chore: remove /v1/inference/completion and implementations (#3622)

# What does this PR do?

the /inference/completion route is gone. this removes the
implementations.

## Test Plan

ci

llama_stack/apis/inference/inference.py

@@ -1008,28 +1008,6 @@ class InferenceProvider(Protocol):
 
     model_store: ModelStore | None = None
 
-    async def completion(
-        self,
-        model_id: str,
-        content: InterleavedContent,
-        sampling_params: SamplingParams | None = None,
-        response_format: ResponseFormat | None = None,
-        stream: bool | None = False,
-        logprobs: LogProbConfig | None = None,
-    ) -> CompletionResponse | AsyncIterator[CompletionResponseStreamChunk]:
-        """Generate a completion for the given content using the specified model.
-
-        :param model_id: The identifier of the model to use. The model must be registered with Llama Stack and available via the /models endpoint.
-        :param content: The content to generate a completion for.
-        :param sampling_params: (Optional) Parameters to control the sampling strategy.
-        :param response_format: (Optional) Grammar specification for guided (structured) decoding.
-        :param stream: (Optional) If True, generate an SSE event stream of the response. Defaults to False.
-        :param logprobs: (Optional) If specified, log probabilities for each token position will be returned.
-        :returns: If stream=False, returns a CompletionResponse with the full completion.
-                 If stream=True, returns an SSE event stream of CompletionResponseStreamChunk.
-        """
-        ...
-
     async def chat_completion(
         self,
         model_id: str,

llama_stack/core/routers/inference.py

@@ -267,47 +267,6 @@ class InferenceRouter(Inference):
         )
         return response
 
-    async def completion(
-        self,
-        model_id: str,
-        content: InterleavedContent,
-        sampling_params: SamplingParams | None = None,
-        response_format: ResponseFormat | None = None,
-        stream: bool | None = False,
-        logprobs: LogProbConfig | None = None,
-    ) -> AsyncGenerator:
-        if sampling_params is None:
-            sampling_params = SamplingParams()
-        logger.debug(
-            f"InferenceRouter.completion: {model_id=}, {stream=}, {content=}, {sampling_params=}, {response_format=}",
-        )
-        model = await self._get_model(model_id, ModelType.llm)
-        provider = await self.routing_table.get_provider_impl(model_id)
-        params = dict(
-            model_id=model_id,
-            content=content,
-            sampling_params=sampling_params,
-            response_format=response_format,
-            stream=stream,
-            logprobs=logprobs,
-        )
-
-        prompt_tokens = await self._count_tokens(content)
-        response = await provider.completion(**params)
-        if stream:
-            return self.stream_tokens_and_compute_metrics(
-                response=response,
-                prompt_tokens=prompt_tokens,
-                model=model,
-            )
-
-        metrics = await self.count_tokens_and_compute_metrics(
-            response=response, prompt_tokens=prompt_tokens, model=model
-        )
-        response.metrics = metrics if response.metrics is None else response.metrics + metrics
-
-        return response
-
     async def openai_completion(
         self,
         model: str,

llama_stack/providers/inline/inference/meta_reference/inference.py

@@ -24,11 +24,7 @@ from llama_stack.apis.inference import (
     ChatCompletionResponseEventType,
     ChatCompletionResponseStreamChunk,
     CompletionMessage,
-    CompletionRequest,
-    CompletionResponse,
-    CompletionResponseStreamChunk,
     InferenceProvider,
-    InterleavedContent,
     LogProbConfig,
     Message,
     ResponseFormat,
@@ -59,10 +55,8 @@ from llama_stack.providers.utils.inference.model_registry import (
 )
 from llama_stack.providers.utils.inference.openai_compat import (
     OpenAIChatCompletionToLlamaStackMixin,
-    OpenAICompletionToLlamaStackMixin,
 )
 from llama_stack.providers.utils.inference.prompt_adapter import (
-    augment_content_with_response_format_prompt,
     chat_completion_request_to_messages,
     convert_request_to_raw,
 )
@@ -82,7 +76,6 @@ def llama_builder_fn(config: MetaReferenceInferenceConfig, model_id: str, llama_
 
 
 class MetaReferenceInferenceImpl(
-    OpenAICompletionToLlamaStackMixin,
     OpenAIChatCompletionToLlamaStackMixin,
     SentenceTransformerEmbeddingMixin,
     InferenceProvider,
@@ -100,6 +93,9 @@ class MetaReferenceInferenceImpl(
         if self.config.create_distributed_process_group:
             self.generator.stop()
 
+    async def openai_completion(self, *args, **kwargs):
+        raise NotImplementedError("OpenAI completion not supported by meta reference provider")
+
     async def should_refresh_models(self) -> bool:
         return False
 
@@ -165,11 +161,6 @@ class MetaReferenceInferenceImpl(
         self.llama_model = llama_model
 
         log.info("Warming up...")
-        await self.completion(
-            model_id=model_id,
-            content="Hello, world!",
-            sampling_params=SamplingParams(max_tokens=10),
-        )
         await self.chat_completion(
             model_id=model_id,
             messages=[UserMessage(content="Hi how are you?")],
@@ -185,137 +176,6 @@ class MetaReferenceInferenceImpl(
         elif request.model != self.model_id:
             raise RuntimeError(f"Model mismatch: request model: {request.model} != loaded model: {self.model_id}")
 
-    async def completion(
-        self,
-        model_id: str,
-        content: InterleavedContent,
-        sampling_params: SamplingParams | None = None,
-        response_format: ResponseFormat | None = None,
-        stream: bool | None = False,
-        logprobs: LogProbConfig | None = None,
-    ) -> CompletionResponse | CompletionResponseStreamChunk:
-        if sampling_params is None:
-            sampling_params = SamplingParams()
-        if logprobs:
-            assert logprobs.top_k == 1, f"Unexpected top_k={logprobs.top_k}"
-
-        content = augment_content_with_response_format_prompt(response_format, content)
-        request = CompletionRequest(
-            model=model_id,
-            content=content,
-            sampling_params=sampling_params,
-            response_format=response_format,
-            stream=stream,
-            logprobs=logprobs,
-        )
-        self.check_model(request)
-        request = await convert_request_to_raw(request)
-
-        if request.stream:
-            return self._stream_completion(request)
-        else:
-            results = await self._nonstream_completion([request])
-            return results[0]
-
-    async def _stream_completion(self, request: CompletionRequest) -> AsyncGenerator:
-        tokenizer = self.generator.formatter.tokenizer
-
-        def impl():
-            stop_reason = None
-
-            for token_results in self.generator.completion([request]):
-                token_result = token_results[0]
-                if token_result.token == tokenizer.eot_id:
-                    stop_reason = StopReason.end_of_turn
-                    text = ""
-                elif token_result.token == tokenizer.eom_id:
-                    stop_reason = StopReason.end_of_message
-                    text = ""
-                else:
-                    text = token_result.text
-
-                logprobs = None
-                if stop_reason is None:
-                    if request.logprobs:
-                        assert len(token_result.logprobs) == 1
-
-                        logprobs = [TokenLogProbs(logprobs_by_token={token_result.text: token_result.logprobs[0]})]
-
-                yield CompletionResponseStreamChunk(
-                    delta=text,
-                    stop_reason=stop_reason,
-                    logprobs=logprobs if request.logprobs else None,
-                )
-
-            if stop_reason is None:
-                yield CompletionResponseStreamChunk(
-                    delta="",
-                    stop_reason=StopReason.out_of_tokens,
-                )
-
-        if self.config.create_distributed_process_group:
-            async with SEMAPHORE:
-                for x in impl():
-                    yield x
-        else:
-            for x in impl():
-                yield x
-
-    async def _nonstream_completion(self, request_batch: list[CompletionRequest]) -> list[CompletionResponse]:
-        tokenizer = self.generator.formatter.tokenizer
-
-        first_request = request_batch[0]
-
-        class ItemState(BaseModel):
-            tokens: list[int] = []
-            logprobs: list[TokenLogProbs] = []
-            stop_reason: StopReason | None = None
-            finished: bool = False
-
-        def impl():
-            states = [ItemState() for _ in request_batch]
-
-            results = []
-            for token_results in self.generator.completion(request_batch):
-                for result in token_results:
-                    idx = result.batch_idx
-                    state = states[idx]
-                    if state.finished or result.ignore_token:
-                        continue
-
-                    state.finished = result.finished
-                    if first_request.logprobs:
-                        state.logprobs.append(TokenLogProbs(logprobs_by_token={result.text: result.logprobs[0]}))
-
-                    state.tokens.append(result.token)
-                    if result.token == tokenizer.eot_id:
-                        state.stop_reason = StopReason.end_of_turn
-                    elif result.token == tokenizer.eom_id:
-                        state.stop_reason = StopReason.end_of_message
-
-            for state in states:
-                if state.stop_reason is None:
-                    state.stop_reason = StopReason.out_of_tokens
-
-                if state.tokens[-1] in self.generator.formatter.tokenizer.stop_tokens:
-                    state.tokens = state.tokens[:-1]
-                content = self.generator.formatter.tokenizer.decode(state.tokens)
-                results.append(
-                    CompletionResponse(
-                        content=content,
-                        stop_reason=state.stop_reason,
-                        logprobs=state.logprobs if first_request.logprobs else None,
-                    )
-                )
-
-            return results
-
-        if self.config.create_distributed_process_group:
-            async with SEMAPHORE:
-                return impl()
-        else:
-            return impl()
-
     async def chat_completion(
         self,
         model_id: str,

llama_stack/providers/inline/inference/meta_reference/model_parallel.py

@@ -27,8 +27,6 @@ class ModelRunner:
     def __call__(self, task: Any):
         if task[0] == "chat_completion":
             return self.llama.chat_completion(task[1])
-        elif task[0] == "completion":
-            return self.llama.completion(task[1])
         else:
             raise ValueError(f"Unexpected task type {task[0]}")
 

llama_stack/providers/inline/inference/sentence_transformers/sentence_transformers.py

@@ -5,9 +5,9 @@
 # the root directory of this source tree.
 
 from collections.abc import AsyncGenerator
+from typing import Any
 
 from llama_stack.apis.inference import (
-    CompletionResponse,
     InferenceProvider,
     LogProbConfig,
     Message,
@@ -18,6 +18,7 @@ from llama_stack.apis.inference import (
     ToolDefinition,
     ToolPromptFormat,
 )
+from llama_stack.apis.inference.inference import OpenAICompletion
 from llama_stack.apis.models import ModelType
 from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import Model, ModelsProtocolPrivate
@@ -26,7 +27,6 @@ from llama_stack.providers.utils.inference.embedding_mixin import (
 )
 from llama_stack.providers.utils.inference.openai_compat import (
     OpenAIChatCompletionToLlamaStackMixin,
-    OpenAICompletionToLlamaStackMixin,
 )
 
 from .config import SentenceTransformersInferenceConfig
@@ -36,7 +36,6 @@ log = get_logger(name=__name__, category="inference")
 
 class SentenceTransformersInferenceImpl(
     OpenAIChatCompletionToLlamaStackMixin,
-    OpenAICompletionToLlamaStackMixin,
     SentenceTransformerEmbeddingMixin,
     InferenceProvider,
     ModelsProtocolPrivate,
@@ -74,17 +73,6 @@ class SentenceTransformersInferenceImpl(
     async def unregister_model(self, model_id: str) -> None:
         pass
 
-    async def completion(
-        self,
-        model_id: str,
-        content: str,
-        sampling_params: SamplingParams | None = None,
-        response_format: ResponseFormat | None = None,
-        stream: bool | None = False,
-        logprobs: LogProbConfig | None = None,
-    ) -> CompletionResponse | AsyncGenerator:
-        raise ValueError("Sentence transformers don't support completion")
-
     async def chat_completion(
         self,
         model_id: str,
@@ -99,3 +87,31 @@ class SentenceTransformersInferenceImpl(
         tool_config: ToolConfig | None = None,
     ) -> AsyncGenerator:
         raise ValueError("Sentence transformers don't support chat completion")
+
+    async def openai_completion(
+        self,
+        # Standard OpenAI completion parameters
+        model: str,
+        prompt: str | list[str] | list[int] | list[list[int]],
+        best_of: int | None = None,
+        echo: bool | None = None,
+        frequency_penalty: float | None = None,
+        logit_bias: dict[str, float] | None = None,
+        logprobs: bool | None = None,
+        max_tokens: int | None = None,
+        n: int | None = None,
+        presence_penalty: float | None = None,
+        seed: int | None = None,
+        stop: str | list[str] | None = None,
+        stream: bool | None = None,
+        stream_options: dict[str, Any] | None = None,
+        temperature: float | None = None,
+        top_p: float | None = None,
+        user: str | None = None,
+        # vLLM-specific parameters
+        guided_choice: list[str] | None = None,
+        prompt_logprobs: int | None = None,
+        # for fill-in-the-middle type completion
+        suffix: str | None = None,
+    ) -> OpenAICompletion:
+        raise NotImplementedError("OpenAI completion not supported by sentence transformers provider")

llama_stack/providers/remote/inference/bedrock/bedrock.py

@@ -6,12 +6,10 @@
 
 import json
 from collections.abc import AsyncGenerator, AsyncIterator
+from typing import Any
 
 from botocore.client import BaseClient
 
-from llama_stack.apis.common.content_types import (
-    InterleavedContent,
-)
 from llama_stack.apis.inference import (
     ChatCompletionRequest,
     ChatCompletionResponse,
@@ -27,6 +25,7 @@ from llama_stack.apis.inference import (
     ToolDefinition,
     ToolPromptFormat,
 )
+from llama_stack.apis.inference.inference import OpenAICompletion
 from llama_stack.providers.remote.inference.bedrock.config import BedrockConfig
 from llama_stack.providers.utils.bedrock.client import create_bedrock_client
 from llama_stack.providers.utils.inference.model_registry import (
@@ -36,7 +35,6 @@ from llama_stack.providers.utils.inference.openai_compat import (
     OpenAIChatCompletionToLlamaStackMixin,
     OpenAICompatCompletionChoice,
     OpenAICompatCompletionResponse,
-    OpenAICompletionToLlamaStackMixin,
     get_sampling_strategy_options,
     process_chat_completion_response,
     process_chat_completion_stream_response,
@@ -89,7 +87,6 @@ class BedrockInferenceAdapter(
     ModelRegistryHelper,
     Inference,
     OpenAIChatCompletionToLlamaStackMixin,
-    OpenAICompletionToLlamaStackMixin,
 ):
     def __init__(self, config: BedrockConfig) -> None:
         ModelRegistryHelper.__init__(self, model_entries=MODEL_ENTRIES)
@@ -109,17 +106,6 @@ class BedrockInferenceAdapter(
         if self._client is not None:
             self._client.close()
 
-    async def completion(
-        self,
-        model_id: str,
-        content: InterleavedContent,
-        sampling_params: SamplingParams | None = None,
-        response_format: ResponseFormat | None = None,
-        stream: bool | None = False,
-        logprobs: LogProbConfig | None = None,
-    ) -> AsyncGenerator:
-        raise NotImplementedError()
-
     async def chat_completion(
         self,
         model_id: str,
@@ -221,3 +207,31 @@ class BedrockInferenceAdapter(
         user: str | None = None,
     ) -> OpenAIEmbeddingsResponse:
         raise NotImplementedError()
+
+    async def openai_completion(
+        self,
+        # Standard OpenAI completion parameters
+        model: str,
+        prompt: str | list[str] | list[int] | list[list[int]],
+        best_of: int | None = None,
+        echo: bool | None = None,
+        frequency_penalty: float | None = None,
+        logit_bias: dict[str, float] | None = None,
+        logprobs: bool | None = None,
+        max_tokens: int | None = None,
+        n: int | None = None,
+        presence_penalty: float | None = None,
+        seed: int | None = None,
+        stop: str | list[str] | None = None,
+        stream: bool | None = None,
+        stream_options: dict[str, Any] | None = None,
+        temperature: float | None = None,
+        top_p: float | None = None,
+        user: str | None = None,
+        # vLLM-specific parameters
+        guided_choice: list[str] | None = None,
+        prompt_logprobs: int | None = None,
+        # for fill-in-the-middle type completion
+        suffix: str | None = None,
+    ) -> OpenAICompletion:
+        raise NotImplementedError("OpenAI completion not supported by the Bedrock provider")

llama_stack/providers/remote/inference/cerebras/cerebras.py

@@ -9,9 +9,6 @@ from urllib.parse import urljoin
 
 from cerebras.cloud.sdk import AsyncCerebras
 
-from llama_stack.apis.common.content_types import (
-    InterleavedContent,
-)
 from llama_stack.apis.inference import (
     ChatCompletionRequest,
     CompletionRequest,
@@ -35,8 +32,6 @@ from llama_stack.providers.utils.inference.openai_compat import (
     get_sampling_options,
     process_chat_completion_response,
     process_chat_completion_stream_response,
-    process_completion_response,
-    process_completion_stream_response,
 )
 from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
 from llama_stack.providers.utils.inference.prompt_adapter import (
@@ -73,48 +68,6 @@ class CerebrasInferenceAdapter(
     async def shutdown(self) -> None:
         pass
 
-    async def completion(
-        self,
-        model_id: str,
-        content: InterleavedContent,
-        sampling_params: SamplingParams | None = None,
-        response_format: ResponseFormat | None = None,
-        stream: bool | None = False,
-        logprobs: LogProbConfig | None = None,
-    ) -> AsyncGenerator:
-        if sampling_params is None:
-            sampling_params = SamplingParams()
-        model = await self.model_store.get_model(model_id)
-        request = CompletionRequest(
-            model=model.provider_resource_id,
-            content=content,
-            sampling_params=sampling_params,
-            response_format=response_format,
-            stream=stream,
-            logprobs=logprobs,
-        )
-        if stream:
-            return self._stream_completion(
-                request,
-            )
-        else:
-            return await self._nonstream_completion(request)
-
-    async def _nonstream_completion(self, request: CompletionRequest) -> CompletionResponse:
-        params = await self._get_params(request)
-
-        r = await self._cerebras_client.completions.create(**params)
-
-        return process_completion_response(r)
-
-    async def _stream_completion(self, request: CompletionRequest) -> AsyncGenerator:
-        params = await self._get_params(request)
-
-        stream = await self._cerebras_client.completions.create(**params)
-
-        async for chunk in process_completion_stream_response(stream):
-            yield chunk
-
     async def chat_completion(
         self,
         model_id: str,

llama_stack/providers/remote/inference/databricks/databricks.py

@@ -9,14 +9,9 @@ from typing import Any
 
 from databricks.sdk import WorkspaceClient
 
-from llama_stack.apis.common.content_types import (
-    InterleavedContent,
-)
 from llama_stack.apis.inference import (
     ChatCompletionResponse,
     ChatCompletionResponseStreamChunk,
-    CompletionResponse,
-    CompletionResponseStreamChunk,
     Inference,
     LogProbConfig,
     Message,
@@ -63,17 +58,6 @@ class DatabricksInferenceAdapter(
     async def shutdown(self) -> None:
         pass
 
