Merge remote-tracking branch 'origin/main' into HEAD

# Conflicts:
#	tests/unit/litellm_core_utils/llm_response_utils/test_convert_dict_to_response.py

.github/scripts/smoke_test_native_wheel.py

@@ -7,6 +7,8 @@ import zipfile
 from pathlib import Path
 from typing import Final
 
+COST_MAP: Final = Path(__file__).resolve().parents[2] / "model_prices_and_context_window.json"
+
 CHILD_SCRIPT: Final = """
 from importlib.util import module_from_spec, spec_from_file_location
 from pathlib import Path
@@ -34,6 +36,9 @@ else:
 after = module.gil_stats()
 if not isinstance(after.get("releases"), int):
     raise AssertionError(f"native module unusable after panic: {after!r}")
+
+if module.bundled_model_cost_map() != Path(sys.argv[2]).read_bytes():
+    raise AssertionError("the wheel embeds a different model_prices_and_context_window.json than this checkout")
 """
 
 
@@ -55,7 +60,9 @@ def main() -> int:
 
         native_path: Final = Path(temporary_directory) / Path(native_members[0].filename).name
         native_path.write_bytes(archive.read(native_members[0]))
-        result: Final = subprocess.run((sys.executable, "-c", CHILD_SCRIPT, str(native_path)), check=False)
+        result: Final = subprocess.run(
+            (sys.executable, "-c", CHILD_SCRIPT, str(native_path), str(COST_MAP)), check=False
+        )
 
     if result.returncode != 0:
         sys.stderr.write(f"native wheel smoke test exited with status {result.returncode}\n")

.github/workflows/cost-map-guard.yml

@@ -46,3 +46,24 @@ jobs:
           HEAD_REF: ${{ github.event.pull_request.head.ref }}
         run: |
           uv run --no-project python ci_cd/cost_map_guard.py --base "$MERGE_BASE" --head "$HEAD_SHA" --head-ref "$HEAD_REF"
+      - name: Check whether the pull request changes the cost map
+        id: changed
+        env:
+          MERGE_BASE: ${{ steps.revisions.outputs.merge_base }}
+          HEAD_SHA: ${{ github.event.pull_request.head.sha }}
+        run: |
+          if git diff --quiet "$MERGE_BASE" "$HEAD_SHA" -- model_prices_and_context_window.json; then
+            echo "cost_map=false" >> "$GITHUB_OUTPUT"
+          else
+            echo "cost_map=true" >> "$GITHUB_OUTPUT"
+          fi
+      - if: steps.changed.outputs.cost_map == 'true'
+        run: rustup toolchain install --no-self-update
+      - name: Load the cost map through model-catalog
+        if: steps.changed.outputs.cost_map == 'true'
+        env:
+          HEAD_SHA: ${{ github.event.pull_request.head.sha }}
+        run: |
+          git show "${HEAD_SHA}:model_prices_and_context_window.json" > "$RUNNER_TEMP/model_prices_and_context_window.json"
+          cargo run --locked --quiet --manifest-path litellm-rust/Cargo.toml -p litellm-model-catalog --bin check-registry \
+            -- "$RUNNER_TEMP/model_prices_and_context_window.json"

.github/workflows/test-unit.yml

@@ -322,6 +322,7 @@ jobs:
               tests/unit/llms/openai_like
               tests/unit/llms/opencode
               tests/unit/llms/opensandbox
+              tests/unit/llms/opensearch
               tests/unit/llms/openrouter
               tests/unit/llms/ovhcloud
               tests/unit/llms/parallel_ai

backend/Dockerfile

@@ -24,6 +24,7 @@ ENV UV_PYTHON_DOWNLOADS=0
 COPY pyproject.toml README.md rust-toolchain.toml ./
 COPY .cargo ./.cargo
 COPY litellm-rust ./litellm-rust
+COPY model_prices_and_context_window.json ./
 
 RUN --mount=type=cache,target=/root/.cache/uv \
     mkdir -p litellm/rust_bridge /wheels && \

gateway/Dockerfile

@@ -40,6 +40,7 @@ ENV UV_PYTHON_DOWNLOADS=0
 COPY pyproject.toml README.md rust-toolchain.toml ./
 COPY .cargo ./.cargo
 COPY litellm-rust ./litellm-rust
+COPY model_prices_and_context_window.json ./
 
 RUN --mount=type=cache,target=/root/.cache/uv \
     mkdir -p litellm/rust_bridge /wheels && \

litellm-rust/Cargo.lock

@@ -4345,6 +4345,7 @@ dependencies = [
  "litellm-inference-transcription",
  "litellm-llms",
  "litellm-llms-types",
+ "litellm-model-catalog",
  "litellm-router-types",
  "litellm-secrets",
  "litellm-secrets-aws",

litellm-rust/crates/core-utils/src/exception_mapping_utils/openai.rs

@@ -53,6 +53,7 @@ pub(super) const RULES: &[Rule] = &[
         |mapping| {
             mapping.error_str.contains("invalid_request_error")
                 && !mapping.error_str.contains("Incorrect API key provided")
+                && !matches!(mapping.status, Some(401 | 404))
         },
         PublicError::BadRequest,
     ),
@@ -132,6 +133,23 @@ mod tests {
         assert_eq!(classified(Some(400), text), Some(expected));
     }
 
+    #[rstest::rstest]
+    #[case::unauthorized(Some(401), None)]
+    #[case::not_found(Some(404), None)]
+    #[case::forbidden(Some(403), Some(PublicError::BadRequest))]
+    #[case::rate_limited(Some(429), Some(PublicError::BadRequest))]
+    #[case::server_error(Some(500), Some(PublicError::BadRequest))]
+    #[case::no_status(None, Some(PublicError::BadRequest))]
+    fn invalid_request_errors_leave_401_and_404_to_the_status_mapping(
+        #[case] status: Option<u16>,
+        #[case] expected: Option<PublicError>,
+    ) {
+        assert_eq!(
+            classified(status, "invalid_request_error bad field"),
+            expected
+        );
+    }
+
     #[rstest::rstest]
     #[case::rate_limit_before_context_window(
         "rate limit and This model's maximum context length is 10",

litellm-rust/crates/model-catalog/AGENTS.md

@@ -25,3 +25,5 @@ A new `N`, image size or resolution in an existing key shape parses with no Rust
 ## Tests
 
 `BUNDLED_REGISTRY` embeds the root `model_prices_and_context_window.json`, so the tests over it fail a JSON change with a bad value or a key no type reads
+
+`Catalog::problems` is the one definition of a mergeable cost map. The bundled test asserts it is empty, and the cost map guard workflow runs it on a pull request's JSON through `cargo run --bin check-registry <path>`

litellm-rust/crates/model-catalog/Cargo.toml

@@ -10,6 +10,7 @@ schema = ["dep:schemars", "litellm-cost/schema"]
 
 [dependencies]
 litellm-cost = { workspace = true, features = ["serde"] }
+litellm-error.workspace = true
 
 indexmap = { version = "2.14.0", features = ["serde"] }
 jiff = { version = "0.2.37", default-features = false, features = ["std", "tzdb-bundle-always"] }
@@ -24,5 +25,4 @@ time.workspace = true
 
 [dev-dependencies]
 jsonschema = { version = "0.55.1", default-features = false }
-litellm-error.workspace = true
 rstest.workspace = true

litellm-rust/crates/model-catalog/src/bin/check-registry.rs

@@ -0,0 +1,26 @@
+use std::{env, fs, path::PathBuf, process::ExitCode};
+
+use litellm_model_catalog::Catalog;
+
+fn main() -> ExitCode {
+    let Some(path) = env::args_os().nth(1).map(PathBuf::from) else {
+        eprintln!("usage: check-registry <model_prices_and_context_window.json>");
+        return ExitCode::from(2);
+    };
+    let body = match fs::read(&path) {
+        Ok(body) => body,
+        Err(error) => {
+            eprintln!("{}: {error}", path.display());
+            return ExitCode::FAILURE;
+        }
+    };
+    let problems = Catalog::problems(&body);
+    for problem in &problems {
+        eprintln!("{}", litellm_error::Report::new(problem));
+    }
+    if problems.is_empty() {
+        ExitCode::SUCCESS
+    } else {
+        ExitCode::FAILURE
+    }
+}

litellm-rust/crates/model-catalog/src/catalog.rs

@@ -5,7 +5,7 @@ use indexmap::IndexMap;
 use serde::Deserialize;
 use serde_json::Value;
 
-use crate::error::{Error, Loaded, Rejects, Repr};
+use crate::error::{Error, Loaded, Rejects, Repr, pointer_child, pointer_for_key};
 use crate::fallback::{FallbackGeneralizations, FallbackRule};
 use crate::model::Model;
 use crate::store::{Rows, Store};
@@ -211,6 +211,36 @@ impl Catalog {
         })
     }
 
+    pub fn problems(body: &[u8]) -> Vec<Error> {
+        let Loaded {
+            value: catalog,
+            errors,
+        } = match Self::parse(body) {
+            Ok(loaded) => loaded,
+            Err(fatal) => return vec![fatal],
+        };
+        let unread = catalog.rows().entries.iter().flat_map(|(model, entry)| {
+            EntryWire::read(entry.fields())
+                .unknown
+                .into_iter()
+                .map(move |(key, _)| {
+                    Error::new(
+                        pointer_child(&pointer_for_key(model), &key),
+                        Repr::UnreadField,
+                    )
+                })
+        });
+        let no_fallback_rules = catalog
+            .fallback_rules()
+            .is_none_or(<[_]>::is_empty)
+            .then(|| Error::from_key(FALLBACK_GENERALIZATIONS, Repr::NoFallbackRules));
+        errors
+            .into_iter()
+            .chain(unread)
+            .chain(no_fallback_rules)
+            .collect()
+    }
+
     pub fn row_errors(model: &str, fields: &Fields) -> Vec<Error> {
         let (_, _, errors) = read_row(fields);
         errors
@@ -404,3 +434,85 @@ fn unknown_timezone(wire: &EntryWire) -> Option<Error> {
         )
     })
 }
+
+#[cfg(test)]
+mod tests {
+    use rstest::rstest;
+
+    use super::*;
+
+    const RULES: &str = r#""fallback_generalizations":{"rules":[{"name":"family","pattern":"^new-","model_info":{"mode":"chat"}}]}"#;
+
+    #[rstest]
+    #[case::clean(format!(r#"{{{RULES},"m":{{"litellm_provider":"test"}}}}"#), vec![])]
+    #[case::not_json("{".to_owned(), vec![("", "json")])]
+    #[case::row_error(format!(r#"{{{RULES},"m":{{}}}}"#), vec![("/m/litellm_provider", "missing_provider")])]
+    #[case::unread_key(
+        format!(r#"{{{RULES},"m":{{"litellm_provider":"test","typo_cost":1}}}}"#),
+        vec![("/m/typo_cost", "unread_field")]
+    )]
+    #[case::empty_model_key(
+        format!(r#"{{{RULES},"":{{"litellm_provider":"test","future_field":true}}}}"#),
+        vec![("//future_field", "unread_field")]
+    )]
+    #[case::unread_key_on_invalid_row(
+        format!(r#"{{{RULES},"m":{{"typo_cost":1}}}}"#),
+        vec![("/m/litellm_provider", "missing_provider"), ("/m/typo_cost", "unread_field")]
+    )]
+    #[case::escaped_unread_key_on_invalid_row(
+        format!(r#"{{{RULES},"open/router~v1":{{"typo/cost~":1}}}}"#),
+        vec![("/open~1router~0v1/litellm_provider", "missing_provider"), ("/open~1router~0v1/typo~1cost~0", "unread_field")]
+    )]
+    #[case::no_rules(
+        r#"{"m":{"litellm_provider":"test"}}"#.to_owned(),
+        vec![("/fallback_generalizations", "no_fallback_rules")]
+    )]
+    #[case::empty_rules(
+        r#"{"fallback_generalizations":{"rules":[]},"m":{"litellm_provider":"test"}}"#.to_owned(),
+        vec![("/fallback_generalizations", "no_fallback_rules")]
+    )]
+    fn problems_list_what_a_cost_map_change_must_fix(
+        #[case] body: String,
+        #[case] expected: Vec<(&str, &str)>,
+    ) {
+        let problems: Vec<_> = Catalog::problems(body.as_bytes())
+            .iter()
+            .map(|problem| (problem.path().to_owned(), <&str>::from(problem.kind())))
+            .collect();
+        let expected: Vec<_> = expected
+            .into_iter()
+            .map(|(path, kind)| (path.to_owned(), kind))
+            .collect();
+        assert_eq!(problems, expected);
+    }
+
+    #[rstest]
+    fn runtime_parsing_preserves_custom_fields_and_recovers_valid_rows() {
+        let body = br#"{
+            "good":{"litellm_provider":"test","custom_metadata":true},
+            "bad":{"litellm_provider":"test","input_cost_per_token":"wrong"}
+        }"#;
+        let loaded = Catalog::parse(body).unwrap();
+        assert_eq!(loaded.errors.len(), 1);
+        assert_eq!(loaded.errors[0].path(), "/bad/input_cost_per_token");
+        let (catalog, rejected) = loaded.value.without_rejected(&loaded.errors);
+        assert_eq!(rejected.len(), 1);
+        assert!(catalog.lookup("bad").is_none());
+        let good = catalog.lookup("good").unwrap();
+        assert!(good.entry.model().is_some());
+        assert_eq!(
+            good.entry.field("custom_metadata"),
+            Some(&Value::Bool(true))
+        );
+        assert!(
+            Catalog::problems(body)
+                .iter()
+                .any(|problem| problem.kind() == crate::ErrorKind::UnreadField)
+        );
+        assert!(
+            Catalog::problems(body)
+                .iter()
+                .any(|problem| problem.kind() == crate::ErrorKind::NoFallbackRules)
+        );
+    }
+}

litellm-rust/crates/model-catalog/src/error.rs

@@ -109,6 +109,12 @@ pub(crate) enum Repr {
         #[source]
         cause: Option<DateCause>,
     },
+    #[error("not a field litellm reads")]
+    #[strum_discriminants(strum(serialize = "unread_field"))]
+    UnreadField,
+    #[error("has no rules")]
+    #[strum_discriminants(strum(serialize = "no_fallback_rules"))]
+    NoFallbackRules,
 }
 
 /// What a problem takes out of the served map
@@ -148,7 +154,7 @@ impl Error {
             | Repr::NoOffPeakHours
             | Repr::EmptyList
             | Repr::InvalidDate { .. } => Rejects::Row,
-            Repr::MissingProvider => Rejects::Nothing,
+            Repr::MissingProvider | Repr::UnreadField | Repr::NoFallbackRules => Rejects::Nothing,
         }
     }
 }
@@ -334,6 +340,8 @@ mod tests {
     #[case::no_off_peak_hours(Repr::NoOffPeakHours, Rejects::Row)]
     #[case::empty_list(Repr::EmptyList, Rejects::Row)]
     #[case::invalid_date(Repr::InvalidDate { cause: None }, Rejects::Row)]
+    #[case::unread_field(Repr::UnreadField, Rejects::Nothing)]
+    #[case::no_fallback_rules(Repr::NoFallbackRules, Rejects::Nothing)]
     fn policy_classifies_each_error(#[case] error: Repr, #[case] expected: Rejects) {
         assert_eq!(Error::from(error).rejects(), expected);
     }

litellm-rust/crates/model-catalog/tests/catalog.rs

@@ -366,18 +366,14 @@ fn written_is_the_document_in_order_including_sections_and_bad_entries() {
 }
 
 #[rstest]
-fn the_bundled_registry_parses_with_no_problems_and_every_key_understood() {
-    let loaded = Catalog::parse(BUNDLED_REGISTRY).unwrap();
-    let catalog = loaded.value;
-    assert!(loaded.errors.is_empty(), "{:#?}", loaded.errors);
-    let unrecognized: Vec<_> = catalog.unrecognized().collect();
-    assert!(unrecognized.is_empty(), "{unrecognized:#?}");
-    assert!(
-        catalog
-            .fallback_rules()
-            .is_some_and(|rules| !rules.is_empty())
-    );
+fn the_bundled_registry_has_no_problems() {
+    let problems: Vec<_> = Catalog::problems(BUNDLED_REGISTRY)
+        .iter()
+        .map(report)
+        .collect();
+    assert!(problems.is_empty(), "{problems:#?}");
     const REFERENCE_MODEL_COUNT: usize = 4303;
+    let catalog = Catalog::parse(BUNDLED_REGISTRY).unwrap().value;
     assert!(catalog.model_count() as f64 >= REFERENCE_MODEL_COUNT as f64 * 0.9);
 }
 

litellm-rust/crates/python-bridge/AGENTS.md

@@ -106,6 +106,18 @@ GIL handling to `litellm-host-python`.
 
 ## Data Handling
 
+- Reuse large immutable bundled data and parsed catalogs across calls, including in
+  replacement APIs. Share Rust storage and cache repeated Python projections instead
+  of reparsing, serializing or copying the full payload on every access
+- Cache owned Python objects with `PyOnceLock<Py<T>>`, then bind and clone references
+  while attached. Do not use blocking `OnceLock::get_or_init` while attached
+  ([PyO3 initialization](https://pyo3.rs/v0.29.2/faq.html),
+  [reference ownership](https://pyo3.rs/v0.29.2/types.html))
+- Never reuse Python objects across interpreters or interpreter reinitialization.
+  Once-only caches are for immutable data. Reloadable/custom data needs versioned
+  entries or invalidation, and caller-mutable Python containers must not be shared
+- Test unchanged contents and allocation reuse, not just equal values. Keep this
+  coverage when replacing a bundled-data accessor with a catalog API
 - OCR payloads can contain personal data and large base64 images. Do not log
   payloads or provider responses.
 - Avoid copying large payloads more than needed. The current JSON round-trip is

litellm-rust/crates/python-bridge/Cargo.toml

@@ -49,6 +49,7 @@ litellm-secrets-types.workspace = true
 litellm-llms-types.workspace = true
 litellm-router-types.workspace = true
 litellm-host-python.workspace = true
+litellm-model-catalog.workspace = true
 litellm-token-counter = { path = "../token-counter", default-features = false }
 pyo3.workspace = true
 prost.workspace = true

litellm-rust/crates/python-bridge/src/lib.rs

@@ -8,6 +8,7 @@ mod execution;
 mod http;
 mod logger;
 mod marshal;
+mod model_registry;
 mod preflight;
 mod python_settings;
 mod routes;
@@ -30,6 +31,8 @@ mod _native {
     #[pymodule_export]
     use crate::logger::NativeDiagnosticProcessor;
     #[pymodule_export]
+    use crate::model_registry::bundled_model_cost_map;
+    #[pymodule_export]
     use crate::routes::audio_transcription::{atranscription, transcription};
     #[pymodule_export]
     use crate::routes::chat_completions::{acompletion, completion};
@@ -110,6 +113,7 @@ mod tests {
                 "NativeClickHouseSpendConfig",
                 "NativeClickHouseSpendStorage",
                 "trace_encode_error",
+                "bundled_model_cost_map",
                 "TokenCounter",
                 "Tokenizer",
                 "gil_stats",

litellm-rust/crates/python-bridge/src/model_registry.rs

@@ -0,0 +1,30 @@
+use pyo3::prelude::*;
+use pyo3::sync::PyOnceLock;
+use pyo3::types::PyBytes;
+
+#[pyfunction]
+pub fn bundled_model_cost_map(py: Python<'_>) -> Bound<'_, PyBytes> {
+    static BYTES: PyOnceLock<Py<PyBytes>> = PyOnceLock::new();
+    BYTES
+        .get_or_init(py, || {
+            PyBytes::new(py, litellm_model_catalog::BUNDLED_REGISTRY).unbind()
+        })
+        .bind(py)
+        .clone()
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[rstest::rstest]
+    fn repeated_calls_reuse_the_bundled_registry_bytes() {
+        Python::initialize();
+        let first = Python::attach(|py| bundled_model_cost_map(py).unbind());
+        Python::attach(|py| {
+            let second = bundled_model_cost_map(py);
+            assert!(first.bind(py).is(&second));
+            assert_eq!(second.as_bytes(), litellm_model_catalog::BUNDLED_REGISTRY);
+        });
+    }
+}

litellm/exceptions.py

@@ -139,6 +139,7 @@ class AuthenticationError(openai.AuthenticationError):
         litellm_debug_info: str | None = None,
         max_retries: int | None = None,
         num_retries: int | None = None,
+        body: Mapping[str, object] | None = None,
     ):
         self.status_code = 401
         self.message = f"litellm.AuthenticationError: {message}"
@@ -152,7 +153,7 @@ class AuthenticationError(openai.AuthenticationError):
             request=httpx.Request(method="GET", url="https://litellm.ai"),  # mock request object
         )
         super().__init__(
-            self.message, response=self.response, body=None
+            self.message, response=self.response, body=body
         )  # Call the base class constructor with the parameters it needs
 
     def __str__(self):
@@ -189,6 +190,7 @@ class NotFoundError(openai.NotFoundError):
         litellm_debug_info: str | None = None,
         max_retries: int | None = None,
         num_retries: int | None = None,
+        body: Mapping[str, object] | None = None,
     ):
         self.status_code = 404
         self.message = f"litellm.NotFoundError: {message}"
@@ -202,7 +204,7 @@ class NotFoundError(openai.NotFoundError):
             request=httpx.Request(method="GET", url="https://litellm.ai"),  # mock request object
         )
         super().__init__(
-            self.message, response=self.response, body=None
+            self.message, response=self.response, body=body
         )  # Call the base class constructor with the parameters it needs
 
     def __str__(self):
@@ -321,6 +323,17 @@ class UnsupportedEndpointError(BadRequestError):
     pass
 
 
+class EndpointNotSupportedByModelError(BadRequestError):
+    def __init__(self, message: str, model: str, llm_provider: str = "") -> None:
+        super().__init__(  # pyright: ignore[reportUnknownMemberType]  # inherited public exception constructor is untyped
+            message=message, model=model, llm_provider=llm_provider
+        )
+        self.message = message
+        self.type = "invalid_request_error"
+        self.param = "model"
+        self.code = "400"
+
+
 class UnprocessableEntityError(openai.UnprocessableEntityError):
     def __init__(
         self,

litellm/litellm_core_utils/exception_mapping_utils.py

@@ -413,6 +413,25 @@ def _map_openai_exception(
             body=getattr(original_exception, "body", None),
         )
     elif "invalid_request_error" in error_str and "Incorrect API key provided" not in error_str:
+        invalid_request_status: Final = getattr(original_exception, "status_code", None)
+        if invalid_request_status == 401:
+            raise AuthenticationError(
+                message=f"{exception_provider} - {message}",
+                llm_provider=custom_llm_provider,
+                model=model,
+                response=response,
+                litellm_debug_info=extra_information,
+                body=getattr(original_exception, "body", None),
+            )
+        if invalid_request_status == 404:
+            raise NotFoundError(
+                message=f"{exception_provider} - {message}",
+                model=model,
+                llm_provider=custom_llm_provider,
+                response=response,
+                litellm_debug_info=extra_information,
+                body=getattr(original_exception, "body", None),
+            )
         raise BadRequestError(
             message=f"{exception_provider} - {message}",
             llm_provider=custom_llm_provider,

litellm/litellm_core_utils/health_check_helpers.py

@@ -31,7 +31,13 @@ EXTRACTION_HEALTH_CHECK_FIELDS: Final[Mapping[str, Mapping[str, str]]] = Mapping
 )
 
 
-def native_health_check_mode(model: str, custom_llm_provider: str | None) -> Literal["anthropic_messages"] | None:
+def native_health_check_mode(
+    model: str, custom_llm_provider: str | None
+) -> Literal["anthropic_messages", "realtime"] | None:
+    if custom_llm_provider == "soniox":
+        from litellm.llms.soniox.common_utils import soniox_health_check_mode
+
+        return soniox_health_check_mode(model)
     if custom_llm_provider != "bedrock_mantle":
         return None
     from litellm.llms.bedrock_mantle.common_utils import mantle_health_check_mode

litellm/litellm_core_utils/llm_response_utils/convert_dict_to_response.py

@@ -19,6 +19,7 @@ from litellm.litellm_core_utils.prompt_templates.common_utils import (
 from litellm.llms.ollama.common_utils import OllamaResponseIdNormalizer
 from litellm.types.llms.databricks import DatabricksTool
 from litellm.types.llms.openai import (
+    ChatCompletionAnnotation,
     ChatCompletionReasoningItem,
     ChatCompletionThinkingBlock,
     ImageURLListItem,
@@ -59,6 +60,31 @@ _CHOICES_FIELDS: Final[frozenset] = frozenset(Choices.model_fields.keys())
 _MODEL_RESPONSE_FIELDS: Final[frozenset] = frozenset(ModelResponse.model_fields.keys()) | {"usage"}
 
 
+_ANNOTATION_ENTRIES: Final = TypeAdapter(tuple[object, ...])
+
+
+def _is_provider_specific_annotation(annotation: object) -> bool:
+    match annotation:
+        case {"type": "url_citation"}:
+            return False
+        case {"type": _}:
+            return True
+        case _:
+            return False
+
+
+def _annotation_entries(annotations: object) -> tuple[object, ...]:
+    return _ANNOTATION_ENTRIES.validate_python(annotations) if isinstance(annotations, list) else ()
+
+
+def partition_annotations(annotations: object) -> tuple[object, tuple[object, ...]]:
+    entries: Final = _annotation_entries(annotations)
+    others: Final = tuple(annotation for annotation in entries if _is_provider_specific_annotation(annotation))
+    if not others:
+        return annotations, ()
+    return [annotation for annotation in entries if not _is_provider_specific_annotation(annotation)] or None, others
+
+
 def _normalize_images_for_message(
     images: list[dict] | None,
 ) -> list[ImageURLListItem] | None:
@@ -787,6 +813,9 @@ def convert_to_model_response_object(
                     provider_specific_fields = dict(choice["message"].get("provider_specific_fields", None) or {})
                     for f in choice["message"].keys() - _MESSAGE_FIELDS:
                         provider_specific_fields[f] = choice["message"][f]
+                    annotations, other_annotations = partition_annotations(choice["message"].get("annotations", None))
+                    if other_annotations:
+                        provider_specific_fields["annotations"] = list(other_annotations)
 
                     # Handle reasoning models that display `reasoning_content` within `content`
                     reasoning_content, content = extract_reasoning_content(choice["message"])
@@ -809,7 +838,9 @@ def convert_to_model_response_object(
                         reasoning_content=reasoning_content,
                         thinking_blocks=thinking_blocks,
                         reasoning_items=choice["message"].get("reasoning_items", None),
-                        annotations=choice["message"].get("annotations", None),
+                        annotations=cast(  # cast-ok: provider annotation dicts pass through whole, keeping keys such as url_citation.content
+                            list[ChatCompletionAnnotation] | None, annotations
+                        ),
                         images=_normalize_images_for_message(choice["message"].get("images", None)),
                     )
                     finish_reason = choice.get("finish_reason", None)

litellm/litellm_core_utils/prompt_templates/common_utils.py

@@ -424,6 +424,109 @@ def anthropic_image_source_to_openai_url(image_source: Mapping[str, object]) ->
     return None
 
 
+@dataclass(frozen=True, slots=True)
+class InlineDocument:
+    filename: str
+    file_data: str
+
+
+def _document_media_type(source: object) -> str:
+    match source:
+        case {"media_type": str() as media_type} if media_type:
+            return media_type
+        case _:
+            return "application/pdf"
+
+
+def anthropic_inline_document(document: Mapping[str, object]) -> InlineDocument | None:
+    source: Final = document.get("source")
+    match source:
+        case {"type": "base64", "data": str() as data} if data:
+            title: Final = document.get("title")
+            return InlineDocument(
+                filename=title if isinstance(title, str) and title else "document.pdf",
+                file_data=f"data:{_document_media_type(source)};base64,{data}",
+            )
+        case _:
+            return None
+
+
+def is_pdf_file_part(part: object) -> bool:
+    match part:
+        case {"type": "file", "file": {"file_data": str() as file_data}} if file_data.startswith("data:"):
+            return file_data.startswith("data:application/pdf")
+        case {"type": "file", "file": {"format": str() as file_format}}:
+            return file_format == "application/pdf"
+        case {"type": "file", "file": {"filename": str() as filename}}:
+            return filename.lower().endswith(".pdf")
+        case _:
+            return False
+
+
+_CONTENT_PARTS: Final = TypeAdapter(tuple[object, ...])
+
+
+def content_parts(message: Mapping[str, object]) -> Sequence[object]:
+    content: Final = message.get("content")
+    return _CONTENT_PARTS.validate_python(content) if isinstance(content, list) else ()
+
+
+def _has_pdf_file_part(message: Mapping[str, object]) -> bool:
+    return any(is_pdf_file_part(part) for part in content_parts(message))
+
+
+def messages_have_pdf_file_part(messages: Sequence[Mapping[str, object]]) -> bool:
+    return any(_has_pdf_file_part(message) for message in messages)
+
+
+def _message_without_pdf_file_parts(message: AllMessageValues) -> AllMessageValues:
+    if not _has_pdf_file_part(message):
+        return message
+    kept: Final = [part for part in content_parts(message) if not is_pdf_file_part(part)]
+    return cast(AllMessageValues, {**message, "content": kept})  # cast-ok: same message with fewer content parts
+
+
+def _is_pdf_only_user_turn(message: AllMessageValues) -> bool:
+    parts: Final = content_parts(message)
+    return message["role"] == "user" and bool(parts) and all(is_pdf_file_part(part) for part in parts)
+
+
+def messages_without_pdf_file_parts(messages: Sequence[AllMessageValues]) -> tuple[AllMessageValues, ...]:
+    return tuple(
+        _message_without_pdf_file_parts(message) for message in messages if not _is_pdf_only_user_turn(message)
+    )
+
+
+_CONTENT_PART: Final = TypeAdapter(Mapping[str, object])
+
+
+def _file_part_as_image_url(part: object) -> object:
+    match part:
+        case {"type": "file", "file": {"file_data": str() as file_data}} if file_data:
+            fields: Final = _CONTENT_PART.validate_python(part)
+            return {key: value for key, value in fields.items() if key != "file"} | {
+                "type": "image_url",
+                "image_url": {"url": file_data},
+            }
+        case _:
+            return part
+
+
+def message_with_file_parts_as_image_urls(message: Mapping[str, object]) -> Mapping[str, object]:
+    parts: Final = content_parts(message)
+    if not parts:
+        return message
+    return {**message, "content": [_file_part_as_image_url(part) for part in parts]}
+
+
+def _typed_message_with_file_parts_as_image_urls(message: AllMessageValues) -> AllMessageValues:
+    return cast(AllMessageValues, message_with_file_parts_as_image_urls(message))  # cast-ok: same message, image parts
+
+
+def messages_with_file_parts_as_image_urls(messages: Sequence[AllMessageValues]) -> tuple[AllMessageValues, ...]:
+    return tuple(_typed_message_with_file_parts_as_image_urls(message) for message in messages)
+
+
 def _image_part_url(part: Mapping[str, object]) -> str | None:
     """The image URL carried by one content part, whichever of the three dialects wrote it."""
     part_type: Final = part.get("type")

litellm/litellm_core_utils/realtime_streaming.py

@@ -20,6 +20,7 @@ from litellm.constants import (
     REALTIME_SESSION_SUCCESS_LOGGED_KEY,
 )
 from litellm.litellm_core_utils.logging_worker import GLOBAL_LOGGING_WORKER
+from litellm.llms.base_llm.realtime.transcription_protocol import RealtimeTranscriptionProtocolError
 from litellm.llms.base_llm.realtime.transformation import BaseRealtimeConfig, RealtimeBackend
 from litellm.types.llms.openai import (
     OpenAIRealtimeEvents,
@@ -1281,24 +1282,29 @@ class RealTimeStreaming:
             verbose_logger.exception("Connection closed in backend to client send messages - %s", e)
             close: Final = backend_close_from(e)
             self._flush_unbilled_transcription_usage()
-            if self._backend_refused_session(close):
-                await self.log_backend_refusal(e)
-            else:
-                await self.log_messages()
+            await self._log_session_end(e if self._backend_refused_session(close) else None)
             return close
         except asyncio.CancelledError:
             self._flush_unbilled_transcription_usage()
-            await self.log_messages()
+            await self._log_session_end(None)
             raise
         except Exception as e:
             verbose_logger.exception("Error in backend to client send messages: %s", e)
             self._flush_unbilled_transcription_usage()
-            await self.log_messages()
+            await self._log_session_end(None)
             return BackendClose(code=1011, reason="proxy failed while relaying the upstream websocket")
 
     def _backend_refused_session(self, close: BackendClose) -> bool:
         return close.code != 1000 and not self._backend_sent_frames
 
+    async def _log_session_end(self, transport_refusal: Exception | None) -> None:
+        provider_refusal: Final = None if self.provider_config is None else self.provider_config.session_refusal()
+        refusal: Final = provider_refusal or transport_refusal
+        if refusal is None:
+            await self.log_messages()
+            return
+        await self.log_backend_refusal(refusal)
+
     async def log_backend_refusal(self, error: Exception) -> None:
         if not self.logging_obj:
             return
@@ -1691,7 +1697,13 @@ class RealTimeStreaming:
                 # ``_maybe_send_guardrail_turn_detection_update`` would retry.
                 if msg_type == "session.update" and self.provider_config is None:
                     await self._send_session_gate_once()
-                sent = await self._send_to_backend(message)
+                try:
+                    sent = await self._send_to_backend(message)
+                except RealtimeTranscriptionProtocolError as e:
+                    await self.websocket.send_text(realtime_error_event(str(e), error_type="invalid_request_error"))
+                    if self._uses_deferred_backend_setup():
+                        raise
+                    continue
                 if guardrail_turn_detection_injected and sent:
                     self._guardrail_turn_detection_update_sent = True
 

litellm/litellm_core_utils/streaming_chunk_builder_utils.py

@@ -20,6 +20,7 @@ from litellm.litellm_core_utils.core_helpers import (
 )
 from litellm.litellm_core_utils.hidden_params import HIDDEN_PARAMS_ATTR, set_hidden_params
 from litellm.litellm_core_utils.llm_cost_calc.guardrail_cost import BEDROCK_TRACE_FIELDS, object_items, object_mapping
+from litellm.litellm_core_utils.llm_response_utils.convert_dict_to_response import partition_annotations
 from litellm.litellm_core_utils.prompt_templates.common_utils import (
     get_content_from_model_response,  # pyright: ignore[reportUnknownVariableType]  # response param accepts an untyped dict
 )
@@ -1703,10 +1704,17 @@ def stream_chunk_builder(
             # Merge annotations from ALL chunks — providers may spread
             # them across multiple streaming chunks or send them only in
             # the final chunk.
-            all_annotations: Final[list] = []
+            all_annotations: Final[list[object]] = []
             for ac in annotation_chunks:
                 all_annotations.extend(ac["choices"][0]["delta"]["annotations"])
-            response["choices"][0]["message"]["annotations"] = all_annotations
+            message_annotations, provider_annotations = partition_annotations(all_annotations)
+            if message_annotations is not None:
+                response["choices"][0]["message"]["annotations"] = message_annotations
+            if provider_annotations:
+                response.choices[0].message.provider_specific_fields = {
+                    **(response.choices[0].message.provider_specific_fields or {}),
+                    "annotations": list(provider_annotations),
+                }
 
         audio_chunks: Final = [
             chunk
@@ -1769,7 +1777,10 @@ def stream_chunk_builder(
 
             if combined_provider_fields:
                 _choice = response.choices[0]
-                _choice.message.provider_specific_fields = combined_provider_fields
+                _choice.message.provider_specific_fields = {
+                    **(_choice.message.provider_specific_fields or {}),
+                    **combined_provider_fields,
+                }
 
         completion_output = get_content_from_model_response(response)
 

litellm/llms/anthropic/messages/adapters/transformation.py

@@ -123,6 +123,7 @@ from openai.types.chat.chat_completion_chunk import Choice as OpenAIStreamingCho
 
 from litellm.litellm_core_utils.prompt_templates.common_utils import (
     anthropic_image_source_to_openai_url,
+    anthropic_inline_document,
     parse_tool_call_arguments,
     reasoning_content_from_thinking_blocks,
     with_prompt_cache_breakpoint,
@@ -185,6 +186,8 @@ from litellm.types.llms.openai import (
     AllMessageValues,
     ChatCompletionAssistantMessage,
     ChatCompletionAssistantToolCall,
+    ChatCompletionFileObject,
+    ChatCompletionFileObjectFile,
     ChatCompletionImageObject,
     ChatCompletionImageUrlObject,
     ChatCompletionRedactedThinkingBlock,
@@ -498,7 +501,9 @@ class LiteLLMAnthropicMessagesAdapter:
         for m in ordered_messages:
             user_message: ChatCompletionUserMessage | None = None
             tool_message_list: list[ChatCompletionToolMessage] = []
-            new_user_content_list: list[ChatCompletionTextObject | ChatCompletionImageObject] = []
+            new_user_content_list: list[
+                ChatCompletionTextObject | ChatCompletionImageObject | ChatCompletionFileObject
+            ] = []
             if m["role"] == "system":
                 system_message = self._translate_midturn_system_message_to_openai(m, model)
                 if system_message is not None:
@@ -531,13 +536,8 @@ class LiteLLMAnthropicMessagesAdapter:
                                     self._add_prompt_cache_breakpoint_if_present(content, image_obj)
                                 )
                         elif content.get("type") == "document":
-                            # Convert Anthropic document format (PDF, etc.) to OpenAI format
-                            source = content.get("source", {})
-                            openai_image_url = self._translate_anthropic_image_to_openai(cast(dict, source))
-
-                            if openai_image_url:
-                                image_url_obj = ChatCompletionImageUrlObject(url=openai_image_url)
-                                doc_obj = ChatCompletionImageObject(type="image_url", image_url=image_url_obj)
+                            doc_obj = self._translate_anthropic_document_to_openai(content)
+                            if doc_obj is not None:
                                 self._add_cache_control_if_applicable(content, doc_obj, model)
                                 new_user_content_list.append(doc_obj)
                         elif content.get("type") == "tool_result":
@@ -1357,7 +1357,7 @@ class LiteLLMAnthropicMessagesAdapter:
                 text: Final = tool_result_text_part(block)
                 return None if text is None else ChatCompletionTextObject(type="text", text=text)
             case "image" | "document":
-                return self._tool_result_image_part(block.get("source"))
+                return self._source_image_url_part(block.get("source"))
             case "tool_reference":
                 return ChatCompletionToolReferenceObject(
                     type="tool_reference", tool_name=str(block.get("tool_name") or "")
@@ -1365,7 +1365,18 @@ class LiteLLMAnthropicMessagesAdapter:
             case _:
                 return None
 
-    def _tool_result_image_part(self, image_source: object) -> ChatCompletionImageObject | None:
+    def _translate_anthropic_document_to_openai(
+        self, document: Mapping[str, object]
+    ) -> ChatCompletionFileObject | ChatCompletionImageObject | None:
+        inline_document: Final = anthropic_inline_document(document)
+        if inline_document is None:
+            return self._source_image_url_part(document.get("source"))
+        return ChatCompletionFileObject(
+            type="file",
+            file=ChatCompletionFileObjectFile(filename=inline_document.filename, file_data=inline_document.file_data),
+        )
+
+    def _source_image_url_part(self, image_source: object) -> ChatCompletionImageObject | None:
         if not isinstance(image_source, dict):
             return None
         openai_image_url = self._translate_anthropic_image_to_openai(image_source)

litellm/llms/anthropic/messages/responses_adapters/transformation.py

@@ -13,6 +13,7 @@ from typing import Any, Final, cast
 from litellm.litellm_core_utils.prompt_templates.common_utils import (
     TOOL_RESULT_IMAGE_BOUNDARY,
     TOOL_RESULT_IMAGE_PLACEHOLDER,
+    anthropic_inline_document,
     encrypted_reasoning_signature,
     responses_reasoning_items_from_thinking_blocks,
     with_prompt_cache_breakpoint,
@@ -106,27 +107,18 @@ class LiteLLMAnthropicToResponsesAPIAdapter:
         block: Mapping[str, object],
     ) -> dict[str, str] | None:  # mutable-ok: API message payload
         """Convert an Anthropic document block to a Responses input_file part."""
+        inline_document: Final = anthropic_inline_document(block)
+        if inline_document is not None:
+            return {
+                "type": "input_file",
+                "filename": inline_document.filename,
+                "file_data": inline_document.file_data,
+            }
         raw_source: Final = block.get("source")
         if not isinstance(raw_source, Mapping):
             return None
         source: Final = cast(Mapping[str, object], raw_source)  # cast-ok: untrusted client payload
-        source_type: Final = source.get("type")
-        if source_type == "base64":
-            data: Final = source.get("data")
-            if not isinstance(data, str) or not data:
-                return None
-            raw_media_type: Final = source.get("media_type")
-            media_type: Final = (
-                raw_media_type if isinstance(raw_media_type, str) and raw_media_type else "application/pdf"
-            )
-            raw_title: Final = block.get("title")
-            filename: Final = raw_title if isinstance(raw_title, str) and raw_title else "document.pdf"
-            return {
-                "type": "input_file",
-                "filename": filename,
-                "file_data": f"data:{media_type};base64,{data}",
-            }
-        if source_type == "url":
+        if source.get("type") == "url":
             url: Final = source.get("url")
             if not isinstance(url, str) or not url:
                 return None

litellm/llms/azure_ai/chat/transformation.py

@@ -1,7 +1,7 @@
 import copy
 import enum
 import re
-from collections.abc import AsyncIterator, Collection, Iterator
+from collections.abc import AsyncIterator, Collection, Iterator, Mapping
 from typing import TYPE_CHECKING, Final, cast
 
 import httpx
@@ -10,10 +10,11 @@ from httpx import Response
 import litellm
 from litellm._logging import verbose_logger
 from litellm.litellm_core_utils.prompt_templates.common_utils import (
-    audio_or_image_in_message_content,
+    content_parts,
     convert_content_list_to_str,
     drop_tool_reference_parts_from_tool_messages,
     filter_value_from_dict,
+    is_pdf_file_part,
 )
 from litellm.llms.azure.common_utils import BaseAzureLLM
 from litellm.llms.azure_ai.common_utils import (
@@ -46,6 +47,20 @@ NON_OPENAI_SPEC_MESSAGE_FIELDS: Final = (
     "cache_control",
 )
 
