e2e install pack and run the package

Co-authored-by: Alex Yang <himself65@outlook.com>

.changeset/smart-eels-design.md

@@ -0,0 +1,5 @@
+---
+"llamaindex": patch
+---
+
+fix: update `VectorIndexRetriever` constructor parameters' type.

.devcontainer/devcontainer.json

@@ -4,6 +4,6 @@
     "ghcr.io/devcontainers/features/node:1": {},
     "ghcr.io/devcontainers-contrib/features/turborepo-npm:1": {},
     "ghcr.io/devcontainers-contrib/features/typescript:2": {},
-    "ghcr.io/devcontainers-contrib/features/pnpm:2": {}
-  }
+    "ghcr.io/devcontainers-contrib/features/pnpm:2": {},
+  },
 }

.github/workflows/e2e.yml

@@ -21,6 +21,9 @@ jobs:
         node-version: [18, 20]
         python-version: ["3.11"]
         os: [macos-latest, windows-latest]
+    defaults:
+      run:
+        shell: bash
     runs-on: ${{ matrix.os }}
     steps:
       - uses: actions/checkout@v4
@@ -47,7 +50,7 @@ jobs:
         run: pnpm run build
         working-directory: ./packages/create-llama
       - name: Run Playwright tests
-        run: pnpm exec playwright test
+        run: pnpm run e2e
         env:
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
         working-directory: ./packages/create-llama

.github/workflows/lint_on_push_or_pull.yml

@@ -14,6 +14,8 @@ jobs:
     steps:
       - uses: actions/checkout@v4
       - uses: pnpm/action-setup@v2
+        with:
+          version: latest
       - name: Setup Node.js
         uses: actions/setup-node@v4
         with:

.github/workflows/test.yml

@@ -32,7 +32,35 @@ jobs:
       - name: Install dependencies
         run: pnpm install
       - name: Build
-        run: pnpm run build
-        working-directory: ./packages/core
+        run: pnpm run build --filter llamaindex
       - name: Run Type Check
         run: pnpm run type-check
+      - name: Run Circular Dependency Check
+        run: pnpm run circular-check
+        working-directory: ./packages/core
+  typecheck-examples:
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v4
+      - uses: pnpm/action-setup@v2
+      - name: Setup Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version-file: ".nvmrc"
+          cache: "pnpm"
+      - name: Install dependencies
+        run: pnpm install
+      - name: Build
+        run: pnpm run build --filter llamaindex
+      - name: Copy examples
+        run: rsync -rv --exclude=node_modules ./examples ${{ runner.temp }}
+      - name: Pack
+        run: pnpm pack --pack-destination ${{ runner.temp }}
+        working-directory: packages/core
+      - name: Install llamaindex
+        run: npm add ${{ runner.temp }}/*.tgz
+        working-directory: ${{ runner.temp }}/examples
+      - name: Run Type Check
+        run: npx tsc --project ./tsconfig.json
+        working-directory: ${{ runner.temp }}/examples

.husky/pre-commit

@@ -1,6 +1,3 @@
-#!/usr/bin/env sh
-. "$(dirname -- "$0")/_/husky.sh"
-
 pnpm format
 pnpm lint
 npx lint-staged

.husky/pre-push

@@ -1,4 +1 @@
-#!/usr/bin/env sh
-. "$(dirname -- "$0")/_/husky.sh"
-
 pnpm test

.npmrc

@@ -1 +1,2 @@
 auto-install-peers = true
+enable-pre-post-scripts = true

.prettierignore

@@ -1,4 +1,6 @@
 apps/docs/i18n
+apps/docs/docs/api
 pnpm-lock.yaml
 lib/
 dist/
+.docusaurus/

.vscode/settings.json

@@ -8,5 +8,11 @@
   "jest.rootPath": "./packages/core",
   "[python]": {
     "editor.defaultFormatter": "ms-python.black-formatter"
+  },
+  "[jsonc]": {
+    "editor.defaultFormatter": "esbenp.prettier-vscode"
+  },
+  "[json]": {
+    "editor.defaultFormatter": "esbenp.prettier-vscode"
   }
 }

README.md

@@ -11,6 +11,10 @@ Use your own data with large language models (LLMs, OpenAI ChatGPT and others) i
 
 Documentation: https://ts.llamaindex.ai/
 
+Try examples online:
+
+[![Open in Stackblitz](https://developer.stackblitz.com/img/open_in_stackblitz.svg)](https://stackblitz.com/github/run-llama/LlamaIndexTS/tree/main/examples)
+
 ## What is LlamaIndex.TS?
 
 LlamaIndex.TS aims to be a lightweight, easy to use set of libraries to help you integrate large language models into your applications with your own data.
@@ -52,9 +56,9 @@ async function main() {
 
   // Query the index
   const queryEngine = index.asQueryEngine();
-  const response = await queryEngine.query(
-    "What did the author do in college?",
-  );
+  const response = await queryEngine.query({
+    query: "What did the author do in college?",
+  });
 
   // Output response
   console.log(response.toString());
@@ -66,7 +70,7 @@ main();
 Then you can run it using
 
 ```bash
-pnpx ts-node example.ts
+pnpm dlx ts-node example.ts
 ```
 
 ## Playground
@@ -101,6 +105,9 @@ export const runtime = "nodejs"; // default
 // next.config.js
 /** @type {import('next').NextConfig} */
 const nextConfig = {
+  experimental: {
+    serverComponentsExternalPackages: ["pdf2json"],
+  },
   webpack: (config) => {
     config.resolve.alias = {
       ...config.resolve.alias,

apps/docs/CHANGELOG.md

@@ -0,0 +1,13 @@
+# docs
+
+## 0.0.2
+
+### Patch Changes
+
+- 0f64084: docs: update API references
+
+## 0.0.1
+
+### Patch Changes
+
+- 3154f52: chore: add qdrant readme

apps/docs/docs/end_to_end.md

@@ -1,49 +0,0 @@
----
-sidebar_position: 4
----
-
-# End to End Examples
-
-We include several end-to-end examples using LlamaIndex.TS in the repository
-
-Check out the examples below or try them out and complete them in minutes with interactive Github Codespace tutorials provided by Dev-Docs [here](https://codespaces.new/team-dev-docs/lits-dev-docs-playground?devcontainer_path=.devcontainer%2Fjavascript_ltsquickstart%2Fdevcontainer.json):
-
-## [Chat Engine](https://github.com/run-llama/LlamaIndexTS/blob/main/examples/chatEngine.ts)
-
-Read a file and chat about it with the LLM.
-
-## [Vector Index](https://github.com/run-llama/LlamaIndexTS/blob/main/examples/vectorIndex.ts)
-
-Create a vector index and query it. The vector index will use embeddings to fetch the top k most relevant nodes. By default, the top k is 2.
-
-## [Summary Index](https://github.com/run-llama/LlamaIndexTS/blob/main/examples/summaryIndex.ts)
-
-Create a list index and query it. This example also use the `LLMRetriever`, which will use the LLM to select the best nodes to use when generating answer.
-
-## [Save / Load an Index](https://github.com/run-llama/LlamaIndexTS/blob/main/examples/storageContext.ts)
-
-Create and load a vector index. Persistance to disk in LlamaIndex.TS happens automatically once a storage context object is created.
-
-## [Customized Vector Index](https://github.com/run-llama/LlamaIndexTS/blob/main/examples/vectorIndexCustomize.ts)
-
-Create a vector index and query it, while also configuring the the `LLM`, the `ServiceContext`, and the `similarity_top_k`.
-
-## [OpenAI LLM](https://github.com/run-llama/LlamaIndexTS/blob/main/examples/openai.ts)
-
-Create an OpenAI LLM and directly use it for chat.
-
-## [Llama2 DeuceLLM](https://github.com/run-llama/LlamaIndexTS/blob/main/examples/llamadeuce.ts)
-
-Create a Llama-2 LLM and directly use it for chat.
-
-## [SubQuestionQueryEngine](https://github.com/run-llama/LlamaIndexTS/blob/main/examples/subquestion.ts)
-
-Uses the `SubQuestionQueryEngine`, which breaks complex queries into multiple questions, and then aggreates a response across the answers to all sub-questions.
-
-## [Low Level Modules](https://github.com/run-llama/LlamaIndexTS/blob/main/examples/lowlevel.ts)
-
-This example uses several low-level components, which removes the need for an actual query engine. These components can be used anywhere, in any application, or customized and sub-classed to meet your own needs.
-
-## [JSON Entity Extraction](https://github.com/run-llama/LlamaIndexTS/blob/main/examples/jsonExtract.ts)
-
-Features OpenAI's chat API (using [`json_mode`](https://platform.openai.com/docs/guides/text-generation/json-mode)) to extract a JSON object from a sales call transcript.

apps/docs/docs/examples/_category_.yml

@@ -0,0 +1,2 @@
+label: Examples
+position: 2

apps/docs/docs/examples/agent.mdx

@@ -0,0 +1,85 @@
+# Agents
+
+A built-in agent that can take decisions and reasoning based on the tools provided to it.
+
+## OpenAI Agent
+
+```ts
+import { FunctionTool, OpenAIAgent } from "llamaindex";
+
+// Define a function to sum two numbers
+function sumNumbers({ a, b }: { a: number; b: number }): number {
+  return a + b;
+}
+
+// Define a function to divide two numbers
+function divideNumbers({ a, b }: { a: number; b: number }): number {
+  return a / b;
+}
+
+// Define the parameters of the sum function as a JSON schema
+const sumJSON = {
+  type: "object",
+  properties: {
+    a: {
+      type: "number",
+      description: "The first number",
+    },
+    b: {
+      type: "number",
+      description: "The second number",
+    },
+  },
+  required: ["a", "b"],
+};
+
+// Define the parameters of the divide function as a JSON schema
+const divideJSON = {
+  type: "object",
+  properties: {
+    a: {
+      type: "number",
+      description: "The dividend to divide",
+    },
+    b: {
+      type: "number",
+      description: "The divisor to divide by",
+    },
+  },
+  required: ["a", "b"],
+};
+
+async function main() {
+  // Create a function tool from the sum function
+  const sumFunctionTool = new FunctionTool(sumNumbers, {
+    name: "sumNumbers",
+    description: "Use this function to sum two numbers",
+    parameters: sumJSON,
+  });
+
+  // Create a function tool from the divide function
+  const divideFunctionTool = new FunctionTool(divideNumbers, {
+    name: "divideNumbers",
+    description: "Use this function to divide two numbers"
+    parameters: divideJSON,
+  });
+
+  // Create an OpenAIAgent with the function tools
+  const agent = new OpenAIAgent({
+    tools: [sumFunctionTool, divideFunctionTool],
+    verbose: true,
+  });
+
+  // Chat with the agent
+  const response = await agent.chat({
+    message: "How much is 5 + 5? then divide by 2",
+  });
+
+  // Print the response
+  console.log(String(response));
+}
+
+main().then(() => {
+  console.log("Done");
+});
+```

apps/docs/docs/examples/chat_engine.mdx

@@ -0,0 +1,12 @@
+---
+sidebar_position: 1
+---
+
+import CodeBlock from "@theme/CodeBlock";
+import CodeSource from "!raw-loader!../../../../examples/chatEngine";
+
+# Chat Engine
+
+Chat Engine is a class that allows you to create a chatbot from a retriever. It is a wrapper around a retriever that allows you to chat with it in a conversational manner.
+
+<CodeBlock language="ts">{CodeSource}</CodeBlock>

apps/docs/docs/examples/more_examples.mdx

@@ -0,0 +1,7 @@
+---
+sidebar_position: 5
+---
+
+# More examples
+
+You can check out more examples in the [examples](https://github.com/run-llama/LlamaIndexTS/tree/main/examples) folder of the repository.

