Release 0.6.9 (#1252)

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

.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:
@@ -104,6 +106,7 @@ jobs:
           - nextjs-edge-runtime
           - nextjs-node-runtime
           - waku-query-engine
+          - llama-parse-browser
     runs-on: ubuntu-latest
     name: Build LlamaIndex Example (${{ matrix.packages }})
     steps:
@@ -145,6 +148,9 @@ jobs:
       - name: Pack @llamaindex/openai
         run: pnpm pack --pack-destination ${{ runner.temp }}
         working-directory: packages/llm/openai
+      - name: Pack @llamaindex/groq
+        run: pnpm pack --pack-destination ${{ runner.temp }}
+        working-directory: packages/llm/groq
       - name: Pack @llamaindex/core
         run: pnpm pack --pack-destination ${{ runner.temp }}
         working-directory: packages/core

README.md

@@ -167,11 +167,13 @@ 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(

apps/docs/CHANGELOG.md

@@ -1,5 +1,81 @@
 # docs
 
+## 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
+
+- Updated dependencies [11feef8]
+  - llamaindex@0.6.0
+  - @llamaindex/examples@0.0.8
+
 ## 0.0.68
 
 ### 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_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/docs/modules/workflows.mdx

@@ -0,0 +1,168 @@
+import CodeBlock from "@theme/CodeBlock";
+import CodeSource from "!raw-loader!../../../../examples/workflow/joke.ts";
+
+# Workflows
+
+A `Workflow` in LlamaIndexTS is an event-driven abstraction used to chain together several events. Workflows are made up of `steps`, with each step responsible for handling certain event types and emitting new events.
+
+Workflows in LlamaIndexTS work by defining step functions that handle specific event types and emit new events.
+
+When a step function is added to a workflow, you need to specify the input and optionally the output event types (used for validation). The specification of the input events ensures each step only runs when an accepted event is ready.
+
+You can create a `Workflow` to do anything! Build an agent, a RAG flow, an extraction flow, or anything else you want.
+
+## Getting Started
+
+As an illustrative example, let's consider a naive workflow where a joke is generated and then critiqued.
+
+<CodeBlock language="ts">{CodeSource}</CodeBlock>
+
+There's a few moving pieces here, so let's go through this piece by piece.
+
+### Defining Workflow Events
+
+```typescript
+export class JokeEvent extends WorkflowEvent<{ joke: string }> {}
+```
+
+Events are user-defined classes that extend `WorkflowEvent` and contain arbitrary data provided as template argument. In this case, our workflow relies on a single user-defined event, the `JokeEvent` with a `joke` attribute of type `string`.
+
+### Setting up the Workflow Class
+
+```typescript
+const llm = new OpenAI();
+...
+const jokeFlow = new Workflow({ verbose: true });
+```
+
+Our workflow is implemented by initiating the `Workflow` class. For simplicity, we created a `OpenAI` llm instance.
+
+### Workflow Entry Points
+
+```typescript
+const generateJoke = async (_context: Context, ev: StartEvent) => {
+  const prompt = `Write your best joke about ${ev.data.input}.`;
+  const response = await llm.complete({ prompt });
+  return new JokeEvent({ joke: response.text });
+};
+```
+
+Here, we come to the entry-point of our workflow. While events are user-defined, there are two special-case events, the `StartEvent` and the `StopEvent`. Here, the `StartEvent` signifies where to send the initial workflow input.
+
+The `StartEvent` is a bit of a special object since it can hold arbitrary attributes. Here, we accessed the topic with `ev.data.input`.
+
+At this point, you may have noticed that we haven't explicitly told the workflow what events are handled by which steps.
+
+To do so, we use the `addStep` method which adds a step to the workflow. The first argument is the event type that the step will handle, and the second argument is the previously defined step function:
+
+```typescript
+jokeFlow.addStep(StartEvent, generateJoke);
+```
+
+### Workflow Exit Points
+
+```typescript
+const critiqueJoke = async (_context: Context, ev: JokeEvent) => {
+  const prompt = `Give a thorough critique of the following joke: ${ev.data.joke}`;
+  const response = await llm.complete({ prompt });
+  return new StopEvent({ result: response.text });
+};
+```
+
+Here, we have our second, and last step, in the workflow. We know its the last step because the special `StopEvent` is returned. When the workflow encounters a returned `StopEvent`, it immediately stops the workflow and returns whatever the result was.
+
+In this case, the result is a string, but it could be a map, array, or any other object.
+
+Don't forget to add the step to the workflow:
+
+```typescript
+jokeFlow.addStep(JokeEvent, critiqueJoke);
+```
+
+### Running the Workflow
+
+```typescript
+const result = await jokeFlow.run("pirates");
+console.log(result.data.result);
+```
+
+Lastly, we run the workflow. The `.run()` method is async, so we use await here to wait for the result.
+
+### Validating Workflows
+
+To tell the workflow what events are produced by each step, you can optionally provide a third argument to `addStep` to specify the output event type:
+
+```typescript
+jokeFlow.addStep(StartEvent, generateJoke, { outputs: JokeEvent });
+jokeFlow.addStep(JokeEvent, critiqueJoke, { outputs: StopEvent });
+```
+
+To validate a workflow, you need to call the `validate` method:
+
+```typescript
+jokeFlow.validate();
+```
+
+To automatically validate a workflow when you run it, you can set the `validate` flag to `true` at initialization:
+
+```typescript
+const jokeFlow = new Workflow({ verbose: true, validate: true });
+```
+
+## Working with Global Context/State
+
+Optionally, you can choose to use global context between steps. For example, maybe multiple steps access the original `query` input from the user. You can store this in global context so that every step has access.
+
+```typescript
+import { Context } from "@llamaindex/core/workflow";
+
+const query = async (context: Context, ev: MyEvent) => {
+  // get the query from the context
+  const query = context.get("query");
+  // do something with context and event
+  const val = ...
+  const result = ...
+  // store in context
+  context.set("key", val);
+
+  return new StopEvent({ result });
+};
+```
+
+## Waiting for Multiple Events
+
+The context does more than just hold data, it also provides utilities to buffer and wait for multiple events.
+
+For example, you might have a step that waits for a query and retrieved nodes before synthesizing a response:
+
+```typescript
+const synthesize = async (context: Context, ev: QueryEvent | RetrieveEvent) => {
+  const events = context.collectEvents(ev, [QueryEvent | RetrieveEvent]);
+  if (!events) {
+    return;
+  }
+  const prompt = events
+    .map((event) => {
+      if (event instanceof QueryEvent) {
+        return `Answer this query using the context provided: ${event.data.query}`;
+      } else if (event instanceof RetrieveEvent) {
+        return `Context: ${event.data.context}`;
+      }
+      return "";
+    })
+    .join("\n");
+
+  const response = await llm.complete({ prompt });
+  return new StopEvent({ result: response.text });
+};
+```
+
+Using `ctx.collectEvents()` we can buffer and wait for ALL expected events to arrive. This function will only return events (in the requested order) once all events have arrived.
+
+## Manually Triggering Events
+
+Normally, events are triggered by returning another event during a step. However, events can also be manually dispatched using the `ctx.sendEvent(event)` method within a workflow.
+
+## Examples
+
+You can find many useful examples of using workflows in the [examples folder](https://github.com/run-llama/LlamaIndexTS/blob/main/examples/workflow).

apps/docs/package.json

@@ -1,6 +1,6 @@
 {
   "name": "docs",
-  "version": "0.0.68",
+  "version": "0.0.78",
   "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": [

examples/CHANGELOG.md

@@ -1,5 +1,14 @@
 # examples
 
+## 0.0.8
+
+### Patch Changes
+
+- 11feef8: Add workflows
+- Updated dependencies [11feef8]
+  - @llamaindex/core@0.2.0
+  - llamaindex@0.6.0
+
 ## 0.0.7
 
 ### Patch Changes

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/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/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/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/openai_o1.ts

