mirror of
https://github.com/BerriAI/litellm.git
synced 2026-11-03 07:23:33 +00:00
8 commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
yujonglee
|
7b60fd061a
|
refactor(rust): adopt one error discipline across litellm-rust with source chains and Report (#48042)
* build(rust): upgrade reqwest to 0.13 and move MCP out of litellm-http The workspace moves to reqwest 0.13, so litellm-http no longer carries a second reqwest just for rmcp. The mcp feature, its pool and its proxy conversion go away, and the gateway hands rmcp a pooled NoRedirect client that already carries the TLS, proxy and timeout settings The Client test helpers collapse into for_test, a no-redirect client without TLS or proxy settings, and wrap_for_test for the few tests that need a hand-built reqwest client Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * fix(http): honor transport settings for Rust WebSockets The Responses WebSocket connected through tokio-tungstenite with its own native-roots TLS, so it ignored the configured CA bundle, verify flag, client certificate, proxies and timeouts. The handshake now goes through a pooled reqwest client (ClientVariant::WebSocket, HTTP/1.1, no redirects) and upgrades the response, so it shares the settings every other outbound request uses The upgrade response is checked for the Sec-WebSocket-Accept key, the Upgrade and Connection headers and a subprotocol that was offered Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * refactor(http): build outbound requests as reqwest::Request OutboundRequest was a hand-rolled copy of reqwest::Request. Routes now build a reqwest::Request directly and RequestSigner reads the body bytes back out of it, so the signer still covers exactly what is sent. SigV4 signing moves onto HeaderMap and takes the method, which drops the BTreeMap plumbing and the header casing table Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * refactor(http): make litellm_http::Error the one opaque error transport::Error and media::Error fold into litellm_http::Error, an Arc<Repr> newtype whose private variants keep the typed reqwest, io, rustls and tungstenite causes and own every message. Callers can no longer build an error with a message of their own. They ask through dispatched, is_request, status, upstream_response and tls_source One From<reqwest::Error> now classifies connect, network and timeout and strips the URL. The WebSocket handshake reports each failed check as its own variant and owns its connect timeout. OCR drops the media variants it mirrored, and inference::Error maps a header the signer computes to Unsupported for every route, not only chat The error rules move out of AGENTS.md into the rust-error skill, which also records the decisions behind this design Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * docs(rust): split rust-error skill into thin SKILL.md with references Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * refactor(rust-error): render causes once and ask predicates at the boundaries The four WebSocket handshake variants collapse into Handshake(HandshakeFault), and media download failures no longer answer dispatched() since they happen before the provider call ErrorDetail in llms and auth-types, the inference error and the OCR error now store their cause in a field named cause with no #[source], so to_string() carries it exactly once and source() is None. Tests that walked the chain now destructure the variant and downcast the cause The gateway asks is_unsupported() and is_missing_credential() on the inference and OCR errors instead of matching through nested enums, and litellm_auth::Error answers is_missing_credential() for both The rust-error skill gains axum and pyo3 references, boundaries.md describes the real predicate order and the known divergences, the testing example asserts dispatched() correctly, and shape, classification, conversions, messages-and-causes and recipes agree with the code again * docs(rust): focus the rust-error gateway page on the error flow and add a rust-gateway skill The former axum page toured gateway-inference: its status match, the envelope table, an extractor, the unsupported route and the Sse closure. Those details belong to the gateway crates and go stale with them. The page is now gateway.md and follows an error from the provider failure through each From into IntoResponse, where it is classified once and rendered by surface and framing. The gateway-structure material moves to a new rust-gateway skill: envelopes per API surface, extractors and path registration, SSE relay, and the router test harness. * test(inference): pin provider URL redaction on every route's transport error Chat, Messages, Responses and transcription each get a route-level test that fails if a reqwest error reaches the caller with its URL. The Messages stream decoder also takes body errors through litellm_http::Error like every other reqwest error Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * docs(rust-error): adopt std source chains and add litellm-error Report Messages describe their own layer and the cause is the source, boundaries render the chain once with litellm_error::Report. Corrects skill claims the audit found stale against the code. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * refactor(rust-error): keep causes as sources across http, auth, secrets, cache and utility crates Messages describe their own layer and every cause is a typed source, predicates match exhaustively, and boundaries render with litellm_error::Report. Media downloads no longer count as dispatched, Error::upstream bounds the provider body itself, and a forwarded header SigV4 cannot sign is the caller's fault. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * refactor(rust-error): keep causes as sources in llms and the gateway crates ErrorDetail loses its string variant, OCR provider variants become data, host faults stop counting as the caller's, a tunnelled transport error classifies the same on every stream path, and each gateway crate has one Error with one status() and extractors that reject in its envelope. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * refactor(rust-error): nest llms errors in inference and ask predicates in route tests litellm_inference::Error nests litellm_llms::Error instead of mirroring its variants, host driver faults get their own variant, every predicate matches exhaustively, and route tests ask predicates instead of matching transport variants. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * refactor(rust-error): map bridge and gateway-inference errors once and render them with Report RustUpstreamError has one constructor that always sets headers, argument checks raise typed errors mapped once, OCR asks upstream_response(), the Responses stream error event comes from the error type, gateway-inference loses its string variants, and auth faults classify through one litellm_auth predicate on every route. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * fix(rust-error): carry OCR provider headers as a HeaderMap and keep outages off the caller The OCR provider error and get_error_class take reqwest's HeaderMap, the bridge turns it into Python's (name, value) pairs once. Credential acquisition and provider SDK failures are no longer the caller's fault, the UI hides auth and backend causes, and chat keeps a provider answer as is. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * docs(rust-error): correct skill claims against the aligned code Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * refactor(rust-error): fix the remaining sweep findings in gateway, llms, auth and cache crates Auth rejections hide their backend cause on every inference renderer, path parameters reject in the surface envelope, Textract's unsupported document and Azure DI's unparseable poll URL get typed variants, the credential chain exposes its last failure as the source, gateway-mcp asks predicates over rmcp errors, and the S3 and Azure blob transports classify through litellm_http::Error. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * refactor(rust-error): fix the remaining sweep findings on the Python side host-python keeps a native error's chain and returns blocking-pool cancellation instead of panicking, Vault transport failures convert through litellm_http::Error so no URL reaches Python, the bridge asks predicates instead of matching three levels into Vault errors, and the last string-built PyErrs become typed errors mapped once. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * build(deps): refresh compatible dependencies flagged by OSV * refactor(rust): remove redundant client construction comment * test(rust): cover configured MCP proxy and CA bundle * fix(http): preserve native WebSocket trust and optional protocols * fix(http): allow plain WebSockets without native roots * fix(http): normalize provider URLs before building outbound requests * fix(rust): preserve OCR download status and reduce error size * fix(rust): preserve upstream bodies and bound diagnostics only * test(rust): preserve bridge serialization boundary in body regression --------- Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com> Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
||
|
devin-ai-integration[bot]
|
66db132627
|
refactor(rust): add shared llms wire type derives (#43730) | ||
|
devin-ai-integration[bot]
|
268e8bb735
|
refactor(rust): share anthropic types, request helpers, and streaming contracts across crates (#43426)
* refactor(rust): standardize Azure Messages module path * docs(rust): define shared types crate boundaries * refactor(rust): share request helpers and type Anthropic blocks * docs(rust): format shared type invariants as bullets * test(rust): parameterize repeated cases with rstest * refactor(rust): move Responses transform result into llms * fix(anthropic): validate chat and batch responses * docs(rust): clarify API format ownership boundaries * docs: clarify Rust error message construction * refactor(auth): keep shared Rust errors provider-neutral * refactor(rust): separate format contracts from provider policy * fix(rust): type Anthropic chat response text collection * fix(rust): pass audio secret sources through hosts * fix(rust): unblock batch lint and OCR error assertions * test(rust): assert response failures at the adapter boundary * refactor(rust): declare error messages with typed context * wip * fix(rust): adapt Bedrock error details * style(rust): cargo fmt bedrock audio transcription Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(rust): adapt tests and dead code to typed error details Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(rust): keep converse error contracts and read env secrets without litellm Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * ci(rust): raise the native wheel size gate to 45 MB Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(rust): tolerate missing usage in converse responses on the transcription route Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --------- Co-authored-by: Yujong Lee <yujong@berri.ai> Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
||
|
devin-ai-integration[bot]
|
0abd9267c1
|
feat(tokenizer): preserve Python defaults with opt-in Rust dispatch (#42174)
* ci: benchmark and gate an installed release wheel Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * ci: simplify installed-wheel benchmark check Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * feat(rust): add native tokenizer codec Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * refactor(tokenizer): route Python tokenization through the Rust extension Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * style(lint): format tokenizer call Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(packaging): restore runtime dependencies and native images Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(tokenizer): preserve Python SDK behavior with Rust tokenizers * fix(tokenizer): restore compatibility paths Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * refactor(tokenizer): count custom tokenizers directly Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(tokenizer): preserve caller-supplied Python tokenizer counts * fix(tokenizer): reuse packaged vocabularies in the native wheel * refactor(rust_bridge): route token counting through the catalog as