diff --git a/.github/actions/run-and-record-tests/action.yml b/.github/actions/run-and-record-tests/action.yml
index d240381c5..a5aa31af4 100644
--- a/.github/actions/run-and-record-tests/action.yml
+++ b/.github/actions/run-and-record-tests/action.yml
@@ -66,11 +66,11 @@ runs:
       shell: bash
       run: |
         echo "Checking for recording changes"
-        git status --porcelain tests/integration/recordings/
+        git status --porcelain tests/integration/
 
-        if [[ -n $(git status --porcelain tests/integration/recordings/) ]]; then
+        if [[ -n $(git status --porcelain tests/integration/) ]]; then
           echo "New recordings detected, committing and pushing"
-          git add tests/integration/recordings/
+          git add tests/integration/
 
           git commit -m "Recordings update from CI (suite: ${{ inputs.suite }})"
           git fetch origin ${{ github.ref_name }}
diff --git a/llama_stack/core/routers/vector_io.py b/llama_stack/core/routers/vector_io.py
index 79789ef0a..dc7b3a694 100644
--- a/llama_stack/core/routers/vector_io.py
+++ b/llama_stack/core/routers/vector_io.py
@@ -55,30 +55,18 @@ class VectorIORouter(VectorIO):
         logger.debug("VectorIORouter.shutdown")
         pass
 
-    async def _get_first_embedding_model(self) -> tuple[str, int] | None:
-        """Get the first available embedding model identifier."""
-        try:
-            # Get all models from the routing table
-            all_models = await self.routing_table.get_all_with_type("model")
+    async def _get_embedding_model_dimension(self, embedding_model_id: str) -> int:
+        """Get the embedding dimension for a specific embedding model."""
+        all_models = await self.routing_table.get_all_with_type("model")
 
-            # Filter for embedding models
-            embedding_models = [
-                model
-                for model in all_models
-                if hasattr(model, "model_type") and model.model_type == ModelType.embedding
-            ]
-
-            if embedding_models:
-                dimension = embedding_models[0].metadata.get("embedding_dimension", None)
+        for model in all_models:
+            if model.identifier == embedding_model_id and model.model_type == ModelType.embedding:
+                dimension = model.metadata.get("embedding_dimension")
                 if dimension is None:
-                    raise ValueError(f"Embedding model {embedding_models[0].identifier} has no embedding dimension")
-                return embedding_models[0].identifier, dimension
-            else:
-                logger.warning("No embedding models found in the routing table")
-                return None
-        except Exception as e:
-            logger.error(f"Error getting embedding models: {e}")
-            return None
+                    raise ValueError(f"Embedding model '{embedding_model_id}' has no embedding_dimension in metadata")
+                return int(dimension)
+
+        raise ValueError(f"Embedding model '{embedding_model_id}' not found or not an embedding model")
 
     async def register_vector_db(
         self,
@@ -129,20 +117,30 @@ class VectorIORouter(VectorIO):
         # Extract llama-stack-specific parameters from extra_body
         extra = params.model_extra or {}
         embedding_model = extra.get("embedding_model")
-        embedding_dimension = extra.get("embedding_dimension", 384)
+        embedding_dimension = extra.get("embedding_dimension")
         provider_id = extra.get("provider_id")
 
         logger.debug(f"VectorIORouter.openai_create_vector_store: name={params.name}, provider_id={provider_id}")
 
-        # If no embedding model is provided, use the first available one
-        # TODO: this branch will soon be deleted so you _must_ provide the embedding_model when
-        # creating a vector store
+        # Require explicit embedding model specification
         if embedding_model is None:
-            embedding_model_info = await self._get_first_embedding_model()
-            if embedding_model_info is None:
-                raise ValueError("No embedding model provided and no embedding models available in the system")
-            embedding_model, embedding_dimension = embedding_model_info
-            logger.info(f"No embedding model specified, using first available: {embedding_model}")
+            raise ValueError("embedding_model is required in extra_body when creating a vector store")
+
+        if embedding_dimension is None:
+            embedding_dimension = await self._get_embedding_model_dimension(embedding_model)
+
+        # Auto-select provider if not specified
+        if provider_id is None:
+            num_providers = len(self.routing_table.impls_by_provider_id)
+            if num_providers == 0:
+                raise ValueError("No vector_io providers available")
+            if num_providers > 1:
+                available_providers = list(self.routing_table.impls_by_provider_id.keys())
+                raise ValueError(
+                    f"Multiple vector_io providers available. Please specify provider_id in extra_body. "
+                    f"Available providers: {available_providers}"
+                )
+            provider_id = list(self.routing_table.impls_by_provider_id.keys())[0]
 
         vector_db_id = f"vs_{uuid.uuid4()}"
         registered_vector_db = await self.routing_table.register_vector_db(
diff --git a/llama_stack/core/server/auth_providers.py b/llama_stack/core/server/auth_providers.py
index 38188c49a..05a21c8d4 100644
--- a/llama_stack/core/server/auth_providers.py
+++ b/llama_stack/core/server/auth_providers.py
@@ -5,13 +5,11 @@
 # the root directory of this source tree.
 