+LIST_ONLY_CONTENT_PART_TYPES: Final = frozenset({"image_url", "input_audio"})
+
+
+def _is_list_only_content_part(part: object) -> bool:
+    match part:
+        case {"type": str() as part_type} if part_type in LIST_ONLY_CONTENT_PART_TYPES:
+            return True
+        case _:
+            return is_pdf_file_part(part)
+
+
+def _has_list_only_content_part(message: Mapping[str, object]) -> bool:
+    return any(_is_list_only_content_part(part) for part in content_parts(message))
+
 
 class AzureAIChatCompletionResponseIterator(OpenAIChatCompletionResponseIterator):
     def chunk_parser(self, chunk: dict[str, object]) -> ModelResponseStream:  # mutable-ok: mirrors override contract
@@ -246,7 +261,7 @@ class AzureAIStudioConfig(OpenAIConfig):
                these with "Extra inputs are not permitted", which breaks multi-turn
                Anthropic-format clients that echo thinking blocks back as history.
             2. Transforms list content to a string.
-            3. If message contains an image or audio, send as is (user-intended)
+            3. If message contains an image, audio, or PDF file part, send as is (user-intended)
 
         Operates on a deep copy so the caller's messages keep their thinking blocks
         and provider metadata, which a fallback to another provider still needs.
@@ -257,8 +272,7 @@ class AzureAIStudioConfig(OpenAIConfig):
             for field in NON_OPENAI_SPEC_MESSAGE_FIELDS:
                 filter_value_from_dict(message_dict, field)
 
-            # Do nothing if the message contains an image or audio
-            if audio_or_image_in_message_content(message):
+            if _has_list_only_content_part(message):
                 continue
 
             texts = convert_content_list_to_str(message=message)

litellm/llms/base_llm/realtime/transformation.py

@@ -4,7 +4,6 @@ from types import TracebackType
 from typing import TYPE_CHECKING, Any, Protocol
 
 import httpx
-from typing_extensions import Self
 
 from litellm.types.llms.openai import OpenAIRealtimeStreamSessionEvents
 from litellm.types.realtime import (
@@ -24,7 +23,7 @@ else:
 
 
 class RealtimeBackend(Protocol):
-    async def __aenter__(self) -> Self: ...
+    async def __aenter__(self) -> object: ...
 
     async def __aexit__(
         self,
@@ -97,9 +96,15 @@ class BaseRealtimeConfig(ABC):
     def unbilled_usage_on_session_close(self, model: str) -> RealtimeInputAudioTranscriptionUsage | None:
         return None
 
+    def session_refusal(self) -> Exception | None:
+        return None
+
     async def open_backend(self, url: str, headers: Mapping[str, str]) -> RealtimeBackend | None:
         return None
 
+    def wrap_backend(self, backend: RealtimeBackend) -> RealtimeBackend:
+        return backend
+
     def transform_session_created_event(
         self,
         model: str,

litellm/llms/base_llm/vector_store/transformation.py

@@ -226,6 +226,9 @@ class BaseVectorStoreConfig:
         ``get_complete_url`` adds a query string, like Azure, override this."""
         return f"{api_base}/{encoded_vector_store_id}"
 
+    def get_vector_store_list_url(self, api_base: str) -> str:
+        return api_base
+
     def get_error_class(self, error_message: str, status_code: int, headers: dict | httpx.Headers) -> BaseLLMException:
         from ..chat.transformation import BaseLLMException
 

litellm/llms/custom_httpx/llm_http_handler.py

@@ -6923,7 +6923,7 @@ class BaseLLMHTTPHandler:
         try:
             ssl_context: Final = realtime_ssl_for_url(url)
             provider_backend: Final = await provider_config.open_backend(url, headers)
-            backend_ws: Final = (
+            backend_ws: Final = provider_config.wrap_backend(
                 provider_backend
                 if provider_backend is not None
                 else await self._open_realtime_backend_ws(websockets, url, headers, ssl_context)
@@ -6961,9 +6961,7 @@ class BaseLLMHTTPHandler:
                         await backend_ws.send(_session_config)
                         realtime_streaming.session_configuration_request = _session_config
 
-                # For providers that defer setup until client session.update, optionally
-                # send synthetic session.created to unblock clients waiting on connect.
-                if not provider_config.requires_session_configuration():
+                if not _session_config:
                     synthetic_session: Final = provider_config.transform_session_created_event(
                         model=model,
                         logging_session_id=logging_obj.litellm_trace_id,
@@ -11152,7 +11150,7 @@ class BaseLLMHTTPHandler:
             litellm_params=dict(litellm_params),
         )
 
-        url: Final = api_base
+        url: Final = vector_store_provider_config.get_vector_store_list_url(api_base)
 
         params: Final[dict[str, object]] = {}
         if after is not None:
@@ -11231,7 +11229,7 @@ class BaseLLMHTTPHandler:
             litellm_params=dict(litellm_params),
         )
 
-        url: Final = api_base
+        url: Final = vector_store_provider_config.get_vector_store_list_url(api_base)
 
         params: Final[dict[str, object]] = {}
         if after is not None:

litellm/llms/gigachat/chat/transformation.py

@@ -38,6 +38,15 @@ else:
 
 _EMPTY_FUNCTION: Final[Mapping[str, object]] = MappingProxyType({})
 _JSON_VALUE: Final = TypeAdapter(object)
+_CONTENT_PART: Final = TypeAdapter(Mapping[str, object])
+
+
+def _file_part_data(part: Mapping[str, object]) -> str | None:
+    match part:
+        case {"type": "file", "file": {"file_data": str() as file_data}} if file_data:
+            return file_data
+        case _:
+            return None
 
 
 def is_valid_json(value: str) -> bool:
@@ -295,7 +304,7 @@ class GigaChatConfig(BaseConfig):
 
     def _transform_list_content(self, content: Sequence) -> tuple[str, Sequence[object]]:
         """
-        Extract text and image attachments from a multimodal message content list.
+        Extract text, image and file attachments from a multimodal message content list.
 
         Args:
             content: List of content parts (OpenAI multimodal format)
@@ -320,6 +329,10 @@ class GigaChatConfig(BaseConfig):
                         file_id = self._upload_image(upload_url)
                         if file_id:
                             attachments.append(file_id)
+                elif (file_data := _file_part_data(_CONTENT_PART.validate_python(part))) and (
+                    uploaded_file_id := self._upload_image(file_data)
+                ):
+                    attachments.append(uploaded_file_id)
         text: Final = "\n".join(texts) if texts else ""
         return text, attachments
 

litellm/llms/oci/chat/transformation.py

@@ -20,6 +20,7 @@ import litellm
 from litellm._logging import verbose_logger
 from litellm.constants import DEFAULT_OCI_CHAT_MAX_TOKENS
 from litellm.litellm_core_utils.logging_utils import track_llm_api_timing
+from litellm.litellm_core_utils.prompt_templates.common_utils import messages_with_file_parts_as_image_urls
 from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper
 from litellm.litellm_core_utils.url_utils import join_api_base
 from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException
@@ -681,7 +682,9 @@ class OCIChatConfig(BaseConfig):
                 servingMode=serving_mode,
                 chatRequest=OCIChatRequestPayload(
                     apiFormat=vendor.value,
-                    messages=adapt_messages_to_generic_oci_standard(messages),
+                    messages=adapt_messages_to_generic_oci_standard(
+                        list(messages_with_file_parts_as_image_urls(messages))
+                    ),
                     **self._get_optional_params(vendor, optional_params, model),
                 ),
             )

litellm/llms/opensearch/vector_stores/transformation.py

@@ -0,0 +1,415 @@
+import hashlib
+import json
+import re
+from collections.abc import Mapping, Sequence
+from functools import partial
+from typing import TYPE_CHECKING, Final, Literal, NoReturn
+from urllib.parse import quote, urlsplit
+
+import httpx
+from pydantic import ConfigDict, Field, TypeAdapter, ValidationError
+
+from litellm._logging import verbose_logger
+from litellm.exceptions import AuthenticationError, BadRequestError
+from litellm.litellm_core_utils.core_helpers import drops_unsupported_params
+from litellm.litellm_core_utils.optional_imports import ensure_optional_import
+from litellm.llms.base_llm.chat.transformation import BaseLLMException
+from litellm.llms.base_llm.vector_store.transformation import (
+    BaseQueryEmbeddingVectorStoreConfig,
+    VectorStoreEmbeddingExecutor,
+)
+from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM, SignsRequestsWithAWS, sign_aws_json_post
+from litellm.types.llms.base import LiteLLMBaseModel
+from litellm.types.llms.bedrock import AwsAuthParams
+from litellm.types.router import GenericLiteLLMParams
+from litellm.types.utils import EmbeddingResponse
+from litellm.types.vector_stores import (
+    BaseVectorStoreAuthCredentials,
+    VectorStoreCreateOptionalRequestParams,
+    VectorStoreIndexEndpoints,
+    VectorStoreResultContent,
+    VectorStoreSearchOptionalRequestParams,
+    VectorStoreSearchResponse,
+    VectorStoreSearchResult,
+)
+
+if TYPE_CHECKING:
+    from botocore.credentials import Credentials
+
+    from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+
+DEFAULT_EMBEDDING_FIELD: Final = "embedding"
+DEFAULT_TEXT_FIELD: Final = "text"
+DEFAULT_MAX_NUM_RESULTS: Final = 10
+MAX_MAX_NUM_RESULTS: Final = 50
+_PARAM_PREFIX: Final = "opensearch_"
+_AWS_HOSTS: Final = (
+    re.compile(r"\.(?P<region>[a-z0-9-]+)\.(?P<service>aoss|aoss-fips|es)\.amazonaws\.com(?:\.cn)?\Z"),
+    re.compile(r"\.(?P<service>aoss|aos)\.(?P<region>[a-z0-9-]+)\.on\.aws\Z"),
+)
+_ACCOUNT_ID_LABEL: Final = re.compile(r"\A\d{12}\.")
+_QUERY_VECTOR: Final = TypeAdapter(tuple[float, ...])
+_ATTRIBUTE_VALUE_TYPES: Final = (str, int, float, bool)
+_SEARCH_ONLY_MESSAGE: Final = (
+    "The OpenSearch vector store is search-only. Create the k-NN index in OpenSearch, "
+    "then register it here with its name as opensearch_index_name or vector_store_id."
+)
+_PER_ACCOUNT_MESSAGE: Final = (
+    "api_base is an OpenSearch Serverless per-account endpoint, which needs a collection header the vector store "
+    "does not send. Use the collection endpoint instead, for example https://<collection-id>.<region>.aoss.amazonaws.com."
+)
+_SIGV4_ONLY_MESSAGE: Final = (
+    "The OpenSearch vector store signs every request with AWS SigV4 and does not support api_key. "
+    "Remove api_key and give the vector store AWS credentials instead."
+)
+_CREDENTIALS_HINT: Final = (
+    "Set aws_access_key_id and aws_secret_access_key, aws_role_name, or aws_profile_name on the vector store, "
+    "or run LiteLLM with an AWS identity."
+)
+
+
+def _bad_request(message: str) -> BadRequestError:
+    return BadRequestError(message=message, model=None, llm_provider="opensearch")
+
+
+def _unauthenticated(reason: str) -> AuthenticationError:
+    return AuthenticationError(
+        message=f"No usable AWS credentials to sign the OpenSearch request ({reason}). {_CREDENTIALS_HINT}",
+        llm_provider="opensearch",
+        model=None,
+    )
+
+
+def _usable_credentials(credentials: object) -> "Credentials":
+    from botocore.credentials import Credentials
+
+    if isinstance(credentials, Credentials):
+        return credentials
+    raise _unauthenticated("none were found")
+
+
+class _OpenSearchParams(LiteLLMBaseModel):
+    model_config = ConfigDict(frozen=True, extra="ignore")
+
+    litellm_embedding_model: str | None = None
+    opensearch_embedding_field: str | None = None
+    opensearch_index_name: str | None = None
+    opensearch_text_field: str | None = None
+
+    @property
+    def embedding_field(self) -> str:
+        return self.opensearch_embedding_field or DEFAULT_EMBEDDING_FIELD
+
+    @property
+    def text_field(self) -> str:
+        return self.opensearch_text_field or DEFAULT_TEXT_FIELD
+
+    def index_name(self, vector_store_id: str) -> str:
+        return self.opensearch_index_name or vector_store_id
+
+    def require_embedding_model(self) -> str:
+        if not self.litellm_embedding_model:
+            raise _bad_request(
+                "litellm_embedding_model is required for the OpenSearch vector store. It must be the model that "
+                f"produced the vectors in '{self.embedding_field}', for example openai/text-embedding-3-small."
+            )
+        return self.litellm_embedding_model
+
+
+class _SigningRegion(LiteLLMBaseModel):
+    model_config = ConfigDict(frozen=True, extra="ignore")
+
+    aws_region_name: str | None = None
+
+
+class _SearchOptions(LiteLLMBaseModel):
+    model_config = ConfigDict(frozen=True, extra="ignore")
+
+    max_num_results: int | None = None
+    filters: Mapping[str, object] | None = None
+    ranking_options: Mapping[str, object] | None = None
+    rewrite_query: bool | None = None
+
+    @property
+    def unsupported(self) -> tuple[str, ...]:
+        requested: Final = (
+            ("filters", self.filters),
+            ("ranking_options", self.ranking_options),
+            ("rewrite_query", self.rewrite_query),
+        )
+        return tuple(name for name, value in requested if value)
+
+    @property
+    def k(self) -> int:
+        if self.max_num_results is None:
+            return DEFAULT_MAX_NUM_RESULTS
+        if not 1 <= self.max_num_results <= MAX_MAX_NUM_RESULTS:
+            raise _bad_request(
+                f"max_num_results must be between 1 and {MAX_MAX_NUM_RESULTS}, got {self.max_num_results}"
+            )
+        return self.max_num_results
+
+
+_KNOWN_PARAMS: Final = frozenset(name for name in _OpenSearchParams.model_fields if name.startswith(_PARAM_PREFIX))
+
+
+class _LoggedSearch(LiteLLMBaseModel):
+    model_config = ConfigDict(frozen=True, extra="ignore")
+
+    query: str | tuple[str, ...] = ""
+    litellm_params: _OpenSearchParams = _OpenSearchParams()
+
+    @property
+    def search_query(self) -> str:
+        return BaseQueryEmbeddingVectorStoreConfig.query_text(self.query)
+
+
+class _Hit(LiteLLMBaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    id: str = Field(alias="_id")
+    score: float | None = Field(default=None, alias="_score")
+    source: Mapping[str, object] = Field(default_factory=dict, alias="_source")
+
+
+class _Hits(LiteLLMBaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    hits: Sequence[_Hit]
+
+
+class _SearchResponse(LiteLLMBaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    hits: _Hits
+
+
+def aws_signing_target(url: str) -> tuple[Literal["aoss", "es"], str | None]:
+    hostname: Final = urlsplit(url).hostname or ""
+    match: Final = next(filter(None, (pattern.search(hostname) for pattern in _AWS_HOSTS)), None)
+    if match is None:
+        return "es", None
+    return ("aoss" if match["service"].startswith("aoss") else "es"), match["region"]
+
+
+def _search_result(hit: _Hit, params: _OpenSearchParams) -> VectorStoreSearchResult:
+    text: Final = hit.source.get(params.text_field)
+    filename: Final = hit.source.get("filename")
+    return VectorStoreSearchResult(
+        score=hit.score,
+        content=[VectorStoreResultContent(type="text", text=text)] if isinstance(text, str) else [],
+        file_id=hit.id,
+        filename=filename if isinstance(filename, str) else None,
+        attributes={
+            key: value
+            for key, value in hit.source.items()
+            if key not in (params.text_field, params.embedding_field) and isinstance(value, _ATTRIBUTE_VALUE_TYPES)
+        },
+    )
+
+
+class OpenSearchVectorStoreConfig(BaseQueryEmbeddingVectorStoreConfig, SignsRequestsWithAWS):
+    def __init__(self, aws: BaseAWSLLM | None = None) -> None:
+        self._aws: Final = aws if aws is not None else BaseAWSLLM()
+
+    def get_auth_credentials(self, litellm_params: Mapping[str, object]) -> BaseVectorStoreAuthCredentials:
+        return BaseVectorStoreAuthCredentials()
+
+    def get_vector_store_endpoints_by_type(self) -> VectorStoreIndexEndpoints:
+        return VectorStoreIndexEndpoints(read=[], write=[])
+
+    def validate_environment(
+        self, headers: Mapping[str, object], litellm_params: GenericLiteLLMParams | None
+    ) -> dict[str, object]:  # mutable-ok: the shared HTTP handler requires writable headers
+        params: Final = dict(litellm_params or {})
+        self._signing_credentials(params, self._signing_region(params, str(params.get("api_base") or "")))
+        return {**headers, "Content-Type": "application/json"}
+
+    def _signing_region(self, params: Mapping[str, object], api_base: str) -> str:
+        host_region: Final = aws_signing_target(api_base)[1]
+        return self._aws.get_aws_region_name_for_non_llm_api_calls(
+            host_region or _SigningRegion.model_validate(params).aws_region_name
+        )
+
+    def _signing_credentials(self, params: Mapping[str, object], region: str) -> "Credentials":
+        if params.get("api_key"):
+            raise _bad_request(_SIGV4_ONLY_MESSAGE)
+        auth: Final = AwsAuthParams.model_validate(params)
+        if bool(auth.aws_access_key_id) != bool(auth.aws_secret_access_key):
+            raise _unauthenticated("aws_access_key_id and aws_secret_access_key must be set together")
+        ensure_optional_import("botocore")
+        from botocore.exceptions import (
+            CredentialRetrievalError,
+            NoCredentialsError,
+            PartialCredentialsError,
+            ProfileNotFound,
+        )
+
+        try:
+            return _usable_credentials(self._aws.resolve_credentials(auth, region))
+        except CredentialRetrievalError as error:
+            verbose_logger.warning("OpenSearch vector store AWS credential source failed: %s", error)
+            raise _unauthenticated("the AWS credential source failed, see the LiteLLM logs") from None
+        except (NoCredentialsError, PartialCredentialsError, ProfileNotFound) as error:
+            raise _unauthenticated(str(error)) from None
+
+    def get_complete_url(self, api_base: str | None, litellm_params: Mapping[str, object]) -> str:
+        parsed: Final = urlsplit(api_base or "")
+        if parsed.scheme not in ("http", "https") or not parsed.hostname:
+            raise _bad_request(
+                "api_base must be the OpenSearch endpoint URL, for example "
+                "https://<collection-id>.<region>.aoss.amazonaws.com or https://<domain-endpoint>."
+            )
+        if _ACCOUNT_ID_LABEL.match(parsed.hostname) and aws_signing_target(api_base or "")[0] == "aoss":
+            raise _bad_request(_PER_ACCOUNT_MESSAGE)
+        return (api_base or "").rstrip("/")
+
+    def get_vector_store_url(self, api_base: str, encoded_vector_store_id: str) -> NoReturn:
+        raise _bad_request(_SEARCH_ONLY_MESSAGE)
+
+    def get_vector_store_list_url(self, api_base: str) -> NoReturn:
+        raise _bad_request(_SEARCH_ONLY_MESSAGE)
+
+    @staticmethod
+    def _params(
+        litellm_params: Mapping[str, object],
+        optional_params: VectorStoreSearchOptionalRequestParams,
+        extra_body: Mapping[str, object] | None,
+    ) -> tuple[_OpenSearchParams, int]:
+        unknown: Final = sorted(
+            key for key in litellm_params if key.startswith(_PARAM_PREFIX) and key not in _KNOWN_PARAMS
+        )
+        if unknown:
+            raise _bad_request(
+                f"Unknown OpenSearch vector store parameter(s): {', '.join(unknown)}. "
+                f"Supported: {', '.join(sorted(_KNOWN_PARAMS))}."
+            )
+        if extra_body:
+            raise _bad_request("The OpenSearch vector store does not support extra_body.")
+        try:
+            options: Final = _SearchOptions.model_validate(optional_params)
+        except ValidationError as error:
+            raise _bad_request(f"Invalid vector store search parameters: {error}") from None
+        if options.unsupported and not drops_unsupported_params(litellm_params.get("drop_params")):
+            raise _bad_request(
+                f"The OpenSearch vector store does not support {', '.join(options.unsupported)} yet. "
+                "Remove it from the request, or set drop_params to drop it."
+            )
+        try:
+            return _OpenSearchParams.model_validate(litellm_params), options.k
+        except ValidationError:
+            raise _bad_request(
+                "opensearch_embedding_field, opensearch_index_name, opensearch_text_field, and litellm_embedding_model "
+                "must be strings."
+            ) from None
+
+    @staticmethod
+    def _search_request(
+        vector_store_id: str,
+        embedding: EmbeddingResponse,
+        k: int,
+        params: _OpenSearchParams,
+        api_base: str,
+    ) -> tuple[str, dict[str, object]]:  # mutable-ok: the provider contract returns a writable JSON request body
+        vector: Final = _QUERY_VECTOR.validate_python(embedding.data[0]["embedding"])  # pyright: ignore[reportUnknownMemberType]  # EmbeddingResponse.data is an untyped list, the vector is validated here
+        return (
+            f"{api_base}/{quote(params.index_name(vector_store_id), safe='')}/_search",
+            {
+                "size": k,
+                "query": {"knn": {params.embedding_field: {"vector": vector, "k": k}}},
+                "_source": {"excludes": [params.embedding_field]},
+            },
+        )
+
+    def transform_search_vector_store_request(
+        self,
+        vector_store_id: str,
+        query: str | Sequence[str],
+        vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams,
+        api_base: str,
+        litellm_logging_obj: "LiteLLMLoggingObj",
+        litellm_params: Mapping[str, object],
+        extra_body: Mapping[str, object] | None = None,
+        embedding_executor: VectorStoreEmbeddingExecutor | None = None,
+    ) -> tuple[str, dict[str, object]]:  # mutable-ok: the provider contract returns a writable JSON request body
+        params, k = self._params(litellm_params, vector_store_search_optional_params, extra_body)
+        embedding: Final = self.query_embedding_executor(embedding_executor, None).embed(
+            params.require_embedding_model(), self.query_text(query), self.query_embedding_configuration(litellm_params)
+        )
+        return self._search_request(vector_store_id, embedding, k, params, api_base)
+
+    async def atransform_search_vector_store_request(
+        self,
+        vector_store_id: str,
+        query: str | Sequence[str],
+        vector_store_search_optional_params: VectorStoreSearchOptionalRequestParams,
+        api_base: str,
+        litellm_logging_obj: "LiteLLMLoggingObj",
+        litellm_params: Mapping[str, object],
+        extra_body: Mapping[str, object] | None = None,
+        embedding_executor: VectorStoreEmbeddingExecutor | None = None,
+    ) -> tuple[str, dict[str, object]]:  # mutable-ok: the provider contract returns a writable JSON request body
+        params, k = self._params(litellm_params, vector_store_search_optional_params, extra_body)
+        embedding: Final = await self.query_embedding_executor(embedding_executor, None).aembed(
+            params.require_embedding_model(), self.query_text(query), self.query_embedding_configuration(litellm_params)
+        )
+        return self._search_request(vector_store_id, embedding, k, params, api_base)
+
+    def sign_request(
+        self,
+        headers: Mapping[str, str],
+        optional_params: Mapping[str, object],
+        request_data: Mapping[str, object],
+        api_base: str,
+        api_key: str | None = None,
+    ) -> tuple[dict[str, str], bytes]:  # mutable-ok: the shared HTTP handler requires writable headers
+        region: Final = self._signing_region(optional_params, api_base)
+        body: Final = json.dumps(request_data)
+        signed: Final = sign_aws_json_post(
+            get_credentials=partial(self._signing_credentials, optional_params, region),
+            service_name=aws_signing_target(api_base)[0],
+            aws_region_name=region,
+            url=api_base,
+            body=body,
+            headers={
+                "Content-Type": "application/json",
+                "x-amz-content-sha256": hashlib.sha256(body.encode()).hexdigest(),
+            },
+        )
+        signed_headers: Final = dict(signed.headers.items())
+        signed_names: Final = frozenset(name.lower() for name in signed_headers)
+        return {
+            **{name: value for name, value in headers.items() if name.lower() not in signed_names},
+            **signed_headers,
+        }, body.encode()
+
+    def transform_search_vector_store_response(
+        self, response: httpx.Response, litellm_logging_obj: "LiteLLMLoggingObj"
+    ) -> VectorStoreSearchResponse:
+        if not response.is_success:
+            raise BaseLLMException(status_code=response.status_code, message=response.text, headers=response.headers)
+        try:
+            hits: Final = _SearchResponse.model_validate_json(response.content).hits.hits
+        except ValidationError:
+            raise BaseLLMException(
+                status_code=502,
+                message="OpenSearch answered the search without a hits.hits list.",
+                headers=response.headers,
+            ) from None
+        logged: Final = _LoggedSearch.model_validate(litellm_logging_obj.model_call_details)
+        return VectorStoreSearchResponse(
+            object="vector_store.search_results.page",
+            search_query=logged.search_query,
+            data=[_search_result(hit, logged.litellm_params) for hit in hits],
+        )
+
+    def validate_create_vector_store(self) -> NoReturn:
+        raise _bad_request(_SEARCH_ONLY_MESSAGE)
+
+    def transform_create_vector_store_request(
+        self, vector_store_create_optional_params: VectorStoreCreateOptionalRequestParams, api_base: str
+    ) -> NoReturn:
+        raise _bad_request(_SEARCH_ONLY_MESSAGE)
+
+    def transform_create_vector_store_response(self, response: httpx.Response) -> NoReturn:
+        raise _bad_request(_SEARCH_ONLY_MESSAGE)

litellm/llms/sap/chat/transformation.py

@@ -11,6 +11,7 @@ import httpx
 from pydantic import BaseModel
 
 import litellm
+from litellm.litellm_core_utils.prompt_templates.common_utils import message_with_file_parts_as_image_urls
 from litellm.litellm_core_utils.url_utils import join_api_base
 from litellm.types.llms.openai import AllMessageValues
 from litellm.types.utils import ModelResponse
@@ -104,7 +105,7 @@ def _message_role_mapping(role: str) -> type[ChatMessage]:
 def _messages_to_sap_template(messages: list[AllMessageValues]) -> list:
     template: Final = []
     for message in messages:
-        folded_message = _fold_message_cache_control(message)
+        folded_message = _fold_message_cache_control(message_with_file_parts_as_image_urls(message))
         message_role_class = _message_role_mapping(str(folded_message["role"]))
         validated_message = validate_dict(folded_message, message_role_class)
         template.append(validated_message)

litellm/llms/snowflake/chat/transformation.py

@@ -112,7 +112,7 @@ def _convert_image_url_to_anthropic(block: Mapping[str, object]) -> object:
     pdf/text data URIs become document blocks rather than malformed image blocks.
     """
     image_url: Final = block.get("image_url")
-    url: Final = image_url if isinstance(image_url, str) else _image_url_field(image_url, "url")
+    url: Final = image_url if isinstance(image_url, str) else _str_field(image_url, "url")
     if not url:
         return block
 
@@ -121,18 +121,31 @@ def _convert_image_url_to_anthropic(block: Mapping[str, object]) -> object:
         if select_anthropic_content_block_type_for_file(_data_uri_media_type(url)) == "document"
         else create_anthropic_image_param(
             image_url if isinstance(image_url, dict) else url,
-            format=_image_url_field(image_url, "format"),
+            format=_str_field(image_url, "format"),
             is_bedrock_invoke=True,
         )
     )
+    return _with_cache_control(converted, block)
+
+
+def _convert_file_to_anthropic(block: Mapping[str, object]) -> object:
+    file_data: Final = _str_field(block.get("file"), "file_data")
+    if not file_data:
+        return block
+    return _with_cache_control(
+        anthropic_process_openai_file_message({"type": "file", "file": {"file_data": file_data}}), block
+    )
+
+
+def _with_cache_control(converted: Mapping[str, object], block: Mapping[str, object]) -> Mapping[str, object]:
     cache_control: Final = block.get("cache_control")
     if cache_control is None:
         return converted
     return {**converted, "cache_control": cache_control}
 
 
-def _image_url_field(image_url: object, key: str) -> str | None:
-    value: Final = image_url.get(key) if isinstance(image_url, dict) else None
+def _str_field(container: object, key: str) -> str | None:
+    value: Final = container.get(key) if isinstance(container, dict) else None
     return value if isinstance(value, str) else None
 
 
@@ -141,15 +154,23 @@ def _data_uri_media_type(url: str) -> str:
     return match.group(1) if match else ""
 
 
-def _convert_image_url_blocks_to_anthropic(content: object) -> object:
+def _convert_media_block_to_anthropic(block: object) -> object:
+    mapping: Final = as_str_mapping(block)
+    if mapping is None:
+        return block
+    match mapping.get("type"):
+        case "image_url":
+            return _convert_image_url_to_anthropic(mapping)
+        case "file":
+            return _convert_file_to_anthropic(mapping)
+        case _:
+            return block
+
+
+def _convert_media_blocks_to_anthropic(content: object) -> object:
     if not isinstance(content, list):
         return content
-    return [
-        _convert_image_url_to_anthropic(block)
-        if isinstance(block, Mapping) and block.get("type") == "image_url"
-        else block
-        for block in content
-    ]
+    return [_convert_media_block_to_anthropic(block) for block in content]
 
 
 def _convert_tool_result_to_anthropic(
@@ -391,7 +412,7 @@ class SnowflakeConfig(SnowflakeBaseConfig, OpenAIGPTConfig):
                 conversation.append(
                     {
                         "role": role,
-                        "content": _convert_image_url_blocks_to_anthropic(content),
+                        "content": _convert_media_blocks_to_anthropic(content),
                     }
                 )
 

litellm/llms/soniox/common_utils.py

@@ -3,7 +3,7 @@ Shared utilities for the Soniox provider (https://soniox.com).
 """
 
 from collections.abc import Mapping, Sequence
-from typing import Final, TypeAlias
+from typing import Final, Literal, TypeAlias
 
 from litellm.litellm_core_utils.audio_utils.subtitle_utils import (
     SubtitleToken,
@@ -17,6 +17,8 @@ SonioxToken: TypeAlias = Mapping[str, object]
 # Soniox API base URL.
 SONIOX_API_BASE: Final[str] = "https://api.soniox.com"
 
+SONIOX_REALTIME_MODEL_PREFIX: Final = "stt-rt-"
+
 # Default polling interval in seconds when waiting for an async transcription
 # to finish. Mirrors the Soniox SDK default.
 SONIOX_DEFAULT_POLL_INTERVAL: Final[float] = 1.0
@@ -54,6 +56,10 @@ class SonioxException(BaseLLMException):
     """Provider-specific exception class for Soniox."""
 
 
+def soniox_health_check_mode(model: str) -> Literal["realtime"] | None:
+    return "realtime" if model.startswith(SONIOX_REALTIME_MODEL_PREFIX) else None
+
+
 def get_soniox_api_key(api_key: str | None = None) -> str | None:
     """Resolve the Soniox API key from arg or env var."""
     # Local import to avoid a circular import: litellm.secret_managers.main

