Claude 1M Context Window: Büyük Codebase'i Tek Seferde Analiz Et

1response = client.messages.create(
2    model="claude-opus-4-7",
3    context_window_size="1M",  # Opsiyonel, 200k default
4    max_tokens=4096,
5    messages=[{
6        "role": "user",
7        "content": huge_content
8    }]
9)

1import os
2 
3def load_codebase(root: str) -> str:
4    files = []
5    for dirpath, _, filenames in os.walk(root):
6        for fname in filenames:
7            if fname.endswith(('.py', '.ts', '.md')):
8                path = os.path.join(dirpath, fname)
9                with open(path) as f:
10                    content = f.read()
11                    files.append(f"# FILE: {path}\n{content}")
12    return "\n\n".join(files)
13 
14codebase = load_codebase("./my_project")
15# ~80k satır kod → ~600k token
16 
17response = client.messages.create(
18    model="claude-opus-4-7",
19    context_window_size="1M",
20    max_tokens=8192,
21    messages=[{
22        "role": "user",
23        "content": f"Bu codebase'de potansiyel güvenlik açıkları, race condition'lar ve performans sorunlarını bul:\n\n{codebase}"
24    }]
25)

1100 AI research paper → "Bu 100 paper'daki recent trend'leri özetle,
2çelişen bulguları listele, en önemli 5 paper'ı rank et"

1"Bu 300 sayfalık Almanca kitabın tamamını Türkçe'ye çevir, tutarlı
2terminoloji kullan"

1"Bu 200-sayfa merger agreement'ı analiz et. Risk'ler, olağandışı
2clause'lar, karşı tarafın avantajlı olduğu maddeler."

1"Bu kullanıcının 2 yıllık support history'sini ve tüm bu conversation'ı
2oku. Ne çok şikayet ediyor? Nasıl daha iyi yaklaşabiliriz?"

1Input: 1M × $15/1M = $15
2Output: 10k × $75/1M = $0.75
3Total: $15.75

1First request(cache write): $18.75 + $0.75 = $19.50
2Next 9 requests(cache read): 9 × ($1.50 + $0.75) = $20.25
3Total: $39.75 (10 request, $3.98/request avg)

110 × $15.75 = $157.50

1with client.messages.stream(
2    model="claude-opus-4-7",
3    context_window_size="1M",
4    messages=[{"role": "user", "content": huge_content + question}]
5) as stream:
6    for text in stream.text_stream:
7        print(text, end="", flush=True)

1response = client.messages.create(
2    model="claude-opus-4-7",
3    context_window_size="1M",
4    extra_headers={"anthropic-beta": "prompt-caching-2024-07-31"},
5    max_tokens=4096,
6    messages=[{
7        "role": "user",
8        "content": [
9            # Cache breakpoint 1: Large codebase (persistent for session)
10            {
11                "type": "text",
12                "text": full_codebase,  # 800k token
13                "cache_control": {"type": "ephemeral", "ttl": "1h"}  # 1-hour cache (beta)
14            },
15 
16            # Cache breakpoint 2: System instructions
17            {
18                "type": "text",
19                "text": SYSTEM_PROMPT,
20                "cache_control": {"type": "ephemeral"}
21            },
22 
23            # Query-specific (not cached)
24            {
25                "type": "text",
26                "text": user_question
27            }
28        ]
29    }]
30)

1from anthropic import Anthropic
2 
3client = Anthropic()
4 
5count = client.beta.messages.count_tokens(
6    model="claude-opus-4-7",
7    messages=[{"role": "user", "content": my_content}]
8)
9print(f"Token sayısı: {count.input_tokens}")

1MAX_CONTEXT = 900_000  # 1M'in altında buffer
2 
3if count.input_tokens > MAX_CONTEXT:
4    # Prune strategy:
5    # 1. Remove duplicate files
6    # 2. Summarize older conversations
7    # 3. Use RAG for rarely-accessed docs
8    content = prune_content(content, target=MAX_CONTEXT)

1# Settings
2/effort xhigh          # xhigh effort, Opus 4.7
3/context 1M            # 1M context window
4 
5# Usage
6/codebase              # Tüm project'i context'e ekle
7# Şimdi Claude Code'a soru sor — tüm project bağlamında cevap

1from anthropic import Anthropic
2from pathlib import Path
3 
4def prepare_1m_context(project_root: Path, critical_paths: list[str] = None) -> str:
5    """1M context için codebase optimize et."""
6    sections = []
7 
8    # 1. Critical files first (high attention)
9    if critical_paths:
10        sections.append("# CRITICAL FILES (early attention)\n")
11        for path in critical_paths:
12            with open(project_root / path) as f:
13                sections.append(f"## {path}\n```\n{f.read()}\n```\n")
14 
15    # 2. Main source files (middle — OK for general context)
16    sections.append("\n# MAIN SOURCE FILES\n")
17    for py in project_root.rglob("*.py"):
18        if py.stat().st_size < 50_000 and py.name != "__init__.py":
19            sections.append(f"## {py.relative_to(project_root)}\n```python\n{py.read_text()}\n```\n")
20 
21    # 3. Recent changes (end — high attention for questions about changes)
22    sections.append("\n# RECENT CHANGES (high attention)\n")
23    # git log output...
24 
25    return "\n".join(sections)
26 
27client = Anthropic()
28context = prepare_1m_context(
29    project_root=Path("./my_project"),
30    critical_paths=["src/core/auth.py", "src/api/endpoints.py"]
31)
32 
33response = client.messages.create(
34    model="claude-opus-4-7",
35    context_window_size="1M",
36    extra_headers={"anthropic-beta": "prompt-caching-2024-07-31"},
37    max_tokens=4096,
38    messages=[{
39        "role": "user",
40        "content": [
41            {"type": "text", "text": context, "cache_control": {"type": "ephemeral", "ttl": "1h"}},
42            {"type": "text", "text": "Bu codebase'de refactor önerilerini 10 madde ile listele."}
43        ]
44    }]
45)