apps/docs/docs/examples/save_load_index.mdx

@@ -0,0 +1,10 @@
+---
+sidebar_position: 4
+---
+
+import CodeBlock from "@theme/CodeBlock";
+import CodeSource from "!raw-loader!../../../../examples/storageContext";
+
+# Save/Load an Index
+
+<CodeBlock language="ts">{CodeSource}</CodeBlock>

apps/docs/docs/examples/summary_index.mdx

@@ -0,0 +1,10 @@
+---
+sidebar_position: 3
+---
+
+import CodeBlock from "@theme/CodeBlock";
+import CodeSource from "!raw-loader!../../../../examples/summaryIndex";
+
+# Summary Index
+
+<CodeBlock language="ts">{CodeSource}</CodeBlock>

apps/docs/docs/examples/vector_index.mdx

@@ -0,0 +1,10 @@
+---
+sidebar_position: 2
+---
+
+import CodeBlock from "@theme/CodeBlock";
+import CodeSource from "!raw-loader!../../../../examples/vectorIndex";
+
+# Vector Index
+
+<CodeBlock language="ts">{CodeSource}</CodeBlock>

apps/docs/docs/getting_started/_category_.yml

@@ -0,0 +1,2 @@
+label: Getting Started
+position: 1

apps/docs/docs/getting_started/concepts.md

@@ -2,7 +2,7 @@
 sidebar_position: 3
 ---
 
-# High-Level Concepts
+# Concepts
 
 LlamaIndex.TS helps you build LLM-powered applications (e.g. Q&A, chatbot) over custom data.
 
@@ -18,7 +18,7 @@ LlamaIndex uses a two stage method when using an LLM with your data:
 1. **indexing stage**: preparing a knowledge base, and
 2. **querying stage**: retrieving relevant context from the knowledge to assist the LLM in responding to a question
 
-![](./_static/concepts/rag.jpg)
+![](../_static/concepts/rag.jpg)
 
 This process is also known as Retrieval Augmented Generation (RAG).
 
@@ -30,14 +30,14 @@ Let's explore each stage in detail.
 
 LlamaIndex.TS help you prepare the knowledge base with a suite of data connectors and indexes.
 
-![](./_static/concepts/indexing.jpg)
+![](../_static/concepts/indexing.jpg)
 
-[**Data Loaders**](./modules/high_level/data_loader.md):
+[**Data Loaders**](../modules/data_loader.md):
 A data connector (i.e. `Reader`) ingest data from different data sources and data formats into a simple `Document` representation (text and simple metadata).
 
-[**Documents / Nodes**](./modules/high_level/documents_and_nodes.md): A `Document` is a generic container around any data source - for instance, a PDF, an API output, or retrieved data from a database. A `Node` is the atomic unit of data in LlamaIndex and represents a "chunk" of a source `Document`. It's a rich representation that includes metadata and relationships (to other nodes) to enable accurate and expressive retrieval operations.
+[**Documents / Nodes**](../modules/documents_and_nodes/index.md): A `Document` is a generic container around any data source - for instance, a PDF, an API output, or retrieved data from a database. A `Node` is the atomic unit of data in LlamaIndex and represents a "chunk" of a source `Document`. It's a rich representation that includes metadata and relationships (to other nodes) to enable accurate and expressive retrieval operations.
 
-[**Data Indexes**](./modules/high_level/data_index.md):
+[**Data Indexes**](../modules/data_index.md):
 Once you've ingested your data, LlamaIndex helps you index data into a format that's easy to retrieve.
 
 Under the hood, LlamaIndex parses the raw documents into intermediate representations, calculates vector embeddings, and stores your data in-memory or to disk.
@@ -56,23 +56,23 @@ LlamaIndex provides composable modules that help you build and integrate RAG pip
 
 These building blocks can be customized to reflect ranking preferences, as well as composed to reason over multiple knowledge bases in a structured way.
 
-![](./_static/concepts/querying.jpg)
+![](../_static/concepts/querying.jpg)
 
 #### Building Blocks
 
-[**Retrievers**](./modules/low_level/retriever.md):
+[**Retrievers**](../modules/retriever.md):
 A retriever defines how to efficiently retrieve relevant context from a knowledge base (i.e. index) when given a query.
 The specific retrieval logic differs for difference indices, the most popular being dense retrieval against a vector index.
 
-[**Response Synthesizers**](./modules/low_level/response_synthesizer.md):
+[**Response Synthesizers**](../modules/response_synthesizer.md):
 A response synthesizer generates a response from an LLM, using a user query and a given set of retrieved text chunks.
 
 #### Pipelines
 
-[**Query Engines**](./modules/high_level/query_engine.md):
+[**Query Engines**](../modules/query_engines):
 A query engine is an end-to-end pipeline that allow you to ask question over your data.
 It takes in a natural language query, and returns a response, along with reference context retrieved and passed to the LLM.
 
-[**Chat Engines**](./modules/high_level/chat_engine.md):
+[**Chat Engines**](../modules/chat_engine.md):
 A chat engine is an end-to-end pipeline for having a conversation with your data
 (multiple back-and-forth instead of a single question & answer).

