Batch fix all inks to TypeScript API reference (#2214)

2026-07-01 22:14:03 -04:00 · 2025-10-02 10:06:09 -07:00
parent fe862ea8a4
commit fd53bececf
60 changed files with 111 additions and 111 deletions
@@ -23,6 +23,6 @@ LlamaIndex.TS comes with a few built-in agents, but you can also create your own

 ## Api References

- [OpenAIAgent](/docs/api/classes/OpenAIAgent)
- [AnthropicAgent](/docs/api/classes/AnthropicAgent)
- [ReActAgent](/docs/api/classes/ReActAgent)
+- [OpenAIAgent](/typescript/framework-api-reference/classes/openaiagent/)
+- [AnthropicAgent](/typescript/framework-api-reference/classes/anthropicagent/)
+- [ReActAgent](/typescript/framework-api-reference/classes/reactagent/)
@@ -12,7 +12,7 @@ To use workflows install this package:
 npm i @llamaindex/workflow-core
 ```

-This contains the core functionality for the workflow system. You can read more about the core concepts in the [workflow-core](/docs/workflows) section.
+This contains the core functionality for the workflow system. You can read more about the core concepts in the [workflow-core](/typescript/workflows/) section.

 In contrast, the `@llamaindex/workflow` package contains more utiltities, such as prebuilt agents.

@@ -19,6 +19,6 @@ const index = await VectorStoreIndex.fromDocuments([document]);

 ## API Reference

- [SummaryIndex](/docs/api/classes/SummaryIndex)
- [VectorStoreIndex](/docs/api/classes/VectorStoreIndex)
- [KeywordTableIndex](/docs/api/classes/KeywordTableIndex)
+- [SummaryIndex](/typescript/framework-api-reference/classes/summaryindex/)
+- [VectorStoreIndex](/typescript/framework-api-reference/classes/vectorstoreindex/)
+- [KeywordTableIndex](/typescript/framework-api-reference/classes/keywordtableindex/)
@@ -13,5 +13,5 @@ document = new Document({ text: "text", metadata: { key: "val" } });

 ## API Reference

- [Document](/docs/api/classes/Document)
- [TextNode](/docs/api/classes/TextNode)
+- [Document](/typescript/framework-api-reference/classes/document/)
+- [TextNode](/typescript/framework-api-reference/classes/textnode/)
@@ -107,4 +107,4 @@ main().catch(console.error);

 ## API Reference

- [IngestionPipeline](/docs/api/classes/IngestionPipeline)
+- [IngestionPipeline](/typescript/framework-api-reference/classes/ingestionpipeline/)
@@ -80,4 +80,4 @@ main().catch(console.error);

 ## API Reference

- [TransformComponent](/docs/api/classes/TransformComponent)
+- [TransformComponent](/typescript/framework-api-reference/classes/transformcomponent/)
@@ -43,7 +43,7 @@ main().then(() => console.log("done"));

 ## API Reference

- [SummaryExtractor](/docs/api/classes/SummaryExtractor)
- [QuestionsAnsweredExtractor](/docs/api/classes/QuestionsAnsweredExtractor)
- [TitleExtractor](/docs/api/classes/TitleExtractor)
- [KeywordExtractor](/docs/api/classes/KeywordExtractor)
+- [SummaryExtractor](/typescript/framework-api-reference/classes/summaryextractor/)
+- [QuestionsAnsweredExtractor](/typescript/framework-api-reference/classes/questionsansweredextractor/)
+- [TitleExtractor](/typescript/framework-api-reference/classes/titleextractor/)
+- [KeywordExtractor](/typescript/framework-api-reference/classes/keywordextractor/)
@@ -195,6 +195,6 @@ Try it out ⬇️

 ## API Reference

- [SentenceSplitter](/docs/api/classes/SentenceSplitter)
- [MarkdownNodeParser](/docs/api/classes/MarkdownNodeParser)
- [CodeSplitter](/docs/api/classes/CodeSplitter)
+- [SentenceSplitter](/typescript/framework-api-reference/classes/sentencesplitter/)
+- [MarkdownNodeParser](/typescript/framework-api-reference/classes/markdownnodeparser/)
+- [CodeSplitter](/typescript/framework-api-reference/classes/codesplitter/)
@@ -36,4 +36,4 @@ Copy the URL in your browser and select the server you want your bot to join.