-    async def completion(
-        self,
-        model_id: str,
-        content: InterleavedContent,
-        sampling_params: SamplingParams | None = None,
-        response_format: ResponseFormat | None = None,
-        stream: bool | None = False,
-        logprobs: LogProbConfig | None = None,
-    ) -> CompletionResponse | AsyncIterator[CompletionResponseStreamChunk]:
-        raise NotImplementedError()
-
     async def openai_completion(
         self,
         model: str,

llama_stack/providers/remote/inference/fireworks/fireworks.py

@@ -8,14 +8,9 @@ from collections.abc import AsyncGenerator
 
 from fireworks.client import Fireworks
 
-from llama_stack.apis.common.content_types import (
-    InterleavedContent,
-)
 from llama_stack.apis.inference import (
     ChatCompletionRequest,
     ChatCompletionResponse,
-    CompletionRequest,
-    CompletionResponse,
     Inference,
     LogProbConfig,
     Message,
@@ -37,13 +32,10 @@ from llama_stack.providers.utils.inference.openai_compat import (
     get_sampling_options,
     process_chat_completion_response,
     process_chat_completion_stream_response,
-    process_completion_response,
-    process_completion_stream_response,
 )
 from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
 from llama_stack.providers.utils.inference.prompt_adapter import (
     chat_completion_request_to_prompt,
-    completion_request_to_prompt,
     request_has_media,
 )
 
@@ -94,79 +86,6 @@ class FireworksInferenceAdapter(OpenAIMixin, ModelRegistryHelper, Inference, Nee
             return prompt[len("<|begin_of_text|>") :]
         return prompt
 
-    async def completion(
-        self,
-        model_id: str,
-        content: InterleavedContent,
-        sampling_params: SamplingParams | None = None,
-        response_format: ResponseFormat | None = None,
-        stream: bool | None = False,
-        logprobs: LogProbConfig | None = None,
-    ) -> AsyncGenerator:
-        if sampling_params is None:
-            sampling_params = SamplingParams()
-        model = await self.model_store.get_model(model_id)
-        request = CompletionRequest(
-            model=model.provider_resource_id,
-            content=content,
-            sampling_params=sampling_params,
-            response_format=response_format,
-            stream=stream,
-            logprobs=logprobs,
-        )
-        if stream:
-            return self._stream_completion(request)
-        else:
-            return await self._nonstream_completion(request)
-
-    async def _nonstream_completion(self, request: CompletionRequest) -> CompletionResponse:
-        params = await self._get_params(request)
-        r = await self._get_client().completion.acreate(**params)
-        return process_completion_response(r)
-
-    async def _stream_completion(self, request: CompletionRequest) -> AsyncGenerator:
-        params = await self._get_params(request)
-
-        # Wrapper for async generator similar
-        async def _to_async_generator():
-            stream = self._get_client().completion.create(**params)
-            for chunk in stream:
-                yield chunk
-
-        stream = _to_async_generator()
-        async for chunk in process_completion_stream_response(stream):
-            yield chunk
-
-    def _build_options(
-        self,
-        sampling_params: SamplingParams | None,
-        fmt: ResponseFormat,
-        logprobs: LogProbConfig | None,
-    ) -> dict:
-        options = get_sampling_options(sampling_params)
-        options.setdefault("max_tokens", 512)
-
-        if fmt:
-            if fmt.type == ResponseFormatType.json_schema.value:
-                options["response_format"] = {
-                    "type": "json_object",
-                    "schema": fmt.json_schema,
-                }
-            elif fmt.type == ResponseFormatType.grammar.value:
-                options["response_format"] = {
-                    "type": "grammar",
-                    "grammar": fmt.bnf,
-                }
-            else:
-                raise ValueError(f"Unknown response format {fmt.type}")
-
-        if logprobs and logprobs.top_k:
-            options["logprobs"] = logprobs.top_k
-            if options["logprobs"] <= 0 or options["logprobs"] >= 5:
-                raise ValueError("Required range: 0 < top_k < 5")
-
-        return options
-
     async def chat_completion(
         self,
         model_id: str,
@@ -222,22 +141,46 @@ class FireworksInferenceAdapter(OpenAIMixin, ModelRegistryHelper, Inference, Nee
         async for chunk in process_chat_completion_stream_response(stream, request):
             yield chunk
 
-    async def _get_params(self, request: ChatCompletionRequest | CompletionRequest) -> dict:
+    def _build_options(
+        self,
+        sampling_params: SamplingParams | None,
+        fmt: ResponseFormat | None,
+        logprobs: LogProbConfig | None,
+    ) -> dict:
+        options = get_sampling_options(sampling_params)
+        options.setdefault("max_tokens", 512)
+
+        if fmt:
+            if fmt.type == ResponseFormatType.json_schema.value:
+                options["response_format"] = {
+                    "type": "json_object",
+                    "schema": fmt.json_schema,
+                }
+            elif fmt.type == ResponseFormatType.grammar.value:
+                options["response_format"] = {
+                    "type": "grammar",
+                    "grammar": fmt.bnf,
+                }
+            else:
+                raise ValueError(f"Unknown response format {fmt.type}")
+
+        if logprobs and logprobs.top_k:
+            options["logprobs"] = logprobs.top_k
+            if options["logprobs"] <= 0 or options["logprobs"] >= 5:
+                raise ValueError("Required range: 0 < top_k < 5")
+
+        return options
+
+    async def _get_params(self, request: ChatCompletionRequest) -> dict:
         input_dict = {}
         media_present = request_has_media(request)
 
         llama_model = self.get_llama_model(request.model)
-        if isinstance(request, ChatCompletionRequest):
-            # TODO: tools are never added to the request, so we need to add them here
-            if media_present or not llama_model:
-                input_dict["messages"] = [
-                    await convert_message_to_openai_dict(m, download=True) for m in request.messages
-                ]
-            else:
-                input_dict["prompt"] = await chat_completion_request_to_prompt(request, llama_model)
+        # TODO: tools are never added to the request, so we need to add them here
+        if media_present or not llama_model:
+            input_dict["messages"] = [await convert_message_to_openai_dict(m, download=True) for m in request.messages]
         else:
-            assert not media_present, "Fireworks does not support media for Completion requests"
-            input_dict["prompt"] = await completion_request_to_prompt(request)
+            input_dict["prompt"] = await chat_completion_request_to_prompt(request, llama_model)
 
         # Fireworks always prepends with BOS
         if "prompt" in input_dict:

llama_stack/providers/remote/inference/nvidia/nvidia.py

@@ -9,16 +9,10 @@ from collections.abc import AsyncIterator
 
 from openai import NOT_GIVEN, APIConnectionError
 
-from llama_stack.apis.common.content_types import (
-    InterleavedContent,
-)
 from llama_stack.apis.inference import (
     ChatCompletionRequest,
     ChatCompletionResponse,
     ChatCompletionResponseStreamChunk,
-    CompletionRequest,
-    CompletionResponse,
-    CompletionResponseStreamChunk,
     Inference,
     LogProbConfig,
     Message,
@@ -37,14 +31,10 @@ from llama_stack.providers.utils.inference.openai_compat import (
     convert_openai_chat_completion_stream,
 )
 from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
-from llama_stack.providers.utils.inference.prompt_adapter import content_has_media
 
 from . import NVIDIAConfig
 from .openai_utils import (
     convert_chat_completion_request,
-    convert_completion_request,
-    convert_openai_completion_choice,
-    convert_openai_completion_stream,
 )
 from .utils import _is_nvidia_hosted
 
@@ -109,48 +99,6 @@ class NVIDIAInferenceAdapter(OpenAIMixin, Inference):
         """
         return f"{self._config.url}/v1" if self._config.append_api_version else self._config.url
 
-    async def completion(
-        self,
-        model_id: str,
-        content: InterleavedContent,
-        sampling_params: SamplingParams | None = None,
-        response_format: ResponseFormat | None = None,
-        stream: bool | None = False,
-        logprobs: LogProbConfig | None = None,
-    ) -> CompletionResponse | AsyncIterator[CompletionResponseStreamChunk]:
-        if sampling_params is None:
-            sampling_params = SamplingParams()
-        if content_has_media(content):
-            raise NotImplementedError("Media is not supported")
-
-        # ToDo: check health of NeMo endpoints and enable this
-        # removing this health check as NeMo customizer endpoint health check is returning 404
-        # await check_health(self._config)  # this raises errors
-
-        provider_model_id = await self._get_provider_model_id(model_id)
-        request = convert_completion_request(
-            request=CompletionRequest(
-                model=provider_model_id,
-                content=content,
-                sampling_params=sampling_params,
-                response_format=response_format,
-                stream=stream,
-                logprobs=logprobs,
-            ),
-            n=1,
-        )
-
-        try:
-            response = await self.client.completions.create(**request)
-        except APIConnectionError as e:
-            raise ConnectionError(f"Failed to connect to NVIDIA NIM at {self._config.url}: {e}") from e
-
-        if stream:
-            return convert_openai_completion_stream(response)
-        else:
-            # we pass n=1 to get only one completion
-            return convert_openai_completion_choice(response.choices[0])
-
     async def openai_embeddings(
         self,
         model: str,

llama_stack/providers/remote/inference/ollama/ollama.py

@@ -13,7 +13,6 @@ from ollama import AsyncClient as AsyncOllamaClient
 
 from llama_stack.apis.common.content_types import (
     ImageContentItem,
-    InterleavedContent,
     TextContentItem,
 )
 from llama_stack.apis.common.errors import UnsupportedModelError
@@ -21,9 +20,6 @@ from llama_stack.apis.inference import (
     ChatCompletionRequest,
     ChatCompletionResponse,
     ChatCompletionResponseStreamChunk,
-    CompletionRequest,
-    CompletionResponse,
-    CompletionResponseStreamChunk,
     GrammarResponseFormat,
     InferenceProvider,
     JsonSchemaResponseFormat,
@@ -55,13 +51,10 @@ from llama_stack.providers.utils.inference.openai_compat import (
     get_sampling_options,
     process_chat_completion_response,
     process_chat_completion_stream_response,
-    process_completion_response,
-    process_completion_stream_response,
 )
 from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
 from llama_stack.providers.utils.inference.prompt_adapter import (
     chat_completion_request_to_prompt,
-    completion_request_to_prompt,
     convert_image_content_to_url,
     request_has_media,
 )
@@ -168,67 +161,6 @@ class OllamaInferenceAdapter(
             raise ValueError("Model store not set")
         return await self.model_store.get_model(model_id)
 
-    async def completion(
-        self,
-        model_id: str,
-        content: InterleavedContent,
-        sampling_params: SamplingParams | None = None,
-        response_format: ResponseFormat | None = None,
-        stream: bool | None = False,
-        logprobs: LogProbConfig | None = None,
-    ) -> CompletionResponse | AsyncGenerator[CompletionResponseStreamChunk, None]:
-        if sampling_params is None:
-            sampling_params = SamplingParams()
-        model = await self._get_model(model_id)
-        if model.provider_resource_id is None:
-            raise ValueError(f"Model {model_id} has no provider_resource_id set")
-        request = CompletionRequest(
-            model=model.provider_resource_id,
-            content=content,
-            sampling_params=sampling_params,
-            response_format=response_format,
-            stream=stream,
-            logprobs=logprobs,
-        )
-        if stream:
-            return self._stream_completion(request)
-        else:
-            return await self._nonstream_completion(request)
-
-    async def _stream_completion(
-        self, request: CompletionRequest
-    ) -> AsyncGenerator[CompletionResponseStreamChunk, None]:
-        params = await self._get_params(request)
-
-        async def _generate_and_convert_to_openai_compat():
-            s = await self.ollama_client.generate(**params)
-            async for chunk in s:
-                choice = OpenAICompatCompletionChoice(
-                    finish_reason=chunk["done_reason"] if chunk["done"] else None,
-                    text=chunk["response"],
-                )
-                yield OpenAICompatCompletionResponse(
-                    choices=[choice],
-                )
-
-        stream = _generate_and_convert_to_openai_compat()
-        async for chunk in process_completion_stream_response(stream):
-            yield chunk
-
-    async def _nonstream_completion(self, request: CompletionRequest) -> CompletionResponse:
-        params = await self._get_params(request)
-        r = await self.ollama_client.generate(**params)
-
-        choice = OpenAICompatCompletionChoice(
-            finish_reason=r["done_reason"] if r["done"] else None,
-            text=r["response"],
-        )
-        response = OpenAICompatCompletionResponse(
-            choices=[choice],
-        )
-
-        return process_completion_response(response)
-
     async def chat_completion(
         self,
         model_id: str,
@@ -262,7 +194,7 @@ class OllamaInferenceAdapter(
         else:
             return await self._nonstream_chat_completion(request)
 
-    async def _get_params(self, request: ChatCompletionRequest | CompletionRequest) -> dict:
+    async def _get_params(self, request: ChatCompletionRequest) -> dict:
         sampling_options = get_sampling_options(request.sampling_params)
         # This is needed since the Ollama API expects num_predict to be set
         # for early truncation instead of max_tokens.
@@ -272,21 +204,16 @@ class OllamaInferenceAdapter(
         input_dict: dict[str, Any] = {}
         media_present = request_has_media(request)
         llama_model = self.get_llama_model(request.model)
-        if isinstance(request, ChatCompletionRequest):
-            if media_present or not llama_model:
-                contents = [await convert_message_to_openai_dict_for_ollama(m) for m in request.messages]
-                # flatten the list of lists
-                input_dict["messages"] = [item for sublist in contents for item in sublist]
-            else:
-                input_dict["raw"] = True
-                input_dict["prompt"] = await chat_completion_request_to_prompt(
-                    request,
-                    llama_model,
-                )
+        if media_present or not llama_model:
+            contents = [await convert_message_to_openai_dict_for_ollama(m) for m in request.messages]
+            # flatten the list of lists
+            input_dict["messages"] = [item for sublist in contents for item in sublist]
         else:
-            assert not media_present, "Ollama does not support media for Completion requests"
-            input_dict["prompt"] = await completion_request_to_prompt(request)
             input_dict["raw"] = True
+            input_dict["prompt"] = await chat_completion_request_to_prompt(
+                request,
+                llama_model,
+            )
 
         if fmt := request.response_format:
             if isinstance(fmt, JsonSchemaResponseFormat):

llama_stack/providers/remote/inference/passthrough/passthrough.py

@@ -9,7 +9,6 @@ from typing import Any
 
 from llama_stack_client import AsyncLlamaStackClient
 
-from llama_stack.apis.common.content_types import InterleavedContent
 from llama_stack.apis.inference import (
     ChatCompletionResponse,
     ChatCompletionResponseStreamChunk,
@@ -86,37 +85,6 @@ class PassthroughInferenceAdapter(Inference):
             provider_data=provider_data,
         )
 
-    async def completion(
-        self,
-        model_id: str,
-        content: InterleavedContent,
-        sampling_params: SamplingParams | None = None,
-        response_format: ResponseFormat | None = None,
-        stream: bool | None = False,
-        logprobs: LogProbConfig | None = None,
-    ) -> AsyncGenerator:
-        if sampling_params is None:
-            sampling_params = SamplingParams()
-        client = self._get_client()
-        model = await self.model_store.get_model(model_id)
-
-        request_params = {
-            "model_id": model.provider_resource_id,
-            "content": content,
-            "sampling_params": sampling_params,
-            "response_format": response_format,
-            "stream": stream,
-            "logprobs": logprobs,
-        }
-
-        request_params = {key: value for key, value in request_params.items() if value is not None}
-
-        # cast everything to json dict
-        json_params = self.cast_value_to_json_dict(request_params)
-
-        # only pass through the not None params
-        return await client.inference.completion(**json_params)
-
     async def chat_completion(
         self,
         model_id: str,

llama_stack/providers/remote/inference/runpod/runpod.py

@@ -14,7 +14,6 @@ from llama_stack.apis.inference import OpenAIEmbeddingsResponse
 from llama_stack.providers.utils.inference.model_registry import ModelRegistryHelper, build_hf_repo_model_entry
 from llama_stack.providers.utils.inference.openai_compat import (
     OpenAIChatCompletionToLlamaStackMixin,
-    OpenAICompletionToLlamaStackMixin,
     get_sampling_options,
     process_chat_completion_response,
     process_chat_completion_stream_response,
@@ -55,7 +54,6 @@ class RunpodInferenceAdapter(
     ModelRegistryHelper,
     Inference,
     OpenAIChatCompletionToLlamaStackMixin,
-    OpenAICompletionToLlamaStackMixin,
 ):
     def __init__(self, config: RunpodImplConfig) -> None:
         ModelRegistryHelper.__init__(self, stack_to_provider_models_map=RUNPOD_SUPPORTED_MODELS)
@@ -67,17 +65,6 @@ class RunpodInferenceAdapter(
     async def shutdown(self) -> None:
         pass
 
-    async def completion(
-        self,
-        model: str,
-        content: InterleavedContent,
-        sampling_params: SamplingParams | None = None,
-        response_format: ResponseFormat | None = None,
-        stream: bool | None = False,
-        logprobs: LogProbConfig | None = None,
-    ) -> AsyncGenerator:
-        raise NotImplementedError()
-
     async def chat_completion(
         self,
         model: str,

llama_stack/providers/remote/inference/tgi/tgi.py

@@ -10,13 +10,9 @@ from collections.abc import AsyncGenerator
 from huggingface_hub import AsyncInferenceClient, HfApi
 from pydantic import SecretStr
 
-from llama_stack.apis.common.content_types import (
-    InterleavedContent,
-)
 from llama_stack.apis.inference import (
     ChatCompletionRequest,
     ChatCompletionResponse,
-    CompletionRequest,
     Inference,
     LogProbConfig,
     Message,
@@ -44,13 +40,10 @@ from llama_stack.providers.utils.inference.openai_compat import (
     get_sampling_options,
     process_chat_completion_response,
     process_chat_completion_stream_response,
-    process_completion_response,
-    process_completion_stream_response,
 )
 from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
 from llama_stack.providers.utils.inference.prompt_adapter import (
     chat_completion_request_to_model_input_info,
-    completion_request_to_prompt_model_input_info,
 )
 
 from .config import InferenceAPIImplConfig, InferenceEndpointImplConfig, TGIImplConfig
@@ -122,31 +115,6 @@ class _HfAdapter(
     async def unregister_model(self, model_id: str) -> None:
         pass
 
