LlamaIndex RAG Application Builder

Build production-ready retrieval-augmented generation (RAG) applications using LlamaIndex, the leading framework for connecting LLMs to your data. This skill guides you through using LlamaIndex core abstractions to create data-aware LLM applications.

What This Skill Does

This skill helps you leverage LlamaIndex core (v0.14.13+) to build RAG applications by:

Instructions

1. Install LlamaIndex Core

First, install the core package:

```bash

pip install llama-index-core

```

For specific integrations (vector stores, LLMs, readers), install the needed packages:

```bash

Example integrations

pip install llama-index-llms-openai

pip install llama-index-vector-stores-pinecone

pip install llama-index-embeddings-huggingface

```

2. Load and Index Documents

Create a basic RAG pipeline by loading documents and creating an index:

```python

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

Load documents from a directory

documents = SimpleDirectoryReader("./data").load_data()

Create an index from documents

index = VectorStoreIndex.from_documents(documents)

```

3. Query Your Index

Set up a query engine to retrieve relevant context and generate responses:

```python

Create a query engine

query_engine = index.as_query_engine()

Query your data

response = query_engine.query("What is the main topic of these documents?")

print(response)

```

4. Customize Components

LlamaIndex core provides abstractions you can extend:

**Custom LLM:**

```python

from llama_index.core.llms import LLM

from llama_index.core import Settings

Use a specific LLM integration

from llama_index.llms.openai import OpenAI

Settings.llm = OpenAI(model="gpt-4", temperature=0.1)

```

**Custom Embeddings:**

```python

from llama_index.embeddings.huggingface import HuggingFaceEmbedding

Settings.embed_model = HuggingFaceEmbedding(

model_name="BAAI/bge-small-en-v1.5"

)

```

**Custom Vector Store:**

```python

from llama_index.vector_stores.pinecone import PineconeVectorStore

import pinecone

Initialize Pinecone

pc = pinecone.Pinecone(api_key="your-api-key")

index = pc.Index("your-index-name")

Create vector store

vector_store = PineconeVectorStore(pinecone_index=index)

Use in index creation

index = VectorStoreIndex.from_documents(

documents,

vector_store=vector_store

)

```

5. Implement Advanced Retrieval

Use LlamaIndex's retrieval abstractions for sophisticated patterns:

```python

from llama_index.core import QueryBundle

from llama_index.core.retrievers import VectorIndexRetriever

from llama_index.core.query_engine import RetrieverQueryEngine

Create custom retriever

retriever = VectorIndexRetriever(

index=index,

similarity_top_k=5

)

Build query engine with custom retriever

query_engine = RetrieverQueryEngine(retriever=retriever)

```

6. Persist and Load Indexes

Save your index to disk for reuse:

```python

Persist index

index.storage_context.persist(persist_dir="./storage")

Load index later

from llama_index.core import StorageContext, load_index_from_storage

storage_context = StorageContext.from_defaults(persist_dir="./storage")

index = load_index_from_storage(storage_context)

```

7. Handle Data Updates

Update your index as data changes:

```python

Insert new documents

new_docs = SimpleDirectoryReader("./new_data").load_data()

for doc in new_docs:

index.insert(doc)

Refresh existing documents

index.refresh_ref_docs(documents)

```

Key Abstractions

LlamaIndex core provides these foundational abstractions:

Integration Pattern

LlamaIndex follows an extensible architecture:

1. **Core** (`llama-index-core`): Base abstractions and interfaces

2. **Integrations** (`llama-index-{component}-{provider}`): Specific implementations

When building applications:

Examples

**Basic RAG app:**

```python

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

docs = SimpleDirectoryReader("docs").load_data()

index = VectorStoreIndex.from_documents(docs)

response = index.as_query_engine().query("Summarize the key points")

```

**Multi-source RAG:**

```python

from llama_index.core import VectorStoreIndex

from llama_index.readers.file import PDFReader

from llama_index.readers.web import SimpleWebPageReader

Load from multiple sources

pdf_docs = PDFReader().load_data("./reports")

web_docs = SimpleWebPageReader().load_data(["https://example.com"])

Combine and index

all_docs = pdf_docs + web_docs

index = VectorStoreIndex.from_documents(all_docs)

```

Resources

Constraints