 ## API Reference

- [DiscordReader](/docs/api/classes/DiscordReader)
+- [DiscordReader](/typescript/framework-api-reference/classes/discordreader/)
@@ -48,13 +48,13 @@ It is a simple reader that reads all files from a directory and its subdirectori

 Currently, the following readers are mapped to specific file types:

- [TextFileReader](/docs/api/classes/TextFileReader): `.txt`
- [PDFReader](/docs/api/classes/PDFReader): `.pdf`
- [CSVReader](/docs/api/classes/CSVReader): `.csv`
- [MarkdownReader](/docs/api/classes/MarkdownReader): `.md`
- [DocxReader](/docs/api/classes/DocxReader): `.docx`
- [HTMLReader](/docs/api/classes/HTMLReader): `.htm`, `.html`
- [ImageReader](/docs/api/classes/ImageReader): `.jpg`, `.jpeg`, `.png`, `.gif`
+- [TextFileReader](/typescript/framework-api-reference/classes/textfilereader/): `.txt`
+- [PDFReader](/typescript/framework-api-reference/classes/pdfreader/): `.pdf`
+- [CSVReader](/typescript/framework-api-reference/classes/csvreader/): `.csv`
+- [MarkdownReader](/typescript/framework-api-reference/classes/markdownreader/): `.md`
+- [DocxReader](/typescript/framework-api-reference/classes/docxreader/): `.docx`
+- [HTMLReader](/typescript/framework-api-reference/classes/htmlreader/): `.htm`, `.html`
+- [ImageReader](/typescript/framework-api-reference/classes/imagereader/): `.jpg`, `.jpeg`, `.png`, `.gif`

 You can modify the reader three different ways:

@@ -118,4 +118,4 @@ const text = csv.getText()

 ## API Reference

- [SimpleDirectoryReader](/docs/api/classes/SimpleDirectoryReader)
+- [SimpleDirectoryReader](/typescript/framework-api-reference/classes/simpledirectoryreader/)
@@ -152,4 +152,4 @@ Output:

 ## API Reference

- [JSONReader](/docs/api/classes/JSONReader)
+- [JSONReader](/typescript/framework-api-reference/classes/jsonreader/)
@@ -60,4 +60,4 @@ Below a full example of `LlamaParse` integrated in `SimpleDirectoryReader` with

 ## API Reference

- [SimpleDirectoryReader](/docs/api/classes/SimpleDirectoryReader)
+- [SimpleDirectoryReader](/typescript/framework-api-reference/classes/simpledirectoryreader/)
@@ -98,4 +98,4 @@ You can assign any other values of the JSON response to the Document as needed.

 ## API Reference

- [SimpleDirectoryReader](/docs/api/classes/SimpleDirectoryReader)
+- [SimpleDirectoryReader](/typescript/framework-api-reference/classes/simpledirectoryreader/)
@@ -6,11 +6,11 @@ Chat stores manage chat history by storing sequences of messages in a structured

 ## Available Chat Stores

- [SimpleChatStore](/docs/api/classes/SimpleChatStore): A simple in-memory chat store with support for [persisting](/typescript/framework/modules/data/stores#local-storage) data to disk.
+- [SimpleChatStore](/typescript/framework-api-reference/classes/simplechatstore/): A simple in-memory chat store with support for [persisting](/typescript/framework/modules/data/stores#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](/docs/api/classes/BaseChatStore)
+- [BaseChatStore](/typescript/framework-api-reference/classes/basechatstore/)

@@ -6,8 +6,8 @@ Document stores contain ingested document chunks, i.e. [Node](/typescript/framew

 ## Available Document Stores

- [SimpleDocumentStore](/docs/api/classes/SimpleDocumentStore): A simple in-memory document store with support for [persisting](/typescript/framework/modules/data/stores#local-storage) data to disk.
- [PostgresDocumentStore](/docs/api/classes/PostgresDocumentStore): A PostgreSQL document store, see [PostgreSQL Storage](/typescript/framework/modules/data/stores#postgresql-storage).