 import ssl
-import time
 from abc import ABC, abstractmethod
-from asyncio import Lock
 from urllib.parse import parse_qs, urljoin, urlparse
 
 import httpx
-from jose import jwt
+import jwt
 from pydantic import BaseModel, Field
 
 from llama_stack.apis.common.errors import TokenValidationError
@@ -98,9 +96,7 @@ class OAuth2TokenAuthProvider(AuthProvider):
 
     def __init__(self, config: OAuth2TokenAuthConfig):
         self.config = config
-        self._jwks_at: float = 0.0
-        self._jwks: dict[str, str] = {}
-        self._jwks_lock = Lock()
+        self._jwks_client: jwt.PyJWKClient | None = None
 
     async def validate_token(self, token: str, scope: dict | None = None) -> User:
         if self.config.jwks:
@@ -109,23 +105,60 @@ class OAuth2TokenAuthProvider(AuthProvider):
             return await self.introspect_token(token, scope)
         raise ValueError("One of jwks or introspection must be configured")
 
+    def _get_jwks_client(self) -> jwt.PyJWKClient:
+        if self._jwks_client is None:
+            ssl_context = None
+            if not self.config.verify_tls:
+                # Disable SSL verification if verify_tls is False
+                ssl_context = ssl.create_default_context()
+                ssl_context.check_hostname = False
+                ssl_context.verify_mode = ssl.CERT_NONE
+            elif self.config.tls_cafile:
+                # Use custom CA file if provided
+                ssl_context = ssl.create_default_context(
+                    cafile=self.config.tls_cafile.as_posix(),
+                )
+                # If verify_tls is True and no tls_cafile, ssl_context remains None (use system defaults)
+
+            # Prepare headers for JWKS request - this is needed for Kubernetes to authenticate
+            # to the JWK endpoint, we must use the token in the config to authenticate
+            headers = {}
+            if self.config.jwks and self.config.jwks.token:
+                headers["Authorization"] = f"Bearer {self.config.jwks.token}"
+
+            self._jwks_client = jwt.PyJWKClient(
+                self.config.jwks.uri if self.config.jwks else None,
+                cache_keys=True,
+                max_cached_keys=10,
+                lifespan=self.config.jwks.key_recheck_period if self.config.jwks else None,
+                headers=headers,
+                ssl_context=ssl_context,
+            )
+        return self._jwks_client
+
     async def validate_jwt_token(self, token: str, scope: dict | None = None) -> User:
         """Validate a token using the JWT token."""
-        await self._refresh_jwks()
-
         try:
-            header = jwt.get_unverified_header(token)
-            kid = header["kid"]
-            if kid not in self._jwks:
-                raise ValueError(f"Unknown key ID: {kid}")
-            key_data = self._jwks[kid]
-            algorithm = header.get("alg", "RS256")
+            jwks_client: jwt.PyJWKClient = self._get_jwks_client()
+            signing_key = jwks_client.get_signing_key_from_jwt(token)
+            algorithm = jwt.get_unverified_header(token)["alg"]
             claims = jwt.decode(
                 token,
-                key_data,
+                signing_key.key,
                 algorithms=[algorithm],
                 audience=self.config.audience,
                 issuer=self.config.issuer,
+                options={"verify_exp": True, "verify_aud": True, "verify_iss": True},
+            )
+
+            # Decode and verify the JWT
+            claims = jwt.decode(
+                token,
+                signing_key.key,
+                algorithms=[algorithm],
+                audience=self.config.audience,
+                issuer=self.config.issuer,
+                options={"verify_exp": True, "verify_aud": True, "verify_iss": True},
             )
         except Exception as exc:
             raise ValueError("Invalid JWT token") from exc
@@ -201,37 +234,6 @@ class OAuth2TokenAuthProvider(AuthProvider):
         else:
             return "Authentication required. Please provide a valid OAuth2 Bearer token in the Authorization header"
 
-    async def _refresh_jwks(self) -> None:
-        """
-        Refresh the JWKS cache.
-
-        This is a simple cache that expires after a certain amount of time (defined by `key_recheck_period`).
-        If the cache is expired, we refresh the JWKS from the JWKS URI.
-
-        Notes: for Kubernetes which doesn't fully implement the OIDC protocol:
-            * It doesn't have user authentication flows
-            * It doesn't have refresh tokens
-        """
-        async with self._jwks_lock:
-            if self.config.jwks is None:
-                raise ValueError("JWKS is not configured")
-            if time.time() - self._jwks_at > self.config.jwks.key_recheck_period:
-                headers = {}
-                if self.config.jwks.token:
-                    headers["Authorization"] = f"Bearer {self.config.jwks.token}"
-                verify = self.config.tls_cafile.as_posix() if self.config.tls_cafile else self.config.verify_tls
-                async with httpx.AsyncClient(verify=verify) as client:
-                    res = await client.get(self.config.jwks.uri, timeout=5, headers=headers)
-                    res.raise_for_status()
-                    jwks_data = res.json()["keys"]
-                    updated = {}
-                    for k in jwks_data:
-                        kid = k["kid"]
-                        # Store the entire key object as it may be needed for different algorithms
-                        updated[kid] = k
-                    self._jwks = updated
-                    self._jwks_at = time.time()
-
 