@@ -0,0 +1,13 @@
+import { OpenAI } from "llamaindex";
+
+(async () => {
+  const llm = new OpenAI({ model: "o1-preview", temperature: 1 });
+
+  const prompt = `What are three compounds we should consider investigating to advance research 
+  into new antibiotics? Why should we consider them?
+  `;
+
+  // complete api
+  const response = await llm.complete({ prompt });
+  console.log(response.text);
+})();

examples/package.json

@@ -1,12 +1,12 @@
 {
   "name": "@llamaindex/examples",
   "private": true,
-  "version": "0.0.7",
+  "version": "0.0.8",
   "dependencies": {
     "@aws-crypto/sha256-js": "^5.2.0",
     "@azure/identity": "^4.4.1",
     "@datastax/astra-db-ts": "^1.4.1",
-    "@llamaindex/core": "^0.1.0",
+    "@llamaindex/core": "^0.2.0",
     "@notionhq/client": "^2.2.15",
     "@pinecone-database/pinecone": "^3.0.2",
     "@zilliz/milvus2-sdk-node": "^2.4.6",
@@ -14,14 +14,14 @@
     "commander": "^12.1.0",
     "dotenv": "^16.4.5",
     "js-tiktoken": "^1.0.14",
-    "llamaindex": "^0.5.0",
+    "llamaindex": "^0.6.0",
     "mongodb": "^6.7.0",
     "pathe": "^1.1.2"
   },
   "devDependencies": {
     "@types/node": "^22.5.1",
     "tsx": "^4.19.0",
-    "typescript": "^5.5.4"
+    "typescript": "^5.6.2"
   },
   "scripts": {
     "lint": "eslint ."

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

@@ -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/pg-vector-store/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/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/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/utils";
 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/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/vectorIndexFromVectorStore.ts

@@ -1,11 +1,10 @@
 import {
+  getResponseSynthesizer,
   OpenAI,
   OpenAIEmbedding,
-  ResponseSynthesizer,
   RetrieverQueryEngine,
   Settings,
   TextNode,
-  TreeSummarize,
   VectorIndexRetriever,
   VectorStore,
   VectorStoreIndex,
@@ -165,10 +164,7 @@ async function main() {
       similarityTopK: 500,
     });
 
-    const responseSynthesizer = new ResponseSynthesizer({
-      responseBuilder: new TreeSummarize(),
-    });
-
+    const responseSynthesizer = getResponseSynthesizer("tree_summarize");
     return new RetrieverQueryEngine(retriever, responseSynthesizer, {
       filter,
     });

examples/workflow/README.md

@@ -0,0 +1,7 @@
+# Workflow Examples
+
+These examples demonstrate LlamaIndexTS's workflow system. Check out [its documentation](https://ts.llamaindex.ai/modules/workflows) for more information.
+
+## Running the Examples
+
+To run the examples, make sure to run them from the parent folder called `examples`). For example, to run the joke workflow, run `npx tsx workflow/joke.ts`.

examples/workflow/app-creator.ts

@@ -0,0 +1,122 @@
+import {
+  Context,
+  StartEvent,
+  StopEvent,
+  Workflow,
+  WorkflowEvent,
+} from "@llamaindex/core/workflow";
+import { OpenAI } from "llamaindex";
+
+const MAX_REVIEWS = 3;
+
+// Using the o1-preview model (see https://platform.openai.com/docs/guides/reasoning?reasoning-prompt-examples=coding-planning)
+const llm = new OpenAI({ model: "o1-preview", temperature: 1 });
+
+// example specification from https://platform.openai.com/docs/guides/reasoning?reasoning-prompt-examples=coding-planning
+const specification = `Python app that takes user questions and looks them up in a 
+database where they are mapped to answers. If there is a close match, it retrieves 
+the matched answer. If there isn't, it asks the user to provide an answer and 
+stores the question/answer pair in the database.`;
+
+// Create custom event types
+export class MessageEvent extends WorkflowEvent<{ msg: string }> {}
+export class CodeEvent extends WorkflowEvent<{ code: string }> {}
+export class ReviewEvent extends WorkflowEvent<{
+  review: string;
+  code: string;
+}> {}
+
+// Helper function to truncate long strings
+const truncate = (str: string) => {
+  const MAX_LENGTH = 60;
+  if (str.length <= MAX_LENGTH) return str;
+  return str.slice(0, MAX_LENGTH) + "...";
+};
+
+// the architect is responsible for writing the structure and the initial code based on the specification
+const architect = async (context: Context, ev: StartEvent) => {
+  // get the specification from the start event and save it to context
+  context.set("specification", ev.data.input);
+  const spec = context.get("specification");
+  // write a message to send an update to the user
+  context.writeEventToStream(
+    new MessageEvent({
+      msg: `Writing app using this specification: ${truncate(spec)}`,
+    }),
+  );
+  const prompt = `Build an app for this specification: <spec>${spec}</spec>. Make a plan for the directory structure you'll need, then return each file in full. Don't supply any reasoning, just code.`;
+  const code = await llm.complete({ prompt });
+  return new CodeEvent({ code: code.text });
+};
+
+// the coder is responsible for updating the code based on the review
+const coder = async (context: Context, ev: ReviewEvent) => {
+  // get the specification from the context
+  const spec = context.get("specification");
+  // get the latest review and code
+  const { review, code } = ev.data;
+  // write a message to send an update to the user
+  context.writeEventToStream(
+    new MessageEvent({
+      msg: `Update code based on review: ${truncate(review)}`,
+    }),
+  );
+  const prompt = `We need to improve code that should implement this specification: <spec>${spec}</spec>. Here is the current code: <code>${code}</code>. And here is a review of the code: <review>${review}</review>. Improve the code based on the review, keep the specification in mind, and return the full updated code. Don't supply any reasoning, just code.`;
+  const updatedCode = await llm.complete({ prompt });
+  return new CodeEvent({ code: updatedCode.text });
+};
+
+// the reviewer is responsible for reviewing the code and providing feedback
+const reviewer = async (context: Context, ev: CodeEvent) => {
+  // get the specification from the context
+  const spec = context.get("specification");
+  // get latest code from the event
+  const { code } = ev.data;
+  // update and check the number of reviews
+  const numberReviews = context.get("numberReviews", 0) + 1;
+  context.set("numberReviews", numberReviews);
+  if (numberReviews > MAX_REVIEWS) {
+    // the we've done this too many times - return the code
+    context.writeEventToStream(
+      new MessageEvent({
+        msg: `Already reviewed ${numberReviews - 1} times, stopping!`,
+      }),
+    );
+    return new StopEvent({ result: code });
+  }
+  // write a message to send an update to the user
+  context.writeEventToStream(
+    new MessageEvent({ msg: `Review #${numberReviews}: ${truncate(code)}` }),
+  );
+  const prompt = `Review this code: <code>${code}</code>. Check if the code quality and whether it correctly implements this specification: <spec>${spec}</spec>. If you're satisfied, just return 'Looks great', nothing else. If not, return a review with a list of changes you'd like to see.`;
+  const review = (await llm.complete({ prompt })).text;
+  if (review.includes("Looks great")) {
+    // the reviewer is satisfied with the code, let's return the review
+    context.writeEventToStream(
+      new MessageEvent({
+        msg: `Reviewer says: ${review}`,
+      }),
+    );
+    return new StopEvent({ result: code });
+  }
+
+  return new ReviewEvent({ review, code });
+};
+
+const codeAgent = new Workflow({ validate: true });
+codeAgent.addStep(StartEvent, architect, { outputs: CodeEvent });
+codeAgent.addStep(ReviewEvent, coder, { outputs: CodeEvent });
+codeAgent.addStep(CodeEvent, reviewer, { outputs: ReviewEvent });
+
+// Usage
+async function main() {
+  const run = codeAgent.run(specification);
+  for await (const event of codeAgent.streamEvents()) {
+    const msg = (event as MessageEvent).data.msg;
+    console.log(`${msg}\n`);
+  }
+  const result = await run;
+  console.log("Final code:\n", result.data.result);
+}
+
+main().catch(console.error);

