Initial commit
This commit is contained in:
Zhongwei Li
14
.claude-plugin/plugin.json
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14
.claude-plugin/plugin.json
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{
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"name": "ai-ml-integration",
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"description": "AI/ML integration patterns with LangChain, RAG, vector databases, and prompt engineering",
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"version": "1.0.0",
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"author": {
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"name": "Brock"
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},
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"agents": [
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"./agents"
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],
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"commands": [
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"./commands"
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]
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}
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3
README.md
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3
README.md
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# ai-ml-integration
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AI/ML integration patterns with LangChain, RAG, vector databases, and prompt engineering
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90
agents/ai-integrator.md
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90
agents/ai-integrator.md
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# AI/ML Integrator Agent
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You are an autonomous agent specialized in integrating AI/ML capabilities using LangChain, RAG, vector databases, and modern LLM frameworks.
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## Your Mission
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Build production-ready AI-powered features using LLMs, embeddings, vector databases, and retrieval-augmented generation.
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## Core Responsibilities
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### 1. Design AI Architecture
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- Choose appropriate LLM (GPT-4, Claude, Llama)
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- Design RAG pipeline
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- Select vector database
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- Plan prompt engineering strategy
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- Design evaluation metrics
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### 2. Implement RAG Pipeline
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```typescript
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import { OpenAIEmbeddings } from 'langchain/embeddings/openai';
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import { PineconeStore } from 'langchain/vectorstores/pinecone';
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import { RetrievalQAChain } from 'langchain/chains';
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// Setup vector store
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const vectorStore = await PineconeStore.fromDocuments(
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documents,
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new OpenAIEmbeddings(),
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{ pineconeIndex: index }
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);
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// Create RAG chain
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const chain = RetrievalQAChain.fromLLM(
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model,
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vectorStore.asRetriever()
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);
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const answer = await chain.call({ query: 'User question' });
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```
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### 3. Engineer Prompts
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- Design effective system prompts
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- Implement few-shot learning
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- Use structured outputs
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- Test and iterate
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### 4. Implement Memory
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- Conversation history
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- Summary memory
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- Entity memory
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- Session management
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### 5. Build Agents
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- Tool-using agents
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- Custom tools
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- Multi-step reasoning
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- Error handling
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### 6. Optimize Performance
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- Cache embeddings
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- Batch processing
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- Stream responses
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- Cost optimization
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### 7. Evaluate Quality
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- Test outputs
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- A/B testing
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- Monitor hallucinations
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- User feedback loop
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## Best Practices
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- Use appropriate models for tasks
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- Implement caching
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- Handle rate limits
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- Validate outputs
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- Monitor costs
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- Test thoroughly
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- Secure API keys
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- Implement fallbacks
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## Deliverables
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1. RAG pipeline setup
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2. Vector database integration
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3. Prompt templates
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4. Agent implementations
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5. Evaluation framework
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6. Monitoring setup
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7. Documentation
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437
commands/ai-patterns.md
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437
commands/ai-patterns.md
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# AI/ML Integration Patterns
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Comprehensive patterns for integrating AI/ML capabilities with LangChain, RAG, vector databases, and prompt engineering.
