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feat(voyage): add voyage-4 family + voyage-context-4, fix contextual list[str] input
- Add voyage-4, voyage-4-large, voyage-4-lite, voyage-4-nano (/bin/bash) and voyage-context-4 to the cost/context map (both root and backup json). - Contextual embeddings: normalize flat list[str] input. Voyage rejects a flat list[str] unless input_type='query', so wrap each string as its own single-chunk document otherwise; single str -> [[str]]; list[list[str]] passes through. Keeps list[str] when input_type='query'. - Add tests for input normalization and voyage-4 family pricing.
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fzowl
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4 changed files with 197 additions and 1 deletions
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@ -106,11 +106,42 @@ class VoyageContextualEmbeddingConfig(BaseEmbeddingConfig):
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headers: dict,
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) -> dict:
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return {
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"inputs": input,
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"inputs": self._transform_contextual_inputs(input, optional_params),
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"model": model,
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**optional_params,
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}
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@staticmethod
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def _transform_contextual_inputs(
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input: Union[AllEmbeddingInputValues, List[List[str]]],
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optional_params: dict,
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) -> List[List[str]]:
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"""
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Voyage's contextualized embeddings API expects ``inputs`` to be a
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``list[list[str]]`` (each inner list is a document made of chunks that
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share context).
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It also accepts a flat ``list[str]`` - but only when
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``input_type == "query"``. In every other case a flat list must be
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wrapped so each string becomes its own single-chunk document, otherwise
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the API rejects the request with a 400.
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Reference: https://docs.voyageai.com/reference/contextualized-embeddings-api
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"""
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# Single string -> one document with a single chunk.
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if isinstance(input, str):
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return [[input]]
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# Flat list[str]: keep as list[str] when the API allows it
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# (input_type="query"), otherwise wrap each string as its own document.
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if isinstance(input, list) and all(isinstance(i, str) for i in input):
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if optional_params.get("input_type") == "query":
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return input # type: ignore[return-value]
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return [[i] for i in input]
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# Already list[list[str]] (or another shape) -> pass through unchanged.
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return input # type: ignore[return-value]
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def transform_embedding_response(
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self,
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model: str,
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@ -27345,6 +27345,38 @@
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"mode": "embedding",
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"output_cost_per_token": 0.0
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},
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"voyage/voyage-4": {
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"input_cost_per_token": 6e-08,
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"litellm_provider": "voyage",
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"max_input_tokens": 32000,
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"max_tokens": 32000,
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"mode": "embedding",
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"output_cost_per_token": 0.0
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},
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"voyage/voyage-4-large": {
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"input_cost_per_token": 1.2e-07,
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"litellm_provider": "voyage",
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"max_input_tokens": 32000,
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"max_tokens": 32000,
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"mode": "embedding",
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"output_cost_per_token": 0.0
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},
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"voyage/voyage-4-lite": {
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"input_cost_per_token": 2e-08,
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"litellm_provider": "voyage",
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"max_input_tokens": 32000,
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"max_tokens": 32000,
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"mode": "embedding",
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"output_cost_per_token": 0.0
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},
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"voyage/voyage-4-nano": {
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"input_cost_per_token": 0.0,
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"litellm_provider": "voyage",
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"max_input_tokens": 32000,
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"max_tokens": 32000,
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"mode": "embedding",
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"output_cost_per_token": 0.0
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},
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"voyage/voyage-code-2": {
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"input_cost_per_token": 1.2e-07,
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"litellm_provider": "voyage",
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@ -27369,6 +27401,14 @@
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"mode": "embedding",
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"output_cost_per_token": 0.0
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},
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"voyage/voyage-context-4": {
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"input_cost_per_token": 1.2e-07,
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"litellm_provider": "voyage",
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"max_input_tokens": 120000,
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"max_tokens": 120000,
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"mode": "embedding",
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"output_cost_per_token": 0.0
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},
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"voyage/voyage-finance-2": {
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"input_cost_per_token": 1.2e-07,
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"litellm_provider": "voyage",
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@ -27345,6 +27345,38 @@
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"mode": "embedding",
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"output_cost_per_token": 0.0
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},
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"voyage/voyage-4": {
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"input_cost_per_token": 6e-08,
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"litellm_provider": "voyage",
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"max_input_tokens": 32000,
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"max_tokens": 32000,
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"mode": "embedding",
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"output_cost_per_token": 0.0
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},
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"voyage/voyage-4-large": {
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"input_cost_per_token": 1.2e-07,
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"litellm_provider": "voyage",
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"max_input_tokens": 32000,
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"max_tokens": 32000,
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"mode": "embedding",
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"output_cost_per_token": 0.0
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},
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"voyage/voyage-4-lite": {
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"input_cost_per_token": 2e-08,
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"litellm_provider": "voyage",
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"max_input_tokens": 32000,