examples/workflow/collect-events.ts

@@ -0,0 +1,70 @@
+import {
+  Context,
+  StartEvent,
+  StopEvent,
+  Workflow,
+  WorkflowEvent,
+} from "@llamaindex/core/workflow";
+import { OpenAI } from "llamaindex";
+
+// Create LLM instance
+const llm = new OpenAI();
+
+// Create custom event types
+export class JokeEvent extends WorkflowEvent<{ joke: string }> {}
+export class CritiqueEvent extends WorkflowEvent<{ critique: string }> {}
+export class AnalysisEvent extends WorkflowEvent<{ analysis: string }> {}
+
+const generateJoke = async (_context: Context, ev: StartEvent) => {
+  const prompt = `Write your best joke about ${ev.data.input}.`;
+  const response = await llm.complete({ prompt });
+  return new JokeEvent({ joke: response.text });
+};
+
+const critiqueJoke = async (_context: Context, ev: JokeEvent) => {
+  const prompt = `Give a thorough critique of the following joke: ${ev.data.joke}`;
+  const response = await llm.complete({ prompt });
+  return new CritiqueEvent({ critique: response.text });
+};
+
+const analyzeJoke = async (_context: Context, ev: JokeEvent) => {
+  const prompt = `Give a thorough analysis of the following joke: ${ev.data.joke}`;
+  const response = await llm.complete({ prompt });
+  return new AnalysisEvent({ analysis: response.text });
+};
+
+const reportJoke = async (
+  context: Context,
+  ev: AnalysisEvent | CritiqueEvent,
+) => {
+  const events = context.collectEvents(ev, [AnalysisEvent, CritiqueEvent]);
+  if (!events) {
+    return;
+  }
+  const subPrompts = events.map((event) => {
+    if (event instanceof AnalysisEvent) {
+      return `Analysis: ${event.data.analysis}`;
+    } else if (event instanceof CritiqueEvent) {
+      return `Critique: ${event.data.critique}`;
+    }
+    return "";
+  });
+
+  const prompt = `Based on the following information about a joke:\n${subPrompts.join("\n")}\nProvide a comprehensive report on the joke's quality and impact.`;
+  const response = await llm.complete({ prompt });
+  return new StopEvent({ result: response.text });
+};
+
+const jokeFlow = new Workflow();
+jokeFlow.addStep(StartEvent, generateJoke);
+jokeFlow.addStep(JokeEvent, critiqueJoke);
+jokeFlow.addStep(JokeEvent, analyzeJoke);
+jokeFlow.addStep([AnalysisEvent, CritiqueEvent], reportJoke);
+
+// Usage
+async function main() {
+  const result = await jokeFlow.run("pirates");
+  console.log(result.data.result);
+}
+
+main().catch(console.error);

examples/workflow/joke.ts

@@ -0,0 +1,38 @@
+import {
+  Context,
+  StartEvent,
+  StopEvent,
+  Workflow,
+  WorkflowEvent,
+} from "@llamaindex/core/workflow";
+import { OpenAI } from "llamaindex";
+
+// Create LLM instance
+const llm = new OpenAI();
+
+// Create a custom event type
+export class JokeEvent extends WorkflowEvent<{ joke: string }> {}
+
+const generateJoke = async (_context: Context, ev: StartEvent) => {
+  const prompt = `Write your best joke about ${ev.data.input}.`;
+  const response = await llm.complete({ prompt });
+  return new JokeEvent({ joke: response.text });
+};
+
+const critiqueJoke = async (_context: Context, ev: JokeEvent) => {
+  const prompt = `Give a thorough critique of the following joke: ${ev.data.joke}`;
+  const response = await llm.complete({ prompt });
+  return new StopEvent({ result: response.text });
+};
+
+const jokeFlow = new Workflow({ verbose: true });
+jokeFlow.addStep(StartEvent, generateJoke);
+jokeFlow.addStep(JokeEvent, critiqueJoke);
+
+// Usage
+async function main() {
+  const result = await jokeFlow.run("pirates");
+  console.log(result.data.result);
+}
+
+main().catch(console.error);

examples/workflow/stream-events.ts

@@ -0,0 +1,49 @@
+import {
+  Context,
+  StartEvent,
+  StopEvent,
+  Workflow,
+  WorkflowEvent,
+} from "@llamaindex/core/workflow";
+import { OpenAI } from "llamaindex";
+
+// Create LLM instance
+const llm = new OpenAI();
+
+// Create custom event types
+export class JokeEvent extends WorkflowEvent<{ joke: string }> {}
+export class MessageEvent extends WorkflowEvent<{ msg: string }> {}
+
+const generateJoke = async (context: Context, ev: StartEvent) => {
+  context.writeEventToStream(
+    new MessageEvent({ msg: `Generating a joke about: ${ev.data.input}` }),
+  );
+  const prompt = `Write your best joke about ${ev.data.input}.`;
+  const response = await llm.complete({ prompt });
+  return new JokeEvent({ joke: response.text });
+};
+
+const critiqueJoke = async (context: Context, ev: JokeEvent) => {
+  context.writeEventToStream(
+    new MessageEvent({ msg: `Write a critique of this joke: ${ev.data.joke}` }),
+  );
+  const prompt = `Give a thorough critique of the following joke: ${ev.data.joke}`;
+  const response = await llm.complete({ prompt });
+  return new StopEvent({ result: response.text });
+};
+
+const jokeFlow = new Workflow();
+jokeFlow.addStep(StartEvent, generateJoke);
+jokeFlow.addStep(JokeEvent, critiqueJoke);
+
+// Usage
+async function main() {
+  const run = jokeFlow.run("pirates");
+  for await (const event of jokeFlow.streamEvents()) {
+    console.log((event as MessageEvent).data.msg);
+  }
+  const result = await run;
+  console.log(result.data.result);
+}
+
+main().catch(console.error);

examples/workflow/timeout.ts

@@ -0,0 +1,37 @@
+import {
+  Context,
+  StartEvent,
+  StopEvent,
+  Workflow,
+} from "@llamaindex/core/workflow";
+
+const longRunning = async (_context: Context, ev: StartEvent) => {
+  await new Promise((resolve) => setTimeout(resolve, 2000)); // Wait for 2 seconds
+  return new StopEvent({ result: "We waited 2 seconds" });
+};
+
+async function timeout() {
+  const workflow = new Workflow({ verbose: true, timeout: 1 });
+  workflow.addStep(StartEvent, longRunning);
+  // This will timeout
+  try {
+    await workflow.run("Let's start");
+  } catch (error) {
+    console.error(error);
+  }
+}
+
+async function notimeout() {
+  // Increase timeout to 3 seconds - no timeout
+  const workflow = new Workflow({ verbose: true, timeout: 3 });
+  workflow.addStep(StartEvent, longRunning);
+  const result = await workflow.run("Let's start");
+  console.log(result.data.result);
+}
+
+async function main() {
+  await timeout();
+  await notimeout();
+}
+
+main().catch(console.error);

examples/workflow/validation.ts

@@ -0,0 +1,53 @@
+import {
+  Context,
+  StartEvent,
+  StopEvent,
+  Workflow,
+  WorkflowEvent,
+} from "@llamaindex/core/workflow";
+import { OpenAI } from "llamaindex";
+
+// Create LLM instance
+const llm = new OpenAI();
+
+// Create a custom event type
+export class JokeEvent extends WorkflowEvent<{ joke: string }> {}
+
+const generateJoke = async (_context: Context, ev: StartEvent) => {
+  const prompt = `Write your best joke about ${ev.data.input}.`;
+  const response = await llm.complete({ prompt });
+  return new JokeEvent({ joke: response.text });
+};
+
+const critiqueJoke = async (_context: Context, ev: JokeEvent) => {
+  const prompt = `Give a thorough critique of the following joke: ${ev.data.joke}`;
+  const response = await llm.complete({ prompt });
+  return new StopEvent({ result: response.text });
+};
+
+async function validateFails() {
+  try {
+    const jokeFlow = new Workflow({ verbose: true, validate: true });
+    jokeFlow.addStep(StartEvent, generateJoke, { outputs: StopEvent });
+    jokeFlow.addStep(JokeEvent, critiqueJoke, { outputs: StopEvent });
+    await jokeFlow.run("pirates");
+  } catch (e) {
+    console.error("Validation failed:", e);
+  }
+}
+
+async function validate() {
+  const jokeFlow = new Workflow({ verbose: true, validate: true });
+  jokeFlow.addStep(StartEvent, generateJoke, { outputs: JokeEvent });
+  jokeFlow.addStep(JokeEvent, critiqueJoke, { outputs: StopEvent });
+  const result = await jokeFlow.run("pirates");
+  console.log(result.data.result);
+}
+
+// Usage
+async function main() {
+  await validateFails();
+  await validate();
+}
+
+main().catch(console.error);