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## LangChain Integration
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### Basic LLM Setup
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```typescript
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import { ChatOpenAI } from 'langchain/chat_models/openai';
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import { HumanMessage, SystemMessage } from 'langchain/schema';
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const model = new ChatOpenAI({
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openAIApiKey: process.env.OPENAI_API_KEY,
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modelName: 'gpt-4-turbo-preview',
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temperature: 0.7,
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});
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async function chat(userMessage: string) {
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const messages = [
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new SystemMessage('You are a helpful assistant.'),
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new HumanMessage(userMessage),
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];
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const response = await model.invoke(messages);
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return response.content;
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}
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```
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### Chains for Sequential Processing
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```typescript
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import { LLMChain } from 'langchain/chains';
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import { PromptTemplate } from 'langchain/prompts';
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const summaryTemplate = `
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Summarize the following text in 2-3 sentences:
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Text: {text}
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Summary:`;
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const prompt = new PromptTemplate({
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template: summaryTemplate,
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inputVariables: ['text'],
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});
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const chain = new LLMChain({
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llm: model,
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prompt,
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});
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const result = await chain.call({ text: 'Long text here...' });
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```
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## RAG (Retrieval Augmented Generation)
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### Vector Store Setup
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```typescript
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import { OpenAIEmbeddings } from 'langchain/embeddings/openai';
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import { PineconeStore } from 'langchain/vectorstores/pinecone';
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import { Pinecone } from '@pinecone-database/pinecone';
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import { RecursiveCharacterTextSplitter } from 'langchain/text_splitter';
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// Initialize Pinecone
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const pinecone = new Pinecone({
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apiKey: process.env.PINECONE_API_KEY,
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});
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const index = pinecone.Index('my-index');
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// Create embeddings
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const embeddings = new OpenAIEmbeddings({
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openAIApiKey: process.env.OPENAI_API_KEY,
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});
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// Split documents
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const textSplitter = new RecursiveCharacterTextSplitter({
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chunkSize: 1000,
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chunkOverlap: 200,
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});
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const docs = await textSplitter.createDocuments([longText]);
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// Store in vector database
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await PineconeStore.fromDocuments(docs, embeddings, {
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pineconeIndex: index,
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namespace: 'my-namespace',
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});
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```
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### RAG Chain
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```typescript
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import { RetrievalQAChain } from 'langchain/chains';
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import { ChatOpenAI } from 'langchain/chat_models/openai';
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// Create vector store
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const vectorStore = await PineconeStore.fromExistingIndex(embeddings, {
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pineconeIndex: index,
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namespace: 'my-namespace',
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});
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// Create retriever
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const retriever = vectorStore.asRetriever({
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k: 4, // Return top 4 most relevant documents
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});
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// Create RAG chain
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const chain = RetrievalQAChain.fromLLM(
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new ChatOpenAI({ modelName: 'gpt-4' }),
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retriever
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);
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// Query
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const response = await chain.call({
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query: 'What is the return policy?',
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});
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console.log(response.text);
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```
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### Advanced RAG with Contextual Compression
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```typescript
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import { ContextualCompressionRetriever } from 'langchain/retrievers/contextual_compression';
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import { LLMChainExtractor } from 'langchain/retrievers/document_compressors/chain_extract';
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const baseRetriever = vectorStore.asRetriever();
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const compressor = LLMChainExtractor.fromLLM(
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new ChatOpenAI({
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modelName: 'gpt-3.5-turbo',
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temperature: 0,
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})
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);
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const retriever = new ContextualCompressionRetriever({
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baseCompressor: compressor,
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baseRetriever,
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});
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const relevantDocs = await retriever.getRelevantDocuments(
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'What is the pricing structure?'
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);
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```
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## Prompt Engineering
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### Few-Shot Learning
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```typescript
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import { FewShotPromptTemplate, PromptTemplate } from 'langchain/prompts';
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const examples = [
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{
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question: 'Who is the CEO of Tesla?',
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answer: 'Elon Musk is the CEO of Tesla.',
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},
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{
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question: 'When was Apple founded?',
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answer: 'Apple was founded in 1976.',
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},
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];
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const examplePrompt = new PromptTemplate({
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inputVariables: ['question', 'answer'],
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template: 'Q: {question}\nA: {answer}',
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});
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const fewShotPrompt = new FewShotPromptTemplate({
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examples,
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examplePrompt,
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prefix: 'Answer the following questions accurately:',
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suffix: 'Q: {input}\nA:',
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inputVariables: ['input'],
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});
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const formatted = await fewShotPrompt.format({
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input: 'Who founded Microsoft?',
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});
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```
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### Structured Output
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```typescript
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import { z } from 'zod';
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import { StructuredOutputParser } from 'langchain/output_parsers';
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const parser = StructuredOutputParser.fromZodSchema(
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z.object({
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name: z.string().describe('The person\'s name'),
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age: z.number().describe('The person\'s age'),
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occupation: z.string().describe('The person\'s occupation'),
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})
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);
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const prompt = PromptTemplate.fromTemplate(
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`Extract information about the person.
