdf`
-
-## 0.3.16
-
-### Patch Changes
-
-- 11ae926: feat: add numCandidates setting to MongoDBAtlasVectorStore for tuning queries
-- 631f000: feat: DeepInfra LLM implementation
-- 1378ec4: feat: set default model to `gpt-4o`
-- 6b1ded4: add gpt4o-mode, invalidate cache and skip diagonal text to LlamaParseReader
-- 4d4bd85: Show error message if agent tool is called with partial JSON
-- 24a9d1e: add json mode and image retrieval to LlamaParseReader
-- 45952de: add concurrency management for SimpleDirectoryReader
-- 54230f0: feat: Gemini GA release models
-- a29d835: setDocumentHash should be async
-- 73819bf: Unify metadata and ID handling of documents, allow files to be read by `Buffer`
-
 ## 0.3.15
 
 ### Patch Changes

packages/core/CONTRIBUTING.md

@@ -41,7 +41,7 @@ To run them, run
 pnpm run test
 ```
 
-To write new test cases write them in [packages/core/src/tests](/packages/llamaindex/src/tests)
+To write new test cases write them in [packages/core/src/tests](/packages/core/src/tests)
 
 We use Jest https://jestjs.io/ to write our test cases. Jest comes with a bunch of built in assertions using the expect function: https://jestjs.io/docs/expect
 

packages/core/e2e/CHANGELOG.md

@@ -1,11 +1,5 @@
 # @llamaindex/core-e2e
 
-## 0.0.7
-
-### Patch Changes
-
-- bf25ff6: fix: polyfill for cloudflare worker
-
 ## 0.0.6
 
 ### Patch Changes

packages/core/e2e/examples/cloudflare-worker-agent/CHANGELOG.md

@@ -1,50 +1,5 @@
 # @llamaindex/cloudflare-worker-agent-test
 
-## 0.0.20
-
-### Patch Changes
-
-- Updated dependencies [3c47910]
-- Updated dependencies [ed467a9]
-- Updated dependencies [cba5406]
-  - llamaindex@0.4.1
-
-## 0.0.19
-
-### Patch Changes
-
-- Updated dependencies [436bc41]
-- Updated dependencies [a44e54f]
-- Updated dependencies [a51ed8d]
-- Updated dependencies [d3b635b]
-  - llamaindex@0.4.0
-
-## 0.0.18
-
-### Patch Changes
-
-- bf25ff6: fix: polyfill for cloudflare worker
-- Updated dependencies [6bc5bdd]
-- Updated dependencies [bf25ff6]
-- Updated dependencies [e6d6576]
-  - llamaindex@0.3.17
-
-## 0.0.17
-
-### Patch Changes
-
-- Updated dependencies [11ae926]
-- Updated dependencies [631f000]
-- Updated dependencies [1378ec4]
-- Updated dependencies [6b1ded4]
-- Updated dependencies [4d4bd85]
-- Updated dependencies [24a9d1e]
-- Updated dependencies [45952de]
-- Updated dependencies [54230f0]
-- Updated dependencies [a29d835]
-- Updated dependencies [73819bf]
-  - llamaindex@0.3.16
-
 ## 0.0.16
 
 ### Patch Changes

packages/core/e2e/examples/cloudflare-worker-agent/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@llamaindex/cloudflare-worker-agent-test",
-  "version": "0.0.20",
+  "version": "0.0.16",
   "type": "module",
   "private": true,
   "scripts": {
@@ -12,13 +12,13 @@
     "cf-typegen": "wrangler types"
   },
   "devDependencies": {
-    "@cloudflare/vitest-pool-workers": "^0.4.3",
-    "@cloudflare/workers-types": "^4.20240605.0",
+    "@cloudflare/vitest-pool-workers": "^0.2.6",
+    "@cloudflare/workers-types": "^4.20240502.0",
     "@vitest/runner": "1.3.0",
     "@vitest/snapshot": "1.3.0",
-    "typescript": "^5.5.2",
+    "typescript": "^5.4.5",
     "vitest": "1.3.0",
-    "wrangler": "^3.60.1"
+    "wrangler": "^3.53.1"
   },
   "dependencies": {
     "llamaindex": "workspace:*"

packages/core/e2e/examples/cloudflare-worker-agent/src/index.ts

@@ -10,18 +10,16 @@ export default {
     const agent = new OpenAIAgent({
       tools: [],
     });
-    console.log(1);
     const responseStream = await agent.chat({
       stream: true,
       message: "Hello? What is the weather today?",
     });
-    console.log(2);
     const textEncoder = new TextEncoder();
     const response = responseStream.pipeThrough<Uint8Array>(
       // @ts-expect-error: see https://github.com/cloudflare/workerd/issues/2067
       new TransformStream({
         transform: (chunk, controller) => {
-          controller.enqueue(textEncoder.encode(chunk.delta));