+- [SimpleDocumentStore](/typescript/framework-api-reference/classes/simpledocumentstore/): A simple in-memory document store with support for [persisting](/typescript/framework/modules/data/stores#local-storage) data to disk.
+- [PostgresDocumentStore](/typescript/framework-api-reference/classes/postgresdocumentstore/): A PostgreSQL document store, see [PostgreSQL Storage](/typescript/framework/modules/data/stores#postgresql-storage).

 Check the [LlamaIndexTS Github](https://github.com/run-llama/LlamaIndexTS) for the most up to date overview of integrations.

@@ -33,4 +33,4 @@ const storageContext = await storageContextFromDefaults({

 ## API Reference

- [BaseDocumentStore](/docs/api/classes/BaseDocumentStore)
+- [BaseDocumentStore](/typescript/framework-api-reference/classes/basedocumentstore/)
@@ -6,8 +6,8 @@ Index stores are underlying storage components that contain metadata(i.e. inform

 ## Available Index Stores

- [SimpleIndexStore](/docs/api/classes/SimpleIndexStore): A simple in-memory index store with support for [persisting](/typescript/framework/modules/data/stores#local-storage) data to disk.
- [PostgresIndexStore](/docs/api/classes/PostgresIndexStore): A PostgreSQL index store, , see [PostgreSQL Storage](/typescript/framework/modules/data/stores#postgresql-storage).
+- [SimpleIndexStore](/typescript/framework-api-reference/classes/simpleindexstore/): A simple in-memory index store with support for [persisting](/typescript/framework/modules/data/stores#local-storage) data to disk.
+- [PostgresIndexStore](/typescript/framework-api-reference/classes/postgresindexstore/): A PostgreSQL index store, , see [PostgreSQL Storage](/typescript/framework/modules/data/stores#postgresql-storage).

 Check the [LlamaIndexTS Github](https://github.com/run-llama/LlamaIndexTS) for the most up to date overview of integrations.

@@ -33,4 +33,4 @@ const storageContext = await storageContextFromDefaults({

 ## API Reference

- [BaseIndexStore](/docs/api/classes/BaseIndexStore)
+- [BaseIndexStore](/typescript/framework-api-reference/classes/baseindexstore/)
@@ -6,11 +6,11 @@ Key-Value Stores represent underlying storage components used in [Document Store

 ## Available Key-Value Stores

- [SimpleKVStore](/docs/api/classes/SimpleKVStore): A simple Key-Value store with support of [persisting](/typescript/framework/modules/data/stores#local-storage) data to disk.
- [PostgresKVStore](/docs/api/classes/PostgresKVStore): A PostgreSQL Key-Value store, see [PostgreSQL Storage](/typescript/framework/modules/data/stores#postgresql-storage).
+- [SimpleKVStore](/typescript/framework-api-reference/classes/simplekvstore/): A simple Key-Value store with support of [persisting](/typescript/framework/modules/data/stores#local-storage) data to disk.
+- [PostgresKVStore](/typescript/framework-api-reference/classes/postgreskvstore/): A PostgreSQL Key-Value store, see [PostgreSQL Storage](/typescript/framework/modules/data/stores#postgresql-storage).

 Check the [LlamaIndexTS Github](https://github.com/run-llama/LlamaIndexTS) for the most up to date overview of integrations.

 ## API Reference

- [BaseKVStore](/docs/api/classes/BaseKVStore)
+- [BaseKVStore](/typescript/framework-api-reference/classes/basekvstore/)
@@ -8,14 +8,14 @@ Vector stores save embedding vectors of your ingested document chunks.

 Available Vector Stores are shown on the sidebar to the left. Additionally the following integrations exist without separate documentation:

- [SimpleVectorStore](/docs/api/classes/SimpleVectorStore): A simple in-memory vector store with optional [persistance](/typescript/framework/modules/data/stores#local-storage) to disk.