 class CustomAuthProvider(AuthProvider):
     """Custom authentication provider that uses an external endpoint."""
diff --git a/llama_stack/providers/registry/inference.py b/llama_stack/providers/registry/inference.py
index ab1434669..35afb296d 100644
--- a/llama_stack/providers/registry/inference.py
+++ b/llama_stack/providers/registry/inference.py
@@ -277,7 +277,7 @@ Available Models:
             pip_packages=["litellm"],
             module="llama_stack.providers.remote.inference.watsonx",
             config_class="llama_stack.providers.remote.inference.watsonx.WatsonXConfig",
-            provider_data_validator="llama_stack.providers.remote.inference.watsonx.WatsonXProviderDataValidator",
+            provider_data_validator="llama_stack.providers.remote.inference.watsonx.config.WatsonXProviderDataValidator",
             description="IBM WatsonX inference provider for accessing AI models on IBM's WatsonX platform.",
         ),
         RemoteProviderSpec(
diff --git a/llama_stack/providers/remote/inference/watsonx/config.py b/llama_stack/providers/remote/inference/watsonx/config.py
index 022dc5ee7..8d8df13b4 100644
--- a/llama_stack/providers/remote/inference/watsonx/config.py
+++ b/llama_stack/providers/remote/inference/watsonx/config.py
@@ -7,18 +7,18 @@
 import os
 from typing import Any
 
-from pydantic import BaseModel, ConfigDict, Field
+from pydantic import BaseModel, Field
 
 from llama_stack.providers.utils.inference.model_registry import RemoteInferenceProviderConfig
 from llama_stack.schema_utils import json_schema_type
 
 
 class WatsonXProviderDataValidator(BaseModel):
-    model_config = ConfigDict(
-        from_attributes=True,
-        extra="forbid",
+    watsonx_project_id: str | None = Field(
+        default=None,
+        description="IBM WatsonX project ID",
     )
-    watsonx_api_key: str | None
+    watsonx_api_key: str | None = None
 
 
 @json_schema_type
diff --git a/llama_stack/providers/remote/inference/watsonx/watsonx.py b/llama_stack/providers/remote/inference/watsonx/watsonx.py
index 654d61f34..2c051719b 100644
--- a/llama_stack/providers/remote/inference/watsonx/watsonx.py
+++ b/llama_stack/providers/remote/inference/watsonx/watsonx.py
@@ -4,42 +4,259 @@
 # 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 AsyncIterator
 from typing import Any
 
+import litellm
 import requests
 
-from llama_stack.apis.inference import ChatCompletionRequest
+from llama_stack.apis.inference.inference import (
+    OpenAIChatCompletion,
+    OpenAIChatCompletionChunk,
+    OpenAIChatCompletionRequestWithExtraBody,
+    OpenAIChatCompletionUsage,
+    OpenAICompletion,
+    OpenAICompletionRequestWithExtraBody,
+    OpenAIEmbeddingsRequestWithExtraBody,
+    OpenAIEmbeddingsResponse,
+)
 from llama_stack.apis.models import Model
 from llama_stack.apis.models.models import ModelType
+from llama_stack.log import get_logger
 from llama_stack.providers.remote.inference.watsonx.config import WatsonXConfig
 from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin
+from llama_stack.providers.utils.inference.openai_compat import prepare_openai_completion_params
+from llama_stack.providers.utils.telemetry.tracing import get_current_span
+
+logger = get_logger(name=__name__, category="providers::remote::watsonx")
 
 
 class WatsonXInferenceAdapter(LiteLLMOpenAIMixin):
     _model_cache: dict[str, Model] = {}
 