-    async def completion(
-        self,
-        model_id: str,
-        content: InterleavedContent,
-        sampling_params: SamplingParams | None = None,
-        response_format: ResponseFormat | None = None,
-        stream: bool | None = False,
-        logprobs: LogProbConfig | None = None,
-    ) -> AsyncGenerator:
-        if sampling_params is None:
-            sampling_params = SamplingParams()
-        model = await self.model_store.get_model(model_id)
-        request = CompletionRequest(
-            model=model.provider_resource_id,
-            content=content,
-            sampling_params=sampling_params,
-            response_format=response_format,
-            stream=stream,
-            logprobs=logprobs,
-        )
-        if stream:
-            return self._stream_completion(request)
-        else:
-            return await self._nonstream_completion(request)
-
     def _get_max_new_tokens(self, sampling_params, input_tokens):
         return min(
             sampling_params.max_tokens or (self.max_tokens - input_tokens),
@@ -180,53 +148,6 @@ class _HfAdapter(
 
         return options
 
-    async def _get_params_for_completion(self, request: CompletionRequest) -> dict:
-        prompt, input_tokens = await completion_request_to_prompt_model_input_info(request)
-
-        return dict(
-            prompt=prompt,
-            stream=request.stream,
-            details=True,
-            max_new_tokens=self._get_max_new_tokens(request.sampling_params, input_tokens),
-            stop_sequences=["<|eom_id|>", "<|eot_id|>"],
-            **self._build_options(request.sampling_params, request.response_format),
-        )
-
-    async def _stream_completion(self, request: CompletionRequest) -> AsyncGenerator:
-        params = await self._get_params_for_completion(request)
-
-        async def _generate_and_convert_to_openai_compat():
-            s = await self.hf_client.text_generation(**params)
-            async for chunk in s:
-                token_result = chunk.token
-                finish_reason = None
-                if chunk.details:
-                    finish_reason = chunk.details.finish_reason
-
-                choice = OpenAICompatCompletionChoice(text=token_result.text, finish_reason=finish_reason)
-                yield OpenAICompatCompletionResponse(
-                    choices=[choice],
-                )
-
-        stream = _generate_and_convert_to_openai_compat()
-        async for chunk in process_completion_stream_response(stream):
-            yield chunk
-
-    async def _nonstream_completion(self, request: CompletionRequest) -> AsyncGenerator:
-        params = await self._get_params_for_completion(request)
-        r = await self.hf_client.text_generation(**params)
-
-        choice = OpenAICompatCompletionChoice(
-            finish_reason=r.details.finish_reason,
-            text="".join(t.text for t in r.details.tokens),
-        )
-
-        response = OpenAICompatCompletionResponse(
-            choices=[choice],
-        )
-
-        return process_completion_response(response)
-
     async def chat_completion(
         self,
         model_id: str,

llama_stack/providers/remote/inference/together/together.py

@@ -10,13 +10,9 @@ from openai import AsyncOpenAI
 from together import AsyncTogether
 from together.constants import BASE_URL
 
-from llama_stack.apis.common.content_types import (
-    InterleavedContent,
-)
 from llama_stack.apis.inference import (
     ChatCompletionRequest,
     ChatCompletionResponse,
-    CompletionRequest,
     Inference,
     LogProbConfig,
     Message,
@@ -39,13 +35,10 @@ from llama_stack.providers.utils.inference.openai_compat import (
     get_sampling_options,
     process_chat_completion_response,
     process_chat_completion_stream_response,
-    process_completion_response,
-    process_completion_stream_response,
 )
 from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
 from llama_stack.providers.utils.inference.prompt_adapter import (
     chat_completion_request_to_prompt,
-    completion_request_to_prompt,
     request_has_media,
 )
 
@@ -81,31 +74,6 @@ class TogetherInferenceAdapter(OpenAIMixin, ModelRegistryHelper, Inference, Need
     async def shutdown(self) -> None:
         pass
 
-    async def completion(
-        self,
-        model_id: str,
-        content: InterleavedContent,
-        sampling_params: SamplingParams | None = None,
-        response_format: ResponseFormat | None = None,
-        stream: bool | None = False,
-        logprobs: LogProbConfig | None = None,
-    ) -> AsyncGenerator:
-        if sampling_params is None:
-            sampling_params = SamplingParams()
-        model = await self.model_store.get_model(model_id)
-        request = CompletionRequest(
-            model=model.provider_resource_id,
-            content=content,
-            sampling_params=sampling_params,
-            response_format=response_format,
-            stream=stream,
-            logprobs=logprobs,
-        )
-        if stream:
-            return self._stream_completion(request)
-        else:
-            return await self._nonstream_completion(request)
-
     def _get_client(self) -> AsyncTogether:
         together_api_key = None
         config_api_key = self.config.api_key.get_secret_value() if self.config.api_key else None
@@ -127,19 +95,6 @@ class TogetherInferenceAdapter(OpenAIMixin, ModelRegistryHelper, Inference, Need
             api_key=together_client.api_key,
         )
 
-    async def _nonstream_completion(self, request: CompletionRequest) -> ChatCompletionResponse:
-        params = await self._get_params(request)
-        client = self._get_client()
-        r = await client.completions.create(**params)
-        return process_completion_response(r)
-
-    async def _stream_completion(self, request: CompletionRequest) -> AsyncGenerator:
-        params = await self._get_params(request)
-        client = self._get_client()
-        stream = await client.completions.create(**params)
-        async for chunk in process_completion_stream_response(stream):
-            yield chunk
-
     def _build_options(
         self,
         sampling_params: SamplingParams | None,
@@ -219,18 +174,14 @@ class TogetherInferenceAdapter(OpenAIMixin, ModelRegistryHelper, Inference, Need
         async for chunk in process_chat_completion_stream_response(stream, request):
             yield chunk
 
-    async def _get_params(self, request: ChatCompletionRequest | CompletionRequest) -> dict:
+    async def _get_params(self, request: ChatCompletionRequest) -> dict:
         input_dict = {}
         media_present = request_has_media(request)
         llama_model = self.get_llama_model(request.model)
-        if isinstance(request, ChatCompletionRequest):
-            if media_present or not llama_model:
-                input_dict["messages"] = [await convert_message_to_openai_dict(m) for m in request.messages]
-            else:
-                input_dict["prompt"] = await chat_completion_request_to_prompt(request, llama_model)
+        if media_present or not llama_model:
+            input_dict["messages"] = [await convert_message_to_openai_dict(m) for m in request.messages]
         else:
-            assert not media_present, "Together does not support media for Completion requests"
-            input_dict["prompt"] = await completion_request_to_prompt(request)
+            input_dict["prompt"] = await chat_completion_request_to_prompt(request, llama_model)
 
         params = {
             "model": request.model,

llama_stack/providers/remote/inference/vllm/vllm.py

@@ -15,7 +15,6 @@ from openai.types.chat.chat_completion_chunk import (
 )
 
 from llama_stack.apis.common.content_types import (
-    InterleavedContent,
     TextDelta,
     ToolCallDelta,
     ToolCallParseStatus,
@@ -27,9 +26,6 @@ from llama_stack.apis.inference import (
     ChatCompletionResponseEventType,
     ChatCompletionResponseStreamChunk,
     CompletionMessage,
-    CompletionRequest,
-    CompletionResponse,
-    CompletionResponseStreamChunk,
     GrammarResponseFormat,
     Inference,
     JsonSchemaResponseFormat,
@@ -64,14 +60,8 @@ from llama_stack.providers.utils.inference.openai_compat import (
     convert_tool_call,
     get_sampling_options,
     process_chat_completion_stream_response,
-    process_completion_response,
-    process_completion_stream_response,
 )
 from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
-from llama_stack.providers.utils.inference.prompt_adapter import (
-    completion_request_to_prompt,
-    request_has_media,
-)
 
 from .config import VLLMInferenceAdapterConfig
 
@@ -363,33 +353,6 @@ class VLLMInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin, Inference, ModelsPro
     def get_extra_client_params(self):
         return {"http_client": httpx.AsyncClient(verify=self.config.tls_verify)}
 
-    async def completion(  # type: ignore[override]  # Return type more specific than base class  which is allows for both streaming and non-streaming responses.
-        self,
-        model_id: str,
-        content: InterleavedContent,
-        sampling_params: SamplingParams | None = None,
-        response_format: ResponseFormat | None = None,
-        stream: bool | None = False,
-        logprobs: LogProbConfig | None = None,
-    ) -> CompletionResponse | AsyncGenerator[CompletionResponseStreamChunk, None]:
-        if sampling_params is None:
-            sampling_params = SamplingParams()
-        model = await self._get_model(model_id)
-        if model.provider_resource_id is None:
-            raise ValueError(f"Model {model_id} has no provider_resource_id set")
-        request = CompletionRequest(
-            model=model.provider_resource_id,
-            content=content,
-            sampling_params=sampling_params,
-            response_format=response_format,
-            stream=stream,
-            logprobs=logprobs,
-        )
-        if stream:
-            return self._stream_completion(request)
-        else:
-            return await self._nonstream_completion(request)
-
     async def chat_completion(
         self,
         model_id: str,
@@ -474,24 +437,6 @@ class VLLMInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin, Inference, ModelsPro
         async for chunk in res:
             yield chunk
 
-    async def _nonstream_completion(self, request: CompletionRequest) -> CompletionResponse:
-        if self.client is None:
-            raise RuntimeError("Client is not initialized")
-        params = await self._get_params(request)
-        r = await self.client.completions.create(**params)
-        return process_completion_response(r)
-
-    async def _stream_completion(
-        self, request: CompletionRequest
-    ) -> AsyncGenerator[CompletionResponseStreamChunk, None]:
-        if self.client is None:
-            raise RuntimeError("Client is not initialized")
-        params = await self._get_params(request)
-
-        stream = await self.client.completions.create(**params)
-        async for chunk in process_completion_stream_response(stream):
-            yield chunk
-
     async def register_model(self, model: Model) -> Model:
         try:
             model = await self.register_helper.register_model(model)
@@ -511,7 +456,7 @@ class VLLMInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin, Inference, ModelsPro
             )
         return model
 
-    async def _get_params(self, request: ChatCompletionRequest | CompletionRequest) -> dict:
+    async def _get_params(self, request: ChatCompletionRequest) -> dict:
         options = get_sampling_options(request.sampling_params)
         if "max_tokens" not in options:
             options["max_tokens"] = self.config.max_tokens
@@ -521,11 +466,7 @@ class VLLMInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin, Inference, ModelsPro
         if isinstance(request, ChatCompletionRequest) and request.tools:
             input_dict = {"tools": _convert_to_vllm_tools_in_request(request.tools)}
 
-        if isinstance(request, ChatCompletionRequest):
-            input_dict["messages"] = [await convert_message_to_openai_dict(m, download=True) for m in request.messages]
-        else:
-            assert not request_has_media(request), "vLLM does not support media for Completion requests"
-            input_dict["prompt"] = await completion_request_to_prompt(request)
+        input_dict["messages"] = [await convert_message_to_openai_dict(m, download=True) for m in request.messages]
 
         if fmt := request.response_format:
             if isinstance(fmt, JsonSchemaResponseFormat):

llama_stack/providers/remote/inference/watsonx/watsonx.py

@@ -11,7 +11,6 @@ from ibm_watsonx_ai.foundation_models import Model
 from ibm_watsonx_ai.metanames import GenTextParamsMetaNames as GenParams
 from openai import AsyncOpenAI
 
-from llama_stack.apis.common.content_types import InterleavedContent
 from llama_stack.apis.inference import (
     ChatCompletionRequest,
     ChatCompletionResponse,
@@ -43,8 +42,6 @@ from llama_stack.providers.utils.inference.openai_compat import (
     prepare_openai_completion_params,
     process_chat_completion_response,
     process_chat_completion_stream_response,
-    process_completion_response,
-    process_completion_stream_response,
 )
 from llama_stack.providers.utils.inference.prompt_adapter import (
     chat_completion_request_to_prompt,
@@ -87,31 +84,6 @@ class WatsonXInferenceAdapter(Inference, ModelRegistryHelper):
     async def shutdown(self) -> None:
         pass
 
-    async def completion(
-        self,
-        model_id: str,
-        content: InterleavedContent,
-        sampling_params: SamplingParams | None = None,
-        response_format: ResponseFormat | None = None,
-        stream: bool | None = False,
-        logprobs: LogProbConfig | None = None,
-    ) -> AsyncGenerator:
-        if sampling_params is None:
-            sampling_params = SamplingParams()
-        model = await self.model_store.get_model(model_id)
-        request = CompletionRequest(
-            model=model.provider_resource_id,
-            content=content,
-            sampling_params=sampling_params,
-            response_format=response_format,
-            stream=stream,
-            logprobs=logprobs,
-        )
-        if stream:
-            return self._stream_completion(request)
-        else:
-            return await self._nonstream_completion(request)
-
     def _get_client(self, model_id) -> Model:
         config_api_key = self._config.api_key.get_secret_value() if self._config.api_key else None
         config_url = self._config.url
@@ -128,40 +100,6 @@ class WatsonXInferenceAdapter(Inference, ModelRegistryHelper):
             )
         return self._openai_client
 
-    async def _nonstream_completion(self, request: CompletionRequest) -> ChatCompletionResponse:
-        params = await self._get_params(request)
-        r = self._get_client(request.model).generate(**params)
-        choices = []
-        if "results" in r:
-            for result in r["results"]:
-                choice = OpenAICompatCompletionChoice(
-                    finish_reason=result["stop_reason"] if result["stop_reason"] else None,
-                    text=result["generated_text"],
-                )
-                choices.append(choice)
-        response = OpenAICompatCompletionResponse(
-            choices=choices,
-        )
-        return process_completion_response(response)
-
-    async def _stream_completion(self, request: CompletionRequest) -> AsyncGenerator:
-        params = await self._get_params(request)
-
-        async def _generate_and_convert_to_openai_compat():
-            s = self._get_client(request.model).generate_text_stream(**params)
-            for chunk in s:
-                choice = OpenAICompatCompletionChoice(
-                    finish_reason=None,
-                    text=chunk,
-                )
-                yield OpenAICompatCompletionResponse(
-                    choices=[choice],
-                )
-
-        stream = _generate_and_convert_to_openai_compat()
-        async for chunk in process_completion_stream_response(stream):
-            yield chunk
-
     async def chat_completion(
         self,
         model_id: str,

llama_stack/providers/utils/inference/litellm_openai_mixin.py

@@ -4,14 +4,11 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from collections.abc import AsyncGenerator, AsyncIterator
+from collections.abc import AsyncIterator
 from typing import Any
 
 import litellm
 
-from llama_stack.apis.common.content_types import (
-    InterleavedContent,
-)
 from llama_stack.apis.inference import (
     ChatCompletionRequest,
     ChatCompletionResponse,
@@ -108,17 +105,6 @@ class LiteLLMOpenAIMixin(
             else model_id
         )
 
-    async def completion(
-        self,
-        model_id: str,
-        content: InterleavedContent,
-        sampling_params: SamplingParams | None = None,
-        response_format: ResponseFormat | None = None,
-        stream: bool | None = False,
-        logprobs: LogProbConfig | None = None,
-    ) -> AsyncGenerator:
-        raise NotImplementedError("LiteLLM does not support completion requests")
-
     async def chat_completion(
         self,
         model_id: str,

llama_stack/providers/utils/inference/openai_compat.py

@@ -103,8 +103,6 @@ from llama_stack.apis.inference import (
     JsonSchemaResponseFormat,
     Message,
     OpenAIChatCompletion,
-    OpenAICompletion,
-    OpenAICompletionChoice,
     OpenAIEmbeddingData,
     OpenAIMessageParam,
     OpenAIResponseFormatParam,
@@ -1281,76 +1279,6 @@ async def prepare_openai_completion_params(**params):
     return completion_params
 
 
-class OpenAICompletionToLlamaStackMixin:
-    async def openai_completion(
-        self,
-        model: str,
-        prompt: str | list[str] | list[int] | list[list[int]],
-        best_of: int | None = None,
-        echo: bool | None = None,
-        frequency_penalty: float | None = None,
-        logit_bias: dict[str, float] | None = None,
-        logprobs: bool | None = None,
-        max_tokens: int | None = None,
-        n: int | None = None,
-        presence_penalty: float | None = None,
-        seed: int | None = None,
-        stop: str | list[str] | None = None,
-        stream: bool | None = None,
-        stream_options: dict[str, Any] | None = None,
-        temperature: float | None = None,
-        top_p: float | None = None,
-        user: str | None = None,
-        guided_choice: list[str] | None = None,
-        prompt_logprobs: int | None = None,
-        suffix: str | None = None,
-    ) -> OpenAICompletion:
-        if stream:
-            raise ValueError(f"{self.__class__.__name__} doesn't support streaming openai completions")
-
-        # This is a pretty hacky way to do emulate completions -
-        # basically just de-batches them...
-        prompts = [prompt] if not isinstance(prompt, list) else prompt
-
-        sampling_params = _convert_openai_sampling_params(
-            max_tokens=max_tokens,
-            temperature=temperature,
-            top_p=top_p,
-        )
-
-        choices = []
-        # "n" is the number of completions to generate per prompt
-        n = n or 1
-        for _i in range(0, n):
-            # and we may have multiple prompts, if batching was used
-
-            for prompt in prompts:
-                result = self.completion(
-                    model_id=model,
-                    content=prompt,
-                    sampling_params=sampling_params,
-                )
-
-                index = len(choices)
-                text = result.content
-                finish_reason = _convert_stop_reason_to_openai_finish_reason(result.stop_reason)
-
-                choice = OpenAICompletionChoice(
-                    index=index,
-                    text=text,
-                    finish_reason=finish_reason,
-                )
-                choices.append(choice)
-
-        return OpenAICompletion(
-            id=f"cmpl-{uuid.uuid4()}",
-            choices=choices,
-            created=int(time.time()),
-            model=model,
-            object="text_completion",
-        )
-
-
 class OpenAIChatCompletionToLlamaStackMixin:
     async def openai_chat_completion(
         self,

llama_stack/providers/utils/inference/prompt_adapter.py

@@ -229,28 +229,6 @@ async def convert_image_content_to_url(
         return base64.b64encode(content).decode("utf-8")
 