apps/docs/docs/getting_started/environments.md

@@ -1,5 +1,5 @@
 ---
-sidebar_position: 5
+sidebar_position: 2
 ---
 
 # Environments

apps/docs/docs/getting_started/installation.mdx

@@ -1,5 +1,5 @@
 ---
-sidebar_position: 1
+sidebar_position: 0
 ---
 
 # Installation and Setup
@@ -58,6 +58,6 @@ Our examples use OpenAI by default. You'll need to set up your Open AI key like
 export OPENAI_API_KEY="sk-......" # Replace with your key from https://platform.openai.com/account/api-keys
 ```
 
-If you want to have it automatically loaded every time, add it to your .zshrc/.bashrc.
+If you want to have it automatically loaded every time, add it to your `.zshrc/.bashrc`.
 
 WARNING: do not check in your OpenAI key into version control.

apps/docs/docs/getting_started/starter.md

@@ -1,5 +1,5 @@
 ---
-sidebar_position: 2
+sidebar_position: 1
 ---
 
 # Starter Tutorial
@@ -36,9 +36,9 @@ async function main() {
 
   // Query the index
   const queryEngine = index.asQueryEngine();
-  const response = await queryEngine.query(
-    "What did the author do in college?",
-  );
+  const response = await queryEngine.query({
+    query: "What did the author do in college?",
+  });
 
   // Output response
   console.log(response.toString());

apps/docs/docs/introduction.md

@@ -37,9 +37,9 @@ For more complex applications, our lower-level APIs allow advanced users to cust
 
 `npm install llamaindex`
 
-Our documentation includes [Installation Instructions](./installation.mdx) and a [Starter Tutorial](./starter.md) to build your first application.
+Our documentation includes [Installation Instructions](./getting_started/installation.mdx) and a [Starter Tutorial](./getting_started/starter.md) to build your first application.
 
-Once you're up and running, [High-Level Concepts](./concepts.md) has an overview of LlamaIndex's modular architecture. For more hands-on practical examples, look through our [End-to-End Tutorials](./end_to_end.md).
+Once you're up and running, [High-Level Concepts](./getting_started/concepts.md) has an overview of LlamaIndex's modular architecture. For more hands-on practical examples, look through our Examples section on the sidebar.
 
 ## 🗺️ Ecosystem
 

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

@@ -0,0 +1 @@
+label: "Agents"

apps/docs/docs/modules/agent/index.md

@@ -0,0 +1,14 @@
+# Agents
+
+An “agent” is an automated reasoning and decision engine. It takes in a user input/query and can make internal decisions for executing that query in order to return the correct result. The key agent components can include, but are not limited to:
+
+- Breaking down a complex question into smaller ones
+- Choosing an external Tool to use + coming up with parameters for calling the Tool
+- Planning out a set of tasks
+- Storing previously completed tasks in a memory module
+
+## Getting Started
+
+LlamaIndex.TS comes with a few built-in agents, but you can also create your own. The built-in agents include:
+
+- [OpenAI Agent](./openai.mdx)

apps/docs/docs/modules/agent/openai.mdx

@@ -0,0 +1,183 @@
+# OpenAI Agent
+
+OpenAI API that supports function calling, it’s never been easier to build your own agent!
+
+In this notebook tutorial, we showcase how to write your own OpenAI agent
+
+## Setup
+
+First, you need to install the `llamaindex` package. You can do this by running the following command in your terminal:
+
+```bash
+pnpm i llamaindex
+```
+
+Then we can define a function to sum two numbers and another function to divide two numbers.
+
+```ts
+function sumNumbers({ a, b }: { a: number; b: number }): number {
+  return a + b;
+}
+
+// Define a function to divide two numbers
+function divideNumbers({ a, b }: { a: number; b: number }): number {
+  return a / b;
+}
+```
+
+## Create a function tool
+
+Now we can create a function tool from the sum function and another function tool from the divide function.
+
+For the parameters of the sum function, we can define a JSON schema.
+
+### JSON Schema
+
+```ts
+const sumJSON = {
+  type: "object",
+  properties: {
+    a: {
+      type: "number",
+      description: "The first number",
+    },
+    b: {
+      type: "number",
+      description: "The second number",
+    },
+  },
+  required: ["a", "b"],
+};
+
+const divideJSON = {
+  type: "object",
+  properties: {
+    a: {
+      type: "number",
+      description: "The dividend a to divide",
+    },
+    b: {
+      type: "number",
+      description: "The divisor b to divide by",
+    },
+  },
+  required: ["a", "b"],
+};
+
+const sumFunctionTool = new FunctionTool(sumNumbers, {
+  name: "sumNumbers",
+  description: "Use this function to sum two numbers",
+  parameters: sumJSON,
+});
+
+const divideFunctionTool = new FunctionTool(divideNumbers, {
+  name: "divideNumbers",
+  description: "Use this function to divide two numbers",
+  parameters: divideJSON,
+});
+```
+
+## Create an OpenAIAgent
+
+Now we can create an OpenAIAgent with the function tools.
+
+```ts
+const worker = new OpenAIAgent({
+  tools: [sumFunctionTool, divideFunctionTool],
+  verbose: true,
+});
+```
+
+## Chat with the agent
+
+Now we can chat with the agent.
+
+```ts
+const response = await worker.chat({
+  message: "How much is 5 + 5? then divide by 2",
+});
+
+console.log(String(response));
+```
+
+## Full code
+
+```ts
+import { FunctionTool, OpenAIAgent } from "llamaindex";
+
+// Define a function to sum two numbers
+function sumNumbers({ a, b }: { a: number; b: number }): number {
+  return a + b;
+}
+
+// Define a function to divide two numbers
+function divideNumbers({ a, b }: { a: number; b: number }): number {
+  return a / b;
+}
+
+// Define the parameters of the sum function as a JSON schema
+const sumJSON = {
+  type: "object",
+  properties: {
+    a: {
+      type: "number",
+      description: "The first number",
+    },
+    b: {
+      type: "number",
+      description: "The second number",
+    },
+  },
+  required: ["a", "b"],
+};
+
+// Define the parameters of the divide function as a JSON schema
+const divideJSON = {
+  type: "object",
+  properties: {
+    a: {
+      type: "number",
+      description: "The argument a to divide",
+    },
+    b: {
+      type: "number",
+      description: "The argument b to divide",
+    },
+  },
+  required: ["a", "b"],
+};
+
+async function main() {
+  // Create a function tool from the sum function
+  const sumFunctionTool = new FunctionTool(sumNumbers, {
+    name: "sumNumbers",
+    description: "Use this function to sum two numbers",
+    parameters: sumJSON,
+  });
+
+  // Create a function tool from the divide function
+  const divideFunctionTool = new FunctionTool(divideNumbers, {
+    name: "divideNumbers",
+    description: "Use this function to divide two numbers",
+    parameters: divideJSON,
+  });
+
+  // Create an OpenAIAgent with the function tools
+  const agent = new OpenAIAgent({
+    tools: [sumFunctionTool, divideFunctionTool],
+    verbose: true,
+  });
+
+  // Chat with the agent
+  const response = await agent.chat({
+    message: "How much is 5 + 5? then divide by 2",
+  });
+
+  // Print the response
+  console.log(String(response));
+}
+
+main().then(() => {
+  console.log("Done");
+});
+```

apps/docs/docs/modules/chat_engine.md

@@ -25,5 +25,5 @@ for await (const chunk of stream) {
 
 ## Api References
 
-- [ContextChatEngine](../../api/classes/ContextChatEngine.md)
-- [CondenseQuestionChatEngine](../../api/classes/ContextChatEngine.md)
+- [ContextChatEngine](../api/classes/ContextChatEngine.md)
+- [CondenseQuestionChatEngine](../api/classes/ContextChatEngine.md)

apps/docs/docs/modules/data_index.md

@@ -1,5 +1,5 @@
 ---
-sidebar_position: 2
+sidebar_position: 4
 ---
 
 # Index
@@ -19,5 +19,5 @@ const index = await VectorStoreIndex.fromDocuments([document]);
 
 ## API Reference
 
-- [SummaryIndex](../../api/classes/SummaryIndex.md)
-- [VectorStoreIndex](../../api/classes/VectorStoreIndex.md)
+- [SummaryIndex](../api/classes/SummaryIndex.md)
+- [VectorStoreIndex](../api/classes/VectorStoreIndex.md)

apps/docs/docs/modules/data_loader.md

@@ -1,5 +1,5 @@
 ---
-sidebar_position: 1
+sidebar_position: 3
 ---
 
 # Reader / Loader
@@ -14,4 +14,4 @@ documents = new SimpleDirectoryReader().loadData("./data");
 
 ## API Reference
 
-- [SimpleDirectoryReader](../../api/classes/SimpleDirectoryReader.md)
+- [SimpleDirectoryReader](../api/classes/SimpleDirectoryReader.md)

apps/docs/docs/modules/documents_and_nodes/_category_.yml

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

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

@@ -1,5 +1,5 @@
 ---
-sidebar_position: 0
+sidebar_position: 1
 ---
 
 # Documents and Nodes
@@ -14,5 +14,5 @@ document = new Document({ text: "text", metadata: { key: "val" } });
 
 ## API Reference
 
-- [Document](../../api/classes/Document.md)
-- [TextNode](../../api/classes/TextNode.md)
+- [Document](../api/classes/Document.md)
+- [TextNode](../api/classes/TextNode.md)

apps/docs/docs/modules/documents_and_nodes/metadata_extraction.md

@@ -0,0 +1,45 @@
+# Metadata Extraction Usage Pattern
+
+You can use LLMs to automate metadata extraction with our `Metadata Extractor` modules.
+
+Our metadata extractor modules include the following "feature extractors":
+
+- `SummaryExtractor` - automatically extracts a summary over a set of Nodes
+- `QuestionsAnsweredExtractor` - extracts a set of questions that each Node can answer
+- `TitleExtractor` - extracts a title over the context of each Node by document and combine them
+- `KeywordExtractor` - extracts keywords over the context of each Node
+
+Then you can chain the `Metadata Extractors` with the `IngestionPipeline` to extract metadata from a set of documents.
+
+```ts
+import {
+  IngestionPipeline,
+  TitleExtractor,
+  QuestionsAnsweredExtractor,
+  Document,
+  OpenAI,
+} from "llamaindex";
+
+async function main() {
+  const pipeline = new IngestionPipeline({
+    transformations: [
+      new TitleExtractor(),
+      new QuestionsAnsweredExtractor({
+        questions: 5,
+      }),
+    ],
+  });
+
+  const nodes = await pipeline.run({
+    documents: [
+      new Document({ text: "I am 10 years old. John is 20 years old." }),
+    ],
+  });
+
+  for (const node of nodes) {
+    console.log(node.metadata);
+  }
+}
+
+main().then(() => console.log("done"));
+```

apps/docs/docs/modules/embedding.md

@@ -1,5 +1,5 @@
 ---
-sidebar_position: 1
+sidebar_position: 3
 ---
 
 # Embedding
@@ -18,5 +18,5 @@ const serviceContext = serviceContextFromDefaults({ embedModel: openaiEmbeds });
 
 ## API Reference
 
-- [OpenAIEmbedding](../../api/classes/OpenAIEmbedding.md)
-- [ServiceContext](../../api/interfaces/ServiceContext.md)
+- [OpenAIEmbedding](../api/classes/OpenAIEmbedding.md)
+- [ServiceContext](../api/interfaces//ServiceContext.md)

apps/docs/docs/modules/high_level/_category_.yml

@@ -1 +0,0 @@
-label: High-Level Modules

apps/docs/docs/modules/index.md

@@ -1,31 +0,0 @@
-# Core Modules
-
-LlamaIndex.TS offers several core modules, seperated into high-level modules for quickly getting started, and low-level modules for customizing key components as you need.
-
-## High-Level Modules
-
