skill-seekers-reference/examples/llama-index-query-engine/README.md

LlamaIndex Query Engine Example

Complete example showing how to build a query engine using Skill Seekers nodes with LlamaIndex.

What This Example Does

1. Loads Skill Seekers-generated LlamaIndex Nodes
2. Creates a persistent VectorStoreIndex
3. Demonstrates query engine capabilities
4. Provides interactive chat mode with memory

Prerequisites

# Install dependencies
pip install llama-index llama-index-llms-openai llama-index-embeddings-openai

# Set API key
export OPENAI_API_KEY=sk-...

Generate Nodes

First, generate LlamaIndex nodes using Skill Seekers:

# Option 1: Use preset config (e.g., Django)
skill-seekers scrape --config configs/django.json
skill-seekers package output/django --target llama-index

# Option 2: From GitHub repo
skill-seekers github --repo django/django --name django
skill-seekers package output/django --target llama-index

# Output: output/django-llama-index.json

Run the Example

cd examples/llama-index-query-engine

# Run the quickstart script
python quickstart.py

What You'll See

1. Nodes loaded from JSON file
2. Index created with embeddings
3. Example queries demonstrating the query engine
4. Interactive chat mode with conversational memory

Example Output

============================================================
LLAMAINDEX QUERY ENGINE QUICKSTART
============================================================

Step 1: Loading nodes...
✅ Loaded 180 nodes
   Categories: {'overview': 1, 'models': 45, 'views': 38, ...}

Step 2: Creating index...
✅ Index created and persisted to: ./storage
   Nodes indexed: 180

Step 3: Running example queries...

============================================================
EXAMPLE QUERIES
============================================================

QUERY: What is this documentation about?
------------------------------------------------------------
ANSWER:
This documentation covers Django, a high-level Python web framework
that encourages rapid development and clean, pragmatic design...

SOURCES:
  1. overview (SKILL.md) - Score: 0.85
  2. models (models.md) - Score: 0.78

============================================================
INTERACTIVE CHAT MODE
============================================================
Ask questions about the documentation (type 'quit' to exit)

You: How do I create a model?

Features Demonstrated

• Query Engine - Semantic search over documentation
• Chat Engine - Conversational interface with memory
• Source Attribution - Shows which nodes contributed to answers
• Persistence - Index saved to disk for reuse

Files in This Example

• quickstart.py - Complete working example
• README.md - This file
• requirements.txt - Python dependencies

Next Steps

1. Customize - Modify for your specific documentation
2. Experiment - Try different index types (Tree, Keyword)
3. Extend - Add filters, custom retrievers, hybrid search
4. Deploy - Build a production query engine

Troubleshooting

"Documents not found"

• Make sure you've generated nodes first
• Check the DOCS_PATH in quickstart.py matches your output location

"OpenAI API key not found"

• Set environment variable: export OPENAI_API_KEY=sk-...

"Module not found"

• Install dependencies: pip install -r requirements.txt

Advanced Usage

Load Persisted Index

from llama_index.core import load_index_from_storage, StorageContext

# Load existing index
storage_context = StorageContext.from_defaults(persist_dir="./storage")
index = load_index_from_storage(storage_context)

Query with Filters

from llama_index.core.vector_stores import MetadataFilters, ExactMatchFilter

filters = MetadataFilters(
    filters=[ExactMatchFilter(key="category", value="models")]
)

query_engine = index.as_query_engine(filters=filters)

Streaming Responses

query_engine = index.as_query_engine(streaming=True)
response = query_engine.query("Explain Django models")

for text in response.response_gen:
    print(text, end="", flush=True)

Related Examples

• LangChain RAG Pipeline
• Pinecone Integration

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