mirror of
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feat: (re-)enable Databricks inference adapter (#3500)
# What does this PR do? add/enable the Databricks inference adapter Databricks inference adapter was broken, closes #3486 - remove deprecated completion / chat_completion endpoints - enable dynamic model listing w/o refresh, listing is not async - use SecretStr instead of str for token - backward incompatible change: for consistency with databricks docs, env DATABRICKS_URL -> DATABRICKS_HOST and DATABRICKS_API_TOKEN -> DATABRICKS_TOKEN - databricks urls are custom per user/org, add special recorder handling for databricks urls - add integration test --setup databricks - enable chat completions tests - enable embeddings tests - disable n > 1 tests - disable embeddings base64 tests - disable embeddings dimensions tests note: reasoning models, e.g. gpt oss, fail because databricks has a custom, incompatible response format ## Test Plan ci and ``` ./scripts/integration-tests.sh --stack-config server:ci-tests --setup databricks --subdirs inference --pattern openai ``` note: databricks needs to be manually added to the ci-tests distro for replay testing
This commit is contained in:
Matthew Farrellee
GitHub
parent
9406a998b9
commit
d07ebce4d9
24 changed files with 11649 additions and 101 deletions
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@ -9,13 +9,13 @@ Databricks inference provider for running models on Databricks' unified analytic
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| Field | Type | Required | Default | Description |
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|-------|------|----------|---------|-------------|
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| `url` | `<class 'str'>` | No | | The URL for the Databricks model serving endpoint |
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| `api_token` | `<class 'str'>` | No | | The Databricks API token |
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| `api_token` | `<class 'pydantic.types.SecretStr'>` | No | | The Databricks API token |
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## Sample Configuration
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```yaml
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url: ${env.DATABRICKS_URL:=}
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api_token: ${env.DATABRICKS_API_TOKEN:=}
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url: ${env.DATABRICKS_HOST:=}
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api_token: ${env.DATABRICKS_TOKEN:=}
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```
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@ -142,7 +142,7 @@ def available_providers() -> list[ProviderSpec]:
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api=Api.inference,
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adapter_type="databricks",
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provider_type="remote::databricks",
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pip_packages=[],
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pip_packages=["databricks-sdk"],
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module="llama_stack.providers.remote.inference.databricks",
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config_class="llama_stack.providers.remote.inference.databricks.DatabricksImplConfig",
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description="Databricks inference provider for running models on Databricks' unified analytics platform.",
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@ -5,10 +5,11 @@
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# the root directory of this source tree.
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from .config import DatabricksImplConfig
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from .databricks import DatabricksInferenceAdapter
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async def get_adapter_impl(config: DatabricksImplConfig, _deps):
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from .databricks import DatabricksInferenceAdapter
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assert isinstance(config, DatabricksImplConfig), f"Unexpected config type: {type(config)}"
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impl = DatabricksInferenceAdapter(config)
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await impl.initialize()
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@ -6,7 +6,7 @@
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from typing import Any
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from pydantic import BaseModel, Field
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from pydantic import BaseModel, Field, SecretStr
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from llama_stack.schema_utils import json_schema_type
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@ -17,16 +17,16 @@ class DatabricksImplConfig(BaseModel):
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default=None,
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description="The URL for the Databricks model serving endpoint",
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)
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api_token: str = Field(
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default=None,
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api_token: SecretStr = Field(
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default=SecretStr(None),
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description="The Databricks API token",
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)
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@classmethod
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def sample_run_config(
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cls,
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url: str = "${env.DATABRICKS_URL:=}",
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api_token: str = "${env.DATABRICKS_API_TOKEN:=}",
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url: str = "${env.DATABRICKS_HOST:=}",
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api_token: str = "${env.DATABRICKS_TOKEN:=}",
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**kwargs: Any,
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) -> dict[str, Any]:
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return {
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@ -4,23 +4,26 @@
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from collections.abc import AsyncGenerator
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from collections.abc import AsyncIterator
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from typing import Any
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from openai import OpenAI
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from databricks.sdk import WorkspaceClient
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from llama_stack.apis.common.content_types import (
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InterleavedContent,
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InterleavedContentItem,
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)
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from llama_stack.apis.inference import (
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ChatCompletionRequest,
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ChatCompletionResponse,
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ChatCompletionResponseStreamChunk,
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CompletionResponse,
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CompletionResponseStreamChunk,
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EmbeddingsResponse,
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EmbeddingTaskType,
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Inference,
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LogProbConfig,
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Message,
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OpenAIEmbeddingsResponse,
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OpenAICompletion,
