RAG (Retrieval-Augmented Generation) vs Uzun bağlam (1M+ token) Comparison

# OpenAI Responses API — File Search (a managed RAG tool)
# As of September 2026, official docs: platform.openai.com/docs/guides/tools-file-search
from openai import OpenAI

client = OpenAI()

response = client.responses.create(
    model="gpt-6-astra",
    input="Q3 finansal raporunda net kâr marjı neydi?",
    tools=[{
        "type": "file_search",
        "vector_store_ids": ["vs_abc123"],
    }],
)

print(response.output_text)

# This hosted tool manages embedding + indexing + retrieval on OpenAI's side
# (works with no code). Cost: $2.50 per 1k tool calls
# + storage $0.10/GB/day (first 1GB free). Source: platform.openai.com/docs/pricing

# Claude API — making a 1M-token context cheaper with prompt caching
# Source: platform.claude.com/docs/en/build-with-claude/prompt-caching
import anthropic

client = anthropic.Anthropic()

response = client.messages.create(
    model="claude-sonnet-5",
    max_tokens=1024,
    system=[
        {
            "type": "text",
            "text": full_knowledge_base_text,  # e.g. 800K tokens of internal documentation
            "cache_control": {"type": "ephemeral"},
        }
    ],
    messages=[{"role": "user", "content": "Onboarding sürecinde hangi adımlar zorunlu?"}],
)

# First call: billed at the full input price (cache write).
# Subsequent calls (within a short window, e.g. 5 min): the cache-read price applies
# — a small fraction of the base input price (see the official pricing page).