RUST_OPT_IN Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(spend_tracking): compare tokenizer groups by value Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * chore(deps): re-resolve filelock under the <4.0 pin Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(llms): align transformation override signatures with base configs Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * build(rust): use fat LTO to keep the native wheel under the 35 MB limit Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * feat(tokenizer): preserve Python defaults with opt-in Rust dispatch * test(proxy): tolerate missing litellm.utils.Tokenizer when patching it Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(proxy): patch the tokenizer dispatch function instead of the removed alias Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * feat(tokenizer): give the Rust wrappers the tiktoken and tokenizers surface Callers of litellm.encoding and litellm.create_tokenizer must see the same read-only API whichever backend the catalog selects. - OpenAIEncoding mirrors tiktoken.Encoding: n_vocab, max_token_value, token_byte_values, encode_single_token, encode_with_unstable, encode_to_numpy, decode_with_offsets, is_special_token, repr; the Rust tiktoken crate keeps a Vocabulary beside each CoreBPE and reports the requested encoding name (gpt2 stays gpt2). - HuggingFaceTokenizer mirrors the read-only tokenizers.Tokenizer surface (token_to_id, id_to_token, get_vocab, get_vocab_size, get_added_tokens_decoder, num_special_tokens_to_add, padding, truncation, encode_special_tokens, from_buffer); HuggingFaceEncoding gains the char/word/token lookups, pad, truncate, set_sequence_id and merge. Mutators stay on the Python tokenizer. - from_json/from_pretrained claim the fork gate only when the huggingface feature is compiled in; the surrogate fallback matches on the Codec. - Tokenizer caching is keyed on the same catalog Context the dispatch runs on; rust_tokenizer reads the encoding name without loading an encoding; LITELLM_RUST parsing is cached. - Drop the unused tiktoken_encoding_for_model export and Error::Download. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * fix(tokenizer): close the exhaustive matches with assert_never CodeQL reads a `match` over a Literal with no default arm as an implicit `None` return. `assert_never` makes the exhaustiveness explicit for both the HuggingFace tokenizer loader and the Rust token-counter factory. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * feat(tokenizer): derive the fast counter from the shared tokenizer The count-only counter (`fast` feature) and the codec each parsed the same artifact: TokenCounter took the Anthropic JSON and the tiktoken rank files from Python while Tokenizer loaded them again. One parse now serves both. - FastTokenizer builds from a model another loader holds: `from_shared` takes the Arc<tokenizers::Tokenizer> the HF codec keeps, and `from_*_pairs` take the ranks the tiktoken vocabulary already parsed. - `FastCounter::fast_counter` in the core crate derives it from either codec; encodings the fast scanner does not reproduce are refused. - Native `Tokenizer.count(text, fast=False)` opts into that counter, built once per tokenizer on first use; `TokenCounter.from_tokenizer(tokenizer, fast=False)` replaces the JSON and rank-file constructors. - The Python route counts over the native tokenizers the codec path shares (`native_encoding`, `native_anthropic`) and no longer reads rank files; the packaged Anthropic tokenizer has one loader, `tokenizer_dispatch.anthropic`. - Public wrappers gain `count(text, fast=False)`. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> --------- Co-authored-by: Yujong Lee <yujong@berri.ai> Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> Co-authored-by: Claude Fable 5.1 <noreply@anthropic.com> |
||
|
Yujong Lee
|
84c26978a3 | refactor(rust): split token counter backends | ||
|
Yujong Lee
|
e850232f02 |
test(rust): assert merge cost scales linearly instead of a wall-clock bound
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
||
|
devin-ai-integration[bot]
|
359b7a8489
|
feat(rust): count tiktoken cl100k_base admission tokens in Rust (#40777)
* feat(rust): count tiktoken cl100k_base admission tokens in Rust The Rust admission token counter only had the Anthropic tokenizer, so every other model (OpenAI gpt-4 family, Azure, Gemini, Bedrock non-Claude, Mistral) tokenized with tiktoken on the Python inference worker. Add an exact cl100k_base counter to litellm-token-counter: the vendored rank file (base64 token / rank lines, the bytes Python's tiktoken uses) is parsed into a byte-level BPE model and the cl100k split pattern is a handwritten scanner over the shared Unicode classes, so no regex engine runs per request. Both tokenizers share the message, tool and reply-priming accounting. The PyO3 TokenCounter gains a from_cl100k_ranks constructor; Python reads the rank file and passes it in, the way claude_json_str already works. The bridge selects the counter through the same predicates litellm.token_counter uses (huggingface_tokenizer_kind, openai_tokenizer_encoding), declines o200k_base, downloaded HuggingFace and custom tokenizers to Python, and budget reservation counts once per distinct tokenizer a request names. The legacy gpt-3.5-turbo-0301 message accounting (4 per message, -1 per name) stays in Python: the selector declines it through the predicate token_counter itself uses. * feat(rust): count tiktoken o200k_base admission tokens in Rust (#40794) Add a handwritten o200k_base split scanner and TokenCounter::from_o200k_ranks next to the cl100k_base counter, sharing MergeRanks and the request accounting. The Python bridge selects it when openai_tokenizer_encoding names o200k_base, so gpt-4o, gpt-4.1, gpt-5, o1/o3/o4 and chatgpt-4o requests stop tokenizing on the Python worker under LITELLM_RUST=true Co-authored-by: yassin <yassin@berri.ai> --------- Co-authored-by: yassin <yassin@berri.ai> Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
||
|
devin-ai-integration[bot]
|
46a185d3cd
|
feat(rust_bridge): count budget-check input tokens in Rust on all LLM routes (#40381)
* 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> |