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ResponseFormat,
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SamplingParams,
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TextTruncation,
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@ -29,49 +32,50 @@ from llama_stack.apis.inference import (
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ToolDefinition,
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ToolPromptFormat,
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)
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from llama_stack.models.llama.sku_types import CoreModelId
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from llama_stack.apis.models import Model, ModelType
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from llama_stack.log import get_logger
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from llama_stack.providers.utils.inference.model_registry import (
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ModelRegistryHelper,
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build_hf_repo_model_entry,
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)
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from llama_stack.providers.utils.inference.openai_compat import (
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OpenAIChatCompletionToLlamaStackMixin,
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OpenAICompletionToLlamaStackMixin,
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get_sampling_options,
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process_chat_completion_response,
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process_chat_completion_stream_response,
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)
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from llama_stack.providers.utils.inference.prompt_adapter import (
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chat_completion_request_to_prompt,
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ProviderModelEntry,
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)
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from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
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from .config import DatabricksImplConfig
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SAFETY_MODELS_ENTRIES = []
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logger = get_logger(name=__name__, category="inference::databricks")
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# https://docs.databricks.com/aws/en/machine-learning/model-serving/foundation-model-overview
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MODEL_ENTRIES = [
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build_hf_repo_model_entry(
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"databricks-meta-llama-3-1-70b-instruct",
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CoreModelId.llama3_1_70b_instruct.value,
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# source: https://docs.databricks.com/aws/en/machine-learning/foundation-model-apis/supported-models
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EMBEDDING_MODEL_ENTRIES = {
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"databricks-gte-large-en": ProviderModelEntry(
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provider_model_id="databricks-gte-large-en",
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metadata={
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"embedding_dimension": 1024,
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"context_length": 8192,
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},
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),
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build_hf_repo_model_entry(
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"databricks-meta-llama-3-1-405b-instruct",
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CoreModelId.llama3_1_405b_instruct.value,
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"databricks-bge-large-en": ProviderModelEntry(
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provider_model_id="databricks-bge-large-en",
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metadata={
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"embedding_dimension": 1024,
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"context_length": 512,
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},
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),
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] + SAFETY_MODELS_ENTRIES
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}
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class DatabricksInferenceAdapter(
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ModelRegistryHelper,
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OpenAIMixin,
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Inference,
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OpenAIChatCompletionToLlamaStackMixin,
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OpenAICompletionToLlamaStackMixin,
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):
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def __init__(self, config: DatabricksImplConfig) -> None:
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ModelRegistryHelper.__init__(self, model_entries=MODEL_ENTRIES)
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self.config = config
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def get_api_key(self) -> str:
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return self.config.api_token.get_secret_value()
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def get_base_url(self) -> str:
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return f"{self.config.url}/serving-endpoints"
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async def initialize(self) -> None:
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return
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@ -80,72 +84,54 @@ class DatabricksInferenceAdapter(
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async def completion(
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self,
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model: str,
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model_id: str,
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content: InterleavedContent,
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sampling_params: SamplingParams | None = None,
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response_format: ResponseFormat | None = None,
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stream: bool | None = False,
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logprobs: LogProbConfig | None = None,
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) -> AsyncGenerator:
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) -> CompletionResponse | AsyncIterator[CompletionResponseStreamChunk]:
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raise NotImplementedError()
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async def openai_completion(
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self,
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model: str,
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prompt: str | list[str] | list[int] | list[list[int]],
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best_of: int | None = None,
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echo: bool | None = None,
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frequency_penalty: float | None = None,
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logit_bias: dict[str, float] | None = None,
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logprobs: bool | None = None,
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max_tokens: int | None = None,
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n: int | None = None,
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presence_penalty: float | None = None,
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seed: int | None = None,
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stop: str | list[str] | None = None,
