claude-rag-skills

# RAG Pipeline Setup Guide for [COMPANY]

## Step 1: Data Ingestion
```python
# Claude Code snippet for data ingestion
from langchain.document_loaders import PyPDFLoader

loader = PyPDFLoader("[DATA_PATH]/documents/")
documents = loader.load()
```

## Step 2: Vectorization
```python
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import Chroma

text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
docs = text_splitter.split_documents(documents)
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
vectorstore = Chroma.from_documents(docs, embeddings)
```

## Step 3: Retrieval
```python
from langchain.chains import RetrievalQA
from langchain.llms import Claude

llm = Claude(temperature=0)
qa_chain = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=vectorstore.as_retriever())
```

## Optimization Tips
- Ensure chunk size and overlap are optimized for your document type.
- Monitor and update embeddings regularly to maintain retrieval accuracy.
- Implement caching for frequently accessed documents to improve performance.