Ingesting Entire Enterprise Codebases

Gemini 1.5 Pro and Gemini 2.0 Flash feature an unprecedented 2-million token context window. In contrast to legacy LLM workflows that relied on aggressive, lossy RAG chunking and vector embeddings, 2M tokens fundamentally changes the paradigm of AI software engineering.

By supplying an entire repository directly into the model context, the self-attention layers construct a global cross-file dependency graph across classes, interfaces, and function signatures with zero retrieval retrieval loss.

from google import genai
from google.genai import types

client = genai.Client()

# Ingest an entire multi-file project directory
project_context = ""
for file_path in ["core.py", "types.py", "router.py", "database.py"]:
    with open(file_path, "r") as f:
        project_context += f"
--- FILE: {file_path} ---
" + f.read()

response = client.models.generate_content(
    model="gemini-2.0-flash",
    contents=[
        project_context,
        "Perform a static architectural audit: trace memory leaks in router.py across database connection lifecycles."
    ],
    config=types.GenerateContentConfig(
        temperature=0.2,
        max_output_tokens=2048,
    )
)
print(response.text)