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stream: bool | None = None,
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stream_options: dict[str, Any] | None = None,
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temperature: float | None = None,
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top_p: float | None = None,
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user: str | None = None,
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guided_choice: list[str] | None = None,
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prompt_logprobs: int | None = None,
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suffix: str | None = None,
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) -> OpenAICompletion:
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raise NotImplementedError()
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async def chat_completion(
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self,
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model: str,
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model_id: str,
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messages: list[Message],
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sampling_params: SamplingParams | None = None,
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response_format: ResponseFormat | None = None,
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tools: list[ToolDefinition] | None = None,
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tool_choice: ToolChoice | None = ToolChoice.auto,
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tool_prompt_format: ToolPromptFormat | None = None,
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response_format: ResponseFormat | None = None,
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stream: bool | None = False,
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logprobs: LogProbConfig | None = None,
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tool_config: ToolConfig | None = None,
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) -> AsyncGenerator:
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if sampling_params is None:
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sampling_params = SamplingParams()
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request = ChatCompletionRequest(
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model=model,
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messages=messages,
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sampling_params=sampling_params,
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tools=tools or [],
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stream=stream,
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logprobs=logprobs,
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tool_config=tool_config,
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)
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client = OpenAI(base_url=self.config.url, api_key=self.config.api_token)
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if stream:
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return self._stream_chat_completion(request, client)
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else:
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return await self._nonstream_chat_completion(request, client)
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async def _nonstream_chat_completion(
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self, request: ChatCompletionRequest, client: OpenAI
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) -> ChatCompletionResponse:
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params = self._get_params(request)
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r = client.completions.create(**params)
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return process_chat_completion_response(r, request)
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async def _stream_chat_completion(self, request: ChatCompletionRequest, client: OpenAI) -> AsyncGenerator:
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params = self._get_params(request)
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async def _to_async_generator():
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s = client.completions.create(**params)
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for chunk in s:
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yield chunk
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stream = _to_async_generator()
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async for chunk in process_chat_completion_stream_response(stream, request):
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yield chunk
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def _get_params(self, request: ChatCompletionRequest) -> dict:
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return {
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"model": request.model,
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"prompt": chat_completion_request_to_prompt(request, self.get_llama_model(request.model)),
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"stream": request.stream,
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**get_sampling_options(request.sampling_params),
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}
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) -> ChatCompletionResponse | AsyncIterator[ChatCompletionResponseStreamChunk]:
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raise NotImplementedError()
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async def embeddings(
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self,
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@ -157,12 +143,39 @@ class DatabricksInferenceAdapter(
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) -> EmbeddingsResponse:
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raise NotImplementedError()
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async def openai_embeddings(
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self,
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model: str,
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input: str | list[str],
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encoding_format: str | None = "float",
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dimensions: int | None = None,
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user: str | None = None,
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) -> OpenAIEmbeddingsResponse:
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raise NotImplementedError()
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async def list_models(self) -> list[Model] | None:
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self._model_cache = {} # from OpenAIMixin
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ws_client = WorkspaceClient(host=self.config.url, token=self.get_api_key()) # TODO: this is not async
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endpoints = ws_client.serving_endpoints.list()
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for endpoint in endpoints:
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model = Model(
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provider_id=self.__provider_id__,
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provider_resource_id=endpoint.name,
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identifier=endpoint.name,
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)
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if endpoint.task == "llm/v1/chat":
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model.model_type = ModelType.llm # this is redundant, but informative
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elif endpoint.task == "llm/v1/embeddings":
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if endpoint.name not in EMBEDDING_MODEL_ENTRIES:
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logger.warning(f"No metadata information available for embedding model {endpoint.name}, skipping.")