 
-async def completion_request_to_prompt(request: CompletionRequest) -> str:
-    content = augment_content_with_response_format_prompt(request.response_format, request.content)
-    request.content = content
-    request = await convert_request_to_raw(request)
-
-    formatter = ChatFormat(tokenizer=Tokenizer.get_instance())
-    model_input = formatter.encode_content(request.content)
-    return formatter.tokenizer.decode(model_input.tokens)
-
-
-async def completion_request_to_prompt_model_input_info(
-    request: CompletionRequest,
-) -> tuple[str, int]:
-    content = augment_content_with_response_format_prompt(request.response_format, request.content)
-    request.content = content
-    request = await convert_request_to_raw(request)
-
-    formatter = ChatFormat(tokenizer=Tokenizer.get_instance())
-    model_input = formatter.encode_content(request.content)
-    return (formatter.tokenizer.decode(model_input.tokens), len(model_input.tokens))
-
-
 def augment_content_with_response_format_prompt(response_format, content):
     if fmt_prompt := response_format_prompt(response_format):
         if isinstance(content, list):

tests/integration/recordings/responses/0ff78129bb3a.json

@@ -1,167 +0,0 @@
-{
-  "request": {
-    "method": "POST",
-    "url": "http://localhost:11434/api/generate",
-    "headers": {},
-    "body": {
-      "model": "llama3.2:3b-instruct-fp16",
-      "raw": true,
-      "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant. You have access to functions, but you should only use them if they are required.\nYou are an expert in composing functions. You are given a question and a set of possible functions.\nBased on the question, you may or may not need to make one function/tool call to achieve the purpose.\n\nIf you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]\nIf you decide to invoke a function, you SHOULD NOT include any other text in the response. besides the function call in the above format.\nFor a boolean parameter, be sure to use `True` or `False` (capitalized) for the value.\n\n\nHere is a list of functions in JSON format that you can invoke.\n\n[\n    {\n        \"name\": \"greet_everyone\",\n        \"description\": \"\",\n        \"parameters\": {\n            \"type\": \"dict\",\n            \"required\": [\"url\"],\n            \"properties\": {\n                \"url\": {\n                    \"type\": \"string\",\n                    \"description\": \"\"\n                }\n            }\n        }\n    },\n    {\n        \"name\": \"get_boiling_point\",\n        \"description\": \"\nReturns the boiling point of a liquid in Celsius or Fahrenheit.\n\n:param liquid_name: The name of the liquid\n:param celsius: Whether to return the boiling point in Celsius\n:return: The boiling point of the liquid in Celcius or Fahrenheit\n\",\n        \"parameters\": {\n            \"type\": \"dict\",\n            \"required\": [\"liquid_name\", \"celsius\"],\n            \"properties\": {\n                \"liquid_name\": {\n                    \"type\": \"string\",\n                    \"description\": \"\"\n                },\n                \"celsius\": {\n                    \"type\": \"boolean\",\n                    \"description\": \"\"\n                }\n            }\n        }\n    }\n]\n\nYou can answer general questions or invoke tools when necessary.\nIn addition to tool calls, you should also augment your responses by using the tool outputs.\nYou are a helpful assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nSay hi to the world. Use tools to do so.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n[greet_everyone(url=\"world\")]<|eot_id|><|start_header_id|>ipython<|end_header_id|>\n\nHello, world!<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
-      "options": {
-        "temperature": 0.0
-      },
-      "stream": true
-    },
-    "endpoint": "/api/generate",
-    "model": "llama3.2:3b-instruct-fp16"
-  },
-  "response": {
-    "body": [
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-07-29T23:26:18.143606Z",
-          "done": false,
-          "done_reason": null,
-          "total_duration": null,
-          "load_duration": null,
-          "prompt_eval_count": null,
-          "prompt_eval_duration": null,
-          "eval_count": null,
-          "eval_duration": null,
-          "response": "How",
-          "thinking": null,
-          "context": null
-        }
-      },
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-07-29T23:26:18.186151Z",
-          "done": false,
-          "done_reason": null,
-          "total_duration": null,
-          "load_duration": null,
-          "prompt_eval_count": null,
-          "prompt_eval_duration": null,
-          "eval_count": null,
-          "eval_duration": null,
-          "response": " can",
-          "thinking": null,
-          "context": null
-        }
-      },
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-07-29T23:26:18.229036Z",
-          "done": false,
-          "done_reason": null,
-          "total_duration": null,
-          "load_duration": null,
-          "prompt_eval_count": null,
-          "prompt_eval_duration": null,
-          "eval_count": null,
-          "eval_duration": null,
-          "response": " I",
-          "thinking": null,
-          "context": null
-        }
-      },
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-07-29T23:26:18.271516Z",
-          "done": false,
-          "done_reason": null,
-          "total_duration": null,
-          "load_duration": null,
-          "prompt_eval_count": null,
-          "prompt_eval_duration": null,
-          "eval_count": null,
-          "eval_duration": null,
-          "response": " assist",
-          "thinking": null,
-          "context": null
-        }
-      },
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-07-29T23:26:18.316272Z",
-          "done": false,
-          "done_reason": null,
-          "total_duration": null,
-          "load_duration": null,
-          "prompt_eval_count": null,
-          "prompt_eval_duration": null,
-          "eval_count": null,
-          "eval_duration": null,
-          "response": " you",
-          "thinking": null,
-          "context": null
-        }
-      },
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-07-29T23:26:18.361005Z",
-          "done": false,
-          "done_reason": null,
-          "total_duration": null,
-          "load_duration": null,
-          "prompt_eval_count": null,
-          "prompt_eval_duration": null,
-          "eval_count": null,
-          "eval_duration": null,
-          "response": " further",
-          "thinking": null,
-          "context": null
-        }
-      },
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-07-29T23:26:18.404689Z",
-          "done": false,
-          "done_reason": null,
-          "total_duration": null,
-          "load_duration": null,
-          "prompt_eval_count": null,
-          "prompt_eval_duration": null,
-          "eval_count": null,
-          "eval_duration": null,
-          "response": "?",
-          "thinking": null,
-          "context": null
-        }
-      },
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-07-29T23:26:18.447699Z",
-          "done": true,
-          "done_reason": "stop",
-          "total_duration": 456939083,
-          "load_duration": 79653292,
-          "prompt_eval_count": 471,
-          "prompt_eval_duration": 71724667,
-          "eval_count": 8,
-          "eval_duration": 304859000,
-          "response": "",
-          "thinking": null,
-          "context": null
-        }
-      }
-    ],
-    "is_streaming": true
-  }
-}

tests/integration/recordings/responses/1b8394f90636.json

@@ -1,41 +0,0 @@
-{
-  "request": {
-    "method": "POST",
-    "url": "http://localhost:11434/api/generate",
-    "headers": {},
-    "body": {
-      "model": "llama3.2:3b-instruct-fp16",
-      "prompt": "<|begin_of_text|>Complete the sentence using one word: Roses are red, violets are ",
-      "raw": true,
-      "options": {
-        "temperature": 0.0,
-        "max_tokens": 50,
-        "num_predict": 50
-      },
-      "stream": false
-    },
-    "endpoint": "/api/generate",
-    "model": "llama3.2:3b-instruct-fp16"
-  },
-  "response": {
-    "body": {
-      "__type__": "ollama._types.GenerateResponse",
-      "__data__": {
-        "model": "llama3.2:3b-instruct-fp16",
-        "created_at": "2025-09-03T17:36:13.821929Z",
-        "done": true,
-        "done_reason": "stop",
-        "total_duration": 1907912167,
-        "load_duration": 90979292,
-        "prompt_eval_count": 18,
-        "prompt_eval_duration": 77350291,
-        "eval_count": 43,
-        "eval_duration": 1738568334,
-        "response": " _______.\n\nThe best answer is blue. The traditional nursery rhyme goes like this:\n\nRoses are red,\nViolets are blue,\nSugar is sweet,\nAnd so are you! (Or something similar.)",
-        "thinking": null,
-        "context": null
-      }
-    },
-    "is_streaming": false
-  }
-}

tests/integration/recordings/responses/1b92be674e2a.json

@@ -1,39 +0,0 @@
-{
-  "request": {
-    "method": "POST",
-    "url": "http://localhost:11434/api/generate",
-    "headers": {},
-    "body": {
-      "model": "llama3.2:3b-instruct-fp16",
-      "raw": true,
-      "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWho is the CEO of Meta?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
-      "options": {
-        "temperature": 0.0
-      },
-      "stream": false
-    },
-    "endpoint": "/api/generate",
-    "model": "llama3.2:3b-instruct-fp16"
-  },
-  "response": {
-    "body": {
-      "__type__": "ollama._types.GenerateResponse",
-      "__data__": {
-        "model": "llama3.2:3b-instruct-fp16",
-        "created_at": "2025-09-03T17:39:38.236797Z",
-        "done": true,
-        "done_reason": "stop",
-        "total_duration": 1296281500,
-        "load_duration": 283393917,
-        "prompt_eval_count": 23,
-        "prompt_eval_duration": 75453042,
-        "eval_count": 24,
-        "eval_duration": 936860125,
-        "response": "Mark Zuckerberg is the founder, chairman and CEO of Meta, which he originally founded as Facebook in 2004.",
-        "thinking": null,
-        "context": null
-      }
-    },
-    "is_streaming": false
-  }
-}

tests/integration/recordings/responses/211b1562d4e6.json

@@ -1,39 +0,0 @@
-{
-  "request": {
-    "method": "POST",
-    "url": "http://localhost:11434/api/generate",
-    "headers": {},
-    "body": {
-      "model": "llama3.2:3b-instruct-fp16",
-      "raw": true,
-      "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhich planet do humans live on?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
-      "options": {
-        "temperature": 0.0
-      },
-      "stream": false
-    },
-    "endpoint": "/api/generate",
-    "model": "llama3.2:3b-instruct-fp16"
-  },
-  "response": {
-    "body": {
-      "__type__": "ollama._types.GenerateResponse",
-      "__data__": {
-        "model": "llama3.2:3b-instruct-fp16",
-        "created_at": "2025-09-03T17:36:17.894986Z",
-        "done": true,
-        "done_reason": "stop",
-        "total_duration": 363397458,
-        "load_duration": 86692791,
-        "prompt_eval_count": 23,
-        "prompt_eval_duration": 68658541,
-        "eval_count": 6,
-        "eval_duration": 207389084,
-        "response": "Humans live on Earth.",
-        "thinking": null,
-        "context": null
-      }
-    },
-    "is_streaming": false
-  }
-}

tests/integration/recordings/responses/239e4503608a.json

@@ -0,0 +1,806 @@
+{
+  "request": {
+    "method": "POST",
+    "url": "http://0.0.0.0:11434/v1/v1/embeddings",
+    "headers": {},
+    "body": {
+      "model": "nomic-embed-text:137m-v1.5-fp16",
+      "input": [
+        "What inspires neural networks?"
+      ],
+      "encoding_format": "float"
+    },
+    "endpoint": "/v1/embeddings",
+    "model": "nomic-embed-text:137m-v1.5-fp16"
+  },
+  "response": {
+    "body": {
+      "__type__": "openai.types.create_embedding_response.CreateEmbeddingResponse",
+      "__data__": {
+        "data": [
+          {
+            "embedding": [
+              -0.0050316164,
+              0.07984447,
+              -0.15915774,
+              -0.015208397,
+              0.06857012,
+              -0.025208611,
+              0.013689548,
+              0.01110039,
+              -0.021925347,
+              -0.014392589,
+              -0.0557497,
+              0.048096333,
+              0.124248095,
+              0.05381016,
+              -0.032023083,
+              0.03293363,
+              -0.07727248,
+              -0.01613264,
+              -0.0012452743,
+              -0.015702942,
+              -0.067251004,
+              -0.028757395,
+              0.034863908,
+              -0.0017118178,
+              0.0616299,
+              0.021848574,
+              -0.022553956,
+              -0.033664376,
+              0.01553894,
+              0.009967761,
+              0.08114387,
+              -0.066336334,
+              -0.025725907,
+              0.0058821645,
+              -0.072110265,
+              -0.015364161,
+              0.031697143,
+              -0.015320406,
+              0.011826234,
+              0.05202543,
+              -0.008305483,
+              -0.013734584,
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+          "prompt_tokens": 6,
+          "total_tokens": 6
+        }
+      }
+    },
+    "is_streaming": false
+  }
+}

tests/integration/recordings/responses/27ef1a50dc19.json

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+{
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+      "input": [
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+      "encoding_format": "float"
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+    "endpoint": "/v1/embeddings",
+    "model": "nomic-embed-text:137m-v1.5-fp16"
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+            ],
+            "index": 0,
+            "object": "embedding"
+          }
+        ],
+        "model": "nomic-embed-text:137m-v1.5-fp16",
+        "object": "list",
+        "usage": {
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+      }
+    },
+    "is_streaming": false
+  }
+}

tests/integration/recordings/responses/390f0c7dac96.json

@@ -1,39 +0,0 @@
-{
-  "request": {
-    "method": "POST",
-    "url": "http://localhost:11434/api/generate",
-    "headers": {},
-    "body": {
-      "model": "llama3.2:3b-instruct-fp16",
-      "raw": true,
-      "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nTest metrics generation 1<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
-      "options": {
-        "temperature": 0.0
-      },
-      "stream": false
-    },
-    "endpoint": "/api/generate",
-    "model": "llama3.2:3b-instruct-fp16"
-  },
-  "response": {
-    "body": {
-      "__type__": "ollama._types.GenerateResponse",
-      "__data__": {
-        "model": "llama3.2:3b-instruct-fp16",
-        "created_at": "2025-08-11T15:51:18.170868Z",
-        "done": true,
-        "done_reason": "stop",
-        "total_duration": 5240614083,
-        "load_duration": 9823416,
-        "prompt_eval_count": 21,
-        "prompt_eval_duration": 21000000,
-        "eval_count": 310,
-        "eval_duration": 5209000000,
-        "response": "This is the start of a test. I'll provide some sample data and you can try to generate metrics based on it.\n\n**Data:**\n\nLet's say we have a dataset of user interactions with an e-commerce website. The data includes:\n\n| User ID | Product Name | Purchase Date | Quantity | Price |\n| --- | --- | --- | --- | --- |\n| 1 | iPhone 13 | 2022-01-01 | 2 | 999.99 |\n| 1 | MacBook Air | 2022-01-05 | 1 | 1299.99 |\n| 2 | Samsung TV | 2022-01-10 | 3 | 899.99 |\n| 3 | iPhone 13 | 2022-01-15 | 1 | 999.99 |\n| 4 | MacBook Pro | 2022-01-20 | 2 | 1799.99 |\n\n**Task:**\n\nYour task is to generate the following metrics based on this data:\n\n1. Average order value (AOV)\n2. Conversion rate\n3. Average revenue per user (ARPU)\n4. Customer lifetime value (CLV)\n\nPlease provide your answers in a format like this:\n\n| Metric | Value |\n| --- | --- |\n| AOV | 1234.56 |\n| Conversion Rate | 0.25 |\n| ARPU | 1000.00 |\n| CLV | 5000.00 |\n\nGo ahead and generate the metrics!",
-        "thinking": null,
-        "context": null
-      }
-    },
-    "is_streaming": false
-  }
-}

tests/integration/recordings/responses/3b60c09d6c4f.json

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+{
+  "request": {
+    "method": "POST",
+    "url": "http://0.0.0.0:11434/v1/v1/embeddings",
+    "headers": {},
+    "body": {
+      "model": "nomic-embed-text:137m-v1.5-fp16",
+      "input": [
+        "The secret string is foobazbar."
+      ],
+      "encoding_format": "float"
+    },
+    "endpoint": "/v1/embeddings",
+    "model": "nomic-embed-text:137m-v1.5-fp16"
+  },
+  "response": {
+    "body": {
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+}

tests/integration/recordings/responses/3c3f13cb7794.json

@@ -1,221 +0,0 @@
-{
-  "request": {
-    "method": "POST",
-    "url": "http://localhost:11434/api/generate",
-    "headers": {},
-    "body": {
-      "model": "llama3.2:3b-instruct-fp16",
-      "raw": true,
-      "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhat's the name of the Sun in latin?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
-      "options": {
-        "temperature": 0.0
-      },
-      "stream": true
-    },
-    "endpoint": "/api/generate",
-    "model": "llama3.2:3b-instruct-fp16"
-  },
-  "response": {
-    "body": [
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-09-03T17:36:18.136699Z",
-          "done": false,
-          "done_reason": null,
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-          "load_duration": null,
-          "prompt_eval_count": null,
-          "prompt_eval_duration": null,
-          "eval_count": null,
-          "eval_duration": null,
-          "response": "The",
-          "thinking": null,
-          "context": null
-        }
-      },
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-09-03T17:36:18.177622Z",
-          "done": false,
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-          "response": " Latin",
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-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
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-          "created_at": "2025-09-03T17:36:18.218104Z",
-          "done": false,
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-          "prompt_eval_duration": null,
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-          "eval_duration": null,
-          "response": " word",
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-          "context": null
-        }
-      },
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
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-        "__type__": "ollama._types.GenerateResponse",
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-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-09-03T17:36:18.299715Z",
-          "done": false,
-          "done_reason": null,
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-          "eval_duration": null,
-          "response": " \"",
-          "thinking": null,
-          "context": null
-        }
-      },
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-09-03T17:36:18.341602Z",
-          "done": false,
-          "done_reason": null,
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-          "load_duration": null,
-          "prompt_eval_count": null,
-          "prompt_eval_duration": null,
-          "eval_count": null,
-          "eval_duration": null,
-          "response": "Sun",
-          "thinking": null,
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-        }
-      },
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-09-03T17:36:18.385504Z",
-          "done": false,
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-          "prompt_eval_duration": null,
-          "eval_count": null,
-          "eval_duration": null,
-          "response": "\"",
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-          "context": null
-        }
-      },
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-09-03T17:36:18.429427Z",
-          "done": false,
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-          "eval_count": null,
-          "eval_duration": null,
-          "response": " is",
-          "thinking": null,
-          "context": null
-        }
-      },
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-09-03T17:36:18.473547Z",
-          "done": false,
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-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-09-03T17:36:18.516327Z",
-          "done": false,
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-          "eval_count": null,
-          "eval_duration": null,
-          "response": ".",
-          "thinking": null,
-          "context": null
-        }
-      },
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-09-03T17:36:18.559332Z",
-          "done": true,
-          "done_reason": "stop",
-          "total_duration": 628034000,
-          "load_duration": 116384417,
-          "prompt_eval_count": 26,
-          "prompt_eval_duration": 87798792,
-          "eval_count": 11,
-          "eval_duration": 423189583,
-          "response": "",
-          "thinking": null,
-          "context": null
-        }
-      }
-    ],
-    "is_streaming": true
-  }
-}

tests/integration/recordings/responses/40f524d1934a.json

@@ -1,221 +0,0 @@
-{
-  "request": {
-    "method": "POST",
-    "url": "http://localhost:11434/api/generate",
-    "headers": {},
-    "body": {
-      "model": "llama3.2:3b-instruct-fp16",
-      "raw": true,
-      "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant. You have access to functions, but you should only use them if they are required.\nYou are an expert in composing functions. You are given a question and a set of possible functions.\nBased on the question, you may or may not need to make one function/tool call to achieve the purpose.\n\nIf you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]\nIf you decide to invoke a function, you SHOULD NOT include any other text in the response. besides the function call in the above format.\nFor a boolean parameter, be sure to use `True` or `False` (capitalized) for the value.\n\n\nHere is a list of functions in JSON format that you can invoke.\n\n[\n    {\n        \"name\": \"get_weather\",\n        \"description\": \"Get the current weather\",\n        \"parameters\": {\n            \"type\": \"dict\",\n            \"required\": [\"location\"],\n            \"properties\": {\n                \"location\": {\n                    \"type\": \"string\",\n                    \"description\": \"The city and state (both required), e.g. San Francisco, CA.\"\n                }\n            }\n        }\n    }\n]\n\nYou can answer general questions or invoke tools when necessary.\nIn addition to tool calls, you should also augment your responses by using the tool outputs.\nPretend you are a weather assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhat's the weather like in San Francisco?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
-      "options": {
-        "temperature": 0.0
-      },
-      "stream": true
-    },
-    "endpoint": "/api/generate",
-    "model": "llama3.2:3b-instruct-fp16"
-  },
-  "response": {
-    "body": [
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-08-01T20:56:51.314693Z",
-          "done": false,
-          "done_reason": null,
-          "total_duration": null,
-          "load_duration": null,
-          "prompt_eval_count": null,
-          "prompt_eval_duration": null,
-          "eval_count": null,
-          "eval_duration": null,
-          "response": "[",
-          "thinking": null,
-          "context": null
-        }
-      },
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-08-01T20:56:51.362989Z",
-          "done": false,
-          "done_reason": null,
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-          "eval_duration": null,
-          "response": "get",
-          "thinking": null,
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-        }
-      },
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-08-01T20:56:51.408403Z",
-          "done": false,
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-          "response": "_weather",
-          "thinking": null,
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-        }
-      },
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-08-01T20:56:51.455832Z",
-          "done": false,
-          "done_reason": null,
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-          "response": "(location",
-          "thinking": null,
-          "context": null
-        }
-      },
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-08-01T20:56:51.50384Z",
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-          "response": "=\"",
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-          "response": "San",
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tests/integration/recordings/responses/44fb9cf5875f.json