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",
@@ -31,7 +31,7 @@
     "prettier": "^3.3.3",
     "prettier-plugin-organize-imports": "^4.0.0",
     "turbo": "^2.1.0",
-    "typescript": "^5.5.4"
+    "typescript": "^5.6.2"
   },
   "packageManager": "pnpm@9.5.0",
   "pnpm": {

packages/autotool/CHANGELOG.md

@@ -1,5 +1,84 @@
 # @llamaindex/autotool
 
+## 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
+
+- Updated dependencies [11feef8]
+  - llamaindex@0.6.0
+
 ## 2.0.1
 
 ### Patch Changes

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

@@ -1,5 +1,90 @@
 # @llamaindex/autotool-01-node-example
 
+## 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
+
+- Updated dependencies [11feef8]
+  - llamaindex@0.6.0
+  - @llamaindex/autotool@3.0.0
+
 ## 0.0.8
 
 ### 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": "0.0.8"
+  "version": "0.0.18"
 }

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

@@ -1,5 +1,90 @@
 # @llamaindex/autotool-02-next-example
 
+## 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
+
+- Updated dependencies [11feef8]
+  - llamaindex@0.6.0
+  - @llamaindex/autotool@3.0.0
+
 ## 0.1.52
 
 ### 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": "0.1.52",
+  "version": "0.1.62",
   "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": "2.0.1",
+  "version": "3.0.9",
   "description": "auto transpile your JS function to LLM Agent compatible",
   "files": [
     "dist",
@@ -51,7 +51,7 @@
     "unplugin": "^1.12.2"
   },
   "peerDependencies": {
-    "llamaindex": "^0.5.27",
+    "llamaindex": "workspace:*",
     "openai": "^4",
     "typescript": "^4"
   },
@@ -72,10 +72,10 @@
     "@types/node": "^22.5.1",
     "bunchee": "5.3.2",
     "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 +1,47 @@
 # @llamaindex/cloud
 
+## 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/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@llamaindex/cloud",
-  "version": "0.2.4",
+  "version": "0.2.8",
   "type": "module",
   "license": "MIT",
   "scripts": {
@@ -26,6 +26,20 @@
         "types": "./dist/api.d.ts",
         "default": "./dist/api.js"
       }
+    },
+    "./reader": {
+      "require": {
+        "types": "./dist/reader.d.cts",
+        "default": "./dist/reader.cjs"
+      },
+      "import": {
+        "types": "./dist/reader.d.ts",
+        "default": "./dist/reader.js"
+      },
+      "default": {
+        "types": "./dist/reader.d.ts",
+        "default": "./dist/reader.js"
+      }
     }
   },
   "repository": {
@@ -36,6 +50,15 @@
   "devDependencies": {
     "@hey-api/client-fetch": "^0.2.4",
     "@hey-api/openapi-ts": "^0.53.0",
+    "@llamaindex/core": "workspace:^0.2.3",
+    "@llamaindex/env": "workspace:^0.1.11",
     "bunchee": "5.3.2"
+  },
+  "peerDependencies": {
+    "@llamaindex/core": "workspace:^0.2.3",
+    "@llamaindex/env": "workspace:^0.1.11"
+  },
+  "dependencies": {
+    "magic-bytes.js": "^1.10.0"
   }
 }

packages/cloud/src/reader.ts

@@ -1,92 +1,17 @@
+import { createClient, createConfig, type Client } 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 {
+  ParsingService,
+  type Body_upload_file_api_v1_parsing_upload_post,
+  type ParserLanguages,
+} 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",
@@ -181,6 +106,15 @@ const SUPPORT_FILE_EXT: string[] = [
   ".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.
  * See https://github.com/run-llama/llama_parse
@@ -188,8 +122,8 @@ 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 +133,7 @@ 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 +170,48 @@ 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 =
@@ -266,126 +226,145 @@ 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> {
+  private async createJob(data: Uint8Array): Promise<string> {
     // Load data, set the mime type
-    const { mime, extension } = await LlamaParseReader.getMimeType(data);
+    const { mime } = await LlamaParseReader.getMimeType(data);
 
     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], {
+        type: mime,
+      }),
       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 {
@@ -401,43 +380,38 @@ 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 +441,8 @@ 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;
@@ -551,15 +525,20 @@ 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 arrayBuffer = (await response.data) as ArrayBuffer;
     const buffer = new Uint8Array(arrayBuffer);
     // Write the image buffer to the specified imagePath
     await fs.writeFile(imagePath, buffer);

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 +1,60 @@
 # @llamaindex/community
 
+## 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
+
+- Updated dependencies [11feef8]
+  - @llamaindex/core@0.2.0
+
 ## 0.0.33
 
 ### Patch Changes

packages/community/README.md

@@ -7,6 +7,7 @@
 - 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 querying Knowledge Base
 
 ## LICENSE
 

packages/community/package.json

@@ -1,7 +1,7 @@
 {
   "name": "@llamaindex/community",
   "description": "Community package for LlamaIndexTS",
-  "version": "0.0.33",
+  "version": "0.0.40",
   "type": "module",
   "types": "dist/type/index.d.ts",
   "main": "dist/cjs/index.js",
@@ -47,6 +47,7 @@
   },
   "dependencies": {
     "@aws-sdk/client-bedrock-runtime": "^3.642.0",
+    "@aws-sdk/client-bedrock-agent-runtime": "^3.642.0",
     "@llamaindex/core": "workspace:*",
     "@llamaindex/env": "workspace:*"
   }

packages/community/src/index.ts

@@ -3,3 +3,4 @@ export {
   BEDROCK_MODEL_MAX_TOKENS,
   Bedrock,
 } from "./llm/bedrock/index.js";
+export { AmazonKnowledgeBaseRetriever } from "./retrievers/bedrock.js";

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 +1,67 @@
 # @llamaindex/core
 
+## 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
+
+- 11feef8: Add workflows
+
 ## 0.1.12
 
