Llamaindex

LlamaIndex is a community skill for building LLM-powered data applications using the LlamaIndex framework, covering document ingestion, index construction, query engines, retrieval-augmented generation, and agent workflows for connecting language models to custom data sources.

What Is This?

Overview

LlamaIndex provides patterns for building applications that connect large language models to external data. It covers document loading from files, databases, and APIs using data connectors, text splitting and chunking strategies for optimal retrieval granularity, index construction with vector stores, keyword tables, and knowledge graphs, query engine configuration for retrieval-augmented generation with custom prompts, and agent tools that combine multiple data sources with reasoning capabilities. The skill enables developers to build RAG pipelines that ground LLM responses in specific datasets rather than relying solely on model training data.

Who Should Use This

This skill serves developers building question-answering systems over private document collections, teams implementing RAG pipelines for enterprise knowledge bases, and AI engineers creating LLM agents that interact with structured and unstructured data sources.

Why Use It?

Problems It Solves

Language models cannot access private or recent data beyond their training cutoff without retrieval mechanisms. Splitting documents into chunks that preserve semantic meaning while fitting context windows requires careful text processing. Selecting the right index type and retrieval strategy for different query patterns needs experimentation. Combining retrieval results with LLM prompts to produce grounded answers demands structured pipelines.

Core Highlights

Data connectors load documents from over 100 sources including PDFs, databases, and Slack channels. Index builder creates vector, keyword, and graph indexes from chunked documents. Query engine retrieves relevant chunks and synthesizes answers using LLM prompts. Agent framework combines tools, data sources, and reasoning for multi-step workflows.

How to Use It?

Basic Usage

from llama_index.core import (
    VectorStoreIndex,
    SimpleDirectoryReader,
    Settings)
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import (
    OpenAIEmbedding)

Settings.llm = OpenAI(model="gpt-4o")
Settings.embed_model = OpenAIEmbedding(
    model="text-embedding-3-small")

documents = SimpleDirectoryReader(
    "./data").load_data()
print(f"Loaded {len(documents)} documents")

index = VectorStoreIndex.from_documents(
    documents)
query_engine = index.as_query_engine(
    similarity_top_k=3)

response = query_engine.query(
    "What are the main findings?")
print(response)
print(f"Sources: {len(response.source_nodes)}")

Real-World Examples

from llama_index.core import (
    VectorStoreIndex, StorageContext)
from llama_index.core.node_parser import (
    SentenceSplitter)
from llama_index.vector_stores.chroma import (
    ChromaVectorStore)
import chromadb

class RAGPipeline:
    def __init__(self, collection_name: str):
        self.chroma = chromadb.PersistentClient(
            path="./chroma_db")
        collection = self.chroma\
            .get_or_create_collection(
                collection_name)
        vector_store = ChromaVectorStore(
            chroma_collection=collection)
        self.storage = StorageContext\
            .from_defaults(
                vector_store=vector_store)
        self.splitter = SentenceSplitter(
            chunk_size=512,
            chunk_overlap=50)

    def ingest(self, documents: list):
        nodes = self.splitter\
            .get_nodes_from_documents(
                documents)
        self.index = VectorStoreIndex(
            nodes,
            storage_context=self.storage)
        return len(nodes)

    def query(self, question: str,
              top_k: int = 5) -> dict:
        engine = self.index.as_query_engine(
            similarity_top_k=top_k)
        response = engine.query(question)
        sources = [{
            "text": n.node.text[:200],
            "score": round(n.score, 4)}
            for n in response.source_nodes]
        return {"answer": str(response),
                "sources": sources}

pipeline = RAGPipeline("docs")
result = pipeline.query(
    "Summarize the key points")
print(result["answer"])

Advanced Tips

Experiment with chunk sizes between 256 and 1024 tokens to find the retrieval granularity that balances context quality with relevance for your specific dataset. Use metadata filters on vector store queries to narrow retrieval scope by document type, date, or source. Implement a reranker after initial retrieval to improve the quality of chunks passed to the LLM prompt.

When to Use It?

Use Cases

Build a document question-answering system over company internal knowledge bases with citation tracking. Create a customer support chatbot that retrieves answers from product documentation and FAQ collections. Implement a research assistant that queries multiple paper collections and synthesizes comparative summaries.

Related Topics

Retrieval-augmented generation, vector databases, document embeddings, LLM application development, and semantic search.

Important Notes

Requirements

Python with llama-index package installed. An LLM provider API key such as OpenAI for generation and embedding. A vector store like ChromaDB or Pinecone for persistent index storage.

Usage Recommendations

Do: use persistent vector stores for production deployments to avoid re-indexing on every application restart. Monitor retrieval quality by logging source node scores and reviewing relevance of retrieved chunks. Test different chunk sizes and overlap settings to optimize retrieval for your document types.

Don't: index entire documents without chunking, which limits retrieval precision for specific questions. Rely on default settings without evaluating retrieval quality on representative queries. Skip metadata extraction from documents when it could improve filtering and retrieval accuracy.

Limitations

Retrieval quality depends heavily on embedding model choice and chunking strategy for the specific data domain. Large document collections require vector store infrastructure that adds operational complexity. Query latency includes both retrieval and LLM generation time, which may not meet real-time requirements.