- [AstraDBVectorStore](/docs/api/classes/AstraDBVectorStore): A cloud-native, scalable Database-as-a-Service built on Apache Cassandra, see [datastax.com](https://www.datastax.com/products/datastax-astra)
- [ChromaVectorStore](/docs/api/classes/ChromaVectorStore): An open-source vector database, focused on ease of use and performance, see [trychroma.com](https://www.trychroma.com/)
- [MilvusVectorStore](/docs/api/classes/MilvusVectorStore): An open-source, high-performance, highly scalable vector database, see [milvus.io](https://milvus.io/)
- [MongoDBAtlasVectorSearch](/docs/api/classes/MongoDBAtlasVectorSearch): A cloud-based vector search solution for MongoDB, see [mongodb.com](https://www.mongodb.com/products/platform/atlas-vector-search)
- [PGVectorStore](/docs/api/classes/PGVectorStore): An open-source vector store built on PostgreSQL, see [pgvector Github](https://github.com/pgvector/pgvector)
- [PineconeVectorStore](/docs/api/classes/PineconeVectorStore): A managed, cloud-native vector database, see [pinecone.io](https://www.pinecone.io/)
- [WeaviateVectorStore](/docs/api/classes/WeaviateVectorStore): An open-source, ai-native vector database, see [weaviate.io](https://weaviate.io/)
+- [SimpleVectorStore](/typescript/framework-api-reference/classes/simplevectorstore/): A simple in-memory vector store with optional [persistance](/typescript/framework/modules/data/stores#local-storage) to disk.
+- [AstraDBVectorStore](/typescript/framework-api-reference/classes/astradbvectorstore/): A cloud-native, scalable Database-as-a-Service built on Apache Cassandra, see [datastax.com](https://www.datastax.com/products/datastax-astra)
+- [ChromaVectorStore](/typescript/framework-api-reference/classes/chromavectorstore/): An open-source vector database, focused on ease of use and performance, see [trychroma.com](https://www.trychroma.com/)
+- [MilvusVectorStore](/typescript/framework-api-reference/classes/milvusvectorstore/): An open-source, high-performance, highly scalable vector database, see [milvus.io](https://milvus.io/)
+- [MongoDBAtlasVectorSearch](/typescript/framework-api-reference/classes/mongodbatlasvectorsearch/): A cloud-based vector search solution for MongoDB, see [mongodb.com](https://www.mongodb.com/products/platform/atlas-vector-search)
+- [PGVectorStore](/typescript/framework-api-reference/classes/pgvectorstore/): An open-source vector store built on PostgreSQL, see [pgvector Github](https://github.com/pgvector/pgvector)
+- [PineconeVectorStore](/typescript/framework-api-reference/classes/pineconevectorstore/): A managed, cloud-native vector database, see [pinecone.io](https://www.pinecone.io/)
+- [WeaviateVectorStore](/typescript/framework-api-reference/classes/weaviatevectorstore/): 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.

@@ -99,4 +99,4 @@ main().catch(console.error);

 ## API Reference

- [QdrantVectorStore](/docs/api/classes/QdrantVectorStore)
+- [QdrantVectorStore](/typescript/framework-api-reference/classes/qdrantvectorstore/)
@@ -181,4 +181,4 @@ main().catch(console.error);

 ## API Reference

- [SupabaseVectorStore](/docs/api/classes/SupabaseVectorStore)
+- [SupabaseVectorStore](/typescript/framework-api-reference/classes/supabasevectorstore/)
@@ -62,4 +62,4 @@ the response is not correct with a score of 2.5

 ## API Reference

- [CorrectnessEvaluator](/docs/api/classes/CorrectnessEvaluator)
+- [CorrectnessEvaluator](/typescript/framework-api-reference/classes/correctnessevaluator/)
@@ -81,4 +81,4 @@ the response is faithful

 ## API Reference

- [FaithfulnessEvaluator](/docs/api/classes/FaithfulnessEvaluator)
+- [FaithfulnessEvaluator](/typescript/framework-api-reference/classes/faithfulnessevaluator/)
@@ -75,4 +75,4 @@ the response is relevant

 ## API Reference

- [RelevancyEvaluator](/docs/api/classes/RelevancyEvaluator)
+- [RelevancyEvaluator](/typescript/framework-api-reference/classes/relevancyevaluator/)
@@ -85,4 +85,4 @@ For questions or feedback, please contact us at [feedback@deepinfra.com](mailto:

 ## API Reference

- [DeepInfraEmbedding](/docs/api/classes/DeepInfraEmbedding)
+- [DeepInfraEmbedding](/typescript/framework-api-reference/classes/deepinfraembedding/)
@@ -43,4 +43,4 @@ Settings.embedModel = new GeminiEmbedding({

 ## API Reference

- [GeminiEmbedding](/docs/api/classes/GeminiEmbedding)
+- [GeminiEmbedding](/typescript/framework-api-reference/classes/geminiembedding/)
@@ -46,4 +46,4 @@ Settings.embedModel = new HuggingFaceEmbedding({

 ## API Reference

- [HuggingFaceEmbedding](/docs/api/classes/HuggingFaceEmbedding)
+- [HuggingFaceEmbedding](/typescript/framework-api-reference/classes/huggingfaceembedding/)
@@ -55,11 +55,11 @@ Settings.embedModel = new OllamaEmbedding({
 Most available embeddings are listed in the sidebar on the left.