 ### Patch Changes

packages/core/package.json

@@ -1,7 +1,7 @@
 {
   "name": "@llamaindex/core",
   "type": "module",
-  "version": "0.1.12",
+  "version": "0.2.6",
   "description": "LlamaIndex Core Module",
   "exports": {
     "./node-parser": {
@@ -143,6 +143,90 @@
         "types": "./dist/indices/index.d.ts",
         "default": "./dist/indices/index.js"
       }
+    },
+    "./workflow": {
+      "require": {
+        "types": "./dist/workflow/index.d.cts",
+        "default": "./dist/workflow/index.cjs"
+      },
+      "import": {
+        "types": "./dist/workflow/index.d.ts",
+        "default": "./dist/workflow/index.js"
+      },
+      "default": {
+        "types": "./dist/workflow/index.d.ts",
+        "default": "./dist/workflow/index.js"
+      }
+    },
+    "./memory": {
+      "require": {
+        "types": "./dist/memory/index.d.cts",
+        "default": "./dist/memory/index.cjs"
+      },
+      "import": {
+        "types": "./dist/memory/index.d.ts",
+        "default": "./dist/memory/index.js"
+      },
+      "default": {
+        "types": "./dist/memory/index.d.ts",
+        "default": "./dist/memory/index.js"
+      }
+    },
+    "./storage/chat-store": {
+      "require": {
+        "types": "./dist/storage/chat-store/index.d.cts",
+        "default": "./dist/storage/chat-store/index.cjs"
+      },
+      "import": {
+        "types": "./dist/storage/chat-store/index.d.ts",
+        "default": "./dist/storage/chat-store/index.js"
+      },
+      "default": {
+        "types": "./dist/storage/chat-store/index.d.ts",
+        "default": "./dist/storage/chat-store/index.js"
+      }
+    },
+    "./response-synthesizers": {
+      "require": {
+        "types": "./dist/response-synthesizers/index.d.cts",
+        "default": "./dist/response-synthesizers/index.cjs"
+      },
+      "import": {
+        "types": "./dist/response-synthesizers/index.d.ts",
+        "default": "./dist/response-synthesizers/index.js"
+      },
+      "default": {
+        "types": "./dist/response-synthesizers/index.d.ts",
+        "default": "./dist/response-synthesizers/index.js"
+      }
+    },
+    "./chat-engine": {
+      "require": {
+        "types": "./dist/chat-engine/index.d.cts",
+        "default": "./dist/chat-engine/index.cjs"
+      },
+      "import": {
+        "types": "./dist/chat-engine/index.d.ts",
+        "default": "./dist/chat-engine/index.js"
+      },
+      "default": {
+        "types": "./dist/chat-engine/index.d.ts",
+        "default": "./dist/chat-engine/index.js"
+      }
+    },
+    "./retriever": {
+      "require": {
+        "types": "./dist/retriever/index.d.cts",
+        "default": "./dist/retriever/index.cjs"
+      },
+      "import": {
+        "types": "./dist/retriever/index.d.ts",
+        "default": "./dist/retriever/index.js"
+      },
+      "default": {
+        "types": "./dist/retriever/index.d.ts",
+        "default": "./dist/retriever/index.js"
+      }
     }
   },
   "files": [
@@ -158,14 +242,17 @@
     "url": "https://github.com/himself65/LlamaIndexTS.git"
   },
   "devDependencies": {
+    "@edge-runtime/vm": "^4.0.3",
     "ajv": "^8.17.1",
     "bunchee": "5.3.2",
+    "happy-dom": "^15.7.4",
     "natural": "^8.0.1",
     "python-format-js": "^1.4.3"
   },
   "dependencies": {
     "@llamaindex/env": "workspace:*",
     "@types/node": "^22.5.1",
+    "magic-bytes.js": "^1.10.0",
     "zod": "^3.23.8"
   }
 }

packages/core/src/chat-engine/index.ts

@@ -0,0 +1,36 @@
+import type { ChatMessage, MessageContent } from "../llms";
+import type { BaseMemory } from "../memory";
+import { EngineResponse } from "../schema";
+
+export interface BaseChatEngineParams<
+  AdditionalMessageOptions extends object = object,
+> {
+  message: MessageContent;
+  /**
+   * Optional chat history if you want to customize the chat history.
+   */
+  chatHistory?:
+    | ChatMessage<AdditionalMessageOptions>[]
+    | BaseMemory<AdditionalMessageOptions>;
+}
+
+export interface StreamingChatEngineParams<
+  AdditionalMessageOptions extends object = object,
+> extends BaseChatEngineParams<AdditionalMessageOptions> {
+  stream: true;
+}
+
+export interface NonStreamingChatEngineParams<
+  AdditionalMessageOptions extends object = object,
+> extends BaseChatEngineParams<AdditionalMessageOptions> {
+  stream?: false;
+}
+
+export abstract class BaseChatEngine {
+  abstract chat(params: NonStreamingChatEngineParams): Promise<EngineResponse>;
+  abstract chat(
+    params: StreamingChatEngineParams,
+  ): Promise<AsyncIterable<EngineResponse>>;
+
+  abstract chatHistory: ChatMessage[] | Promise<ChatMessage[]>;
+}

packages/core/src/embeddings/base.ts

@@ -23,23 +23,34 @@ export abstract class BaseEmbedding extends TransformComponent {
   embedBatchSize = DEFAULT_EMBED_BATCH_SIZE;
   embedInfo?: EmbeddingInfo;
 
-  constructor() {
-    super(
-      async (
-        nodes: BaseNode[],
-        options?: BaseEmbeddingOptions,
-      ): Promise<BaseNode[]> => {
-        const texts = nodes.map((node) => node.getContent(MetadataMode.EMBED));
+  protected constructor(
+    transformFn?: (
+      nodes: BaseNode[],
+      options?: BaseEmbeddingOptions,
+    ) => Promise<BaseNode[]>,
+  ) {
+    if (transformFn) {
+      super(transformFn);
+    } else {
+      super(
+        async (
+          nodes: BaseNode[],
+          options?: BaseEmbeddingOptions,
+        ): Promise<BaseNode[]> => {
+          const texts = nodes.map((node) =>
+            node.getContent(MetadataMode.EMBED),
+          );
 
-        const embeddings = await this.getTextEmbeddingsBatch(texts, options);
+          const embeddings = await this.getTextEmbeddingsBatch(texts, options);
 
-        for (let i = 0; i < nodes.length; i++) {
-          nodes[i]!.embedding = embeddings[i];
-        }
+          for (let i = 0; i < nodes.length; i++) {
+            nodes[i]!.embedding = embeddings[i];
+          }
 
-        return nodes;
-      },
-    );
+          return nodes;
+        },
+      );
+    }
   }
 
   similarity(

packages/core/src/embeddings/index.ts

@@ -1,4 +1,5 @@
 export { BaseEmbedding, batchEmbeddings } from "./base";
 export type { BaseEmbeddingOptions, EmbeddingInfo } from "./base";
+export { MultiModalEmbedding } from "./muti-model";
 export { truncateMaxTokens } from "./tokenizer";
 export { DEFAULT_SIMILARITY_TOP_K, SimilarityType, similarity } from "./utils";

packages/core/src/embeddings/muti-model.ts

@@ -0,0 +1,81 @@
+import type { MessageContentDetail } from "../llms";
+import {
+  ImageNode,
+  MetadataMode,
+  ModalityType,
+  splitNodesByType,
+  type BaseNode,
+  type ImageType,
+} from "../schema";
+import { extractImage, extractSingleText } from "../utils";
+import {
+  BaseEmbedding,
+  batchEmbeddings,
+  type BaseEmbeddingOptions,
+} from "./base";
+
+/*
+ * Base class for Multi Modal embeddings.
+ */
+export abstract class MultiModalEmbedding extends BaseEmbedding {
+  abstract getImageEmbedding(images: ImageType): Promise<number[]>;
+
+  protected constructor() {
+    super(
+      async (
+        nodes: BaseNode[],
+        options?: BaseEmbeddingOptions,
+      ): Promise<BaseNode[]> => {
+        const nodeMap = splitNodesByType(nodes);
+        const imageNodes = nodeMap[ModalityType.IMAGE] ?? [];
+        const textNodes = nodeMap[ModalityType.TEXT] ?? [];
+
+        const embeddings = await batchEmbeddings(
+          textNodes.map((node) => node.getContent(MetadataMode.EMBED)),
+          this.getTextEmbeddings.bind(this),
+          this.embedBatchSize,
+          options,
+        );
+        for (let i = 0; i < textNodes.length; i++) {
+          textNodes[i]!.embedding = embeddings[i];
+        }
+
+        const imageEmbeddings = await batchEmbeddings(
+          imageNodes.map((n) => (n as ImageNode).image),
+          this.getImageEmbeddings.bind(this),
+          this.embedBatchSize,
+          options,
+        );
+        for (let i = 0; i < imageNodes.length; i++) {
+          imageNodes[i]!.embedding = imageEmbeddings[i];
+        }
+
+        return nodes;
+      },
+    );
+  }
+
+  /**
+   * Optionally override this method to retrieve multiple image embeddings in a single request
+   * @param images
+   */
+  async getImageEmbeddings(images: ImageType[]): Promise<number[][]> {
+    return Promise.all(
+      images.map((imgFilePath) => this.getImageEmbedding(imgFilePath)),
+    );
+  }
+
+  async getQueryEmbedding(
+    query: MessageContentDetail,
+  ): Promise<number[] | null> {
+    const image = extractImage(query);
+    if (image) {
+      return await this.getImageEmbedding(image);
+    }
+    const text = extractSingleText(query);
+    if (text) {
+      return await this.getTextEmbedding(text);
+    }
+    return null;
+  }
+}