litellm/llms/soniox/realtime/transformation.py

@@ -0,0 +1,609 @@
+import asyncio
+import time
+from collections import deque
+from collections.abc import Awaitable, Callable, Mapping
+from itertools import chain, groupby
+from types import MappingProxyType, TracebackType
+from typing import Final
+from urllib.parse import urlparse, urlunparse
+
+from pydantic import BaseModel, ConfigDict, JsonValue, ValidationError
+from typing_extensions import NotRequired, ReadOnly, Required, Self, TypedDict
+
+from litellm import verbose_logger
+from litellm._uuid import uuid
+from litellm.constants import REALTIME_WEBSOCKET_MAX_MESSAGE_SIZE_BYTES
+from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+from litellm.llms.base_llm.realtime.transcription_protocol import (
+    SESSION_UPDATE_EVENT_TYPES,
+    RealtimeTranscriptionProtocolError,
+    TranscriptionSessionUpdate,
+    completed_event,
+    decode_pcm16_append,
+    delta_event,
+    duration_usage,
+    error_event,
+    json_object,
+    parse_transcription_session_update,
+    speech_event,
+    transcription_session,
+    transcription_session_created_event,
+    uses_server_vad,
+)
+from litellm.llms.base_llm.realtime.transformation import BaseRealtimeConfig, RealtimeBackend
+from litellm.llms.custom_httpx.http_handler import realtime_ssl_for_url
+from litellm.llms.soniox.common_utils import SonioxException, get_soniox_api_key
+from litellm.types.llms.openai import (
+    OpenAIRealtimeErrorEvent,
+    OpenAIRealtimeEvents,
+    OpenAIRealtimeInputAudioTranscriptionCompleted,
+    OpenAIRealtimeInputAudioTranscriptionDelta,
+    OpenAIRealtimeTranscriptionSessionCreated,
+)
+from litellm.types.realtime import (
+    RealtimeInputAudioTranscriptionUsage,
+    RealtimeResponseTransformInput,
+    RealtimeResponseTypedDict,
+)
+
+DEFAULT_SONIOX_REALTIME_URL: Final = "wss://stt-rt.soniox.com/transcribe-websocket"
+WEBSOCKET_SCHEMES: Final = MappingProxyType({"wss": "wss", "ws": "ws", "https": "wss", "http": "ws"})
+DEFAULT_SAMPLE_RATE: Final = 24_000
+KEEPALIVE_INTERVAL_SECONDS: Final = 5.0
+_MAX_PENDING_FRAMES: Final = 64
+_FINALIZE: Final = '{"type":"finalize"}'
+_KEEPALIVE: Final = '{"type":"keepalive"}'
+_END_STREAM: Final = ""
+_HEALTH_CHECK_SILENCE: Final = bytes(DEFAULT_SAMPLE_RATE // 5)
+_CONTROL_FRAMES: Final = frozenset((_FINALIZE, _KEEPALIVE, _END_STREAM))
+SESSION_STARTED_FRAME: Final = '{"type":"litellm.soniox.session_started"}'
+_ENDPOINT_TOKEN: Final = "<end>"
+_FINALIZED_TOKEN: Final = "<fin>"
+
+
+class SonioxProtocolError(RealtimeTranscriptionProtocolError):
+    pass
+
+
+class SonioxRealtimeOptions(BaseModel):
+    model_config = ConfigDict(frozen=True, extra="ignore")
+
+    language_hints: tuple[str, ...] | None = None
+    language_hints_strict: bool | None = None
+    enable_language_identification: bool | None = None
+    enable_speaker_diarization: bool | None = None
+    context: JsonValue | None = None
+    translation: JsonValue | None = None
+    client_reference_id: str | None = None
+    max_endpoint_delay_ms: int | None = None
+    endpoint_sensitivity: float | None = None
+    endpoint_latency_adjustment_level: int | None = None
+
+
+class SonioxStartRequest(SonioxRealtimeOptions):
+    model: str
+    audio_format: str
+    sample_rate: int
+    num_channels: int
+    enable_endpoint_detection: bool
+
+
+class SonioxSpeechLabels(TypedDict, total=False):
+    speaker: ReadOnly[str]
+    language: ReadOnly[str]
+
+
+class SonioxTranscriptSegment(SonioxSpeechLabels):
+    text: Required[ReadOnly[str]]
+
+
+class SonioxTranscriptionDelta(OpenAIRealtimeInputAudioTranscriptionDelta, SonioxSpeechLabels):
+    pass
+
+
+class SonioxTranscriptionCompleted(OpenAIRealtimeInputAudioTranscriptionCompleted):
+    segments: NotRequired[ReadOnly[tuple[SonioxTranscriptSegment, ...]]]
+
+
+class _SonioxToken(BaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    text: str
+    is_final: bool = False
+    translation_status: str | None = None
+    speaker: str | None = None
+    language: str | None = None
+
+    @property
+    def labelled(self) -> bool:
+        return self.speaker is not None or self.language is not None
+
+
+class _SonioxResponse(BaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    tokens: tuple[_SonioxToken, ...] = ()
+    total_audio_proc_ms: int | None = None
+    error_code: int | None = None
+    error_message: str | None = None
+    finished: bool = False
+
+
+def _sample_rate(update: TranscriptionSessionUpdate | None) -> int:
+    audio_format: Final = None if update is None else update.audio_format
+    if audio_format is None:
+        return DEFAULT_SAMPLE_RATE
+    if not audio_format.is_pcm16:
+        raise SonioxProtocolError("Soniox realtime requires pcm16 input audio")
+    if audio_format.channels not in (None, 1):
+        raise SonioxProtocolError("Soniox realtime requires mono input audio")
+    if audio_format.rate is None:
+        return DEFAULT_SAMPLE_RATE
+    if audio_format.rate <= 0:
+        raise SonioxProtocolError("sample rate must be positive")
+    return audio_format.rate
+
+
+def _language_hints(
+    language: str | None, configured: tuple[str, ...] | None, strict: bool | None
+) -> tuple[str, ...] | None:
+    if not language or (strict and configured):
+        return configured
+    return (language, *(hint for hint in configured or () if hint != language))
+
+
+def _uses_endpoint_detection(update: TranscriptionSessionUpdate, drop_params: bool) -> bool:
+    return uses_server_vad(update, "Soniox realtime", drop_params, SonioxProtocolError)
+
+
+def _ignored_settings(update: TranscriptionSessionUpdate) -> tuple[str, ...]:
+    vad_tuning: Final = (f"turn_detection.{key}" for key in sorted(update.turn_detection or {}) if key != "type")
+    return (*update.unsupported_transcription_keys, *vad_tuning)
+
+
+def build_start_request(
+    model: str, update: TranscriptionSessionUpdate | None, options: SonioxRealtimeOptions, drop_params: bool = False
+) -> SonioxStartRequest:
+    return SonioxStartRequest(
+        **options.model_dump(exclude={"language_hints"}),
+        language_hints=_language_hints(
+            None if update is None else update.language, options.language_hints, options.language_hints_strict
+        ),
+        model=model,
+        audio_format="pcm_s16le",
+        sample_rate=_sample_rate(update),
+        num_channels=1,
+        enable_endpoint_detection=update is None or _uses_endpoint_detection(update, drop_params),
+    )
+
+
+def _session_created(session_id: str, start_request: SonioxStartRequest) -> OpenAIRealtimeTranscriptionSessionCreated:
+    session: Final = transcription_session(
+        session_id=session_id,
+        model=start_request.model,
+        sample_rate=start_request.sample_rate,
+        language=start_request.language_hints[0] if start_request.language_hints else None,
+        server_vad=start_request.enable_endpoint_detection,
+    )
+    return transcription_session_created_event(session)
+
+
+def _changes_started_rate(update: TranscriptionSessionUpdate, started: SonioxStartRequest) -> bool:
+    audio_format: Final = update.audio_format
+    if audio_format is None:
+        return False
+    requested_rate: Final = _sample_rate(update)
+    return audio_format.rate is not None and requested_rate != started.sample_rate
+
+
+def _changes_started_stream(update: TranscriptionSessionUpdate, started: SonioxStartRequest, drop_params: bool) -> bool:
+    turn_detection_set: Final = update.turn_detection is not None or update.turn_detection_disabled
+    return (
+        _changes_started_rate(update, started)
+        or (
+            update.language is not None
+            and _language_hints(update.language, started.language_hints, started.language_hints_strict)
+            != started.language_hints
+        )
+        or (turn_detection_set and _uses_endpoint_detection(update, drop_params) != started.enable_endpoint_detection)
+    )
+
+
+def build_soniox_realtime_url(api_base: str | None) -> str:
+    if api_base is None:
+        return DEFAULT_SONIOX_REALTIME_URL
+    parsed: Final = urlparse(api_base)
+    scheme: Final = WEBSOCKET_SCHEMES.get(parsed.scheme)
+    if scheme is None:
+        raise ValueError("Soniox realtime api_base must be a wss://, ws://, https://, or http:// URL")
+    return urlunparse(parsed._replace(scheme=scheme))
+
+
+def _labels(token: _SonioxToken) -> SonioxSpeechLabels:
+    speaker: Final[SonioxSpeechLabels] = {} if token.speaker is None else {"speaker": token.speaker}
+    language: Final[SonioxSpeechLabels] = {} if token.language is None else {"language": token.language}
+    return {**speaker, **language}
+
+
+def _delta(item_id: str, token: _SonioxToken) -> SonioxTranscriptionDelta:
+    return {**delta_event(item_id, token.text), **_labels(token)}
+
+
+def _segment(run: tuple[_SonioxToken, ...]) -> SonioxTranscriptSegment:
+    return {**_labels(run[0]), "text": "".join(token.text for token in run).strip()}
+
+
+def _segments(tokens: tuple[_SonioxToken, ...]) -> tuple[SonioxTranscriptSegment, ...]:
+    return tuple(_segment(tuple(run)) for _, run in groupby(tokens, key=lambda token: (token.speaker, token.language)))
+
+
+def _new_item_id() -> str:
+    return f"item_{uuid.uuid4().hex}"
+
+
+def _completed(
+    item_id: str, tokens: tuple[_SonioxToken, ...], usage: RealtimeInputAudioTranscriptionUsage | None
+) -> OpenAIRealtimeInputAudioTranscriptionCompleted:
+    completed: Final = completed_event(item_id, "".join(token.text for token in tokens).strip(), usage)
+    if not any(token.labelled for token in tokens):
+        return completed
+    labelled: Final[SonioxTranscriptionCompleted] = {**completed, "segments": _segments(tokens)}
+    return labelled
+
+
+def _soniox_error(response: _SonioxResponse) -> SonioxException | None:
+    if response.error_code is None and response.error_message is None:
+        return None
+    return SonioxException(
+        status_code=response.error_code or 500,
+        message=f"Soniox realtime error {response.error_code}: {response.error_message}",
+    )
+
+
+class SonioxEventTransformer:
+    def __init__(self, *, drops_originals: bool) -> None:
+        self._drops_originals: Final = drops_originals
+        self._item_id: str | None = None
+        self._final_tokens: Final[list[_SonioxToken]] = []  # mutable-ok: a turn's tokens append in O(1) each
+        self._finalizes: Final[deque[bool]] = deque()  # mutable-ok: pending finalizes queue and pop in O(1) each
+        self._oldest_commit_answered: bool = False
+        self._processed_ms: int = 0
+        self._billed_ms: int = 0
+        self._refusal: SonioxException | None = None
+
+    @property
+    def refusal(self) -> SonioxException | None:
+        return self._refusal
+
+    def transform(self, payload: str) -> tuple[OpenAIRealtimeEvents, ...]:
+        try:
+            response: Final = _SonioxResponse.model_validate_json(payload)
+        except ValidationError:
+            raise SonioxProtocolError("Soniox returned an invalid realtime response") from None
+        self._bill(response.total_audio_proc_ms)
+        error: Final = _soniox_error(response)
+        if error is not None:
+            return (self._report(error),)
+        return tuple(chain.from_iterable(self._token_events(token) for token in response.tokens))
+
+    def queue_finalize(self, *, discards_turn: bool) -> None:
+        if discards_turn and not self._finalizes:
+            self._item_id = None
+            self._final_tokens.clear()
+        self._finalizes.append(discards_turn)
+
+    def take_unbilled_usage(self, stream_ms: int = 0) -> RealtimeInputAudioTranscriptionUsage | None:
+        billable_ms: Final = max(self._processed_ms, stream_ms)
+        unbilled_ms: Final = billable_ms - self._billed_ms
+        if unbilled_ms <= 0:
+            return None
+        self._billed_ms = billable_ms
+        return duration_usage(unbilled_ms / 1000)
+
+    def _report(self, error: SonioxException) -> OpenAIRealtimeErrorEvent:
+        if self._processed_ms == 0:
+            self._refusal = error
+        return error_event(error.message)
+
+    def _bill(self, total_audio_proc_ms: int | None) -> None:
+        if total_audio_proc_ms is not None and total_audio_proc_ms > self._processed_ms:
+            self._processed_ms = total_audio_proc_ms
+
+    def _token_events(self, token: _SonioxToken) -> tuple[OpenAIRealtimeEvents, ...]:
+        if token.text == _FINALIZED_TOKEN:
+            return self._finalized()
+        if self._discarding:
+            return ()
+        if token.text == _ENDPOINT_TOKEN:
+            return self._end_turn()
+        started: Final = self._start()
+        if not token.is_final or (self._drops_originals and token.translation_status == "original"):
+            return started
+        self._final_tokens.append(token)
+        return (*started, _delta(self._current_item_id(), token))
+
+    @property
+    def _discarding(self) -> bool:
+        return bool(self._finalizes) and self._finalizes[0]
+
+    def _finalized(self) -> tuple[OpenAIRealtimeEvents, ...]:
+        discarded: Final = self._finalizes.popleft() if self._finalizes else None
+        answered: Final = self._oldest_commit_answered
+        self._oldest_commit_answered = False
+        if discarded:
+            return ()
+        if self._item_id is not None:
+            return self._complete(speech_stopped=False)
+        if discarded is None or answered:
+            return ()
+        return (_completed(_new_item_id(), (), self.take_unbilled_usage()),)
+
+    def _end_turn(self) -> tuple[OpenAIRealtimeEvents, ...]:
+        events: Final = self._complete(speech_stopped=True)
+        if events and self._finalizes:
+            self._oldest_commit_answered = True
+        return events
+
+    def _start(self) -> tuple[OpenAIRealtimeEvents, ...]:
+        if self._item_id is not None:
+            return ()
+        item_id: Final = _new_item_id()
+        self._item_id = item_id
+        return (speech_event("input_audio_buffer.speech_started", item_id),)
+
+    def _current_item_id(self) -> str:
+        assert self._item_id is not None
+        return self._item_id
+
+    def _complete(self, *, speech_stopped: bool) -> tuple[OpenAIRealtimeEvents, ...]:
+        item_id: Final = self._item_id
+        if item_id is None:
+            return ()
+        tokens: Final = tuple(self._final_tokens)
+        self._item_id = None
+        self._final_tokens.clear()
+        stopped: Final[tuple[OpenAIRealtimeEvents, ...]] = (
+            (speech_event("input_audio_buffer.speech_stopped", item_id),) if speech_stopped else ()
+        )
+        return (*stopped, _completed(item_id, tokens, self.take_unbilled_usage()))
+
+
+class SonioxRealtimeBackend:
+    def __init__(
+        self,
+        inner: RealtimeBackend,
+        *,
+        keepalive_interval: float = KEEPALIVE_INTERVAL_SECONDS,
+        clock: Callable[[], float] = time.monotonic,
+        sleep: Callable[[float], Awaitable[None]] = asyncio.sleep,
+    ) -> None:
+        self._inner: Final = inner
+        self._interval: Final = keepalive_interval
+        self._clock: Final = clock
+        self._sleep: Final = sleep
+        self._frames: Final[asyncio.Queue[str | bytes | Exception]] = asyncio.Queue(maxsize=_MAX_PENDING_FRAMES)
+        self._last_sent: float = clock()
+        self._started: bool = False
+        self._ended: bool = False
+        self._tasks: tuple[asyncio.Task[None], ...] = ()
+
+    async def __aenter__(self) -> Self:
+        await self._inner.__aenter__()
+        self._tasks = (asyncio.create_task(self._pump()), asyncio.create_task(self._keepalive()))
+        return self
+
+    async def __aexit__(
+        self,
+        exc_type: type[BaseException] | None,
+        exc_value: BaseException | None,
+        traceback: TracebackType | None,
+    ) -> None:
+        self._stop()
+        await self._inner.__aexit__(exc_type, exc_value, traceback)
+
+    async def send(self, message: str | bytes) -> None:
+        self._last_sent = self._clock()
+        self._ended = self._ended or message == _END_STREAM
+        await self._inner.send(message)
+        if not self._started and isinstance(message, str) and message not in _CONTROL_FRAMES:
+            self._started = True
+            await self._frames.put(SESSION_STARTED_FRAME)
+
+    async def recv(self, decode: bool | None = None) -> str | bytes:
+        frame: Final = await self._frames.get()
+        if isinstance(frame, Exception):
+            raise frame
+        return frame
+
+    async def close(self) -> None:
+        self._stop()
+        await self._inner.close()
+
+    def _stop(self) -> None:
+        for task in self._tasks:
+            task.cancel()
+
+    async def _pump(self) -> None:
+        while True:
+            try:
+                frame = await self._inner.recv()
+            except Exception as e:  # noqa: BLE001  # recv() re-raises it, so an upstream failure never hangs the reader
+                await self._frames.put(e)
+                return
+            await self._frames.put(frame)
+
+    async def _keepalive(self) -> None:
+        while not self._ended:
+            await self._sleep(self._interval)
+            if self._ended or self._clock() - self._last_sent < self._interval:
+                continue
+            try:
+                await self.send(_KEEPALIVE)
+            except Exception as e:  # noqa: BLE001  # stops only keepalives; the pump reports the broken socket
+                verbose_logger.debug("Soniox realtime: stopping keepalive after send failed: %s", e)
+                return
+
+
+class SonioxRealtimeConfig(BaseRealtimeConfig):
+    def __init__(
+        self,
+        *,
+        options: SonioxRealtimeOptions | None = None,
+        wrap: Callable[[RealtimeBackend], RealtimeBackend] = SonioxRealtimeBackend,
+        clock: Callable[[], float] = time.monotonic,
+        drop_params: bool = False,
+    ) -> None:
+        self._options: Final = options or SonioxRealtimeOptions()
+        self._drop_params: Final = drop_params
+        self._wrap: Final = wrap
+        self._clock: Final = clock
+        self._transformer: Final = SonioxEventTransformer(drops_originals=self._options.translation is not None)
+        self._start_request: SonioxStartRequest | None = None
+        self._session_id: str = f"sess_{uuid.uuid4().hex}"
+        self._started_at: float | None = None
+
+    @classmethod
+    def from_litellm_params(cls, litellm_params: Mapping[str, object], *, drop_params: bool = False) -> Self:
+        return cls(options=SonioxRealtimeOptions.model_validate(dict(litellm_params)), drop_params=drop_params)
+
+    def requires_session_configuration(self) -> bool:
+        return True
+
+    def validate_environment(
+        self,
+        headers: dict[str, str],  # mutable-ok: BaseRealtimeConfig contract
+        model: str,
+        api_key: str | None = None,
+    ) -> dict[str, str]:  # mutable-ok: BaseRealtimeConfig contract
+        resolved_key: Final = get_soniox_api_key(api_key)
+        if not resolved_key:
+            raise ValueError("Missing Soniox API key. Set SONIOX_API_KEY or api_key in the deployment litellm_params")
+        return {**headers, "Authorization": f"Bearer {resolved_key}"}
+
+    def get_complete_url(self, api_base: str | None, model: str, api_key: str | None = None) -> str:
+        return build_soniox_realtime_url(api_base)
+
+    def wrap_backend(self, backend: RealtimeBackend) -> RealtimeBackend:
+        return self._wrap(backend)
+
+    def transform_session_created_event(
+        self,
+        model: str,
+        logging_session_id: str,
+        session_configuration_request: str | None = None,
+    ) -> OpenAIRealtimeTranscriptionSessionCreated:
+        self._session_id = logging_session_id
+        return _session_created(logging_session_id, build_start_request(model, None, self._options))
+
+    def transform_realtime_request(
+        self,
+        message: str,
+        model: str,
+        session_configuration_request: str | None = None,
+    ) -> tuple[str | bytes, ...]:
+        request: Final = json_object(message, SonioxProtocolError)
+        event_type: Final = request.get("type")
+        if event_type is None:
+            raise SonioxProtocolError("Soniox realtime takes OpenAI realtime events, and this message has no type")
+        if event_type in SESSION_UPDATE_EVENT_TYPES:
+            update: Final = parse_transcription_session_update(message, SonioxProtocolError)
+            ignored: Final = _ignored_settings(update)
+            if ignored:
+                verbose_logger.warning("Soniox realtime: ignoring unsupported session settings %s", ignored)
+            return self._start(model, update)
+        if event_type == "input_audio_buffer.append":
+            audio: Final = decode_pcm16_append(request.get("audio"), error=SonioxProtocolError)
+            return (*self._start(model, None), audio)
+        if event_type == "input_audio_buffer.commit":
+            return self._finalize(model, discards_turn=False)
+        if event_type == "input_audio_buffer.clear":
+            return () if self._start_request is None else self._finalize(model, discards_turn=True)
+        if event_type == "input_audio_buffer.end":
+            return (*self._start(model, None), _END_STREAM)
+        verbose_logger.debug("Soniox realtime: dropping unsupported client event %s", event_type)
+        return ()
+
+    def _finalize(self, model: str, *, discards_turn: bool) -> tuple[str, ...]:
+        start: Final = self._start(model, None)
+        self._transformer.queue_finalize(discards_turn=discards_turn)
+        return (*start, _FINALIZE)
+
+    def session_refusal(self) -> Exception | None:
+        return self._transformer.refusal
+
+    def unbilled_usage_on_session_close(self, model: str) -> RealtimeInputAudioTranscriptionUsage | None:
+        started_at: Final = self._started_at
+        if self._transformer.refusal is not None:
+            return None
+        if started_at is None:
+            return self._transformer.take_unbilled_usage()
+        return self._transformer.take_unbilled_usage(int((self._clock() - started_at) * 1000))
+
+    def transform_realtime_response(
+        self,
+        message: str | bytes,
+        model: str,
+        logging_obj: LiteLLMLoggingObj,
+        realtime_response_transform_input: RealtimeResponseTransformInput,
+    ) -> RealtimeResponseTypedDict:
+        payload: Final = message.decode("utf-8") if isinstance(message, bytes) else message
+        result: Final[RealtimeResponseTypedDict] = {
+            "response": list(self._backend_events(payload)),
+            "current_output_item_id": realtime_response_transform_input.get("current_output_item_id"),
+            "current_response_id": realtime_response_transform_input.get("current_response_id"),
+            "current_delta_chunks": realtime_response_transform_input.get("current_delta_chunks"),
+            "current_conversation_id": realtime_response_transform_input.get("current_conversation_id"),
+            "current_item_chunks": realtime_response_transform_input.get("current_item_chunks"),
+            "current_delta_type": realtime_response_transform_input.get("current_delta_type"),
+            "session_configuration_request": realtime_response_transform_input.get("session_configuration_request"),
+        }
+        return result
+
+    def _backend_events(self, payload: str) -> tuple[OpenAIRealtimeEvents, ...]:
+        if payload != SESSION_STARTED_FRAME:
+            return self._transformer.transform(payload)
+        assert self._start_request is not None
+        return (_session_created(self._session_id, self._start_request),)
+
+    def _start(self, model: str, update: TranscriptionSessionUpdate | None) -> tuple[str, ...]:
+        started: Final = self._start_request
+        if started is not None:
+            if update is not None and _changes_started_stream(update, started, self._drop_params):
+                raise SonioxProtocolError("Soniox realtime can't change the session once the stream has started")
+            return ()
+        start_request: Final = build_start_request(model, update, self._options, self._drop_params)
+        self._start_request = start_request
+        self._started_at = self._clock()
+        return (start_request.model_dump_json(exclude_none=True),)
+
+
+def _finishes_stream(frame: str | bytes) -> bool:
+    response: Final = _SonioxResponse.model_validate_json(frame)
+    error: Final = _soniox_error(response)
+    if error is not None:
+        raise error
+    return response.finished
+
+
+async def soniox_realtime_health_check(
+    model: str, api_base: str | None, api_key: str | None, options: SonioxRealtimeOptions
+) -> bool:
+    import websockets
+
+    config: Final = SonioxRealtimeConfig()
+    url: Final = config.get_complete_url(api_base, model)
+    headers: Final = config.validate_environment({}, model, api_key)
+    start_request: Final = build_start_request(model, None, options)
+    async with websockets.connect(
+        url,
+        additional_headers=headers,
+        max_size=REALTIME_WEBSOCKET_MAX_MESSAGE_SIZE_BYTES,
+        ssl=realtime_ssl_for_url(url),
+    ) as backend:
+        await backend.send(start_request.model_dump_json(exclude_none=True))
+        await backend.send(_HEALTH_CHECK_SILENCE)
+        await backend.send(_END_STREAM)
+        async for frame in backend:
+            if _finishes_stream(frame):
+                return True
+    raise SonioxException(status_code=502, message="Soniox closed the realtime stream without finishing it")

litellm/llms/vertex_ai/vertex_ai_partner_models/main.py

@@ -9,6 +9,7 @@ import httpx
 
 import litellm
 from litellm import LlmProviders
+from litellm.litellm_core_utils.prompt_templates.common_utils import messages_with_file_parts_as_image_urls
 from litellm.types.llms.vertex_ai import VertexPartnerProvider
 from litellm.utils import ModelResponse
 
@@ -217,7 +218,7 @@ class VertexAIPartnerModels(VertexBase):
                 return base_llm_http_handler.completion(
                     model=model,
                     stream=stream,
-                    messages=messages,
+                    messages=list(messages_with_file_parts_as_image_urls(messages)),
                     acompletion=acompletion,
                     api_base=api_base,
                     model_response=model_response,

litellm/main.py

@@ -108,6 +108,10 @@ from litellm.litellm_core_utils.mock_functions import (
 from litellm.litellm_core_utils.prompt_templates.common_utils import (
     get_content_from_model_response as get_content_from_model_response,  # noqa: PLC0414  # public re-export
 )
+from litellm.litellm_core_utils.prompt_templates.common_utils import (
+    messages_have_pdf_file_part,
+    messages_without_pdf_file_parts,
+)
 from litellm.litellm_core_utils.provider_affinity import add_provider_affinity_header
 from litellm.litellm_core_utils.request_timeout_resolver import (
     get_configured_request_timeout,
@@ -191,11 +195,13 @@ from litellm.utils import (
     get_requester_metadata,
     get_secret,
     get_standard_openai_params,
+    is_explicitly_disabled_factory,
     mock_completion_streaming_obj,
     pre_process_non_default_params,
     read_config_args,
     should_run_mock_completion,
     supports_httpx_timeout,
+    supports_pdf_input,
     validate_and_fix_openai_messages,
     validate_and_fix_openai_tools,
     validate_and_fix_thinking_param,
@@ -393,6 +399,125 @@ def _resolve_control_options(kwargs: Mapping[str, object], model: str) -> Contro
             return assert_never(control)
 
 
+PROVIDERS_WITHOUT_DOCUMENT_INPUT: Final = frozenset({"ollama", "ollama_chat"})
+PROVIDERS_DROPPING_UNDECLARED_PDF_INPUT: Final = frozenset({"azure_ai"})
+
+
+def _cost_map_pdf_input_support(model: str, custom_llm_provider: str) -> bool | None:
+    if custom_llm_provider in PROVIDERS_WITHOUT_DOCUMENT_INPUT or is_explicitly_disabled_factory(
+        model, custom_llm_provider, "supports_pdf_input"
+    ):
+        return False
+    if supports_pdf_input(model, custom_llm_provider):
+        return True
+    return None
+
+
+def _deployment_pdf_input_support(model: str, base_model: str, custom_llm_provider: str) -> bool | None:
+    supports: Final = (
+        _cost_map_pdf_input_support(model, custom_llm_provider),
+        _cost_map_pdf_input_support(base_model, custom_llm_provider),
+    )
+    if False in supports:
+        return False
+    if True in supports:
+        return True
+    return None
+
+
+def _declared_pdf_input_support(
+    model: str, custom_llm_provider: str, model_info: Mapping[str, object] | None, base_model: str | None
+) -> bool | None:
+    match model_info:
+        case {"supports_pdf_input": bool() as declared}:
+            return declared
+        case _ if base_model:
+            return _deployment_pdf_input_support(model, base_model, custom_llm_provider)
+        case _:
+            return _cost_map_pdf_input_support(model, custom_llm_provider)
+
+
+def _pdf_input_unreadable(
+    model: str,
+    custom_llm_provider: str,
+    model_info: Mapping[str, object] | None,
+    base_model: str | None,
+    drop_params: bool,
+) -> bool:
+    match _declared_pdf_input_support(model, custom_llm_provider, model_info, base_model):
+        case False:
+            return True
+        case None:
+            return drop_params and custom_llm_provider in PROVIDERS_DROPPING_UNDECLARED_PDF_INPUT
+        case True:
+            return False
+
+
+def _messages_without_unreadable_pdfs(
+    messages: Sequence[AllMessageValues],
+    model: str,
+    custom_llm_provider: str,
+    model_info: Mapping[str, object] | None,
+    base_model: str | None,
+    drop_params: bool,
+) -> tuple[AllMessageValues, ...] | None:
+    if not messages_have_pdf_file_part(messages) or not _pdf_input_unreadable(
+        model, custom_llm_provider, model_info, base_model, drop_params
+    ):
+        return None
+    if not drop_params:
+        raise litellm.BadRequestError(
+            message=(
+                f"{custom_llm_provider}/{model} does not accept PDF input. "
+                "Remove the PDF, or set drop_params=True to send the request without it"
+            ),
+            model=model,
+            llm_provider=custom_llm_provider,
+        )
+    readable_messages: Final = messages_without_pdf_file_parts(messages)
+    if not any(message["role"] == "user" for message in readable_messages):
+        raise litellm.BadRequestError(
+            message=(
+                f"{custom_llm_provider}/{model} does not accept PDF input, "
+                "and no user message is left once the PDF is dropped"
+            ),
+            model=model,
+            llm_provider=custom_llm_provider,
+        )
+    return readable_messages
+
+
+def _messages_readable_by_model(
+    messages: Sequence[AllMessageValues],
+    model: str,
+    custom_llm_provider: str,
+    model_info: Mapping[str, object] | None,
+    base_model: str | None,
+    drop_params: bool,
+    ensure_alternating_roles: bool | None,
+    user_continue_message: ChatCompletionUserMessage | None,
+    assistant_continue_message: ChatCompletionAssistantMessage | None,
+) -> tuple[AllMessageValues, ...] | None:
+    pdf_free_messages: Final = _messages_without_unreadable_pdfs(
+        messages=messages,
+        model=model,
+        custom_llm_provider=custom_llm_provider,
+        model_info=model_info,
+        base_model=base_model,
+        drop_params=drop_params,
+    )
+    if pdf_free_messages is None:
+        return None
+    return tuple(
+        get_completion_messages(
+            messages=list(pdf_free_messages),
+            ensure_alternating_roles=ensure_alternating_roles or False,
+            user_continue_message=user_continue_message,
+            assistant_continue_message=assistant_continue_message,
+        )
+    )
+
+
 ####### COMPLETION ENDPOINTS ################
 
 
@@ -4234,7 +4359,6 @@ def _complete_sagemaker(ctx: CompletionDispatchContext) -> _CompletionDispatchRe
 _ADDITIONAL_DROP_PARAMS_ADAPTER: Final = TypeAdapter(list[str])
 _OPTIONAL_PARAMS_ADAPTER: Final = TypeAdapter(dict[str, object])
 _OLLAMA_EMBEDDING_HEADERS_ADAPTER: Final = TypeAdapter(dict[str, str])
-_REQUEST_KWARGS_ADAPTER: Final = TypeAdapter(Mapping[str, object])
 _MODEL_INFO_ADAPTER: Final = TypeAdapter(Mapping[str, object])
 _PROVIDER_ROUTING_HINTS: Final = TypeAdapter(ProviderRoutingHints)
 _BASE_URL_HINT: Final = TypeAdapter(BaseUrlRoutingHint)
@@ -5558,9 +5682,15 @@ def completion(
     )
     headers = kwargs.get("headers", None) or extra_headers
 
-    ensure_alternating_roles: Final[bool | None] = kwargs.get("ensure_alternating_roles", None)
-    user_continue_message: Final[ChatCompletionUserMessage | None] = kwargs.get("user_continue_message", None)
-    assistant_continue_message: ChatCompletionAssistantMessage | None = kwargs.get("assistant_continue_message", None)
+    ensure_alternating_roles: Final = cast(  # cast-ok: read from untyped kwargs
+        bool | None, kwargs.get("ensure_alternating_roles", None)
+    )
+    user_continue_message: Final = cast(  # cast-ok: read from untyped kwargs
+        ChatCompletionUserMessage | None, kwargs.get("user_continue_message", None)
+    )
+    assistant_continue_message: Final = cast(  # cast-ok: read from untyped kwargs
+        ChatCompletionAssistantMessage | None, kwargs.get("assistant_continue_message", None)
+    )
     headers = {} if headers is None else dict(headers)
     if extra_headers is not None:
         headers.update(extra_headers)
@@ -5928,15 +6058,17 @@ def completion(
         def optional_params_for_model_info(deployment_info: Mapping[str, object] | None) -> dict[str, object]:
             return get_optional_params(**mapping_args, model_info=deployment_info, **mapping_special_params)
 
-        optional_params = optional_params_for_model_info(
+        validated_model_info: Final = (
             _MODEL_INFO_ADAPTER.validate_python(model_info) if isinstance(model_info, dict) else None
         )
+        deployment_base_model: Final = base_model if isinstance(base_model, str) else None
+        optional_params = optional_params_for_model_info(validated_model_info)
         fakes_stream: Final = partial(
             _completion_fakes_stream,
             model,
             custom_llm_provider,
             provider_config,
-            base_model=base_model if isinstance(base_model, str) else None,
+            base_model=deployment_base_model,
             bridges_to_responses_api=bridges_to_responses_api is True,
         )
         resolve_timeout: Final = partial(
@@ -5974,16 +6106,29 @@ def completion(
             functions_unsupported_model: Final[list[object]] = TypeAdapter(list[object]).validate_python(
                 optional_params.pop("functions_unsupported_model")
             )
-            request_kwargs: Final = _REQUEST_KWARGS_ADAPTER.validate_python(kwargs)
             messages = function_call_prompt(
                 messages=messages,
                 functions=functions_unsupported_model,
                 model=model,
                 llm_provider=custom_llm_provider,
-                drop_params=litellm.drop_params is True
-                or normalize_drop_params(request_kwargs.get("drop_params")) is True,
+                drop_params=drop_unsupported_reasoning,
             )
 
+        readable_messages: Final = _messages_readable_by_model(
+            messages=cast(list[AllMessageValues], messages),  # cast-ok: completion types messages as a bare List
+            model=model,
+            custom_llm_provider=custom_llm_provider,
+            model_info=validated_model_info,
+            base_model=deployment_base_model,
+            drop_params=drop_unsupported_reasoning,
+            ensure_alternating_roles=ensure_alternating_roles,
+            user_continue_message=user_continue_message,
+            assistant_continue_message=assistant_continue_message,
+        )
+        messages = (  # rebind-ok: completion() rewrites messages step by step
+            messages if readable_messages is None else list(readable_messages)
+        )
+
         # For logging - save the values of the litellm-specific params passed in
         requested_litellm_params: Final = get_litellm_params(
             acompletion=acompletion,

litellm/model_prices_and_context_window_backup.json

@@ -4049,6 +4049,7 @@
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
         "supports_parallel_function_calling": true,
+        "supports_pdf_input": false,
         "supports_response_schema": true,
         "supports_tool_choice": true
     },
@@ -12437,7 +12438,8 @@
         "supported_output_modalities": [
             "text"
         ],
-        "supports_native_streaming": true
+        "supports_native_streaming": true,
+        "supports_pdf_input": false
     },
     "azure_ai/FLUX-1.1-pro": {
         "deprecation_date": "2027-01-05",
@@ -12502,6 +12504,7 @@
         "output_cost_per_token": 1.85e-06,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -12516,6 +12519,7 @@
         "output_cost_per_token": 3.828e-06,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -12531,6 +12535,7 @@
         "output_cost_per_token": 3.52e-06,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -12546,6 +12551,7 @@
         "output_cost_per_token": 4.84e-06,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -12560,6 +12566,7 @@
         "output_cost_per_token": 4.84e-06,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -12574,6 +12581,7 @@
         "output_cost_per_token": 7.26e-06,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -12594,6 +12602,7 @@
             "text"
         ],
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -12616,6 +12625,7 @@
             "text"
         ],
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true,
@@ -12638,6 +12648,7 @@
             "text"
         ],
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true,
@@ -12660,6 +12671,7 @@
             "text"
         ],
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true,
@@ -12687,6 +12699,7 @@
             "text"
         ],
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true,
@@ -12703,6 +12716,7 @@
         "output_cost_per_token": 1.32e-06,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -12724,6 +12738,7 @@
             "text"
         ],
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true,
@@ -12744,6 +12759,7 @@
             "text"
         ],
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true,
@@ -12765,6 +12781,7 @@
             "text"
         ],
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -12833,6 +12850,7 @@
             "text"
         ],
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -12847,6 +12865,7 @@
         "output_cost_per_token": 7.1e-07,
         "source": "https://learn.microsoft.com/en-us/azure/foundry/foundry-models/concepts/models-sold-directly-by-azure",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_tool_choice": true
     },
     "azure_ai/Llama-4-Maverick-17B-128E-Instruct-FP8": {
@@ -12859,6 +12878,7 @@
         "output_cost_per_token": 1e-06,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_tool_choice": true,
         "supports_vision": true
     },
@@ -12872,6 +12892,7 @@
         "output_cost_per_token": 7.8e-07,
         "source": "https://azure.microsoft.com/en-us/blog/introducing-the-llama-4-herd-in-azure-ai-foundry-and-azure-databricks/",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_tool_choice": true,
         "supports_vision": true
     },
@@ -12884,6 +12905,7 @@
         "mode": "chat",
         "output_cost_per_token": 3.7e-07,
         "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models",
+        "supports_pdf_input": false,
         "supports_tool_choice": true
     },
     "azure_ai/Meta-Llama-3.1-70B-Instruct": {
@@ -12895,6 +12917,7 @@
         "mode": "chat",
         "output_cost_per_token": 3.54e-06,
         "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models",
+        "supports_pdf_input": false,
         "supports_tool_choice": true
     },
     "azure_ai/Phi-3-medium-128k-instruct": {
@@ -12906,6 +12929,7 @@
         "mode": "chat",
         "output_cost_per_token": 6.8e-07,
         "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models",
+        "supports_pdf_input": false,
         "supports_tool_choice": true,
         "supports_vision": false
     },
@@ -12918,6 +12942,7 @@
         "mode": "chat",
         "output_cost_per_token": 6.8e-07,
         "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models",
+        "supports_pdf_input": false,
         "supports_tool_choice": true,
         "supports_vision": false
     },
@@ -12930,6 +12955,7 @@
         "mode": "chat",
         "output_cost_per_token": 5.2e-07,
         "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models",
+        "supports_pdf_input": false,
         "supports_tool_choice": true,
         "supports_vision": false
     },
@@ -12942,6 +12968,7 @@
         "mode": "chat",
         "output_cost_per_token": 5.2e-07,
         "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models",
+        "supports_pdf_input": false,
         "supports_tool_choice": true,
         "supports_vision": false
     },
@@ -12954,6 +12981,7 @@
         "mode": "chat",
         "output_cost_per_token": 6e-07,
         "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models",
+        "supports_pdf_input": false,
         "supports_tool_choice": true,
         "supports_vision": false
     },
@@ -12966,6 +12994,7 @@
         "mode": "chat",
         "output_cost_per_token": 6e-07,
         "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models",
+        "supports_pdf_input": false,
         "supports_tool_choice": true,
         "supports_vision": false
     },
@@ -12978,6 +13007,7 @@
         "mode": "chat",
         "output_cost_per_token": 6.4e-07,
         "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models",
+        "supports_pdf_input": false,
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         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true,
@@ -67258,6 +67457,7 @@
         "output_cost_per_token": 1.5e-06,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -67272,6 +67472,7 @@
         "output_cost_per_token": 3.1e-07,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -67286,6 +67487,7 @@
         "output_cost_per_token": 5.5e-06,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -67300,6 +67502,7 @@
         "output_cost_per_token": 6.25e-07,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -67316,6 +67519,7 @@
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
         "supports_parallel_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_response_schema": true,
@@ -67331,6 +67535,7 @@
         "output_cost_per_token": 3.2e-06,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_reasoning": true,
         "supports_tool_choice": true
     },
@@ -67349,6 +67554,7 @@
         "supported_output_modalities": [
             "text"
         ],
+        "supports_pdf_input": false,
         "supports_response_schema": true,
         "supports_vision": true
     },
@@ -67358,7 +67564,8 @@
         "output_cost_per_token": 5.4e-06,
         "litellm_provider": "azure_ai",
         "mode": "chat",
-        "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'"
+        "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
+        "supports_pdf_input": false
     },
     "azure_ai/deepseek-v3-0324": {
         "deprecation_date": "2026-07-13",
@@ -67366,7 +67573,8 @@
         "output_cost_per_token": 4.56e-06,
         "litellm_provider": "azure_ai",
         "mode": "chat",
-        "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'"
+        "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
+        "supports_pdf_input": false
     },
     "azure_ai/deepseek-v3.1": {
         "deprecation_date": "2026-07-13",
@@ -67374,7 +67582,8 @@
         "output_cost_per_token": 4.94e-06,
         "litellm_provider": "azure_ai",
         "mode": "chat",
-        "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'"
+        "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
+        "supports_pdf_input": false
     },
     "azure_ai/grok-3": {
         "deprecation_date": "2026-05-01",
@@ -67382,7 +67591,8 @@
         "output_cost_per_token": 1.5e-05,
         "litellm_provider": "azure_ai",
         "mode": "chat",
-        "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'"
+        "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
+        "supports_pdf_input": false
     },
     "azure_ai/grok-3-mini": {
         "deprecation_date": "2026-05-01",
@@ -67390,7 +67600,8 @@
         "output_cost_per_token": 1.27e-06,
         "litellm_provider": "azure_ai",
         "mode": "chat",
-        "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'"
+        "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
+        "supports_pdf_input": false
     },
     "azure_ai/grok-4-fast-non-reasoning": {
         "deprecation_date": "2026-05-01",
@@ -67398,7 +67609,8 @@
         "output_cost_per_token": 5e-07,
         "litellm_provider": "azure_ai",
         "mode": "chat",
-        "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'"
+        "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
+        "supports_pdf_input": false
     },
     "azure_ai/grok-4-fast-reasoning": {
         "deprecation_date": "2026-05-01",
@@ -67406,7 +67618,8 @@
         "output_cost_per_token": 5e-07,
         "litellm_provider": "azure_ai",
         "mode": "chat",
-        "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'"
+        "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
+        "supports_pdf_input": false
     },
     "bedrock/us-gov-west-1/nvidia.nemotron-nano-3-30b": {
         "input_cost_per_token": 7.2e-08,
@@ -73076,6 +73289,7 @@
         "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing",
         "supported_regions": ["global"],
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_response_schema": true,
@@ -73691,6 +73905,7 @@
         "output_cost_per_token": 3.5e-06,
         "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/microsoft/",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -74745,6 +74960,7 @@
             "text"
         ],
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": false,
         "supports_reasoning": true,
         "supports_tool_choice": true,
@@ -74993,6 +75209,7 @@
         "output_cost_per_token_above_200k_tokens": 1.2e-05,
         "source": "https://docs.x.ai/developers/models/grok-4.7",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_response_schema": true,

litellm/provider_endpoints_support_backup.json

@@ -3724,7 +3724,7 @@
         "ocr": false,
         "search": false,
         "moderations": false,
-        "realtime": false,
+        "realtime": true,
         "video_generations": false,
         "batches": false,
         "messages": false,
@@ -4910,6 +4910,33 @@
         "vector_stores_search": true
       }
     },
+    "opensearch": {
+      "display_name": "Amazon OpenSearch (`opensearch`)",
+      "url": "https://docs.litellm.ai/docs/providers/opensearch_vector_stores",
+      "endpoints": {
+        "chat_completions": false,
+        "responses": false,
+        "text_completion": false,
+        "embeddings": false,
+        "image_generations": false,
+        "image_edits": false,
+        "audio_transcriptions": false,
+        "audio_speech": false,
+        "rerank": false,
+        "ocr": false,
+        "search": false,
+        "moderations": false,
+        "realtime": false,
+        "video_generations": false,
+        "batches": false,
+        "messages": false,
+        "interactions": false,
+        "systemone": false,
+        "a2a": false,
+        "generateContent": false,
+        "vector_stores_search": true
+      }
+    },
     "helicone": {
       "display_name": "Helicone (`helicone`)",
       "url": "https://docs.litellm.ai/docs/providers/helicone",

litellm/proxy/common_request_processing.py

@@ -130,6 +130,7 @@ from litellm.proxy.guardrails.auto_router_compression import arm_pre_call as _ar
 from litellm.proxy.hooks.parallel_request_limiter_v3 import get_or_create_request_stash
 from litellm.proxy.native_compaction import with_proxy_compaction_executor
 from litellm.proxy.route_llm_request import (
+    endpoint_unsupported_by_model,
     route_request,
 )
 from litellm.proxy.telemetry.request_context import bind_call_id as bind_telemetry_call_id
@@ -872,6 +873,17 @@ _DECODED_JSON: Final = TypeAdapter(object)
 _ROUTER_SETTINGS_ADAPTER: Final[TypeAdapter[Mapping[str, object] | None]] = TypeAdapter(Mapping[str, object] | None)
 
 
+def _fallback_probe_data(
+    pristine: object, fallback_model: str, key_aliases: object, group_alias: object
+) -> Mapping[str, object]:
+    return {
+        **_REQUEST_FIELDS.validate_python(pristine),
+        "model": resolve_served_model(
+            fallback_model, _DECODED_JSON.validate_python(litellm.model_alias_map), key_aliases, group_alias
+        ),
+    }
+
+
 def _request_model(data: Mapping[str, object]) -> str | list[str] | None:
     try:
         return _REQUEST_MODEL.validate_python(data.get("model"), strict=True)
@@ -2679,6 +2691,17 @@ class ProxyBaseLLMRequestProcessing:
                 original_model,
                 fallback_models,
             )
+            raw_router_settings: Final = _DECODED_JSON.validate_python(
+                _REQUEST_FIELDS.validate_python(rate_limited_data).get("router_settings_override")
+            )
+            group_alias: Final = _DECODED_JSON.validate_python(
+                _REQUEST_FIELDS.validate_python(raw_router_settings).get("model_group_alias")
+                if isinstance(raw_router_settings, Mapping)
+                else None
+            )
+            key_aliases: Final[object] = cast(  # cast-ok: auth aliases are dynamically typed at runtime
+                object, user_api_key_dict.aliases
+            )
             try:
                 for fallback_model in fallback_models:
                     if fallback_model == original_model:
@@ -2694,6 +2717,13 @@ class ProxyBaseLLMRequestProcessing:
                     self.data = independent_snapshot(pristine)
                     self.data["model"] = fallback_model
                     try:
+                        if endpoint_unsupported_by_model(
+                            route_type,
+                            _fallback_probe_data(pristine, fallback_model, key_aliases, group_alias),
+                            llm_router,
+                            user_api_key_dict.team_id,
+                        ):
+                            continue
                         return await self.common_processing_pre_call_logic(
                             request=request,
                             general_settings=general_settings,

litellm/proxy/common_utils/callback_utils.py

@@ -436,10 +436,7 @@ def get_model_group_from_litellm_kwargs(kwargs: dict) -> str | None:
 def get_model_group_from_request_data(data: dict) -> str | None:
     _metadata: Final = data.get("metadata", None) or {}
     _model_group: Final = _metadata.get("model_group", None)
-    if _model_group is not None:
-        return _model_group
-
-    return None
+    return _model_group if isinstance(_model_group, str) else None
 
 
 def get_remaining_tokens_and_requests_from_request_data(data: dict) -> dict[str, str]:

litellm/proxy/guardrails/guardrail_hooks/prompt_security/prompt_security.py

@@ -647,9 +647,10 @@ class PromptSecurityGuardrail(CustomGuardrail):
     async def _process_document_item(self, item: dict, user_api_key_alias: str | None) -> dict:
         """Process and sanitize document/file items."""
         doc_data: Final = item.get("document") or item.get("file") or item
+        url_key: Final = "file_data" if isinstance(doc_data, dict) and not doc_data.get("url") else "url"
 
         if isinstance(doc_data, dict):
-            url = doc_data.get("url", "")
+            url = doc_data.get(url_key) or ""
             doc_content = doc_data.get("data", "")
         else:
             url = doc_data if isinstance(doc_data, str) else ""
@@ -698,7 +699,7 @@ class PromptSecurityGuardrail(CustomGuardrail):
                     if url.startswith("data:") and header:
                         sanitized_url: Final = f"{header},{sanitized_encoded}"
                         if isinstance(doc_data, dict):
-                            doc_data["url"] = sanitized_url
+                            doc_data[url_key] = sanitized_url
                     elif isinstance(doc_data, dict):
                         doc_data["data"] = sanitized_encoded
 

litellm/proxy/management_endpoints/fallback_management_endpoints.py

@@ -20,7 +20,7 @@ from litellm._logging import verbose_proxy_logger
 from litellm.proxy._types import UserAPIKeyAuth
 from litellm.proxy.auth.model_checks import get_all_fallbacks
 from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
-from litellm.proxy.utils import PrismaClient, evict_config_param, invalidate_config_param
+from litellm.proxy.utils import PrismaClient, invalidate_config_param
 
 if TYPE_CHECKING:
     from fastapi import APIRouter, Depends, HTTPException, status
@@ -48,22 +48,50 @@ FallbackRule = dict[str, list[str]]
 StoredFallback = Annotated[FallbackRule | dict[str, object] | str, Field(union_mode="left_to_right")]
 _STORED_FALLBACKS: Final = TypeAdapter(list[StoredFallback])
 _JSON_RULES: Final = TypeAdapter(list[JsonValue])
+_STORED_ROUTER_SETTINGS: Final = TypeAdapter(dict[str, JsonValue])
 
 
 def _rule_covers(entry: StoredFallback, model: str) -> bool:
     return isinstance(entry, dict) and model in entry
 