 Additionally the following integrations exist without separate documentation:

- [ClipEmbedding](/docs/api/classes/ClipEmbedding) using `@xenova/transformers`
- [FireworksEmbedding](/docs/api/classes/FireworksEmbedding) see [fireworks.ai](https://fireworks.ai/)
+- [ClipEmbedding](/typescript/framework-api-reference/classes/clipembedding/) using `@xenova/transformers`
+- [FireworksEmbedding](/typescript/framework-api-reference/classes/fireworksembedding/) 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](/docs/api/classes/OpenAIEmbedding)
+- [OpenAIEmbedding](/typescript/framework-api-reference/classes/openaiembedding/)
@@ -25,4 +25,4 @@ const results = await queryEngine.query({

 ## API Reference

- [JinaAIEmbedding](/docs/api/classes/JinaAIEmbedding)
+- [JinaAIEmbedding](/typescript/framework-api-reference/classes/jinaaiembedding/)
@@ -34,4 +34,4 @@ const results = await queryEngine.query({

 ## API Reference

- [MistralAIEmbedding](/docs/api/classes/MistralAIEmbedding)
+- [MistralAIEmbedding](/typescript/framework-api-reference/classes/mistralaiembedding/)
@@ -102,4 +102,4 @@ console.log(result); // Perfectly customized embeddings, ready to serve.

 ## API Reference

- [MixedbreadAIEmbeddings](/docs/api/classes/MixedbreadAIEmbeddings)
+- [MixedbreadAIEmbeddings](/typescript/framework-api-reference/classes/mixedbreadaiembeddings/)
@@ -39,4 +39,4 @@ const results = await queryEngine.query({

 ## API Reference

- [OllamaEmbedding](/docs/api/classes/OllamaEmbedding)
+- [OllamaEmbedding](/typescript/framework-api-reference/classes/ollamaembedding/)
@@ -31,4 +31,4 @@ const results = await queryEngine.query({

 ## API Reference

- [OpenAIEmbedding](/docs/api/classes/OpenAIEmbedding)
+- [OpenAIEmbedding](/typescript/framework-api-reference/classes/openaiembedding/)
@@ -27,4 +27,4 @@ const results = await queryEngine.query({

 ## API Reference

- [TogetherEmbedding](/docs/api/classes/TogetherEmbedding)
+- [TogetherEmbedding](/typescript/framework-api-reference/classes/togetherembedding/)
@@ -31,4 +31,4 @@ const results = await queryEngine.query({

 ## API Reference

- [VoyageAIEmbedding](/docs/api/classes/VoyageAIEmbedding)
+- [VoyageAIEmbedding](/typescript/framework-api-reference/classes/voyageaiembedding/)
@@ -76,4 +76,4 @@ async function main() {

 ## API Reference

- [Anthropic](/docs/api/classes/Anthropic)
+- [Anthropic](/typescript/framework-api-reference/classes/anthropic/)
@@ -40,5 +40,5 @@ See the [Azure examples](https://github.com/run-llama/LlamaIndexTS/tree/main/exa

 ## API Reference

- [AzureOpenAI](/docs/api/classes/AzureOpenAI)
- [AzureOpenAIEmbedding](/docs/api/classes/AzureOpenAIEmbedding)
+- [AzureOpenAI](/typescript/framework-api-reference/classes/azureopenai/)
+- [AzureOpenAIEmbedding](/typescript/framework-api-reference/classes/azureopenaiembedding/)
@@ -96,4 +96,4 @@ If you have any feedback, please reach out to us at [feedback@deepinfra.com](mai

 ## API Reference

- [DeepInfra](/docs/api/classes/DeepInfra)
+- [DeepInfra](/typescript/framework-api-reference/classes/deepinfra/)
@@ -53,4 +53,4 @@ Currently does not support function calling.