@@ -1,39 +0,0 @@
-{
-  "request": {
-    "method": "POST",
-    "url": "http://localhost:11434/api/generate",
-    "headers": {},
-    "body": {
-      "model": "llama3.2:3b-instruct-fp16",
-      "raw": true,
-      "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nTest trace 1<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
-      "options": {
-        "temperature": 0.0
-      },
-      "stream": false
-    },
-    "endpoint": "/api/generate",
-    "model": "llama3.2:3b-instruct-fp16"
-  },
-  "response": {
-    "body": {
-      "__type__": "ollama._types.GenerateResponse",
-      "__data__": {
-        "model": "llama3.2:3b-instruct-fp16",
-        "created_at": "2025-09-03T17:41:49.581065Z",
-        "done": true,
-        "done_reason": "stop",
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-        "prompt_eval_count": 20,
-        "prompt_eval_duration": 74456583,
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-        "eval_duration": 2134471458,
-        "response": "It seems like you're trying to test the system, but I'm not sure what specific functionality or feature you'd like to test. Could you please provide more context or clarify what you're looking for? I'll do my best to assist you!",
-        "thinking": null,
-        "context": null
-      }
-    },
-    "is_streaming": false
-  }
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tests/integration/recordings/responses/4597743bcd2a.json

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-{
-  "request": {
-    "method": "POST",
-    "url": "http://localhost:11434/api/generate",
-    "headers": {},
-    "body": {
-      "model": "llama3.2:3b-instruct-fp16",
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-      "options": {
-        "temperature": 0.0
-      },
-      "stream": true
-    },
-    "endpoint": "/api/generate",
-    "model": "llama3.2:3b-instruct-fp16"
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-  "response": {
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tests/integration/recordings/responses/47004e2babf0.json

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+      "input": [
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+      "encoding_format": "float"
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+    "endpoint": "/v1/embeddings",
+    "model": "nomic-embed-text:137m-v1.5-fp16"
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+    "body": {
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+            "index": 0,
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+        ],
+        "model": "nomic-embed-text:137m-v1.5-fp16",
+        "object": "list",
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+      }
+    },
+    "is_streaming": false
+  }
+}

tests/integration/recordings/responses/48d2fb183a2a.json

@@ -1,86 +0,0 @@
-{
-  "request": {
-    "method": "POST",
-    "url": "http://localhost:11434/api/generate",
-    "headers": {},
-    "body": {
-      "model": "llama3.2:3b-instruct-fp16",
-      "raw": true,
-      "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant. Michael Jordan was born in 1963. He played basketball for the Chicago Bulls for 15 seasons.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nPlease give me information about Michael Jordan.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nPlease respond in JSON format with the schema: {\"$defs\": {\"NBAStats\": {\"properties\": {\"year_for_draft\": {\"title\": \"Year For Draft\", \"type\": \"integer\"}, \"num_seasons_in_nba\": {\"title\": \"Num Seasons In Nba\", \"type\": \"integer\"}}, \"required\": [\"year_for_draft\", \"num_seasons_in_nba\"], \"title\": \"NBAStats\", \"type\": \"object\"}}, \"properties\": {\"first_name\": {\"title\": \"First Name\", \"type\": \"string\"}, \"last_name\": {\"title\": \"Last Name\", \"type\": \"string\"}, \"year_of_birth\": {\"title\": \"Year Of Birth\", \"type\": \"integer\"}, \"nba_stats\": {\"$ref\": \"#/$defs/NBAStats\"}}, \"required\": [\"first_name\", \"last_name\", \"year_of_birth\", \"nba_stats\"], \"title\": \"AnswerFormat\", \"type\": \"object\"}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
-      "format": {
-        "$defs": {
-          "NBAStats": {
-            "properties": {
-              "year_for_draft": {
-                "title": "Year For Draft",
-                "type": "integer"
-              },
-              "num_seasons_in_nba": {
-                "title": "Num Seasons In Nba",
-                "type": "integer"
-              }
-            },
-            "required": [
-              "year_for_draft",
-              "num_seasons_in_nba"
-            ],
-            "title": "NBAStats",
-            "type": "object"
-          }
-        },
-        "properties": {
-          "first_name": {
-            "title": "First Name",
-            "type": "string"
-          },
-          "last_name": {
-            "title": "Last Name",
-            "type": "string"
-          },
-          "year_of_birth": {
-            "title": "Year Of Birth",
-            "type": "integer"
-          },
-          "nba_stats": {
-            "$ref": "#/$defs/NBAStats"
-          }
-        },
-        "required": [
-          "first_name",
-          "last_name",
-          "year_of_birth",
-          "nba_stats"
-        ],
-        "title": "AnswerFormat",
-        "type": "object"
-      },
-      "options": {
-        "temperature": 0.0
-      },
-      "stream": false
-    },
-    "endpoint": "/api/generate",
-    "model": "llama3.2:3b-instruct-fp16"
-  },
-  "response": {
-    "body": {
-      "__type__": "ollama._types.GenerateResponse",
-      "__data__": {
-        "model": "llama3.2:3b-instruct-fp16",
-        "created_at": "2025-09-03T17:36:40.283084Z",
-        "done": true,
-        "done_reason": "stop",
-        "total_duration": 2900042958,
-        "load_duration": 83372125,
-        "prompt_eval_count": 259,
-        "prompt_eval_duration": 352890750,
-        "eval_count": 60,
-        "eval_duration": 2462885208,
-        "response": "{\n  \"first_name\": \"Michael\",\n  \"last_name\": \"Jordan\",\n  \"year_of_birth\": 1963,\n  \"nba_stats\": {\n    \"year_for_draft\": 1984,\n    \"num_seasons_in_nba\": 15\n  }\n}",
-        "thinking": null,
-        "context": null
-      }
-    },
-    "is_streaming": false
-  }
-}

tests/integration/recordings/responses/5357765a9ac9.json

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+{
+  "request": {
+    "method": "POST",
+    "url": "http://0.0.0.0:11434/v1/v1/embeddings",
+    "headers": {},
+    "body": {
+      "model": "nomic-embed-text:137m-v1.5-fp16",
+      "input": [
+        "This is a test file 0"
+      ],
+      "encoding_format": "float"
+    },
+    "endpoint": "/v1/embeddings",
+    "model": "nomic-embed-text:137m-v1.5-fp16"
+  },
+  "response": {
+    "body": {
+      "__type__": "openai.types.create_embedding_response.CreateEmbeddingResponse",
+      "__data__": {
+        "data": [
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+            ],
+            "index": 0,
+            "object": "embedding"
+          }
+        ],
+        "model": "nomic-embed-text:137m-v1.5-fp16",
+        "object": "list",
+        "usage": {
+          "prompt_tokens": 6,
+          "total_tokens": 6
+        }
+      }
+    },
+    "is_streaming": false
+  }
+}

tests/integration/recordings/responses/561746e1c8de.json

@@ -1,221 +0,0 @@
-{
-  "request": {
-    "method": "POST",
-    "url": "http://localhost:11434/api/generate",
-    "headers": {},
-    "body": {
-      "model": "llama3.2:3b-instruct-fp16",
-      "raw": true,
-      "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant. You have access to functions, but you should only use them if they are required.\nYou are an expert in composing functions. You are given a question and a set of possible functions.\nBased on the question, you may or may not need to make one function/tool call to achieve the purpose.\n\nIf you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]\nIf you decide to invoke a function, you SHOULD NOT include any other text in the response. besides the function call in the above format.\nFor a boolean parameter, be sure to use `True` or `False` (capitalized) for the value.\n\n\nHere is a list of functions in JSON format that you can invoke.\n\n[\n    {\n        \"name\": \"get_weather\",\n        \"description\": \"Get the current weather\",\n        \"parameters\": {\n            \"type\": \"dict\",\n            \"required\": [\"location\"],\n            \"properties\": {\n                \"location\": {\n                    \"type\": \"string\",\n                    \"description\": \"The city and state (both required), e.g. San Francisco, CA.\"\n                }\n            }\n        }\n    }\n]\n\nYou can answer general questions or invoke tools when necessary.\nIn addition to tool calls, you should also augment your responses by using the tool outputs.\nPretend you are a weather assistant.\nYou MUST use one of the provided functions/tools to answer the user query.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhat's the weather like in San Francisco, CA?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
-      "options": {
-        "temperature": 0.0
-      },
-      "stream": true
-    },
-    "endpoint": "/api/generate",
-    "model": "llama3.2:3b-instruct-fp16"
-  },
-  "response": {
-    "body": [
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-09-03T17:36:20.465701Z",
-          "done": false,
-          "done_reason": null,
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-      },
-      {
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-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
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-        "__data__": {
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-          "created_at": "2025-09-03T17:36:20.877784Z",
-          "done": true,
-          "done_reason": "stop",
-          "total_duration": 612057417,
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-          "prompt_eval_count": 341,
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-          "eval_count": 11,
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-    "is_streaming": true
-  }
-}

tests/integration/recordings/responses/563b994bb7d1.json

@@ -1,39 +0,0 @@
-{
-  "request": {
-    "method": "POST",
-    "url": "http://localhost:11434/api/generate",
-    "headers": {},
-    "body": {
-      "model": "llama3.2:3b-instruct-fp16",
-      "raw": true,
-      "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant. You have access to functions, but you should only use them if they are required.\nYou are an expert in composing functions. You are given a question and a set of possible functions.\nBased on the question, you may or may not need to make one function/tool call to achieve the purpose.\n\nIf you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]\nIf you decide to invoke a function, you SHOULD NOT include any other text in the response. besides the function call in the above format.\nFor a boolean parameter, be sure to use `True` or `False` (capitalized) for the value.\n\n\nHere is a list of functions in JSON format that you can invoke.\n\n[\n    {\n        \"name\": \"get_weather\",\n        \"description\": \"Get the current weather\",\n        \"parameters\": {\n            \"type\": \"dict\",\n            \"required\": [\"location\"],\n            \"properties\": {\n                \"location\": {\n                    \"type\": \"string\",\n                    \"description\": \"The city and state (both required), e.g. San Francisco, CA.\"\n                }\n            }\n        }\n    }\n]\n\nYou can answer general questions or invoke tools when necessary.\nIn addition to tool calls, you should also augment your responses by using the tool outputs.\nPretend you are a weather assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhat's the weather like in San Francisco, CA?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
-      "options": {
-        "temperature": 0.0
-      },
-      "stream": false
-    },
-    "endpoint": "/api/generate",
-    "model": "llama3.2:3b-instruct-fp16"
-  },
-  "response": {
-    "body": {
-      "__type__": "ollama._types.GenerateResponse",
-      "__data__": {
-        "model": "llama3.2:3b-instruct-fp16",
-        "created_at": "2025-09-03T17:36:19.594923Z",
-        "done": true,
-        "done_reason": "stop",
-        "total_duration": 988472417,
-        "load_duration": 117976625,
-        "prompt_eval_count": 326,
-        "prompt_eval_duration": 451625542,
-        "eval_count": 11,
-        "eval_duration": 418313417,
-        "response": "[get_weather(location=\"San Francisco, CA\")]",
-        "thinking": null,
-        "context": null
-      }
-    },
-    "is_streaming": false
-  }
-}

tests/integration/recordings/responses/5f5d16afadb4.json

@@ -1,221 +0,0 @@
-{
-  "request": {
-    "method": "POST",
-    "url": "http://localhost:11434/api/generate",
-    "headers": {},
-    "body": {
-      "model": "llama3.2:3b-instruct-fp16",
-      "raw": true,
-      "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant. You have access to functions, but you should only use them if they are required.\nYou are an expert in composing functions. You are given a question and a set of possible functions.\nBased on the question, you may or may not need to make one function/tool call to achieve the purpose.\n\nIf you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]\nIf you decide to invoke a function, you SHOULD NOT include any other text in the response. besides the function call in the above format.\nFor a boolean parameter, be sure to use `True` or `False` (capitalized) for the value.\n\n\nHere is a list of functions in JSON format that you can invoke.\n\n[\n    {\n        \"name\": \"get_weather\",\n        \"description\": \"Get the current weather\",\n        \"parameters\": {\n            \"type\": \"dict\",\n            \"required\": [\"location\"],\n            \"properties\": {\n                \"location\": {\n                    \"type\": \"string\",\n                    \"description\": \"The city and state (both required), e.g. San Francisco, CA.\"\n                }\n            }\n        }\n    }\n]\n\nYou can answer general questions or invoke tools when necessary.\nIn addition to tool calls, you should also augment your responses by using the tool outputs.\nPretend you are a weather assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhat's the weather like in San Francisco, CA?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
-      "options": {
-        "temperature": 0.0
-      },
-      "stream": true
-    },
-    "endpoint": "/api/generate",
-    "model": "llama3.2:3b-instruct-fp16"
-  },
-  "response": {
-    "body": [
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-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-09-03T17:36:19.808372Z",
-          "done": false,
-          "done_reason": null,
-          "total_duration": null,
-          "load_duration": null,
-          "prompt_eval_count": null,
-          "prompt_eval_duration": null,
-          "eval_count": null,
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-          "response": "[",
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-        "__data__": {
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-        "__data__": {
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-          "created_at": "2025-09-03T17:36:20.226017Z",
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-    ],
-    "is_streaming": true
-  }
-}

tests/integration/recordings/responses/6cc063bbd7d3.json

@@ -1,383 +0,0 @@
-{
-  "request": {
-    "method": "POST",
-    "url": "http://localhost:11434/api/generate",
-    "headers": {},
-    "body": {
-      "model": "llama3.2:3b-instruct-fp16",
-      "raw": true,
-      "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhat is the name of the US captial?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
-      "options": {
-        "temperature": 0.0
-      },
-      "stream": true
-    },
-    "endpoint": "/api/generate",
-    "model": "llama3.2:3b-instruct-fp16"
-  },
-  "response": {
-    "body": [
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-09-03T17:42:17.402486Z",
-          "done": false,
-          "done_reason": null,
-          "total_duration": null,
-          "load_duration": null,
-          "prompt_eval_count": null,
-          "prompt_eval_duration": null,
-          "eval_count": null,
-          "eval_duration": null,
-          "response": "The",
-          "thinking": null,
-          "context": null
-        }
-      },
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-09-03T17:42:17.444334Z",
-          "done": false,
-          "done_reason": null,
-          "total_duration": null,
-          "load_duration": null,
-          "prompt_eval_count": null,
-          "prompt_eval_duration": null,
-          "eval_count": null,
-          "eval_duration": null,
-          "response": " capital",
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-          "context": null
-        }
-      },
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-09-03T17:42:17.484625Z",
-          "done": false,
-          "done_reason": null,
-          "total_duration": null,
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-          "prompt_eval_count": null,
-          "prompt_eval_duration": null,
-          "eval_count": null,
-          "eval_duration": null,
-          "response": " of",
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-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-09-03T17:42:17.525063Z",
-          "done": false,
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-          "response": " the",
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-        "__data__": {
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-          "created_at": "2025-09-03T17:42:17.565015Z",
-          "done": false,
-          "done_reason": null,
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-          "eval_duration": null,
-          "response": " United",
-          "thinking": null,
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-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
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-          "created_at": "2025-09-03T17:42:17.60499Z",
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-        "__data__": {
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-          "eval_duration": null,
-          "response": ".C",
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-      {
-        "__type__": "ollama._types.GenerateResponse",
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-          "created_at": "2025-09-03T17:42:17.847273Z",
-          "done": false,
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-          "response": ".",
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-          "eval_duration": null,
-          "response": "short",
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-      {
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-          "created_at": "2025-09-03T17:42:17.969744Z",
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-          "created_at": "2025-09-03T17:42:18.010869Z",
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-          "response": " District",
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-      },
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-09-03T17:42:18.051109Z",
-          "done": false,
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-          "prompt_eval_duration": null,
-          "eval_count": null,
-          "eval_duration": null,
-          "response": " of",
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-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-09-03T17:42:18.093266Z",
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-          "eval_count": null,
-          "eval_duration": null,
-          "response": " Columbia",
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-      },
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-09-03T17:42:18.135749Z",
-          "done": false,
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-          "prompt_eval_duration": null,
-          "eval_count": null,
-          "eval_duration": null,
-          "response": ").",
-          "thinking": null,
-          "context": null
-        }
-      },
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-09-03T17:42:18.176649Z",
-          "done": true,
-          "done_reason": "stop",
-          "total_duration": 907420000,
-          "load_duration": 66756750,
-          "prompt_eval_count": 26,
-          "prompt_eval_duration": 62900875,
-          "eval_count": 20,
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-          "response": "",
-          "thinking": null,
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-      }
-    ],
-    "is_streaming": true
-  }
-}