+    provider_data_api_key_field: str = "watsonx_api_key"
+
     def __init__(self, config: WatsonXConfig):
+        self.available_models = None
+        self.config = config
+        api_key = config.auth_credential.get_secret_value() if config.auth_credential else None
         LiteLLMOpenAIMixin.__init__(
             self,
             litellm_provider_name="watsonx",
-            api_key_from_config=config.auth_credential.get_secret_value() if config.auth_credential else None,
+            api_key_from_config=api_key,
             provider_data_api_key_field="watsonx_api_key",
+            openai_compat_api_base=self.get_base_url(),
+        )
+
+    async def openai_chat_completion(
+        self,
+        params: OpenAIChatCompletionRequestWithExtraBody,
+    ) -> OpenAIChatCompletion | AsyncIterator[OpenAIChatCompletionChunk]:
+        """
+        Override parent method to add timeout and inject usage object when missing.
+        This works around a LiteLLM defect where usage block is sometimes dropped.
+        """
+
+        # Add usage tracking for streaming when telemetry is active
+        stream_options = params.stream_options
+        if params.stream and get_current_span() is not None:
+            if stream_options is None:
+                stream_options = {"include_usage": True}
+            elif "include_usage" not in stream_options:
+                stream_options = {**stream_options, "include_usage": True}
+
+        model_obj = await self.model_store.get_model(params.model)
+
+        request_params = await prepare_openai_completion_params(
+            model=self.get_litellm_model_name(model_obj.provider_resource_id),
+            messages=params.messages,
+            frequency_penalty=params.frequency_penalty,
+            function_call=params.function_call,
+            functions=params.functions,
+            logit_bias=params.logit_bias,
+            logprobs=params.logprobs,
+            max_completion_tokens=params.max_completion_tokens,
+            max_tokens=params.max_tokens,
+            n=params.n,
+            parallel_tool_calls=params.parallel_tool_calls,
+            presence_penalty=params.presence_penalty,
+            response_format=params.response_format,
+            seed=params.seed,
+            stop=params.stop,
+            stream=params.stream,
+            stream_options=stream_options,
+            temperature=params.temperature,
+            tool_choice=params.tool_choice,
+            tools=params.tools,
+            top_logprobs=params.top_logprobs,
+            top_p=params.top_p,
+            user=params.user,
+            api_key=self.get_api_key(),
+            api_base=self.api_base,
+            # These are watsonx-specific parameters
+            timeout=self.config.timeout,
+            project_id=self.config.project_id,
+        )
+
+        result = await litellm.acompletion(**request_params)
+
+        # If not streaming, check and inject usage if missing
+        if not params.stream:
+            # Use getattr to safely handle cases where usage attribute might not exist
+            if getattr(result, "usage", None) is None:
+                # Create usage object with zeros
+                usage_obj = OpenAIChatCompletionUsage(
+                    prompt_tokens=0,
+                    completion_tokens=0,
+                    total_tokens=0,
+                )
+                # Use model_copy to create a new response with the usage injected
+                result = result.model_copy(update={"usage": usage_obj})
+            return result
+
+        # For streaming, wrap the iterator to normalize chunks
+        return self._normalize_stream(result)
+
+    def _normalize_chunk(self, chunk: OpenAIChatCompletionChunk) -> OpenAIChatCompletionChunk:
+        """
+        Normalize a chunk to ensure it has all expected attributes.
+        This works around LiteLLM not always including all expected attributes.
+        """
+        # Ensure chunk has usage attribute with zeros if missing
+        if not hasattr(chunk, "usage") or chunk.usage is None:
+            usage_obj = OpenAIChatCompletionUsage(
+                prompt_tokens=0,
+                completion_tokens=0,
+                total_tokens=0,
+            )
+            chunk = chunk.model_copy(update={"usage": usage_obj})
+
+        # Ensure all delta objects in choices have expected attributes
+        if hasattr(chunk, "choices") and chunk.choices:
+            normalized_choices = []
+            for choice in chunk.choices:
+                if hasattr(choice, "delta") and choice.delta:
+                    delta = choice.delta
+                    # Build update dict for missing attributes
+                    delta_updates = {}
+                    if not hasattr(delta, "refusal"):
+                        delta_updates["refusal"] = None
+                    if not hasattr(delta, "reasoning_content"):
+                        delta_updates["reasoning_content"] = None
+
+                    # If we need to update delta, create a new choice with updated delta
+                    if delta_updates:
+                        new_delta = delta.model_copy(update=delta_updates)
+                        new_choice = choice.model_copy(update={"delta": new_delta})
+                        normalized_choices.append(new_choice)
+                    else:
+                        normalized_choices.append(choice)
+                else:
+                    normalized_choices.append(choice)
+
+            # If we modified any choices, create a new chunk with updated choices
+            if any(normalized_choices[i] is not chunk.choices[i] for i in range(len(chunk.choices))):
+                chunk = chunk.model_copy(update={"choices": normalized_choices})
+
+        return chunk
+
+    async def _normalize_stream(
+        self, stream: AsyncIterator[OpenAIChatCompletionChunk]
+    ) -> AsyncIterator[OpenAIChatCompletionChunk]:
+        """
+        Normalize all chunks in the stream to ensure they have expected attributes.
+        This works around LiteLLM sometimes not including expected attributes.
+        """
+        try:
+            async for chunk in stream:
+                # Normalize and yield each chunk immediately
+                yield self._normalize_chunk(chunk)
+        except Exception as e:
+            logger.error(f"Error normalizing stream: {e}", exc_info=True)
+            raise
+
+    async def openai_completion(
+        self,
+        params: OpenAICompletionRequestWithExtraBody,
+    ) -> OpenAICompletion:
+        """
+        Override parent method to add watsonx-specific parameters.
+        """
+        from llama_stack.providers.utils.inference.openai_compat import prepare_openai_completion_params
+
+        model_obj = await self.model_store.get_model(params.model)
+
+        request_params = await prepare_openai_completion_params(
+            model=self.get_litellm_model_name(model_obj.provider_resource_id),
+            prompt=params.prompt,
+            best_of=params.best_of,
+            echo=params.echo,
+            frequency_penalty=params.frequency_penalty,
+            logit_bias=params.logit_bias,
+            logprobs=params.logprobs,
+            max_tokens=params.max_tokens,
+            n=params.n,
+            presence_penalty=params.presence_penalty,
+            seed=params.seed,
+            stop=params.stop,
+            stream=params.stream,
+            stream_options=params.stream_options,
+            temperature=params.temperature,
+            top_p=params.top_p,
+            user=params.user,
+            suffix=params.suffix,
+            api_key=self.get_api_key(),
+            api_base=self.api_base,
+            # These are watsonx-specific parameters
+            timeout=self.config.timeout,
+            project_id=self.config.project_id,
+        )
+        return await litellm.atext_completion(**request_params)
+
+    async def openai_embeddings(
+        self,
+        params: OpenAIEmbeddingsRequestWithExtraBody,
+    ) -> OpenAIEmbeddingsResponse:
+        """
+        Override parent method to add watsonx-specific parameters.
+        """
+        model_obj = await self.model_store.get_model(params.model)
+
+        # Convert input to list if it's a string
+        input_list = [params.input] if isinstance(params.input, str) else params.input
+
+        # Call litellm embedding function with watsonx-specific parameters
+        response = litellm.embedding(
+            model=self.get_litellm_model_name(model_obj.provider_resource_id),
+            input=input_list,
+            api_key=self.get_api_key(),
+            api_base=self.api_base,
+            dimensions=params.dimensions,
+            # These are watsonx-specific parameters
+            timeout=self.config.timeout,
+            project_id=self.config.project_id,
+        )
+
+        # Convert response to OpenAI format
+        from llama_stack.apis.inference import OpenAIEmbeddingUsage
+        from llama_stack.providers.utils.inference.litellm_openai_mixin import b64_encode_openai_embeddings_response
+
+        data = b64_encode_openai_embeddings_response(response.data, params.encoding_format)
+
+        usage = OpenAIEmbeddingUsage(
+            prompt_tokens=response["usage"]["prompt_tokens"],
+            total_tokens=response["usage"]["total_tokens"],
+        )
+
+        return OpenAIEmbeddingsResponse(
+            data=data,
+            model=model_obj.provider_resource_id,
+            usage=usage,
         )
-        self.available_models = None
-        self.config = config
 
