mirror of https://github.com/magnus919/agent-skills.git synced 2026-09-19 07:27:04 +03:00

Files

T

Magnus Hedemark 3e86349539 feat: add llamaindex — expert skill for LlamaIndex framework

Comprehensive skill covering:
- Core architecture (7 primitives, Settings, data flow)
- RAG strategies (basic through advanced with hybrid retrieval, reranking)
- Multi-agent orchestration (AgentWorkflow, handoff bug fix)
- Event-driven workflows (durable execution, checkpoint/resume)
- Production deployment (llama-deploy, debugging, observability)
- PropertyGraphIndex (knowledge graphs, hybrid retrieval)
- Evaluation and span-attached observability
- Integration ecosystem (vector stores, LlamaHub, LlamaParse)

9 reference files, 4 templates, 1 verification script.
MIT licensed. 100% AI agent portable (no platform-specific content).

Signed-off-by: Magnus Hedemark <magnus919@pm.me>

2026-07-09 13:32:53 -04:00

2.5 KiB

Raw Blame History

LlamaIndex Integration Ecosystem

Vector Stores

Store	Production	Best For
Pinecone	Yes	Managed, high-scale
Qdrant	Yes	Self-hosted or managed
Weaviate	Yes	Hybrid search + graph
Chroma	Yes	Embeddings + metadata
pgvector	Yes	PostgreSQL native
Milvus	Yes	Billion-scale
SimpleVectorStore	No	Dev/test only

from llama_index.vector_stores.qdrant import QdrantVectorStore
from qdrant_client import QdrantClient

vector_store = QdrantVectorStore(
    client=QdrantClient(url="http://localhost:6333"),
    collection_name="my_docs",
)

index = VectorStoreIndex.from_documents(documents, vector_store=vector_store)

LlamaHub Data Connectors

200+ data loaders available:

pip install llama-index-readers-notion
from llama_index.readers.notion import NotionPageReader
documents = NotionPageReader(integration_token="...").load_data()

Available connectors include: PDFs, Notion, Confluence, Slack, GitHub, S3, JIRA, SAP, Salesforce, Google Drive, SQL databases, web pages, Discord, YouTube.

LlamaParse Document Parsing

pip install llama-parse
from llama_parse import LlamaParse

parser = LlamaParse(result_type="markdown",
    parsing_instruction="Extract tables and preserve layout.")
documents = parser.load_data("./complex_document.pdf")

Key capabilities: LLM-powered parsing, JSON output mode, multi-model support (GPT-4.1, Gemini 2.5 Pro), auto skew correction, MCP integration.

LangChain Interoperability

from llama_index.core.langchain_helpers.agents import (
    IndexToolConfig, LlamaIndexTool
)

tool_config = IndexToolConfig(
    query_engine=query_engine,
    name="vector_index",
    description="Useful for answering queries about documents",
)
rag_tool = LlamaIndexTool.from_tool_config(tool_config)
# Use rag_tool with any LangChain agent

Framework Comparison

Framework	Lead With	Best For	License
LlamaIndex	Data ingestion + retrieval	RAG-heavy apps, heterogeneous sources	MIT
LangChain + LangGraph	Chain/graph abstractions	Multi-agent state machines, checkpoints	MIT
Haystack	Pipeline composition	Production NLP search pipelines	Apache 2.0
DSPy	Compiled prompt programs	Optimization-driven prompt programming	MIT

LlamaIndex excels when you have multiple data sources, multiple indexes, hybrid retrieval, and metadata filtering. For single-source, single-strategy RAG, the abstraction may not earn its weight.

2.5 KiB Raw Blame History