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{format_instructions}
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Text: {text}
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Output:`
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);
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const input = await prompt.format({
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text: 'John Doe is a 30-year-old software engineer.',
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format_instructions: parser.getFormatInstructions(),
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});
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const response = await model.invoke(input);
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const parsed = await parser.parse(response.content);
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// { name: 'John Doe', age: 30, occupation: 'software engineer' }
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```
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## Memory and Conversation
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### Conversation Buffer Memory
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```typescript
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import { ConversationChain } from 'langchain/chains';
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import { BufferMemory } from 'langchain/memory';
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const memory = new BufferMemory();
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const chain = new ConversationChain({
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llm: model,
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memory,
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});
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// First turn
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await chain.call({ input: 'Hi, my name is John.' });
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// Response: Hello John! How can I help you today?
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// Second turn (remembers name)
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await chain.call({ input: 'What is my name?' });
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// Response: Your name is John.
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```
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### Summary Memory
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```typescript
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import { ConversationSummaryMemory } from 'langchain/memory';
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const memory = new ConversationSummaryMemory({
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llm: new ChatOpenAI({ modelName: 'gpt-3.5-turbo' }),
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maxTokenLimit: 2000,
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});
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const chain = new ConversationChain({ llm: model, memory });
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```
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## Agents
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### Tool-Using Agent
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```typescript
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import { initializeAgentExecutorWithOptions } from 'langchain/agents';
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import { Calculator } from 'langchain/tools/calculator';
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import { SerpAPI } from 'langchain/tools';
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const tools = [
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new Calculator(),
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new SerpAPI(process.env.SERPAPI_API_KEY),
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];
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const executor = await initializeAgentExecutorWithOptions(tools, model, {
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agentType: 'zero-shot-react-description',
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verbose: true,
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});
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const result = await executor.call({
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input: 'What is the current temperature in San Francisco and what is 25% of 80?',
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});
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```
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### Custom Tool
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```typescript
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import { DynamicTool } from 'langchain/tools';
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const databaseTool = new DynamicTool({
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name: 'database-query',
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description: 'Useful for querying the database. Input should be a SQL query.',
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func: async (query: string) => {
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const results = await db.query(query);
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return JSON.stringify(results);
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},
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});
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const tools = [databaseTool];
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const executor = await initializeAgentExecutorWithOptions(tools, model, {
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agentType: 'zero-shot-react-description',
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});
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```
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## Streaming Responses
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```typescript
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import { ChatOpenAI } from 'langchain/chat_models/openai';
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const model = new ChatOpenAI({
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streaming: true,
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callbacks: [