     def get_base_url(self) -> str:
         return self.config.url
 
-    async def _get_params(self, request: ChatCompletionRequest) -> dict[str, Any]:
-        # Get base parameters from parent
-        params = await super()._get_params(request)
-
-        # Add watsonx.ai specific parameters
-        params["project_id"] = self.config.project_id
-        params["time_limit"] = self.config.timeout
-        return params
-
     # Copied from OpenAIMixin
     async def check_model_availability(self, model: str) -> bool:
         """
diff --git a/llama_stack/providers/utils/memory/openai_vector_store_mixin.py b/llama_stack/providers/utils/memory/openai_vector_store_mixin.py
index 100c78a9a..d46e9bbd9 100644
--- a/llama_stack/providers/utils/memory/openai_vector_store_mixin.py
+++ b/llama_stack/providers/utils/memory/openai_vector_store_mixin.py
@@ -353,14 +353,11 @@ class OpenAIVectorStoreMixin(ABC):
         provider_vector_db_id = extra.get("provider_vector_db_id")
         embedding_model = extra.get("embedding_model")
         embedding_dimension = extra.get("embedding_dimension", 768)
-        provider_id = extra.get("provider_id")
-
+        # use provider_id set by router; fallback to provider's own ID when used directly via --stack-config
+        provider_id = extra.get("provider_id") or getattr(self, "__provider_id__", None)
         # Derive the canonical vector_db_id (allow override, else generate)
         vector_db_id = provider_vector_db_id or generate_object_id("vector_store", lambda: f"vs_{uuid.uuid4()}")
 
-        if provider_id is None:
-            raise ValueError("Provider ID is required")
-
         if embedding_model is None:
             raise ValueError("Embedding model is required")
 
@@ -369,6 +366,9 @@ class OpenAIVectorStoreMixin(ABC):
             raise ValueError("Embedding dimension is required")
 
         # Register the VectorDB backing this vector store
+        if provider_id is None:
+            raise ValueError("Provider ID is required but was not provided")
+
         vector_db = VectorDB(
             identifier=vector_db_id,
             embedding_dimension=embedding_dimension,
diff --git a/pyproject.toml b/pyproject.toml
index 81997c249..d55de794d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -34,7 +34,7 @@ dependencies = [
     "openai>=1.107",                                  # for expires_after support
     "prompt-toolkit",
     "python-dotenv",
-    "python-jose[cryptography]",
+    "pyjwt[crypto]>=2.10.0",                          # Pull crypto to support RS256 for jwt. Requires 2.10.0+ for ssl_context support.
     "pydantic>=2.11.9",
     "rich",
     "starlette",
diff --git a/tests/integration/inference/test_openai_completion.py b/tests/integration/inference/test_openai_completion.py
index 3f0cffb2d..65f773889 100644
--- a/tests/integration/inference/test_openai_completion.py
+++ b/tests/integration/inference/test_openai_completion.py
@@ -58,7 +58,6 @@ def skip_if_model_doesnt_support_openai_completion(client_with_models, model_id)
         #  does not work with the specified model, gpt-5-mini. Please choose different model and try
         #  again. You can learn more about which models can be used with each operation here:
         #  https://go.microsoft.com/fwlink/?linkid=2197993.'}}"}
-        "remote::watsonx",  # return 404 when hitting the /openai/v1 endpoint
         "remote::llama-openai-compat",
     ):
         pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support OpenAI completions.")
@@ -68,6 +67,7 @@ def skip_if_doesnt_support_completions_logprobs(client_with_models, model_id):
     provider_type = provider_from_model(client_with_models, model_id).provider_type
     if provider_type in (
         "remote::ollama",  # logprobs is ignored
+        "remote::watsonx",
     ):
         pytest.skip(f"Model {model_id} hosted by {provider_type} doesn't support /v1/completions logprobs.")
 