 
-async def _router_settings_fresh_from_db(proxy_config: "ProxyConfig") -> dict[str, object]:
-    await evict_config_param(ROUTER_SETTINGS_PARAM)
-    config: Final = await proxy_config.get_config()
-    return config.get(ROUTER_SETTINGS_PARAM, {})
+async def _reload_config_file_ownership(proxy_config: "ProxyConfig") -> None:
+    await proxy_config.get_config()
 
 
-def _reject_config_owned_rules(proxy_config: "ProxyConfig", fallback_key: str, rules: Sequence[StoredFallback]) -> None:
+async def _stored_router_settings(prisma_client: PrismaClient) -> Mapping[str, JsonValue]:
+    try:
+        row: Final = await ConfigRepository(prisma_client, use_writer=True).get_param(ROUTER_SETTINGS_PARAM)
+    except json.JSONDecodeError:
+        return {}
+    if row is None or not isinstance(row.param_value, Mapping):
+        return {}
+    return _STORED_ROUTER_SETTINGS.validate_python(row.param_value)
+
+
+def _editable_rules(
+    proxy_config: "ProxyConfig", stored_settings: Mapping[str, JsonValue], fallback_key: str
+) -> list[StoredFallback]:
+    file_settings: Final = proxy_config.router_settings
+    if not file_settings.owned_by_config(fallback_key):
+        return _STORED_FALLBACKS.validate_python(stored_settings.get(fallback_key) or [])
+    file_rules: Final = file_settings.get(fallback_key)
+    return _STORED_FALLBACKS.validate_python(file_rules if isinstance(file_rules, list) else [])
+
+
+async def _write_rules(
+    prisma_client: PrismaClient,
+    proxy_config: "ProxyConfig",
+    stored_settings: Mapping[str, JsonValue],
+    fallback_key: str,
+    rules: Sequence[StoredFallback],
+) -> None:
     proxy_config.reject_config_owned_writes(
         section_name=ROUTER_SETTINGS_PARAM, changed_keys={fallback_key: _JSON_RULES.validate_python(rules)}
     )
+    if proxy_config.router_settings.owned_by_config(fallback_key):
+        return
+    await _persist_router_settings(prisma_client, {**stored_settings, fallback_key: rules})
 
 
 async def _persist_router_settings(prisma_client: PrismaClient, router_settings: Mapping[str, object]) -> None:
@@ -162,7 +190,8 @@ async def create_fallback(
                 },
             )
 
-        router_settings: Final = await _router_settings_fresh_from_db(proxy_config)
+        await _reload_config_file_ownership(proxy_config)
+        router_settings: Final = await _stored_router_settings(prisma_client)
 
         # Get the appropriate fallback list based on type
         fallback_key = "fallbacks"
@@ -172,7 +201,7 @@ async def create_fallback(
             fallback_key = "content_policy_fallbacks"
 
         # Get existing fallbacks
-        existing_fallbacks: Final = _STORED_FALLBACKS.validate_python(router_settings.get(fallback_key) or [])
+        existing_fallbacks: Final = _editable_rules(proxy_config, router_settings, fallback_key)
 
         # Update or add the fallback configuration
         fallback_updated = False
@@ -187,12 +216,7 @@ async def create_fallback(
             # Add new fallback
             existing_fallbacks.append({data.model: data.fallback_models})
 
-        _reject_config_owned_rules(proxy_config, fallback_key, existing_fallbacks)
-
-        # Update router settings
-        router_settings[fallback_key] = existing_fallbacks
-
-        await _persist_router_settings(prisma_client, router_settings)
+        await _write_rules(prisma_client, proxy_config, router_settings, fallback_key, existing_fallbacks)
 
         # Update the in-memory router configuration
         setattr(llm_router, fallback_key, existing_fallbacks)
@@ -320,7 +344,8 @@ async def delete_fallback(
                 },
             )
 
-        router_settings: Final = await _router_settings_fresh_from_db(proxy_config)
+        await _reload_config_file_ownership(proxy_config)
+        router_settings: Final = await _stored_router_settings(prisma_client)
 
         # Get the appropriate fallback list based on type
         fallback_key = "fallbacks"
@@ -330,7 +355,7 @@ async def delete_fallback(
             fallback_key = "content_policy_fallbacks"
 
         # Get existing fallbacks
-        existing_fallbacks: Final = _STORED_FALLBACKS.validate_python(router_settings.get(fallback_key) or [])
+        existing_fallbacks: Final = _editable_rules(proxy_config, router_settings, fallback_key)
 
         updated_fallbacks: Final = [rule for rule in existing_fallbacks if not _rule_covers(rule, model)]
 
@@ -340,12 +365,7 @@ async def delete_fallback(
                 detail={"error": f"No {fallback_type} fallbacks configured for model '{model}'"},
             )
 
-        _reject_config_owned_rules(proxy_config, fallback_key, updated_fallbacks)
-
-        # Update router settings
-        router_settings[fallback_key] = updated_fallbacks
-
-        await _persist_router_settings(prisma_client, router_settings)
+        await _write_rules(prisma_client, proxy_config, router_settings, fallback_key, updated_fallbacks)
 
         # Update the in-memory router configuration
         setattr(llm_router, fallback_key, updated_fallbacks)

litellm/proxy/public_endpoints/provider_create_fields.json

@@ -3612,7 +3612,7 @@
         "key": "api_key",
         "label": "Soniox API Key",
         "placeholder": null,
-        "tooltip": "Currently only the async Speech-to-Text REST API (api.soniox.com) is supported. Realtime STT (stt-rt.soniox.com) and TTS (tts-rt.soniox.com) are not yet available.",
+        "tooltip": "Supports async Speech-to-Text (api.soniox.com) on /v1/audio/transcriptions and realtime Speech-to-Text (stt-rt.soniox.com) on /v1/realtime. TTS (tts-rt.soniox.com) is not yet available.",
         "required": true,
         "field_type": "password",
         "options": null,

litellm/proxy/rag_endpoints/endpoints.py

@@ -54,6 +54,7 @@ from litellm.proxy.rag_endpoints.upload_security import (
 from litellm.proxy.vector_store_endpoints.endpoints import (
     build_request_data_from_managed_vector_store,
     reject_caller_embedding_selection_params,
+    reject_caller_opensearch_index_name,
 )
 from litellm.proxy.vector_store_endpoints.utils import (
     assert_user_can_access_vector_store_id,
@@ -913,6 +914,11 @@ async def rag_query(
             **retrieval_config,
             **store_data,
         }
+        reject_caller_opensearch_index_name(
+            caller_body=retrieval_config,
+            request_fields=merged_retrieval_config,
+            source="retrieval_config",
+        )
         effective_vector_store_config: Final = (
             managed_store_params
             if managed_store is not None

litellm/proxy/response_api_endpoints/endpoints.py

@@ -34,6 +34,7 @@ from litellm._logging import verbose_proxy_logger
 from litellm.anthropic_interface.exceptions.exception_mapping_utils import AnthropicExceptionMapping
 from litellm.caching.dual_cache import DualCache
 from litellm.constants import EMPTY_MAPPING, MANAGED_BACKGROUND_RESPONSE_METADATA_KEY, TERMINAL_RESPONSE_STATUSES
+from litellm.exceptions import EndpointNotSupportedByModelError
 from litellm.integrations.custom_guardrail import ModifyResponseException
 from litellm.litellm_core_utils.core_helpers import as_str_mapping, get_metadata_variable_name_from_kwargs
 from litellm.litellm_core_utils.internal_call_metadata import is_background_response, requests_background
@@ -2086,6 +2087,14 @@ def _routing_hints_from_first_ws_frame(first_message: str) -> Mapping[str, objec
 
 
 def _responses_ws_failure_frame(failure: Exception) -> str:
+    if isinstance(failure, EndpointNotSupportedByModelError):
+        return json.dumps(
+            {
+                "type": "error",
+                "status": 400,
+                "error": {"type": "invalid_request_error", "message": failure.message.split("\n\n", 1)[0]},
+            }
+        )
     proxy_exception: Final = handle_exception_on_proxy(failure)
     status: Final = int(proxy_exception.code) if str(proxy_exception.code).isdigit() else 500
     error_type: Final = (
@@ -2444,7 +2453,12 @@ async def _responses_websocket_session(
             with contextlib.suppress(Exception):
                 await websocket.send_text(_responses_ws_failure_frame(e))
             with contextlib.suppress(Exception):
-                await websocket.close(code=1011, reason="Internal server error")
+                await websocket.close(
+                    code=1008 if isinstance(e, EndpointNotSupportedByModelError) else 1011,
+                    reason="Pre-call error"
+                    if isinstance(e, EndpointNotSupportedByModelError)
+                    else "Internal server error",
+                )
             return crash_row
         return bill_socket_failure(outcome) if isinstance(outcome, Exception) else None
 

litellm/proxy/route_llm_request.py

@@ -431,6 +431,28 @@ def raise_if_required_body_param_missing(
     )
 
 
+def endpoint_unsupported_by_model(
+    route_type: str,
+    data: Mapping[str, object],
+    llm_router: LitellmRouter | None,
+    team_id: str | None,
+) -> bool:
+    model: Final = data.get("model")
+    if not isinstance(model, str) or llm_router is None:
+        return False
+    metadata: Final = data.get("metadata")
+    request_kwargs: Final = {
+        **data,
+        "metadata": {
+            **(metadata if isinstance(metadata, Mapping) else {}),
+            "user_api_key_team_id": team_id,
+        },
+    }
+    return bool(llm_router.get_routable_deployments(model, request_kwargs)) and not llm_router.get_routable_deployments(
+        model, request_kwargs, call_type=route_type
+    )
+
+
 class MockTestingParamsDisabledError(HTTPException):
     def __init__(self, params: tuple[str, ...]):
         super().__init__(

litellm/proxy/spend_tracking/spend_tracking_utils.py

@@ -464,7 +464,11 @@ def _sl_attribution_fallback(
 ) -> str:
     if standard_logging_payload is None:
         return ""
-    return standard_logging_payload.get(field) or ""
+    return _string_or_empty(standard_logging_payload.get(field))
+
+
+def _string_or_empty(value: object) -> str:
+    return value if isinstance(value, str) else ""
 
 
 def _deployment_provider(deployment: DeploymentTypedDict) -> str | None:
@@ -682,7 +686,7 @@ def get_logging_payload(
     _model_id: Final = metadata.get("model_info", {}).get("id", "") or _sl_attribution_fallback(
         standard_logging_payload, "model_id"
     )
-    _model_group: Final = metadata.get("model_group", "") or _sl_attribution_fallback(
+    _model_group: Final = _string_or_empty(metadata.get("model_group")) or _sl_attribution_fallback(
         standard_logging_payload, "model_group"
     )
     _api_base: Final = litellm_params.get("api_base", "") or _sl_attribution_fallback(

litellm/proxy/vector_store_endpoints/endpoints.py

@@ -17,6 +17,7 @@ from litellm.proxy._types import CommonProxyErrors, UserAPIKeyAuth
 from litellm.proxy.auth.user_api_key_auth import user_api_key_auth
 from litellm.proxy.common_request_processing import ProxyBaseLLMRequestProcessing
 from litellm.proxy.common_utils.http_parsing_utils import read_raw_json_body
+from litellm.proxy.route_llm_request import JSON_OBJECT_ADAPTER
 from litellm.proxy.utils import jsonify_object
 from litellm.proxy.vector_store_endpoints.utils import (
     assert_proxy_admin_for_vector_store_index_management,
@@ -24,6 +25,7 @@ from litellm.proxy.vector_store_endpoints.utils import (
     get_litellm_managed_vector_store,
 )
 from litellm.repositories.table_repositories import ManagedVectorStoreIndexRepository
+from litellm.types.utils import LlmProviders
 from litellm.types.vector_stores import IndexCreateRequest, IndexListResponse
 from litellm.vector_stores.utils import VectorStoreWriteBody, callers_metadata_snapshot
 from litellm.vector_stores.vector_store_registry import VectorStoreIndexRegistry
@@ -52,6 +54,24 @@ def reject_caller_embedding_selection_params(payload: Mapping[str, object], sour
         )
 
 
+def reject_caller_opensearch_index_name(
+    caller_body: Mapping[str, object], request_fields: Mapping[str, object], source: str
+) -> None:
+    provider: Final = request_fields.get("custom_llm_provider")
+    if (
+        isinstance(provider, str)
+        and provider.split("/", 1)[0] == LlmProviders.OPENSEARCH.value
+        and "opensearch_index_name" in caller_body
+    ):
+        raise HTTPException(
+            status_code=400,
+            detail={
+                "error": f"'opensearch_index_name' cannot be set in {source}. "
+                "The index comes from the vector store's server-side registration."
+            },
+        )
+
+
 ########################################################
 # OpenAI Compatible Endpoints
 ########################################################
@@ -138,11 +158,17 @@ async def vector_store_search(
     data = await read_request_body(request=request)  # rebind-ok: pre-existing rebinding on a rename-only line
     reject_caller_embedding_selection_params(payload=data, source="the search request body")
     data["vector_store_id"] = vector_store_id
+    caller_body: Final = JSON_OBJECT_ADAPTER.validate_python(data)
 
     # Check for legacy vector store registry (non-managed vector stores)
     data = await _update_request_data_with_litellm_managed_vector_store_registry(
         data=data, vector_store_id=vector_store_id, user_api_key_dict=user_api_key_dict
     )
+    reject_caller_opensearch_index_name(
+        caller_body=caller_body,
+        request_fields=JSON_OBJECT_ADAPTER.validate_python(data),
+        source="the search request body",
+    )
 
     # The managed_vector_stores pre-call hook will handle:
     # 1. Decoding managed vector store IDs

litellm/realtime_api/README.md

@@ -4,6 +4,6 @@ Supported endpoints:
 - WebSocket: `/v1/realtime` (with `intent=transcription` for transcription-only sessions)
 - HTTP: `/v1/realtime/client_secrets`, `/v1/realtime/transcription_sessions`
 
-Supported providers: OpenAI, Azure OpenAI, Bedrock, Vertex AI, xAI.
+Supported providers: OpenAI, Azure OpenAI, Bedrock, Vertex AI, xAI, Gemini, Meta, Soniox.
 
 For user-facing documentation and usage examples, see the litellm-docs repo.

litellm/realtime_api/main.py

@@ -7,6 +7,7 @@ from dataclasses import dataclass
 from types import MappingProxyType
 from typing import TYPE_CHECKING, Any, Final, Literal, NoReturn, cast
 
+from pydantic import TypeAdapter
 from typing_extensions import ReadOnly, TypedDict, assert_never
 
 import litellm
@@ -55,6 +56,7 @@ from ..llms.openai.realtime.handler import (
     confirm_session_started,
 )
 from ..llms.openai.workload_identity import openai_static_api_key, resolve_openai_bearer_token_async
+from ..llms.soniox.realtime.transformation import SonioxRealtimeOptions, soniox_realtime_health_check
 from ..llms.vertex_ai.audio_transcription.realtime_transformation import is_vertex_speech_to_text_model
 from ..llms.vertex_ai.realtime.transformation import VertexAIRealtimeConfig, vertex_realtime_config
 from ..llms.vertex_ai.vertex_llm_base import VertexBase
@@ -75,6 +77,7 @@ vertex_llm_base: Final = VertexBase()
 base_llm_http_handler = BaseLLMHTTPHandler()
 _EMPTY_MODEL_PARAMS: Final[Mapping[str, object]] = MappingProxyType({})
 _EMPTY_AUTH_HEADERS: Final[Mapping[str, str]] = MappingProxyType({})
+_DEPLOYMENT_PARAMS: Final = TypeAdapter(Mapping[str, object])
 
 
 def _model_params_with_stored_credentials(model_params: Mapping[str, object]) -> Mapping[str, object]:
@@ -527,6 +530,7 @@ async def _arealtime(
         custom_llm_provider=_custom_llm_provider,
     )
 
+    deployment_params: Final = _DEPLOYMENT_PARAMS.validate_python(kwargs)
     if live_session_start is not None:
         await _alive_session(
             model=model,
@@ -537,7 +541,7 @@ async def _arealtime(
             api_base=dynamic_api_base or litellm_params.api_base,
             api_key=dynamic_api_key,
             litellm_metadata=_build_litellm_metadata(kwargs),
-            litellm_params=kwargs,
+            litellm_params=deployment_params,
         )
         return
 
@@ -548,6 +552,7 @@ async def _arealtime(
             model=model,
             provider=LlmProviders(_custom_llm_provider),
             drop_params=drop_params,
+            litellm_params=deployment_params,
         )
     if provider_config is not None:
         await base_llm_http_handler.async_realtime(
@@ -882,6 +887,13 @@ async def realtime_health_check(
         )
     elif custom_llm_provider == "openai":
         url = openai_realtime.construct_url(api_base=openai_api_base, query_params={"model": model})
+    elif custom_llm_provider == "soniox":
+        return await soniox_realtime_health_check(
+            model=model,
+            api_base=resolved_api_base,
+            api_key=health_check_api_key,
+            options=SonioxRealtimeOptions.model_validate(dict(resolved_params)),
+        )
     elif custom_llm_provider == "xai":
         url = xai_realtime.construct_url(
             api_base=resolved_api_base or "https://api.x.ai/v1", query_params={"model": model}

litellm/router.py

@@ -287,6 +287,11 @@ from litellm.router_utils.embedding_batch_size_check import (
     embedding_deployment_selection,
     log_rejection_before_provider_call,
 )
+from litellm.router_utils.endpoint_eligibility import (
+    eligible_deployments,
+    endpoint_selection,
+    warn_on_unenforced_endpoint_flag,
+)
 from litellm.router_utils.fallback_event_handlers import (
     MID_STREAM_FALLBACK_CONTROLS_KEY,
     AttemptedFallbackTargets,
@@ -1449,7 +1454,7 @@ class Router:
         self.assistants_config = assistants_config
         self.search_tools = search_tools or []
         self.guardrail_list = guardrail_list or []
-        self.deployment_names: list = []  # names of models under litellm_params. ex. azure/chatgpt-v-2
+        self.deployment_names: list[str] = []  # names of models under litellm_params. ex. azure/chatgpt-v-2
         self.deployment_latency_map = {}
         ### CACHING ###
         cache_type: Literal["local", "redis", "redis-semantic", "s3", "disk"] = "local"  # default to an in-memory cache
@@ -6054,12 +6059,13 @@ class Router:
             kwargs.setdefault("metadata", {}).update({"model_group": model})
 
             # pick the one that is available (lowest TPM/RPM)
-            deployment: Final = self.get_available_deployment(
-                model=model,
-                messages=messages,
-                specific_deployment=kwargs.pop("specific_deployment", None),
-                request_kwargs=kwargs,
-            )
+            with endpoint_selection("text_completion", model=model):
+                deployment: Final = self.get_available_deployment(
+                    model=model,
+                    messages=messages,
+                    specific_deployment=kwargs.pop("specific_deployment", None),
+                    request_kwargs=kwargs,
+                )
 
             data: Final = deployment["litellm_params"].copy()
             for k, v in self.default_litellm_params.items():
@@ -6915,6 +6921,7 @@ class Router:
         """
         model_group: Final = cast(str, initial_kwargs.get("model"))  # cast-ok: kwargs always carries the model group
         retry_kwargs: Final = mid_stream_retry_kwargs(initial_kwargs)
+        handler: Final = retry_kwargs.get("original_generic_function")
         healthy_deployments, all_deployments = await self._async_get_healthy_deployments(
             model=model_group,
             parent_otel_span=get_parent_otel_span_from_kwargs(retry_kwargs),
@@ -6945,7 +6952,10 @@ class Router:
             )
             carry_superseded_attempt_usage(retry_kwargs)
             try:
-                response = await attempt_function(**retry_kwargs)
+                with endpoint_selection(
+                    getattr(handler, "__name__", None), getattr(handler, "__module__", None), model=model_group
+                ):
+                    response = await attempt_function(**retry_kwargs)
             except Exception as retry_error:  # noqa: BLE001  # the fallback chain judges the failed retry
                 wrapped = _stream_fallback_error_for_raised(retry_error, model_group, False, e.llm_provider)
                 if wrapped is None:
@@ -9315,9 +9325,18 @@ class Router:
         Handler for making a call to the .completion()/.embeddings()/etc. functions.
         """
         model_group: Final = kwargs.get("model")
-        response = original_function(*args, **kwargs)
-        if coroutine_checker.is_async_callable(response) or inspect.isawaitable(response):
-            response = await response
+        generic_function: Final = _OPAQUE_VALUE_ADAPTER.validate_python(kwargs.get("original_generic_function"))
+        endpoint_function: Final = (
+            generic_function if callable(generic_function) else _OPAQUE_VALUE_ADAPTER.validate_python(original_function)
+        )
+        with endpoint_selection(
+            getattr(endpoint_function, "__name__", None),
+            getattr(endpoint_function, "__module__", None),
+            model=_OPAQUE_VALUE_ADAPTER.validate_python(model_group),
+        ):
+            response = original_function(*args, **kwargs)
+            if coroutine_checker.is_async_callable(response) or inspect.isawaitable(response):
+                response = await response
         await self.increment_deployment_usage_for_response(response=response, request_kwargs=kwargs)
         ## PROCESS RESPONSE HEADERS
         response = await self.set_response_headers(response=response, model_group=model_group, request_kwargs=kwargs)
@@ -10638,6 +10657,7 @@ class Router:
             declared_params: Final = _LITELLM_PARAMS_ADAPTER.validate_python(_litellm_params)
             warn_on_provider_credential_mismatch(model_name=_model_name, litellm_params=declared_params)
             warn_on_ignored_model_capability_flags(model_name=_model_name, litellm_params=declared_params)
+            warn_on_unenforced_endpoint_flag(model_name=_model_name, model_info=_model_info)
             deployment = without_model_capability_flags(
                 Deployment(
                     **deployment_info,
@@ -11441,6 +11461,9 @@ class Router:
         declared_params: Final = deployment.litellm_params.model_dump(exclude_none=True)
         warn_on_provider_credential_mismatch(model_name=deployment.model_name, litellm_params=declared_params)
         warn_on_ignored_model_capability_flags(model_name=deployment.model_name, litellm_params=declared_params)
+        warn_on_unenforced_endpoint_flag(
+            model_name=deployment.model_name, model_info=deployment.model_info.model_dump()
+        )
         served: Final = with_admin_api_key_secret_reference(without_model_capability_flags(deployment))
 
         # add to model list
@@ -14797,9 +14820,15 @@ class Router:
         """
         return [m for m in self.model_list if m["litellm_params"]["model"] == model]
 
-    def get_routable_deployments(self, model: str) -> tuple[DeploymentTypedDict, ...]:
+    def get_routable_deployments(
+        self,
+        model: str,
+        request_kwargs: Mapping[str, object] = MappingProxyType({}),
+        call_type: str | None = None,
+    ) -> tuple[DeploymentTypedDict, ...]:
         try:
-            _, candidates = self._common_checks_available_deployment(model=model, request_kwargs={})  # pyright: ignore[reportUnknownMemberType, reportUnknownVariableType]  # its signature leaves input and the result untyped
+            with endpoint_selection(call_type, model=model):
+                _, candidates = self._common_checks_available_deployment(model=model, request_kwargs=request_kwargs)  # pyright: ignore[reportUnknownMemberType, reportUnknownVariableType]  # its signature leaves input and the result untyped
         except (litellm.BadRequestError, ValueError):
             return ()
         if isinstance(candidates, dict):
@@ -15075,7 +15104,10 @@ class Router:
                 model=model,
                 llm_provider="",
             )
-        return selectable
+        eligible: Final = eligible_deployments(model, selectable)
+        if isinstance(eligible, tuple):
+            return list(eligible)
+        raise eligible
 
     def _filter_reserved_deployments(
         self,

litellm/router_utils/endpoint_eligibility.py

@@ -0,0 +1,120 @@
+from collections.abc import Generator, Mapping, Sequence
+from contextlib import contextmanager
+from contextvars import ContextVar
+from types import MappingProxyType
+from typing import Final, TypeVar
+
+from pydantic import BaseModel, ConfigDict, TypeAdapter, ValidationError, field_validator
+
+from litellm._logging import verbose_router_logger
+from litellm.exceptions import EndpointNotSupportedByModelError
+
+CHAT_ENDPOINT_BY_CALL: Final[Mapping[str, str]] = MappingProxyType(
+    {
+        "completion": "/v1/chat/completions",
+        "acompletion": "/v1/chat/completions",
+        "_completion": "/v1/chat/completions",
+        "_acompletion": "/v1/chat/completions",
+        "text_completion": "/v1/completions",
+        "atext_completion": "/v1/completions",
+        "_atext_completion": "/v1/completions",
+        "anthropic_messages": "/v1/messages",
+        "aresponses": "/v1/responses",
+        "agenerate_content": "generateContent",
+        "agenerate_content_stream": "streamGenerateContent",
+        "acreate_interaction": "/v1beta/interactions",
+        "litellm.interactions.main.acreate": "/v1beta/interactions",
+        "litellm.interactions.main.create": "/v1beta/interactions",
+        "_aresponses_websocket": "/v1/responses",
+        "acompact_responses": "/v1/responses/compact",
+    }
+)
+
+
+def _endpoint_path(endpoint: str) -> str:
+    rooted: Final = "/" + endpoint.strip().strip("/").casefold()
+    path: Final = rooted.removeprefix("/openai").removeprefix("/v1beta").removeprefix("/v1")
+    return "/" + path.rsplit(":", 1)[-1] if ":" in path else path
+
+
+CHAT_ENDPOINTS: Final = frozenset(_endpoint_path(endpoint) for endpoint in CHAT_ENDPOINT_BY_CALL.values())
+_Deployment: Final = TypeVar("_Deployment", bound=Mapping[str, object])
+_ENFORCE_FLAG: Final = TypeAdapter(bool | None)
+_REQUEST_SELECTION: Final[ContextVar[tuple[str | None, str | None]]] = ContextVar(
+    "router_request_endpoint", default=(None, None)
+)
+
+
+class _EndpointDeclaration(BaseModel):
+    model_config = ConfigDict(frozen=True)
+
+    enforce_supported_endpoints: bool | None = None
+    supported_endpoints: tuple[str, ...] | None = None
+
+    @field_validator("supported_endpoints", mode="before")
+    @classmethod
+    def _one_endpoint_as_a_list(cls, value: object) -> object:
+        return (value,) if isinstance(value, str) else value
+
+
+@contextmanager
+def endpoint_selection(call_type: object, module: object = None, model: object = None) -> Generator[None]:
+    qualified_call: Final = f"{module}.{call_type}" if module == "litellm.interactions.main" else call_type
+    endpoint: Final = CHAT_ENDPOINT_BY_CALL.get(qualified_call) if isinstance(qualified_call, str) else None
+    token: Final = _REQUEST_SELECTION.set((endpoint, model if isinstance(model, str) else None))
+    try:
+        yield
+    finally:
+        _REQUEST_SELECTION.reset(token)
+
+
+def _enforced_endpoints(model_info: object) -> tuple[str, ...] | None:
+    try:
+        declaration: Final = _EndpointDeclaration.model_validate(model_info)
+    except ValidationError:
+        return None
+    return declaration.supported_endpoints if declaration.enforce_supported_endpoints else None
+
+
+def warn_on_unenforced_endpoint_flag(model_name: str, model_info: Mapping[str, object]) -> str | None:
+    try:
+        flagged: Final = _ENFORCE_FLAG.validate_python(model_info.get("enforce_supported_endpoints"))
+    except ValidationError:
+        return None
+    if not flagged or _enforced_endpoints(model_info) is not None:
+        return None
+    warning: Final = (
+        f"Deployment '{model_name}' sets enforce_supported_endpoints without a supported_endpoints list of "
+        'strings, so litellm enforces nothing for it. Set supported_endpoints, e.g. ["/v1/embeddings"].'
+    )
+    verbose_router_logger.warning(warning)
+    return warning
+
+
+def non_chat_endpoints(deployment: Mapping[str, object]) -> frozenset[str] | None:
+    declared: Final = _enforced_endpoints(deployment.get("model_info"))
+    if declared is None:
+        return None
+    endpoints: Final = frozenset(declared)
+    normalized: Final = frozenset(_endpoint_path(endpoint) for endpoint in endpoints)
+    return endpoints if normalized.isdisjoint(CHAT_ENDPOINTS) else None
+
+
+def eligible_deployments(
+    model: str, deployments: Sequence[_Deployment]
+) -> tuple[_Deployment, ...] | EndpointNotSupportedByModelError:
+    endpoint, requested_model = _REQUEST_SELECTION.get()
+    if endpoint is None or not deployments:
+        return tuple(deployments)
+    eligible: Final = tuple(deployment for deployment in deployments if non_chat_endpoints(deployment) is None)
+    if eligible:
+        return eligible
+    requested: Final = requested_model or model
+    supported: Final = ", ".join(
+        sorted(frozenset[str]().union(*(non_chat_endpoints(deployment) or frozenset() for deployment in deployments)))
+    )
+    return EndpointNotSupportedByModelError(
+        message=f"{endpoint} is not supported by model={requested}. Supported endpoints: {supported or 'none'}",
+        model=model,
+        llm_provider="",
+    )

litellm/rust_bridge/_native.pyi

@@ -14,6 +14,7 @@ class ForkedAfterNativeRuntimeStarted(RuntimeError): ...
 class ProcessReservedForForking(RuntimeError): ...
 
 def trace_encode_error(message: str) -> bytes: ...
+def bundled_model_cost_map() -> bytes: ...
 
 @final
 class NativeClickHouseSpendConfig:
@@ -220,6 +221,7 @@ __all__ = [
     "aocr",
     "aresponses",
     "atranscription",
+    "bundled_model_cost_map",
     "completion",
     "embedding",
     "gil_stats",

litellm/types/router.py

@@ -270,6 +270,7 @@ def _whole_float_as_int(value: object) -> object:
 
 
 class ModelInfo(MirroredPricingParams):
+    enforce_supported_endpoints: bool | None = None
     id: str | None  # Allow id to be optional on input, but it will always be present as a str in the model instance
     db_model: bool = False  # used for proxy - to separate models which are stored in the db vs. config.
     updated_at: datetime.datetime | None = None

litellm/types/utils.py

@@ -4170,7 +4170,12 @@ class CustomPricingLiteLLMParams(MirroredPricingParams):
 DEPLOYMENT_SCOPED_PRICING_FIELDS: Final[frozenset[str]] = frozenset({"off_peak_pricing"})
 
 DEPLOYMENT_SCOPED_CAPABILITY_FIELDS: Final[frozenset[str]] = frozenset(
-    {"supports_regex_lookaround", "supports_assistant_prefill", "supports_bedrock_native_structured_output"}
+    {
+        "supports_regex_lookaround",
+        "supports_assistant_prefill",
+        "supports_bedrock_native_structured_output",
+        "supports_pdf_input",
+    }
 )
 
 DEPLOYMENT_SCOPED_TOKEN_COUNT_FIELDS: Final[frozenset[str]] = frozenset({"token_count_framing", "token_count_model"})
@@ -4582,6 +4587,7 @@ class LlmProviders(str, Enum):
     S3_VECTORS = "s3_vectors"
     VALKEY = "valkey"
     MONGODB = "mongodb"
+    OPENSEARCH = "opensearch"
     HELICONE = "helicone"
     HYPERBOLIC = "hyperbolic"
     RECRAFT = "recraft"

litellm/utils.py

@@ -10460,6 +10460,22 @@ class ProviderConfigManager:
             return BedrockVectorStore.get_initialized_custom_logger()
         return None
 
+    @staticmethod
+    def _get_vertex_ai_vector_stores_config(api_type: str | None) -> BaseVectorStoreConfig | None:
+        if api_type == "rag_api" or api_type is None:
+            from litellm.llms.vertex_ai.vector_stores.rag_api.transformation import (
+                VertexVectorStoreConfig,
+            )
+
+            return VertexVectorStoreConfig()
+        if api_type == "search_api":
+            from litellm.llms.vertex_ai.vector_stores.search_api.transformation import (
+                VertexSearchAPIVectorStoreConfig,
+            )
+
+            return VertexSearchAPIVectorStoreConfig()
+        return None
+
     @staticmethod
     def get_provider_vector_stores_config(
         provider: LlmProviders,
@@ -10481,18 +10497,7 @@ class ProviderConfigManager:
 
             return AzureOpenAIVectorStoreConfig()
         elif litellm.LlmProviders.VERTEX_AI == provider:
-            if api_type == "rag_api" or api_type is None:  # default to rag_api
-                from litellm.llms.vertex_ai.vector_stores.rag_api.transformation import (
-                    VertexVectorStoreConfig,
-                )
-
-                return VertexVectorStoreConfig()
-            elif api_type == "search_api":
-                from litellm.llms.vertex_ai.vector_stores.search_api.transformation import (
-                    VertexSearchAPIVectorStoreConfig,
-                )
-
-                return VertexSearchAPIVectorStoreConfig()
+            return ProviderConfigManager._get_vertex_ai_vector_stores_config(api_type)
         elif litellm.LlmProviders.BEDROCK == provider:
             from litellm.llms.bedrock.vector_stores.transformation import (
                 BedrockVectorStoreConfig,
@@ -10547,6 +10552,12 @@ class ProviderConfigManager:
             )
 
             return MongoDBVectorStoreConfig()
+        elif litellm.LlmProviders.OPENSEARCH == provider:
+            from litellm.llms.opensearch.vector_stores.transformation import (
+                OpenSearchVectorStoreConfig,
+            )
+
+            return OpenSearchVectorStoreConfig()
         return None
 
     @staticmethod
@@ -10759,6 +10770,7 @@ class ProviderConfigManager:
         model: str,
         provider: LlmProviders,
         drop_params: bool = False,
+        litellm_params: Mapping[str, object] = MappingProxyType({}),
     ) -> BaseRealtimeConfig | None:
         if LlmProviders.GEMINI == provider:
             from litellm.llms.gemini.realtime.transformation import GeminiRealtimeConfig
@@ -10768,6 +10780,10 @@ class ProviderConfigManager:
             from litellm.llms.meta.realtime.transformation import MetaRealtimeConfig
 
             return MetaRealtimeConfig(drop_params=drop_params)
+        if LlmProviders.SONIOX == provider:
+            from litellm.llms.soniox.realtime.transformation import SonioxRealtimeConfig
+
+            return SonioxRealtimeConfig.from_litellm_params(litellm_params, drop_params=drop_params)
         return None
 
     @staticmethod

migrations/Dockerfile

@@ -43,6 +43,7 @@ ENV UV_PYTHON_DOWNLOADS=0
 COPY pyproject.toml README.md rust-toolchain.toml ./
 COPY .cargo ./.cargo
 COPY litellm-rust ./litellm-rust
+COPY model_prices_and_context_window.json ./
 
