mirror of
https://github.com/BerriAI/litellm.git
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fix(ci): sync openrouter and vercel_ai_gateway prices without crashing on unpriced catalog rows
This commit is contained in:
mateo-berri
parent
fd36e0cde1
commit
9c5db54d53
3 changed files with 178 additions and 74 deletions
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@ -1,6 +1,34 @@
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import asyncio
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import aiohttp
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import json
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from types import MappingProxyType
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from typing import Final
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import aiohttp
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from pydantic import BaseModel
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COST_MAP_PATHS: Final = (
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"model_prices_and_context_window.json",
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"litellm/model_prices_and_context_window_backup.json",
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)
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OPENROUTER_URL: Final = "https://openrouter.ai/api/v1/models"
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VERCEL_AI_GATEWAY_URL: Final = "https://ai-gateway.vercel.sh/v1/models"
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VERCEL_TYPE_TO_MODE: Final = MappingProxyType({"language": "chat", "embedding": "embedding"})
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class VercelPricing(BaseModel):
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input: float | None = None
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output: float | None = None
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input_cache_read: float | None = None
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input_cache_write: float | None = None
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class VercelModel(BaseModel):
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id: str
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type: str
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context_window: int | None = None
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max_tokens: int | None = None
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pricing: VercelPricing | None = None
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# Asynchronously fetch data from a given URL
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async def fetch_data(url):
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@ -31,17 +59,11 @@ def sync_local_data_with_remote(local_data, remote_data):
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for key in (set(remote_data) - set(local_data)):
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local_data[key] = remote_data[key]
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# Write data to the json file
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def write_to_file(file_path, data):
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try:
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# Open the file in write mode
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with open(file_path, "w") as file:
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# Dump the data as JSON into the file
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json.dump(data, file, indent=4)
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print("Values updated successfully.")
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except Exception as e:
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# Print an error message if writing to file fails
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print("Error updating JSON file:", e)
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def write_to_file(file_path: str, data: dict[str, object]) -> None:
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with open(file_path, "w", encoding="utf-8") as file:
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file.write(json.dumps(data, indent=4, ensure_ascii=False) + "\n")
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print(f"Wrote {file_path}.")
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# Update the existing models and add the missing models for OpenRouter
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def transform_openrouter_data(data):
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@ -81,40 +103,30 @@ def transform_openrouter_data(data):
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return transformed
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# Update the existing models and add the missing models for Vercel AI Gateway
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def transform_vercel_ai_gateway_data(data):
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transformed = {}
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for row in data:
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obj = {}
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def _vercel_entry(model: VercelModel) -> dict[str, object] | None:
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mode: Final = VERCEL_TYPE_TO_MODE[model.type]
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pricing: Final = model.pricing if model.pricing is not None else VercelPricing()
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if pricing.input is None or (mode == "chat" and pricing.output is None):
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print(f"Skipping vercel_ai_gateway/{model.id}: the catalog lists no per-token price for it.")
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return None
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candidate: Final = {
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"max_tokens": model.max_tokens,
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"max_input_tokens": model.context_window,
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"max_output_tokens": model.max_tokens,
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"input_cost_per_token": pricing.input,
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"output_cost_per_token": pricing.output if pricing.output is not None else 0.0,
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"cache_read_input_token_cost": pricing.input_cache_read,
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"cache_creation_input_token_cost": pricing.input_cache_write,
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"litellm_provider": "vercel_ai_gateway",
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"mode": mode,
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}
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return {key: value for key, value in candidate.items() if value is not None}
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if "context_window" in row:
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obj['max_tokens'] = row["context_window"]
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obj['max_input_tokens'] = row["context_window"]
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if "pricing" in row:
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if "input" in row["pricing"] and row["pricing"]["input"] is not None:
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obj['input_cost_per_token'] = float(row["pricing"]["input"])
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if "output" in row["pricing"] and row["pricing"]["output"] is not None:
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obj['output_cost_per_token'] = float(row["pricing"]["output"])
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if "max_tokens" in row:
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obj['max_output_tokens'] = row['max_tokens']
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# Handle cache pricing if available
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if "pricing" in row:
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if "input_cache_read" in row["pricing"] and row["pricing"]["input_cache_read"] is not None:
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obj['cache_read_input_token_cost'] = float(f"{float(row['pricing']['input_cache_read']):e}")