packages/core/src/global/settings/callback-manager.ts

@@ -6,8 +6,14 @@ import type {
   ToolCall,
   ToolOutput,
 } from "../../llms";
+import type { QueryEndEvent, QueryStartEvent } from "../../query-engine";
+import type {
+  SynthesizeEndEvent,
+  SynthesizeStartEvent,
+} from "../../response-synthesizers";
+import type { RetrieveEndEvent, RetrieveStartEvent } from "../../retriever";
 import { TextNode } from "../../schema";
-import { EventCaller, getEventCaller } from "../../utils/event-caller";
+import { EventCaller, getEventCaller } from "../../utils";
 import type { UUID } from "../type";
 
 export type LLMStartEvent = {
@@ -60,6 +66,12 @@ export interface LlamaIndexEventMaps {
   "chunking-end": ChunkingEndEvent;
   "node-parsing-start": NodeParsingStartEvent;
   "node-parsing-end": NodeParsingEndEvent;
+  "query-start": QueryStartEvent;
+  "query-end": QueryEndEvent;
+  "synthesize-start": SynthesizeStartEvent;
+  "synthesize-end": SynthesizeEndEvent;
+  "retrieve-start": RetrieveStartEvent;
+  "retrieve-end": RetrieveEndEvent;
 }
 
 export class LlamaIndexCustomEvent<T = any> extends CustomEvent<T> {
@@ -119,16 +131,29 @@ export class CallbackManager {
   dispatchEvent<K extends keyof LlamaIndexEventMaps>(
     event: K,
     detail: LlamaIndexEventMaps[K],
+    sync = false,
   ) {
     const cbs = this.#handlers.get(event);
     if (!cbs) {
       return;
     }
-    queueMicrotask(() => {
+    if (typeof queueMicrotask === "undefined") {
+      console.warn(
+        "queueMicrotask is not available, dispatching synchronously",
+      );
+      sync = true;
+    }
+    if (sync) {
       cbs.forEach((handler) =>
         handler(LlamaIndexCustomEvent.fromEvent(event, { ...detail })),
       );
-    });
+    } else {
+      queueMicrotask(() => {
+        cbs.forEach((handler) =>
+          handler(LlamaIndexCustomEvent.fromEvent(event, { ...detail })),
+        );
+      });
+    }
   }
 }
 

packages/core/src/indices/prompt-helper.ts

@@ -1,10 +1,13 @@
 import { type Tokenizer, tokenizers } from "@llamaindex/env";
 import {
   DEFAULT_CHUNK_OVERLAP_RATIO,
+  DEFAULT_CHUNK_SIZE,
   DEFAULT_CONTEXT_WINDOW,
   DEFAULT_NUM_OUTPUTS,
   DEFAULT_PADDING,
+  Settings,
 } from "../global";
+import type { LLMMetadata } from "../llms";
 import { SentenceSplitter } from "../node-parser";
 import type { PromptTemplate } from "../prompts";
 
@@ -133,4 +136,29 @@ export class PromptHelper {
     const combinedStr = textChunks.join("\n\n");
     return textSplitter.splitText(combinedStr);
   }
+
+  static fromLLMMetadata(
+    metadata: LLMMetadata,
+    options?: {
+      chunkOverlapRatio?: number;
+      chunkSizeLimit?: number;
+      tokenizer?: Tokenizer;
+      separator?: string;
+    },
+  ) {
+    const {
+      chunkOverlapRatio = DEFAULT_CHUNK_OVERLAP_RATIO,
+      chunkSizeLimit = DEFAULT_CHUNK_SIZE,
+      tokenizer = Settings.tokenizer,
+      separator = " ",
+    } = options ?? {};
+    return new PromptHelper({
+      contextWindow: metadata.contextWindow,
+      numOutput: metadata.maxTokens ?? DEFAULT_NUM_OUTPUTS,
+      chunkOverlapRatio,
+      chunkSizeLimit,
+      tokenizer,
+      separator,
+    });
+  }
 }

packages/core/src/memory/base.ts

@@ -0,0 +1,83 @@
+import { Settings } from "../global";
+import type { ChatMessage } from "../llms";
+import { type BaseChatStore, SimpleChatStore } from "../storage/chat-store";
+import { extractText } from "../utils";
+
+export const DEFAULT_TOKEN_LIMIT_RATIO = 0.75;
+export const DEFAULT_CHAT_STORE_KEY = "chat_history";
+
+/**
+ * A ChatMemory is used to keep the state of back and forth chat messages
+ */
+export abstract class BaseMemory<
+  AdditionalMessageOptions extends object = object,
+> {
+  /**
+   * Retrieves messages from the memory, optionally including transient messages.
+   * Compared to getAllMessages, this method a) allows for transient messages to be included in the retrieval and b) may return a subset of the total messages by applying a token limit.
+   * @param transientMessages Optional array of temporary messages to be included in the retrieval.
+   * These messages are not stored in the memory but are considered for the current interaction.
+   * @returns An array of chat messages, either synchronously or as a Promise.
+   */
+  abstract getMessages(
+    transientMessages?: ChatMessage<AdditionalMessageOptions>[] | undefined,
+  ):
+    | ChatMessage<AdditionalMessageOptions>[]
+    | Promise<ChatMessage<AdditionalMessageOptions>[]>;
+
+  /**
+   * Retrieves all messages stored in the memory.
+   * @returns An array of all chat messages, either synchronously or as a Promise.
+   */
+  abstract getAllMessages():
+    | ChatMessage<AdditionalMessageOptions>[]
+    | Promise<ChatMessage<AdditionalMessageOptions>[]>;
+
+  /**
+   * Adds a new message to the memory.
+   * @param messages The chat message to be added to the memory.
+   */
+  abstract put(messages: ChatMessage<AdditionalMessageOptions>): void;
+
+  /**
+   * Clears all messages from the memory.
+   */
+  abstract reset(): void;
+
+  protected _tokenCountForMessages(messages: ChatMessage[]): number {
+    if (messages.length === 0) {
+      return 0;
+    }
+
+    const tokenizer = Settings.tokenizer;
+    const str = messages.map((m) => extractText(m.content)).join(" ");
+    return tokenizer.encode(str).length;
+  }
+}
+
+export abstract class BaseChatStoreMemory<
+  AdditionalMessageOptions extends object = object,
+> extends BaseMemory<AdditionalMessageOptions> {
+  protected constructor(
+    public chatStore: BaseChatStore<AdditionalMessageOptions> = new SimpleChatStore<AdditionalMessageOptions>(),
+    public chatStoreKey: string = DEFAULT_CHAT_STORE_KEY,
+  ) {
+    super();
+  }
+
+  getAllMessages(): ChatMessage<AdditionalMessageOptions>[] {
+    return this.chatStore.getMessages(this.chatStoreKey);
+  }
+
+  put(messages: ChatMessage<AdditionalMessageOptions>) {
+    this.chatStore.addMessage(this.chatStoreKey, messages);
+  }
+
+  set(messages: ChatMessage<AdditionalMessageOptions>[]) {
+    this.chatStore.setMessages(this.chatStoreKey, messages);
+  }
+
+  reset() {
+    this.chatStore.deleteMessages(this.chatStoreKey);
+  }
+}

