RAGWire with Anthropic¶

Use Anthropic Claude as the metadata extraction LLM paired with any embedding provider.

Anthropic does not provide embedding models. You need a separate embedding provider — Ollama (local/free) or OpenAI are recommended.

Prerequisites¶

Anthropic API key — console.anthropic.com
RAGWire installed: pip install "ragwire[anthropic]"
Qdrant running: docker run -d -p 6333:6333 qdrant/qdrant

1. Install Dependencies¶

# Anthropic for LLM + Ollama for embeddings (fully local embeddings, no cost)
pip install "ragwire[anthropic]" "ragwire[ollama]"

# Or with OpenAI for embeddings
pip install "ragwire[anthropic]" "ragwire[openai]"

pip install fastembed               # For hybrid search

2. Set API Key¶

# Linux / macOS
export ANTHROPIC_API_KEY="sk-ant-..."

# Windows (PowerShell)
$env:ANTHROPIC_API_KEY="sk-ant-..."

Or add it to a .env file at the project root:

ANTHROPIC_API_KEY=sk-ant-...

3. Configuration¶

Claude LLM + Ollama Embeddings (recommended — free embeddings)¶

embeddings:
  provider: "ollama"
  model: "nomic-embed-text"
  base_url: "http://localhost:11434"

llm:
  provider: "anthropic"
  model: "claude-haiku-4-5-20251001"   # Fastest, cheapest — ideal for extraction
  # model: "claude-sonnet-4-6"         # Best speed/intelligence balance
  # model: "claude-opus-4-6"           # Most intelligent

vectorstore:
  url: "http://localhost:6333"
  collection_name: "my_docs"
  use_sparse: true
  force_recreate: false

retriever:
  search_type: "hybrid"
  top_k: 5
  auto_filter: false   # set true to enable LLM-based filter extraction from every query

Claude LLM + OpenAI Embeddings¶

embeddings:
  provider: "openai"
  model: "text-embedding-3-small"

llm:
  provider: "anthropic"
  model: "claude-haiku-4-5-20251001"   # 200K context, $1/$5 per MTok

vectorstore:
  url: "http://localhost:6333"
  collection_name: "my_docs"
  use_sparse: true
  force_recreate: false

retriever:
  search_type: "hybrid"
  top_k: 5
  auto_filter: false   # set true to enable LLM-based filter extraction from every query

4. Python Usage¶

from ragwire import RAGWire

rag = RAGWire("config.yaml")

# Ingest
stats = rag.ingest_documents(["data/Apple_10k_2025.pdf"])
print(f"Chunks created: {stats['chunks_created']}")

# Retrieve
results = rag.retrieve("What is Apple's total revenue?", top_k=5)
for doc in results:
    print(doc.metadata.get("company_name"), doc.page_content[:200])

5. Run the Example¶

python examples/basic_usage.py

Available Models¶

Model	Context	Price (in/out per MTok)	Notes
`claude-haiku-4-5-20251001`	200K	$1 / $5	Fastest, cheapest — recommended for extraction
`claude-sonnet-4-6`	1M	$3 / $15	Best speed/intelligence balance
`claude-opus-4-6`	1M	$5 / $25	Most intelligent

Notes¶

The API key can also be passed directly in config: api_key: "sk-ant-..." — but environment variables are preferred.
Claude Haiku is the recommended model for metadata extraction — it is fast, cheap, and follows JSON instructions reliably.