skill-seekers-reference/docs/integrations/LLAMA_INDEX.md

Using Skill Seekers with LlamaIndex

Last Updated: February 5, 2026 Status: Production Ready Difficulty: Easy ⭐

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🎯 The Problem

Building knowledge bases and query engines with LlamaIndex requires well-structured documentation. Manually preparing documents is:

• Labor-Intensive - Scraping, chunking, and formatting takes hours
• Inconsistent - Manual processes lead to quality variations
• Hard to Update - Documentation changes require complete rework

Example:

"When building a LlamaIndex query engine for FastAPI documentation, you need to extract 300+ pages, structure them properly, and maintain consistent metadata. This typically takes 3-5 hours."

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✨ The Solution

Use Skill Seekers as essential preprocessing before LlamaIndex:

1. Generate LlamaIndex Nodes from any documentation source
2. Pre-structured with IDs and rich metadata
3. Ready for indexes (VectorStoreIndex, TreeIndex, KeywordTableIndex)
4. One command - complete documentation in minutes

Result: Skill Seekers outputs JSON files with LlamaIndex Node format, ready to build indexes and query engines.

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🚀 Quick Start (5 Minutes)

Prerequisites

• Python 3.10+
• LlamaIndex installed: pip install llama-index
• OpenAI API key (for embeddings): export OPENAI_API_KEY=sk-...

Installation

# Install Skill Seekers
pip install skill-seekers

# Verify installation
skill-seekers --version

Generate LlamaIndex Nodes

# Example: Django framework documentation
skill-seekers scrape --config configs/django.json

# Package as LlamaIndex Nodes
skill-seekers package output/django --target llama-index

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

Build Query Engine

from llama_index.core.schema import TextNode
from llama_index.core import VectorStoreIndex
import json

# Load nodes
with open("output/django-llama-index.json") as f:
    nodes_data = json.load(f)

# Convert to LlamaIndex Nodes
nodes = [
    TextNode(
        text=node["text"],
        metadata=node["metadata"],
        id_=node["id_"]
    )
    for node in nodes_data
]

print(f"Loaded {len(nodes)} nodes")

# Create index
index = VectorStoreIndex(nodes)

# Create query engine
query_engine = index.as_query_engine()

# Query
response = query_engine.query("How do I create a Django model?")
print(response)

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📖 Detailed Setup Guide

Step 1: Choose Your Documentation Source

Option A: Use Preset Config (Fastest)

# Available presets: django, fastapi, vue, etc.
skill-seekers scrape --config configs/django.json

Option B: From GitHub Repository

# Scrape from GitHub repo
skill-seekers github --repo django/django --name django-skill

Option C: Custom Documentation

# Create custom config
skill-seekers scrape --config configs/my-docs.json

Step 2: Generate LlamaIndex Format

# Convert to LlamaIndex Nodes
skill-seekers package output/django --target llama-index

# Output structure:
# output/django-llama-index.json
# [
#   {
#     "text": "...",
#     "metadata": {
#       "source": "django",
#       "category": "models",
#       "file": "models.md"
#     },
#     "id_": "unique-hash-id",
#     "embedding": null
#   }
# ]

What You Get:

• ✅ Pre-structured nodes with unique IDs
• ✅ Rich metadata (source, category, file, type)
• ✅ Clean text (code blocks preserved)
• ✅ Ready for indexing

Step 3: Create Vector Store Index

from llama_index.core.schema import TextNode
from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.core.storage.docstore import SimpleDocumentStore
from llama_index.core.storage.index_store import SimpleIndexStore
from llama_index.core.vector_stores import SimpleVectorStore
import json

# Load nodes
with open("output/django-llama-index.json") as f:
    nodes_data = json.load(f)

nodes = [
    TextNode(
        text=node["text"],
        metadata=node["metadata"],
        id_=node["id_"]
    )
    for node in nodes_data
]

# Create index
index = VectorStoreIndex(nodes)

# Persist for later use
index.storage_context.persist(persist_dir="./storage")

print(f"✅ Index created with {len(nodes)} nodes")

Load Persisted Index:

from llama_index.core import load_index_from_storage, StorageContext

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

print("✅ Index loaded from storage")

Step 4: Create Query Engine

Basic Query Engine:

# Create query engine
query_engine = index.as_query_engine(
    similarity_top_k=3,  # Return top 3 relevant chunks
    response_mode="compact"
)

# Query
response = query_engine.query("How do I create a Django model?")
print(response)

Chat Engine (Conversational):

from llama_index.core.chat_engine import CondenseQuestionChatEngine

# Create chat engine with memory
chat_engine = index.as_chat_engine(
    chat_mode="condense_question",
    verbose=True
)

# Chat
response = chat_engine.chat("Tell me about Django models")
print(response)

# Follow-up (maintains context)
response = chat_engine.chat("How do I add fields?")
print(response)

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🎨 Advanced Usage

Custom Index Types

Tree Index (For Summarization):

from llama_index.core import TreeIndex

tree_index = TreeIndex(nodes)
query_engine = tree_index.as_query_engine()

# Better for summarization queries
response = query_engine.query("Summarize Django's ORM capabilities")

Keyword Table Index (For Keyword Search):

from llama_index.core import KeywordTableIndex

keyword_index = KeywordTableIndex(nodes)
query_engine = keyword_index.as_query_engine()

# Better for keyword-based queries
response = query_engine.query("foreign key relationships")

Query with Filters

from llama_index.core.vector_stores import MetadataFilters, ExactMatchFilter

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

query_engine = index.as_query_engine(
    similarity_top_k=3,
    filters=filters
)