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{
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handleLLMNewToken(token: string) {
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process.stdout.write(token);
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},
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},
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],
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});
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await model.invoke([new HumanMessage('Tell me a story')]);
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```
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## Embeddings for Semantic Search
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```typescript
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import { OpenAIEmbeddings } from 'langchain/embeddings/openai';
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const embeddings = new OpenAIEmbeddings();
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// Generate embeddings
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const queryEmbedding = await embeddings.embedQuery('What is machine learning?');
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// Store in database with pgvector
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await db.query(
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'INSERT INTO documents (content, embedding) VALUES ($1, $2)',
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['Machine learning is...', queryEmbedding]
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);
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// Semantic search
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const results = await db.query(
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'SELECT content FROM documents ORDER BY embedding <=> $1 LIMIT 5',
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[queryEmbedding]
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);
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```
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## Document Loaders
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```typescript
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import { PDFLoader } from 'langchain/document_loaders/fs/pdf';
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import { CSVLoader } from 'langchain/document_loaders/fs/csv';
|
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import { GithubRepoLoader } from 'langchain/document_loaders/web/github';
|
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|
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// Load PDF
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const pdfLoader = new PDFLoader('document.pdf');
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const pdfDocs = await pdfLoader.load();
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// Load CSV
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const csvLoader = new CSVLoader('data.csv');
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const csvDocs = await csvLoader.load();
|
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|
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// Load GitHub repo
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const githubLoader = new GithubRepoLoader(
|
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'https://github.com/user/repo',
|
||||
{ branch: 'main', recursive: true }
|
||||
);
|
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const githubDocs = await githubLoader.load();
|
||||
```
|
||||
|
||||
## Evaluation
|
||||
|
||||
```typescript
|
||||
import { loadEvaluator } from 'langchain/evaluation';
|
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|
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// Evaluate response quality
|
||||
const evaluator = await loadEvaluator('qa');
|
||||
|
||||
const result = await evaluator.evaluateStrings({
|
||||
prediction: 'The capital of France is Paris.',
|
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input: 'What is the capital of France?',
|
||||
reference: 'Paris is the capital of France.',
|
||||
});
|
||||
|
||||
console.log(result);
|
||||
// { score: 1.0, reasoning: '...' }
|
||||
```
|
||||
|
||||
## Best Practices
|
||||
|
||||
### 1. Prompt Design
|
||||
- Be specific and clear
|
||||
- Provide context and examples
|
||||
- Use system messages for role definition
|
||||
- Iterate and test prompts
|
||||
|
||||
### 2. RAG Optimization
|
||||
- Chunk documents appropriately (1000-2000 chars)
|
||||
- Use overlap for context preservation
|
||||
- Implement re-ranking for better results
|
||||
- Monitor retrieval quality
|
||||
|
||||
### 3. Cost Management
|
||||
- Use appropriate models (GPT-3.5 vs GPT-4)
|
||||
- Implement caching
|
||||
- Batch requests when possible
|
||||
- Monitor token usage
|
||||
|
||||
### 4. Error Handling
|
||||
- Handle rate limits
|
||||
- Implement retries with exponential backoff
|
||||
- Validate LLM outputs
|
||||
- Provide fallbacks
|
||||
|
||||
### 5. Security
|
||||
- Sanitize user inputs
|
||||
- Implement output filtering
|
||||
- Rate limit API calls
|
||||
- Protect API keys
|
||||
|
||||
### 6. Testing
|
||||
- Test prompts with various inputs
|
||||
- Evaluate output quality
|
||||
- Monitor for hallucinations
|
||||
- A/B test different approaches
|
||||
|
||||
## Vector Database Options
|
||||
|
||||
- **Pinecone**: Managed, scalable
|
||||
- **Weaviate**: Open-source, feature-rich
|
||||
- **Qdrant**: Fast, efficient
|
||||
- **ChromaDB**: Simple, lightweight
|
||||
- **pgvector**: PostgreSQL extension
|
||||
- **Milvus**: Open-source, distributed
|
||||
|
||||
## Model Selection Guide
|
||||
|
||||
- **GPT-4**: Complex reasoning, highest quality
|
||||
- **GPT-3.5-Turbo**: Fast, cost-effective
|
||||
- **Claude**: Long context, safety-focused
|
||||
- **Llama 2**: Open-source, self-hosted
|
||||
- **Mistral**: Open-source, efficient
|
||||
49
plugin.lock.json
Normal file
49
plugin.lock.json
Normal file
@@ -0,0 +1,49 @@
|
||||
{
|
||||
"$schema": "internal://schemas/plugin.lock.v1.json",
|
||||
"pluginId": "gh:Dieshen/claude_marketplace:plugins/ai-ml-integration",
|
||||
"normalized": {
|
||||
"repo": null,
|
||||
"ref": "refs/tags/v20251128.0",
|
||||
"commit": "6a899f0c0d22713821ac6ef9a9a524f0e8d2cefb",
|
||||
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Reference in New Issue
Block a user