@@ -110,6 +110,7 @@ def skip_if_doesnt_support_n(client_with_models, model_id):
         # Error code 400 - {'message': '"n" > 1 is not currently supported', 'type': 'invalid_request_error', 'param': 'n', 'code': 'wrong_api_format'}
         "remote::cerebras",
         "remote::databricks",  # Bad request: parameter "n" must be equal to 1 for streaming mode
+        "remote::watsonx",
     ):
         pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support n param.")
 
@@ -124,7 +125,6 @@ def skip_if_model_doesnt_support_openai_chat_completion(client_with_models, mode
         "remote::databricks",
         "remote::cerebras",
         "remote::runpod",
-        "remote::watsonx",  # watsonx returns 404 when hitting the /openai/v1 endpoint
     ):
         pytest.skip(f"Model {model_id} hosted by {provider.provider_type} doesn't support OpenAI chat completions.")
 
@@ -508,6 +508,12 @@ def test_openai_chat_completion_non_streaming_with_file(openai_client, client_wi
     assert "hello world" in normalized_content
 
 
+def skip_if_doesnt_support_completions_stop_sequence(client_with_models, model_id):
+    provider_type = provider_from_model(client_with_models, model_id).provider_type
+    if provider_type in ("remote::watsonx",):  # openai.BadRequestError: Error code: 400
+        pytest.skip(f"Model {model_id} hosted by {provider_type} doesn't support /v1/completions stop sequence.")
+
+
 @pytest.mark.parametrize(
     "test_case",
     [
@@ -516,6 +522,7 @@ def test_openai_chat_completion_non_streaming_with_file(openai_client, client_wi
 )
 def test_openai_completion_stop_sequence(client_with_models, openai_client, text_model_id, test_case):
     skip_if_model_doesnt_support_openai_completion(client_with_models, text_model_id)
+    skip_if_doesnt_support_completions_stop_sequence(client_with_models, text_model_id)
 
     tc = TestCase(test_case)
 
diff --git a/tests/integration/inference/test_openai_embeddings.py b/tests/integration/inference/test_openai_embeddings.py
index 2827284c8..fc2f66b9c 100644
--- a/tests/integration/inference/test_openai_embeddings.py
+++ b/tests/integration/inference/test_openai_embeddings.py
@@ -50,11 +50,15 @@ def skip_if_model_doesnt_support_encoding_format_base64(client, model_id):
 