 RUN --mount=type=cache,target=/root/.cache/uv \
     mkdir -p litellm/rust_bridge /wheels && \

model_prices_and_context_window.json

@@ -4049,6 +4049,7 @@
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
         "supports_parallel_function_calling": true,
+        "supports_pdf_input": false,
         "supports_response_schema": true,
         "supports_tool_choice": true
     },
@@ -12437,7 +12438,8 @@
         "supported_output_modalities": [
             "text"
         ],
-        "supports_native_streaming": true
+        "supports_native_streaming": true,
+        "supports_pdf_input": false
     },
     "azure_ai/FLUX-1.1-pro": {
         "deprecation_date": "2027-01-05",
@@ -12502,6 +12504,7 @@
         "output_cost_per_token": 1.85e-06,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -12516,6 +12519,7 @@
         "output_cost_per_token": 3.828e-06,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -12531,6 +12535,7 @@
         "output_cost_per_token": 3.52e-06,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -12546,6 +12551,7 @@
         "output_cost_per_token": 4.84e-06,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -12560,6 +12566,7 @@
         "output_cost_per_token": 4.84e-06,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -12574,6 +12581,7 @@
         "output_cost_per_token": 7.26e-06,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -12594,6 +12602,7 @@
             "text"
         ],
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -12616,6 +12625,7 @@
             "text"
         ],
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true,
@@ -12638,6 +12648,7 @@
             "text"
         ],
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true,
@@ -12660,6 +12671,7 @@
             "text"
         ],
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true,
@@ -12687,6 +12699,7 @@
             "text"
         ],
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true,
@@ -12703,6 +12716,7 @@
         "output_cost_per_token": 1.32e-06,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -12724,6 +12738,7 @@
             "text"
         ],
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true,
@@ -12744,6 +12759,7 @@
             "text"
         ],
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true,
@@ -12765,6 +12781,7 @@
             "text"
         ],
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -12833,6 +12850,7 @@
             "text"
         ],
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -12847,6 +12865,7 @@
         "output_cost_per_token": 7.1e-07,
         "source": "https://learn.microsoft.com/en-us/azure/foundry/foundry-models/concepts/models-sold-directly-by-azure",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_tool_choice": true
     },
     "azure_ai/Llama-4-Maverick-17B-128E-Instruct-FP8": {
@@ -12859,6 +12878,7 @@
         "output_cost_per_token": 1e-06,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_tool_choice": true,
         "supports_vision": true
     },
@@ -12872,6 +12892,7 @@
         "output_cost_per_token": 7.8e-07,
         "source": "https://azure.microsoft.com/en-us/blog/introducing-the-llama-4-herd-in-azure-ai-foundry-and-azure-databricks/",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_tool_choice": true,
         "supports_vision": true
     },
@@ -12884,6 +12905,7 @@
         "mode": "chat",
         "output_cost_per_token": 3.7e-07,
         "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models",
+        "supports_pdf_input": false,
         "supports_tool_choice": true
     },
     "azure_ai/Meta-Llama-3.1-70B-Instruct": {
@@ -12895,6 +12917,7 @@
         "mode": "chat",
         "output_cost_per_token": 3.54e-06,
         "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models",
+        "supports_pdf_input": false,
         "supports_tool_choice": true
     },
     "azure_ai/Phi-3-medium-128k-instruct": {
@@ -12906,6 +12929,7 @@
         "mode": "chat",
         "output_cost_per_token": 6.8e-07,
         "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models",
+        "supports_pdf_input": false,
         "supports_tool_choice": true,
         "supports_vision": false
     },
@@ -12918,6 +12942,7 @@
         "mode": "chat",
         "output_cost_per_token": 6.8e-07,
         "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models",
+        "supports_pdf_input": false,
         "supports_tool_choice": true,
         "supports_vision": false
     },
@@ -12930,6 +12955,7 @@
         "mode": "chat",
         "output_cost_per_token": 5.2e-07,
         "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models",
+        "supports_pdf_input": false,
         "supports_tool_choice": true,
         "supports_vision": false
     },
@@ -12942,6 +12968,7 @@
         "mode": "chat",
         "output_cost_per_token": 5.2e-07,
         "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models",
+        "supports_pdf_input": false,
         "supports_tool_choice": true,
         "supports_vision": false
     },
@@ -12954,6 +12981,7 @@
         "mode": "chat",
         "output_cost_per_token": 6e-07,
         "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models",
+        "supports_pdf_input": false,
         "supports_tool_choice": true,
         "supports_vision": false
     },
@@ -12966,6 +12994,7 @@
         "mode": "chat",
         "output_cost_per_token": 6e-07,
         "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models",
+        "supports_pdf_input": false,
         "supports_tool_choice": true,
         "supports_vision": false
     },
@@ -12978,6 +13007,7 @@
         "mode": "chat",
         "output_cost_per_token": 6.4e-07,
         "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models",
+        "supports_pdf_input": false,
         "supports_tool_choice": true,
         "supports_vision": false
     },
@@ -12990,6 +13020,7 @@
         "mode": "chat",
         "output_cost_per_token": 5.2e-07,
         "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models",
+        "supports_pdf_input": false,
         "supports_tool_choice": true,
         "supports_vision": false
     },
@@ -13002,6 +13033,7 @@
         "mode": "chat",
         "output_cost_per_token": 5.2e-07,
         "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models",
+        "supports_pdf_input": false,
         "supports_tool_choice": true,
         "supports_vision": true
     },
@@ -13015,6 +13047,7 @@
         "output_cost_per_token": 5e-07,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_tool_choice": true,
         "supports_vision": false
     },
@@ -13027,7 +13060,8 @@
         "mode": "chat",
         "output_cost_per_token": 3e-07,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
-        "supports_function_calling": true
+        "supports_function_calling": true,
+        "supports_pdf_input": false
     },
     "azure_ai/Phi-4-multimodal-instruct": {
         "deprecation_date": "2027-01-17",
@@ -13041,6 +13075,7 @@
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_audio_input": true,
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_vision": true
     },
     "azure_ai/Phi-4-mini-reasoning": {
@@ -13052,7 +13087,8 @@
         "mode": "chat",
         "output_cost_per_token": 3e-07,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
-        "supports_function_calling": true
+        "supports_function_calling": true,
+        "supports_pdf_input": false
     },
     "azure_ai/Phi-4-reasoning": {
         "deprecation_date": "2027-01-17",
@@ -13064,6 +13100,7 @@
         "output_cost_per_token": 5e-07,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_tool_choice": true,
         "supports_reasoning": true
     },
@@ -13106,6 +13143,7 @@
         "output_cost_per_token": 1e-05,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_tool_choice": true
     },
     "azure_ai/doc-intelligence/prebuilt-read": {
@@ -13144,6 +13182,7 @@
         "mode": "chat",
         "output_cost_per_token": 5.4e-06,
         "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models",
+        "supports_pdf_input": false,
         "supports_reasoning": true,
         "supports_tool_choice": true
     },
@@ -13202,6 +13241,7 @@
         "source": "https://learn.microsoft.com/en-us/azure/foundry/foundry-models/concepts/models-sold-directly-by-azure",
         "supports_assistant_prefill": true,
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -13217,6 +13257,7 @@
         "source": "https://learn.microsoft.com/en-us/azure/foundry/foundry-models/concepts/models-sold-directly-by-azure",
         "supports_assistant_prefill": true,
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -13230,6 +13271,7 @@
         "mode": "chat",
         "output_cost_per_token": 4.56e-06,
         "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models",
+        "supports_pdf_input": false,
         "supports_tool_choice": true
     },
     "azure_ai/deepseek-v4-pro": {
@@ -13242,6 +13284,7 @@
         "output_cost_per_token": 3.48e-06,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_reasoning": true,
         "supports_tool_choice": true,
         "cache_read_input_token_cost": 1.45e-07,
@@ -13257,6 +13300,7 @@
         "output_cost_per_token": 5.1e-07,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_reasoning": true,
         "supports_tool_choice": true,
         "cache_read_input_token_cost": 2.8e-08,
@@ -13273,6 +13317,7 @@
         "output_cost_per_token": 1.32e-06,
         "source": "https://learn.microsoft.com/en-us/azure/foundry/foundry-models/concepts/models-sold-directly-by-azure",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_tool_choice": true
@@ -13306,6 +13351,7 @@
         "output_cost_per_token": 1.5e-05,
         "source": "https://learn.microsoft.com/en-us/azure/foundry/foundry-models/concepts/models-sold-directly-by-azure",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_web_search": true
@@ -13323,6 +13369,7 @@
         "output_cost_per_token_above_200k_tokens": 5e-06,
         "source": "https://learn.microsoft.com/en-us/azure/foundry/foundry-models/concepts/models-sold-directly-by-azure",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_response_schema": true,
@@ -13343,6 +13390,7 @@
         "output_cost_per_token_above_200k_tokens": 1.2e-05,
         "source": "https://azure.microsoft.com/en-us/pricing/details/ai-foundry-models/grok/",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_response_schema": true,
@@ -13361,6 +13409,7 @@
         "output_cost_per_token": 2.5e-06,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
@@ -13379,6 +13428,7 @@
         "output_cost_per_token": 2.5e-06,
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_prompt_caching": true,
         "supports_response_schema": true,
         "supports_tool_choice": true,
@@ -13395,6 +13445,7 @@
         "mode": "chat",
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
         "supports_response_schema": true,
         "supports_tool_choice": true,
         "supports_web_search": true
@@ -13409,6 +13460,7 @@
         "mode": "chat",
         "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
         "supports_function_calling": true,
+        "supports_pdf_input": false,
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-        "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models"
+        "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models",
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         "supports_function_calling": true,
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         "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models",
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         "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models",
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         "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models",
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         "source": "https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/retired-models",
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@@ -13591,6 +13656,7 @@
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@@ -52006,6 +52072,7 @@
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@@ -52026,6 +52093,7 @@
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@@ -52794,6 +52863,7 @@
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@@ -52811,6 +52881,7 @@
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         "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing",
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@@ -53131,6 +53204,7 @@
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@@ -53149,6 +53223,7 @@
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@@ -53164,6 +53239,7 @@
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@@ -53179,6 +53255,7 @@
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@@ -61177,6 +61254,126 @@
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+        "source": "https://soniox.com/pricing",
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@@ -67202,6 +67399,7 @@
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+        "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
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+        "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
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+        "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
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+        "source": "https://prices.azure.com/api/retail/prices?$filter=serviceName%20eq%20'Foundry%20Models'%20and%20armRegionName%20eq%20'eastus'%20and%20priceType%20eq%20'Consumption'",
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@@ -73076,6 +73289,7 @@
         "source": "https://cloud.google.com/gemini-enterprise-agent-platform/generative-ai/pricing",
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@@ -74745,6 +74960,7 @@
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@@ -74993,6 +75209,7 @@
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         "supports_prompt_caching": true,
         "supports_reasoning": true,
         "supports_response_schema": true,

packaging/litellm-core/pyproject.toml

@@ -56,6 +56,7 @@ editable-profile = "dev"
 include = [
     { path = "rust-toolchain.toml", format = "sdist" },
     { path = ".cargo/config.toml", format = "sdist" },
+    { path = "model_prices_and_context_window.json", format = "sdist" },
     "litellm/router_strategy/complexity_router/artifacts/*.json",
     "litellm/router_strategy/complexity_router/fuse_presets.json",
     "litellm/proxy/model_insights_tasks.json",

provider_endpoints_support.json

@@ -3724,7 +3724,7 @@
         "ocr": false,
         "search": false,
         "moderations": false,
-        "realtime": false,
+        "realtime": true,
         "video_generations": false,
         "batches": false,
         "messages": false,
@@ -4910,6 +4910,33 @@
         "vector_stores_search": true
       }
     },
+    "opensearch": {
+      "display_name": "Amazon OpenSearch (`opensearch`)",
+      "url": "https://docs.litellm.ai/docs/providers/opensearch_vector_stores",
+      "endpoints": {
+        "chat_completions": false,
+        "responses": false,
+        "text_completion": false,
+        "embeddings": false,
+        "image_generations": false,
+        "image_edits": false,
+        "audio_transcriptions": false,
+        "audio_speech": false,
+        "rerank": false,
+        "ocr": false,
+        "search": false,
+        "moderations": false,
+        "realtime": false,
+        "video_generations": false,
+        "batches": false,
+        "messages": false,
+        "interactions": false,
+        "systemone": false,
+        "a2a": false,
+        "generateContent": false,
+        "vector_stores_search": true
+      }
+    },
     "helicone": {
       "display_name": "Helicone (`helicone`)",
       "url": "https://docs.litellm.ai/docs/providers/helicone",

pyproject.toml

@@ -335,6 +335,7 @@ include = [
     "litellm/proxy/public_endpoints/whats_new.json",
     "litellm/proxy/common_utils/codex_base_instructions.md",
     "litellm/proxy/common_utils/codex_bundled_models_0.159.3.json",
+    { path = "model_prices_and_context_window.json", format = "sdist" },
 ]
 exclude = [
     "litellm/proxy/enterprise",
@@ -357,6 +358,7 @@ cache-keys = [
     { file = "litellm-rust/Cargo.lock" },
     { file = "litellm-rust/Cargo.toml" },
     { file = "litellm-rust/crates/**/*" },
+    { file = "model_prices_and_context_window.json" },
 ]
 constraint-dependencies = [
     "tornado>=6.5.8",

scripts/build_core_distribution.py

@@ -9,7 +9,15 @@ from pathlib import Path
 from typing import Final
 
 ROOT: Final = Path(__file__).resolve().parents[1]
-SOURCES: Final = ("litellm", "litellm-rust", ".cargo", "rust-toolchain.toml", "README.md", "LICENSE")
+SOURCES: Final = (
+    "litellm",
+    "litellm-rust",
+    ".cargo",
+    "rust-toolchain.toml",
+    "model_prices_and_context_window.json",
+    "README.md",
+    "LICENSE",
+)
 
 
 def stage_core_distribution(source: Path, destination: Path) -> None:

tests/base_sdk_tests/test_core_distribution.py

@@ -132,7 +132,9 @@ def test_core_artifacts_exclude_local_configuration(distributions: tuple[Path, P
             assert not any(member.name.endswith(f"/{name}") for member in archive.getmembers())
 
 
-@pytest.mark.parametrize("relative_path", ["rust-toolchain.toml", ".cargo/config.toml"])
+@pytest.mark.parametrize(
+    "relative_path", ["rust-toolchain.toml", ".cargo/config.toml", "model_prices_and_context_window.json"]
+)
 def test_core_sdist_preserves_native_build_configuration(distribution_directory: Path, relative_path: str) -> None:
     sdists: Final = tuple(distribution_directory.glob("litellm_core-*.tar.gz"))
     assert len(sdists) == 1

tests/e2e/coverage_registry/llm_nonconversational.yaml

@@ -2,6 +2,7 @@
 - {id: llm.completions.openai.basic.nonstream.works, module: llm, tier: P0, subject_endpoint: completions, route: openai, capability: basic, streaming: nonstream, assertions: [works], source: "test_completions_endpoint_e2e.py", rationale: "Legacy text /completions endpoint, second-highest production request volume"}
 - {id: llm.embeddings.openai.basic.nonstream.works, module: llm, tier: P0, subject_endpoint: embeddings, route: openai, capability: basic, streaming: nonstream, assertions: [works], source: "test_embeddings_endpoint_e2e.py:23", rationale: "Core endpoint, live vector response"}
 - {id: llm.embeddings.openai.input_validation.nonstream.works, module: llm, tier: P1, subject_endpoint: embeddings, route: openai, capability: input_validation, streaming: nonstream, assertions: [works], source: "vendor strategy §9.3 / LIT-4778", rationale: "Missing model/input on /embeddings return client errors"}
+- {id: llm.embeddings.openai.supported_endpoints_gate.nonstream.works, module: llm, tier: P1, subject_endpoint: embeddings, route: openai, capability: input_validation, streaming: nonstream, assertions: [works], fail_before_fix: proven, source: "test_embedding_only_supported_endpoints_e2e.py", rationale: "Explicit endpoint restrictions preserve real embeddings, refuse chat with a gateway 400, and route chat through a live fallback; zero-provider-call contracts live in integration/routing/test_non_chat_model_endpoint_gate.py"}
 - {id: llm.embeddings.openai.basic.nonstream.cost_logged, module: llm, tier: P0, subject_endpoint: embeddings, route: openai, capability: basic, streaming: nonstream, assertions: [cost_logged], source: "SPEND_TRACKING_COVERAGE_MATRIX.md:34", rationale: "Cost tracking on embeddings"}
 - {id: llm.embeddings.openai.passthrough.nonstream.cost_logged, module: llm, tier: P0, subject_endpoint: embeddings, route: openai, capability: basic, streaming: nonstream, assertions: [cost_logged], source: "test_passthrough_e2e.py", rationale: "POST /openai_passthrough/v1/embeddings is costed; the route wrote no spend row at all, so budgets never saw traffic OpenAI was billing for (GitHub issue #36646)"}
 - {id: llm.embeddings.azure_openai.basic.nonstream.works, module: llm, tier: P0, subject_endpoint: embeddings, route: azure_openai, capability: basic, streaming: nonstream, assertions: [works], source: "llms/azure/azure.py", rationale: "Azure embeddings via translation"}

tests/e2e/llm_translation/test_embedding_only_supported_endpoints_e2e.py

@@ -0,0 +1,119 @@
+"""Live provider checks for embeddings, the chat refusal, and chat fallback from an explicitly restricted model."""
+
+from __future__ import annotations
+
+import dataclasses
+from typing import Final
+
+import pytest
+from e2e_config import unique_marker
+from e2e_metadata import Domain, Mode, Provider, Route, Subject, meta
+from lifecycle import ResourceManager
+from models import LiteLLMParamsBody, ModelInfoBody, ModelMode, ModelNewBody
+from openai import BadRequestError, OpenAI
+from proxy_client import ProxyClient
+from sdk_clients import NO_PROXY_CACHE, SdkClients
+
+pytestmark = pytest.mark.e2e
+
+EMBEDDING_BACKEND: Final = "openai/text-embedding-3-small"
+CHAT_FALLBACK_BACKEND: Final = "openai/gpt-5.4-nano"
+EMBEDDINGS_ONLY: Final = ("/v1/embeddings",)
+PROMPT: Final = "Say this is a test!"
+
+
+def _subject(route: Route) -> Subject:
+    return Subject(
+        domain=Domain.LLM_TRANSLATION,
+        route=route,
+        providers=(Provider.OPENAI,),
+        models=(EMBEDDING_BACKEND,),
+        mode=Mode.NONSTREAM,
+    )
+
+
+def _register(
+    proxy: ProxyClient,
+    resources: ResourceManager,
+    prefix: str,
+    supported_endpoints: tuple[str, ...] | None,
+    backend: str = EMBEDDING_BACKEND,
+    mode: ModelMode = "embedding",
+) -> str:
+    model: Final = f"{prefix}-{unique_marker()}"
+    model_id: Final = proxy.register_model(
+        ModelNewBody(
+            model_name=model,
+            litellm_params=LiteLLMParamsBody(model=backend, api_key="os.environ/OPENAI_API_KEY"),
+            model_info=ModelInfoBody(
+                enforce_supported_endpoints=supported_endpoints is not None,
+                mode=mode,
+                supported_endpoints=None if supported_endpoints is None else list(supported_endpoints),
+            ),
+        )
+    )
+    resources.defer(lambda: proxy.delete_model(model_id))
+    return model
+
+
+def _openai_at(sdk: SdkClients, api_prefix: str, key: str) -> OpenAI:
+    return dataclasses.replace(sdk, base_url=f"{sdk.base_url}{api_prefix}").openai(key)
+
+
+class TestEmbeddingOnlySupportedEndpoints:
+    @pytest.mark.covers("llm.embeddings.openai.supported_endpoints_gate.nonstream.works")
+    @meta(_subject(Route.EMBEDDINGS))
+    @pytest.mark.parametrize(
+        ("prefix", "supported_endpoints"),
+        (
+            pytest.param("e2e-embed-only", EMBEDDINGS_ONLY, id="embed-only"),
+            pytest.param("e2e-embed-control", None, id="control"),
+        ),
+    )
+    def test_embeddings_return_a_vector(
+        self,
+        proxy: ProxyClient,
+        resources: ResourceManager,
+        sdk: SdkClients,
+        prefix: str,
+        supported_endpoints: tuple[str, ...] | None,
+    ) -> None:
+        model: Final = _register(proxy, resources, prefix, supported_endpoints)
+        embeddings: Final = sdk.openai(resources.key()).embeddings.create(
+            model=model, input=PROMPT, extra_body=NO_PROXY_CACHE
+        )
+        assert embeddings.data, f"/embeddings returned no data for {model}: {embeddings!r}"
+        assert any(component != 0.0 for component in embeddings.data[0].embedding), (
+            f"/embeddings returned an empty or all-zero vector for {model}"
+        )
+
+    @pytest.mark.covers("llm.embeddings.openai.supported_endpoints_gate.nonstream.works")
+    @meta(dataclasses.replace(_subject(Route.CHAT_COMPLETIONS), models=(EMBEDDING_BACKEND, CHAT_FALLBACK_BACKEND)))
+    def test_chat_completions_with_a_fallback_are_answered_by_the_fallback(
+        self, proxy: ProxyClient, resources: ResourceManager, sdk: SdkClients
+    ) -> None:
+        model: Final = _register(proxy, resources, "e2e-embed-only", EMBEDDINGS_ONLY)
+        fallback: Final = _register(proxy, resources, "e2e-chat-fallback", None, CHAT_FALLBACK_BACKEND, "chat")
+        completion: Final = _openai_at(sdk, "/v1", resources.key()).chat.completions.create(
+            model=model,
+            messages=[{"role": "user", "content": PROMPT}],
+            extra_body={**NO_PROXY_CACHE, "fallbacks": [fallback]},
+        )
+        assert completion.choices and completion.choices[0].message.content, (
+            f"the fallback {fallback} should answer a chat call to {model}, got {completion!r}"
+        )
+
+    @pytest.mark.covers("llm.embeddings.openai.supported_endpoints_gate.nonstream.works")
+    @meta(_subject(Route.CHAT_COMPLETIONS))
+    def test_chat_completions_without_a_fallback_are_refused_by_the_gateway(
+        self, proxy: ProxyClient, resources: ResourceManager, sdk: SdkClients
+    ) -> None:
+        model: Final = _register(proxy, resources, "e2e-embed-only", EMBEDDINGS_ONLY)
+        with pytest.raises(BadRequestError) as refused:
+            _ = _openai_at(sdk, "/v1", resources.key()).chat.completions.create(
+                model=model, messages=[{"role": "user", "content": PROMPT}], extra_body=NO_PROXY_CACHE
+            )
+        assert (
+            f"/v1/chat/completions is not supported by model={model}. Supported endpoints: /v1/embeddings"
+            in refused.value.message
+        ), f"chat on {model} should be refused by the gateway before any provider call, got {refused.value!r}"

tests/e2e/models.py

@@ -1416,7 +1416,7 @@ class LiteLLMParamsBody(BaseModel):
     order: int | None = None
 
 
-ModelMode = Literal["chat", "batch", "realtime", "image_generation"]
+ModelMode = Literal["chat", "embedding", "batch", "realtime", "image_generation"]
 
 
 class TokenCountModelBody(BaseModel, frozen=True):
@@ -1425,6 +1425,7 @@ class TokenCountModelBody(BaseModel, frozen=True):
 
 
 class ModelInfoBody(BaseModel):
+    enforce_supported_endpoints: bool | None = None
     # id is left unset so the proxy assigns a unique model_id per deployment.
     # Pinning it to the model_name made re-registrations of a fixed-name model
     # (e.g. the batch suite's openai-batch) collide on the model_id unique

tests/integration/management/test_fallback_management_public_names.py

@@ -1,6 +1,7 @@
 import uuid
 from collections.abc import Iterator
 from concurrent.futures import ThreadPoolExecutor
+from contextlib import contextmanager
 from functools import partial
 from itertools import chain
 from pathlib import Path
@@ -10,6 +11,7 @@ import httpx
 import psutil
 import pytest
 from pydantic import JsonValue
+from redis import Redis
 
 from tests.integration._support.client import (
     Gateway,
@@ -27,7 +29,7 @@ from tests.integration._support.process import (
     setup_only_proxy_run,
     worker_configuration,
 )
-from tests.integration._support.redis_process import owned_redis
+from tests.integration._support.redis_process import OwnedRedis, owned_redis
 
 pytestmark: Final = [pytest.mark.timeout(300), pytest.mark.shares_worker_state]
 
@@ -385,6 +387,39 @@ def test_fallback_write_survives_a_redis_outage(gateway: Gateway, tmp_path: Path
                 cache.start()
 
 
+@contextmanager
+def _refusing_cache_deletes(cache: OwnedRedis) -> Iterator[None]:
+    with Redis(host=cache.host, port=cache.port) as client:
+        client.acl_setuser("default", enabled=True, commands=["-del", "-unlink"])
+        try:
+            yield
+        finally:
+            client.acl_setuser("default", enabled=True, commands=["+del", "+unlink"])
+
+
+@pytest.mark.usefixtures("migrated_database")
+def test_consecutive_creates_keep_every_rule_when_redis_refuses_the_cache_eviction(
+    gateway: Gateway, tmp_path: Path
+) -> None:
+    with owned_redis(tmp_path) as cache:
+        overrides: Final = {"REDIS_HOST": cache.host, "REDIS_PORT": str(cache.port)}
+        with owned_proxy_process(gateway, tmp_path, overrides, database_setup=()) as owned:
+            with owned.gateway.scenario() as scenario:
+                target: Final = scenario.model(model=f"openai/x3t-{uuid.uuid4().hex}")
+                first: Final = scenario.model(model=f"openai/x3a-{uuid.uuid4().hex}")
+                second: Final = scenario.model(model=f"openai/x3b-{uuid.uuid4().hex}")
+                for model in (target, first, second):
+                    _wait_until_served(owned.gateway, model)
+                with _refusing_cache_deletes(cache):
+                    created: Final = tuple(
+                        _create_fallback(owned.gateway, scenario, model, [target]) for model in (first, second)
+                    )
+                    assert all(response.status_code == 200 for response in created), [r.text for r in created]
+                    covered: Final = _covered_models(_stored_fallbacks())
+                    assert {first, second} <= covered, covered
+                    assert EVICTION_WARNING in owned.log.read_text(), owned.log.read_text()[-3000:]
+
+
 @pytest.mark.usefixtures("migrated_database")
 def test_fallback_write_survives_a_worker_kill(gateway: Gateway, tmp_path: Path) -> None:
     with owned_proxy_process(gateway, tmp_path, {}, workers=2, database_setup=()) as owned:

tests/integration/messages_endpoint/chat_bridge/test_anthropic_messages_pdf_document_wire.py

@@ -0,0 +1,421 @@
+import json
+import uuid
+from collections.abc import Callable, Sequence
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Final
+
+import anthropic
+import pytest
+from integration._support.client import Gateway, Scenario, string_value
+from integration._support.pdf_document import LETTER, Page, document, encoded, pdf_bytes
+from integration._support.process import owned_proxy_process
+from integration._support.wire import Reply, Request, Wire, wire_server
+from pydantic import JsonValue, TypeAdapter
+
+_API_KEY: Final = "synthetic-pdf-document-key"
+_ANSWER: Final = "The codeword is PELICAN-42."
+_TITLE: Final = "Quarterly report"
+_URL: Final = "https://example.com/brief.pdf"
+_SAP_TOKEN_TARGET: Final = "/oauth/token"
+_JSON_OBJECT: Final = TypeAdapter(dict[str, JsonValue])
+_PARTS: Final = TypeAdapter(list[dict[str, JsonValue]])
+
+
+def _pdf(text: str) -> str:
+    return encoded(pdf_bytes((Page(text=text), LETTER)))
+
+
+def _chat_completion(identity: str, model: str) -> Reply:
+    return Reply(
+        body=json.dumps(
+            {
+                "id": identity,
+                "object": "chat.completion",
+                "created": 1,
+                "model": model,
+                "choices": [
+                    {"index": 0, "message": {"role": "assistant", "content": _ANSWER}, "finish_reason": "stop"}
+                ],
+                "usage": {"prompt_tokens": 914, "completion_tokens": 9, "total_tokens": 923},
+            }
+        ).encode()
+    )
+
+
+def _only_body(wire: Wire) -> dict[str, JsonValue]:
+    received: Final = tuple(
+        request
+        for request in wire.drain()
+        if request.method == "POST" and not request.target.startswith(_SAP_TOKEN_TARGET)
+    )
+    assert len(received) == 1, received
+    return _JSON_OBJECT.validate_json(received[0].body)
+
+
+def _user_parts(body: dict[str, JsonValue], key: str) -> list[dict[str, JsonValue]]:
+    turns: Final = _PARTS.validate_python(body[key])
+    assert len(turns) == 1, turns
+    return _PARTS.validate_python(turns[0]["content" if key == "messages" else "parts"])
+
+
+def test_anthropic_sdk_pdf_documents_reach_azure_as_file_parts_named_by_title(gateway: Gateway) -> None:
+    marker: Final = uuid.uuid4().hex
+    backend: Final = f"gpt-pdf-{marker[:8]}"
+    identity: Final = f"chatcmpl-{marker}"
+    titled: Final = _pdf(f"PELICAN-42 {marker}")
+    untitled: Final = _pdf(f"ORCHID-77 {marker}")
+    question: Final = f"What codeword does each document carry? {marker}"
+    with wire_server(lambda _: _chat_completion(identity, backend)) as wire, gateway.scenario() as scenario:
+        model: Final = scenario.model(
+            model=f"azure/{backend}", api_base=wire.url, api_key=_API_KEY, api_version="2024-10-21"
+        )
+        client: Final = anthropic.Anthropic(base_url=str(gateway.client.base_url), api_key=gateway.key, max_retries=0)
+        message: Final = client.messages.create(
+            model=model,
+            max_tokens=64,
+            messages=[
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "document",
+                            "title": _TITLE,
+                            "source": {"type": "base64", "media_type": "application/pdf", "data": titled},
+                        },
+                        {
+                            "type": "document",
+                            "source": {"type": "base64", "media_type": "application/pdf", "data": untitled},
+                        },
+                        {"type": "document", "source": {"type": "url", "url": _URL}},
+                        {"type": "text", "text": question},
+                    ],
+                }
+            ],
+        )
+        assert message.id == identity
+        assert [(block.type, getattr(block, "text", None)) for block in message.content] == [("text", _ANSWER)]
+        assert _user_parts(_only_body(wire), "messages") == [
+            {"type": "file", "file": {"filename": _TITLE, "file_data": f"data:application/pdf;base64,{titled}"}},
+            {
+                "type": "file",
+                "file": {"filename": "document.pdf", "file_data": f"data:application/pdf;base64,{untitled}"},
+            },
+            {"type": "image_url", "image_url": {"url": _URL}},
+            {"type": "text", "text": question},
+        ]
+
+
+@dataclass(frozen=True, slots=True)
+class _Reader:
+    deploy: Callable[[Scenario, Wire], str]
+    reply: Callable[[Request], Reply]
+    pdf_parts: Callable[[dict[str, JsonValue]], Sequence[JsonValue]]
+
+
+def _gemini_reply(_: Request) -> Reply:
+    return Reply(
+        body=json.dumps(
+            {
+                "candidates": [
+                    {"content": {"parts": [{"text": _ANSWER}], "role": "model"}, "finishReason": "STOP", "index": 0}
+                ],
+                "usageMetadata": {"promptTokenCount": 300, "candidatesTokenCount": 9, "totalTokenCount": 309},
+                "modelVersion": "gemini-2.5-flash",
+            }
+        ).encode()
+    )
+
+
+def _bedrock_reply(_: Request) -> Reply:
+    return Reply(
+        body=json.dumps(
+            {
+                "output": {"message": {"role": "assistant", "content": [{"text": _ANSWER}]}},
+                "stopReason": "end_turn",
+                "usage": {"inputTokens": 300, "outputTokens": 9, "totalTokens": 309},
+                "metrics": {"latencyMs": 1},
+            }
+        ).encode()
+    )
+
+
+def _anthropic_reply(_: Request) -> Reply:
+    return Reply(
+        body=json.dumps(
+            {
+                "id": "msg_pdf",
+                "type": "message",
+                "role": "assistant",
+                "model": "claude-sonnet-4-5",
+                "content": [{"type": "text", "text": _ANSWER}],
+                "stop_reason": "end_turn",
+                "usage": {"input_tokens": 300, "output_tokens": 9},
+            }
+        ).encode()
+    )
+
+
+def _sap_reply(request: Request) -> Reply:
+    if request.target.startswith(_SAP_TOKEN_TARGET):
+        return Reply(body=json.dumps({"access_token": "scripted", "expires_in": 3600}).encode())
+    match request.target:
+        case "/v2/lm/deployments":
+            deployment: Final = {
+                "scenarioId": "orchestration",
+                "configurationId": "orchestration-config",
+                "deploymentUrl": f"http://{request.headers['host']}/sap-deployment",
+                "createdAt": "2026-01-01T00:00:00Z",
+            }
+            return Reply(body=json.dumps({"resources": [deployment]}).encode())
+        case "/v2/lm/configurations/orchestration-config":
+            return Reply(body=json.dumps({"executableId": "orchestration"}).encode())
+        case _:
+            completion: Final = _JSON_OBJECT.validate_json(_chat_completion("chatcmpl-sap-pdf", "claude").body)
+            return Reply(body=json.dumps({"final_result": completion}).encode())
+
+
+def _snowflake(scenario: Scenario, wire: Wire) -> str:
+    return scenario.model(model="snowflake/claude-sonnet-4-5", api_base=wire.url, api_key=_API_KEY)
+
+
+def _sap(scenario: Scenario, wire: Wire) -> str:
+    service_key: Final = {
+        "clientid": "scripted",
+        "clientsecret": _API_KEY,
+        "url": wire.url,
+        "serviceurls": {"AI_API_URL": wire.url},
+    }
+    return scenario.model(model="sap/anthropic--claude-4.5-sonnet", api_base=wire.url, api_key=json.dumps(service_key))
+
+
+def _snowflake_pdf_parts(body: dict[str, JsonValue]) -> Sequence[JsonValue]:
+    return tuple(part.get("source") for part in _user_parts(body, "messages") if part.get("type") == "document")
+
+
+def _sap_pdf_parts(body: dict[str, JsonValue]) -> Sequence[JsonValue]:
+    config: Final = _JSON_OBJECT.validate_python(body["config"])
+    templating: Final = _JSON_OBJECT.validate_python(
+        _JSON_OBJECT.validate_python(config["modules"])["prompt_templating"]
+    )
+    prompt: Final = _JSON_OBJECT.validate_python(templating["prompt"])
+    turns: Final = _PARTS.validate_python(prompt["template"])
+    assert len(turns) == 1, turns
+    parts: Final = _PARTS.validate_python(turns[0]["content"])
+    return tuple(
+        _JSON_OBJECT.validate_python(part["image_url"]).get("url") for part in parts if part.get("type") == "image_url"
+    )
+
+
+def _gemini(scenario: Scenario, wire: Wire) -> str:
+    return scenario.model(model="gemini/gemini-2.5-flash", api_base=wire.url, api_key=_API_KEY)
+
+
+def _bedrock(scenario: Scenario, wire: Wire) -> str:
+    return scenario.model(
+        model="bedrock/converse/us.amazon.nova-2-lite-v1:0",
+        api_key=None,
+        aws_access_key_id="AKIDSYNTHETIC",
+        aws_secret_access_key="synthetic-secret",
+        aws_region_name="us-east-1",
+        aws_bedrock_runtime_endpoint=wire.url,
+    )
+
+
+def _gemini_pdf_parts(body: dict[str, JsonValue]) -> Sequence[JsonValue]:
+    return tuple(part.get("inline_data") for part in _user_parts(body, "contents") if "inline_data" in part)
+
+
+def _bedrock_pdf_parts(body: dict[str, JsonValue]) -> Sequence[JsonValue]:
+    documents: Final = tuple(
+        _JSON_OBJECT.validate_python(part["document"]) for part in _user_parts(body, "messages") if "document" in part
+    )
+    return tuple({"format": item.get("format"), "source": item.get("source")} for item in documents)
+
+
+@pytest.mark.parametrize(
+    ("reader", "expected"),
+    [
+        pytest.param(
+            _Reader(_gemini, _gemini_reply, _gemini_pdf_parts),
+            lambda data: [{"mime_type": "application/pdf", "data": data}],
+            id="gemini",
+        ),
+        pytest.param(
+            _Reader(_bedrock, _bedrock_reply, _bedrock_pdf_parts),
+            lambda data: [{"format": "pdf", "source": {"bytes": data}}],
+            id="bedrock-converse",
+        ),
+        pytest.param(
+            _Reader(_snowflake, _anthropic_reply, _snowflake_pdf_parts),
+            lambda data: [{"type": "base64", "media_type": "application/pdf", "data": data}],
+            id="snowflake-claude",
+        ),
+        pytest.param(
+            _Reader(_sap, _sap_reply, _sap_pdf_parts),
+            lambda data: [f"data:application/pdf;base64,{data}"],
+            id="sap-orchestration",
+        ),
+    ],
+)
+def test_messages_pdf_still_reaches_providers_that_read_pdfs_in_their_own_document_shape(
+    gateway: Gateway, reader: _Reader, expected: Callable[[str], Sequence[JsonValue]]
+) -> None:
+    marker: Final = uuid.uuid4().hex
+    data: Final = _pdf(f"PELICAN-42 {marker}")
+    with wire_server(reader.reply) as wire, gateway.scenario() as scenario:
+        model: Final = reader.deploy(scenario, wire)
+        response: Final = gateway.request(
+            "POST",
+            "/v1/messages",
+            {
+                "model": model,
+                "max_tokens": 64,
+                "messages": [
+                    {
+                        "role": "user",
+                        "content": [
+                            document({"type": "base64", "media_type": "application/pdf", "data": data}, title=_TITLE),
+                            {"type": "text", "text": f"What is the codeword? {marker}"},
+                        ],
+                    }
+                ],
+            },
+            headers={"anthropic-version": "2023-06-01"},
+        )
+        assert response.status_code == 200, response.text
+        assert _JSON_OBJECT.validate_json(response.content)["content"] == [{"type": "text", "text": _ANSWER}]
+        assert list(reader.pdf_parts(_only_body(wire))) == list(expected(data))
+
+
+def _prompt_security(job: str) -> Callable[[Request], Reply]:
+    def respond(request: Request) -> Reply:
+        match (request.method, request.target.partition("?")[0]):
+            case ("POST", "/api/sanitizeFile"):
+                return Reply(body=json.dumps({"jobId": job}).encode())
+            case ("GET", "/api/sanitizeFile"):
+                return Reply(
+                    body=json.dumps(
+                        {
+                            "status": "done",
+                            "content": "",
+                            "metadata": {"action": "block", "violations": ["Malicious File"]},
+                        }
+                    ).encode()
+                )
+            case _:
+                return Reply(body=json.dumps({"result": {"prompt": {"action": "allow"}}}).encode())
+
+    return respond
+
+
+def _delete_guardrail(gateway: Gateway, identity: str) -> None:
+    response: Final = gateway.request("DELETE", f"/guardrails/{identity}")
+    assert response.status_code == 200, response.text
+
+
+def test_prompt_security_still_sanitizes_a_messages_pdf_and_blocks_it_before_the_provider(gateway: Gateway) -> None:
+    marker: Final = uuid.uuid4().hex
+    pdf: Final = pdf_bytes((Page(text=f"PELICAN-42 {marker}"), LETTER))
+    with (
+        wire_server(lambda _: _chat_completion(f"chatcmpl-{marker}", "gpt-pdf")) as provider,
+        wire_server(_prompt_security(f"job-{marker}")) as sink,
+        gateway.scenario() as scenario,
+    ):
+        model: Final = scenario.model(
+            model=f"azure/gpt-pdf-{marker[:8]}", api_base=provider.url, api_key=_API_KEY, api_version="2024-10-21"
+        )
+        name: Final = f"pdf-prompt-security-{marker}"
+        created: Final = gateway.post(
+            "/guardrails",
+            {
+                "guardrail": {
+                    "guardrail_name": name,
+                    "litellm_params": {
+                        "guardrail": "prompt_security",
+                        "mode": "pre_call",
+                        "api_key": "synthetic-prompt-security-key",
+                        "api_base": sink.url,
+                    },
+                }
+            },
+        )
+        scenario.cleanups.callback(_delete_guardrail, gateway, string_value(created["guardrail_id"]))
+        response: Final = gateway.request(
+            "POST",
+            "/v1/messages",
+            {
+                "model": model,
+                "max_tokens": 64,
+                "guardrails": [name],
+                "messages": [
+                    {
+                        "role": "user",
+                        "content": [
+                            document({"type": "base64", "media_type": "application/pdf", "data": encoded(pdf)}),
+                            {"type": "text", "text": f"Summarize this PDF {marker}"},
+                        ],
+                    }
+                ],
+            },
+            headers={"anthropic-version": "2023-06-01"},
+        )
+        assert response.status_code == 400, response.text
+        assert "Malicious File" in response.text
+        assert provider.drain() == ()
+        uploads: Final = tuple(
+            request for request in sink.drain() if (request.method, request.target) == ("POST", "/api/sanitizeFile")
+        )
+        assert len(uploads) == 1, uploads
+        assert pdf in uploads[0].body
+
+
+def _gigachat_reply(file_id: str) -> Callable[[Request], Reply]:
+    def respond(request: Request) -> Reply:
+        if request.target.endswith("/files"):
+            return Reply(body=json.dumps({"id": file_id, "object": "file", "purpose": "general"}).encode())
+        return _chat_completion(f"chatcmpl-{file_id}", "GigaChat-Pro")
+
+    return respond
+
+
+def test_anthropic_sdk_pdf_document_reaches_gigachat_as_an_uploaded_attachment(
+    gateway: Gateway, tmp_path: Path
+) -> None:
+    marker: Final = uuid.uuid4().hex
+    file_id: Final = f"file-{marker}"
+    pdf: Final = pdf_bytes((Page(text=f"PELICAN-42 {marker}"), LETTER))
+    with (
+        wire_server(_gigachat_reply(file_id)) as wire,
+        owned_proxy_process(gateway, tmp_path, {"GIGACHAT_ACCESS_TOKEN": _API_KEY}) as owned,
+        owned.gateway.scenario() as scenario,
+    ):
+        model: Final = scenario.model(model="gigachat/GigaChat-Pro", api_base=f"{wire.url}/v1", api_key=_API_KEY)
+        client: Final = anthropic.Anthropic(
+            base_url=str(owned.gateway.client.base_url), api_key=owned.gateway.key, max_retries=0
+        )
+        message: Final = client.messages.create(
+            model=model,
+            max_tokens=64,
+            messages=[
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "document",
+                            "source": {"type": "base64", "media_type": "application/pdf", "data": encoded(pdf)},
+                        },
+                        {"type": "text", "text": f"What is the codeword? {marker}"},
+                    ],
+                }
+            ],
+        )
+        assert [(block.type, getattr(block, "text", None)) for block in message.content] == [("text", _ANSWER)]
+    upload, chat = (request for request in wire.drain() if request.method == "POST")
+    assert upload.target == "/v1/files", upload.target
+    assert upload.headers["authorization"] == f"Bearer {_API_KEY}", sorted(upload.headers)
+    assert pdf in upload.body
+    assert b"application/pdf" in upload.body
+    assert chat.target == "/v1/chat/completions", chat.target
+    turns: Final = _PARTS.validate_python(_JSON_OBJECT.validate_json(chat.body)["messages"])
+    assert turns[-1]["attachments"] == [file_id], chat.body

