II.
LibraryProcess overview
Reference · livelib-process:ai-agents-conversational--rag-pipeline-implementation
rag-pipeline-implementation overview
RAG Pipeline Design and Implementation - Comprehensive process for building RAG pipelines including document ingestion, chunking strategies, embedding generation, vector storage, retrieval, and generation.
Attributes
displayName
rag-pipeline-implementation
description
RAG Pipeline Design and Implementation - Comprehensive process for building RAG pipelines including
document ingestion, chunking strategies, embedding generation, vector storage, retrieval, and generation.
libraryPath
library/specializations/ai-agents-conversational/rag-pipeline-implementation.js
specialization
ai-agents-conversational
references
- - LlamaIndex RAG: https://docs.llamaindex.ai/en/stable/ - LangChain RAG: https://python.langchain.com/docs/use_cases/question_answering/ - Pinecone: https://docs.pinecone.io/
example
const result = await orchestrate('specializations/ai-agents-conversational/rag-pipeline-implementation', {
pipelineName: 'docs-qa-system',
documentSources: ['confluence', 'github-docs'],
vectorDb: 'pinecone',
embeddingModel: 'text-embedding-3-small'
});
usesAgents
- rag-architect
- rag-evaluator
usesSkills
- document-loaders
- text-splitters
- embedding-models
- vector-store-configs
- rag-prompt-templates
Outgoing edges
lib_applies_to_domain1
- domain:software-engineering·DomainSoftware Engineering
lib_belongs_to_specialization1
- specialization:ai-agents-conversational·Specialization
lib_implements_workflow2
- workflow:release-management·Workflow
- workflow:data-pipeline-deployment·WorkflowData Pipeline Deployment
Incoming edges
None.