+          controller.enqueue(textEncoder.encode(chunk.response.delta));
         },
       }),
     );

packages/core/e2e/examples/nextjs-agent/CHANGELOG.md

@@ -1,49 +1,5 @@
 # @llamaindex/next-agent-test
 
-## 0.1.20
-
-### Patch Changes
-
-- Updated dependencies [3c47910]
-- Updated dependencies [ed467a9]
-- Updated dependencies [cba5406]
-  - llamaindex@0.4.1
-
-## 0.1.19
-
-### Patch Changes
-
-- Updated dependencies [436bc41]
-- Updated dependencies [a44e54f]
-- Updated dependencies [a51ed8d]
-- Updated dependencies [d3b635b]
-  - llamaindex@0.4.0
-
-## 0.1.18
-
-### Patch Changes
-
-- Updated dependencies [6bc5bdd]
-- Updated dependencies [bf25ff6]
-- Updated dependencies [e6d6576]
-  - llamaindex@0.3.17
-
-## 0.1.17
-
-### Patch Changes
-
-- Updated dependencies [11ae926]
-- Updated dependencies [631f000]
-- Updated dependencies [1378ec4]
-- Updated dependencies [6b1ded4]
-- Updated dependencies [4d4bd85]
-- Updated dependencies [24a9d1e]
-- Updated dependencies [45952de]
-- Updated dependencies [54230f0]
-- Updated dependencies [a29d835]
-- Updated dependencies [73819bf]
-  - llamaindex@0.3.16
-
 ## 0.1.16
 
 ### Patch Changes

packages/core/e2e/examples/nextjs-agent/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@llamaindex/next-agent-test",
-  "version": "0.1.20",
+  "version": "0.1.16",
   "private": true,
   "scripts": {
     "dev": "next dev",
@@ -9,20 +9,20 @@
     "lint": "next lint"
   },
   "dependencies": {
-    "ai": "^3.2.1",
+    "ai": "^3.1.3",
     "llamaindex": "workspace:*",
-    "next": "14.2.4",
+    "next": "14.2.3",
     "react": "18.3.1",
     "react-dom": "18.3.1"
   },
   "devDependencies": {
     "@types/node": "^20.12.11",
-    "@types/react": "^18.3.3",
+    "@types/react": "^18.3.1",
     "@types/react-dom": "^18.3.0",
     "eslint": "^8.57.0",
     "eslint-config-next": "14.2.3",
     "postcss": "^8",
-    "tailwindcss": "^3.4.4",
-    "typescript": "^5.5.2"
+    "tailwindcss": "^3.4.1",
+    "typescript": "^5.4.5"
   }
 }

packages/core/e2e/examples/nextjs-agent/src/actions/index.tsx

@@ -23,7 +23,7 @@ export async function chatWithAgent(
           uiStream.update("response:");
         },
         write: async (message) => {
-          uiStream.append(message.delta);
+          uiStream.append(message.response.delta);
         },
       }),
     )

packages/core/e2e/examples/nextjs-agent/src/app/page.tsx

@@ -1,12 +1,12 @@
 "use client";
 import { chatWithAgent } from "@/actions";
-import type { ReactNode } from "react";
+import type { JSX } from "react";
 import { useFormState } from "react-dom";
 
 export const runtime = "edge";
 
 export default function Home() {
-  const [state, action] = useFormState<ReactNode>(async () => {
+  const [state, action] = useFormState<JSX.Element | null>(async () => {
     return chatWithAgent("hello!", []);
   }, null);
   return (

packages/core/e2e/examples/nextjs-edge-runtime/CHANGELOG.md

@@ -1,49 +1,5 @@
 # test-edge-runtime
 
-## 0.1.19
-
-### Patch Changes
-
-- Updated dependencies [3c47910]
-- Updated dependencies [ed467a9]
-- Updated dependencies [cba5406]
-  - llamaindex@0.4.1
-
-## 0.1.18
-
-### Patch Changes
-
-- Updated dependencies [436bc41]
-- Updated dependencies [a44e54f]
-- Updated dependencies [a51ed8d]
-- Updated dependencies [d3b635b]
-  - llamaindex@0.4.0
-
-## 0.1.17
-
-### Patch Changes
-
-- Updated dependencies [6bc5bdd]
-- Updated dependencies [bf25ff6]
-- Updated dependencies [e6d6576]
-  - llamaindex@0.3.17
-
-## 0.1.16
-
-### Patch Changes
-
-- Updated dependencies [11ae926]
-- Updated dependencies [631f000]
-- Updated dependencies [1378ec4]
-- Updated dependencies [6b1ded4]
-- Updated dependencies [4d4bd85]
-- Updated dependencies [24a9d1e]
-- Updated dependencies [45952de]
-- Updated dependencies [54230f0]
-- Updated dependencies [a29d835]
-- Updated dependencies [73819bf]
-  - llamaindex@0.3.16
-
 ## 0.1.15
 
 ### Patch Changes