tests/integration/providers/test_chat_provider_annotations_wire.py

@@ -0,0 +1,190 @@
+import json
+import uuid
+from collections.abc import Callable, Iterator
+from itertools import chain
+from typing import Final
+
+import httpx
+from integration._support.client import Gateway
+from integration._support.wire import Reply, Request, Wire, wire_server
+from integration.caching.response_cache_case import await_entry, redis_store
+from pydantic import JsonValue, TypeAdapter
+from redis import Redis
+
+_BACKEND: Final = "z-ai/glm-5.2"
+_API_KEY: Final = "synthetic-openrouter-key"
+_ANSWER: Final = "The report covers revenue."
+_URL_CITATION: Final[dict[str, JsonValue]] = {
+    "type": "url_citation",
+    "url_citation": {
+        "url": "https://example.com/report",
+        "title": "Report",
+        "start_index": 0,
+        "end_index": 10,
+        "content": "cited passage",
+    },
+}
+_FILE_ANNOTATION: Final[dict[str, JsonValue]] = {
+    "type": "file",
+    "file": {"hash": "sha-report", "name": "report.pdf", "content": [{"type": "text", "text": "parsed page one"}]},
+}
+_USAGE: Final[dict[str, JsonValue]] = {"prompt_tokens": 40, "completion_tokens": 6, "total_tokens": 46}
+_JSON_OBJECT: Final = TypeAdapter(dict[str, JsonValue])
+_CHOICES: Final = TypeAdapter(list[dict[str, JsonValue]])
+
+
+def _completion(identity: str) -> bytes:
+    return json.dumps(
+        {
+            "id": identity,
+            "object": "chat.completion",
+            "created": 1,
+            "model": _BACKEND,
+            "choices": [
+                {
+                    "index": 0,
+                    "message": {
+                        "role": "assistant",
+                        "content": _ANSWER,
+                        "annotations": [_URL_CITATION, _FILE_ANNOTATION],
+                    },
+                    "finish_reason": "stop",
+                }
+            ],
+            "usage": _USAGE,
+        }
+    ).encode()
+
+
+def _frame(frame: dict[str, JsonValue]) -> bytes:
+    return b"data: " + json.dumps(frame).encode() + b"\n\n"
+
+
+def _stream(identity: str) -> Reply:
+    chunk: Final[dict[str, JsonValue]] = {
+        "id": identity,
+        "object": "chat.completion.chunk",
+        "created": 1,
+        "model": _BACKEND,
+    }
+    return Reply(
+        content_type="text/event-stream",
+        chunks=(
+            _frame({**chunk, "choices": [{"index": 0, "delta": {"role": "assistant", "content": _ANSWER}}]}),
+            _frame({**chunk, "choices": [{"index": 0, "delta": {"annotations": [_URL_CITATION, _FILE_ANNOTATION]}}]}),
+            _frame({**chunk, "choices": [{"index": 0, "delta": {}, "finish_reason": "stop"}], "usage": _USAGE}),
+            b"data: [DONE]\n\n",
+        ),
+    )
+
+
+def _responder(identity: str) -> Callable[[Request], Reply]:
+    def respond(request: Request) -> Reply:
+        if _JSON_OBJECT.validate_json(request.body).get("stream") is True:
+            return _stream(identity)
+        return Reply(body=_completion(identity))
+
+    return respond
+
+
+def _chat_body(model: str, marker: str, stream: bool) -> dict[str, JsonValue]:
+    return {
+        "model": model,
+        "messages": [{"role": "user", "content": f"Summarize the report {marker}"}],
+        "stream": stream,
+    }
+
+
+def _posted(wire: Wire) -> int:
+    return sum(1 for request in wire.drain() if request.method == "POST")
+
+
+def _message(response: httpx.Response) -> dict[str, JsonValue]:
+    assert response.status_code == 200, response.text
+    choices: Final = _CHOICES.validate_python(_JSON_OBJECT.validate_json(response.content)["choices"])
+    return _JSON_OBJECT.validate_python(choices[0]["message"])
+
+
+def _assert_split(message: dict[str, JsonValue]) -> None:
+    assert message["content"] == _ANSWER, message
+    assert message["annotations"] == [_URL_CITATION], message
+    assert _JSON_OBJECT.validate_python(message["provider_specific_fields"])["annotations"] == [_FILE_ANNOTATION]
+
+
+def test_file_annotation_reaches_the_caller_in_provider_specific_fields_and_survives_a_cache_hit(
+    gateway: Gateway,
+) -> None:
+    marker: Final = uuid.uuid4().hex
+    identity: Final = f"gen-{marker}"
+    with wire_server(_responder(identity)) as wire, gateway.scenario() as scenario:
+        model: Final = scenario.model(model=f"openrouter/{_BACKEND}", api_base=wire.url, api_key=_API_KEY)
+        first: Final = gateway.request("POST", "/v1/chat/completions", _chat_body(model, marker, stream=False))
+        _assert_split(_message(first))
+        assert _posted(wire) == 1
+        await_entry(redis_store(), identity)
+        cached: Final = gateway.request("POST", "/v1/chat/completions", _chat_body(model, marker, stream=False))
+        assert cached.headers.get("x-litellm-cache-key"), dict(cached.headers)
+        _assert_split(_message(cached))
+        assert _posted(wire) == 0
+
+
+def _deltas(response: httpx.Response) -> Iterator[dict[str, JsonValue]]:
+    for line in response.iter_lines():
+        if not line.startswith("data: ") or line == "data: [DONE]":
+            continue
+        choices = _CHOICES.validate_python(_JSON_OBJECT.validate_json(line.removeprefix("data: "))["choices"])
+        yield from (_JSON_OBJECT.validate_python(choice["delta"]) for choice in choices)
+
+
+def _streamed(gateway: Gateway, body: dict[str, JsonValue]) -> tuple[dict[str, JsonValue], ...]:
+    with gateway.client.stream(
+        "POST", "/v1/chat/completions", json=body, headers={"Authorization": f"Bearer {gateway.key}"}
+    ) as response:
+        assert response.status_code == 200, response.read()
+        return tuple(_deltas(response))
+
+
+def _listed(value: JsonValue) -> list[JsonValue]:
+    return value if isinstance(value, list) else []
+
+
+def _provider_fields(delta: dict[str, JsonValue]) -> dict[str, JsonValue]:
+    return _JSON_OBJECT.validate_python(delta.get("provider_specific_fields") or {})
+
+
+def _annotations(deltas: tuple[dict[str, JsonValue], ...]) -> list[JsonValue]:
+    return list(chain.from_iterable(_listed(delta.get("annotations")) for delta in deltas))
+
+
+def _provider_annotations(deltas: tuple[dict[str, JsonValue], ...]) -> list[JsonValue]:
+    return list(chain.from_iterable(_listed(_provider_fields(delta).get("annotations")) for delta in deltas))
+
+
+def _text(deltas: tuple[dict[str, JsonValue], ...]) -> str:
+    return "".join(str(delta.get("content") or "") for delta in deltas)
+
+
+def _cached_message(store: Redis, key: str) -> dict[str, JsonValue]:
+    raw: Final = store.get(key)
+    assert isinstance(raw, bytes), key
+    response: Final = _JSON_OBJECT.validate_json(raw)["response"]
+    stored: Final = _JSON_OBJECT.validate_json(response) if isinstance(response, str) else response
+    choices: Final = _CHOICES.validate_python(_JSON_OBJECT.validate_python(stored)["choices"])
+    return _JSON_OBJECT.validate_python(choices[0]["message"])
+
+
+def test_streamed_annotations_reach_the_caller_as_sent_and_the_cached_stream_splits_them(gateway: Gateway) -> None:
+    marker: Final = uuid.uuid4().hex
+    identity: Final = f"gen-{marker}"
+    with wire_server(_responder(identity)) as wire, gateway.scenario() as scenario:
+        model: Final = scenario.model(model=f"openrouter/{_BACKEND}", api_base=wire.url, api_key=_API_KEY)
+        live: Final = _streamed(gateway, _chat_body(model, marker, stream=True))
+        assert _text(live) == _ANSWER, live
+        assert _annotations(live) == [_URL_CITATION, _FILE_ANNOTATION], live
+        assert _posted(wire) == 1
+        store: Final = redis_store()
+        _assert_split(_cached_message(store, await_entry(store, identity)))
+        replayed: Final = _streamed(gateway, _chat_body(model, marker, stream=True))
+        assert _text(replayed) == _ANSWER, replayed
+        assert _provider_annotations(replayed) == [_FILE_ANNOTATION], replayed
+        assert _posted(wire) == 0

tests/integration/providers/test_pdf_input_support_wire.py

@@ -0,0 +1,390 @@
+import json
+import uuid
+from collections.abc import Callable, Mapping, Sequence
+from concurrent.futures import ThreadPoolExecutor
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Final
+
+import httpx
+import pytest
+from integration._support.client import Gateway, Scenario
+from integration._support.openai_wire import chat_reply
+from integration._support.pdf_document import (
+    LETTER,
+    chat_body,
+    chat_file,
+    messages_body,
+    pdf_data_url,
+    pdf_document,
+    responses_body,
+    responses_input_file,
+)
+from integration._support.wire import Reply, Request, Wire, wire_server
+from pydantic import JsonValue, TypeAdapter
+
+_API_KEY: Final = "synthetic-pdf-gate-key"
+_ANSWER: Final = "The report covers revenue."
+_FILENAME: Final = "report.pdf"
+_PDF: Final = pdf_data_url((LETTER,))
+_JSON_OBJECT: Final = TypeAdapter(dict[str, JsonValue])
+_MESSAGES: Final = TypeAdapter(list[JsonValue])
+_COST_MAP_PATH: Final = Path(__file__).parents[3] / "litellm" / "model_prices_and_context_window_backup.json"
+
+
+@dataclass(frozen=True, slots=True)
+class _Entrypoint:
+    path: str
+    body: Callable[[str, str], Mapping[str, JsonValue]]
+    stream: bool = False
+    provider: str = "hosted_vllm"
+
+
+_ENTRYPOINTS: Final = (
+    pytest.param(
+        _Entrypoint("/v1/chat/completions", lambda model, text: chat_body(model, [chat_file(_PDF, _FILENAME)], text)),
+        id="chat-completions",
+    ),
+    pytest.param(
+        _Entrypoint(
+            "/v1/chat/completions",
+            lambda model, text: chat_body(model, [chat_file(_PDF, _FILENAME)], text, stream=True),
+            stream=True,
+        ),
+        id="chat-completions-stream",
+    ),
+    pytest.param(
+        _Entrypoint("/v1/messages", lambda model, text: messages_body(model, [pdf_document((LETTER,))], text)),
+        id="messages",
+    ),
+    pytest.param(
+        _Entrypoint(
+            "/v1/responses",
+            lambda model, text: responses_body(model, [responses_input_file(_PDF, _FILENAME)], text),
+            provider="lm_studio",
+        ),
+        id="responses-bridge",
+    ),
+)
+
+
+def _azure_ai_chat_model_pdf_input(entry: JsonValue) -> bool | None:
+    match entry:
+        case {"litellm_provider": "azure_ai", "mode": "chat", "supports_pdf_input": bool() as supports}:
+            return supports
+        case _:
+            return None
+
+
+def _azure_ai_chat_model(reads_pdfs: bool) -> str:
+    entries: Final = _JSON_OBJECT.validate_json(_COST_MAP_PATH.read_bytes())
+    matching: Final = sorted(
+        name for name, entry in entries.items() if _azure_ai_chat_model_pdf_input(entry) is reads_pdfs
+    )
+    assert matching, f"the cost map marks no azure_ai chat model with supports_pdf_input {reads_pdfs}"
+    return matching[0]
+
+
+def _ollama_reply(backend: str) -> Reply:
+    return Reply(
+        body=json.dumps(
+            {
+                "model": backend,
+                "created_at": "2026-01-01T00:00:00Z",
+                "message": {"role": "assistant", "content": _ANSWER},
+                "done": True,
+                "done_reason": "stop",
+                "prompt_eval_count": 5,
+                "eval_count": 3,
+            }
+        ).encode()
+    )
+
+
+def _responder(backend: str) -> Callable[[Request], Reply]:
+    def respond(request: Request) -> Reply:
+        if request.target.endswith("/api/chat"):
+            return _ollama_reply(backend)
+        body: Final = _JSON_OBJECT.validate_json(request.body)
+        return chat_reply(f"chatcmpl-{uuid.uuid4().hex}", backend, _ANSWER, stream=body.get("stream") is True)
+
+    return respond
+
+
+def _post(gateway: Gateway, path: str, body: Mapping[str, JsonValue]) -> httpx.Response:
+    return gateway.request("POST", path, body, headers={"anthropic-version": "2023-06-01"})
+
+
+def _post_streaming(gateway: Gateway, path: str, body: Mapping[str, JsonValue]) -> httpx.Response:
+    with gateway.client.stream(
+        "POST", path, json=dict(body), headers={"Authorization": f"Bearer {gateway.key}"}
+    ) as response:
+        response.read()
+        return response
+
+
+def _send(gateway: Gateway, entrypoint: _Entrypoint, model: str, text: str) -> httpx.Response:
+    body: Final = entrypoint.body(model, text)
+    if entrypoint.stream:
+        return _post_streaming(gateway, entrypoint.path, body)
+    return _post(gateway, entrypoint.path, body)
+
+
+def _error_message(response: httpx.Response) -> str:
+    match _JSON_OBJECT.validate_json(response.content):
+        case {"error": {"message": str() as message}}:
+            return message
+        case _:
+            raise AssertionError(response.text)
+
+
+def _assert_refused_before_the_provider(response: httpx.Response, wire: Wire, backend: str) -> None:
+    assert response.status_code == 400, response.text
+    message: Final = _error_message(response)
+    assert "PDF" in message and backend in message, message
+    assert wire.drain() == ()
+
+
+def _bodies(wire: Wire) -> tuple[dict[str, JsonValue], ...]:
+    return tuple(_JSON_OBJECT.validate_json(request.body) for request in wire.drain() if request.method == "POST")
+
+
+def _only_body(wire: Wire) -> dict[str, JsonValue]:
+    bodies: Final = _bodies(wire)
+    assert len(bodies) == 1, bodies
+    return bodies[0]
+
+
+def _assert_reaches_the_provider(gateway: Gateway, wire: Wire, model: str) -> None:
+    response: Final = _post(gateway, "/v1/chat/completions", chat_body(model, [], f"text only {uuid.uuid4().hex}"))
+    assert response.status_code == 200, response.text
+    assert len(_bodies(wire)) == 1
+
+
+def _openai_compatible(
+    scenario: Scenario,
+    wire: Wire,
+    backend: str,
+    model_info: Mapping[str, JsonValue],
+    provider: str = "hosted_vllm",
+    **parameters: JsonValue,
+) -> str:
+    return scenario.model(
+        model=f"{provider}/{backend}",
+        api_base=f"{wire.url}/v1",
+        api_key=_API_KEY,
+        model_info=model_info,
+        **parameters,
+    )
+
+
+def _backend() -> str:
+    return f"pdf-gate-{uuid.uuid4().hex[:12]}"
+
+
+@pytest.mark.parametrize("entrypoint", _ENTRYPOINTS)
+def test_pdf_to_a_deployment_marked_unable_to_read_it_is_refused_before_the_provider(
+    gateway: Gateway, entrypoint: _Entrypoint
+) -> None:
+    backend: Final = _backend()
+    with wire_server(_responder(backend)) as wire, gateway.scenario() as scenario:
+        model: Final = _openai_compatible(scenario, wire, backend, {"supports_pdf_input": False}, entrypoint.provider)
+        response: Final = _send(gateway, entrypoint, model, f"Summarize the report {uuid.uuid4().hex}")
+        _assert_refused_before_the_provider(response, wire, backend)
+        _assert_reaches_the_provider(gateway, wire, model)
+
+
+def _base_model_deployment(scenario: Scenario, wire: Wire) -> tuple[str, str]:
+    backend: Final = f"pdf-gate-{uuid.uuid4().hex[:12]}"
+    model: Final = scenario.model(
+        model=f"azure_ai/{backend}",
+        api_base=wire.url,
+        api_key=_API_KEY,
+        model_info={"base_model": _azure_ai_chat_model(reads_pdfs=False)},
+    )
+    return model, backend
+
+
+def _litellm_params_base_model_deployment(scenario: Scenario, wire: Wire) -> tuple[str, str]:
+    backend: Final = _backend()
+    model: Final = scenario.model(
+        model=f"azure_ai/{backend}",
+        api_base=wire.url,
+        api_key=_API_KEY,
+        base_model=_azure_ai_chat_model(reads_pdfs=False),
+    )
+    return model, backend
+
+
+def _cost_map_deployment(scenario: Scenario, wire: Wire) -> tuple[str, str]:
+    marked: Final = _azure_ai_chat_model(reads_pdfs=False)
+    return scenario.model(model=marked, api_base=wire.url, api_key=_API_KEY), marked.removeprefix("azure_ai/")
+
+
+def _ollama_deployment(scenario: Scenario, wire: Wire) -> tuple[str, str]:
+    backend: Final = _backend()
+    return scenario.model(model=f"ollama_chat/{backend}", api_base=wire.url, api_key=None), backend
+
+
+@pytest.mark.parametrize(
+    "deploy",
+    [
+        pytest.param(_base_model_deployment, id="model-info-base-model"),
+        pytest.param(_litellm_params_base_model_deployment, id="litellm-params-base-model"),
+        pytest.param(_cost_map_deployment, id="cost-map"),
+        pytest.param(_ollama_deployment, id="ollama"),
+    ],
+)
+def test_pdf_to_a_model_its_declaration_marks_unable_is_refused_before_the_provider(
+    gateway: Gateway, deploy: Callable[[Scenario, Wire], tuple[str, str]]
+) -> None:
+    with wire_server(_responder("scripted")) as wire, gateway.scenario() as scenario:
+        model, backend = deploy(scenario, wire)
+        response: Final = _post(
+            gateway,
+            "/v1/chat/completions",
+            chat_body(model, [chat_file(_PDF, _FILENAME)], f"Summarize the report {uuid.uuid4().hex}"),
+        )
+        _assert_refused_before_the_provider(response, wire, backend)
+        _assert_reaches_the_provider(gateway, wire, model)
+
+
+def _pdf_turns(question: str) -> list[JsonValue]:
+    return [
+        {"role": "user", "content": [chat_file(_PDF, _FILENAME), {"type": "text", "text": question}]},
+        {"role": "assistant", "content": "It is a quarterly report."},
+        {"role": "user", "content": [chat_file(_PDF, "appendix.pdf")]},
+        {"role": "user", "content": "Who wrote it?"},
+    ]
+
+
+def _answer(response: httpx.Response) -> JsonValue:
+    match _JSON_OBJECT.validate_json(response.content):
+        case {"choices": [{"message": {"content": content}}]}:
+            return content
+        case _:
+            raise AssertionError(response.text)
+
+
+def _turns_without_pdfs(question: str) -> list[JsonValue]:
+    return [
+        {"role": "user", "content": [{"type": "text", "text": question}]},
+        {"role": "assistant", "content": "It is a quarterly report."},
+        {"role": "user", "content": "Who wrote it?"},
+    ]
+
+
+@pytest.mark.parametrize(
+    ("deployment_params", "request_params"),
+    [
+        pytest.param({"drop_params": True}, {}, id="deployment-drop-params"),
+        pytest.param({}, {"drop_params": True}, id="request-drop-params"),
+    ],
+)
+def test_drop_params_sends_the_conversation_without_the_pdfs_a_model_cannot_read(
+    gateway: Gateway, deployment_params: Mapping[str, JsonValue], request_params: Mapping[str, JsonValue]
+) -> None:
+    backend: Final = _backend()
+    question: Final = f"Summarize the report {uuid.uuid4().hex}"
+    with wire_server(_responder(backend)) as wire, gateway.scenario() as scenario:
+        model: Final = _openai_compatible(scenario, wire, backend, {"supports_pdf_input": False}, **deployment_params)
+        response: Final = _post(
+            gateway,
+            "/v1/chat/completions",
+            {"model": model, "max_tokens": 64, "messages": _pdf_turns(question), **request_params},
+        )
+        assert response.status_code == 200, response.text
+        assert _answer(response) == _ANSWER
+        assert _only_body(wire)["messages"] == _turns_without_pdfs(question)
+
+
+def test_drop_params_refuses_a_request_left_with_no_user_turn_once_the_pdf_is_dropped(gateway: Gateway) -> None:
+    backend: Final = _backend()
+    with wire_server(_responder(backend)) as wire, gateway.scenario() as scenario:
+        model: Final = _openai_compatible(scenario, wire, backend, {"supports_pdf_input": False}, drop_params=True)
+        response: Final = _post(
+            gateway,
+            "/v1/chat/completions",
+            {
+                "model": model,
+                "max_tokens": 64,
+                "messages": [
+                    {"role": "system", "content": f"Answer from the attachment {uuid.uuid4().hex}"},
+                    {"role": "user", "content": [chat_file(_PDF, _FILENAME)]},
+                ],
+            },
+        )
+        _assert_refused_before_the_provider(response, wire, backend)
+        _assert_reaches_the_provider(gateway, wire, model)
+
+
+def _file_and_question(question: str) -> list[JsonValue]:
+    return [{"role": "user", "content": [chat_file(_PDF, _FILENAME), {"type": "text", "text": question}]}]
+
+
+@pytest.mark.parametrize(
+    ("model_info", "deployment_params", "keeps_pdf"),
+    [
+        pytest.param({}, {}, True, id="undeclared"),
+        pytest.param({}, {"drop_params": True}, False, id="undeclared-drop-params"),
+        pytest.param({"supports_pdf_input": True}, {"drop_params": True}, True, id="declared-drop-params"),
+        pytest.param(
+            {},
+            {"drop_params": True, "base_model": _azure_ai_chat_model(reads_pdfs=True)},
+            True,
+            id="base-model-reads-drop-params",
+        ),
+    ],
+)
+def test_azure_ai_model_outside_the_cost_map_gets_the_pdf_unless_drop_params_finds_no_declaration(
+    gateway: Gateway, model_info: Mapping[str, JsonValue], deployment_params: Mapping[str, JsonValue], keeps_pdf: bool
+) -> None:
+    backend: Final = _backend()
+    question: Final = f"Summarize the report {uuid.uuid4().hex}"
+    with wire_server(_responder(backend)) as wire, gateway.scenario() as scenario:
+        model: Final = scenario.model(
+            model=f"azure_ai/{backend}",
+            api_base=wire.url,
+            api_key=_API_KEY,
+            model_info=model_info,
+            **deployment_params,
+        )
+        response: Final = _post(
+            gateway, "/v1/chat/completions", chat_body(model, [chat_file(_PDF, _FILENAME)], question)
+        )
+        assert response.status_code == 200, response.text
+        expected: Final[list[JsonValue]] = (
+            _file_and_question(question) if keeps_pdf else [{"role": "user", "content": question}]
+        )
+        assert _only_body(wire)["messages"] == expected
+
+
+def _sibling_pdf_request(gateway: Gateway, model: str) -> httpx.Response:
+    return _post(
+        gateway,
+        "/v1/chat/completions",
+        chat_body(model, [chat_file(_PDF, _FILENAME)], f"Summarize the report {uuid.uuid4().hex}"),
+    )
+
+
+def _forwarded_file_parts(bodies: Sequence[dict[str, JsonValue]]) -> tuple[JsonValue, ...]:
+    return tuple(_MESSAGES.validate_python(body["messages"])[0] for body in bodies)
+
+
+def test_pdf_opt_out_on_one_deployment_leaves_its_sibling_on_the_same_model_reading_pdfs(gateway: Gateway) -> None:
+    backend: Final = _backend()
+    burst: Final = 6
+    with wire_server(_responder(backend)) as wire, gateway.scenario() as scenario:
+        sibling: Final = _openai_compatible(scenario, wire, backend, {})
+        marked: Final = _openai_compatible(scenario, wire, backend, {"supports_pdf_input": False})
+        with ThreadPoolExecutor(max_workers=burst) as pool:
+            responses: Final = tuple(pool.map(lambda _: _sibling_pdf_request(gateway, sibling), range(burst)))
+        assert [response.status_code for response in responses] == [200] * burst, [r.text for r in responses]
+        forwarded: Final = _forwarded_file_parts(_bodies(wire))
+        assert len(forwarded) == burst, forwarded
+        assert all(
+            _MESSAGES.validate_python(_JSON_OBJECT.validate_python(message)["content"])[0] == chat_file(_PDF, _FILENAME)
+            for message in forwarded
+        ), forwarded
+        refused: Final = _sibling_pdf_request(gateway, marked)
+        _assert_refused_before_the_provider(refused, wire, backend)

tests/unit/AGENTS.md

@@ -42,6 +42,9 @@ Green if `send_batched` drops every row. pydantic doubles in 12 of 203 files, fa
 `tests/unit/<path>` mirrors `litellm/<path>`, so a changed file selects its tests by path, not a mapping
 file. New unit tests go here
 
+A new directory under `tests/unit` runs in CI only once a shard in `.github/workflows/test-unit.yml` names it.
+`pytest tests/unit/test_assert_ci_coverage.py` checks that, so run it before you push
+
 ## Writing it so a human can read it
 
 A class only when tests share an arrange

tests/unit/litellm_core_utils/llm_response_utils/test_convert_dict_to_response.py

@@ -3013,3 +3013,53 @@ def test_a_finite_provider_cost_breakdown_with_non_string_keys_is_kept():
 
     assert response.usage.cost == {1: 0.5}
 
+
+_URL_CITATION: Final = {
+    "type": "url_citation",
+    "url_citation": {
+        "url": "https://example.com",
+        "title": "Example",
+        "content": "cited snippet",
+        "start_index": 0,
+        "end_index": 4,
+    },
+}
+_FILE_ANNOTATION: Final = {
+    "type": "file",
+    "file": {"hash": "abc123", "name": "report.pdf", "content": [{"type": "text", "text": "parsed page"}]},
+}
+
+
+def _message_with_annotations(annotations: list[dict[str, object]]) -> Message:
+    response: Final = {
+        "id": "gen-1",
+        "object": "chat.completion",
+        "created": 1712697600,
+        "model": "z-ai/glm-5.3",
+        "choices": [
+            {
+                "index": 0,
+                "finish_reason": "stop",
+                "message": {"role": "assistant", "content": "done", "annotations": annotations},
+            }
+        ],
+        "usage": {"completion_tokens": 1, "prompt_tokens": 1, "total_tokens": 2},
+    }
+    return convert_to_model_response_object(
+        response_object=response, model_response_object=ModelResponse(), response_type="completion"
+    ).choices[0].message
+
+
+def test_annotations_typed_other_than_url_citation_move_to_provider_specific_fields() -> None:
+    untyped_annotation: Final = {"start_index": 0, "end_index": 4}
+    message: Final = _message_with_annotations([_URL_CITATION, untyped_annotation, _FILE_ANNOTATION])
+
+    assert message.annotations == [_URL_CITATION, untyped_annotation]
+    assert message.provider_specific_fields["annotations"] == [_FILE_ANNOTATION]
+
+
+def test_url_citation_annotations_keep_provider_keys() -> None:
+    message: Final = _message_with_annotations([_URL_CITATION])
+
+    assert message.annotations == [_URL_CITATION]
+    assert "annotations" not in (message.provider_specific_fields or {})

tests/unit/litellm_core_utils/test_exception_mapping_utils.py

@@ -24,6 +24,7 @@ from litellm.llms.base_llm.chat.transformation import BaseLLMException
 from litellm.llms.bedrock.common_utils import BedrockError
 from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
 from litellm.llms.openai.common_utils import OpenAIError
+from litellm.proxy.common_utils.openai_error_payload import openai_error_type
 from litellm.types.utils import LlmProviders
 
 
@@ -1015,6 +1016,138 @@ def test_azure_404_with_invalid_request_error_type_maps_to_not_found():
     assert "Response with id 'resp_abc' not found." in excinfo.value.message
 
 
+@pytest.mark.parametrize("provider", ["openai", "groq", "deepseek"])
+@pytest.mark.parametrize(
+    "status_code, message, expected_exception",
+    [
+        (400, "Invalid 'temperature'", litellm.BadRequestError),
+        (401, "Invalid API key", litellm.AuthenticationError),
+        (404, "The model does not exist", litellm.NotFoundError),
+    ],
+)
+def test_openai_invalid_request_error_respects_status_code(
+    provider: str, status_code: int, message: str, expected_exception: type[openai.APIStatusError]
+) -> None:
+    error_message: Final = json.dumps({"error": {"message": message, "type": "invalid_request_error"}})
+    original_exception: Final = OpenAIError(status_code=status_code, message=error_message, headers={})
+
+    with pytest.raises(openai.APIError) as excinfo:
+        exception_type(
+            model="some-model",
+            original_exception=original_exception,
+            custom_llm_provider=provider,
+        )
+
+    assert type(excinfo.value) is expected_exception
+    assert excinfo.value.status_code == status_code
+    assert message in excinfo.value.message
+
+
+@pytest.mark.parametrize("provider", ["openai", "groq", "deepseek"])
+def test_openai_invalid_request_error_without_a_status_code_maps_to_bad_request(provider: str) -> None:
+    message: Final = "Missing required parameter: 'input'."
+    original_exception: Final = Exception(json.dumps({"error": {"message": message, "type": "invalid_request_error"}}))
+
+    with pytest.raises(openai.APIError) as excinfo:
+        exception_type(
+            model="some-model",
+            original_exception=original_exception,
+            custom_llm_provider=provider,
+        )
+
+    assert type(excinfo.value) is litellm.BadRequestError
+    assert message in excinfo.value.message
+
+
+@pytest.mark.parametrize("provider", ["openai", "groq", "deepseek"])
+@pytest.mark.parametrize("status_code", [403, 409, 422, 429, 500, 503])
+def test_openai_invalid_request_error_keeps_bad_request_for_statuses_other_than_401_and_404(
+    provider: str, status_code: int
+) -> None:
+    message: Final = "Cannot cancel a batch that is already completed."
+    error_message: Final = json.dumps({"error": {"message": message, "type": "invalid_request_error"}})
+    original_exception: Final = OpenAIError(status_code=status_code, message=error_message, headers={})
+
+    with pytest.raises(openai.APIError) as excinfo:
+        exception_type(
+            model="some-model",
+            original_exception=original_exception,
+            custom_llm_provider=provider,
+        )
+
+    assert type(excinfo.value) is litellm.BadRequestError
+    assert message in excinfo.value.message
+
+
+def _openai_sdk_error(
+    sdk_error: type[openai.APIStatusError], status_code: int, provider_error: dict[str, str | None]
+) -> openai.APIStatusError:
+    request: Final = httpx.Request("POST", "https://api.openai.com/v1/chat/completions")
+    return sdk_error(
+        f"Error code: {status_code} - {{'error': {provider_error}}}",
+        response=httpx.Response(status_code, request=request),
+        body=provider_error,
+    )
+
+
+@pytest.mark.parametrize(
+    "sdk_error, status_code, expected_exception, param, code",
+    [
+        (openai.AuthenticationError, 401, litellm.AuthenticationError, "organization", "mismatched_organization"),
+        (openai.NotFoundError, 404, litellm.NotFoundError, "id", None),
+    ],
+)
+def test_openai_invalid_request_error_401_and_404_keep_the_provider_error_fields(
+    sdk_error: type[openai.APIStatusError],
+    status_code: int,
+    expected_exception: type[openai.APIStatusError],
+    param: str,
+    code: str | None,
+) -> None:
+    provider_error: Final = {
+        "message": "The request was rejected.",
+        "type": "invalid_request_error",
+        "param": param,
+        "code": code,
+    }
+
+    with pytest.raises(openai.APIError) as excinfo:
+        exception_type(
+            model="gpt-5-mini",
+            original_exception=_openai_sdk_error(sdk_error, status_code, provider_error),
+            custom_llm_provider="openai",
+        )
+
+    assert type(excinfo.value) is expected_exception
+    assert (excinfo.value.status_code, excinfo.value.type, excinfo.value.param, excinfo.value.code) == (
+        status_code,
+        "invalid_request_error",
+        param,
+        code,
+    )
+    assert excinfo.value.body == provider_error
+    assert openai_error_type(excinfo.value, status_code) == "invalid_request_error"
+    assert excinfo.value.message.count(expected_exception.__name__) == 1
+
+
+def test_openai_incorrect_api_key_keeps_the_proxy_authentication_error_type() -> None:
+    provider_error: Final = {
+        "message": "Incorrect API key provided: sk-litellmqa.",
+        "type": "invalid_request_error",
+        "param": None,
+        "code": "invalid_api_key",
+    }
+
+    with pytest.raises(litellm.AuthenticationError) as excinfo:
+        exception_type(
+            model="gpt-5-mini",
+            original_exception=_openai_sdk_error(openai.AuthenticationError, 401, provider_error),
+            custom_llm_provider="openai",
+        )
+
+    assert openai_error_type(excinfo.value, 401) == "authentication_error"
+
+
 class _UpstreamHTTPError(Exception):
     def __init__(self, status_code: int) -> None:
         super().__init__("upstream failure")

tests/unit/litellm_core_utils/test_health_check_helpers.py

@@ -795,9 +795,12 @@ async def test_ahealth_check_keeps_the_noul_probe_for_other_decisions_providers(
         ("us.anthropic.claude-haiku-4-5-20251001-v1:0", "bedrock", None),
         ("claude-haiku-4-5", "anthropic", None),
         ("anthropic.claude-haiku-4-5", None, None),
+        ("stt-rt-v5", "soniox", "realtime"),
+        ("stt-rt-preview", "soniox", "realtime"),
+        ("stt-async-v5", "soniox", None),
     ),
 )
-def test_native_health_check_mode_is_messages_only_for_mantle_claude(
+def test_native_health_check_mode_routes_mantle_claude_to_messages_and_soniox_realtime_models_to_realtime(
     model: str, custom_llm_provider: str | None, expected: str | None
 ) -> None:
     assert native_health_check_mode(model=model, custom_llm_provider=custom_llm_provider) == expected

tests/unit/litellm_core_utils/test_realtime_streaming.py

@@ -24,6 +24,7 @@ from litellm.litellm_core_utils.realtime_streaming import (
     client_sent_openai_beta_realtime_header,
 )
 from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+from litellm.llms.base_llm.realtime.transcription_protocol import RealtimeTranscriptionProtocolError
 from litellm.llms.base_llm.realtime.transformation import BaseRealtimeConfig
 from litellm.llms.xai.realtime.transformation import XAIRealtimeNormalizer
 from litellm.llms.xai.realtime.usage_meter import XAIRealtimeUsageMeter
@@ -827,9 +828,7 @@ async def test_usage_meter_snapshot_stored_in_model_call_details():
     backend_ws.send = AsyncMock()
     backend_ws.recv = AsyncMock(
         side_effect=[
-            json.dumps(
-                {"type": "session.created", "session": {"turn_detection": {"type": None}}}
-            ).encode(),
+            json.dumps({"type": "session.created", "session": {"turn_detection": {"type": None}}}).encode(),
             json.dumps(
                 {"type": "response.output_audio.delta", "delta": base64.b64encode(b"x" * 24000).decode()}
             ).encode(),
@@ -3187,6 +3186,7 @@ async def test_session_close_flushes_unbilled_transcription_usage():
     provider_config: Final = MagicMock()
     provider_config.transform_realtime_response = MagicMock(return_value=transcript_frame)
     provider_config.unbilled_usage_on_session_close = MagicMock(return_value=usage)
+    provider_config.session_refusal = MagicMock(return_value=None)
 
     streaming: Final = RealTimeStreaming(
         client_ws,
@@ -3320,10 +3320,14 @@ def _backend_ws_closing_with(*frames: bytes | Exception) -> MagicMock:
     return backend_ws
 
 
-def _relay_session(client_ws: MagicMock, backend_ws: MagicMock) -> _RelaySession:
+def _relay_session(
+    client_ws: MagicMock, backend_ws: MagicMock, provider_config: BaseRealtimeConfig | None = None
+) -> _RelaySession:
     logging: Final = _RecordingLogging()
     worker: Final = _InlineLoggingWorker()
-    streaming: Final = RealTimeStreaming(client_ws, backend_ws, logging, model="gpt-realtime", logging_worker=worker)
+    streaming: Final = RealTimeStreaming(
+        client_ws, backend_ws, logging, provider_config=provider_config, model="gpt-realtime", logging_worker=worker
+    )
     return _RelaySession(streaming=streaming, logging=logging, worker=worker)
 
 
@@ -3408,6 +3412,103 @@ async def test_upstream_refusal_before_any_frame_logs_a_failure_not_a_success():
     assert session.logging.logged_sessions == ()
 
 
+class _RefusingRealtimeConfig(_PassthroughRealtimeConfig):
+    def __init__(self, refusal: Exception | None) -> None:
+        self._refusal: Final = refusal
+
+    def requires_session_configuration(self) -> bool:
+        return True
+
+    def session_refusal(self) -> Exception | None:
+        return self._refusal
+
+
+@pytest.mark.asyncio
+async def test_a_session_the_provider_refused_in_band_logs_a_failure_even_after_a_normal_close():
+    refusal: Final = ValueError("Soniox realtime error 401: Invalid API key.")
+    session: Final = _relay_session(
+        _client_ws_that_never_sends(),
+        _backend_ws_closing_with(
+            json.dumps({"type": "error", "error": {"message": str(refusal)}}).encode(),
+            ConnectionClosed(Close(1000, ""), None),
+        ),
+        _RefusingRealtimeConfig(refusal),
+    )
+
+    await session.run()
+
+    assert session.logging.logged_failures == (refusal,)
+    assert session.logging.logged_sessions == ()
+
+
+@pytest.mark.asyncio
+async def test_a_provider_session_without_a_refusal_logs_a_success_after_a_normal_close():
+    session: Final = _relay_session(
+        _client_ws_that_never_sends(),
+        _backend_ws_closing_with(
+            json.dumps({"type": "session.created", "session": {"id": "sess_1"}}).encode(),
+            ConnectionClosed(Close(1000, ""), None),
+        ),
+        _RefusingRealtimeConfig(None),
+    )
+
+    await session.run()
+
+    assert session.logging.logged_failures == ()
+    assert len(session.logging.logged_sessions) == 1
+
+
+class _TranscriptionConfigRejectingSessionUpdates(_PassthroughRealtimeConfig):
+    def __init__(self, *, defers_setup: bool) -> None:
+        self._defers_setup: Final = defers_setup
+
+    def requires_session_configuration(self) -> bool:
+        return not self._defers_setup
+
+    def transform_realtime_request(
+        self, message: str, model: str, session_configuration_request: str | None = None
+    ) -> Sequence[str | bytes]:
+        if json.loads(message).get("type") == "session.update":
+            raise RealtimeTranscriptionProtocolError("requires pcm16 input audio")
+        return (message,)
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    "defers_setup,client_frames_read,audio_reaches_backend",
+    ((True, 1, False), (False, 3, True)),
+    ids=["deferred_setup_session_ends", "eager_setup_session_continues"],
+)
+async def test_a_rejected_transcription_session_update_sends_an_error_and_ends_only_a_deferred_setup_session(
+    defers_setup: bool, client_frames_read: int, audio_reaches_backend: bool
+):
+    append: Final = json.dumps({"type": "input_audio_buffer.append", "audio": "AAE="})
+    client_ws: Final = MagicMock()
+    client_ws.headers = {}
+    client_ws.receive_text = AsyncMock(
+        side_effect=[json.dumps({"type": "session.update", "session": {"type": "transcription"}}), append, RuntimeError]
+    )
+    client_ws.send_text = AsyncMock()
+    backend_ws: Final = MagicMock()
+    backend_ws.send = AsyncMock()
+    streaming: Final = RealTimeStreaming(
+        client_ws,
+        backend_ws,
+        _RecordingLogging(),
+        provider_config=_TranscriptionConfigRejectingSessionUpdates(defers_setup=defers_setup),
+        model="chirp_3",
+        logging_worker=_InlineLoggingWorker(),
+    )
+
+    await asyncio.wait_for(streaming.client_ack_messages(), timeout=2)
+
+    assert _error_events_sent_to(client_ws) == [
+        {"type": "error", "error": {"type": "invalid_request_error", "message": "requires pcm16 input audio"}}
+    ]
+    assert client_ws.receive_text.await_count == client_frames_read
+    assert (append in [call.args[0] for call in backend_ws.send.await_args_list]) is audio_reaches_backend
+
+
 @pytest.mark.asyncio
 async def test_upstream_refusal_after_a_synthetic_session_created_still_logs_a_failure():
     """LIT-6973: deferred Gemini Live setup stores a synthetic ``session.created`` before
@@ -4056,9 +4157,7 @@ _XAI_TYPED_VAD_SESSION: Final = {
         pytest.param(({"type": "realtime", "instructions": "hi"},), id="no-turn-detection"),
         pytest.param(({"type": "realtime", "audio": {"input": {"turn_detection": None}}},), id="push-to-talk"),
         pytest.param((_XAI_TYPED_VAD_SESSION,), id="typed-server-vad"),
-        pytest.param(
-            ({"type": "realtime", "instructions": "hi"}, _XAI_TYPED_VAD_SESSION), id="later-typed-server-vad"
-        ),
+        pytest.param(({"type": "realtime", "instructions": "hi"}, _XAI_TYPED_VAD_SESSION), id="later-typed-server-vad"),
     ],
 )
 async def test_xai_session_update_gate_leaves_out_the_vad_type(
@@ -4070,9 +4169,9 @@ async def test_xai_session_update_gate_leaves_out_the_vad_type(
         monkeypatch, *client_sessions, event_normalizer=XAIRealtimeNormalizer()
     )
 