 ## API Reference

- [DeepSeekLLM](/docs/api/classes/DeepSeekLLM)
+- [DeepSeekLLM](/typescript/framework-api-reference/classes/deepseekllm/)
@@ -65,4 +65,4 @@ main().catch(console.error);

 ## API Reference

- [FireworksLLM](/docs/api/classes/FireworksLLM)
+- [FireworksLLM](/typescript/framework-api-reference/classes/fireworksllm/)
@@ -192,4 +192,4 @@ async function main() {

 ## API Reference

- [Gemini](/docs/api/classes/Gemini)
+- [Gemini](/typescript/framework-api-reference/classes/gemini/)
@@ -59,4 +59,4 @@ const results = await queryEngine.query({

 ## API Reference

- [Groq](/docs/api/classes/Groq)
+- [Groq](/typescript/framework-api-reference/classes/groq/)
@@ -39,11 +39,11 @@ For local LLMs, currently we recommend the use of [Ollama](/typescript/framework

 Most available LLMs are listed in the sidebar on the left. Additionally the following integrations exist without separate documentation:

- [HuggingFaceLLM](/docs/api/classes/HuggingFaceLLM) and [HuggingFaceInferenceAPI](/docs/api/classes/HuggingFaceInferenceAPI).
- [ReplicateLLM](/docs/api/classes/ReplicateLLM) see [replicate.com](https://replicate.com/)
+- [HuggingFaceLLM](/typescript/framework-api-reference/classes/huggingfacellm/) and [HuggingFaceInferenceAPI](/typescript/framework-api-reference/classes/huggingfaceinferenceapi/).
+- [ReplicateLLM](/typescript/framework-api-reference/classes/replicatellm/) 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](/docs/api/classes/OpenAI)
+- [OpenAI](/typescript/framework-api-reference/classes/openai/)
@@ -81,4 +81,4 @@ async function main() {

 ## API Reference

- [MistralAI](/docs/api/classes/MistralAI)
+- [MistralAI](/typescript/framework-api-reference/classes/mistralai/)
@@ -115,4 +115,4 @@ async function main() {

 ## API Reference

- [Ollama](/docs/api/classes/Ollama)
+- [Ollama](/typescript/framework-api-reference/classes/ollama/)
@@ -377,7 +377,7 @@ async function main() {

 ## API Reference

- [OpenAI](/docs/api/classes/OpenAI)
+- [OpenAI](/typescript/framework-api-reference/classes/openai/)


 # OpenAI Live LLM
@@ -119,4 +119,4 @@ Currently does not support function calling.