-- [**Document**](./high_level/documents_and_nodes.md): A document represents a text file, PDF file or other contiguous piece of data.
-
-- [**Node**](./high_level/documents_and_nodes.md): The basic data building block. Most commonly, these are parts of the document split into manageable pieces that are small enough to be fed into an embedding model and LLM.
-
-- [**Reader/Loader**](./high_level/data_loader.md): A reader or loader is something that takes in a document in the real world and transforms into a Document class that can then be used in your Index and queries. We currently support plain text files and PDFs with many many more to come.
-
-- [**Indexes**](./high_level/data_index.md): indexes store the Nodes and the embeddings of those nodes.
-
-- [**QueryEngine**](./high_level/query_engine.md): Query engines are what generate the query you put in and give you back the result. Query engines generally combine a pre-built prompt with selected nodes from your Index to give the LLM the context it needs to answer your query.
-
-- [**ChatEngine**](./high_level/chat_engine.md): A ChatEngine helps you build a chatbot that will interact with your Indexes.
-
-## Low Level Module
-
-- [**LLM**](./low_level/llm.md): The LLM class is a unified interface over a large language model provider such as OpenAI GPT-4, Anthropic Claude, or Meta LLaMA. You can subclass it to write a connector to your own large language model.
-
-- [**Embedding**](./low_level/embedding.md): An embedding is represented as a vector of floating point numbers. OpenAI's text-embedding-ada-002 is our default embedding model and each embedding it generates consists of 1,536 floating point numbers. Another popular embedding model is BERT which uses 768 floating point numbers to represent each Node. We provide a number of utilities to work with embeddings including 3 similarity calculation options and Maximum Marginal Relevance
-
-- [**TextSplitter/NodeParser**](./low_level/node_parser.md): Text splitting strategies are incredibly important to the overall efficacy of the embedding search. Currently, while we do have a default, there's no one size fits all solution. Depending on the source documents, you may want to use different splitting sizes and strategies. Currently we support spliltting by fixed size, splitting by fixed size with overlapping sections, splitting by sentence, and splitting by paragraph. The text splitter is used by the NodeParser when splitting `Document`s into `Node`s.
-
-- [**Retriever**](./low_level/retriever.md): The Retriever is what actually chooses the Nodes to retrieve from the index. Here, you may wish to try retrieving more or fewer Nodes per query, changing your similarity function, or creating your own retriever for each individual use case in your application. For example, you may wish to have a separate retriever for code content vs. text content.
-
-- [**ResponseSynthesizer**](./low_level/response_synthesizer.md): The ResponseSynthesizer is responsible for taking a query string, and using a list of `Node`s to generate a response. This can take many forms, like iterating over all the context and refining an answer, or building a tree of summaries and returning the root summary.
-
-- [**Storage**](./low_level/storage.md): At some point you're going to want to store your indexes, data and vectors instead of re-running the embedding models every time. IndexStore, DocStore, VectorStore, and KVStore are abstractions that let you do that. Combined, they form the StorageContext. Currently, we allow you to persist your embeddings in files on the filesystem (or a virtual in memory file system), but we are also actively adding integrations to Vector Databases.

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

@@ -0,0 +1,2 @@
+label: "Ingestion Pipeline"
+position: 2

apps/docs/docs/modules/ingestion_pipeline/index.md

@@ -0,0 +1,99 @@
+# Ingestion Pipeline
+
+An `IngestionPipeline` uses a concept of `Transformations` that are applied to input data.
+These `Transformations` are applied to your input data, and the resulting nodes are either returned or inserted into a vector database (if given).
+
+## Usage Pattern
+
+The simplest usage is to instantiate an IngestionPipeline like so:
+
+```ts
+import fs from "node:fs/promises";
+
+import {
+  Document,
+  IngestionPipeline,
+  MetadataMode,
+  OpenAIEmbedding,
+  TitleExtractor,
+  SimpleNodeParser,
+} from "llamaindex";
+
+async function main() {
+  // Load essay from abramov.txt in Node
+  const path = "node_modules/llamaindex/examples/abramov.txt";
+
+  const essay = await fs.readFile(path, "utf-8");
+
+  // Create Document object with essay
+  const document = new Document({ text: essay, id_: path });
+  const pipeline = new IngestionPipeline({
+    transformations: [
+      new SimpleNodeParser({ chunkSize: 1024, chunkOverlap: 20 }),
+      new TitleExtractor(),
+      new OpenAIEmbedding(),
+    ],
+  });
+
+  // run the pipeline
+  const nodes = await pipeline.run({ documents: [document] });
+
+  // print out the result of the pipeline run
+  for (const node of nodes) {
+    console.log(node.getContent(MetadataMode.NONE));
+  }
+}
+
+main().catch(console.error);
+```
+
+## Connecting to Vector Databases
+
+When running an ingestion pipeline, you can also chose to automatically insert the resulting nodes into a remote vector store.
+
+Then, you can construct an index from that vector store later on.
+
+```ts
+import fs from "node:fs/promises";
+
+import {
+  Document,
+  IngestionPipeline,
+  MetadataMode,
+  OpenAIEmbedding,
+  TitleExtractor,
+  SimpleNodeParser,
+  QdrantVectorStore,
+  VectorStoreIndex,
+} from "llamaindex";
+
+async function main() {
+  // Load essay from abramov.txt in Node
+  const path = "node_modules/llamaindex/examples/abramov.txt";
+
+  const essay = await fs.readFile(path, "utf-8");
+
+  const vectorStore = new QdrantVectorStore({
+    host: "http://localhost:6333",
+  });
+
+  // Create Document object with essay
+  const document = new Document({ text: essay, id_: path });
+  const pipeline = new IngestionPipeline({
+    transformations: [
+      new SimpleNodeParser({ chunkSize: 1024, chunkOverlap: 20 }),
+      new TitleExtractor(),
+      new OpenAIEmbedding(),
+    ],
+    vectorStore,
+  });
+
+  // run the pipeline
+  const nodes = await pipeline.run({ documents: [document] });
+
+  // create an index
+  const index = VectorStoreIndex.fromVectorStore(vectorStore);
+}
+
+main().catch(console.error);
+```

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

@@ -0,0 +1,77 @@
+# Transformations
+
+A transformation is something that takes a list of nodes as an input, and returns a list of nodes. Each component that implements the Transformatio class has both a `transform` definition responsible for transforming the nodes
+
+Currently, the following components are Transformation objects:
+
+- [SimpleNodeParser](../api/classes/SimpleNodeParser.md)
+- [MetadataExtractor](../documents_and_nodes/metadata_extraction.md)
+- Embeddings
+
+## Usage Pattern
+
+While transformations are best used with with an IngestionPipeline, they can also be used directly.
+
+```ts
+import { SimpleNodeParser, TitleExtractor, Document } from "llamaindex";
+
+async function main() {
+  let nodes = new SimpleNodeParser().getNodesFromDocuments([
+    new Document({ text: "I am 10 years old. John is 20 years old." }),
+  ]);
+
+  const titleExtractor = new TitleExtractor();
+
+  nodes = await titleExtractor.transform(nodes);
+
+  for (const node of nodes) {
+    console.log(node.getContent(MetadataMode.NONE));
+  }
+}
+
+main().catch(console.error);
+```
+
+## Custom Transformations
+
+You can implement any transformation yourself by implementing the `TransformerComponent`.
+
+The following custom transformation will remove any special characters or punctutaion in text.
+
+```ts
+import { TransformerComponent, Node } from "llamaindex";
+
+class RemoveSpecialCharacters extends TransformerComponent {
+  async transform(nodes: Node[]): Promise<Node[]> {
+    for (const node of nodes) {
+      node.text = node.text.replace(/[^\w\s]/gi, "");
+    }
+
+    return nodes;
+  }
+}
+```
+
+These can then be used directly or in any IngestionPipeline.
+
+```ts
+import { IngestionPipeline, Document } from "llamaindex";
+
+async function main() {
+  const pipeline = new IngestionPipeline({
+    transformations: [new RemoveSpecialCharacters()],
+  });
+
+  const nodes = await pipeline.run({
+    documents: [
+      new Document({ text: "I am 10 years old. John is 20 years old." }),
+    ],
+  });
+
+  for (const node of nodes) {
+    console.log(node.getContent(MetadataMode.NONE));
+  }
+}
+
+main().catch(console.error);
+```

apps/docs/docs/modules/llm.md

@@ -0,0 +1,34 @@
+---
+sidebar_position: 3
+---
+
+# LLM
+
+The LLM is responsible for reading text and generating natural language responses to queries. By default, LlamaIndex.TS uses `gpt-3.5-turbo`.
+
+The LLM can be explicitly set in the `ServiceContext` object.
+
+```typescript
+import { OpenAI, serviceContextFromDefaults } from "llamaindex";
+
+const openaiLLM = new OpenAI({ model: "gpt-3.5-turbo", temperature: 0 });
+
+const serviceContext = serviceContextFromDefaults({ llm: openaiLLM });
+```
+
+## Azure OpenAI
+
+To use Azure OpenAI, you only need to set a few environment variables.
+
+For example:
+
+```
+export AZURE_OPENAI_KEY="<YOUR KEY HERE>"
+export AZURE_OPENAI_ENDPOINT="<YOUR ENDPOINT, see https://learn.microsoft.com/en-us/azure/ai-services/openai/quickstart?tabs=command-line%2Cpython&pivots=rest-api>"
+export AZURE_OPENAI_DEPLOYMENT="gpt-4" # or some other deployment name
+```
+
+## API Reference
+
+- [OpenAI](../api/classes/OpenAI.md)
+- [ServiceContext](../api/interfaces//ServiceContext.md)

apps/docs/docs/modules/low_level/_category_.yml

@@ -1 +0,0 @@
-label: Low-Level Modules

apps/docs/docs/modules/low_level/llm.md

@@ -1,22 +0,0 @@
----
-sidebar_position: 0
----
-
-# LLM
-
-The LLM is responsible for reading text and generating natural language responses to queries. By default, LlamaIndex.TS uses `gpt-3.5-turbo`.
-
-The LLM can be explicitly set in the `ServiceContext` object.
-
-```typescript
-import { OpenAI, serviceContextFromDefaults } from "llamaindex";
-
-const openaiLLM = new OpenAI({ model: "gpt-3.5-turbo", temperature: 0 });
-
-const serviceContext = serviceContextFromDefaults({ llm: openaiLLM });
-```
-
-## API Reference
-
-- [OpenAI](../../api/classes/OpenAI.md)
-- [ServiceContext](../../api/interfaces/ServiceContext.md)

apps/docs/docs/modules/node_parser.md

@@ -4,7 +4,7 @@ sidebar_position: 3
 
 # NodeParser
 
-The `NodeParser` in LlamaIndex is responbile for splitting `Document` objects into more manageable `Node` objects. When you call `.fromDocuments()`, the `NodeParser` from the `ServiceContext` is used to do this automatically for you. Alternatively, you can use it to split documents ahead of time.
+The `NodeParser` in LlamaIndex is responsible for splitting `Document` objects into more manageable `Node` objects. When you call `.fromDocuments()`, the `NodeParser` from the `ServiceContext` is used to do this automatically for you. Alternatively, you can use it to split documents ahead of time.
 