# Only searches in "models" category
response = query_engine.query("How do relationships work?")

Custom Retrieval

from llama_index.core.retrievers import VectorIndexRetriever

# Custom retriever with specific settings
retriever = VectorIndexRetriever(
    index=index,
    similarity_top_k=5,
)

# Get source nodes
nodes = retriever.retrieve("django models")

for node in nodes:
    print(f"Score: {node.score:.3f}")
    print(f"Category: {node.metadata['category']}")
    print(f"Text: {node.text[:100]}...\n")

Multi-Source Knowledge Base

# Combine multiple documentation sources
sources = ["django", "fastapi", "flask"]
all_nodes = []

for source in sources:
    with open(f"output/{source}-llama-index.json") as f:
        nodes_data = json.load(f)

    nodes = [
        TextNode(
            text=node["text"],
            metadata=node["metadata"],
            id_=node["id_"]
        )
        for node in nodes_data
    ]
    all_nodes.extend(nodes)

# Create unified index
index = VectorStoreIndex(all_nodes)
print(f"✅ Created index with {len(all_nodes)} nodes from {len(sources)} sources")

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💡 Best Practices

1. Persist Your Indexes

# Save to avoid re-indexing
index.storage_context.persist(persist_dir="./storage")

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

2. Use Streaming for Long Responses

query_engine = index.as_query_engine(
    streaming=True
)

response = query_engine.query("Explain Django in detail")
for text in response.response_gen:
    print(text, end="", flush=True)

3. Add Response Synthesis

from llama_index.core.response_synthesizers import ResponseMode

query_engine = index.as_query_engine(
    response_mode=ResponseMode.TREE_SUMMARIZE,  # Better for long docs
    similarity_top_k=5
)

4. Monitor Performance

import time

start = time.time()
response = query_engine.query("your question")
elapsed = time.time() - start

print(f"Query took {elapsed:.2f}s")
print(f"Used {len(response.source_nodes)} source nodes")

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🔥 Real-World Example

Building a FastAPI Documentation Assistant

Step 1: Generate Nodes

# Scrape FastAPI docs
skill-seekers scrape --config configs/fastapi.json

# Convert to LlamaIndex format
skill-seekers package output/fastapi --target llama-index

Step 2: Build Index and Query Engine

from llama_index.core.schema import TextNode
from llama_index.core import VectorStoreIndex
from llama_index.core.chat_engine import CondenseQuestionChatEngine
import json

# Load nodes
with open("output/fastapi-llama-index.json") as f:
    nodes_data = json.load(f)

nodes = [
    TextNode(
        text=node["text"],
        metadata=node["metadata"],
        id_=node["id_"]
    )
    for node in nodes_data
]

# Create index
index = VectorStoreIndex(nodes)
index.storage_context.persist(persist_dir="./fastapi_index")

print(f"✅ FastAPI index created with {len(nodes)} nodes")

# Create chat engine
chat_engine = index.as_chat_engine(
    chat_mode="condense_question",
    verbose=True
)

# Interactive loop
print("\n🤖 FastAPI Documentation Assistant")
print("Ask me anything about FastAPI (type 'quit' to exit)\n")

while True:
    user_input = input("You: ").strip()

    if user_input.lower() in ['quit', 'exit', 'q']:
        print("👋 Goodbye!")
        break

    if not user_input:
        continue

    response = chat_engine.chat(user_input)
    print(f"\nAssistant: {response}\n")

    # Show sources
    print("Sources:")
    for node in response.source_nodes:
        cat = node.metadata.get('category', 'unknown')
        file = node.metadata.get('file', 'unknown')
        print(f"  - {cat} ({file})")
    print()

Result:

• Complete FastAPI documentation indexed
• Conversational interface with memory
• Source attribution for transparency
• Instant responses (<1 second)

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🐛 Troubleshooting

Issue: Index Too Large

Solution: Use hybrid indexing or split by category

# Create separate indexes per category
categories = set(node["metadata"]["category"] for node in nodes_data)

indexes = {}
for category in categories:
    cat_nodes = [
        TextNode(**node)
        for node in nodes_data
        if node["metadata"]["category"] == category
    ]
    indexes[category] = VectorStoreIndex(cat_nodes)

Issue: Slow Queries

Solution: Reduce similarity_top_k or use caching

query_engine = index.as_query_engine(
    similarity_top_k=2,  # Reduce from 3 to 2
)

Issue: Missing Dependencies

Solution: Install LlamaIndex components

pip install llama-index llama-index-core
pip install llama-index-llms-openai  # For OpenAI LLM
pip install llama-index-embeddings-openai  # For OpenAI embeddings

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📊 Before vs After Comparison

Aspect	Manual Process	With Skill Seekers
Time to Setup	3-5 hours	5 minutes
Node Structure	Manual, inconsistent	Automatic, structured
Metadata	Often missing	Rich, comprehensive
IDs	Manual generation	Auto-generated (stable)
Maintenance	Re-process everything	Re-run one command
Updates	Hours of work	5 minutes

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🤝 Community & Support

• Questions: GitHub Discussions
• Issues: GitHub Issues
• Documentation: https://skillseekersweb.com/
• Twitter: @yUSyUS

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📚 Related Guides

• LangChain Integration
• Pinecone Integration
• RAG Pipelines Overview

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📖 Next Steps

1. Try the Quick Start above
2. Explore different index types (Tree, Keyword, List)
3. Build your query engine with production-ready docs
4. Share your experience - we'd love feedback!

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Last Updated: February 5, 2026 Tested With: LlamaIndex v0.10.0+, OpenAI GPT-4 Skill Seekers Version: v2.9.0+