docs: fix typos, add API references (run-llama#1161)

apps/docs/docs/introduction.md

@@ -21,7 +21,7 @@ LlamaIndex.TS handles several major use cases:
 
 - **Structured Data Extraction**: turning complex, unstructured and semi-structured data into uniform, programmatically accessible formats.
 - **Retrieval-Augmented Generation (RAG)**: answering queries across your internal data by providing LLMs with up-to-date, semantically relevant context including Question and Answer systems and chat bots.
-- **Autonomous Agents**: building software that is capable of intelligently selecting and using tools to accomplish tasks in an interative, unsupervised manner.
+- **Autonomous Agents**: building software that is capable of intelligently selecting and using tools to accomplish tasks in an interactive, unsupervised manner.
 
 ## 👨‍👩‍👧‍👦 Who is LlamaIndex for?
 

apps/docs/docs/modules/chat_engine.md

@@ -27,3 +27,4 @@ for await (const chunk of stream) {
 
 - [ContextChatEngine](../api/classes/ContextChatEngine.md)
 - [CondenseQuestionChatEngine](../api/classes/ContextChatEngine.md)
+- [SimpleChatEngine](../api/classes/SimpleChatEngine.md)

apps/docs/docs/modules/data_index.md

@@ -21,3 +21,4 @@ const index = await VectorStoreIndex.fromDocuments([document]);
 
 - [SummaryIndex](../api/classes/SummaryIndex.md)
 - [VectorStoreIndex](../api/classes/VectorStoreIndex.md)
+- [KeywordTableIndex](../api/classes/KeywordTableIndex.md)

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

@@ -98,3 +98,7 @@ Use the `embedDocuments` method to generate embeddings for the texts.
 const result = await embeddings.embedDocuments(texts);
 console.log(result); // Perfectly customized embeddings, ready to serve.
 ```
+
+## API Reference
+
+- [MixedbreadAIEmbeddings](../../../api/classes/MixedbreadAIEmbeddings.md)

apps/docs/docs/modules/evaluation/index.md

@@ -2,7 +2,7 @@
 
 ## Concept
 
-Evaluation and benchmarking are crucial concepts in LLM development. To improve the perfomance of an LLM app (RAG, agents) you must have a way to measure it.
+Evaluation and benchmarking are crucial concepts in LLM development. To improve the performance of an LLM app (RAG, agents) you must have a way to measure it.
 
 LlamaIndex offers key modules to measure the quality of generated results. We also offer key modules to measure retrieval quality.
 

apps/docs/docs/modules/ingestion_pipeline/transformations.md

@@ -36,7 +36,7 @@ main().catch(console.error);
 
 You can implement any transformation yourself by implementing the `TransformComponent`.
 
-The following custom transformation will remove any special characters or punctutation in text.
+The following custom transformation will remove any special characters or punctuation in text.
 
 ```ts
 import { TransformComponent, TextNode } from "llamaindex";
@@ -75,3 +75,7 @@ async function main() {
 
 main().catch(console.error);
 ```
+
+## API Reference
+
+- [TransformComponent](../../api/classes/TransformComponent.md)

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

@@ -1,5 +1,7 @@
 # DeepSeek LLM
 
+[DeepSeek Platform](https://platform.deepseek.com/)
+
 ## Usage
 
 ```ts
@@ -45,6 +47,6 @@ Currently does not support function calling.
 
 [Currently does not support json-output param while still is very good at json generating.](https://platform.deepseek.com/api-docs/faq#does-your-api-support-json-output)
 
-## API platform
+## API Reference
 
-- [DeepSeek platform](https://platform.deepseek.com/)
+- [DeepSeekLLM](../../../api/classes/DeepSeekLLM.md)

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

@@ -163,3 +163,7 @@ Use the `rerank` method to reorder the documents based on the query.
 const result = await reranker.rerank(documents, query);
 console.log(result); // Perfectly customized results, ready to serve.
 ```
+
+## API Reference
+
+- [MixedbreadAIReranker](../../api/classes/MixedbreadAIReranker.md)

apps/docs/docs/modules/query_engines/index.md

@@ -1,6 +1,6 @@
 # QueryEngine
 
-A query engine wraps a `Retriever` and a `ResponseSynthesizer` into a pipeline, that will use the query string to fetech nodes and then send them to the LLM to generate a response.
+A query engine wraps a `Retriever` and a `ResponseSynthesizer` into a pipeline, that will use the query string to fetch nodes and then send them to the LLM to generate a response.
 
 ```typescript
 const queryEngine = index.asQueryEngine();

apps/docs/docs/modules/retriever.md

@@ -4,7 +4,14 @@ sidebar_position: 5
 
 # Retriever
 
-A retriever in LlamaIndex is what is used to fetch `Node`s from an index using a query string. Aa `VectorIndexRetriever` will fetch the top-k most similar nodes. Meanwhile, a `SummaryIndexRetriever` will fetch all nodes no matter the query.
+A retriever in LlamaIndex is what is used to fetch `Node`s from an index using a query string.
+
+- [VectorIndexRetriever](../api/classes/VectorIndexRetriever.md) will fetch the top-k most similar nodes. Ideal for dense retrieval to find most relevant nodes.
+- [SummaryIndexRetriever](../api/classes/SummaryIndexRetriever.md) will fetch all nodes no matter the query. Ideal when complete context is necessary, e.g. analyzing large datasets.
+- [SummaryIndexLLMRetriever](../api/classes/SummaryIndexLLMRetriever.md) utilizes an LLM to score and filter nodes based on relevancy to the query.
+- [KeywordTableLLMRetriever](../api/classes/KeywordTableLLMRetriever.md) uses an LLM to extract keywords from the query and retrieve relevant nodes based on keyword matches.
+- [KeywordTableSimpleRetriever](../api/classes/KeywordTableSimpleRetriever.md) uses a basic frequency-based approach to extract keywords and retrieve nodes.
+- [KeywordTableRAKERetriever](../api/classes/KeywordTableRAKERetriever.md) uses the RAKE (Rapid Automatic Keyword Extraction) algorithm to extract keywords from the query, focusing on co-occurrence and context for keyword-based retrieval.
 
 ```typescript
 const retriever = vectorIndex.asRetriever({
@@ -14,9 +21,3 @@ const retriever = vectorIndex.asRetriever({
 // Fetch nodes!
 const nodesWithScore = await retriever.retrieve({ query: "query string" });
 ```
-
-## API Reference
-
-- [SummaryIndexRetriever](../api/classes/SummaryIndexRetriever.md)
-- [SummaryIndexLLMRetriever](../api/classes/SummaryIndexLLMRetriever.md)
-- [VectorIndexRetriever](../api/classes/VectorIndexRetriever.md)

packages/llamaindex/src/storage/vectorStore/MongoDBAtlasVectorStore.ts

@@ -129,9 +129,9 @@ export class MongoDBAtlasVectorSearch
    * Function to determine the number of candidates to retrieve for a given query.
    * In case your results are not good, you might tune this value.
    *
-   * {@link https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-stage/|Run Vector Search Queries}
+   * {@link https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-stage/ | Run Vector Search Queries}
    *
-   * {@link https://arxiv.org/abs/1603.09320|Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs}
+   * {@link https://arxiv.org/abs/1603.09320 | Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs}
    *
    *
    * Default: query.similarityTopK * 10