 ## API Reference

- [Perplexity](/docs/api/classes/Perplexity)
+- [Perplexity](/typescript/framework-api-reference/classes/perplexity/)
@@ -83,4 +83,4 @@ async function main() {

 ## API Reference

- [Portkey](/docs/api/classes/Portkey)
+- [Portkey](/typescript/framework-api-reference/classes/portkey/)
@@ -81,4 +81,4 @@ async function main() {

 ## API Reference

- [TogetherLLM](/docs/api/classes/TogetherLLM)
+- [TogetherLLM](/typescript/framework-api-reference/classes/togetherllm/)
@@ -83,4 +83,4 @@ const response = await queryEngine.query({
 ## API Reference

 - [Response Synthesizer](/typescript/framework/modules/rag/response_synthesizer)
- [CompactAndRefine](/docs/api/classes/CompactAndRefine)
+- [CompactAndRefine](/typescript/framework-api-reference/classes/compactandrefine/)
@@ -40,6 +40,6 @@ for await (const chunk of stream) {

 ## Api References

- [ContextChatEngine](/docs/api/classes/ContextChatEngine)
- [CondenseQuestionChatEngine](/docs/api/classes/CondenseQuestionChatEngine)
- [SimpleChatEngine](/docs/api/classes/SimpleChatEngine)
+- [ContextChatEngine](/typescript/framework-api-reference/classes/contextchatengine/)
+- [CondenseQuestionChatEngine](/typescript/framework-api-reference/classes/condensequestionchatengine/)
+- [SimpleChatEngine](/typescript/framework-api-reference/classes/simplechatengine/)
@@ -67,4 +67,4 @@ const response = await queryEngine.query("Where did the author grown up?");

 ## API Reference

- [CohereRerank](/docs/api/classes/CohereRerank)
+- [CohereRerank](/typescript/framework-api-reference/classes/coherererank/)
@@ -112,5 +112,5 @@ const filteredNodes = processor.postprocessNodes(nodes);

 ## API Reference

- [SimilarityPostprocessor](/docs/api/classes/SimilarityPostprocessor)
- [MetadataReplacementPostProcessor](/docs/api/classes/MetadataReplacementPostProcessor)
+- [SimilarityPostprocessor](/typescript/framework-api-reference/classes/similaritypostprocessor/)
+- [MetadataReplacementPostProcessor](/typescript/framework-api-reference/classes/metadatareplacementpostprocessor/)
@@ -70,4 +70,4 @@ const response = await queryEngine.query("Where did the author grown up?");

 ## API Reference

- [JinaAIReranker](/docs/api/classes/JinaAIReranker)
+- [JinaAIReranker](/typescript/framework-api-reference/classes/jinaaireranker/)
@@ -168,4 +168,4 @@ console.log(result); // Perfectly customized results, ready to serve.

 ## API Reference

- [MixedbreadAIReranker](/docs/api/classes/MixedbreadAIReranker)
+- [MixedbreadAIReranker](/typescript/framework-api-reference/classes/mixedbreadaireranker/)
@@ -38,6 +38,6 @@ You can learn more about Tools by taking a look at the LlamaIndex Python documen

 ## API Reference

- [RetrieverQueryEngine](/docs/api/classes/RetrieverQueryEngine)
- [SubQuestionQueryEngine](/docs/api/classes/SubQuestionQueryEngine)
- [QueryEngineTool](/docs/api/classes/QueryEngineTool)
+- [RetrieverQueryEngine](/typescript/framework-api-reference/classes/retrieverqueryengine/)
+- [SubQuestionQueryEngine](/typescript/framework-api-reference/classes/subquestionqueryengine/)
+- [QueryEngineTool](/typescript/framework-api-reference/classes/queryenginetool/)
@@ -150,6 +150,6 @@ main();

 ## API Reference

- [VectorStoreIndex](/docs/api/classes/VectorStoreIndex)
- [ChromaVectorStore](/docs/api/classes/ChromaVectorStore)
+- [VectorStoreIndex](/typescript/framework-api-reference/classes/vectorstoreindex/)
+- [ChromaVectorStore](/typescript/framework-api-reference/classes/chromavectorstore/)
 - [MetadataFilter](/docs/api/interfaces/MetadataFilter)
@@ -172,4 +172,4 @@ main().then(() => console.log("Done"));

 ## API Reference

- [RouterQueryEngine](/docs/api/classes/RouterQueryEngine)
+- [RouterQueryEngine](/typescript/framework-api-reference/classes/routerqueryengine/)
@@ -59,7 +59,7 @@ for await (const chunk of stream) {

 - [getResponseSynthesizer](/docs/api/functions/getResponseSynthesizer)
 - [responseModeSchema](/docs/api/variables/responseModeSchema)
- [Refine](/docs/api/classes/Refine)
- [CompactAndRefine](/docs/api/classes/CompactAndRefine)
- [TreeSummarize](/docs/api/classes/TreeSummarize)
- [MultiModal](/docs/api/classes/MultiModal)
+- [Refine](/typescript/framework-api-reference/classes/refine/)
+- [CompactAndRefine](/typescript/framework-api-reference/classes/compactandrefine/)
+- [TreeSummarize](/typescript/framework-api-reference/classes/treesummarize/)
+- [MultiModal](/typescript/framework-api-reference/classes/multimodal/)
@@ -4,13 +4,13 @@ title: Retriever

 A retriever in LlamaIndex is what is used to fetch `Node`s from an index using a query string.