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if "input_cache_write" in row["pricing"] and row["pricing"]["input_cache_write"] is not None:
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obj['cache_creation_input_token_cost'] = float(f"{float(row['pricing']['input_cache_write']):e}")
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mode = "embedding" if "embedding" in row["id"].lower() else "chat"
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obj.update({"litellm_provider": "vercel_ai_gateway", "mode": mode})
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transformed[f'vercel_ai_gateway/{row["id"]}'] = obj
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return transformed
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def transform_vercel_ai_gateway_data(data: list[dict[str, object]]) -> dict[str, dict[str, object]]:
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models: Final = tuple(VercelModel.model_validate(row) for row in data if row.get("type") in VERCEL_TYPE_TO_MODE)
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entries: Final = ((f"vercel_ai_gateway/{model.id}", _vercel_entry(model)) for model in models)
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return {key: entry for key, entry in entries if entry is not None}
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# Load local data from a specified file
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@ -134,30 +146,16 @@ def load_local_data(file_path):
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return None
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def main():
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local_file_path = "model_prices_and_context_window.json" # Path to the local data file
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openrouter_url = "https://openrouter.ai/api/v1/models" # URL to fetch OpenRouter data
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vercel_ai_gateway_url = "https://ai-gateway.vercel.sh/v1/models" # URL to fetch Vercel AI Gateway data
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local_data = load_local_data(COST_MAP_PATHS[0])
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# Load local data from file
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local_data = load_local_data(local_file_path)
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# Fetch OpenRouter data
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openrouter_data = asyncio.run(fetch_data(openrouter_url))
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# Transform the fetched OpenRouter data
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openrouter_data = transform_openrouter_data(openrouter_data)
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# Fetch Vercel AI Gateway data
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vercel_data = asyncio.run(fetch_data(vercel_ai_gateway_url))
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# Transform the fetched Vercel AI Gateway data
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vercel_data = transform_vercel_ai_gateway_data(vercel_data)
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# Combine both datasets
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openrouter_data = transform_openrouter_data(asyncio.run(fetch_data(OPENROUTER_URL)))
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vercel_data = transform_vercel_ai_gateway_data(asyncio.run(fetch_data(VERCEL_AI_GATEWAY_URL)))
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all_remote_data = {**openrouter_data, **vercel_data}
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# If both local and openrouter data are available, synchronize and save
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if local_data and all_remote_data:
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sync_local_data_with_remote(local_data, all_remote_data)
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write_to_file(local_file_path, local_data)
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for path in COST_MAP_PATHS:
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write_to_file(path, local_data)
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else:
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print("Failed to fetch model data from either local file or URL.")
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@ -2,8 +2,8 @@ name: Updates model_prices_and_context_window.json and Create Pull Request
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on:
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schedule:
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- cron: "0 0 * * 0" # Run every Sundays at midnight
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#- cron: "0 0 * * *" # Run daily at midnight
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- cron: "0 0 * * 0"
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workflow_dispatch:
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permissions:
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contents: write
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@ -21,19 +21,44 @@ jobs:
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uses: ./.github/actions/setup-uv-with-retries
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with:
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version: "0.10.9"
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- name: Update JSON Data
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- name: Look for an already-open price map update PR
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id: existing
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run: |
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uv run --frozen --with 'aiohttp==3.13.3' python ".github/scripts/auto_update_price_and_context_window_file.py"
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open_pr="$(gh pr list --repo "$GITHUB_REPOSITORY" --state open --limit 1000 --json headRefName --jq '[.[].headRefName | select(startswith("litellm_price_map_auto_update_"))] | first // empty')"
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if [ -n "$open_pr" ]; then
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echo "Skipping the sync: the price map update PR on $open_pr is still open"
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fi
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echo "open_pr=$open_pr" >> "$GITHUB_OUTPUT"
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env:
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GH_TOKEN: ${{ secrets.GH_TOKEN || github.token }}
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- name: Update JSON Data
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if: steps.existing.outputs.open_pr == ''
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run: |
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uv run --frozen --with 'aiohttp==3.13.3' python .github/scripts/auto_update_price_and_context_window_file.py
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- name: Regenerate JSON Schema
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if: steps.existing.outputs.open_pr == ''
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run: |
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uv run --frozen python ci_cd/generate_model_prices_schema.py
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- name: Create Pull Request
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if: steps.existing.outputs.open_pr == ''
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run: |
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git add model_prices_and_context_window.json model_prices_and_context_window.schema.json
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git commit -m "Update model_prices_and_context_window.json file: $(date +'%Y-%m-%d')"
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gh pr create --title "Update model_prices_and_context_window.json file" \
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--body "Automated update for model_prices_and_context_window.json" \
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--head auto-update-price-and-context-window-$(date +'%Y-%m-%d') \
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--base main
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if git diff --quiet; then
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echo "Price map already in sync with the OpenRouter and Vercel AI Gateway catalogs; no PR needed."