packages/core/src/memory/chat-memory-buffer.ts

@@ -0,0 +1,71 @@
+import { Settings } from "../global";
+import type { ChatMessage, LLM } from "../llms";
+import { type BaseChatStore } from "../storage/chat-store";
+import { BaseChatStoreMemory, DEFAULT_TOKEN_LIMIT_RATIO } from "./base";
+
+type ChatMemoryBufferOptions<AdditionalMessageOptions extends object = object> =
+  {
+    tokenLimit?: number | undefined;
+    chatStore?: BaseChatStore<AdditionalMessageOptions> | undefined;
+    chatStoreKey?: string | undefined;
+    chatHistory?: ChatMessage<AdditionalMessageOptions>[] | undefined;
+    llm?: LLM<object, AdditionalMessageOptions> | undefined;
+  };
+
+export class ChatMemoryBuffer<
+  AdditionalMessageOptions extends object = object,
+> extends BaseChatStoreMemory<AdditionalMessageOptions> {
+  tokenLimit: number;
+
+  constructor(
+    options?: Partial<ChatMemoryBufferOptions<AdditionalMessageOptions>>,
+  ) {
+    super(options?.chatStore, options?.chatStoreKey);
+
+    const llm = options?.llm ?? Settings.llm;
+    const contextWindow = llm.metadata.contextWindow;
+    this.tokenLimit =
+      options?.tokenLimit ??
+      Math.ceil(contextWindow * DEFAULT_TOKEN_LIMIT_RATIO);
+
+    if (options?.chatHistory) {
+      this.chatStore.setMessages(this.chatStoreKey, options.chatHistory);
+    }
+  }
+
+  getMessages(
+    transientMessages?: ChatMessage<AdditionalMessageOptions>[] | undefined,
+    initialTokenCount: number = 0,
+  ) {
+    const messages = this.getAllMessages();
+
+    if (initialTokenCount > this.tokenLimit) {
+      throw new Error("Initial token count exceeds token limit");
+    }
+
+    // Add input messages as transient messages
+    const messagesWithInput = transientMessages
+      ? [...transientMessages, ...messages]
+      : messages;
+
+    let messageCount = messagesWithInput.length;
+    let currentMessages = messagesWithInput.slice(-messageCount);
+    let tokenCount =
+      this._tokenCountForMessages(messagesWithInput) + initialTokenCount;
+
+    while (tokenCount > this.tokenLimit && messageCount > 1) {
+      messageCount -= 1;
+      if (messagesWithInput.at(-messageCount)!.role === "assistant") {
+        messageCount -= 1;
+      }
+      currentMessages = messagesWithInput.slice(-messageCount);
+      tokenCount =
+        this._tokenCountForMessages(currentMessages) + initialTokenCount;
+    }
+
+    if (tokenCount > this.tokenLimit && messageCount <= 0) {
+      return [];
+    }
+    return messagesWithInput.slice(-messageCount);
+  }
+}

packages/core/src/memory/index.ts

@@ -0,0 +1,3 @@
+export { BaseMemory } from "./base";
+export { ChatMemoryBuffer } from "./chat-memory-buffer";
+export { ChatSummaryMemoryBuffer } from "./summary-memory";

packages/core/src/memory/summary-memory.ts

@@ -1,73 +1,11 @@
-import type { ChatMessage, LLM, MessageType } from "@llamaindex/core/llms";
-import {
-  defaultSummaryPrompt,
-  type SummaryPrompt,
-} from "@llamaindex/core/prompts";
-import { extractText, messagesToHistory } from "@llamaindex/core/utils";
-import { tokenizers, type Tokenizer } from "@llamaindex/env";
-import { OpenAI } from "@llamaindex/openai";
+import { type Tokenizer, tokenizers } from "@llamaindex/env";
+import { Settings } from "../global";
+import type { ChatMessage, LLM, MessageType } from "../llms";
+import { defaultSummaryPrompt, type SummaryPrompt } from "../prompts";
+import { extractText, messagesToHistory } from "../utils";
+import { BaseMemory } from "./base";
 
-/**
- * A ChatHistory is used to keep the state of back and forth chat messages
- */
-export abstract class ChatHistory<
-  AdditionalMessageOptions extends object = object,
-> {
-  abstract get messages(): ChatMessage<AdditionalMessageOptions>[];
-  /**
-   * Adds a message to the chat history.
-   * @param message
-   */
-  abstract addMessage(message: ChatMessage<AdditionalMessageOptions>): void;
-
-  /**
-   * Returns the messages that should be used as input to the LLM.
-   */
-  abstract requestMessages(
-    transientMessages?: ChatMessage<AdditionalMessageOptions>[],
-  ): Promise<ChatMessage<AdditionalMessageOptions>[]>;
-
-  /**
-   * Resets the chat history so that it's empty.
-   */
-  abstract reset(): void;
-
-  /**
-   * Returns the new messages since the last call to this function (or since calling the constructor)
-   */
-  abstract newMessages(): ChatMessage<AdditionalMessageOptions>[];
-}
-
-export class SimpleChatHistory extends ChatHistory {
-  messages: ChatMessage[];
-  private messagesBefore: number;
-
-  constructor(init?: { messages?: ChatMessage[] | undefined }) {
-    super();
-    this.messages = init?.messages ?? [];
-    this.messagesBefore = this.messages.length;
-  }
-
-  addMessage(message: ChatMessage) {
-    this.messages.push(message);
-  }
-
-  async requestMessages(transientMessages?: ChatMessage[]) {
-    return [...(transientMessages ?? []), ...this.messages];
-  }
-
-  reset() {
-    this.messages = [];
-  }
-
-  newMessages() {
-    const newMessages = this.messages.slice(this.messagesBefore);
-    this.messagesBefore = this.messages.length;
-    return newMessages;
-  }
-}
-
-export class SummaryChatHistory extends ChatHistory {
+export class ChatSummaryMemoryBuffer extends BaseMemory {
   /**
    * Tokenizer function that converts text to tokens,
    *  this is used to calculate the number of tokens in a message.
@@ -77,20 +15,18 @@ export class SummaryChatHistory extends ChatHistory {
   messages: ChatMessage[];
   summaryPrompt: SummaryPrompt;
   llm: LLM;
-  private messagesBefore: number;
 
-  constructor(init?: Partial<SummaryChatHistory>) {
+  constructor(options?: Partial<ChatSummaryMemoryBuffer>) {
     super();
-    this.messages = init?.messages ?? [];
-    this.messagesBefore = this.messages.length;
-    this.summaryPrompt = init?.summaryPrompt ?? defaultSummaryPrompt;
-    this.llm = init?.llm ?? new OpenAI();
+    this.messages = options?.messages ?? [];
+    this.summaryPrompt = options?.summaryPrompt ?? defaultSummaryPrompt;
+    this.llm = options?.llm ?? Settings.llm;
     if (!this.llm.metadata.maxTokens) {
       throw new Error(
         "LLM maxTokens is not set. Needed so the summarizer ensures the context window size of the LLM.",
       );
     }
-    this.tokenizer = init?.tokenizer ?? tokenizers.tokenizer();
+    this.tokenizer = options?.tokenizer ?? tokenizers.tokenizer();
     this.tokensToSummarize =
       this.llm.metadata.contextWindow - this.llm.metadata.maxTokens;
     if (this.tokensToSummarize < this.llm.metadata.contextWindow * 0.25) {
@@ -128,12 +64,8 @@ export class SummaryChatHistory extends ChatHistory {
     return { content: response.message.content, role: "memory" };
   }
 
-  addMessage(message: ChatMessage) {
-    this.messages.push(message);
-  }
-
   // Find last summary message
-  private getLastSummaryIndex(): number | null {
+  private get lastSummaryIndex(): number | null {
     const reversedMessages = this.messages.slice().reverse();
     const index = reversedMessages.findIndex(
       (message) => message.role === "memory",
@@ -145,7 +77,7 @@ export class SummaryChatHistory extends ChatHistory {
   }
 
   public getLastSummary(): ChatMessage | null {
-    const lastSummaryIndex = this.getLastSummaryIndex();
+    const lastSummaryIndex = this.lastSummaryIndex;
     return lastSummaryIndex ? this.messages[lastSummaryIndex]! : null;
   }
 