 def skip_if_model_doesnt_support_variable_dimensions(client_with_models, model_id):
     provider = provider_from_model(client_with_models, model_id)
-    if provider.provider_type in (
-        "remote::together",  # returns 400
-        "inline::sentence-transformers",
-        # Error code: 400 - {'error_code': 'BAD_REQUEST', 'message': 'Bad request: json: unknown field "dimensions"\n'}
-        "remote::databricks",
+    if (
+        provider.provider_type
+        in (
+            "remote::together",  # returns 400
+            "inline::sentence-transformers",
+            # Error code: 400 - {'error_code': 'BAD_REQUEST', 'message': 'Bad request: json: unknown field "dimensions"\n'}
+            "remote::databricks",
+            "remote::watsonx",  # openai.BadRequestError: Error code: 400 - {'detail': "litellm.UnsupportedParamsError: watsonx does not support parameters: {'dimensions': 384}
+        )
     ):
         pytest.skip(
             f"Model {model_id} hosted by {provider.provider_type} does not support variable output embedding dimensions."
diff --git a/tests/integration/vector_io/test_openai_vector_stores.py b/tests/integration/vector_io/test_openai_vector_stores.py
index 98c8fe920..3d5052f38 100644
--- a/tests/integration/vector_io/test_openai_vector_stores.py
+++ b/tests/integration/vector_io/test_openai_vector_stores.py
@@ -146,8 +146,6 @@ def test_openai_create_vector_store(
         metadata={"purpose": "testing", "environment": "integration"},
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -175,8 +173,6 @@ def test_openai_list_vector_stores(
         metadata={"type": "test"},
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
     store2 = client.vector_stores.create(
@@ -184,8 +180,6 @@ def test_openai_list_vector_stores(
         metadata={"type": "test"},
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -220,8 +214,6 @@ def test_openai_retrieve_vector_store(
         metadata={"purpose": "retrieval_test"},
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -249,8 +241,6 @@ def test_openai_update_vector_store(
         metadata={"version": "1.0"},
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
     time.sleep(1)
@@ -282,8 +272,6 @@ def test_openai_delete_vector_store(
         metadata={"purpose": "deletion_test"},
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -314,8 +302,6 @@ def test_openai_vector_store_search_empty(
         metadata={"purpose": "search_testing"},
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -346,8 +332,6 @@ def test_openai_vector_store_with_chunks(
         metadata={"purpose": "chunks_testing"},
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -412,8 +396,6 @@ def test_openai_vector_store_search_relevance(
         metadata={"purpose": "relevance_testing"},
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -457,8 +439,6 @@ def test_openai_vector_store_search_with_ranking_options(
         metadata={"purpose": "ranking_testing"},
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -512,8 +492,6 @@ def test_openai_vector_store_search_with_high_score_filter(
         metadata={"purpose": "high_score_filtering"},
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -573,8 +551,6 @@ def test_openai_vector_store_search_with_max_num_results(
         metadata={"purpose": "max_num_results_testing"},
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -608,8 +584,6 @@ def test_openai_vector_store_attach_file(
         name="test_store",
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -688,8 +662,6 @@ def test_openai_vector_store_attach_files_on_creation(
         file_ids=file_ids,
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -735,8 +707,6 @@ def test_openai_vector_store_list_files(
         name="test_store",
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -826,8 +796,6 @@ def test_openai_vector_store_retrieve_file_contents(
         name="test_store",
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -851,8 +819,6 @@ def test_openai_vector_store_retrieve_file_contents(
         attributes=attributes,
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -889,8 +855,6 @@ def test_openai_vector_store_delete_file(
         name="test_store",
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -955,8 +919,6 @@ def test_openai_vector_store_delete_file_removes_from_vector_store(
         name="test_store",
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -1007,8 +969,6 @@ def test_openai_vector_store_update_file(
         name="test_store",
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -1078,8 +1038,6 @@ def test_create_vector_store_files_duplicate_vector_store_name(
         name="test_store_with_files",
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
     assert vector_store.file_counts.completed == 0
@@ -1092,8 +1050,6 @@ def test_create_vector_store_files_duplicate_vector_store_name(
         name="test_store_with_files",
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -1105,8 +1061,6 @@ def test_create_vector_store_files_duplicate_vector_store_name(
         file_id=file_ids[0],
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
     assert created_file.status == "completed"
@@ -1117,8 +1071,6 @@ def test_create_vector_store_files_duplicate_vector_store_name(
         file_id=file_ids[1],
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
     assert created_file_from_non_deleted_vector_store.status == "completed"
@@ -1139,8 +1091,6 @@ def test_openai_vector_store_search_modes(
         metadata={"purpose": "search_mode_testing"},
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -1172,8 +1122,6 @@ def test_openai_vector_store_file_batch_create_and_retrieve(
         name="batch_test_store",
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -1191,8 +1139,6 @@ def test_openai_vector_store_file_batch_create_and_retrieve(
         file_ids=file_ids,
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -1239,8 +1185,6 @@ def test_openai_vector_store_file_batch_list_files(
         name="batch_list_test_store",
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -1258,8 +1202,6 @@ def test_openai_vector_store_file_batch_list_files(
         file_ids=file_ids,
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -1336,8 +1278,6 @@ def test_openai_vector_store_file_batch_cancel(
         name="batch_cancel_test_store",
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -1355,8 +1295,6 @@ def test_openai_vector_store_file_batch_cancel(
         file_ids=file_ids,
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -1395,8 +1333,6 @@ def test_openai_vector_store_file_batch_retrieve_contents(
         name="batch_contents_test_store",
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -1419,8 +1355,6 @@ def test_openai_vector_store_file_batch_retrieve_contents(
         file_ids=file_ids,
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -1472,8 +1406,6 @@ def test_openai_vector_store_file_batch_error_handling(
         name="batch_error_test_store",
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -1485,8 +1417,6 @@ def test_openai_vector_store_file_batch_error_handling(
         file_ids=file_ids,
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
diff --git a/tests/integration/vector_io/test_vector_io.py b/tests/integration/vector_io/test_vector_io.py
index f2205ed0a..653299338 100644
--- a/tests/integration/vector_io/test_vector_io.py
+++ b/tests/integration/vector_io/test_vector_io.py
@@ -52,8 +52,6 @@ def test_vector_db_retrieve(client_with_empty_registry, embedding_model_id, embe
         name=vector_db_name,
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -73,8 +71,6 @@ def test_vector_db_register(client_with_empty_registry, embedding_model_id, embe
         name=vector_db_name,
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -110,8 +106,6 @@ def test_insert_chunks(client_with_empty_registry, embedding_model_id, embedding
         name=vector_db_name,
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -152,8 +146,6 @@ def test_insert_chunks_with_precomputed_embeddings(client_with_empty_registry, e
         name=vector_db_name,
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -202,8 +194,6 @@ def test_query_returns_valid_object_when_identical_to_embedding_in_vdb(
         name=vector_db_name,
         extra_body={
             "embedding_model": embedding_model_id,
-            "embedding_dimension": embedding_dimension,
-            "provider_id": "my_provider",
         },
     )
 