- [VectorIndexRetriever](/docs/api/classes/VectorIndexRetriever) will fetch the top-k most similar nodes. Ideal for dense retrieval to find most relevant nodes.
- [SummaryIndexRetriever](/docs/api/classes/SummaryIndexRetriever) will fetch all nodes no matter the query. Ideal when complete context is necessary, e.g. analyzing large datasets.
- [SummaryIndexLLMRetriever](/docs/api/classes/SummaryIndexLLMRetriever) utilizes an LLM to score and filter nodes based on relevancy to the query.
- [KeywordTableLLMRetriever](/docs/api/classes/KeywordTableLLMRetriever) uses an LLM to extract keywords from the query and retrieve relevant nodes based on keyword matches.
- [KeywordTableSimpleRetriever](/docs/api/classes/KeywordTableSimpleRetriever) uses a basic frequency-based approach to extract keywords and retrieve nodes.
- [KeywordTableRAKERetriever](/docs/api/classes/KeywordTableRAKERetriever) uses the RAKE (Rapid Automatic Keyword Extraction) algorithm to extract keywords from the query, focusing on co-occurrence and context for keyword-based retrieval.
- [Bm25Retriever](/docs/api/classes/Bm25Retriever) uses the BM25 algorithm to extract keywords from the query and retrieve relevant nodes based on keyword matches.
+- [VectorIndexRetriever](/typescript/framework-api-reference/classes/vectorindexretriever/) will fetch the top-k most similar nodes. Ideal for dense retrieval to find most relevant nodes.
+- [SummaryIndexRetriever](/typescript/framework-api-reference/classes/summaryindexretriever/) will fetch all nodes no matter the query. Ideal when complete context is necessary, e.g. analyzing large datasets.
+- [SummaryIndexLLMRetriever](/typescript/framework-api-reference/classes/summaryindexllmretriever/) utilizes an LLM to score and filter nodes based on relevancy to the query.
+- [KeywordTableLLMRetriever](/typescript/framework-api-reference/classes/keywordtablellmretriever/) uses an LLM to extract keywords from the query and retrieve relevant nodes based on keyword matches.
+- [KeywordTableSimpleRetriever](/typescript/framework-api-reference/classes/keywordtablesimpleretriever/) uses a basic frequency-based approach to extract keywords and retrieve nodes.
+- [KeywordTableRAKERetriever](/typescript/framework-api-reference/classes/keywordtablerakeretriever/) uses the RAKE (Rapid Automatic Keyword Extraction) algorithm to extract keywords from the query, focusing on co-occurrence and context for keyword-based retrieval.
+- [Bm25Retriever](/typescript/framework-api-reference/classes/bm25retriever/) uses the BM25 algorithm to extract keywords from the query and retrieve relevant nodes based on keyword matches.

 ```typescript
 const retriever = vectorIndex.asRetriever({
@@ -5,7 +5,7 @@ description: Running LlamaIndex workflows with both API endpoints and a user int

 # LlamaIndex Server

-LlamaIndexServer is a Next.js-based application that allows you to quickly launch your [LlamaIndex Workflows](https://ts.llamaindex.ai/typescript/framework/modules/agents/workflows) and [Agent Workflows](https://ts.llamaindex.ai/typescript/framework/modules/agents/agent_workflow) as an API server with an optional chat UI. It provides a complete environment for running LlamaIndex workflows with both API endpoints and a user interface for interaction.
+LlamaIndexServer is a Next.js-based application that allows you to quickly launch your [LlamaIndex Workflows](/typescript/workflows/) as an API server with an optional chat UI. It provides a complete environment for running LlamaIndex workflows with both API endpoints and a user interface for interaction.

 ## Features