Does it support Retrieval-Augmented Generation (RAG)?

Yes, it provides code for both basic and advanced RAG, including document splitting, retrieval, and cross-encoder reranking.

Which LLM providers does this skill support?

It primarily supports OpenAI and Anthropic, including patterns for the latest models like GPT-4o and Claude 3.5 Sonnet.

Can this skill help with vector databases?

Yes, it includes implementation patterns for vector databases like Pinecone and provides logic for cosine similarity and batch embeddings.

Can I use this for complex AI workflows?

Absolutely, it includes LangChain integration for chain composition, document loading, and branching logic based on model output.

AI/ML Integration

Name: AI/ML Integration
Author: miles990

bymiles990

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Ciencia de Datos y ML

Integrates cutting-edge AI and machine learning capabilities into applications using LLM APIs, vector databases, and RAG architectures.

Acerca de

This skill empowers Claude to architect and implement robust AI-driven features, ranging from simple API integrations with providers like OpenAI and Anthropic to sophisticated Retrieval-Augmented Generation (RAG) pipelines. It provides production-ready implementation patterns for generating text embeddings, managing vector databases with Pinecone, and orchestrating complex AI workflows using LangChain. Whether building intelligent chatbots, semantic search engines, or autonomous agents, this skill ensures best practices for token management, tool use, and context retrieval are followed.

Características Principales

Advanced RAG implementation with cross-encoder reranking and context management
Implementation patterns for LLM tool use and function calling
LangChain orchestration for complex chains and automated document processing
Multi-provider LLM support for OpenAI and Anthropic Claude APIs
1 GitHub stars
Semantic search workflows using text embeddings and vector databases like Pinecone

Casos de Uso

Implementing semantic search for large-scale internal documentation
Developing autonomous agents that leverage external tools and APIs
Building context-aware customer support chatbots using RAG

Acerca de

Características Principales

Advanced RAG implementation with cross-encoder reranking and context management
Implementation patterns for LLM tool use and function calling
LangChain orchestration for complex chains and automated document processing
Multi-provider LLM support for OpenAI and Anthropic Claude APIs
1 GitHub stars
Semantic search workflows using text embeddings and vector databases like Pinecone

Casos de Uso

Implementing semantic search for large-scale internal documentation
Developing autonomous agents that leverage external tools and APIs
Building context-aware customer support chatbots using RAG