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* test(token_counter): price PDF pages for OpenAI and Azure models the way they bill * fix(token_counter): price PDF pages for OpenAI and Azure models the way they bill * fix(token_counter): count azure, data-zone and dated GPT names with their family's encoding * fix(token_counter): price only drawn PDF images and keep other gateways' GPT pricing Detect raster images from the page content stream, including inline images on pages with empty resources, and walk each form once. Strip only openai/, azure/ and Azure data-zone prefixes, bound the model family scan, read Azure-only chat models from the cost map, and keep cl100k_base for listed non-chat OpenAI models. * fix(token_counter): price GPT names behind any prefix like the bare name and walk shared forms per resources Prices a PDF by the model name's last path segment, the same name the proxy counts with, keeps listed non-chat models on Claude pricing, walks each form once per inherited resources dictionary so a shared child form drawing an image is not missed, and resolves PDF pricing only when a message carries a PDF * fix(token_counter): keep the rendered-page count for image pages, parse each form once, and match chat models only A PDF page that draws a raster image keeps the rendered-page estimate, since newer GPT models bill such a page near it. The image walk parses each form once and stops at a fixed number of drawn XObjects, so a form shared under many resources dictionaries no longer slows the count. The family prefix match skips non-chat entries, and ft: fine-tunes count like their base model * fix(token_counter): count Azure-only chat models with o200k like other OpenAI chat models * fix(token_counter): count reservation and project ITPM with the serving deployment's model Budget reservation and project ITPM counted with the requested group name, so a GPT-named group served by Claude reserved a PDF at OpenAI's price. They now count with each deployment's model minus its provider prefix, the name count_tokens uses, and take the largest. A custom OpenAI tokenizer with no model counts with cl100k again instead of raising, and listed Azure gpt- chat entries price as OpenAI. * perf(token_counter): find drawn PDF images with a byte scan instead of parsing every operator pypdf parsed every operator of a page's content stream to find the images it draws, so a compressed page of a million operators took about 30 seconds to price for an OpenAI model. A linear scan of the decoded stream for name Do and inline images finds the same draws. * fix(token-counter): count each team deployment by its PDF pricing and honor base_model Budget reservation and the TPM limiter now look up the deployments that serve the caller's team, by base_model when set. A deployment that reads PDF pages and prices them unlike the group name counts with its own model, any other with the group name. A comment between an XObject name and Do now reads as whitespace, and the timing tests moved to CodSpeed benchmarks. * fix(benchmarks): install pypdf in the benchmark environment The PDF token-count benchmarks parse pages with pypdf, which only the proxy-runtime extra installed, so CodSpeed took the no-parser path. * fix(proxy): count PDF pages for deployments served under multi-segment model names * fix(proxy): count a team's wildcard route the way routing serves it and reserve OpenAI chat's per-PDF charge * fix(proxy): count routing-group and alias members with the caller's team, as routing serves them |
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| .. | ||
| host | ||
| messages | ||
| ocr | ||
| trace | ||
| __init__.py | ||
| AGENTS.md | ||
| native_route_wheel_test.py | ||
| stubtest.ini | ||
| test_bindings.py | ||
| test_catalog.py | ||
| test_clickhouse.py | ||
| test_configuration.py | ||
| test_detectors.py | ||
| test_execution.py | ||
| test_failures.py | ||
| test_fork_guard.py | ||
| test_logger.py | ||
| test_preflight.py | ||
| test_public_call.py | ||
| test_runtime.py | ||
| test_token_counter.py | ||
| test_tokenizer.py | ||
| test_verify_linux_native_wheel.py | ||