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continue
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model.model_type = ModelType.embedding
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model.metadata = EMBEDDING_MODEL_ENTRIES[endpoint.name].metadata
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else:
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logger.warning(f"Unknown model type, skipping: {endpoint}")
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continue
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self._model_cache[endpoint.name] = model
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return list(self._model_cache.values())
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async def register_model(self, model: Model) -> Model:
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if not await self.check_model_availability(model.provider_resource_id):
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raise ValueError(f"Model {model.provider_resource_id} is not available in Databricks workspace.")
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return model
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async def unregister_model(self, model_id: str) -> None:
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pass
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async def should_refresh_models(self) -> bool:
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return False
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@ -267,6 +267,10 @@ async def _patched_inference_method(original_method, self, client_type, endpoint
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raise ValueError(f"Unknown client type: {client_type}")
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url = base_url.rstrip("/") + endpoint
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# Special handling for Databricks URLs to avoid leaking workspace info
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# e.g. https://adb-1234567890123456.7.cloud.databricks.com -> https://...cloud.databricks.com
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if "cloud.databricks.com" in url:
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url = "__databricks__" + url.split("cloud.databricks.com")[-1]
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method = "POST"
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headers = {}
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body = kwargs
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@ -98,6 +98,7 @@ def skip_if_doesnt_support_n(client_with_models, model_id):
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# the entered value was 2. Update the candidateCount value and try again.', 'status': 'INVALID_ARGUMENT'}
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"remote::tgi", # TGI ignores n param silently
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"remote::together", # `n` > 1 is not supported when streaming tokens. Please disable `stream`
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"remote::databricks", # Bad request: parameter "n" must be equal to 1 for streaming mode
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):
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pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support n param.")
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@ -110,7 +111,6 @@ def skip_if_model_doesnt_support_openai_chat_completion(client_with_models, mode
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"inline::vllm",
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"remote::bedrock",
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"remote::cerebras",
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"remote::databricks",
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"remote::runpod",
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"remote::watsonx", # watsonx returns 404 when hitting the /openai/v1 endpoint
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):
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|
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@ -42,6 +42,7 @@ def skip_if_model_doesnt_support_encoding_format_base64(client, model_id):
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provider = provider_from_model(client, model_id)
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if provider.provider_type in (
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"remote::together", # param silently ignored, always returns floats
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"remote::databricks", # param silently ignored, always returns floats
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"remote::fireworks", # param silently ignored, always returns list of floats
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):
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pytest.skip(f"Model {model_id} hosted by {provider.provider_type} does not support encoding_format='base64'.")
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@ -52,6 +53,8 @@ def skip_if_model_doesnt_support_variable_dimensions(client_with_models, model_i
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if provider.provider_type in (
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"remote::together", # returns 400
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"inline::sentence-transformers",
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# Error code: 400 - {'error_code': 'BAD_REQUEST', 'message': 'Bad request: json: unknown field "dimensions"\n'}
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"remote::databricks",
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):
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pytest.skip(
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f"Model {model_id} hosted by {provider.provider_type} does not support variable output embedding dimensions."
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@ -75,7 +78,6 @@ def skip_if_model_doesnt_support_openai_embeddings(client, model_id):
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"inline::meta-reference",
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"remote::bedrock",
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"remote::cerebras",
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"remote::databricks",
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"remote::runpod",
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"remote::sambanova",
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"remote::tgi",
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|
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728
tests/integration/recordings/responses/121a72d1c4cf.json
Normal file
728
tests/integration/recordings/responses/121a72d1c4cf.json
Normal file
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@ -0,0 +1,728 @@
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{
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"request": {
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"method": "POST",
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"url": "__databricks__/serving-endpoints/v1/chat/completions",
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"headers": {},
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"body": {
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"model": "databricks-meta-llama-3-3-70b-instruct",
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"messages": [
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{
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"role": "user",
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"content": "Hello, world!"