-    assert [session["audio"]["input"]["turn_detection"] for session in forwarded] == [
-        {"create_response": False}
-    ] * (len(client_sessions) + 1)
+    assert [session["audio"]["input"]["turn_detection"] for session in forwarded] == [{"create_response": False}] * (
+        len(client_sessions) + 1
+    )
 
 
 @pytest.mark.asyncio
@@ -4206,7 +4305,9 @@ async def test_client_never_sees_the_error_caused_by_the_guardrail_cancel(monkey
     await RealTimeStreaming(client_ws, backend_ws, logging_obj).backend_to_client_send_messages()
 
     client_errors: Final = [
-        event["error"] for call in client_ws.send_text.await_args_list if (event := json.loads(call.args[0]))["type"] == "error"
+        event["error"]
+        for call in client_ws.send_text.await_args_list
+        if (event := json.loads(call.args[0]))["type"] == "error"
     ]
     assert [(error["code"], error.get("event_id")) for error in client_errors] == [
         ("content_policy_violation", None),

tests/unit/llms/anthropic/messages/adapters/test_anthropic_messages_adapters_transformation.py

@@ -5286,6 +5286,40 @@ def test_eager_input_streaming_tool_reaches_bedrock_converse_as_beta():
     assert data["toolConfig"]["tools"][0]["toolSpec"]["inputSchema"]["json"] == EAGER_INPUT_SCHEMA
 
 
+@pytest.mark.parametrize(
+    ("title", "expected_filename"),
+    [(None, "document.pdf"), ("Quarterly report", "Quarterly report")],
+)
+def test_user_pdf_document_reaches_openai_compatible_providers_as_file_part(
+    title: str | None, expected_filename: str
+) -> None:
+    document: Final = _base64_pdf_block() | ({"title": title} if title else {})
+    request_messages: Final = _run_chat_completions_pipeline(
+        [
+            AnthropicMessagesUserMessageParam(
+                role="user",
+                content=[document, {"type": "text", "text": "What is the codeword?"}],
+            )
+        ]
+    )
+
+    assert request_messages == [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "file",
+                    "file": {
+                        "filename": expected_filename,
+                        "file_data": f"data:application/pdf;base64,{TOOL_RESULT_PDF_B64}",
+                    },
+                },
+                {"type": "text", "text": "What is the codeword?"},
+            ],
+        }
+    ]
+
+
 _CHAT_BRIDGE_MODEL: Final = "azure/gpt-5.6"
 _CHAT_BRIDGE_API_BASE: Final = "https://tool-result-text.openai.azure.com"
 _CHAT_BRIDGE_URL: Final = r".*tool-result-text\.openai\.azure\.com/openai/deployments/gpt-5\.6/chat/completions.*"

tests/unit/llms/azure_ai/chat/test_azure_ai_transformation.py

@@ -1101,6 +1101,58 @@ def test_map_azure_model_group(model_group_header, expected_model):
     assert config._map_azure_model_group(model_group_header) == expected_model
 
 
+def test_azure_ai_sends_file_parts_instead_of_flattening_them_to_text() -> None:
+    file_part: Final = {
+        "type": "file",
+        "file": {"filename": "document.pdf", "file_data": "data:application/pdf;base64,JVBERi0xLjQK"},
+    }
+    content: Final = [file_part, {"type": "text", "text": "What is the codeword?"}]
+
+    request: Final = AzureAIStudioConfig().transform_request(
+        model="Kimi-K2.6",
+        messages=[{"role": "user", "content": content}],
+        optional_params={},
+        litellm_params={},
+        headers={},
+    )
+
+    assert request["messages"] == [{"role": "user", "content": content}]
+
+
+def test_azure_ai_passes_dict_message_content_through_for_the_provider_to_judge() -> None:
+    content: Final = {"type": "text", "text": "Say hi"}
+
+    request: Final = AzureAIStudioConfig().transform_request(
+        model="gpt-5.6-sol",
+        messages=[{"role": "user", "content": content}],
+        optional_params={},
+        litellm_params={},
+        headers={},
+    )
+
+    assert request["messages"] == [{"role": "user", "content": content}]
+
+
+@pytest.mark.parametrize(
+    "file",
+    [
+        {"filename": "notes.txt", "file_data": "data:text/plain;base64,aGVsbG8="},
+        {"file_id": "file-abc"},
+    ],
+    ids=["text_data_url", "file_id_only"],
+)
+def test_azure_ai_flattens_a_non_pdf_file_part_to_its_text_as_before(file: dict[str, str]) -> None:
+    request: Final = AzureAIStudioConfig().transform_request(
+        model="Llama-3.3-70B-Instruct",
+        messages=[{"role": "user", "content": [{"type": "text", "text": "summarize"}, {"type": "file", "file": file}]}],
+        optional_params={},
+        litellm_params={},
+        headers={},
+    )
+
+    assert request["messages"] == [{"role": "user", "content": "summarize"}]
+
+
 def test_translate_developer_role_hoists_a_later_developer_message_for_a_model_that_rejects_it(
     declare_mid_conversation_system: Callable[[str, bool], None],
 ) -> None:

tests/unit/llms/custom_httpx/test_llm_http_handler.py

@@ -4911,6 +4911,160 @@ async def test_async_realtime_bridges_a_transcription_session_through_the_provid
     assert [bytes(request.audio) for request in speech_client.requests[1:]] == [b"\x00\x01" * 800, b"\x00\x01" * 800]
 
 
+class _FakeSonioxSocket:
+    def __init__(self) -> None:
+        self.sent: Final[list[str | bytes]] = []
+        self._responses: Final[asyncio.Queue[str]] = asyncio.Queue()
+
+    async def __aenter__(self) -> "_FakeSonioxSocket":
+        return self
+
+    async def __aexit__(self, exc_type, exc_value, traceback) -> None:
+        return None
+
+    async def send(self, message: str | bytes) -> None:
+        self.sent.append(message)
+        if message == '{"type":"finalize"}':
+            tokens = [{"text": "Hello", "is_final": True}, {"text": "<fin>", "is_final": True}]
+            await self._responses.put(json.dumps({"tokens": tokens, "total_audio_proc_ms": 1200}))
+
+    async def recv(self, decode: bool | None = None) -> str:
+        return await self._responses.get()
+
+    async def close(self) -> None:
+        return None
+
+
+@pytest.mark.asyncio
+async def test_async_realtime_bridges_a_soniox_transcription_through_the_wrapped_websocket():
+    import websockets.exceptions  # noqa: F401  # binds the submodule so async_realtime's except clause resolves, as in the proxy process
+
+    from litellm.llms.soniox.realtime.transformation import SonioxRealtimeBackend, SonioxRealtimeConfig
+
+    upstream = _FakeSonioxSocket()
+    wrapped: list[object] = []
+    provider_config = SonioxRealtimeConfig(
+        wrap=lambda backend: wrapped.append(backend) or SonioxRealtimeBackend(backend)
+    )
+    audio = b"\x00\x01" * 800
+    client_ws = _ScriptedClientWebSocket(
+        [
+            json.dumps(
+                {
+                    "type": "session.update",
+                    "session": {
+                        "type": "transcription",
+                        "audio": {
+                            "input": {
+                                "format": {"type": "audio/pcm", "rate": 16000},
+                                "transcription": {"model": "stt-rt-v5", "language": "en"},
+                                "turn_detection": None,
+                            }
+                        },
+                    },
+                }
+            ),
+            json.dumps({"type": "input_audio_buffer.append", "audio": base64.b64encode(audio).decode()}),
+            json.dumps({"type": "input_audio_buffer.commit"}),
+        ],
+        last_event_type="conversation.item.input_audio_transcription.completed",
+    )
+    logging_obj = Mock()
+    logging_obj.litellm_trace_id = "trace_1"
+    logging_obj.model_call_details = {}
+    logging_obj.dispatch_success_handlers = AsyncMock()
+    logging_obj.dispatch_failure_handlers = AsyncMock()
+    handler = BaseLLMHTTPHandler()
+
+    with patch.object(handler, "_open_realtime_backend_ws", AsyncMock(return_value=upstream)) as dial:
+        await handler.async_realtime(
+            model="stt-rt-v5",
+            websocket=client_ws,
+            logging_obj=logging_obj,
+            provider_config=provider_config,
+            headers={},
+            api_key="soniox-key",
+            query_params={"model": "stt-rt-v5", "intent": "transcription"},
+        )
+
+    assert dial.await_args.args[1:3] == (
+        "wss://stt-rt.soniox.com/transcribe-websocket",
+        {"Authorization": "Bearer soniox-key"},
+    )
+    assert wrapped == [upstream]
+    assert json.loads(upstream.sent[0]) == {
+        "model": "stt-rt-v5",
+        "audio_format": "pcm_s16le",
+        "sample_rate": 16000,
+        "num_channels": 1,
+        "enable_endpoint_detection": False,
+        "language_hints": ["en"],
+    }
+    assert upstream.sent[1:] == [audio, '{"type":"finalize"}']
+    events = client_ws.sent_events()
+    assert [event["type"] for event in events] == [
+        "session.created",
+        "session.updated",
+        "input_audio_buffer.speech_started",
+        "conversation.item.input_audio_transcription.delta",
+        "conversation.item.input_audio_transcription.completed",
+    ]
+    assert events[-1]["transcript"] == "Hello"
+    assert events[-1]["usage"] == {"type": "duration", "seconds": 1.2}
+
+
+@pytest.mark.asyncio
+async def test_async_realtime_soniox_rejects_a_bad_session_update_and_still_transcribes_audio_sent_without_one():
+    import websockets.exceptions  # noqa: F401  # binds the submodule so async_realtime's except clause resolves, as in the proxy process
+
+    from litellm.llms.soniox.realtime.transformation import SonioxRealtimeConfig
+
+    upstream = _FakeSonioxSocket()
+    audio = b"\x00\x01" * 800
+    client_ws = _ScriptedClientWebSocket(
+        [
+            json.dumps(
+                {
+                    "type": "session.update",
+                    "session": {"type": "transcription", "audio": {"input": {"format": {"type": "audio/pcmu"}}}},
+                }
+            ),
+            json.dumps({"type": "input_audio_buffer.append", "audio": base64.b64encode(audio).decode()}),
+            json.dumps({"type": "input_audio_buffer.commit"}),
+        ],
+        last_event_type="conversation.item.input_audio_transcription.completed",
+    )
+    logging_obj = Mock()
+    logging_obj.litellm_trace_id = "trace_1"
+    logging_obj.model_call_details = {}
+    logging_obj.dispatch_success_handlers = AsyncMock()
+    logging_obj.dispatch_failure_handlers = AsyncMock()
+    handler = BaseLLMHTTPHandler()
+
+    with patch.object(handler, "_open_realtime_backend_ws", AsyncMock(return_value=upstream)):
+        await handler.async_realtime(
+            model="stt-rt-v5",
+            websocket=client_ws,
+            logging_obj=logging_obj,
+            provider_config=SonioxRealtimeConfig(),
+            headers={},
+            api_key="soniox-key",
+            query_params={"model": "stt-rt-v5", "intent": "transcription"},
+        )
+
+    assert json.loads(upstream.sent[0])["sample_rate"] == 24_000
+    assert upstream.sent[1:] == [audio, '{"type":"finalize"}']
+    events = client_ws.sent_events()
+    errors = [event for event in events if event["type"] == "error"]
+    assert errors == [
+        {
+            "type": "error",
+            "error": {"type": "invalid_request_error", "message": "Soniox realtime requires pcm16 input audio"},
+        }
+    ]
+    assert events[-1]["transcript"] == "Hello"
+
+
 @pytest.mark.asyncio
 async def test_responses_agentic_followup_does_not_repeat_request_params_from_plan_kwargs(monkeypatch):
     """A plan whose kwargs repeat a request param must not crash the Responses follow-up with a duplicate keyword"""

tests/unit/llms/gigachat/chat/test_gigachat_chat_transformation.py

@@ -8,6 +8,7 @@ get_model_response_iterator, and get_error_class.
 """
 
 import json
+from typing import Final
 from unittest.mock import MagicMock, patch
 
 import httpx
@@ -512,6 +513,20 @@ class TestTransformMessages:
         assert result[0]["content"] == "look"
         assert "file-456" in result[0]["attachments"]
 
+    def test_list_content_with_pdf_file_part_uploads_it_as_an_attachment(self) -> None:
+        pdf_data_url: Final = "data:application/pdf;base64,JVBERi0xLjQK"
+        content: Final = [
+            {"type": "text", "text": "summarize"},
+            {"type": "file", "file": {"filename": "report.pdf", "file_data": pdf_data_url}},
+        ]
+        with patch.object(self.config, "_upload_image", return_value="file-789") as upload:
+            result: Final = self.config._transform_messages([
+                {"role": "user", "content": content}
+            ])
+        upload.assert_called_once_with(pdf_data_url)
+        assert result[0]["content"] == "summarize"
+        assert result[0]["attachments"] == ["file-789"]
+
 
 class TestTransformRequest:
     def setup_method(self):

tests/unit/llms/oci/chat/test_oci_chat_transformation.py

@@ -1,4 +1,5 @@
 import datetime
+from typing import Final
 import httpx
 import pytest
 import json
@@ -2164,3 +2165,32 @@ class TestOpenAIPrefixMaxTokensKey:
         chat_request = _oci_chat_request("openai.gpt-5", {"max_tokens": 64})
         assert chat_request["maxCompletionTokens"] == 64
         assert "maxTokens" not in chat_request
+
+
+def test_transform_request_sends_a_pdf_file_part_as_an_image_data_url() -> None:
+    pdf_data_url: Final = "data:application/pdf;base64,JVBERi0xLjQK"
+    transformed_request: Final = OCIChatConfig().transform_request(
+        model="google.gemini-2.5-flash",
+        messages=[
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": "Summarize this."},
+                    {"type": "file", "file": {"filename": "report.pdf", "file_data": pdf_data_url}},
+                ],
+            }
+        ],
+        optional_params={"oci_compartment_id": TEST_COMPARTMENT_ID},
+        litellm_params={},
+        headers={},
+    )
+
+    assert transformed_request["chatRequest"]["messages"] == [
+        {
+            "role": "USER",
+            "content": [
+                {"type": "TEXT", "text": "Summarize this."},
+                {"type": "IMAGE", "imageUrl": {"url": pdf_data_url}},
+            ],
+        }
+    ]

tests/unit/llms/opensearch/vector_stores/test_opensearch_transformation.py

@@ -0,0 +1,482 @@
+import hashlib
+import json
+import re
+from collections.abc import Callable, Mapping
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Final
+from unittest.mock import MagicMock
+
+import httpx
+import openai
+import pytest
+from botocore.auth import SigV4Auth
+from botocore.awsrequest import AWSRequest
+from botocore.credentials import Credentials
+from botocore.exceptions import CredentialRetrievalError
+
+import litellm
+from litellm.litellm_core_utils.litellm_logging import Logging
+from litellm.llms.base_llm.chat.transformation import BaseLLMException
+from litellm.llms.bedrock.base_aws_llm import BaseAWSLLM
+from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
+from litellm.llms.opensearch.vector_stores.transformation import OpenSearchVectorStoreConfig
+from litellm.types.llms.bedrock import AwsAuthParams
+from litellm.types.router import GenericLiteLLMParams
+from litellm.types.utils import EmbeddingResponse
+from litellm.types.vector_stores import VectorStoreSearchResponse
+from litellm.vector_stores import main as vector_stores
+
+ACCESS_KEY: Final = "AKIDOPENSEARCHTEST"
+SECRET_KEY: Final = "opensearch-test-secret"
+QUERY: Final = "How many days a year can I work abroad?"
+QUERY_VECTOR: Final = [0.1, 0.2, 0.3]
+CREDENTIAL_SOURCE_OUTPUT: Final = "credential-process-stderr-detail"
+SERVERLESS: Final = "https://abc123.us-east-1.aoss.amazonaws.com"
+BASE_PARAMS: Final = {
+    "api_base": SERVERLESS,
+    "aws_access_key_id": ACCESS_KEY,
+    "aws_secret_access_key": SECRET_KEY,
+    "litellm_embedding_model": "embedding-alias",
+}
+_AUTHORIZATION: Final = re.compile(
+    r"\AAWS4-HMAC-SHA256 Credential=(?P<key>[^/]+)/(?P<date>\d{8})/(?P<region>[^/]+)/(?P<service>[^/]+)/aws4_request, "
+    r"SignedHeaders=(?P<signed>[^,]+), Signature=(?P<signature>[0-9a-f]{64})\Z"
+)
+
+
+class RecordingEmbeddingExecutor:
+    def __init__(self) -> None:
+        self.call: Final = MagicMock(return_value=EmbeddingResponse(data=[{"embedding": QUERY_VECTOR}]))
+
+    def embed(self, model: str, query: str, configuration: Mapping[str, object]) -> EmbeddingResponse:
+        return self.call(model, query, configuration)
+
+    async def aembed(self, model: str, query: str, configuration: Mapping[str, object]) -> EmbeddingResponse:
+        return self.call(model, query, configuration)
+
+
+def _signature_scope(request: httpx.Request) -> tuple[str, str]:
+    authorization: Final = _AUTHORIZATION.match(request.headers["authorization"])
+    assert authorization is not None, request.headers["authorization"]
+    signed_names: Final = authorization["signed"].split(";")
+    assert {"host", "x-amz-content-sha256", "x-amz-date"} <= set(signed_names), signed_names
+    assert request.headers["x-amz-content-sha256"] == hashlib.sha256(request.content).hexdigest()
+    signed_request: Final = AWSRequest(
+        method=request.method,
+        url=str(request.url),
+        data=request.content,
+        headers={name: request.headers[name] for name in signed_names},
+    )
+    signed_request.context["timestamp"] = request.headers["x-amz-date"]
+    signer: Final = SigV4Auth(Credentials(ACCESS_KEY, SECRET_KEY), authorization["service"], authorization["region"])
+    expected: Final = signer.signature(
+        signer.string_to_sign(signed_request, signer.canonical_request(signed_request)), signed_request
+    )
+    assert authorization["key"] == ACCESS_KEY
+    assert authorization["signature"] == expected, "SigV4 signature does not cover the request that was sent"
+    return authorization["service"], authorization["region"]
+
+
+def _sent(respond: MagicMock) -> tuple[httpx.Request, ...]:
+    return tuple(call.args[0] for call in respond.call_args_list)
+
+
+def _opensearch_hits(text_field: str, embedding_field: str) -> Mapping[str, object]:
+    return {
+        "took": 4,
+        "timed_out": False,
+        "hits": {
+            "total": {"value": 2, "relation": "eq"},
+            "max_score": 0.581,
+            "hits": [
+                {
+                    "_index": "litellm-qa-docs",
+                    "_id": "1%3A0%3Aw6ftW6EB",
+                    "_score": 0.581,
+                    "_source": {
+                        text_field: "Employees may work abroad for at most 37 calendar days per year.",
+                        embedding_field: [0.5, 0.5, 0.5],
+                        "embedding_ada002": [0.25, 0.75],
+                        "filename": "hr-remote-work-policy.md",
+                        "category": "hr",
+                        "revision": 7,
+                        "published": True,
+                        "tags": ["hr", "travel"],
+                        "owner": {"team": "people-ops"},
+                        "reviewed_by": None,
+                    },
+                },
+                {"_index": "litellm-qa-docs", "_id": "doc-2", "_score": 0.42, "_source": {"category": "facilities"}},
+            ],
+        },
+    }
+
+
+EXPECTED_RESULTS: Final = [
+    {
+        "score": 0.581,
+        "content": [{"type": "text", "text": "Employees may work abroad for at most 37 calendar days per year."}],
+        "file_id": "1%3A0%3Aw6ftW6EB",
+        "filename": "hr-remote-work-policy.md",
+        "attributes": {"filename": "hr-remote-work-policy.md", "category": "hr", "revision": 7, "published": True},
+    },
+    {"score": 0.42, "content": [], "file_id": "doc-2", "filename": None, "attributes": {"category": "facilities"}},
+]
+
+
+async def _search(
+    asynchronous: bool,
+    respond: Callable[[httpx.Request], httpx.Response],
+    executor: RecordingEmbeddingExecutor,
+    kwargs: Mapping[str, object],
+) -> VectorStoreSearchResponse:
+    request_kwargs: Final = {
+        **BASE_PARAMS,
+        "vector_store_id": "litellm-qa-docs",
+        "query": QUERY,
+        "custom_llm_provider": "opensearch",
+        "_direct_vector_store_embedding_executor": executor,
+        **kwargs,
+    }
+    if asynchronous:
+        return await litellm.vector_stores.asearch(
+            **request_kwargs, client=AsyncHTTPHandler(transport=httpx.MockTransport(respond))
+        )
+    with httpx.Client(transport=httpx.MockTransport(respond)) as transport:
+        return litellm.vector_stores.search(**request_kwargs, client=HTTPHandler(client=transport))
+
+
+@pytest.mark.parametrize("asynchronous", [False, True])
+@pytest.mark.parametrize(
+    "fields",
+    [{}, {"opensearch_embedding_field": "passage_vector", "opensearch_text_field": "passage"}],
+)
+@pytest.mark.parametrize(
+    "api_base,region_params,service,region",
+    [
+        (SERVERLESS, {}, "aoss", "us-east-1"),
+        (SERVERLESS, {"aws_region_name": "us-west-2"}, "aoss", "us-east-1"),
+        ("https://search-docs-xyz.eu-west-1.es.amazonaws.com/", {}, "es", "eu-west-1"),
+        ("https://vpc-docs-xyz.cn-north-1.es.amazonaws.com.cn", {}, "es", "cn-north-1"),
+        ("https://abc123.us-gov-west-1.aoss-fips.amazonaws.com", {}, "aoss", "us-gov-west-1"),
+        ("https://abc123.aoss.eu-central-1.on.aws", {}, "aoss", "eu-central-1"),
+        ("https://search-docs-xyz.aos.ap-southeast-2.on.aws", {}, "es", "ap-southeast-2"),
+        ("https://search.docs.example", {"aws_region_name": "ap-south-1"}, "es", "ap-south-1"),
+    ],
+)
+@pytest.mark.asyncio
+async def test_search_sends_a_signed_knn_query_and_returns_an_openai_results_page(
+    asynchronous: bool,
+    fields: Mapping[str, str],
+    api_base: str,
+    region_params: Mapping[str, str],
+    service: str,
+    region: str,
+) -> None:
+    executor: Final = RecordingEmbeddingExecutor()
+    embedding_field: Final = fields.get("opensearch_embedding_field", "embedding")
+    text_field: Final = fields.get("opensearch_text_field", "text")
+    respond: Final = MagicMock(return_value=httpx.Response(200, json=_opensearch_hits(text_field, embedding_field)))
+
+    response: Final = await _search(
+        asynchronous,
+        respond,
+        executor,
+        {**fields, **region_params, "api_base": api_base, "max_num_results": 3},
+    )
+
+    assert response == {"object": "vector_store.search_results.page", "search_query": QUERY, "data": EXPECTED_RESULTS}
+    sent: Final = _sent(respond)
+    assert len(sent) == 1
+    assert str(sent[0].url) == f"{api_base.rstrip('/')}/litellm-qa-docs/_search"
+    assert json.loads(sent[0].content) == {
+        "size": 3,
+        "query": {"knn": {embedding_field: {"vector": QUERY_VECTOR, "k": 3}}},
+        "_source": {"excludes": [embedding_field]},
+    }
+    assert _signature_scope(sent[0]) == (service, region)
+    executor.call.assert_called_once_with("embedding-alias", QUERY, {})
+
+
+@pytest.mark.asyncio
+async def test_search_defaults_to_ten_results_like_openai() -> None:
+    respond: Final = MagicMock(return_value=httpx.Response(200, json={"hits": {"hits": []}}))
+
+    response: Final = await _search(True, respond, RecordingEmbeddingExecutor(), {})
+
+    assert response == {"object": "vector_store.search_results.page", "search_query": QUERY, "data": []}
+    body: Final = json.loads(_sent(respond)[0].content)
+    assert (body["size"], body["query"]["knn"]["embedding"]["k"]) == (10, 10)
+
+
+@pytest.mark.parametrize("asynchronous", [False, True])
+@pytest.mark.asyncio
+async def test_a_list_query_is_embedded_and_echoed_as_one_string(asynchronous: bool) -> None:
+    executor: Final = RecordingEmbeddingExecutor()
+    respond: Final = MagicMock(return_value=httpx.Response(200, json={"hits": {"hits": []}}))
+
+    response: Final = await _search(asynchronous, respond, executor, {"query": ["remote work", "abroad"]})
+
+    assert response["search_query"] == "remote work abroad"
+    executor.call.assert_called_once_with("embedding-alias", "remote work abroad", {})
+
+
+@pytest.mark.parametrize("asynchronous", [False, True])
+@pytest.mark.parametrize("status", [403, 404, 429, 500])
+@pytest.mark.asyncio
+async def test_opensearch_error_keeps_its_status(asynchronous: bool, status: int) -> None:
+    def respond(request: httpx.Request) -> httpx.Response:
+        return httpx.Response(status, json={"status": status, "error": {"reason": "upstream refused", "type": "x"}})
+
+    with pytest.raises(openai.APIStatusError) as error:
+        await _search(asynchronous, respond, RecordingEmbeddingExecutor(), {})
+
+    assert error.value.status_code == status
+
+
+@pytest.mark.parametrize(
+    "overrides,message",
+    [
+        ({"filters": {"type": "eq", "key": "category", "value": "hr"}}, "filters"),
+        ({"ranking_options": {"score_threshold": 0.5}}, "ranking_options"),
+        ({"rewrite_query": True}, "rewrite_query"),
+        ({"max_num_results": 0}, "max_num_results"),
+        ({"max_num_results": 51}, "max_num_results"),
+        ({"max_num_results": "ten"}, "Invalid vector store search parameters"),
+        ({"litellm_embedding_model": None}, "litellm_embedding_model"),
+        ({"opensearch_text_feild": "passage"}, "opensearch_text_feild"),
+        ({"opensearch_text_field": 7}, "opensearch_text_field"),
+        ({"opensearch_index_name": ["docs"]}, "opensearch_index_name"),
+        ({"extra_body": {"min_score": 0.5}}, "extra_body"),
+        ({"api_base": None}, "api_base"),
+        ({"api_base": "abc123.us-east-1.aoss.amazonaws.com"}, "api_base"),
+        ({"api_base": "https://123456789012.aoss.us-east-1.on.aws"}, "per-account endpoint"),
+        ({"api_key": "opensearch-basic-auth"}, "api_key"),
+    ],
+)
+@pytest.mark.asyncio
+async def test_unsupported_search_is_rejected_before_embedding_or_any_request(
+    overrides: Mapping[str, object], message: str
+) -> None:
+    executor: Final = RecordingEmbeddingExecutor()
+    respond: Final = MagicMock(return_value=httpx.Response(200, json={"hits": {"hits": []}}))
+
+    with pytest.raises(litellm.BadRequestError, match=message) as error:
+        await _search(True, respond, executor, overrides)
+
+    assert error.value.status_code == 400
+    respond.assert_not_called()
+    executor.call.assert_not_called()
+
+
+_AMBIENT_AWS_ENV: Final = (
+    "AWS_ACCESS_KEY_ID",
+    "AWS_SECRET_ACCESS_KEY",
+    "AWS_SESSION_TOKEN",
+    "AWS_PROFILE",
+    "AWS_PROFILE_NAME",
+    "AWS_ROLE_NAME",
+    "AWS_ROLE_ARN",
+    "AWS_SESSION_NAME",
+    "AWS_WEB_IDENTITY_TOKEN",
+    "AWS_WEB_IDENTITY_TOKEN_FILE",
+    "AWS_CONTAINER_CREDENTIALS_RELATIVE_URI",
+    "AWS_CONTAINER_CREDENTIALS_FULL_URI",
+)
+
+
+@pytest.mark.parametrize("asynchronous", [False, True])
+@pytest.mark.parametrize(
+    "credentials,env",
+    [
+        pytest.param({"aws_session_name": "opensearch-no-credentials"}, {}, id="none-anywhere"),
+        pytest.param({"aws_profile_name": "opensearch-missing-profile"}, {}, id="missing-profile"),
+        pytest.param({}, {"AWS_ACCESS_KEY_ID": "AKIDPARTIAL"}, id="key-without-secret"),
+        pytest.param(
+            {"aws_access_key_id": "AKIDSTOREONLY"},
+            {"AWS_ACCESS_KEY_ID": "AKIDAMBIENT", "AWS_SECRET_ACCESS_KEY": "ambient-secret"},
+            id="store-key-without-secret-beside-ambient-credentials",
+        ),
+        pytest.param(
+            {"aws_secret_access_key": "store-secret-only"},
+            {"AWS_ACCESS_KEY_ID": "AKIDAMBIENT", "AWS_SECRET_ACCESS_KEY": "ambient-secret"},
+            id="store-secret-without-key-beside-ambient-credentials",
+        ),
+    ],
+)
+@pytest.mark.asyncio
+async def test_missing_aws_credentials_are_a_401_before_embedding_or_any_request(
+    asynchronous: bool,
+    credentials: Mapping[str, str],
+    env: Mapping[str, str],
+    monkeypatch: pytest.MonkeyPatch,
+    tmp_path: Path,
+) -> None:
+    for name in _AMBIENT_AWS_ENV:
+        monkeypatch.delenv(name, raising=False)
+    for name in ("AWS_CONFIG_FILE", "AWS_SHARED_CREDENTIALS_FILE", "BOTO_CONFIG"):
+        config_file: Path = tmp_path / name
+        config_file.write_text("")
+        monkeypatch.setenv(name, str(config_file))
+    monkeypatch.setenv("AWS_EC2_METADATA_DISABLED", "true")
+    for name, value in env.items():
+        monkeypatch.setenv(name, value)
+    executor: Final = RecordingEmbeddingExecutor()
+    respond: Final = MagicMock(return_value=httpx.Response(200, json={"hits": {"hits": []}}))
+
+    with pytest.raises(litellm.AuthenticationError, match="No usable AWS credentials") as error:
+        await _search(
+            asynchronous,
+            respond,
+            executor,
+            {"aws_access_key_id": None, "aws_secret_access_key": None, **credentials},
+        )
+
+    assert error.value.status_code == 401
+    respond.assert_not_called()
+    executor.call.assert_not_called()
+
+
+class _FailingCredentialSource(BaseAWSLLM):
+    def resolve_credentials(self, auth_params: AwsAuthParams, aws_region_name: str | None) -> Credentials:
+        raise CredentialRetrievalError(provider="container-role", error_msg=CREDENTIAL_SOURCE_OUTPUT)
+
+
+def test_failing_credential_source_is_a_401_that_keeps_its_output_out_of_the_reply() -> None:
+    config: Final = OpenSearchVectorStoreConfig(aws=_FailingCredentialSource())
+
+    with pytest.raises(litellm.AuthenticationError, match="the AWS credential source failed") as error:
+        config.validate_environment({}, GenericLiteLLMParams(api_base=SERVERLESS))
+
+    assert error.value.status_code == 401
+    assert CREDENTIAL_SOURCE_OUTPUT not in str(error.value)
+
+
+@pytest.mark.parametrize("asynchronous", [False, True])
+@pytest.mark.asyncio
+async def test_opensearch_index_name_picks_the_index_so_the_store_id_can_differ(asynchronous: bool) -> None:
+    respond: Final = MagicMock(return_value=httpx.Response(200, json=_opensearch_hits("text", "embedding")))
+
+    response: Final = await _search(
+        asynchronous,
+        respond,
+        RecordingEmbeddingExecutor(),
+        {"vector_store_id": "east-docs", "opensearch_index_name": "docs/v2"},
+    )
+
+    assert response["data"] == EXPECTED_RESULTS
+    assert str(_sent(respond)[0].url) == f"{SERVERLESS}/docs%2Fv2/_search"
+
+
+@pytest.mark.parametrize(
+    "unsupported",
+    [
+        {"filters": {"type": "eq", "key": "category", "value": "hr"}},
+        {"ranking_options": {"score_threshold": 0.5}},
+        {"rewrite_query": True},
+    ],
+)
+@pytest.mark.asyncio
+async def test_unsupported_search_params_are_dropped_under_drop_params(unsupported: Mapping[str, object]) -> None:
+    respond: Final = MagicMock(return_value=httpx.Response(200, json=_opensearch_hits("text", "embedding")))
+
+    response: Final = await _search(
+        True, respond, RecordingEmbeddingExecutor(), {**unsupported, "drop_params": True, "max_num_results": 3}
+    )
+
+    assert response["data"] == EXPECTED_RESULTS
+    assert json.loads(_sent(respond)[0].content) == {
+        "size": 3,
+        "query": {"knn": {"embedding": {"vector": QUERY_VECTOR, "k": 3}}},
+        "_source": {"excludes": ["embedding"]},
+    }
+
+
+@pytest.mark.asyncio
+async def test_no_op_values_of_unsupported_params_are_accepted() -> None:
+    def respond(request: httpx.Request) -> httpx.Response:
+        return httpx.Response(200, json=_opensearch_hits("text", "embedding"))
+
+    response: Final = await _search(
+        True, respond, RecordingEmbeddingExecutor(), {"filters": {}, "ranking_options": {}, "rewrite_query": False}
+    )
+
+    assert response["data"] == EXPECTED_RESULTS
+
+
+def test_create_is_rejected_because_the_store_is_search_only() -> None:
+    with pytest.raises(litellm.BadRequestError, match="search-only"):
+        litellm.vector_stores.create(custom_llm_provider="opensearch")
+
+
+async def _store_operation(
+    asynchronous: bool, operation: str, respond: Callable[[httpx.Request], httpx.Response]
+) -> object:
+    request_kwargs: Final = {**BASE_PARAMS, "vector_store_id": "litellm-qa-docs", "custom_llm_provider": "opensearch"}
+    if asynchronous:
+        return await getattr(vector_stores, f"a{operation}")(
+            **request_kwargs, client=AsyncHTTPHandler(transport=httpx.MockTransport(respond))
+        )
+    with httpx.Client(transport=httpx.MockTransport(respond)) as transport:
+        return getattr(vector_stores, operation)(**request_kwargs, client=HTTPHandler(client=transport))
+
+
+@pytest.mark.parametrize("asynchronous", [False, True])
+@pytest.mark.parametrize("operation", ["retrieve", "update", "delete"])
+@pytest.mark.asyncio
+async def test_store_operations_besides_search_send_nothing_to_the_index(asynchronous: bool, operation: str) -> None:
+    respond: Final = MagicMock(return_value=httpx.Response(200, json={"acknowledged": True}))
+
+    with pytest.raises(litellm.BadRequestError, match="search-only") as error:
+        await _store_operation(asynchronous, operation, respond)
+
+    assert error.value.status_code == 400
+    respond.assert_not_called()
+
+
+async def _list_stores(asynchronous: bool, respond: Callable[[httpx.Request], httpx.Response]) -> object:
+    request_kwargs: Final = {**BASE_PARAMS, "custom_llm_provider": "opensearch"}
+    if asynchronous:
+        return await vector_stores.alist(
+            **request_kwargs, client=AsyncHTTPHandler(transport=httpx.MockTransport(respond))
+        )
+    with httpx.Client(transport=httpx.MockTransport(respond)) as transport:
+        return vector_stores.list(**request_kwargs, client=HTTPHandler(client=transport))
+
+
+@pytest.mark.parametrize("asynchronous", [False, True])
+@pytest.mark.asyncio
+async def test_listing_stores_sends_nothing_to_the_endpoint(asynchronous: bool) -> None:
+    respond: Final = MagicMock(return_value=httpx.Response(200, json={"cluster_name": "docs", "tagline": "OpenSearch"}))
+
+    with pytest.raises(litellm.BadRequestError, match="search-only") as error:
+        await _list_stores(asynchronous, respond)
+
+    assert error.value.status_code == 400
+    respond.assert_not_called()
+
+
+@pytest.mark.parametrize(
+    "response,status",
+    [
+        (httpx.Response(403, json={"status": 403, "error": {"type": "Forbidden"}}), 403),
+        (httpx.Response(200, json={"took": 1}), 502),
+        (httpx.Response(200, text="<html>gateway</html>"), 502),
+    ],
+)
+def test_response_without_search_hits_is_an_error_not_an_empty_page(response: httpx.Response, status: int) -> None:
+    logging: Final = Logging(
+        model="",
+        messages=[],
+        stream=False,
+        call_type="vector_store_search",
+        start_time=datetime(2026, 10, 20, tzinfo=timezone.utc),
+        litellm_call_id="opensearch-test",
+        function_id="opensearch-test",
+    )
+
+    with pytest.raises(BaseLLMException) as error:
+        OpenSearchVectorStoreConfig().transform_search_vector_store_response(response, logging)
+
+    assert error.value.status_code == status

tests/unit/llms/sap/chat/test_sap_transformation.py

@@ -1,4 +1,6 @@
 import warnings
+from typing import Final
+
 import pytest
 from pydantic import ValidationError
 
@@ -908,6 +910,22 @@ class TestCacheControl:
         assert "cache_control" not in content[0]
         assert content[1].get("cache_control") == {"type": "ephemeral"}
 
+    def test_pdf_file_part_reaches_sap_as_an_image_url_data_url(self) -> None:
+        pdf_data_url: Final = "data:application/pdf;base64,JVBERi0xLjQK"
+        messages: Final = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": "Summarize this."},
+                    {"type": "file", "file": {"filename": "report.pdf", "file_data": pdf_data_url}},
+                ],
+            }
+        ]
+        body: Final = self._transform("anthropic--claude-3-5-sonnet", messages)
+        content: Final = self._template(body)[0]["content"]
+        assert content[1]["type"] == "image_url"
+        assert content[1]["image_url"]["url"] == pdf_data_url
+
     def test_message_level_cache_control_folded_onto_trailing_image_block(self):
         messages = [
             {

tests/unit/llms/snowflake/chat/test_snowflake_chat_transformation.py

@@ -18,6 +18,7 @@ import pytest
 import litellm
 from litellm import completion, acompletion
 from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
+from litellm.llms.anthropic.messages.adapters.transformation import LiteLLMAnthropicMessagesAdapter
 from litellm.llms.snowflake.chat.transformation import SnowflakeConfig, SnowflakeStreamingHandler
 from litellm.types.utils import ModelResponse
 
@@ -479,6 +480,35 @@ class TestSnowflakeCortexClaudeFixes:
             }
         ]
 
+    def test_messages_api_pdf_reaches_cortex_claude_as_a_document_block(self) -> None:
+        translated: Final = LiteLLMAnthropicMessagesAdapter().translate_anthropic_messages_to_openai(
+            messages=[
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "document",
+                            "source": {"type": "base64", "media_type": "application/pdf", "data": "ZmFrZQ=="},
+                            "cache_control": {"type": "ephemeral"},
+                        },
+                        {"type": "text", "text": "What is the codeword?"},
+                    ],
+                }
+            ],
+            model="snowflake/claude-sonnet-4-6",
+        )
+
+        body: Final = self._transform(translated)
+
+        assert body["messages"][0]["content"] == [
+            {
+                "type": "document",
+                "source": {"type": "base64", "media_type": "application/pdf", "data": "ZmFrZQ=="},
+                "cache_control": {"type": "ephemeral"},
+            },
+            {"type": "text", "text": "What is the codeword?"},
+        ]
+
     def test_multipart_tool_result_preserves_text_and_converts_image(self):
         body = self._transform(
             [

tests/unit/llms/soniox/realtime/__init__.py

@@ -0,0 +1 @@
+"""Soniox realtime tests."""