tests/integration/recordings/responses/70adef2c30c4.json

@@ -1,39 +0,0 @@
-{
-  "request": {
-    "method": "POST",
-    "url": "http://localhost:11434/api/generate",
-    "headers": {},
-    "body": {
-      "model": "llama3.2:3b-instruct-fp16",
-      "raw": true,
-      "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhich planet has rings around it with a name starting with letter S?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
-      "options": {
-        "temperature": 0.0
-      },
-      "stream": false
-    },
-    "endpoint": "/api/generate",
-    "model": "llama3.2:3b-instruct-fp16"
-  },
-  "response": {
-    "body": {
-      "__type__": "ollama._types.GenerateResponse",
-      "__data__": {
-        "model": "llama3.2:3b-instruct-fp16",
-        "created_at": "2025-09-03T17:42:17.227488Z",
-        "done": true,
-        "done_reason": "stop",
-        "total_duration": 3003964916,
-        "load_duration": 111221916,
-        "prompt_eval_count": 30,
-        "prompt_eval_duration": 72578583,
-        "eval_count": 70,
-        "eval_duration": 2819555375,
-        "response": "The answer is Saturn! Saturn's ring system is one of the most iconic and well-known in our solar system. The rings are made up of ice particles, rock debris, and dust that orbit around the planet due to its gravitational pull.\n\nWould you like to know more about Saturn's rings or is there something else I can help you with?",
-        "thinking": null,
-        "context": null
-      }
-    },
-    "is_streaming": false
-  }
-}

tests/integration/recordings/responses/731824c54461.json

@@ -1,203 +0,0 @@
-{
-  "request": {
-    "method": "POST",
-    "url": "http://localhost:11434/api/generate",
-    "headers": {},
-    "body": {
-      "model": "llama3.2:3b-instruct-fp16",
-      "raw": true,
-      "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nGive me a sentence that contains the word: hello<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
-      "options": {
-        "temperature": 0.0
-      },
-      "stream": true
-    },
-    "endpoint": "/api/generate",
-    "model": "llama3.2:3b-instruct-fp16"
-  },
-  "response": {
-    "body": [
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-08-18T19:47:58.267146Z",
-          "done": false,
-          "done_reason": null,
-          "total_duration": null,
-          "load_duration": null,
-          "prompt_eval_count": null,
-          "prompt_eval_duration": null,
-          "eval_count": null,
-          "eval_duration": null,
-          "response": "Hello",
-          "thinking": null,
-          "context": null
-        }
-      },
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-08-18T19:47:58.309006Z",
-          "done": false,
-          "done_reason": null,
-          "total_duration": null,
-          "load_duration": null,
-          "prompt_eval_count": null,
-          "prompt_eval_duration": null,
-          "eval_count": null,
-          "eval_duration": null,
-          "response": ",",
-          "thinking": null,
-          "context": null
-        }
-      },
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-08-18T19:47:58.351179Z",
-          "done": false,
-          "done_reason": null,
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-          "prompt_eval_duration": null,
-          "eval_count": null,
-          "eval_duration": null,
-          "response": " how",
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-      },
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-08-18T19:47:58.393262Z",
-          "done": false,
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-          "eval_duration": null,
-          "response": " can",
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-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-08-18T19:47:58.436079Z",
-          "done": false,
-          "done_reason": null,
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-          "prompt_eval_duration": null,
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-          "eval_duration": null,
-          "response": " I",
-          "thinking": null,
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-        }
-      },
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-08-18T19:47:58.478393Z",
-          "done": false,
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-          "response": " assist",
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-        "__type__": "ollama._types.GenerateResponse",
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-          "model": "llama3.2:3b-instruct-fp16",
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-          "response": " you",
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-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-08-18T19:47:58.562885Z",
-          "done": false,
-          "done_reason": null,
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-          "eval_duration": null,
-          "response": " today",
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-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-08-18T19:47:58.604683Z",
-          "done": false,
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-          "eval_duration": null,
-          "response": "?",
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-      },
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-08-18T19:47:58.646586Z",
-          "done": true,
-          "done_reason": "stop",
-          "total_duration": 1011323917,
-          "load_duration": 76575458,
-          "prompt_eval_count": 31,
-          "prompt_eval_duration": 553259250,
-          "eval_count": 10,
-          "eval_duration": 380302792,
-          "response": "",
-          "thinking": null,
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-        }
-      }
-    ],
-    "is_streaming": true
-  }
-}

tests/integration/recordings/responses/7354ec181984.json

@@ -1,39 +0,0 @@
-{
-  "request": {
-    "method": "POST",
-    "url": "http://localhost:11434/api/generate",
-    "headers": {},
-    "body": {
-      "model": "llama3.2:3b-instruct-fp16",
-      "raw": true,
-      "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhat is the smallest country in the world?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
-      "options": {
-        "temperature": 0.0
-      },
-      "stream": false
-    },
-    "endpoint": "/api/generate",
-    "model": "llama3.2:3b-instruct-fp16"
-  },
-  "response": {
-    "body": {
-      "__type__": "ollama._types.GenerateResponse",
-      "__data__": {
-        "model": "llama3.2:3b-instruct-fp16",
-        "created_at": "2025-09-03T17:39:54.374714Z",
-        "done": true,
-        "done_reason": "stop",
-        "total_duration": 6321793333,
-        "load_duration": 182255958,
-        "prompt_eval_count": 25,
-        "prompt_eval_duration": 67964459,
-        "eval_count": 150,
-        "eval_duration": 6070867875,
-        "response": "The smallest country in the world is the Vatican City, which has a total area of approximately 0.44 km\u00b2 (0.17 sq mi). It is an independent city-state located within Rome, Italy, and is home to the Pope and the central government of the Catholic Church.\n\nTo put that into perspective, the Vatican City is smaller than a golf course! Despite its tiny size, it has its own government, currency, postal system, and even its own police force. It's also home to numerous iconic landmarks like St. Peter's Basilica and the Sistine Chapel.\n\nInterestingly, the Vatican City is not only the smallest country in the world but also the most densely populated, with a population of just over 800 people!",
-        "thinking": null,
-        "context": null
-      }
-    },
-    "is_streaming": false
-  }
-}

tests/integration/recordings/responses/75d0dd9d0fa3.json

@@ -1,64 +0,0 @@
-{
-  "request": {
-    "method": "POST",
-    "url": "http://localhost:11434/api/generate",
-    "headers": {},
-    "body": {
-      "model": "llama3.2:3b-instruct-fp16",
-      "prompt": "<|begin_of_text|>Michael Jordan was born in 1963. He played basketball for the Chicago Bulls. He retired in 2003.Please respond in JSON format with the schema: {\"properties\": {\"name\": {\"title\": \"Name\", \"type\": \"string\"}, \"year_born\": {\"title\": \"Year Born\", \"type\": \"string\"}, \"year_retired\": {\"title\": \"Year Retired\", \"type\": \"string\"}}, \"required\": [\"name\", \"year_born\", \"year_retired\"], \"title\": \"AnswerFormat\", \"type\": \"object\"}",
-      "raw": true,
-      "format": {
-        "properties": {
-          "name": {
-            "title": "Name",
-            "type": "string"
-          },
-          "year_born": {
-            "title": "Year Born",
-            "type": "string"
-          },
-          "year_retired": {
-            "title": "Year Retired",
-            "type": "string"
-          }
-        },
-        "required": [
-          "name",
-          "year_born",
-          "year_retired"
-        ],
-        "title": "AnswerFormat",
-        "type": "object"
-      },
-      "options": {
-        "temperature": 0.0,
-        "max_tokens": 50,
-        "num_predict": 50
-      },
-      "stream": false
-    },
-    "endpoint": "/api/generate",
-    "model": "llama3.2:3b-instruct-fp16"
-  },
-  "response": {
-    "body": {
-      "__type__": "ollama._types.GenerateResponse",
-      "__data__": {
-        "model": "llama3.2:3b-instruct-fp16",
-        "created_at": "2025-09-03T17:36:17.508028Z",
-        "done": true,
-        "done_reason": "stop",
-        "total_duration": 1529591917,
-        "load_duration": 84990667,
-        "prompt_eval_count": 119,
-        "prompt_eval_duration": 189045583,
-        "eval_count": 29,
-        "eval_duration": 1254813583,
-        "response": "{ \"name\": \"Michael Jordan\", \"year_born\": \"1963\", \"year_retired\": \"2003\"}\n    ",
-        "thinking": null,
-        "context": null
-      }
-    },
-    "is_streaming": false
-  }
-}

tests/integration/recordings/responses/7bcb0f86c91b.json

@@ -1,39 +0,0 @@
-{
-  "request": {
-    "method": "POST",
-    "url": "http://localhost:11434/api/generate",
-    "headers": {},
-    "body": {
-      "model": "llama3.2:3b-instruct-fp16",
-      "raw": true,
-      "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nTest metrics generation 0<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
-      "options": {
-        "temperature": 0.0
-      },
-      "stream": false
-    },
-    "endpoint": "/api/generate",
-    "model": "llama3.2:3b-instruct-fp16"
-  },
-  "response": {
-    "body": {
-      "__type__": "ollama._types.GenerateResponse",
-      "__data__": {
-        "model": "llama3.2:3b-instruct-fp16",
-        "created_at": "2025-08-11T15:51:12.918723Z",
-        "done": true,
-        "done_reason": "stop",
-        "total_duration": 8868987792,
-        "load_duration": 2793275292,
-        "prompt_eval_count": 21,
-        "prompt_eval_duration": 250000000,
-        "eval_count": 344,
-        "eval_duration": 5823000000,
-        "response": "Here are some common test metrics used to evaluate the performance of a system:\n\n1. **Accuracy**: The proportion of correct predictions or classifications out of total predictions made.\n2. **Precision**: The ratio of true positives (correctly predicted instances) to the sum of true positives and false positives (incorrectly predicted instances).\n3. **Recall**: The ratio of true positives to the sum of true positives and false negatives (missed instances).\n4. **F1-score**: The harmonic mean of precision and recall, providing a balanced measure of both.\n5. **Mean Squared Error (MSE)**: The average squared difference between predicted and actual values.\n6. **Mean Absolute Error (MAE)**: The average absolute difference between predicted and actual values.\n7. **Root Mean Squared Percentage Error (RMSPE)**: The square root of the mean of the squared percentage differences between predicted and actual values.\n8. **Coefficient of Determination (R-squared, R2)**: Measures how well a model fits the data, with higher values indicating better fit.\n9. **Mean Absolute Percentage Error (MAPE)**: The average absolute percentage difference between predicted and actual values.\n10. **Normalized Mean Squared Error (NMSE)**: Similar to MSE, but normalized by the mean of the actual values.\n\nThese metrics can be used for various types of data, including:\n\n* Regression problems (e.g., predicting continuous values)\n* Classification problems (e.g., predicting categorical labels)\n* Time series forecasting\n* Clustering and dimensionality reduction\n\nWhen choosing a metric, consider the specific problem you're trying to solve, the type of data, and the desired level of precision.",
-        "thinking": null,
-        "context": null
-      }
-    },
-    "is_streaming": false
-  }
-}

tests/integration/recordings/responses/87c056adc35c.json

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+{
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+      "input": [
+        "Why are data structures important?"
+      ],
+      "encoding_format": "float"
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+    "endpoint": "/v1/embeddings",
+    "model": "nomic-embed-text:137m-v1.5-fp16"
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+            "index": 0,
+            "object": "embedding"
+          }
+        ],
+        "model": "nomic-embed-text:137m-v1.5-fp16",
+        "object": "list",
+        "usage": {
+          "prompt_tokens": 6,
+          "total_tokens": 6
+        }
+      }
+    },
+    "is_streaming": false
+  }
+}

tests/integration/recordings/responses/9b812cbcb88d.json

@@ -1,39 +0,0 @@
-{
-  "request": {
-    "method": "POST",
-    "url": "http://localhost:11434/api/generate",
-    "headers": {},
-    "body": {
-      "model": "llama3.2:3b-instruct-fp16",
-      "raw": true,
-      "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant. You have access to functions, but you should only use them if they are required.\nYou are an expert in composing functions. You are given a question and a set of possible functions.\nBased on the question, you may or may not need to make one function/tool call to achieve the purpose.\n\nIf you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]\nIf you decide to invoke a function, you SHOULD NOT include any other text in the response. besides the function call in the above format.\nFor a boolean parameter, be sure to use `True` or `False` (capitalized) for the value.\n\n\nHere is a list of functions in JSON format that you can invoke.\n\n[\n    {\n        \"name\": \"get_weather\",\n        \"description\": \"Get the current weather\",\n        \"parameters\": {\n            \"type\": \"dict\",\n            \"required\": [\"location\"],\n            \"properties\": {\n                \"location\": {\n                    \"type\": \"string\",\n                    \"description\": \"The city and state (both required), e.g. San Francisco, CA.\"\n                }\n            }\n        }\n    }\n]\n\nYou can answer general questions or invoke tools when necessary.\nIn addition to tool calls, you should also augment your responses by using the tool outputs.\nPretend you are a weather assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhat's the weather like in San Francisco?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
-      "options": {
-        "temperature": 0.0
-      },
-      "stream": false
-    },
-    "endpoint": "/api/generate",
-    "model": "llama3.2:3b-instruct-fp16"
-  },
-  "response": {
-    "body": {
-      "__type__": "ollama._types.GenerateResponse",
-      "__data__": {
-        "model": "llama3.2:3b-instruct-fp16",
-        "created_at": "2025-08-01T20:56:51.035807Z",
-        "done": true,
-        "done_reason": "stop",
-        "total_duration": 1044135792,
-        "load_duration": 50873709,
-        "prompt_eval_count": 324,
-        "prompt_eval_duration": 511000000,
-        "eval_count": 11,
-        "eval_duration": 481000000,
-        "response": "[get_weather(location=\"San Francisco, CA\")]",
-        "thinking": null,
-        "context": null
-      }
-    },
-    "is_streaming": false
-  }
-}

tests/integration/recordings/responses/9c28ec9ac338.json

@@ -1,347 +0,0 @@
-{
-  "request": {
-    "method": "POST",
-    "url": "http://localhost:11434/api/generate",
-    "headers": {},
-    "body": {
-      "model": "llama3.2:3b-instruct-fp16",
-      "raw": true,
-      "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant. You have access to functions, but you should only use them if they are required.\nYou are an expert in composing functions. You are given a question and a set of possible functions.\nBased on the question, you may or may not need to make one function/tool call to achieve the purpose.\n\nIf you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]\nIf you decide to invoke a function, you SHOULD NOT include any other text in the response. besides the function call in the above format.\nFor a boolean parameter, be sure to use `True` or `False` (capitalized) for the value.\n\n\nHere is a list of functions in JSON format that you can invoke.\n\n[\n    {\n        \"name\": \"greet_everyone\",\n        \"description\": \"\",\n        \"parameters\": {\n            \"type\": \"dict\",\n            \"required\": [\"url\"],\n            \"properties\": {\n                \"url\": {\n                    \"type\": \"string\",\n                    \"description\": \"\"\n                }\n            }\n        }\n    },\n    {\n        \"name\": \"get_boiling_point\",\n        \"description\": \"\n        Returns the boiling point of a liquid in Celsius or Fahrenheit.\n\n        :param liquid_name: The name of the liquid\n        :param celsius: Whether to return the boiling point in Celsius\n        :return: The boiling point of the liquid in Celcius or Fahrenheit\n        \",\n        \"parameters\": {\n            \"type\": \"dict\",\n            \"required\": [\"liquid_name\", \"celsius\"],\n            \"properties\": {\n                \"liquid_name\": {\n                    \"type\": \"string\",\n                    \"description\": \"\"\n                },\n                \"celsius\": {\n                    \"type\": \"boolean\",\n                    \"description\": \"\"\n                }\n            }\n        }\n    }\n]\n\nYou can answer general questions or invoke tools when necessary.\nIn addition to tool calls, you should also augment your responses by using the tool outputs.\nYou are a helpful assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nSay hi to the world. Use tools to do so.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n[greet_everyone(url=\"world\")]<|eot_id|><|start_header_id|>ipython<|end_header_id|>\n\nHello, world!<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHow can I assist you further?<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhat is the boiling point of polyjuice? Use tools to answer.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
-      "options": {
-        "temperature": 0.0
-      },
-      "stream": true
-    },
-    "endpoint": "/api/generate",
-    "model": "llama3.2:3b-instruct-fp16"
-  },
-  "response": {
-    "body": [
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-09-03T17:34:23.434819Z",
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-          "eval_count": null,
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-          "response": "[",
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-      {
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-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-09-03T17:34:23.477986Z",
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-          "response": "get",
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-          "context": null
-        }
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-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
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-          "response": "_point",
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-        "__data__": {
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-          "created_at": "2025-09-03T17:34:23.646447Z",
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-          "eval_duration": null,
-          "response": "(",
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-      {
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-          "prompt_eval_count": null,
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-          "eval_duration": null,
-          "response": "elsius",
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-      },
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
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-          "response": "=True",
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-      },
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
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-      },
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-09-03T17:34:24.155424Z",
-          "done": true,
-          "done_reason": "stop",
-          "total_duration": 896931125,
-          "load_duration": 89697291,
-          "prompt_eval_count": 511,
-          "prompt_eval_duration": 83876750,
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-          "eval_duration": 722156292,
-          "response": "",
-          "thinking": null,
-          "context": null
-        }
-      }
-    ],
-    "is_streaming": true
-  }
-}

tests/integration/recordings/responses/9d84bd0e850f.json

@@ -0,0 +1,806 @@
+{
+  "request": {
+    "method": "POST",
+    "url": "http://0.0.0.0:11434/v1/v1/embeddings",
+    "headers": {},
+    "body": {
+      "model": "nomic-embed-text:137m-v1.5-fp16",
+      "input": [
+        "What is the secret string?"
+      ],
+      "encoding_format": "float"
+    },
+    "endpoint": "/v1/embeddings",
+    "model": "nomic-embed-text:137m-v1.5-fp16"
+  },
+  "response": {
+    "body": {
+      "__type__": "openai.types.create_embedding_response.CreateEmbeddingResponse",
+      "__data__": {
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tests/integration/recordings/responses/a4ef4fd267a0.json