@@ -165,7 +97,7 @@ export class SummaryChatHistory extends ChatHistory {
    * If there's a memory, uses all messages after the last summary message.
    */
   private calcConversationMessages(transformSummary?: boolean): ChatMessage[] {
-    const lastSummaryIndex = this.getLastSummaryIndex();
+    const lastSummaryIndex = this.lastSummaryIndex;
     if (!lastSummaryIndex) {
       // there's no memory, so just use all non-system messages
       return this.nonSystemMessages;
@@ -183,7 +115,7 @@ export class SummaryChatHistory extends ChatHistory {
   }
 
   private calcCurrentRequestMessages(transientMessages?: ChatMessage[]) {
-    // TODO: check order: currently, we're sending:
+    // currently, we're sending:
     // system messages first, then transient messages and then the messages that describe the conversation so far
     return [
       ...this.systemMessages,
@@ -192,7 +124,11 @@ export class SummaryChatHistory extends ChatHistory {
     ];
   }
 
-  async requestMessages(transientMessages?: ChatMessage[]) {
+  reset() {
+    this.messages = [];
+  }
+
+  async getMessages(transientMessages?: ChatMessage[]): Promise<ChatMessage[]> {
     const requestMessages = this.calcCurrentRequestMessages(transientMessages);
 
     // get tokens of current request messages and the transient messages
@@ -222,22 +158,11 @@ export class SummaryChatHistory extends ChatHistory {
     return requestMessages;
   }
 
-  reset() {
-    this.messages = [];
+  async getAllMessages(): Promise<ChatMessage[]> {
+    return this.getMessages();
   }
 
-  newMessages() {
-    const newMessages = this.messages.slice(this.messagesBefore);
-    this.messagesBefore = this.messages.length;
-    return newMessages;
+  put(message: ChatMessage) {
+    this.messages.push(message);
   }
 }
-
-export function getHistory(
-  chatHistory?: ChatMessage[] | ChatHistory,
-): ChatHistory {
-  if (chatHistory instanceof ChatHistory) {
-    return chatHistory;
-  }
-  return new SimpleChatHistory({ messages: chatHistory });
-}

packages/core/src/query-engine/base.ts

@@ -1,5 +1,9 @@
+import { randomUUID } from "@llamaindex/env";
+import { Settings } from "../global";
 import type { MessageContent } from "../llms";
+import { PromptMixin } from "../prompts";
 import { EngineResponse, type NodeWithScore } from "../schema";
+import { wrapEventCaller } from "../utils";
 
 /**
  * @link https://docs.llamaindex.ai/en/stable/api_reference/schema/?h=querybundle#llama_index.core.schema.QueryBundle
@@ -14,16 +18,52 @@ export type QueryBundle = {
 
 export type QueryType = string | QueryBundle;
 
-export interface BaseQueryEngine {
-  query(
-    strOrQueryBundle: QueryType,
-    stream: true,
-  ): Promise<AsyncIterable<EngineResponse>>;
-  query(strOrQueryBundle: QueryType, stream?: false): Promise<EngineResponse>;
+export type BaseQueryParams = {
+  query: QueryType;
+};
 
-  synthesize?(
-    strOrQueryBundle: QueryType,
-    nodes: NodeWithScore[],
-    additionalSources?: Iterator<NodeWithScore>,
-  ): Promise<EngineResponse>;
+export interface StreamingQueryParams extends BaseQueryParams {
+  stream: true;
+}
+
+export interface NonStreamingQueryParams extends BaseQueryParams {
+  stream?: false;
+}
+
+export type QueryFn = (
+  strOrQueryBundle: QueryType,
+  stream?: boolean,
+) => Promise<AsyncIterable<EngineResponse> | EngineResponse>;
+
+export abstract class BaseQueryEngine extends PromptMixin {
+  protected constructor(protected readonly _query: QueryFn) {
+    super();
+  }
+
+  async retrieve(params: QueryType): Promise<NodeWithScore[]> {
+    throw new Error(
+      "This query engine does not support retrieve, use query directly",
+    );
+  }
+
+  query(params: StreamingQueryParams): Promise<AsyncIterable<EngineResponse>>;
+  query(params: NonStreamingQueryParams): Promise<EngineResponse>;
+  @wrapEventCaller
+  async query(
+    params: StreamingQueryParams | NonStreamingQueryParams,
+  ): Promise<EngineResponse | AsyncIterable<EngineResponse>> {
+    const { stream, query } = params;
+    const id = randomUUID();
+    const callbackManager = Settings.callbackManager;
+    callbackManager.dispatchEvent("query-start", {
+      id,
+      query,
+    });
+    const response = await this._query(query, stream);
+    callbackManager.dispatchEvent("query-end", {
+      id,
+      response,
+    });
+    return response;
+  }
 }

packages/core/src/query-engine/index.ts

@@ -1 +1,2 @@
-export type { BaseQueryEngine, QueryBundle, QueryType } from "./base";
+export { BaseQueryEngine, type QueryBundle, type QueryType } from "./base";
+export type { QueryEndEvent, QueryStartEvent } from "./type";

packages/core/src/query-engine/type.ts

@@ -0,0 +1,12 @@
+import { EngineResponse } from "../schema";
+import type { QueryType } from "./base";
+
+export type QueryStartEvent = {
+  id: string;
+  query: QueryType;
+};
+
+export type QueryEndEvent = {
+  id: string;
+  response: EngineResponse | AsyncIterable<EngineResponse>;
+};

packages/core/src/response-synthesizers/base-synthesizer.ts

@@ -0,0 +1,58 @@
+import { randomUUID } from "@llamaindex/env";
+import { Settings } from "../global";
+import { PromptHelper } from "../indices";
+import type { LLM, MessageContent } from "../llms";
+import { PromptMixin } from "../prompts";
+import { EngineResponse, type NodeWithScore } from "../schema";
+import type { SynthesizeQuery } from "./type";
+
+export type BaseSynthesizerOptions = {
+  llm?: LLM;
+  promptHelper?: PromptHelper;
+};
+
+export abstract class BaseSynthesizer extends PromptMixin {
+  llm: LLM;
+  promptHelper: PromptHelper;
+
+  protected constructor(options: Partial<BaseSynthesizerOptions>) {
+    super();
+    this.llm = options.llm ?? Settings.llm;
+    this.promptHelper =
+      options.promptHelper ?? PromptHelper.fromLLMMetadata(this.llm.metadata);
+  }
+
+  protected abstract getResponse(
+    query: MessageContent,
+    textChunks: NodeWithScore[],
+    stream: boolean,
+  ): Promise<EngineResponse | AsyncIterable<EngineResponse>>;
+
+  synthesize(
+    query: SynthesizeQuery,
+    stream: true,
+  ): Promise<AsyncIterable<EngineResponse>>;
+  synthesize(query: SynthesizeQuery, stream?: false): Promise<EngineResponse>;
+  async synthesize(
+    query: SynthesizeQuery,
+    stream = false,
+  ): Promise<EngineResponse | AsyncIterable<EngineResponse>> {
+    const callbackManager = Settings.callbackManager;
+    const id = randomUUID();
+    callbackManager.dispatchEvent("synthesize-start", { id, query });
+    let response: EngineResponse | AsyncIterable<EngineResponse>;
+    if (query.nodes.length === 0) {
+      if (stream) {
+        response = EngineResponse.fromResponse("Empty Response", true);
+      } else {
+        response = EngineResponse.fromResponse("Empty Response", false);
+      }
+    } else {
+      const queryMessage: MessageContent =
+        typeof query.query === "string" ? query.query : query.query.query;
+      response = await this.getResponse(queryMessage, query.nodes, stream);
+    }
+    callbackManager.dispatchEvent("synthesize-end", { id, query, response });
+    return response;
+  }
+}