tests/unit/llms/soniox/realtime/test_soniox_realtime_transformation.py

@@ -0,0 +1,807 @@
+import asyncio
+import base64
+import json
+from typing import Final
+from unittest.mock import MagicMock
+
+import pytest
+
+from litellm.llms.soniox.common_utils import SonioxException
+from litellm.llms.soniox.realtime.transformation import (
+    DEFAULT_SONIOX_REALTIME_URL,
+    SESSION_STARTED_FRAME,
+    SonioxEventTransformer,
+    SonioxRealtimeBackend,
+    SonioxProtocolError,
+    SonioxRealtimeConfig,
+    SonioxRealtimeOptions,
+)
+from litellm.types.llms.openai import OpenAIRealtimeEvents
+from litellm.types.realtime import RealtimeResponseTransformInput
+from litellm.types.utils import LlmProviders
+from litellm.utils import ProviderConfigManager
+
+MODEL: Final = "stt-rt-v5"
+EMPTY_TRANSFORM_INPUT: Final[RealtimeResponseTransformInput] = {
+    "session_configuration_request": None,
+    "current_output_item_id": None,
+    "current_response_id": None,
+    "current_delta_chunks": None,
+    "current_item_chunks": None,
+    "current_conversation_id": None,
+    "current_delta_type": None,
+}
+PCM: Final = b"\x01\x02" * 160
+
+
+def _event(event_type: str, **fields: object) -> str:
+    return json.dumps({"type": event_type, **fields})
+
+
+def _session_update(audio_input: dict[str, object]) -> str:
+    return _event("session.update", session={"type": "transcription", "audio": {"input": audio_input}})
+
+
+def _append() -> str:
+    return _event("input_audio_buffer.append", audio=base64.b64encode(PCM).decode())
+
+
+def _start_request(frames: tuple[str | bytes, ...]) -> dict[str, object]:
+    assert isinstance(frames[0], str)
+    return json.loads(frames[0])
+
+
+def _response(tokens: list[dict[str, object]], total_audio_proc_ms: int | None = None) -> str:
+    return json.dumps({"tokens": tokens, "total_audio_proc_ms": total_audio_proc_ms})
+
+
+def _token(text: str, is_final: bool = True, **fields: object) -> dict[str, object]:
+    return {"text": text, "is_final": is_final, **fields}
+
+
+def _types(events: tuple[OpenAIRealtimeEvents, ...]) -> list[object]:
+    return [event["type"] for event in events]
+
+
+def test_session_update_starts_the_stream_with_client_audio_and_deployment_options():
+    config = SonioxRealtimeConfig(
+        options=SonioxRealtimeOptions(language_hints=("en", "fr"), enable_speaker_diarization=True)
+    )
+
+    frames = config.transform_realtime_request(
+        _session_update(
+            {
+                "format": {"type": "audio/pcm", "rate": 16_000},
+                "transcription": {"model": "client-chosen-model", "language": "fr"},
+                "turn_detection": None,
+            }
+        ),
+        MODEL,
+    )
+
+    assert _start_request(frames) == {
+        "model": MODEL,
+        "audio_format": "pcm_s16le",
+        "sample_rate": 16_000,
+        "num_channels": 1,
+        "enable_endpoint_detection": False,
+        "language_hints": ["fr", "en"],
+        "enable_speaker_diarization": True,
+    }
+
+
+def test_an_empty_transcription_language_keeps_the_deployment_language_hints():
+    config = SonioxRealtimeConfig(options=SonioxRealtimeOptions(language_hints=("fa",)))
+
+    frames = config.transform_realtime_request(_session_update({"transcription": {"language": ""}}), MODEL)
+
+    assert _start_request(frames)["language_hints"] == ["fa"]
+
+
+def test_a_client_language_cannot_widen_strict_deployment_language_hints():
+    config = SonioxRealtimeConfig(options=SonioxRealtimeOptions(language_hints=("fa",), language_hints_strict=True))
+
+    frames = config.transform_realtime_request(_session_update({"transcription": {"language": "en"}}), MODEL)
+
+    assert _start_request(frames)["language_hints"] == ["fa"]
+    assert config.transform_realtime_request(_session_update({"transcription": {"language": "en"}}), MODEL) == ()
+
+
+def test_a_soniox_native_config_frame_is_rejected_without_starting_the_stream():
+    config = SonioxRealtimeConfig()
+
+    with pytest.raises(SonioxProtocolError):
+        config.transform_realtime_request(json.dumps({"model": "stt-rt-v5", "audio_format": "auto"}), MODEL)
+    assert _start_request(config.transform_realtime_request(_append(), MODEL))["sample_rate"] == 24_000
+
+
+def test_audio_before_session_update_starts_a_default_stream_once_and_rejects_a_later_change():
+    config = SonioxRealtimeConfig()
+
+    first = config.transform_realtime_request(_append(), MODEL)
+    second = config.transform_realtime_request(_append(), MODEL)
+    with pytest.raises(SonioxProtocolError, match="once the stream has started"):
+        config.transform_realtime_request(_session_update({"turn_detection": None}), MODEL)
+
+    assert _start_request(first) == {
+        "model": MODEL,
+        "audio_format": "pcm_s16le",
+        "sample_rate": 24_000,
+        "num_channels": 1,
+        "enable_endpoint_detection": True,
+    }
+    assert first[1:] == (PCM,)
+    assert second == (PCM,)
+    assert config.transform_realtime_request(_append(), MODEL) == (PCM,)
+
+
+@pytest.mark.parametrize(
+    ("event_type", "control_frame"),
+    [("input_audio_buffer.commit", '{"type":"finalize"}'), ("input_audio_buffer.end", "")],
+)
+def test_buffer_control_events_map_to_soniox_control_frames(event_type: str, control_frame: str):
+    config = SonioxRealtimeConfig()
+    config.transform_realtime_request(_append(), MODEL)
+
+    assert config.transform_realtime_request(_event(event_type), MODEL) == (control_frame,)
+
+
+@pytest.mark.parametrize("event_type", ["response.create", "input_audio_buffer.clear"])
+def test_events_with_nothing_to_send_upstream_are_dropped_without_starting_the_stream(event_type: str):
+    config = SonioxRealtimeConfig()
+
+    assert config.transform_realtime_request(_event(event_type), MODEL) == ()
+    assert _start_request(config.transform_realtime_request(_append(), MODEL))["model"] == MODEL
+
+
+def _events(config: SonioxRealtimeConfig, tokens: list[dict[str, object]]) -> tuple[OpenAIRealtimeEvents, ...]:
+    return tuple(
+        config.transform_realtime_response(_response(tokens), MODEL, MagicMock(), EMPTY_TRANSFORM_INPUT)["response"]
+    )
+
+
+def test_clear_discards_the_open_turn_and_everything_soniox_finalizes_for_it():
+    config = SonioxRealtimeConfig()
+    config.transform_realtime_request(_append(), MODEL)
+    _events(config, [_token("Hel")])
+
+    assert config.transform_realtime_request(_event("input_audio_buffer.clear"), MODEL) == ('{"type":"finalize"}',)
+    cleared = _events(config, [_token("lo"), _token("<end>"), _token("<fin>")])
+    config.transform_realtime_request(_event("input_audio_buffer.commit"), MODEL)
+    kept = _events(config, [_token("Bye"), _token("<fin>")])
+
+    assert cleared == ()
+    assert kept[-1]["transcript"] == "Bye"
+
+
+def test_clear_after_a_commit_keeps_the_committed_turn_and_drops_only_audio_sent_after_it():
+    config = SonioxRealtimeConfig()
+    config.transform_realtime_request(_append(), MODEL)
+    _events(config, [_token("One")])
+    config.transform_realtime_request(_event("input_audio_buffer.commit"), MODEL)
+    config.transform_realtime_request(_event("input_audio_buffer.clear"), MODEL)
+
+    committed = _events(config, [_token("<fin>")])
+    cleared = _events(config, [_token(" two"), _token("<fin>")])
+    later = _events(config, [_token("Three"), _token("<end>")])
+
+    assert committed[-1]["transcript"] == "One"
+    assert cleared == ()
+    assert later[-1]["transcript"] == "Three"
+
+
+@pytest.mark.parametrize(
+    "audio_input",
+    [
+        {"format": {"type": "audio/pcmu"}},
+        {"format": {"type": "audio/pcm", "rate": 24_000, "channels": 2}},
+        {"format": {"type": "audio/pcm", "rate": 0}},
+    ],
+)
+def test_unsupported_audio_formats_are_rejected_without_starting_the_stream(audio_input: dict[str, object]):
+    config = SonioxRealtimeConfig()
+
+    with pytest.raises(SonioxProtocolError):
+        config.transform_realtime_request(_session_update(audio_input), MODEL)
+    assert _start_request(config.transform_realtime_request(_append(), MODEL))["sample_rate"] == 24_000
+
+
+@pytest.mark.parametrize(
+    ("audio_input", "sample_rate"),
+    [({"format": {"type": "audio/pcm"}}, 24_000), ({"input_audio_format": "pcm16"}, 24_000)],
+)
+def test_pcm16_without_a_rate_streams_at_the_openai_default_rate(audio_input: dict[str, object], sample_rate: int):
+    config = SonioxRealtimeConfig()
+
+    frames = config.transform_realtime_request(_session_update(audio_input), MODEL)
+
+    assert _start_request(frames)["sample_rate"] == sample_rate
+
+
+def test_validate_environment_sends_the_key_as_a_bearer_header(monkeypatch):
+    monkeypatch.setenv("SONIOX_API_KEY", "env-key")
+    config = SonioxRealtimeConfig()
+
+    assert config.validate_environment({"x": "y"}, MODEL, "deployment-key") == {
+        "x": "y",
+        "Authorization": "Bearer deployment-key",
+    }
+    assert config.validate_environment({}, MODEL) == {"Authorization": "Bearer env-key"}
+
+
+def test_validate_environment_rejects_a_missing_key(monkeypatch):
+    monkeypatch.delenv("SONIOX_API_KEY", raising=False)
+
+    with pytest.raises(ValueError, match="SONIOX_API_KEY"):
+        SonioxRealtimeConfig().validate_environment({}, MODEL)
+
+
+def test_get_complete_url_defaults_to_soniox_and_opens_a_websocket_for_http_bases():
+    config = SonioxRealtimeConfig()
+
+    assert config.get_complete_url(None, MODEL) == DEFAULT_SONIOX_REALTIME_URL
+    assert config.get_complete_url("wss://proxy.example/ws", MODEL) == "wss://proxy.example/ws"
+    assert config.get_complete_url("https://proxy.example/ws?region=eu", MODEL) == "wss://proxy.example/ws?region=eu"
+    assert config.get_complete_url("http://localhost:8080/ws", MODEL) == "ws://localhost:8080/ws"
+    with pytest.raises(ValueError, match="wss://"):
+        config.get_complete_url("ftp://proxy.example/ws", MODEL)
+
+
+def test_endpoint_detection_emits_one_openai_turn_billed_by_processed_audio():
+    transformer = SonioxEventTransformer(drops_originals=False)
+
+    provisional = transformer.transform(_response([_token("Hel", is_final=False)], total_audio_proc_ms=500))
+    finals = transformer.transform(
+        _response([_token("Hello"), _token(" world"), _token(" next", is_final=False)], total_audio_proc_ms=1500)
+    )
+    endpoint = transformer.transform(_response([_token("<end>")], total_audio_proc_ms=2000))
+
+    assert _types(provisional) == ["input_audio_buffer.speech_started"]
+    item_id = provisional[0]["item_id"]
+    assert [(event["type"], event.get("delta")) for event in finals] == [
+        ("conversation.item.input_audio_transcription.delta", "Hello"),
+        ("conversation.item.input_audio_transcription.delta", " world"),
+    ]
+    assert _types(endpoint) == [
+        "input_audio_buffer.speech_stopped",
+        "conversation.item.input_audio_transcription.completed",
+    ]
+    assert {event["item_id"] for event in (*finals, *endpoint)} == {item_id}
+    assert all("speaker" not in event and "language" not in event for event in finals)
+    assert endpoint[1]["transcript"] == "Hello world"
+    assert "segments" not in endpoint[1]
+    assert endpoint[1]["usage"] == {"type": "duration", "seconds": 2.0}
+    assert transformer.take_unbilled_usage() is None
+
+
+def test_manual_finalization_completes_the_turn_without_speech_stopped_and_starts_a_new_item():
+    transformer = SonioxEventTransformer(drops_originals=False)
+
+    first = transformer.transform(_response([_token("One"), _token("<fin>")]))
+    second = transformer.transform(_response([_token("Two"), _token("<fin>")]))
+
+    assert _types(first) == [
+        "input_audio_buffer.speech_started",
+        "conversation.item.input_audio_transcription.delta",
+        "conversation.item.input_audio_transcription.completed",
+    ]
+    assert first[-1]["transcript"] == "One"
+    assert "usage" not in first[-1]
+    assert second[-1]["transcript"] == "Two"
+    assert second[0]["item_id"] != first[0]["item_id"]
+
+
+def test_a_commit_without_speech_answers_with_an_empty_turn_billed_for_the_silence():
+    config = SonioxRealtimeConfig()
+    config.transform_realtime_request(_append(), MODEL)
+    config.transform_realtime_request(_event("input_audio_buffer.commit"), MODEL)
+
+    events = tuple(
+        config.transform_realtime_response(
+            _response([_token("<fin>")], total_audio_proc_ms=2000), MODEL, MagicMock(), EMPTY_TRANSFORM_INPUT
+        )["response"]
+    )
+
+    assert _types(events) == ["conversation.item.input_audio_transcription.completed"]
+    assert events[0]["transcript"] == ""
+    assert events[0]["usage"] == {"type": "duration", "seconds": 2.0}
+    assert _events(config, [_token("Next"), _token("<end>")])[0]["item_id"] != events[0]["item_id"]
+
+
+def test_a_commit_soniox_answers_after_its_endpoint_already_ended_the_turn_adds_no_second_turn():
+    config = SonioxRealtimeConfig()
+    config.transform_realtime_request(_append(), MODEL)
+    _events(config, [_token("Hello")])
+    config.transform_realtime_request(_event("input_audio_buffer.commit"), MODEL)
+
+    events = _events(config, [_token("<end>"), _token("<fin>")])
+
+    assert [event["transcript"] for event in events if "transcript" in event] == ["Hello"]
+
+
+def test_a_server_vad_turn_answers_only_the_oldest_pending_commit():
+    config = SonioxRealtimeConfig()
+    config.transform_realtime_request(_append(), MODEL)
+    _events(config, [_token("Hello")])
+    config.transform_realtime_request(_event("input_audio_buffer.commit"), MODEL)
+    config.transform_realtime_request(_append(), MODEL)
+    config.transform_realtime_request(_event("input_audio_buffer.commit"), MODEL)
+
+    events = _events(config, [_token("<end>"), _token("<fin>"), _token("<fin>")])
+
+    assert [event["transcript"] for event in events if "transcript" in event] == ["Hello", ""]
+
+
+def test_a_clear_without_speech_still_answers_nothing():
+    config = SonioxRealtimeConfig()
+    config.transform_realtime_request(_append(), MODEL)
+    config.transform_realtime_request(_event("input_audio_buffer.clear"), MODEL)
+
+    assert _events(config, [_token("<fin>")]) == ()
+
+
+def test_translation_sessions_transcribe_only_the_translated_tokens():
+    transformer = SonioxEventTransformer(drops_originals=True)
+
+    events = transformer.transform(
+        _response(
+            [
+                _token("Hola", translation_status="original"),
+                _token("Hello", translation_status="translation"),
+                _token("<end>"),
+            ]
+        )
+    )
+
+    assert [event.get("delta") for event in events if "delta" in event] == ["Hello"]
+    assert events[-1]["transcript"] == "Hello"
+
+
+def test_translation_sessions_keep_speech_soniox_leaves_untranslated():
+    transformer = SonioxEventTransformer(drops_originals=True)
+
+    events = transformer.transform(
+        _response(
+            [_token("Hola", translation_status="none"), _token(" amigos", translation_status="none"), _token("<end>")]
+        )
+    )
+
+    assert events[-1]["transcript"] == "Hola amigos"
+
+
+def test_diarized_tokens_label_each_delta_and_split_the_completed_turn_into_speaker_segments():
+    transformer = SonioxEventTransformer(drops_originals=False)
+
+    events = transformer.transform(
+        _response(
+            [
+                _token("Hi", speaker="1", language="en"),
+                _token(" doctor", speaker="1", language="en"),
+                _token(" Salam", speaker="2", language="fa"),
+                _token(" Hi", speaker="1", language="en"),
+                _token("<end>"),
+            ]
+        )
+    )
+
+    assert [(event["delta"], event["speaker"], event["language"]) for event in events if "delta" in event] == [
+        ("Hi", "1", "en"),
+        (" doctor", "1", "en"),
+        (" Salam", "2", "fa"),
+        (" Hi", "1", "en"),
+    ]
+    assert events[-1]["transcript"] == "Hi doctor Salam Hi"
+    assert events[-1]["segments"] == (
+        {"speaker": "1", "language": "en", "text": "Hi doctor"},
+        {"speaker": "2", "language": "fa", "text": "Salam"},
+        {"speaker": "1", "language": "en", "text": "Hi"},
+    )
+
+
+def test_translation_sessions_announce_speech_on_the_first_spoken_token_before_any_translation():
+    transformer = SonioxEventTransformer(drops_originals=True)
+
+    events = transformer.transform(_response([_token("Hol", is_final=False, translation_status="original")]))
+
+    assert _types(events) == ["input_audio_buffer.speech_started"]
+
+
+def test_provider_errors_surface_as_openai_error_events():
+    transformer = SonioxEventTransformer(drops_originals=False)
+
+    events = transformer.transform(json.dumps({"error_code": 401, "error_message": "Invalid API key."}))
+
+    assert events == (
+        {"type": "error", "error": {"type": "server_error", "message": "Soniox realtime error 401: Invalid API key."}},
+    )
+
+
+def test_malformed_provider_frames_are_rejected():
+    with pytest.raises(SonioxProtocolError):
+        SonioxEventTransformer(drops_originals=False).transform('{"tokens": "nope"}')
+
+
+def test_audio_processed_after_the_last_turn_is_billed_on_session_close():
+    config = SonioxRealtimeConfig()
+    config.transform_realtime_response(
+        _response([_token("Hi"), _token("<end>")], total_audio_proc_ms=1000), MODEL, MagicMock(), EMPTY_TRANSFORM_INPUT
+    )
+    config.transform_realtime_response(
+        _response([], total_audio_proc_ms=3500), MODEL, MagicMock(), EMPTY_TRANSFORM_INPUT
+    )
+
+    assert config.unbilled_usage_on_session_close(MODEL) == {"type": "duration", "seconds": 2.5}
+    assert config.unbilled_usage_on_session_close(MODEL) is None
+
+
+def test_translation_option_switches_the_config_to_translated_transcripts():
+    config = SonioxRealtimeConfig(
+        options=SonioxRealtimeOptions(translation={"type": "one_way", "target_language": "en"})
+    )
+
+    result = config.transform_realtime_response(
+        _response([_token("Hola", translation_status="original"), _token("Hello", translation_status="translation")]),
+        MODEL,
+        MagicMock(),
+        EMPTY_TRANSFORM_INPUT,
+    )
+
+    assert [event.get("delta") for event in result["response"] if "delta" in event] == ["Hello"]
+
+
+def test_stream_time_since_the_start_request_is_billed_on_close_because_soniox_charges_the_whole_stream():
+    now = [100.0]
+    config = SonioxRealtimeConfig(clock=lambda: now[0])
+    config.wrap_backend(_RecordingBackend())
+    now[0] = 110.0
+    config.transform_realtime_request(_append(), MODEL)
+    config.transform_realtime_response(
+        _response([_token("Hi"), _token("<end>")], total_audio_proc_ms=5000), MODEL, MagicMock(), EMPTY_TRANSFORM_INPUT
+    )
+    now[0] = 130.5
+
+    assert config.unbilled_usage_on_session_close(MODEL) == {"type": "duration", "seconds": 15.5}
+    assert config.unbilled_usage_on_session_close(MODEL) is None
+
+
+def _refusal_after(processed_ms: int, error_code: int = 401) -> SonioxRealtimeConfig:
+    now = [100.0]
+    config = SonioxRealtimeConfig(clock=lambda: now[0])
+    config.transform_realtime_request(_append(), MODEL)
+    if processed_ms:
+        config.transform_realtime_response(
+            _response([_token("Hi")], total_audio_proc_ms=processed_ms), MODEL, MagicMock(), EMPTY_TRANSFORM_INPUT
+        )
+    config.transform_realtime_response(
+        json.dumps({"error_code": error_code, "error_message": "Soniox error.", "total_audio_proc_ms": processed_ms}),
+        MODEL,
+        MagicMock(),
+        EMPTY_TRANSFORM_INPUT,
+    )
+    now[0] = 103.0
+    return config
+
+
+@pytest.mark.parametrize("error_code", [400, 401, 402, 403, 408, 429, 500, 503])
+def test_an_error_before_any_audio_is_processed_is_a_refused_session_that_bills_nothing(error_code: int):
+    config = _refusal_after(processed_ms=0, error_code=error_code)
+
+    refusal = config.session_refusal()
+    assert isinstance(refusal, SonioxException)
+    assert refusal.status_code == error_code
+    assert config.unbilled_usage_on_session_close(MODEL) is None
+
+
+def test_an_error_after_audio_was_processed_keeps_the_session_a_billed_success():
+    config = _refusal_after(processed_ms=1500)
+
+    assert config.session_refusal() is None
+    assert config.unbilled_usage_on_session_close(MODEL) == {"type": "duration", "seconds": 3.0}
+
+
+def test_a_connection_that_never_started_a_stream_bills_nothing():
+    now = [100.0]
+    config = SonioxRealtimeConfig(clock=lambda: now[0])
+    config.wrap_backend(_RecordingBackend())
+    now[0] = 160.0
+
+    assert config.unbilled_usage_on_session_close(MODEL) is None
+
+
+def test_soniox_opts_out_of_deferred_setup_so_client_frames_reach_it_in_order():
+    assert SonioxRealtimeConfig().requires_session_configuration()
+
+
+def test_a_session_update_matching_the_started_stream_is_a_no_op():
+    config = SonioxRealtimeConfig()
+    config.transform_realtime_request(_append(), MODEL)
+
+    assert (
+        config.transform_realtime_request(_session_update({"format": {"type": "audio/pcm", "rate": 24_000}}), MODEL)
+        == ()
+    )
+
+
+def _stream_started_at_16khz_in_french_without_vad() -> SonioxRealtimeConfig:
+    config = SonioxRealtimeConfig()
+    config.transform_realtime_request(
+        _session_update(
+            {
+                "format": {"type": "audio/pcm", "rate": 16_000},
+                "transcription": {"language": "fr"},
+                "turn_detection": None,
+            }
+        ),
+        MODEL,
+    )
+    return config
+
+
+@pytest.mark.parametrize(
+    "follow_up",
+    [
+        {"transcription": {"language": "fr"}},
+        {"format": {"type": "audio/pcm", "rate": 16_000}},
+        {"format": {"type": "audio/pcm"}},
+        {"turn_detection": None},
+    ],
+)
+def test_a_partial_follow_up_update_that_restates_the_stream_is_a_no_op(follow_up: dict[str, object]):
+    config = _stream_started_at_16khz_in_french_without_vad()
+
+    assert config.transform_realtime_request(_session_update(follow_up), MODEL) == ()
+
+
+def test_a_follow_up_update_with_an_unsupported_encoding_is_still_rejected():
+    config = _stream_started_at_16khz_in_french_without_vad()
+
+    with pytest.raises(SonioxProtocolError, match="requires pcm16"):
+        config.transform_realtime_request(_session_update({"format": {"type": "audio/pcmu"}}), MODEL)
+
+
+@pytest.mark.parametrize(
+    "follow_up",
+    [
+        {"transcription": {"language": "de"}},
+        {"format": {"type": "audio/pcm", "rate": 24_000}},
+        {"turn_detection": {"type": "server_vad"}},
+    ],
+)
+def test_a_partial_follow_up_update_that_changes_a_sent_setting_is_rejected(follow_up: dict[str, object]):
+    config = _stream_started_at_16khz_in_french_without_vad()
+
+    with pytest.raises(SonioxProtocolError, match="once the stream has started"):
+        config.transform_realtime_request(_session_update(follow_up), MODEL)
+
+
+def test_session_created_event_advertises_the_deployment_model_for_cost_tracking():
+    config = SonioxRealtimeConfig(options=SonioxRealtimeOptions(language_hints=("de",)))
+
+    event = config.transform_session_created_event(MODEL, "session_1")
+
+    assert event["session"]["id"] == "session_1"
+    assert event["session"]["audio"]["input"]["transcription"] == {"model": MODEL, "language": "de"}
+
+
+def test_backend_session_start_is_announced_with_the_negotiated_session():
+    config = SonioxRealtimeConfig()
+    config.transform_session_created_event(MODEL, "session_1")
+    config.transform_realtime_request(
+        _session_update(
+            {
+                "format": {"type": "audio/pcm", "rate": 16_000},
+                "transcription": {"language": "it"},
+                "turn_detection": None,
+            }
+        ),
+        MODEL,
+    )
+
+    result = config.transform_realtime_response(SESSION_STARTED_FRAME, MODEL, MagicMock(), EMPTY_TRANSFORM_INPUT)
+
+    assert _types(tuple(result["response"])) == ["session.created"]
+    assert result["response"][0]["session"]["id"] == "session_1"
+    assert result["response"][0]["session"]["audio"]["input"] == {
+        "format": {"type": "audio/pcm", "rate": 16_000},
+        "transcription": {"model": MODEL, "language": "it"},
+        "turn_detection": None,
+    }
+
+
+def test_provider_config_manager_passes_only_soniox_options_from_deployment_params():
+    config = ProviderConfigManager.get_provider_realtime_config(
+        MODEL,
+        LlmProviders.SONIOX,
+        litellm_params={
+            "api_key": "secret",
+            "model": "soniox/stt-rt-v5",
+            "audio_format": "mulaw",
+            "language_hints": ["pl"],
+            "context": {"terms": ["LiteLLM"]},
+            "max_endpoint_delay_ms": 800,
+        },
+    )
+
+    assert isinstance(config, SonioxRealtimeConfig)
+    assert _start_request(config.transform_realtime_request(_append(), MODEL)) == {
+        "model": MODEL,
+        "audio_format": "pcm_s16le",
+        "sample_rate": 24_000,
+        "num_channels": 1,
+        "enable_endpoint_detection": True,
+        "language_hints": ["pl"],
+        "context": {"terms": ["LiteLLM"]},
+        "max_endpoint_delay_ms": 800,
+    }
+
+
+SEMANTIC_VAD: Final = {"turn_detection": {"type": "semantic_vad", "eagerness": "high"}}
+
+
+def _soniox_config(drop_params: bool) -> SonioxRealtimeConfig:
+    config = ProviderConfigManager.get_provider_realtime_config(MODEL, LlmProviders.SONIOX, drop_params=drop_params)
+    assert isinstance(config, SonioxRealtimeConfig)
+    return config
+
+
+def test_unsupported_turn_detection_is_rejected_without_drop_params():
+    config = _soniox_config(drop_params=False)
+
+    with pytest.raises(SonioxProtocolError, match="Set drop_params to use server_vad instead"):
+        config.transform_realtime_request(_session_update(SEMANTIC_VAD), MODEL)
+
+
+def test_unsupported_turn_detection_falls_back_to_endpoint_detection_with_drop_params():
+    config = _soniox_config(drop_params=True)
+
+    frames = config.transform_realtime_request(_session_update(SEMANTIC_VAD), MODEL)
+
+    assert _start_request(frames)["enable_endpoint_detection"] is True
+    assert config.transform_realtime_request(_session_update(SEMANTIC_VAD), MODEL) == ()
+
+
+def test_a_follow_up_with_unsupported_turn_detection_is_rejected_without_drop_params():
+    config = _soniox_config(drop_params=False)
+    config.transform_realtime_request(_append(), MODEL)
+
+    with pytest.raises(SonioxProtocolError, match="Set drop_params to use server_vad instead"):
+        config.transform_realtime_request(_session_update(SEMANTIC_VAD), MODEL)
+
+
+class _RecordingBackend:
+    def __init__(self) -> None:
+        self.sent: Final[list[str | bytes]] = []
+        self.upstream: Final[asyncio.Queue[str | Exception]] = asyncio.Queue()
+        self.entered = False
+        self.exited = False
+        self.closed = False
+
+    async def __aenter__(self) -> "_RecordingBackend":
+        self.entered = True
+        return self
+
+    async def __aexit__(self, exc_type, exc_value, traceback) -> None:
+        self.exited = True
+
+    async def send(self, message: str | bytes) -> None:
+        self.sent.append(message)
+
+    async def recv(self, decode: bool | None = None) -> str | bytes:
+        frame = await self.upstream.get()
+        if isinstance(frame, Exception):
+            raise frame
+        return frame
+
+    async def close(self) -> None:
+        self.closed = True
+
+
+class _BrokenBackend(_RecordingBackend):
+    async def send(self, message: str | bytes) -> None:
+        raise ConnectionError("upstream gone")
+
+
+@pytest.mark.asyncio
+async def test_keepalive_is_sent_only_while_the_client_is_idle_and_stops_after_end_of_stream():
+    now = [0.0]
+    ticks: asyncio.Queue[float] = asyncio.Queue()
+
+    async def sleep(_: float) -> None:
+        now[0] = await ticks.get()
+
+    async def tick(at: float) -> None:
+        await ticks.put(at)
+        for _ in range(5):
+            await asyncio.sleep(0)
+
+    inner = _RecordingBackend()
+    backend = SonioxRealtimeBackend(inner, keepalive_interval=5.0, clock=lambda: now[0], sleep=sleep)
+
+    async with backend:
+        await tick(5.0)
+        assert inner.sent == ['{"type":"keepalive"}']
+        now[0] = 7.0
+        await backend.send(PCM)
+        await tick(10.0)
+        assert inner.sent == ['{"type":"keepalive"}', PCM]
+        await backend.send("")
+        await tick(30.0)
+        assert inner.sent == ['{"type":"keepalive"}', PCM, ""]
+
+    assert inner.entered and inner.exited
+
+
+@pytest.mark.asyncio
+async def test_backend_announces_the_session_once_the_start_request_is_sent_while_relaying_upstream_frames():
+    inner = _RecordingBackend()
+    backend = SonioxRealtimeBackend(inner)
+
+    async with backend:
+        pending = asyncio.create_task(backend.recv())
+        await backend.send('{"type":"keepalive"}')
+        await asyncio.sleep(0)
+        assert not pending.done()
+        await backend.send('{"model":"stt-rt-v5"}')
+        assert await pending == SESSION_STARTED_FRAME
+        await backend.send('{"model":"ignored-second-config"}')
+        await inner.upstream.put('{"tokens":[]}')
+        assert await backend.recv() == '{"tokens":[]}'
+
+
+@pytest.mark.asyncio
+async def test_backend_relays_upstream_frames_that_arrive_before_the_session_starts():
+    inner = _RecordingBackend()
+
+    async with SonioxRealtimeBackend(inner) as backend:
+        await inner.upstream.put('{"error_code":401,"error_message":"Invalid API key."}')
+
+        assert await backend.recv() == '{"error_code":401,"error_message":"Invalid API key."}'
+
+
+@pytest.mark.asyncio
+async def test_a_cancelled_read_loses_no_frames_and_upstream_errors_reach_the_reader():
+    inner = _RecordingBackend()
+
+    async with SonioxRealtimeBackend(inner) as backend:
+        cancelled = asyncio.create_task(backend.recv())
+        await asyncio.sleep(0)
+        cancelled.cancel()
+        await asyncio.gather(cancelled, return_exceptions=True)
+        await inner.upstream.put('{"tokens":[]}')
+        await inner.upstream.put(ConnectionError("upstream closed"))
+
+        assert await backend.recv() == '{"tokens":[]}'
+        with pytest.raises(ConnectionError, match="upstream closed"):
+            await backend.recv()
+
+
+@pytest.mark.asyncio
+async def test_closing_the_backend_cancels_pending_reads_and_closes_upstream():
+    inner = _RecordingBackend()
+    backend = SonioxRealtimeBackend(inner)
+    await backend.__aenter__()
+    pending = asyncio.create_task(backend.recv())
+    await backend.send('{"model":"stt-rt-v5"}')
+    assert await pending == SESSION_STARTED_FRAME
+
+    await backend.close()
+    await inner.upstream.put('{"tokens":[]}')
+    await asyncio.sleep(0)
+
+    assert inner.closed
+    assert inner.upstream.qsize() == 1
+
+
+@pytest.mark.asyncio
+async def test_keepalive_stops_after_the_upstream_rejects_a_send():
+    sleeps: list[float] = []
+
+    async def sleep(seconds: float) -> None:
+        sleeps.append(seconds)
+        await asyncio.sleep(0)
+
+    async with SonioxRealtimeBackend(_BrokenBackend(), keepalive_interval=0.0, sleep=sleep):
+        for _ in range(5):
+            await asyncio.sleep(0)
+
+    assert sleeps == [0.0]

tests/unit/llms/test_get_complete_url_trailing_slash.py

@@ -143,6 +143,7 @@ API_BASE_IS_THE_FULL_URL: Final[Mapping[str, str]] = MappingProxyType(
             "duckduckgo.search.transformation.DuckDuckGoSearchConfig",
             "google_pse.search.transformation.GooglePSESearchConfig",
             "searchapi.search.transformation.SearchAPIConfig",
+            "soniox.realtime.transformation.SonioxRealtimeConfig",
             "tinyfish.search.transformation.TinyfishSearchConfig",
             "vertex_ai.text_to_speech.transformation.VertexAITextToSpeechConfig",
             "vertex_ai.vertex_ai_partner_models.anthropic.messages.transformation."

tests/unit/llms/vertex_ai/vertex_ai_partner_models/gemma/test_vertex_ai_gemma_global_endpoint.py

@@ -13,6 +13,7 @@ These tests verify that:
 
 import json
 import os
+from typing import Final
 from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
@@ -422,3 +423,52 @@ async def test_vertex_ai_gemma_vision_passthrough():
         assert isinstance(content, list), f"Expected list content, got: {content}"
         image_parts = [p for p in content if p.get("type") == "image_url"]
         assert image_parts, f"No image_url part in forwarded message content: {content}"
+
+
+@pytest.mark.asyncio
+async def test_vertex_ai_gemma_pdf_file_part_is_sent_as_image_url() -> None:
+    pdf_data_url: Final = "data:application/pdf;base64,JVBERi0xLjQKJcfsj6IKMSAwIG9iago8PD4+CmVuZG9iagp0cmFpbGVyCjw8Pj4KJSVFT0YK"
+    messages: Final = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "text", "text": "What does the PDF say?"},
+                {"type": "file", "file": {"file_data": pdf_data_url, "filename": "report.pdf"}},
+            ],
+        }
+    ]
+
+    mock_response: Final = MagicMock()
+    mock_response.status_code = 200
+    mock_response.headers = {}
+    mock_response.json.return_value = _MOCK_RESPONSE_JSON
+
+    mock_vertexai: Final = MagicMock()
+    mock_vertexai.preview = MagicMock()
+
+    with (
+        patch("litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler") as mock_http_handler,
+        patch(
+            "litellm.llms.vertex_ai.vertex_ai_partner_models.main.VertexAIPartnerModels._ensure_access_token",
+            return_value=("fake-token", "test-project"),
+        ),
+        patch.dict(
+            "sys.modules",
+            {"vertexai": mock_vertexai, "vertexai.preview": mock_vertexai.preview},
+        ),
+        patch.dict(litellm.model_cost, _GEMMA_MODEL_COST_ENTRY, clear=False),
+    ):
+        mock_http_handler.return_value.post = AsyncMock(return_value=mock_response)
+
+        await litellm.acompletion(
+            model="vertex_ai/google/gemma-4-26b-a4b-it-maas",
+            messages=messages,
+            vertex_ai_project="test-project",
+        )
+
+        body: Final = json.loads(mock_http_handler.return_value.post.call_args.kwargs["data"])
+        user_msg: Final = next(m for m in body["messages"] if m["role"] == "user")
+        assert user_msg["content"] == [
+            {"type": "text", "text": "What does the PDF say?"},
+            {"type": "image_url", "image_url": {"url": pdf_data_url}},
+        ]

tests/unit/proxy/common_utils/test_callback_utils.py

@@ -49,6 +49,19 @@ def test_get_remaining_tokens_and_requests_from_request_data():
     }
 
 
+@pytest.mark.parametrize("client_model_group", [7, ["embed-only"], {"name": "embed-only"}])
+def test_remaining_limit_headers_ignore_a_model_group_that_is_not_a_string(client_model_group: object):
+    casedata: Final = {
+        "metadata": {
+            "model_group": client_model_group,
+            f"litellm-key-remaining-requests-{client_model_group}": 100,
+            f"litellm-key-remaining-tokens-{client_model_group}": 200,
+        }
+    }
+
+    assert get_remaining_tokens_and_requests_from_request_data(casedata) == {}
+
+
 @patch(
     "litellm.proxy.common_utils.callback_utils.CustomLogger.get_callback_env_vars",
     return_value=["API_KEY", "MISSING_VAR"],

tests/unit/proxy/guardrails/test_prompt_security_guardrails.py

@@ -1,6 +1,7 @@
 import asyncio
 import base64
 from collections.abc import Mapping, Sequence
+from typing import Final
 from unittest.mock import AsyncMock, patch
 
 import pytest
@@ -8,6 +9,7 @@ from fastapi.exceptions import HTTPException
 from httpx import ReadTimeout, Request, Response
 
 import litellm
+from litellm.llms.anthropic.chat.guardrail_translation.handler import AnthropicMessagesHandler
 from litellm.proxy._types import UserAPIKeyAuth
 from litellm.proxy.guardrails.guardrail_hooks.prompt_security.prompt_security import (
     PromptSecurityGuardrail,
@@ -1247,3 +1249,54 @@ async def test_check_tool_results_enabled(monkeypatch: pytest.MonkeyPatch):
 
     assert "indirect_prompt_injection" in str(excinfo.value.detail)
 
+
+@pytest.mark.asyncio
+async def test_messages_api_pdf_reaches_file_sanitization_and_is_blocked() -> None:
+    sanitize_url: Final = "https://test.prompt.security/api/sanitizeFile"
+    upload: Final = AsyncMock(
+        return_value=Response(200, json={"jobId": "pdf-job"}, request=Request("POST", sanitize_url))
+    )
+    poll: Final = AsyncMock(
+        return_value=Response(
+            200,
+            json={"status": "done", "content": "", "metadata": {"action": "block", "violations": ["Malicious File"]}},
+            request=Request("GET", sanitize_url),
+        )
+    )
+    guardrail: Final = PromptSecurityGuardrail(
+        api_key="test-key",
+        api_base="https://test.prompt.security",
+        guardrail_name="test-guard",
+        event_hook="pre_call",
+        default_on=True,
+    )
+    guardrail.poll_interval = 0
+    pdf: Final = b"%PDF-1.4 pelican"
+    request: Final = {
+        "model": "gpt-5.6",
+        "max_tokens": 64,
+        "messages": [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "document",
+                        "source": {
+                            "type": "base64",
+                            "media_type": "application/pdf",
+                            "data": base64.b64encode(pdf).decode(),
+                        },
+                    },
+                    {"type": "text", "text": "Summarize this PDF"},
+                ],
+            }
+        ],
+    }
+
+    with patch.object(guardrail.async_handler, "post", upload), patch.object(guardrail.async_handler, "get", poll):
+        with pytest.raises(HTTPException) as blocked:
+            await AnthropicMessagesHandler().process_input_messages(request, guardrail)
+
+    assert blocked.value.status_code == 400
+    assert "Malicious File" in str(blocked.value.detail)
+    assert upload.call_args.kwargs["files"]["file"][1] == pdf

tests/unit/proxy/management_endpoints/test_model_management_endpoints.py

@@ -3617,6 +3617,33 @@ class TestDeleteTeamModels:
         assert any(e[0] == "delete_many" for e in prisma.events)
 
 
+@pytest.mark.parametrize(
+    ("model_info_patch", "expected_gate"),
+    [
+        pytest.param({"tpm": 5}, True, id="unrelated-field"),
+        pytest.param({"supported_endpoints": ["/v1/embeddings", "/v1/rerank"]}, True, id="endpoint-list-only"),
+        pytest.param({"enforce_supported_endpoints": False}, False, id="explicit-false"),
+    ],
+)
+def test_model_info_patch_changes_the_endpoint_gate_only_when_it_sends_the_flag(
+    model_info_patch: dict[str, object], expected_gate: bool
+):
+    from litellm.proxy.management_endpoints.model_management_endpoints import update_db_model
+
+    result: Final = update_db_model(
+        db_model=Deployment(
+            model_name="embedding-only",
+            litellm_params=LiteLLM_Params(model="openai/text-embedding-3-small"),
+            model_info=ModelInfo(
+                id="embedding-only", enforce_supported_endpoints=True, supported_endpoints=["/v1/embeddings"]
+            ),
+        ),
+        updated_patch=updateDeployment.model_validate({"model_info": model_info_patch}),
+    )
+
+    assert json.loads(result["model_info"]).get("enforce_supported_endpoints") is expected_gate
+
+
 def _build_db_model_for_blocked_test():
     from litellm.types.router import Deployment, LiteLLM_Params, ModelInfo
 

tests/unit/proxy/public_endpoints/test_public_endpoints.py

@@ -566,6 +566,7 @@ ADD_MODEL_UNLISTED_PROVIDERS: Final = frozenset(
         "mongodb",
         "nano-gpt",
         "neosantara",
+        "opensearch",
         "parasail",
         "pinstripes",
         "poe",