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+        "usage": {
+          "prompt_tokens": 6,
+          "total_tokens": 6
+        }
+      }
+    },
+    "is_streaming": false
+  }
+}

tests/integration/recordings/responses/a6810c23eda8.json

@@ -1,799 +0,0 @@
-{
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-          "created_at": "2025-09-03T17:36:15.692748Z",
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tests/integration/recordings/responses/ae1c22f18ecc.json

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-{
-  "request": {
-    "method": "POST",
-    "url": "http://localhost:11434/api/generate",
-    "headers": {},
-    "body": {
-      "model": "llama3.2:3b-instruct-fp16",
-      "raw": true,
-      "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nTest trace 0<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
-      "options": {
-        "temperature": 0.0
-      },
-      "stream": false
-    },
-    "endpoint": "/api/generate",
-    "model": "llama3.2:3b-instruct-fp16"
-  },
-  "response": {
-    "body": {
-      "__type__": "ollama._types.GenerateResponse",
-      "__data__": {
-        "model": "llama3.2:3b-instruct-fp16",
-        "created_at": "2025-09-03T17:41:47.144448Z",
-        "done": true,
-        "done_reason": "stop",
-        "total_duration": 2462760250,
-        "load_duration": 83668541,
-        "prompt_eval_count": 20,
-        "prompt_eval_duration": 74227125,
-        "eval_count": 58,
-        "eval_duration": 2304346166,
-        "response": "I'm happy to help you with your test, but I don't see what kind of test we are testing. Could you please provide more context or clarify what kind of test you would like me to perform? Is it a programming test, a language proficiency test, or something else?",
-        "thinking": null,
-        "context": null
-      }
-    },
-    "is_streaming": false
-  }
-}

tests/integration/recordings/responses/ae6835cfe70e.json

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-{
-  "request": {
-    "method": "POST",
-    "url": "http://localhost:11434/api/generate",
-    "headers": {},
-    "body": {
-      "model": "llama3.2:3b-instruct-fp16",
-      "raw": true,
-      "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant. You have access to functions, but you should only use them if they are required.\nYou are an expert in composing functions. You are given a question and a set of possible functions.\nBased on the question, you may or may not need to make one function/tool call to achieve the purpose.\n\nIf you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]\nIf you decide to invoke a function, you SHOULD NOT include any other text in the response. besides the function call in the above format.\nFor a boolean parameter, be sure to use `True` or `False` (capitalized) for the value.\n\n\nHere is a list of functions in JSON format that you can invoke.\n\n[\n    {\n        \"name\": \"get_object_namespace_list\",\n        \"description\": \"Get the list of objects in a namespace\",\n        \"parameters\": {\n            \"type\": \"dict\",\n            \"required\": [\"kind\", \"namespace\"],\n            \"properties\": {\n                \"kind\": {\n                    \"type\": \"string\",\n                    \"description\": \"the type of object\"\n                },\n                \"namespace\": {\n                    \"type\": \"string\",\n                    \"description\": \"the name of the namespace\"\n                }\n            }\n        }\n    }\n]\n\nYou can answer general questions or invoke tools when necessary.\nIn addition to tool calls, you should also augment your responses by using the tool outputs.\nYou are a helpful assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhat pods are in the namespace openshift-lightspeed?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n[get_object_namespace_list(kind=\"pod\", namespace=\"openshift-lightspeed\")]<|eot_id|><|start_header_id|>ipython<|end_header_id|>\n\nthe objects are pod1, pod2, pod3<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
-      "options": {
-        "temperature": 0.0
-      },
-      "stream": false
-    },
-    "endpoint": "/api/generate",
-    "model": "llama3.2:3b-instruct-fp16"
-  },
-  "response": {
-    "body": {
-      "__type__": "ollama._types.GenerateResponse",
-      "__data__": {
-        "model": "llama3.2:3b-instruct-fp16",
-        "created_at": "2025-09-03T17:42:18.871277Z",
-        "done": true,
-        "done_reason": "stop",
-        "total_duration": 644170416,
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-        "prompt_eval_count": 386,
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-        "eval_duration": 42446084,
-        "response": "[]",
-        "thinking": null,
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-    },
-    "is_streaming": false
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tests/integration/recordings/responses/b14ff438ca99.json

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-{
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-    "method": "POST",
-    "url": "http://localhost:11434/api/generate",
-    "headers": {},
-    "body": {
-      "model": "llama3.2:3b-instruct-fp16",
-      "raw": true,
-      "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhat is the currency of Japan?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
-      "options": {
-        "temperature": 0.0
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-      "stream": false
-    },
-    "endpoint": "/api/generate",
-    "model": "llama3.2:3b-instruct-fp16"
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-  "response": {
-    "body": {
-      "__type__": "ollama._types.GenerateResponse",
-      "__data__": {
-        "model": "llama3.2:3b-instruct-fp16",
-        "created_at": "2025-09-03T17:39:59.708499Z",
-        "done": true,
-        "done_reason": "stop",
-        "total_duration": 5293681583,
-        "load_duration": 196095541,
-        "prompt_eval_count": 23,
-        "prompt_eval_duration": 72668042,
-        "eval_count": 124,
-        "eval_duration": 5024327166,
-        "response": "The official currency of Japan is the Japanese yen (\u00a5). It is abbreviated as \"JPY\" and its symbol is \u00a5. The yen is divided into 100 sen, although the sen has been officially discontinued since 1967.\n\nYou can exchange your money for yen at banks, currency exchange offices, or use ATMs to withdraw cash from an ATM. Credit cards are also widely accepted in Japan, especially among major retailers and restaurants.\n\nIt's worth noting that some businesses may not accept foreign currencies other than US dollars, so it's a good idea to have some local currency on hand when traveling to Japan.",
-        "thinking": null,
-        "context": null
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-    },
-    "is_streaming": false
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tests/integration/recordings/responses/b37b79e8ef96.json

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+    "headers": {},
+    "body": {
+      "model": "nomic-embed-text:137m-v1.5-fp16",
+      "input": [
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+      "encoding_format": "float"
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+    "endpoint": "/v1/embeddings",
+    "model": "nomic-embed-text:137m-v1.5-fp16"
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+  "response": {
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+}

tests/integration/recordings/responses/b91f1fb4aedb.json

@@ -1,221 +0,0 @@
-{
-  "request": {
-    "method": "POST",
-    "url": "http://localhost:11434/api/generate",
-    "headers": {},
-    "body": {
-      "model": "llama3.2:3b-instruct-fp16",
-      "raw": true,
-      "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant. You have access to functions, but you should only use them if they are required.\nYou are an expert in composing functions. You are given a question and a set of possible functions.\nBased on the question, you may or may not need to make one function/tool call to achieve the purpose.\n\nIf you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]\nIf you decide to invoke a function, you SHOULD NOT include any other text in the response. besides the function call in the above format.\nFor a boolean parameter, be sure to use `True` or `False` (capitalized) for the value.\n\n\nHere is a list of functions in JSON format that you can invoke.\n\n[\n    {\n        \"name\": \"get_weather\",\n        \"description\": \"Get the current weather\",\n        \"parameters\": {\n            \"type\": \"dict\",\n            \"required\": [\"location\"],\n            \"properties\": {\n                \"location\": {\n                    \"type\": \"string\",\n                    \"description\": \"The city and state (both required), e.g. San Francisco, CA.\"\n                }\n            }\n        }\n    }\n]\n\nYou can answer general questions or invoke tools when necessary.\nIn addition to tool calls, you should also augment your responses by using the tool outputs.\nPretend you are a weather assistant.\nYou MUST use one of the provided functions/tools to answer the user query.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhat's the weather like in San Francisco?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
-      "options": {
-        "temperature": 0.0
-      },
-      "stream": true
-    },
-    "endpoint": "/api/generate",
-    "model": "llama3.2:3b-instruct-fp16"
-  },
-  "response": {
-    "body": [
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-08-01T20:56:52.232108Z",
-          "done": false,
-          "done_reason": null,
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-          "prompt_eval_count": null,
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-          "eval_duration": null,
-          "response": "[",
-          "thinking": null,
-          "context": null
-        }
-      },
-      {
-        "__type__": "ollama._types.GenerateResponse",
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-          "created_at": "2025-08-01T20:56:52.278231Z",
-          "done": false,
-          "done_reason": null,
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-          "response": "get",
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-        }
-      },
-      {
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-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-08-01T20:56:52.324826Z",
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-          "response": "_weather",
-          "thinking": null,
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-        }
-      },
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
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-          "created_at": "2025-08-01T20:56:52.371742Z",
-          "done": false,
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-          "response": "(location",
-          "thinking": null,
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-        }
-      },
-      {
-        "__type__": "ollama._types.GenerateResponse",
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-      },
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+    "model": "nomic-embed-text:137m-v1.5-fp16"
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tests/integration/recordings/responses/bc581d1d19f9.json

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+        ],
+        "model": "nomic-embed-text:137m-v1.5-fp16",
+        "object": "list",
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+    },
+    "is_streaming": false
+  }
+}

tests/integration/recordings/responses/bd356b27a085.json

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-{
-  "request": {
-    "method": "POST",
-    "url": "http://localhost:11434/api/generate",
-    "headers": {},
-    "body": {
-      "model": "llama3.2:3b-instruct-fp16",
-      "raw": true,
-      "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant. You have access to functions, but you should only use them if they are required.\nYou are an expert in composing functions. You are given a question and a set of possible functions.\nBased on the question, you may or may not need to make one function/tool call to achieve the purpose.\n\nIf you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]\nIf you decide to invoke a function, you SHOULD NOT include any other text in the response. besides the function call in the above format.\nFor a boolean parameter, be sure to use `True` or `False` (capitalized) for the value.\n\n\nHere is a list of functions in JSON format that you can invoke.\n\n[\n    {\n        \"name\": \"greet_everyone\",\n        \"description\": \"\",\n        \"parameters\": {\n            \"type\": \"dict\",\n            \"required\": [\"url\"],\n            \"properties\": {\n                \"url\": {\n                    \"type\": \"string\",\n                    \"description\": \"\"\n                }\n            }\n        }\n    },\n    {\n        \"name\": \"get_boiling_point\",\n        \"description\": \"\n        Returns the boiling point of a liquid in Celsius or Fahrenheit.\n\n        :param liquid_name: The name of the liquid\n        :param celsius: Whether to return the boiling point in Celsius\n        :return: The boiling point of the liquid in Celcius or Fahrenheit\n        \",\n        \"parameters\": {\n            \"type\": \"dict\",\n            \"required\": [\"liquid_name\", \"celsius\"],\n            \"properties\": {\n                \"liquid_name\": {\n                    \"type\": \"string\",\n                    \"description\": \"\"\n                },\n                \"celsius\": {\n                    \"type\": \"boolean\",\n                    \"description\": \"\"\n                }\n            }\n        }\n    }\n]\n\nYou can answer general questions or invoke tools when necessary.\nIn addition to tool calls, you should also augment your responses by using the tool outputs.\nYou are a helpful assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nSay hi to the world. Use tools to do so.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n[greet_everyone(url=\"world\")]<|eot_id|><|start_header_id|>ipython<|end_header_id|>\n\nHello, world!<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
-      "options": {
-        "temperature": 0.0
-      },
-      "stream": true
-    },
-    "endpoint": "/api/generate",
-    "model": "llama3.2:3b-instruct-fp16"
-  },
-  "response": {
-    "body": [
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-09-03T17:34:22.916043Z",
-          "done": false,
-          "done_reason": null,
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-          "load_duration": null,
-          "prompt_eval_count": null,
-          "prompt_eval_duration": null,
-          "eval_count": null,
-          "eval_duration": null,
-          "response": "How",
-          "thinking": null,
-          "context": null
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-      {
-        "__type__": "ollama._types.GenerateResponse",
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-          "created_at": "2025-09-03T17:34:22.957379Z",
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-          "prompt_eval_duration": null,
-          "eval_count": null,
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-          "response": " can",
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-          "context": null
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-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-09-03T17:34:23.00029Z",
-          "done": false,
-          "done_reason": null,
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-        "__type__": "ollama._types.GenerateResponse",
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-      },
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-09-03T17:34:23.128181Z",
-          "done": false,
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-          "thinking": null,
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-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
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-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-09-03T17:34:23.216706Z",
-          "done": true,
-          "done_reason": "stop",
-          "total_duration": 516060000,
-          "load_duration": 127260334,
-          "prompt_eval_count": 479,
-          "prompt_eval_duration": 87107292,
-          "eval_count": 8,
-          "eval_duration": 299381042,
-          "response": "",
-          "thinking": null,
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-        }
-      }
-    ],
-    "is_streaming": true
-  }
-}

tests/integration/recordings/responses/bd656a9e3f8f.json

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+{
+  "request": {
+    "method": "POST",
+    "url": "http://0.0.0.0:11434/v1/v1/embeddings",
+    "headers": {},
+    "body": {
+      "model": "nomic-embed-text:137m-v1.5-fp16",
+      "input": [
+        "What makes Python different from other languages?"
+      ],
+      "encoding_format": "float"
+    },
+    "endpoint": "/v1/embeddings",
+    "model": "nomic-embed-text:137m-v1.5-fp16"
+  },
+  "response": {
+    "body": {
+      "__type__": "openai.types.create_embedding_response.CreateEmbeddingResponse",
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+}

tests/integration/recordings/responses/c31a86ea6c58.json

@@ -1,39 +0,0 @@
-{
-  "request": {
-    "method": "POST",
-    "url": "http://localhost:11434/api/generate",
-    "headers": {},
-    "body": {
-      "model": "llama3.2:3b",
-      "raw": true,
-      "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nTest metrics generation 0<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
-      "options": {
-        "temperature": 0.0
-      },
-      "stream": false
-    },
-    "endpoint": "/api/generate",
-    "model": "llama3.2:3b"
-  },
-  "response": {
-    "body": {
-      "__type__": "ollama._types.GenerateResponse",
-      "__data__": {
-        "model": "llama3.2:3b",
-        "created_at": "2025-08-11T15:56:06.703788Z",
-        "done": true,
-        "done_reason": "stop",
-        "total_duration": 2722294000,
-        "load_duration": 9736083,
-        "prompt_eval_count": 21,
-        "prompt_eval_duration": 113000000,
-        "eval_count": 324,
-        "eval_duration": 2598000000,
-        "response": "Here are some test metrics that can be used to evaluate the performance of a system:\n\n1. **Accuracy**: The proportion of correct predictions made by the model.\n2. **Precision**: The ratio of true positives (correctly predicted instances) to total positive predictions.\n3. **Recall**: The ratio of true positives to the sum of true positives and false negatives (missed instances).\n4. **F1-score**: The harmonic mean of precision and recall, providing a balanced measure of both.\n5. **Mean Squared Error (MSE)**: The average squared difference between predicted and actual values.\n6. **Mean Absolute Error (MAE)**: The average absolute difference between predicted and actual values.\n7. **Root Mean Squared Percentage Error (RMSPE)**: A variation of MSE that expresses the error as a percentage.\n8. **Coefficient of Determination (R-squared, R2)**: Measures how well the model explains the variance in the data.\n9. **Mean Absolute Percentage Error (MAPE)**: The average absolute percentage difference between predicted and actual values.\n10. **Mean Squared Logarithmic Error (MSLE)**: A variation of MSE that is more suitable for skewed distributions.\n\nThese metrics can be used to evaluate different aspects of a system's performance, such as:\n\n* Classification models: accuracy, precision, recall, F1-score\n* Regression models: MSE, MAE, RMSPE, R2, MSLE\n* Time series forecasting: MAPE, RMSPE\n\nNote that the choice of metric depends on the specific problem and data.",
-        "thinking": null,
-        "context": null
-      }
-    },
-    "is_streaming": false
-  }
-}

tests/integration/recordings/responses/c7582fa7c2c4.json

@@ -1,347 +0,0 @@
-{
-  "request": {
-    "method": "POST",
-    "url": "http://localhost:11434/api/generate",
-    "headers": {},
-    "body": {
-      "model": "llama3.2:3b-instruct-fp16",
-      "raw": true,
-      "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant. You have access to functions, but you should only use them if they are required.\nYou are an expert in composing functions. You are given a question and a set of possible functions.\nBased on the question, you may or may not need to make one function/tool call to achieve the purpose.\n\nIf you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]\nIf you decide to invoke a function, you SHOULD NOT include any other text in the response. besides the function call in the above format.\nFor a boolean parameter, be sure to use `True` or `False` (capitalized) for the value.\n\n\nHere is a list of functions in JSON format that you can invoke.\n\n[\n    {\n        \"name\": \"greet_everyone\",\n        \"description\": \"\",\n        \"parameters\": {\n            \"type\": \"dict\",\n            \"required\": [\"url\"],\n            \"properties\": {\n                \"url\": {\n                    \"type\": \"string\",\n                    \"description\": \"\"\n                }\n            }\n        }\n    },\n    {\n        \"name\": \"get_boiling_point\",\n        \"description\": \"\nReturns the boiling point of a liquid in Celsius or Fahrenheit.\n\n:param liquid_name: The name of the liquid\n:param celsius: Whether to return the boiling point in Celsius\n:return: The boiling point of the liquid in Celcius or Fahrenheit\n\",\n        \"parameters\": {\n            \"type\": \"dict\",\n            \"required\": [\"liquid_name\", \"celsius\"],\n            \"properties\": {\n                \"liquid_name\": {\n                    \"type\": \"string\",\n                    \"description\": \"\"\n                },\n                \"celsius\": {\n                    \"type\": \"boolean\",\n                    \"description\": \"\"\n                }\n            }\n        }\n    }\n]\n\nYou can answer general questions or invoke tools when necessary.\nIn addition to tool calls, you should also augment your responses by using the tool outputs.\nYou are a helpful assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nSay hi to the world. Use tools to do so.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n[greet_everyone(url=\"world\")]<|eot_id|><|start_header_id|>ipython<|end_header_id|>\n\nHello, world!<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHow can I assist you further?<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhat is the boiling point of polyjuice? Use tools to answer.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
-      "options": {
-        "temperature": 0.0
-      },
-      "stream": true
-    },
-    "endpoint": "/api/generate",
-    "model": "llama3.2:3b-instruct-fp16"
-  },
-  "response": {
-    "body": [
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-07-29T23:26:18.64197Z",
-          "done": false,
-          "done_reason": null,
-          "total_duration": null,
-          "load_duration": null,
-          "prompt_eval_count": null,
-          "prompt_eval_duration": null,
-          "eval_count": null,
-          "eval_duration": null,
-          "response": "[",
-          "thinking": null,
-          "context": null
-        }
-      },
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-07-29T23:26:18.687885Z",
-          "done": false,
-          "done_reason": null,
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-          "load_duration": null,
-          "prompt_eval_count": null,
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-          "eval_duration": null,
-          "response": "get",
-          "thinking": null,
-          "context": null
-        }
-      },
-      {
-        "__type__": "ollama._types.GenerateResponse",
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-          "model": "llama3.2:3b-instruct-fp16",
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-          "done": false,
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-          "response": "_bo",
-          "thinking": null,
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-        }
-      },
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-07-29T23:26:18.774191Z",
-          "done": false,
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-}

