OCR transformations, BaseOcrConfig with its response and connection types,
the OCR error, and the HTTP pieces (custom_httpx: http_handler, transport,
media, llm_http_handler with OcrClient and the request/response handler)
now live in litellm-llms at their Python paths. Provider code no longer
reaches into the route: it gets the caller's hooks through a route-neutral
CallHooks trait that core implements over its host, and core dispatches to
llm_http_handler::ocr with the concrete config, the way Python calls
base_llm_http_handler.ocr(provider_config=...).
Core keeps the route: entrypoint, request types, credential fallback,
provider dispatch, the machine and hook glue. ocr/mod.rs no longer
re-exports anything, provider constants moved next to their only users,
and provider tests that drive the whole route moved to core's route test
files. Twenty-two of those were exact copies of tests already there and
were dropped; every one still runs once.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
litellm-providers becomes litellm-llms, mirroring litellm/llms. The Anthropic
batches, count_tokens, Messages stream iterator and chat stream handler, the
OpenAI Responses websocket config and its base trait (with URL and model
helpers), and the StreamTransformer base iterator now live at their Python
paths in that crate. Anthropic stream decode errors move with the iterator,
and core drops its duplicate OAuth prefix constant and the unused framing
dependency.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
litellm-types mirrors litellm/types (chat, Anthropic Messages and Responses
websocket data) and litellm-core-utils mirrors litellm/litellm_core_utils
(provider resolution, prompt factory, core helpers, call arguments). Route
request types move up to their core route module, the transform contracts
move into base_llm, and the three duplicated provider error enums become one
Error in base_llm/chat/transformation.rs, mirroring BaseLLMException.
The empty-text placeholder goes back to the value Python's factory.py uses;
the provider extraction had changed it to a single space.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Extracted from #41733 without the router loop, the cache machine layer, streaming, or the
error, timeout and route-pruning work that moved to #41745
litellm-callbacks holds the contract a native call and its host share: Machine, HostOp,
CallEvent, the in-process run loop, and Passthrough, which is built only by comparing the
caller's inputs with the body the route sends, so a route can never mark a key it rewrote.
litellm-host-python (formerly python-interop) owns the CPython driver and the Execution
handle, and litellm-callbacks-legacy is the @client wrapper as the native call sees it:
function_setup, the deployment hooks, pre_call and post_call, the success and failure fan-out
and the deferred proxy release. OCR is the one route on it, and the old core and bridge
lifecycles are gone
The passthrough rule is the structural fix for the bug #41719 patched in core and #41716
reworks: an inlined remote document no longer counts as the caller's value, so the legacy
adapter never hands the caller's URL back into the body. core/tests/ocr/passthrough.rs pins
it for every route and document source, including that unchanged values stay passthrough,
and callbacks-legacy/tests/payload.rs pins the adapter side with a real pre_call callback
Python OCR integration tests that only exercised core behavior now live as Rust tests, so
tests/test_litellm_rust keeps the cases that need the full Python stack
Providers that cannot fetch a public document URL themselves (Azure AI
mistral document AI, Azure cohere parse, Vertex AI) download it and
inline it as a data URI. When a pre-call callback or debug logging
intercepts the request, the Python host hands the caller's original
document back into the body, so the provider request carried the URL
again and Azure's inline-only check rejected it with "invalid OCR
document data URI". The core now keeps the prepared document when a
hook returns the untouched caller document, while a hook that edits or
replaces the document still wins
Delete litellm/ocr/input.py and the native _ocr_file_document, _ocr_upload_document
and _ocr_mime_type helpers. File documents now project to a typed OcrDocumentInput
and the core lifecycle reads local paths, encodes bytes and asks the host to read
file-like objects through a ReadDocument operation before the provider request
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* feat(rust_bridge): count budget-check input tokens in Rust on all LLM routes
Rust counts input tokens from the raw JSON body with the GIL released inside the existing budget reservation, covering every LLM route the auth dependency guards. It only fires for models on the Anthropic tokenizer when a budget is set, and Python counts whenever Rust is off, missing, or declines a body shape.
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* perf(rust): count byte-level BPE tokens without the GPT-2 split regex (#40594)
The oniguruma run of the ByteLevel pre-tokenizer regex is about 90% of
encode_fast on a 100k token body (100 ms of the ~110 ms Rust admission
count in the gateway pod). A hand-written scanner that yields the same
pieces, then feeds the model directly, counts the same text in 10 ms.
It only engages for tokenizers with the Anthropic shape (optional NFKC,
ByteLevel without prefix space, no post-processor) and falls back to the
full encoder when the text contains an added token. Parity with
encode_fast is tested on random texts, the pieces are compared with the
real pre-tokenizer, and the \p{L}/\p{N}/\s tables are checked against
oniguruma for every code point.
NFKC runs through unicode-normalization-alignments, the crate and
Unicode tables NormalizedString::nfkc already uses, so the fast path
normalizes exactly what the full encoder would. Using the newer
unicode-normalization crate changed the count for 171 code points that
gained compatibility decompositions after Unicode 9 (U+32FF, U+A7F1..).
The fast normalizer is compared with the tokenizer's for every scalar
value and on random texts.
The scanner is built without mutable state: byte_char and mapped_len replace the const table builders and the reusable mapped buffer, and iter::successors replaces the stateful piece iterator. byte_chars_match_the_byte_level_alphabet checks the byte mapping against ByteLevel for every scalar value.
Co-authored-by: yassin <yassin@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(rust_bridge): bound concurrent token-count encodes and share the Anthropic tokenizer predicate
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: yassin <yassin@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(python-bridge): split non-streaming bridge modules
* refactor(python-bridge): bring shared function tracing into route layer
* feat(dev): list Python route functions and call sites
* feat(dev): list Rust route functions and call sites
* docs(dev): record OCR parity gaps across Python and Rust
* feat(dev): list executed SDK calls with runtime tracing
* feat(dev): report Python vs Rust SDK pipeline steps in one CLI
* feat(dev): side-by-side pipeline step report in compare CLI
* fix(dev): drop invalid Final annotations in compare cell loop
* feat(dev): blue python-only and yellow rust-only steps in compare CLI
* feat(dev): vertical layout with section spacing in compare CLI
* fix(dev): validate SDK trace stages across sync and async routes
* refactor(rust): align SDK route call structure with Python
* refactor(python-bridge): share sync and async route call wrappers
* refactor(dev): split compare CLI into fixtures, runtime, and report modules
* fix(ci): run SDK trace tests and satisfy test lint
Adds a chat_completions route module to litellm-core, mirroring the messages
route, plus Anthropic Messages and Bedrock Converse provider configs. The
per-model `rust: true` opt-in now covers /chat/completions for both providers.
The core accepts an allowlisted subset (text conversations, non-streaming) and
returns CoreError::Unsupported for anything else, so tool calls, multimodal
content and streaming fall back to the Python path transparently.
Resolves LIT-5698