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"max_tokens": 32000,
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"mode": "embedding",
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"output_cost_per_token": 0.0
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},
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"voyage/voyage-4-nano": {
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"input_cost_per_token": 0.0,
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"litellm_provider": "voyage",
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"max_input_tokens": 32000,
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"max_tokens": 32000,
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"mode": "embedding",
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"output_cost_per_token": 0.0
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},
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"voyage/voyage-code-2": {
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"input_cost_per_token": 1.2e-07,
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"litellm_provider": "voyage",
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@ -27369,6 +27401,14 @@
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"mode": "embedding",
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"output_cost_per_token": 0.0
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},
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"voyage/voyage-context-4": {
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"input_cost_per_token": 1.2e-07,
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"litellm_provider": "voyage",
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"max_input_tokens": 120000,
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"max_tokens": 120000,
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"mode": "embedding",
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"output_cost_per_token": 0.0
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},
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"voyage/voyage-finance-2": {
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"input_cost_per_token": 1.2e-07,
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"litellm_provider": "voyage",
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@ -71,6 +71,26 @@ class TestVoyageAI(BaseLLMEmbeddingTest):
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assert response.usage.total_tokens > 0
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@pytest.mark.parametrize(
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"model, expected_input_cost",
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[
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("voyage/voyage-4", 6e-08),
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("voyage/voyage-4-large", 1.2e-07),
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("voyage/voyage-4-lite", 2e-08),
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("voyage/voyage-4-nano", 0.0),
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("voyage/voyage-context-4", 1.2e-07),
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],
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)
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def test_voyage_4_family_pricing_registered(model, expected_input_cost):
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"""The voyage-4 family and voyage-context-4 must be in the cost map."""
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os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
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litellm.model_cost = litellm.get_model_cost_map(url="")
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info = litellm.get_model_info(model=model)
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assert info["litellm_provider"] == "voyage"
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assert info["mode"] == "embedding"
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assert info["input_cost_per_token"] == expected_input_cost
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def test_voyage_ai_embedding_extra_params():
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"""Test Voyage AI embedding with extra parameters"""
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try:
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@ -142,6 +162,7 @@ class TestVoyageContextualEmbeddings:
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# Test contextual model detection
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assert config.is_contextualized_embeddings("voyage-context-3") is True
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assert config.is_contextualized_embeddings("voyage-context-4") is True
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assert config.is_contextualized_embeddings("voyage-context-2") is True
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assert config.is_contextualized_embeddings("context-model") is True
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@ -198,6 +219,70 @@ class TestVoyageContextualEmbeddings:
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assert transformed["model"] == "voyage-context-3"
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assert transformed["encoding_format"] == "float"
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def test_contextual_flat_list_str_is_wrapped(self):
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"""Flat list[str] without input_type must be wrapped to list[list[str]]."""
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from litellm.llms.voyage.embedding.transformation_contextual import (
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VoyageContextualEmbeddingConfig,
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)
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config = VoyageContextualEmbeddingConfig()
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transformed = config.transform_embedding_request(
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"voyage-context-4", ["Hello", "world"], {}, {}
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)
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# Voyage rejects a flat list[str] unless input_type="query", so each
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# string becomes its own single-chunk document.
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assert transformed["inputs"] == [["Hello"], ["world"]]
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assert transformed["model"] == "voyage-context-4"
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def test_contextual_flat_list_str_query_stays_flat(self):
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"""Flat list[str] with input_type='query' is sent as-is (list[str])."""
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from litellm.llms.voyage.embedding.transformation_contextual import (
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VoyageContextualEmbeddingConfig,
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)
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config = VoyageContextualEmbeddingConfig()
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transformed = config.transform_embedding_request(
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"voyage-context-4",
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["Hello", "world"],
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{"input_type": "query"},
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{},
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)
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assert transformed["inputs"] == ["Hello", "world"]
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assert transformed["input_type"] == "query"
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def test_contextual_single_string_is_wrapped(self):
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"""A single string is wrapped to a single document with a single chunk."""
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from litellm.llms.voyage.embedding.transformation_contextual import (
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VoyageContextualEmbeddingConfig,
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)
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config = VoyageContextualEmbeddingConfig()
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transformed = config.transform_embedding_request(
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"voyage-context-4", "Hello", {}, {}
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)
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assert transformed["inputs"] == [["Hello"]]
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def test_contextual_nested_input_passthrough(self):
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"""Already-nested list[list[str]] input is passed through unchanged."""
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from litellm.llms.voyage.embedding.transformation_contextual import (
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VoyageContextualEmbeddingConfig,
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)
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config = VoyageContextualEmbeddingConfig()
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nested = [["Hello", "world"], ["Test"]]
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transformed = config.transform_embedding_request(
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"voyage-context-4", nested, {}, {}
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)
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assert transformed["inputs"] == nested
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def test_contextual_embedding_response_transformation(self):
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"""Test response transformation for contextual embeddings"""
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from litellm.llms.voyage.embedding.transformation_contextual import (
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