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}
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],
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"stream": true
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},
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"endpoint": "/v1/chat/completions",
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"model": "databricks-meta-llama-3-3-70b-instruct"
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},
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"response": {
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"body": [
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{
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"__type__": "openai.types.chat.chat_completion_chunk.ChatCompletionChunk",
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"__data__": {
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"id": "chatcmpl_7268e4ee-3b8e-461e-80dc-608e76f3801d",
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"choices": [
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{
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"delta": {
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"content": "",
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"function_call": null,
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"refusal": null,
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"role": "assistant",
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"tool_calls": null
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},
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"finish_reason": null,
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"index": 0,
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"logprobs": null
|
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56
tests/integration/recordings/responses/249b7f0ddde6.json
Normal file
56
tests/integration/recordings/responses/249b7f0ddde6.json
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56
tests/integration/recordings/responses/28648cf8d421.json
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56
tests/integration/recordings/responses/28648cf8d421.json
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56
tests/integration/recordings/responses/29585e055e6f.json
Normal file
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tests/integration/recordings/responses/29585e055e6f.json
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344
tests/integration/recordings/responses/3ef0f9aab128.json
Normal file
344
tests/integration/recordings/responses/3ef0f9aab128.json
Normal file
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1061
tests/integration/recordings/responses/441e2832387f.json
Normal file
1061
tests/integration/recordings/responses/441e2832387f.json
Normal file
File diff suppressed because it is too large
Load diff
1061
tests/integration/recordings/responses/5fa0e98f3d84.json
Normal file
1061
tests/integration/recordings/responses/5fa0e98f3d84.json
Normal file
File diff suppressed because it is too large
Load diff
1061
tests/integration/recordings/responses/6d937e5e9233.json
Normal file
1061
tests/integration/recordings/responses/6d937e5e9233.json
Normal file
File diff suppressed because it is too large
Load diff
83
tests/integration/recordings/responses/7f53b458dad9.json
Normal file
83
tests/integration/recordings/responses/7f53b458dad9.json
Normal file
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3125
tests/integration/recordings/responses/875323ed9913.json
Normal file
3125
tests/integration/recordings/responses/875323ed9913.json
Normal file
File diff suppressed because it is too large
Load diff
1062
tests/integration/recordings/responses/9b9e8cf39b15.json
Normal file
1062
tests/integration/recordings/responses/9b9e8cf39b15.json
Normal file
File diff suppressed because it is too large
Load diff
1062
tests/integration/recordings/responses/d64ffaa0de6f.json
Normal file
1062
tests/integration/recordings/responses/d64ffaa0de6f.json
Normal file
File diff suppressed because it is too large
Load diff
168
tests/integration/recordings/responses/e509387fc329.json
Normal file
168
tests/integration/recordings/responses/e509387fc329.json
Normal file
|
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536
tests/integration/recordings/responses/ecf6f0c51485.json
Normal file
536
tests/integration/recordings/responses/ecf6f0c51485.json
Normal file
|
|
@ -0,0 +1,536 @@
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|||
{
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||||
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|
||||
}
|
||||
}
|
||||
],
|
||||
"is_streaming": true
|
||||
}
|
||||
}
|
||||
1061
tests/integration/recordings/responses/ffd7b58fded8.json
Normal file
1061
tests/integration/recordings/responses/ffd7b58fded8.json
Normal file
File diff suppressed because it is too large
Load diff
|
|
@ -108,6 +108,14 @@ SETUP_DEFINITIONS: dict[str, Setup] = {
|
|||
"embedding_model": "together/togethercomputer/m2-bert-80M-32k-retrieval",
|
||||
},
|
||||
),
|
||||
"databricks": Setup(
|
||||
name="databricks",
|
||||
description="Databricks models",
|
||||
defaults={
|
||||
"text_model": "databricks/databricks-meta-llama-3-3-70b-instruct",
|
||||
"embedding_model": "databricks/databricks-bge-large-en",
|
||||
},
|
||||
),
|
||||
"fireworks": Setup(
|
||||
name="fireworks",
|
||||
description="Fireworks provider with a text model",
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue