llamaindex-patterns

name	llamaindex-patterns
description	LlamaIndex data framework — ingestion, indexing, query engines, chat engines, agents. Use when working with llamaindex patterns.
domain	core
author	oyi77
license	Apache-2.0
subdomain	core-platform
tags	["ai-agent","infrastructure","llamaindex","memory","patterns","self-improvement"]
version	1.0.0

Overview

LlamaIndex is a data framework for connecting LLMs with external data. It provides document loaders, vector stores, query engines, and chat engines for building RAG applications and knowledge-augmented agents.

Capabilities

• Load documents from 160+ sources (PDF, Notion, Slack, databases)
• Build vector indices for semantic search
• Create query engines with retrieval and synthesis
• Build conversational chat engines with memory
• Use agents with tool use and multi-step reasoning
• Implement advanced RAG patterns (routing, fusion, recursive)

When to Use

Trigger phrases:

• "llamaindex patterns"

• "LlamaIndex data framework — ingestion, indexing, query engines, chat engines, ag"

• Building RAG applications over custom data sources

• Needing structured document ingestion pipelines

• Wanting query engines with citations and source tracking

• Building chatbots over knowledge bases

• Implementing agentic RAG with tool use

When NOT to Use

• Task is outside your authorization scope
• You need to implement controls (use implementing-* skills)
• Task is about analysis, not action (use analyzing-* skills)
• You don't have access to target systems
• Task requires compliance expertise (consult professionals)
• Task is about defense, not offense (use defensive skills)

Pseudo Code

# Example workflow for this skill
def execute(input_data):
    # Step 1: Validate input
    if not input_data:
        raise ValueError("Input data is required")

    # Step 2: Process core logic
    result = process(input_data)

    # Step 3: Validate output
    validate_output(result)

    return result

Document Ingestion

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

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

# Build index
index = VectorStoreIndex.from_documents(documents)

Query Engine

from llama_index.core import VectorStoreIndex
from llama_index.llms.openai import OpenAI

llm = OpenAI(model="gpt-4o", temperature=0)

index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine(
    llm=llm,
    similarity_top_k=5,
    response_mode="compact",  # or "tree_summarize", "refine"
)

response = query_engine.query("What are the company's revenue streams?")
print(response.response)
print(response.source_nodes)  # Citations

Chat Engine

chat_engine = index.as_chat_engine(
    chat_mode="condense_plus_context",  # or "context", "condense", "simple"
    llm=llm,
    similarity_top_k=3,
)

# Multi-turn conversation
response1 = chat_engine.chat("What is the return policy?")
response2 = chat_engine.chat("What about international orders?")  # Has context

Custom Document Loaders

from llama_index.core import Document

# From database
import pandas as pd
df = pd.read_sql("SELECT * FROM articles", con=engine)
documents = [
    Document(text=row['content'], metadata={"id": row['id'], "title": row['title']})
    for _, row in df.iterrows()
]

index = VectorStoreIndex.from_documents(documents)

Vector Store Integrations

from llama_index.vector_stores.pinecone import PineconeVectorStore
from llama_index.core import StorageContext

# Pinecone
vector_store = PineconeVectorStore(
    index_name="my-index",
    environment="us-east1-gcp",
)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)

# Qdrant
from llama_index.vector_stores.qdrant import QdrantVectorStore
import qdrant_client

client = qdrant_client.QdrantClient(host="localhost", port=6333)
vector_store = QdrantVectorStore(client=client, collection_name="docs")

Agent with Tools

from llama_index.core.agent import ReActAgent
from llama_index.core.tools import QueryEngineTool, FunctionTool

# Tool from query engine
query_tool = QueryEngineTool.from_defaults(
    query_engine=query_engine,
    name="document_search",
    description="Search company documents for information",
)

# Custom tool
def multiply(a: float, b: float) -> float:
    return a * b

multiply_tool = FunctionTool.from_defaults(fn=multiply)

# Agent
agent = ReActAgent.from_tools(
    tools=[query_tool, multiply_tool],
    llm=llm,
    verbose=True,
)

response = agent.chat("Find the Q3 revenue and multiply by 1.1 for Q4 projection")

Advanced RAG: Router Query Engine

from llama_index.core.query_engine import RouterQueryEngine
from llama_index.core.selectors import LLMSingleSelector

# Multiple query engines
vector_engine = index.as_query_engine()
summary_engine = index.as_query_engine(response_mode="tree_summarize")

router = RouterQueryEngine(
    selector=LLMSingleSelector.from_defaults(),
    query_engines=[vector_engine, summary_engine],
    select_multi=False,
)

response = router.query("Summarize all quarterly reports")

Common Patterns

Pattern	When to Use
SimpleDirectoryReader	Load files from disk
VectorStoreIndex	Build searchable index
as_query_engine()	Single question + answer
as_chat_engine()	Multi-turn conversation
ReActAgent	Agent with tool reasoning
RouterQueryEngine	Route to best engine automatically
response_mode="compact"	Concise answers
response_mode="tree_summarize"	Summarize across many documents

Error Handling

Error	Cause	Fix
IndexEmpty	No documents loaded	Check directory path and file types
Response incomplete	Too few source nodes	Increase similarity_top_k
Embedding dimension mismatch	Wrong model	Use consistent embedding model
Token limit during synthesis	Too many nodes retrieved	Reduce similarity_top_k or use compact mode

How to Use

1. Invoke the skill when relevant domain keywords appear in the request
2. Provide required inputs as specified in the skill definition
3. Review the output for correctness before delivering to the user
4. Combine with related skills for complex multi-step workflows

Verification

After completing this skill, confirm:

• Output meets the defined quality and completeness requirements
• All prerequisites are verified and documented
• Error handling covers edge cases
• Results are accurate and actionable

Process

1. Analyze the task requirements
2. Apply domain expertise
3. Verify output quality

Anti-Rationalization Table

Rationalization	Reality
"I will add monitoring later"	Without monitoring, you cannot detect failures. Add it from day one.
"One model is enough"	Different tasks need different models. Route intelligently.
"Premature optimization"	Infrastructure decisions are hard to change later. Design for scale early.