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exit 0
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fi
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today="$(date +'%Y-%m-%d')"
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branch="litellm_price_map_auto_update_$today"
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git config user.name "github-actions[bot]"
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git config user.email "41898282+github-actions[bot]@users.noreply.github.com"
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git checkout -b "$branch"
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git add model_prices_and_context_window.json litellm/model_prices_and_context_window_backup.json model_prices_and_context_window.schema.json
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git commit -m "chore(models): sync openrouter and vercel_ai_gateway prices $today"
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gh auth setup-git
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git push origin "$branch"
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gh pr create \
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--title "chore(models): sync openrouter and vercel_ai_gateway prices $today" \
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--body "Automated sync of model_prices_and_context_window.json from the OpenRouter and Vercel AI Gateway model catalogs" \
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--head "$branch" \
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--base litellm_internal_staging
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env:
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GH_TOKEN: ${{ secrets.GH_TOKEN }}
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GH_TOKEN: ${{ secrets.GH_TOKEN || github.token }}
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@ -0,0 +1,81 @@
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import importlib.util
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from pathlib import Path
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ROOT = Path(__file__).resolve().parents[2]
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SCRIPT = ROOT / ".github" / "scripts" / "auto_update_price_and_context_window_file.py"
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_spec = importlib.util.spec_from_file_location("auto_update_price_and_context_window_file", SCRIPT)
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assert _spec is not None and _spec.loader is not None
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price_sync = importlib.util.module_from_spec(_spec)
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_spec.loader.exec_module(price_sync)
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LANGUAGE_ROW = {
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"id": "anthropic/claude-sonnet-4.5",
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"type": "language",
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"context_window": 200000,
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"max_tokens": 64000,
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"pricing": {
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"input": "0.000003",
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"output": "0.000015",
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"input_cache_read": "0.0000003",
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"input_cache_write": "0.00000375",
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},
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}
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EMBEDDING_ROW = {
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"id": "google/gemini-embedding-001",
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"type": "embedding",
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"context_window": 2048,
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"max_tokens": 0,
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"pricing": {"input": "0.00000015"},
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}
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UNPRICED_LANGUAGE_ROW = {
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"id": "perplexity/sonar",
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"type": "language",
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"context_window": 128000,
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"max_tokens": 8000,
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}
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VIDEO_ROW = {
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"id": "google/veo-3.1",
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"type": "video",
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"pricing": {"video_duration_pricing": [{"resolution": "720p", "price_per_second": "0.4"}]},
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}
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IMAGE_ROW = {"id": "openai/gpt-image-1", "type": "image"}
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def test_vercel_rows_without_per_token_pricing_are_skipped_instead_of_crashing() -> None:
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result = price_sync.transform_vercel_ai_gateway_data(
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[LANGUAGE_ROW, EMBEDDING_ROW, UNPRICED_LANGUAGE_ROW, VIDEO_ROW, IMAGE_ROW]
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)
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assert result == {
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"vercel_ai_gateway/anthropic/claude-sonnet-4.5": {
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"max_tokens": 64000,
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"max_input_tokens": 200000,
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"max_output_tokens": 64000,
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"input_cost_per_token": 3e-06,
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"output_cost_per_token": 1.5e-05,
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"cache_read_input_token_cost": 3e-07,
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"cache_creation_input_token_cost": 3.75e-06,
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"litellm_provider": "vercel_ai_gateway",
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"mode": "chat",
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},
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"vercel_ai_gateway/google/gemini-embedding-001": {
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"max_tokens": 0,
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"max_input_tokens": 2048,
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"max_output_tokens": 0,
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"input_cost_per_token": 1.5e-07,
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"output_cost_per_token": 0.0,
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"litellm_provider": "vercel_ai_gateway",
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"mode": "embedding",
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},
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}
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def test_write_to_file_matches_the_checked_in_cost_map_format(tmp_path: Path) -> None:
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target = tmp_path / "model_prices_and_context_window.json"
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price_sync.write_to_file(str(target), {"vercel_ai_gateway/x": {"mode": "chat", "input_cost_per_token": 1e-06}})
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assert target.read_text(encoding="utf-8") == (
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'{\n "vercel_ai_gateway/x": {\n "mode": "chat",\n "input_cost_per_token": 1e-06\n }\n}\n'
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)
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