tests/integration/recordings/responses/dac7a32e5db9.json

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-{
-  "request": {
-    "method": "POST",
-    "url": "http://localhost:11434/api/generate",
-    "headers": {},
-    "body": {
-      "model": "llama3.2:3b-instruct-fp16",
-      "raw": true,
-      "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhat is the capital of France?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
-      "options": {
-        "temperature": 0.0
-      },
-      "stream": false
-    },
-    "endpoint": "/api/generate",
-    "model": "llama3.2:3b-instruct-fp16"
-  },
-  "response": {
-    "body": {
-      "__type__": "ollama._types.GenerateResponse",
-      "__data__": {
-        "model": "llama3.2:3b-instruct-fp16",
-        "created_at": "2025-09-03T17:39:36.919474Z",
-        "done": true,
-        "done_reason": "stop",
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-        "prompt_eval_count": 23,
-        "prompt_eval_duration": 67480542,
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-        "eval_duration": 288746541,
-        "response": "The capital of France is Paris.",
-        "thinking": null,
-        "context": null
-      }
-    },
-    "is_streaming": false
-  }
-}

tests/integration/recordings/responses/dd9e7d5913e9.json

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-{
-  "request": {
-    "method": "POST",
-    "url": "http://localhost:11434/api/generate",
-    "headers": {},
-    "body": {
-      "model": "llama3.2:3b-instruct-fp16",
-      "raw": true,
-      "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant. You have access to functions, but you should only use them if they are required.\nYou are an expert in composing functions. You are given a question and a set of possible functions.\nBased on the question, you may or may not need to make one function/tool call to achieve the purpose.\n\nIf you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]\nIf you decide to invoke a function, you SHOULD NOT include any other text in the response. besides the function call in the above format.\nFor a boolean parameter, be sure to use `True` or `False` (capitalized) for the value.\n\n\nHere is a list of functions in JSON format that you can invoke.\n\n[\n    {\n        \"name\": \"get_object_namespace_list\",\n        \"description\": \"Get the list of objects in a namespace\",\n        \"parameters\": {\n            \"type\": \"dict\",\n            \"required\": [\"kind\", \"namespace\"],\n            \"properties\": {\n                \"kind\": {\n                    \"type\": \"string\",\n                    \"description\": \"the type of object\"\n                },\n                \"namespace\": {\n                    \"type\": \"string\",\n                    \"description\": \"the name of the namespace\"\n                }\n            }\n        }\n    }\n]\n\nYou can answer general questions or invoke tools when necessary.\nIn addition to tool calls, you should also augment your responses by using the tool outputs.\nYou are a helpful assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhat pods are in the namespace openshift-lightspeed?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n[get_object_namespace_list(kind=\"pod\", namespace=\"openshift-lightspeed\")]<|eot_id|><|start_header_id|>ipython<|end_header_id|>\n\nthe objects are pod1, pod2, pod3<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
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-      "stream": true
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-    "endpoint": "/api/generate",
-    "model": "llama3.2:3b-instruct-fp16"
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tests/integration/recordings/responses/e19cd96d3d9f.json

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+    "url": "http://0.0.0.0:11434/v1/v1/embeddings",
+    "headers": {},
+    "body": {
+      "model": "nomic-embed-text:137m-v1.5-fp16",
+      "input": [
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+      "encoding_format": "float"
+    },
+    "endpoint": "/v1/embeddings",
+    "model": "nomic-embed-text:137m-v1.5-fp16"
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+  "response": {
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+            ],
+            "index": 0,
+            "object": "embedding"
+          }
+        ],
+        "model": "nomic-embed-text:137m-v1.5-fp16",
+        "object": "list",
+        "usage": {
+          "prompt_tokens": 2,
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+        }
+      }
+    },
+    "is_streaming": false
+  }
+}

tests/integration/recordings/responses/ed9e9b34008d.json

@@ -1,39 +0,0 @@
-{
-  "request": {
-    "method": "POST",
-    "url": "http://localhost:11434/api/generate",
-    "headers": {},
-    "body": {
-      "model": "llama3.2:3b-instruct-fp16",
-      "raw": true,
-      "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWhat is the largest planet in our solar system?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
-      "options": {
-        "temperature": 0.0
-      },
-      "stream": false
-    },
-    "endpoint": "/api/generate",
-    "model": "llama3.2:3b-instruct-fp16"
-  },
-  "response": {
-    "body": {
-      "__type__": "ollama._types.GenerateResponse",
-      "__data__": {
-        "model": "llama3.2:3b-instruct-fp16",
-        "created_at": "2025-09-03T17:39:48.030217Z",
-        "done": true,
-        "done_reason": "stop",
-        "total_duration": 9760536750,
-        "load_duration": 242188583,
-        "prompt_eval_count": 26,
-        "prompt_eval_duration": 83819333,
-        "eval_count": 232,
-        "eval_duration": 9434009042,
-        "response": "The largest planet in our solar system is Jupiter. It is a gas giant, meaning it is primarily composed of hydrogen and helium gases. Jupiter has a diameter of approximately 142,984 kilometers (88,846 miles), which is more than 11 times the diameter of Earth.\n\nJupiter is not only the largest planet in terms of size, but also the most massive planet in our solar system, with a mass that is more than 318 times that of Earth. It has a thick atmosphere and a strong magnetic field, and is known for its distinctive banded appearance, which is caused by strong winds in the upper atmosphere.\n\nJupiter's massive size and gravitational pull have a significant impact on the surrounding space, including the orbits of nearby planets and asteroids. Its moons are also notable, with four large ones: Io, Europa, Ganymede, and Callisto, which are known as the Galilean moons due to their discovery by Galileo Galilei in 1610.\n\nJupiter is a fascinating planet that continues to be studied by astronomers and space agencies around the world, offering insights into the formation and evolution of our solar system.",
-        "thinking": null,
-        "context": null
-      }
-    },
-    "is_streaming": false
-  }
-}

tests/integration/recordings/responses/ef757a75ed08.json

@@ -1,185 +0,0 @@
-{
-  "request": {
-    "method": "POST",
-    "url": "http://localhost:11434/api/generate",
-    "headers": {},
-    "body": {
-      "model": "llama3.2:3b-instruct-fp16",
-      "raw": true,
-      "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant. You have access to functions, but you should only use them if they are required.\nYou are an expert in composing functions. You are given a question and a set of possible functions.\nBased on the question, you may or may not need to make one function/tool call to achieve the purpose.\n\nIf you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]\nIf you decide to invoke a function, you SHOULD NOT include any other text in the response. besides the function call in the above format.\nFor a boolean parameter, be sure to use `True` or `False` (capitalized) for the value.\n\n\nHere is a list of functions in JSON format that you can invoke.\n\n[\n    {\n        \"name\": \"greet_everyone\",\n        \"description\": \"\",\n        \"parameters\": {\n            \"type\": \"dict\",\n            \"required\": [\"url\"],\n            \"properties\": {\n                \"url\": {\n                    \"type\": \"string\",\n                    \"description\": \"\"\n                }\n            }\n        }\n    },\n    {\n        \"name\": \"get_boiling_point\",\n        \"description\": \"\n        Returns the boiling point of a liquid in Celsius or Fahrenheit.\n\n        :param liquid_name: The name of the liquid\n        :param celsius: Whether to return the boiling point in Celsius\n        :return: The boiling point of the liquid in Celcius or Fahrenheit\n        \",\n        \"parameters\": {\n            \"type\": \"dict\",\n            \"required\": [\"liquid_name\", \"celsius\"],\n            \"properties\": {\n                \"liquid_name\": {\n                    \"type\": \"string\",\n                    \"description\": \"\"\n                },\n                \"celsius\": {\n                    \"type\": \"boolean\",\n                    \"description\": \"\"\n                }\n            }\n        }\n    }\n]\n\nYou can answer general questions or invoke tools when necessary.\nIn addition to tool calls, you should also augment your responses by using the tool outputs.\nYou are a helpful assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nSay hi to the world. Use tools to do so.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
-      "options": {
-        "temperature": 0.0
-      },
-      "stream": true
-    },
-    "endpoint": "/api/generate",
-    "model": "llama3.2:3b-instruct-fp16"
-  },
-  "response": {
-    "body": [
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-09-03T17:34:22.272912Z",
-          "done": false,
-          "done_reason": null,
-          "total_duration": null,
-          "load_duration": null,
-          "prompt_eval_count": null,
-          "prompt_eval_duration": null,
-          "eval_count": null,
-          "eval_duration": null,
-          "response": "[g",
-          "thinking": null,
-          "context": null
-        }
-      },
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
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-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-09-03T17:34:22.398576Z",
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-          "model": "llama3.2:3b-instruct-fp16",
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-          "response": "(url",
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-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
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-        "__data__": {
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-          "created_at": "2025-09-03T17:34:22.523773Z",
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-          "response": "world",
-          "thinking": null,
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-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
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-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:3b-instruct-fp16",
-          "created_at": "2025-09-03T17:34:22.607117Z",
-          "done": true,
-          "done_reason": "stop",
-          "total_duration": 1386049708,
-          "load_duration": 96970583,
-          "prompt_eval_count": 456,
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-    "is_streaming": true
-  }
-}

tests/integration/recordings/responses/f3c3afbd9b7e.json

@@ -1,59 +0,0 @@
-{
-  "request": {
-    "method": "POST",
-    "url": "http://localhost:11434/api/generate",
-    "headers": {},
-    "body": {
-      "model": "llama3.2:1b",
-      "raw": true,
-      "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant. You have access to functions, but you should only use them if they are required.\nYou are an expert in composing functions. You are given a question and a set of possible functions.\nBased on the question, you may or may not need to make one function/tool call to achieve the purpose.\n\nIf you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]\nIf you decide to invoke a function, you SHOULD NOT include any other text in the response. besides the function call in the above format.\nFor a boolean parameter, be sure to use `True` or `False` (capitalized) for the value.\n\n\nHere is a list of functions in JSON format that you can invoke.\n\n[\n    {\n        \"name\": \"greet_everyone\",\n        \"description\": \"\",\n        \"parameters\": {\n            \"type\": \"dict\",\n            \"required\": [\"url\"],\n            \"properties\": {\n                \"url\": {\n                    \"type\": \"string\",\n                    \"description\": \"\"\n                }\n            }\n        }\n    },\n    {\n        \"name\": \"get_boiling_point\",\n        \"description\": \"\nReturns the boiling point of a liquid in Celsius or Fahrenheit.\n\n:param liquid_name: The name of the liquid\n:param celsius: Whether to return the boiling point in Celsius\n:return: The boiling point of the liquid in Celcius or Fahrenheit\n\",\n        \"parameters\": {\n            \"type\": \"dict\",\n            \"required\": [\"liquid_name\", \"celsius\"],\n            \"properties\": {\n                \"liquid_name\": {\n                    \"type\": \"string\",\n                    \"description\": \"\"\n                },\n                \"celsius\": {\n                    \"type\": \"boolean\",\n                    \"description\": \"\"\n                }\n            }\n        }\n    }\n]\n\nYou can answer general questions or invoke tools when necessary.\nIn addition to tool calls, you should also augment your responses by using the tool outputs.\nYou are a helpful assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nSay hi to the world. Use tools to do so.<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
-      "options": {
-        "temperature": 0.0
-      },
-      "stream": true
-    },
-    "endpoint": "/api/generate",
-    "model": "llama3.2:1b"
-  },
-  "response": {
-    "body": [
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:1b",
-          "created_at": "2025-07-29T23:23:09.553247Z",
-          "done": false,
-          "done_reason": null,
-          "total_duration": null,
-          "load_duration": null,
-          "prompt_eval_count": null,
-          "prompt_eval_duration": null,
-          "eval_count": null,
-          "eval_duration": null,
-          "response": "Hi",
-          "thinking": null,
-          "context": null
-        }
-      },
-      {
-        "__type__": "ollama._types.GenerateResponse",
-        "__data__": {
-          "model": "llama3.2:1b",
-          "created_at": "2025-07-29T23:23:09.564069Z",
-          "done": true,
-          "done_reason": "stop",
-          "total_duration": 2125493250,
-          "load_duration": 1610279708,
-          "prompt_eval_count": 448,
-          "prompt_eval_duration": 502413125,
-          "eval_count": 2,
-          "eval_duration": 11573709,
-          "response": "",
-          "thinking": null,
-          "context": null
-        }
-      }
-    ],
-    "is_streaming": true
-  }
-}

tests/integration/recordings/responses/f6857bcea729.json

@@ -1,39 +0,0 @@
-{
-  "request": {
-    "method": "POST",
-    "url": "http://localhost:11434/api/generate",
-    "headers": {},
-    "body": {
-      "model": "llama3.2:3b",
-      "raw": true,
-      "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nTest metrics generation 2<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
-      "options": {
-        "temperature": 0.0
-      },
-      "stream": false
-    },
-    "endpoint": "/api/generate",
-    "model": "llama3.2:3b"
-  },
-  "response": {
-    "body": {
-      "__type__": "ollama._types.GenerateResponse",
-      "__data__": {
-        "model": "llama3.2:3b",
-        "created_at": "2025-08-11T15:56:13.082679Z",
-        "done": true,
-        "done_reason": "stop",
-        "total_duration": 2606245291,
-        "load_duration": 9979708,
-        "prompt_eval_count": 21,
-        "prompt_eval_duration": 23000000,
-        "eval_count": 321,
-        "eval_duration": 2572000000,
-        "response": "Here are some test metrics that can be used to evaluate the performance of a system:\n\n1. **Accuracy**: Measures how close the predicted values are to the actual values.\n2. **Precision**: Measures the proportion of true positives among all positive predictions made by the model.\n3. **Recall**: Measures the proportion of true positives among all actual positive instances.\n4. **F1-score**: The harmonic mean of precision and recall, providing a balanced measure of both.\n5. **Mean Squared Error (MSE)**: Measures the average squared difference between predicted and actual values.\n6. **Mean Absolute Error (MAE)**: Measures the average absolute difference between predicted and actual values.\n7. **Root Mean Squared Percentage Error (RMSPE)**: A variation of MSE that expresses errors as a percentage of the actual value.\n8. **Coefficient of Determination (R-squared, R2)**: Measures how well the model explains the variance in the data.\n9. **Mean Absolute Percentage Error (MAPE)**: Measures the average absolute percentage difference between predicted and actual values.\n10. **Mean Squared Logarithmic Error (MSLE)**: A variation of MSE that is more suitable for skewed distributions.\n\nThese metrics can be used to evaluate different aspects of a system's performance, such as:\n\n* Classification models: accuracy, precision, recall, F1-score\n* Regression models: MSE, MAE, RMSPE, R2\n* Time series forecasting: MAPE, MSLE\n\nNote that the choice of metric depends on the specific problem and data.",
-        "thinking": null,
-        "context": null
-      }
-    },
-    "is_streaming": false
-  }
-}

tests/integration/recordings/responses/f80b99430f7e.json

@@ -1,39 +0,0 @@
-{
-  "request": {
-    "method": "POST",
-    "url": "http://localhost:11434/api/generate",
-    "headers": {},
-    "body": {
-      "model": "llama3.2:3b",
-      "raw": true,
-      "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nTest metrics generation 1<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
-      "options": {
-        "temperature": 0.0
-      },
-      "stream": false
-    },
-    "endpoint": "/api/generate",
-    "model": "llama3.2:3b"
-  },
-  "response": {
-    "body": {
-      "__type__": "ollama._types.GenerateResponse",
-      "__data__": {
-        "model": "llama3.2:3b",
-        "created_at": "2025-08-11T15:56:10.465932Z",
-        "done": true,
-        "done_reason": "stop",
-        "total_duration": 3745686709,
-        "load_duration": 9734584,
-        "prompt_eval_count": 21,
-        "prompt_eval_duration": 23000000,
-        "eval_count": 457,
-        "eval_duration": 3712000000,
-        "response": "Here are some test metrics that can be used to evaluate the performance of a system:\n\n**Primary Metrics**\n\n1. **Response Time**: The time it takes for the system to respond to a request.\n2. **Throughput**: The number of requests processed by the system per unit time (e.g., requests per second).\n3. **Error Rate**: The percentage of requests that result in an error.\n\n**Secondary Metrics**\n\n1. **Average Response Time**: The average response time for all requests.\n2. **Median Response Time**: The middle value of the response times, used to detect outliers.\n3. **99th Percentile Response Time**: The response time at which 99% of requests are completed within this time.\n4. **Request Latency**: The difference between the request arrival time and the response time.\n\n**User Experience Metrics**\n\n1. **User Satisfaction (USAT)**: Measured through surveys or feedback forms to gauge user satisfaction with the system's performance.\n2. **First Response Time**: The time it takes for a user to receive their first response from the system.\n3. **Time Spent in System**: The total amount of time a user spends interacting with the system.\n\n**System Resource Metrics**\n\n1. **CPU Utilization**: The percentage of CPU resources being used by the system.\n2. **Memory Usage**: The amount of memory being used by the system.\n3. **Disk I/O Wait Time**: The average time spent waiting for disk I/O operations to complete.\n\n**Security Metrics**\n\n1. **Authentication Success Rate**: The percentage of successful authentication attempts.\n2. **Authorization Success Rate**: The percentage of successful authorization attempts.\n3. **Error Rate (Security)**: The percentage of security-related errors.\n\n**Other Metrics**\n\n1. **Page Load Time**: The time it takes for a page to load.\n2. **Click-Through Rate (CTR)**: The percentage of users who click on a link or button after seeing an ad or notification.\n3. **Conversion Rate**: The percentage of users who complete a desired action (e.g., fill out a form, make a purchase).\n\nThese metrics can be used to evaluate the performance and effectiveness of various aspects of your system, from user experience to security and resource utilization.",
-        "thinking": null,
-        "context": null
-      }
-    },
-    "is_streaming": false
-  }
-}