 ```typescript
 import { Document, SimpleNodeParser } from "llamaindex";
@@ -29,5 +29,5 @@ const textSplits = splitter.splitText("Hello World");
 
 ## API Reference
 
-- [SimpleNodeParser](../../api/classes/SimpleNodeParser.md)
-- [SentenceSplitter](../../api/classes/SentenceSplitter.md)
+- [SimpleNodeParser](../api/classes/SimpleNodeParser.md)
+- [SentenceSplitter](../api/classes/SentenceSplitter.md)

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

@@ -0,0 +1,2 @@
+label: "Query Engines"
+position: 2

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

@@ -1,7 +1,3 @@
----
-sidebar_position: 3
----
-
 # 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.

apps/docs/docs/modules/query_engines/metadata_filtering.md

@@ -0,0 +1,152 @@
+# Metadata Filtering
+
+Metadata filtering is a way to filter the documents that are returned by a query based on the metadata associated with the documents. This is useful when you want to filter the documents based on some metadata that is not part of the document text.
+
+You can also check our multi-tenancy blog post to see how metadata filtering can be used in a multi-tenant environment. [https://blog.llamaindex.ai/building-multi-tenancy-rag-system-with-llamaindex-0d6ab4e0c44b] (the article uses the Python version of LlamaIndex, but the concepts are the same).
+
+## Setup
+
+Firstly if you haven't already, you need to install the `llamaindex` package:
+
+```bash
+pnpm i llamaindex
+```
+
+Then you can import the necessary modules from `llamaindex`:
+
+```ts
+import {
+  ChromaVectorStore,
+  Document,
+  VectorStoreIndex,
+  storageContextFromDefaults,
+} from "llamaindex";
+
+const collectionName = "dog_colors";
+```
+
+## Creating documents with metadata
+
+You can create documents with metadata using the `Document` class:
+
+```ts
+const docs = [
+  new Document({
+    text: "The dog is brown",
+    metadata: {
+      color: "brown",
+      dogId: "1",
+    },
+  }),
+  new Document({
+    text: "The dog is red",
+    metadata: {
+      color: "red",
+      dogId: "2",
+    },
+  }),
+];
+```
+
+## Creating a ChromaDB vector store
+
+You can create a `ChromaVectorStore` to store the documents:
+
+```ts
+const chromaVS = new ChromaVectorStore({ collectionName });
+const serviceContext = await storageContextFromDefaults({
+  vectorStore: chromaVS,
+});
+
+const index = await VectorStoreIndex.fromDocuments(docs, {
+  storageContext: serviceContext,
+});
+```
+
+## Querying the index with metadata filtering
+
+Now you can query the index with metadata filtering using the `preFilters` option:
+
+```ts
+const queryEngine = index.asQueryEngine({
+  preFilters: {
+    filters: [
+      {
+        key: "dogId",
+        value: "2",
+        filterType: "ExactMatch",
+      },
+    ],
+  },
+});
+
+const response = await queryEngine.query({
+  query: "What is the color of the dog?",
+});
+
+console.log(response.toString());
+```
+
+## Full Code
+
+```ts
+import {
+  ChromaVectorStore,
+  Document,
+  VectorStoreIndex,
+  storageContextFromDefaults,
+} from "llamaindex";
+
+const collectionName = "dog_colors";
+
+async function main() {
+  try {
+    const docs = [
+      new Document({
+        text: "The dog is brown",
+        metadata: {
+          color: "brown",
+          dogId: "1",
+        },
+      }),
+      new Document({
+        text: "The dog is red",
+        metadata: {
+          color: "red",
+          dogId: "2",
+        },
+      }),
+    ];
+
+    console.log("Creating ChromaDB vector store");
+    const chromaVS = new ChromaVectorStore({ collectionName });
+    const ctx = await storageContextFromDefaults({ vectorStore: chromaVS });
+
+    console.log("Embedding documents and adding to index");
+    const index = await VectorStoreIndex.fromDocuments(docs, {
+      storageContext: ctx,
+    });
+
+    console.log("Querying index");
+    const queryEngine = index.asQueryEngine({
+      preFilters: {
+        filters: [
+          {
+            key: "dogId",
+            value: "2",
+            filterType: "ExactMatch",
+          },
+        ],
+      },
+    });
+    const response = await queryEngine.query({
+      query: "What is the color of the dog?",
+    });
+    console.log(response.toString());
+  } catch (e) {
+    console.error(e);
+  }
+}
+
+main();
+```

apps/docs/docs/modules/query_engines/router_query_engine.md

@@ -0,0 +1,189 @@
+# Router Query Engine
+
+In this tutorial, we define a custom router query engine that selects one out of several candidate query engines to execute a query.
+
+## Setup
+
+First, we need to install import the necessary modules from `llamaindex`:
+
+```bash
+pnpm i lamaindex
+```
+
+```ts
+import {
+  OpenAI,
+  RouterQueryEngine,
+  SimpleDirectoryReader,
+  SimpleNodeParser,
+  SummaryIndex,
+  VectorStoreIndex,
+  serviceContextFromDefaults,
+} from "llamaindex";
+```
+
+## Loading Data
+
+Next, we need to load some data. We will use the `SimpleDirectoryReader` to load documents from a directory:
+
+```ts
+const documents = await new SimpleDirectoryReader().loadData({
+  directoryPath: "node_modules/llamaindex/examples",
+});
+```
+
+## Service Context
+
+Next, we need to define some basic rules and parse the documents into nodes. We will use the `SimpleNodeParser` to parse the documents into nodes and `ServiceContext` to define the rules (eg. LLM API key, chunk size, etc.):
+
+```ts
+const nodeParser = new SimpleNodeParser({
+  chunkSize: 1024,
+});
+
+const serviceContext = serviceContextFromDefaults({
+  nodeParser,
+  llm: new OpenAI(),
+});
+```
+
+## Creating Indices
+
+Next, we need to create some indices. We will create a `VectorStoreIndex` and a `SummaryIndex`:
+
+```ts
+const vectorIndex = await VectorStoreIndex.fromDocuments(documents, {
+  serviceContext,
+});
+
+const summaryIndex = await SummaryIndex.fromDocuments(documents, {
+  serviceContext,
+});
+```
+
+## Creating Query Engines
+
+Next, we need to create some query engines. We will create a `VectorStoreQueryEngine` and a `SummaryQueryEngine`:
+
+```ts
+const vectorQueryEngine = vectorIndex.asQueryEngine();
+const summaryQueryEngine = summaryIndex.asQueryEngine();
+```
+
+## Creating a Router Query Engine
+
+Next, we need to create a router query engine. We will use the `RouterQueryEngine` to create a router query engine:
+
+We're defining two query engines, one for summarization and one for retrieving specific context. The router query engine will select the most appropriate query engine based on the query.
+
+```ts
+const queryEngine = RouterQueryEngine.fromDefaults({
+  queryEngineTools: [
+    {
+      queryEngine: vectorQueryEngine,
+      description: "Useful for summarization questions related to Abramov",
+    },
+    {
+      queryEngine: summaryQueryEngine,
+      description: "Useful for retrieving specific context from Abramov",
+    },
+  ],
+  serviceContext,
+});
+```
+
+## Querying the Router Query Engine
+
+Finally, we can query the router query engine:
+
+```ts
+const summaryResponse = await queryEngine.query({
+  query: "Give me a summary about his past experiences?",
+});
+
+console.log({
+  answer: summaryResponse.response,
+  metadata: summaryResponse?.metadata?.selectorResult,
+});
+```
+
+## Full code
+
+```ts
+import {
+  OpenAI,
+  RouterQueryEngine,
+  SimpleDirectoryReader,
+  SimpleNodeParser,
+  SummaryIndex,
+  VectorStoreIndex,
+  serviceContextFromDefaults,
+} from "llamaindex";
+
+async function main() {
+  // Load documents from a directory
+  const documents = await new SimpleDirectoryReader().loadData({
+    directoryPath: "node_modules/llamaindex/examples",
+  });
+
+  // Parse the documents into nodes
+  const nodeParser = new SimpleNodeParser({
+    chunkSize: 1024,
+  });
+
+  // Create a service context
+  const serviceContext = serviceContextFromDefaults({
+    nodeParser,
+    llm: new OpenAI(),
+  });
+
+  // Create indices
+  const vectorIndex = await VectorStoreIndex.fromDocuments(documents, {
+    serviceContext,
+  });
+
+  const summaryIndex = await SummaryIndex.fromDocuments(documents, {
+    serviceContext,
+  });
+
+  // Create query engines
+  const vectorQueryEngine = vectorIndex.asQueryEngine();
+  const summaryQueryEngine = summaryIndex.asQueryEngine();
+
+  // Create a router query engine
+  const queryEngine = RouterQueryEngine.fromDefaults({
+    queryEngineTools: [
+      {
+        queryEngine: vectorQueryEngine,
+        description: "Useful for summarization questions related to Abramov",
+      },
+      {
+        queryEngine: summaryQueryEngine,
+        description: "Useful for retrieving specific context from Abramov",
+      },
+    ],
+    serviceContext,
+  });
+
+  // Query the router query engine
+  const summaryResponse = await queryEngine.query({
+    query: "Give me a summary about his past experiences?",
+  });
+
+  console.log({
+    answer: summaryResponse.response,
+    metadata: summaryResponse?.metadata?.selectorResult,
+  });
+
+  const specificResponse = await queryEngine.query({
+    query: "Tell me about abramov first job?",
+  });
+
+  console.log({
+    answer: specificResponse.response,
+    metadata: specificResponse.metadata.selectorResult,
+  });
+}
+
+main().then(() => console.log("Done"));
+```

apps/docs/docs/modules/response_synthesizer.md

@@ -57,8 +57,8 @@ for await (const chunk of stream) {
 
 ## API Reference
 
-- [ResponseSynthesizer](../../api/classes/ResponseSynthesizer.md)
-- [Refine](../../api/classes/Refine.md)
-- [CompactAndRefine](../../api/classes/CompactAndRefine.md)
-- [TreeSummarize](../../api/classes/TreeSummarize.md)
-- [SimpleResponseBuilder](../../api/classes/SimpleResponseBuilder.md)
+- [ResponseSynthesizer](../api/classes/ResponseSynthesizer.md)
+- [Refine](../api/classes/Refine.md)
+- [CompactAndRefine](../api/classes/CompactAndRefine.md)
+- [TreeSummarize](../api/classes/TreeSummarize.md)
+- [SimpleResponseBuilder](../api/classes/SimpleResponseBuilder.md)

apps/docs/docs/modules/retriever.md

@@ -16,6 +16,6 @@ const nodesWithScore = await retriever.retrieve("query string");
 
 ## API Reference
 
-- [SummaryIndexRetriever](../../api/classes/SummaryIndexRetriever.md)
-- [SummaryIndexLLMRetriever](../../api/classes/SummaryIndexLLMRetriever.md)
-- [VectorIndexRetriever](../../api/classes/VectorIndexRetriever.md)
+- [SummaryIndexRetriever](../api/classes/SummaryIndexRetriever.md)
+- [SummaryIndexLLMRetriever](../api/classes/SummaryIndexLLMRetriever.md)
+- [VectorIndexRetriever](../api/classes/VectorIndexRetriever.md)

apps/docs/docs/modules/storage.md

@@ -23,4 +23,4 @@ const index = await VectorStoreIndex.fromDocuments([document], {
 
 ## API Reference
 
-- [StorageContext](../../api/interfaces/StorageContext.md)
+- [StorageContext](../api/interfaces//StorageContext.md)

apps/docs/docs/modules/vector_stores/_category_.yml

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

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

@@ -0,0 +1,86 @@
+# Qdrant Vector Store
+
+To run this example, you need to have a Qdrant instance running. You can run it with Docker:
+
+```bash
+docker pull qdrant/qdrant
+docker run -p 6333:6333 qdrant/qdrant
+```
+
+## Importing the modules
+
+```ts
+import fs from "node:fs/promises";
+import { Document, VectorStoreIndex, QdrantVectorStore } from "llamaindex";
+```
+
+## Load the documents
+
+```ts
+const path = "node_modules/llamaindex/examples/abramov.txt";
+const essay = await fs.readFile(path, "utf-8");
+```
+
+## Setup Qdrant
+
+```ts
+const vectorStore = new QdrantVectorStore({
+  url: "http://localhost:6333",
+});
+```
+
+## Setup the index
+
+```ts
+const document = new Document({ text: essay, id_: path });
+
+const index = await VectorStoreIndex.fromDocuments([document], {
+  vectorStore,
+});
+```
+
+## Query the index
+
+```ts
+const queryEngine = index.asQueryEngine();
+
+const response = await queryEngine.query({
+  query: "What did the author do in college?",
+});
+
+// Output response
+console.log(response.toString());
+```
+
+## Full code
+
+```ts
+import fs from "node:fs/promises";
+import { Document, VectorStoreIndex, QdrantVectorStore } from "llamaindex";
+
+async function main() {
+  const path = "node_modules/llamaindex/examples/abramov.txt";
+  const essay = await fs.readFile(path, "utf-8");
+
+  const vectorStore = new QdrantVectorStore({
+    url: "http://localhost:6333",
+  });
+
+  const document = new Document({ text: essay, id_: path });
+
+  const index = await VectorStoreIndex.fromDocuments([document], {
+    vectorStore,
+  });
+
+  const queryEngine = index.asQueryEngine();
+
+  const response = await queryEngine.query({
+    query: "What did the author do in college?",
+  });
+
+  // Output response
+  console.log(response.toString());
+}
+
+main().catch(console.error);
+```

apps/docs/docs/observability/_category_.yml

@@ -1 +1,2 @@
 label: Observability
+position: 5

apps/docs/docusaurus.config.js

@@ -1,8 +1,9 @@
 // @ts-check
 // Note: type annotations allow type checking and IDEs autocompletion
 
-const lightCodeTheme = require("prism-react-renderer/themes/github");
-const darkCodeTheme = require("prism-react-renderer/themes/dracula");
+const renderer = require("prism-react-renderer");
+const lightCodeTheme = renderer.themes.github;
+const darkCodeTheme = renderer.themes.dracula;
 
 /** @type {import('@docusaurus/types').Config} */
 const config = {
@@ -50,10 +51,11 @@ const config = {
 
   presets: [
     [
-      "classic",
+      "@docusaurus/preset-classic",
       /** @type {import('@docusaurus/preset-classic').Options} */
       ({
         docs: {
+          path: "docs",
           routeBasePath: "/",
           sidebarPath: require.resolve("./sidebars.js"),
           // Please change this to your repo.
@@ -171,6 +173,9 @@ const config = {
       },
     ],
   ],
+  markdown: {
+    format: "detect",
+  },
 };
 
 module.exports = config;

apps/docs/i18n/ar/docusaurus-plugin-content-docs/current/api

@@ -1 +0,0 @@
-../../../../docs/api

apps/docs/i18n/ar/docusaurus-plugin-content-docs/current/api

@@ -0,0 +1 @@
+../../../../docs/api

apps/docs/i18n/ar/docusaurus-plugin-content-docs/current/installation.mdx

@@ -2,15 +2,12 @@
 sidebar_position: 1
 ---
 
-
 # التثبيت والإعداد
 
-```تمت ترجمة هذه الوثيقة تلقائيًا وقد تحتوي على أخطاء. لا تتردد في فتح طلب سحب لاقتراح تغييرات.```
-
+`تمت ترجمة هذه الوثيقة تلقائيًا وقد تحتوي على أخطاء. لا تتردد في فتح طلب سحب لاقتراح تغييرات.`
 
 تأكد من أن لديك NodeJS v18 أو أحدث.
 
-
 ## باستخدام create-llama
 
 أسهل طريقة للبدء مع LlamaIndex هي باستخدام `create-llama`. هذه الأداة سطر الأوامر تمكنك من بدء بناء تطبيق LlamaIndex جديد بسرعة، مع كل شيء معد لك.
@@ -48,13 +45,13 @@ npm run dev
 ```
 
 لبدء خادم التطوير. يمكنك ثم زيارة [http://localhost:3000](http://localhost:3000) لرؤية تطبيقك.
+
 ## التثبيت من NPM
 
 ```bash npm2yarn
 npm install llamaindex
 ```
 
-
 ### المتغيرات البيئية
 
 تستخدم أمثلتنا OpenAI افتراضيًا. ستحتاج إلى إعداد مفتاح Open AI الخاص بك على النحو التالي:
@@ -67,5 +64,4 @@ export OPENAI_API_KEY="sk-......" # استبدله بالمفتاح الخاص
 
 تحذير: لا تقم بإضافة مفتاح OpenAI الخاص بك إلى نظام التحكم في الإصدارات.
 
-
 "

apps/docs/i18n/ar/docusaurus-plugin-content-docs/current/introduction.md

@@ -41,7 +41,7 @@ LlamaIndex.TS هو إطار بيانات لتطبيقات LLM لاستيعاب
 
 تتضمن وثائقنا [تعليمات التثبيت](./installation.mdx) و[دليل البداية](./starter.md) لبناء تطبيقك الأول.
 
-بمجرد أن تكون جاهزًا وتعمل ، يحتوي [مفاهيم عالية المستوى](./concepts.md) على نظرة عامة على الهندسة المعمارية المتعددة المستويات لـ LlamaIndex. لمزيد من الأمثلة العملية التفصيلية ، يمكنك الاطلاع على [دروس النهاية إلى النهاية](./end_to_end.md).
+بمجرد أن تكون جاهزًا وتعمل ، يحتوي [مفاهيم عالية المستوى](./getting_started/concepts.md) على نظرة عامة على الهندسة المعمارية المتعددة المستويات لـ LlamaIndex. لمزيد من الأمثلة العملية التفصيلية ، يمكنك الاطلاع على [دروس النهاية إلى النهاية](./end_to_end.md).
 
 ## 🗺️ النظام البيئي
 

apps/docs/i18n/bg/docusaurus-plugin-content-docs/current/api

@@ -1 +0,0 @@
-../../../../docs/api

apps/docs/i18n/bg/docusaurus-plugin-content-docs/current/api

@@ -0,0 +1 @@
+../../../../docs/api

apps/docs/i18n/bg/docusaurus-plugin-content-docs/current/environments.md

@@ -15,4 +15,3 @@ LlamaIndex в момента официално поддържа NodeJS 18 и No
 ```js
 export const runtime = "nodejs"; // по подразбиране
 ```
-

apps/docs/i18n/bg/docusaurus-plugin-content-docs/current/installation.mdx

@@ -2,15 +2,12 @@
 sidebar_position: 1
 ---
 
-
 # Инсталация и настройка
 
-```Тази документация е преведена автоматично и може да съдържа грешки. Не се колебайте да отворите Pull Request, за да предложите промени.```
-
+`Тази документация е преведена автоматично и може да съдържа грешки. Не се колебайте да отворите Pull Request, за да предложите промени.`
 
 Уверете се, че имате NodeJS v18 или по-нова версия.
 
-
 ## Използване на create-llama
 
 Най-лесният начин да започнете с LlamaIndex е чрез използването на `create-llama`. Този инструмент с команден ред ви позволява бързо да започнете да създавате ново приложение LlamaIndex, като всичко е настроено за вас.
@@ -48,13 +45,13 @@ npm run dev
 ```
 
 за да стартирате сървъра за разработка. След това можете да посетите [http://localhost:3000](http://localhost:3000), за да видите вашето приложение.
+
 ## Инсталация от NPM
 
 ```bash npm2yarn
 npm install llamaindex
 ```
 
-
 ### Променливи на средата
 
 Нашият пример използва OpenAI по подразбиране. Ще трябва да настроите вашия Open AI ключ по следния начин:

apps/docs/i18n/bg/docusaurus-plugin-content-docs/current/introduction.md

@@ -43,7 +43,7 @@ LlamaIndex.TS предоставя основен набор от инструм
 
 Документацията ни включва [Инструкции за инсталиране](./installation.mdx) и [Урок за начинаещи](./starter.md), за да построите първото си приложение.
 
-След като сте готови, [Високо ниво концепции](./concepts.md) представя общ преглед на модулната архитектура на LlamaIndex. За повече практически примери, разгледайте нашите [Уроци от начало до край](./end_to_end.md).
+След като сте готови, [Високо ниво концепции](./getting_started/concepts.md) представя общ преглед на модулната архитектура на LlamaIndex. За повече практически примери, разгледайте нашите [Уроци от начало до край](./end_to_end.md).
 
 ## 🗺️ Екосистема
 

apps/docs/i18n/cat/docusaurus-plugin-content-docs/current/api

@@ -1 +0,0 @@
-../../../../docs/api

apps/docs/i18n/cat/docusaurus-plugin-content-docs/current/api

@@ -0,0 +1 @@
+../../../../docs/api

apps/docs/i18n/cat/docusaurus-plugin-content-docs/current/installation.mdx

@@ -2,15 +2,12 @@
 sidebar_position: 1
 ---
 
-
 # Instal·lació i configuració
 
-```Aquesta documentació s'ha traduït automàticament i pot contenir errors. No dubteu a obrir una Pull Request per suggerir canvis.```
-
+`Aquesta documentació s'ha traduït automàticament i pot contenir errors. No dubteu a obrir una Pull Request per suggerir canvis.`
 
 Assegureu-vos de tenir NodeJS v18 o superior.
 
-
 ## Utilitzant create-llama
 
 La manera més senzilla de començar amb LlamaIndex és utilitzant `create-llama`. Aquesta eina de línia de comandes us permet començar ràpidament a construir una nova aplicació LlamaIndex, amb tot configurat per a vosaltres.
@@ -48,13 +45,13 @@ npm run dev
 ```
 
 per iniciar el servidor de desenvolupament. A continuació, podeu visitar [http://localhost:3000](http://localhost:3000) per veure la vostra aplicació.
+
 ## Instal·lació des de NPM
 
 ```bash npm2yarn
 npm install llamaindex
 ```
 
-
 ### Variables d'entorn
 
 Els nostres exemples utilitzen OpenAI per defecte. Hauràs de configurar la teva clau d'Open AI de la següent manera:

apps/docs/i18n/cat/docusaurus-plugin-content-docs/current/introduction.md

@@ -41,7 +41,7 @@ Per a aplicacions més complexes, les nostres API de nivell inferior permeten al
 
 La nostra documentació inclou [Instruccions d'Instal·lació](./installation.mdx) i un [Tutorial d'Inici](./starter.md) per a construir la vostra primera aplicació.
 
-Un cop tingueu tot a punt, [Conceptes de Nivell Alt](./concepts.md) ofereix una visió general de l'arquitectura modular de LlamaIndex. Per a més exemples pràctics, consulteu els nostres [Tutorials de Principi a Fi](./end_to_end.md).
+Un cop tingueu tot a punt, [Conceptes de Nivell Alt](./getting_started/concepts.md) ofereix una visió general de l'arquitectura modular de LlamaIndex. Per a més exemples pràctics, consulteu els nostres [Tutorials de Principi a Fi](./end_to_end.md).
 
 ## 🗺️ Ecosistema
 

apps/docs/i18n/cs/docusaurus-plugin-content-docs/current/api

@@ -1 +0,0 @@
-../../../../docs/api

apps/docs/i18n/cs/docusaurus-plugin-content-docs/current/api

@@ -0,0 +1 @@
+../../../../docs/api

apps/docs/i18n/cs/docusaurus-plugin-content-docs/current/installation.mdx

@@ -2,15 +2,12 @@
 sidebar_position: 1
 ---
 
-
 # Instalace a nastavení
 
-```Tato dokumentace byla automaticky přeložena a může obsahovat chyby. Neváhejte otevřít Pull Request pro navrhování změn.```
-
+`Tato dokumentace byla automaticky přeložena a může obsahovat chyby. Neváhejte otevřít Pull Request pro navrhování změn.`
 
 Ujistěte se, že máte nainstalovaný NodeJS ve verzi 18 nebo vyšší.
 
-
 ## Použití create-llama
 
 Nejjednodušší způsob, jak začít s LlamaIndexem, je použití `create-llama`. Tento nástroj příkazového řádku vám umožní rychle začít s vytvářením nové aplikace LlamaIndex s přednastaveným prostředím.
@@ -48,13 +45,13 @@ npm run dev
 ```
 
 pro spuštění vývojového serveru. Poté můžete navštívit [http://localhost:3000](http://localhost:3000), abyste viděli vaši aplikaci.
+
 ## Instalace pomocí NPM
 
 ```bash npm2yarn
 npm install llamaindex
 ```
 
-
 ### Proměnné prostředí
 
 Naše příklady výchozí používají OpenAI. Budete potřebovat nastavit svůj Open AI klíč následovně:

apps/docs/i18n/cs/docusaurus-plugin-content-docs/current/introduction.md

@@ -41,7 +41,7 @@ Pro složitější aplikace naše API na nižší úrovni umožňuje pokročilý
 
 Naše dokumentace obsahuje [Návod k instalaci](./installation.mdx) a [Úvodní tutoriál](./starter.md) pro vytvoření vaší první aplikace.
 
-Jakmile jste připraveni, [Vysokoúrovňové koncepty](./concepts.md) poskytují přehled o modulární architektuře LlamaIndexu. Pro více praktických příkladů se podívejte na naše [Tutoriály od začátku do konce](./end_to_end.md).
+Jakmile jste připraveni, [Vysokoúrovňové koncepty](./getting_started/concepts.md) poskytují přehled o modulární architektuře LlamaIndexu. Pro více praktických příkladů se podívejte na naše [Tutoriály od začátku do konce](./end_to_end.md).
 
 ## 🗺️ Ekosystém
 

apps/docs/i18n/da/docusaurus-plugin-content-docs/current/api

@@ -1 +0,0 @@
-../../../../docs/api

apps/docs/i18n/da/docusaurus-plugin-content-docs/current/api

@@ -0,0 +1 @@
+../../../../docs/api

apps/docs/i18n/da/docusaurus-plugin-content-docs/current/installation.mdx

@@ -2,15 +2,12 @@
 sidebar_position: 1
 ---
 
-
 # Installation og opsætning
 
-```Denne dokumentation er blevet automatisk oversat og kan indeholde fejl. Tøv ikke med at åbne en Pull Request for at foreslå ændringer.```
-
+`Denne dokumentation er blevet automatisk oversat og kan indeholde fejl. Tøv ikke med at åbne en Pull Request for at foreslå ændringer.`
 
 Sørg for at have NodeJS v18 eller nyere.
 
-
 ## Brug af create-llama
 
 Den nemmeste måde at komme i gang med LlamaIndex er ved at bruge `create-llama`. Dette CLI-værktøj gør det muligt for dig at hurtigt starte med at bygge en ny LlamaIndex-applikation, hvor alt er sat op for dig.
@@ -48,13 +45,13 @@ npm run dev
 ```
 
 for at starte udviklingsserveren. Du kan derefter besøge [http://localhost:3000](http://localhost:3000) for at se din app.
+
 ## Installation fra NPM
 
 ```bash npm2yarn
 npm install llamaindex
 ```
 
-
 ### Miljøvariabler
 
 Vores eksempler bruger som standard OpenAI. Du skal konfigurere din Open AI-nøgle som følger:

apps/docs/i18n/da/docusaurus-plugin-content-docs/current/introduction.md

@@ -41,7 +41,7 @@ Til mere komplekse applikationer giver vores API'er på lavere niveau avancerede
 
 Vores dokumentation inkluderer [Installationsinstruktioner](./installation.mdx) og en [Starter Tutorial](./starter.md) til at bygge din første applikation.
 
-Når du er i gang, giver [Højniveaukoncepter](./concepts.md) et overblik over LlamaIndex's modulære arkitektur. For flere praktiske eksempler, kan du kigge igennem vores [End-to-End Tutorials](./end_to_end.md).
+Når du er i gang, giver [Højniveaukoncepter](./getting_started/concepts.md) et overblik over LlamaIndex's modulære arkitektur. For flere praktiske eksempler, kan du kigge igennem vores [End-to-End Tutorials](./end_to_end.md).
 
 ## 🗺️ Økosystem
 

apps/docs/i18n/de/docusaurus-plugin-content-docs/current/api

@@ -1 +0,0 @@
-../../../../docs/api

apps/docs/i18n/de/docusaurus-plugin-content-docs/current/api

@@ -0,0 +1 @@
+../../../../docs/api

apps/docs/i18n/de/docusaurus-plugin-content-docs/current/installation.mdx

@@ -2,15 +2,12 @@
 sidebar_position: 1
 ---
 
-
 # Installation und Einrichtung
 
-```Diese Dokumentation wurde automatisch übersetzt und kann Fehler enthalten. Zögern Sie nicht, einen Pull Request zu öffnen, um Änderungen vorzuschlagen.```
-
+`Diese Dokumentation wurde automatisch übersetzt und kann Fehler enthalten. Zögern Sie nicht, einen Pull Request zu öffnen, um Änderungen vorzuschlagen.`
 
 Stellen Sie sicher, dass Sie NodeJS Version 18 oder höher installiert haben.
 
-
 ## Verwendung von create-llama
 
 Der einfachste Weg, um mit LlamaIndex zu beginnen, besteht darin, `create-llama` zu verwenden. Dieses CLI-Tool ermöglicht es Ihnen, schnell eine neue LlamaIndex-Anwendung zu erstellen, bei der alles für Sie eingerichtet ist.
@@ -48,13 +45,13 @@ npm run dev
 ```
 
 um den Entwicklungsserver zu starten. Sie können dann [http://localhost:3000](http://localhost:3000) besuchen, um Ihre App zu sehen.
+
 ## Installation über NPM
 
 ```bash npm2yarn
 npm install llamaindex
 ```
 
-
 ### Umgebungsvariablen
 
 Unsere Beispiele verwenden standardmäßig OpenAI. Sie müssen Ihren OpenAI-Schlüssel wie folgt einrichten:

apps/docs/i18n/de/docusaurus-plugin-content-docs/current/introduction.md

@@ -41,7 +41,7 @@ Für komplexere Anwendungen ermöglichen unsere APIs auf niedrigerer Ebene fortg
 
 Unsere Dokumentation enthält [Installationsanweisungen](./installation.mdx) und ein [Einführungstutorial](./starter.md), um Ihre erste Anwendung zu erstellen.
 
-Sobald Sie bereit sind, bietet [High-Level-Konzepte](./concepts.md) einen Überblick über die modulare Architektur von LlamaIndex. Für praktische Beispiele schauen Sie sich unsere [End-to-End-Tutorials](./end_to_end.md) an.
+Sobald Sie bereit sind, bietet [High-Level-Konzepte](./getting_started/concepts.md) einen Überblick über die modulare Architektur von LlamaIndex. Für praktische Beispiele schauen Sie sich unsere [End-to-End-Tutorials](./end_to_end.md) an.
 
 ## 🗺️ Ökosystem
 

apps/docs/i18n/el/docusaurus-plugin-content-docs/current/api

@@ -1 +0,0 @@
-../../../../docs/api

apps/docs/i18n/el/docusaurus-plugin-content-docs/current/api

@@ -0,0 +1 @@
+../../../../docs/api

apps/docs/i18n/el/docusaurus-plugin-content-docs/current/installation.mdx

@@ -2,15 +2,12 @@
 sidebar_position: 1
 ---
 
-
 # Εγκατάσταση και Ρύθμιση
 
-```Αυτό το έγγραφο έχει μεταφραστεί αυτόματα και μπορεί να περιέχει λάθη. Μη διστάσετε να ανοίξετε ένα Pull Request για να προτείνετε αλλαγές.```
-
+`Αυτό το έγγραφο έχει μεταφραστεί αυτόματα και μπορεί να περιέχει λάθη. Μη διστάσετε να ανοίξετε ένα Pull Request για να προτείνετε αλλαγές.`
 
 Βεβαιωθείτε ότι έχετε το NodeJS v18 ή νεότερη έκδοση.
 
-
 ## Χρήση του create-llama
 
 Ο ευκολότερος τρόπος για να ξεκινήσετε με το LlamaIndex είναι να χρησιμοποιήσετε το `create-llama`. Αυτό το εργαλείο γραμμής εντολών σας επιτρέπει να ξεκινήσετε γρήγορα τη δημιουργία μιας νέας εφαρμογής LlamaIndex, με όλα τα απαραίτητα προεπιλεγμένα ρυθμισμένα για εσάς.
@@ -48,13 +45,13 @@ npm run dev
 ```
 
 για να ξεκινήσετε τον διακομιστή ανάπτυξης. Στη συνέχεια, μπορείτε να επισκεφθείτε τη διεύθυνση [http://localhost:3000](http://localhost:3000) για να δείτε την εφαρμογή σας.
+
 ## Εγκατάσταση από το NPM
 
 ```bash npm2yarn
 npm install llamaindex
 ```
 
-
 ### Μεταβλητές περιβάλλοντος
 
 Τα παραδείγματά μας χρησιμοποιούν το OpenAI από προεπιλογή. Θα πρέπει να ρυθμίσετε το κλειδί σας για το Open AI ως εξής:

apps/docs/i18n/el/docusaurus-plugin-content-docs/current/introduction.md

@@ -43,7 +43,7 @@ slug: /
 
 Η τεκμηρίωσή μας περιλαμβάνει [Οδηγίες Εγκατάστασης](./installation.mdx) και ένα [Εισαγωγικό Εκπαιδευτικό Πρόγραμμα](./starter.md) για να δημιουργήσετε την πρώτη σας εφαρμογή.
 
-Αφού ξεκινήσετε, οι [Υψηλού Επιπέδου Έννοιες](./concepts.md) παρέχουν μια επισκόπηση της μοντουλαρισμένης αρχιτεκτονικής του LlamaIndex. Για περισσότερα πρακτικά παραδείγματα, ρίξτε μια ματιά στα [Ολοκληρωμένα Εκπαιδευτικά Προγράμματα](./end_to_end.md).
+Αφού ξεκινήσετε, οι [Υψηλού Επιπέδου Έννοιες](./getting_started/concepts.md) παρέχουν μια επισκόπηση της μοντουλαρισμένης αρχιτεκτονικής του LlamaIndex. Για περισσότερα πρακτικά παραδείγματα, ρίξτε μια ματιά στα [Ολοκληρωμένα Εκπαιδευτικά Προγράμματα](./end_to_end.md).
 
 ## 🗺️ Οικοσύστημα
 

apps/docs/i18n/es/docusaurus-plugin-content-docs/current/api

@@ -1 +0,0 @@
-../../../../docs/api

apps/docs/i18n/es/docusaurus-plugin-content-docs/current/api

@@ -0,0 +1 @@
+../../../../docs/api

apps/docs/i18n/es/docusaurus-plugin-content-docs/current/installation.mdx

@@ -2,15 +2,12 @@
 sidebar_position: 1
 ---
 
-
 # Instalación y Configuración
 
-```Esta documentación ha sido traducida automáticamente y puede contener errores. No dudes en abrir una Pull Request para sugerir cambios.```
-
+`Esta documentación ha sido traducida automáticamente y puede contener errores. No dudes en abrir una Pull Request para sugerir cambios.`
 
 Asegúrese de tener NodeJS v18 o superior.
 
-
 ## Usando create-llama
 
 La forma más fácil de comenzar con LlamaIndex es usando `create-llama`. Esta herramienta de línea de comandos te permite comenzar rápidamente a construir una nueva aplicación LlamaIndex, con todo configurado para ti.
@@ -48,13 +45,13 @@ npm run dev
 ```
 
 para iniciar el servidor de desarrollo. Luego puedes visitar [http://localhost:3000](http://localhost:3000) para ver tu aplicación.
+
 ## Instalación desde NPM
 
 ```bash npm2yarn
 npm install llamaindex
 ```
 
-
 ### Variables de entorno
 
 Nuestros ejemplos utilizan OpenAI de forma predeterminada. Deberá configurar su clave de Open AI de la siguiente manera:

apps/docs/i18n/es/docusaurus-plugin-content-docs/current/introduction.md

@@ -41,7 +41,7 @@ Para aplicaciones más complejas, nuestras API de nivel inferior permiten a los
 
 Nuestra documentación incluye [Instrucciones de instalación](./installation.mdx) y un [Tutorial de inicio](./starter.md) para construir tu primera aplicación.
 
-Una vez que estés en funcionamiento, [Conceptos de alto nivel](./concepts.md) ofrece una visión general de la arquitectura modular de LlamaIndex. Para obtener ejemplos prácticos más detallados, consulta nuestros [Tutoriales de extremo a extremo](./end_to_end.md).
+Una vez que estés en funcionamiento, [Conceptos de alto nivel](./getting_started/concepts.md) ofrece una visión general de la arquitectura modular de LlamaIndex. Para obtener ejemplos prácticos más detallados, consulta nuestros [Tutoriales de extremo a extremo](./end_to_end.md).
 
 ## 🗺️ Ecosistema
 

apps/docs/i18n/et/docusaurus-plugin-content-docs/current/api

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-../../../../docs/api

apps/docs/i18n/et/docusaurus-plugin-content-docs/current/api

@@ -0,0 +1 @@
+../../../../docs/api

apps/docs/i18n/et/docusaurus-plugin-content-docs/current/installation.mdx

@@ -2,15 +2,12 @@
 sidebar_position: 1
 ---
 
-
 # Paigaldamine ja seadistamine
 
-```See dokumentatsioon on tõlgitud automaatselt ja võib sisaldada vigu. Ärge kartke avada Pull Request, et pakkuda muudatusi.```
-
+`See dokumentatsioon on tõlgitud automaatselt ja võib sisaldada vigu. Ärge kartke avada Pull Request, et pakkuda muudatusi.`
 
 Veenduge, et teil oleks NodeJS versioon 18 või uuem.
 
-
 ## Kasutades create-llama
 
 Lihtsaim viis LlamaIndexiga alustamiseks on kasutada `create-llama` tööriista. See käsurea tööriist võimaldab teil kiiresti alustada uue LlamaIndex rakenduse loomist, kõik on juba teie jaoks seadistatud.
@@ -48,13 +45,13 @@ npm run dev
 ```
 
 arendusserveri käivitamiseks. Seejärel saate külastada [http://localhost:3000](http://localhost:3000), et näha oma rakendust.
+
 ## Paigaldamine NPM-ist
 
 ```bash npm2yarn
 npm install llamaindex
 ```
 
-
 ### Keskkonnamuutujad
 
 Meie näidetes kasutatakse vaikimisi OpenAI-d. Peate oma Open AI võtme seadistama järgmiselt:

apps/docs/i18n/et/docusaurus-plugin-content-docs/current/introduction.md

@@ -41,7 +41,7 @@ Täpsemate rakenduste jaoks võimaldavad meie madalama taseme API-d edasijõudnu
 
 Meie dokumentatsioonis on [paigaldusjuhised](./installation.mdx) ja [algõpetus](./starter.md) oma esimese rakenduse loomiseks.
 
-Kui olete valmis ja töötate, siis [kõrgtasemel kontseptsioonid](./concepts.md) annavad ülevaate LlamaIndexi moodularhitektuurist. Praktiliste näidete jaoks vaadake läbi meie [otsast lõpuni õpetused](./end_to_end.md).
+Kui olete valmis ja töötate, siis [kõrgtasemel kontseptsioonid](./getting_started/concepts.md) annavad ülevaate LlamaIndexi moodularhitektuurist. Praktiliste näidete jaoks vaadake läbi meie [otsast lõpuni õpetused](./end_to_end.md).
 
 ## 🗺️ Ökosüsteem
 

apps/docs/i18n/fa/docusaurus-plugin-content-docs/current/api

@@ -1 +0,0 @@
-../../../../docs/api

apps/docs/i18n/fa/docusaurus-plugin-content-docs/current/api

@@ -0,0 +1 @@
+../../../../docs/api

apps/docs/i18n/fa/docusaurus-plugin-content-docs/current/installation.mdx

@@ -2,15 +2,12 @@
 sidebar_position: 1
 ---
 
-
 # نصب و راه‌اندازی
 
-```undefined```
-
+`undefined`
 
 اطمینان حاصل کنید که NodeJS نسخه 18 یا بالاتر را دارید.
 
-
 ## استفاده از create-llama
 
 ساده‌ترین راه برای شروع با LlamaIndex استفاده از `create-llama` است. این ابزار CLI به شما امکان می‌دهد به سرعت یک برنامه جدید LlamaIndex راه‌اندازی کنید و همه چیز برای شما تنظیم شده باشد.
@@ -48,13 +45,13 @@ npm run dev
 ```
 
 برای راه‌اندازی سرور توسعه. سپس می‌توانید به [http://localhost:3000](http://localhost:3000) بروید تا برنامه خود را مشاهده کنید.
+
 ## نصب از NPM
 
 ```bash npm2yarn
 npm install llamaindex
 ```
 
-
 ### متغیرهای محیطی
 
 مثال‌های ما به طور پیش فرض از OpenAI استفاده می‌کنند. برای اینکه بتوانید از آن استفاده کنید، باید کلید Open AI خود را به صورت زیر تنظیم کنید:
@@ -67,5 +64,4 @@ export OPENAI_API_KEY="sk-......" # جایگزین کنید با کلید خود
 
 هشدار: کلید OpenAI خود را در کنترل نسخه گذاری قرار ندهید.
 
-
 "

apps/docs/i18n/fa/docusaurus-plugin-content-docs/current/introduction.md

@@ -43,7 +43,7 @@ API سطح بالای ما به کاربران مبتدی امکان استفا
 
 مستندات ما شامل [دستورالعمل نصب](./installation.mdx) و [آموزش شروع کار](./starter.md) برای ساخت اولین برنامه شما است.
 
-با راه اندازی و اجرا شدن، [مفاهیم سطح بالا](./concepts.md) یک نمای کلی از معماری ماژولار لاماایندکس را ارائه می دهد. برای مثال های عملی بیشتر، به [آموزش های پایان به پایان](./end_to_end.md) مراجعه کنید.
+با راه اندازی و اجرا شدن، [مفاهیم سطح بالا](./getting_started/concepts.md) یک نمای کلی از معماری ماژولار لاماایندکس را ارائه می دهد. برای مثال های عملی بیشتر، به [آموزش های پایان به پایان](./end_to_end.md) مراجعه کنید.
 
 "
 

apps/docs/i18n/fi/docusaurus-plugin-content-docs/current/api

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-../../../../docs/api

apps/docs/i18n/fi/docusaurus-plugin-content-docs/current/api

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+../../../../docs/api