5bafa8b3a2
PR was blocked by .github/workflows/guard-fork-dependencies.yml: fork PRs cannot modify uv.lock. Reverting: - uv.lock + pyproject.toml black bump (24.10.0 -> 26.3.1) and the 295 files of mechanical Black 26 reformat coupled to it - pyproject.toml diskcache extra change (kept the runtime mitigation in litellm/caching/disk_cache.py via JSONDisk) Kept: - Dockerfile cache narrowing (drops ~660 MB of uv build cache that surfaced cached setuptools as CVE findings) - litellm/caching/disk_cache.py: dc.JSONDisk to neutralize CVE-2025-69872 - ui/litellm-dashboard/package-lock.json + litellm-js/spend-logs/package-lock.json: next/postcss/hono/uuid CVE bumps (these are not blocked by the fork guard) - tests/test_litellm/caching/test_disk_cache.py - tests/code_coverage_tests/liccheck.ini: harmless black authorization Black + gitpython + langchain dep upgrades will need a follow-up from a maintainer pushing a branch in the canonical BerriAI/litellm repo. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
96 lines
3.3 KiB
Python
96 lines
3.3 KiB
Python
"""
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LiteLLM Migration Script!
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Takes a config.yaml and calls /model/new
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Inputs:
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- File path to config.yaml
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- Proxy base url to your hosted proxy
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Step 1: Reads your config.yaml
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Step 2: reads `model_list` and loops through all models
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Step 3: calls `<proxy-base-url>/model/new` for each model
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"""
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import yaml
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import requests
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_in_memory_os_variables = {}
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def migrate_models(config_file, proxy_base_url):
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# Step 1: Read the config.yaml file
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with open(config_file, "r") as f:
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config = yaml.safe_load(f)
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# Step 2: Read the model_list and loop through all models
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model_list = config.get("model_list", [])
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print("model_list: ", model_list)
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for model in model_list:
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model_name = model.get("model_name")
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print("\nAdding model: ", model_name)
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litellm_params = model.get("litellm_params", {})
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api_base = litellm_params.get("api_base", "")
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print("api_base on config.yaml: ", api_base)
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litellm_model_name = litellm_params.get("model", "") or ""
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if "vertex_ai/" in litellm_model_name:
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print("\033[91m\nSkipping Vertex AI model\033[0m", model)
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continue
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for param, value in litellm_params.items():
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if isinstance(value, str) and value.startswith("os.environ/"):
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# check if value is in _in_memory_os_variables
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if value in _in_memory_os_variables:
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new_value = _in_memory_os_variables[value]
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print(
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"\033[92mAlready entered value for \033[0m",
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value,
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"\033[92musing \033[0m",
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new_value,
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)
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else:
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new_value = input(f"Enter value for {value}: ")
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_in_memory_os_variables[value] = new_value
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litellm_params[param] = new_value
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if "api_key" not in litellm_params:
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new_value = input(f"Enter api key for {model_name}: ")
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litellm_params["api_key"] = new_value
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print("\nlitellm_params: ", litellm_params)
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# Confirm before sending POST request
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confirm = input(
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"\033[92mDo you want to send the POST request with the above parameters? (y/n): \033[0m"
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)
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if confirm.lower() != "y":
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print("Aborting POST request.")
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exit()
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# Step 3: Call <proxy-base-url>/model/new for each model
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url = f"{proxy_base_url}/model/new"
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {master_key}",
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}
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data = {"model_name": model_name, "litellm_params": litellm_params}
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print("POSTING data to proxy url", url)
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response = requests.post(url, headers=headers, json=data)
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if response.status_code != 200:
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print(f"Error: {response.status_code} - {response.text}")
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raise Exception(f"Error: {response.status_code} - {response.text}")
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# Print the response for each model
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print(
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f"Response for model '{model_name}': Status Code:{response.status_code} - {response.text}"
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)
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# Usage
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config_file = "config.yaml"
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proxy_base_url = "http://0.0.0.0:4000"
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master_key = "sk-1234"
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print(f"config_file: {config_file}")
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print(f"proxy_base_url: {proxy_base_url}")
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migrate_models(config_file, proxy_base_url)
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