@@ -234,3 +224,35 @@ def test_query_returns_valid_object_when_identical_to_embedding_in_vdb(
     assert len(response.chunks) > 0
     assert response.chunks[0].metadata["document_id"] == "doc1"
     assert response.chunks[0].metadata["source"] == "precomputed"
+
+
+def test_auto_extract_embedding_dimension(client_with_empty_registry, embedding_model_id):
+    vs = client_with_empty_registry.vector_stores.create(
+        name="test_auto_extract", extra_body={"embedding_model": embedding_model_id}
+    )
+    assert vs.id is not None
+
+
+def test_provider_auto_selection_single_provider(client_with_empty_registry, embedding_model_id):
+    providers = [p for p in client_with_empty_registry.providers.list() if p.api == "vector_io"]
+    if len(providers) != 1:
+        pytest.skip(f"Test requires exactly one vector_io provider, found {len(providers)}")
+
+    vs = client_with_empty_registry.vector_stores.create(
+        name="test_auto_provider", extra_body={"embedding_model": embedding_model_id}
+    )
+    assert vs.id is not None
+
+
+def test_provider_id_override(client_with_empty_registry, embedding_model_id):
+    providers = [p for p in client_with_empty_registry.providers.list() if p.api == "vector_io"]
+    if len(providers) != 1:
+        pytest.skip(f"Test requires exactly one vector_io provider, found {len(providers)}")
+
+    provider_id = providers[0].provider_id
+
+    vs = client_with_empty_registry.vector_stores.create(
+        name="test_provider_override", extra_body={"embedding_model": embedding_model_id, "provider_id": provider_id}
+    )
+    assert vs.id is not None
+    assert vs.metadata.get("provider_id") == provider_id
diff --git a/tests/unit/core/routers/test_vector_io.py b/tests/unit/core/routers/test_vector_io.py
new file mode 100644
index 000000000..997df0d78
--- /dev/null
+++ b/tests/unit/core/routers/test_vector_io.py
@@ -0,0 +1,57 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from unittest.mock import AsyncMock, Mock
+
+import pytest
+
+from llama_stack.apis.vector_io import OpenAICreateVectorStoreRequestWithExtraBody
+from llama_stack.core.routers.vector_io import VectorIORouter
+
+
+async def test_single_provider_auto_selection():
+    # provider_id automatically selected during vector store create() when only one provider available
+    mock_routing_table = Mock()
+    mock_routing_table.impls_by_provider_id = {"inline::faiss": "mock_provider"}
+    mock_routing_table.get_all_with_type = AsyncMock(
+        return_value=[
+            Mock(identifier="all-MiniLM-L6-v2", model_type="embedding", metadata={"embedding_dimension": 384})
+        ]
+    )
+    mock_routing_table.register_vector_db = AsyncMock(
+        return_value=Mock(identifier="vs_123", provider_id="inline::faiss", provider_resource_id="vs_123")
+    )
+    mock_routing_table.get_provider_impl = AsyncMock(
+        return_value=Mock(openai_create_vector_store=AsyncMock(return_value=Mock(id="vs_123")))
+    )
+    router = VectorIORouter(mock_routing_table)
+    request = OpenAICreateVectorStoreRequestWithExtraBody.model_validate(
+        {"name": "test_store", "embedding_model": "all-MiniLM-L6-v2"}
+    )
+
+    result = await router.openai_create_vector_store(request)
+    assert result.id == "vs_123"
+
+
+async def test_create_vector_stores_multiple_providers_missing_provider_id_error():
+    # if multiple providers are available, vector store create will error without provider_id
+    mock_routing_table = Mock()
+    mock_routing_table.impls_by_provider_id = {
+        "inline::faiss": "mock_provider_1",
+        "inline::sqlite-vec": "mock_provider_2",
+    }
+    mock_routing_table.get_all_with_type = AsyncMock(
+        return_value=[
+            Mock(identifier="all-MiniLM-L6-v2", model_type="embedding", metadata={"embedding_dimension": 384})
+        ]
+    )
+    router = VectorIORouter(mock_routing_table)
+    request = OpenAICreateVectorStoreRequestWithExtraBody.model_validate(
+        {"name": "test_store", "embedding_model": "all-MiniLM-L6-v2"}
+    )
+
+    with pytest.raises(ValueError, match="Multiple vector_io providers available"):
+        await router.openai_create_vector_store(request)
diff --git a/tests/unit/server/test_auth.py b/tests/unit/server/test_auth.py
index 9dbabe195..04ae89db8 100644
--- a/tests/unit/server/test_auth.py
+++ b/tests/unit/server/test_auth.py
@@ -5,7 +5,8 @@
 # the root directory of this source tree.
 
 import base64
-from unittest.mock import AsyncMock, patch
+import json
+from unittest.mock import AsyncMock, Mock, patch
 
 import pytest
 from fastapi import FastAPI
@@ -374,7 +375,7 @@ async def mock_jwks_response(*args, **kwargs):
 
 @pytest.fixture
 def jwt_token_valid():
-    from jose import jwt
+    import jwt
 
     return jwt.encode(
         {
@@ -389,8 +390,30 @@ def jwt_token_valid():
     )
 
 
-@patch("httpx.AsyncClient.get", new=mock_jwks_response)
-def test_valid_oauth2_authentication(oauth2_client, jwt_token_valid):
+@pytest.fixture
+def mock_jwks_urlopen():
+    """Mock urllib.request.urlopen for PyJWKClient JWKS requests."""
+    with patch("urllib.request.urlopen") as mock_urlopen:
+        # Mock the JWKS response for PyJWKClient
+        mock_response = Mock()
+        mock_response.read.return_value = json.dumps(
+            {
+                "keys": [
+                    {
+                        "kid": "1234567890",
+                        "kty": "oct",
+                        "alg": "HS256",
+                        "use": "sig",
+                        "k": base64.b64encode(b"foobarbaz").decode(),
+                    }
+                ]
+            }
+        ).encode()
+        mock_urlopen.return_value.__enter__.return_value = mock_response
+        yield mock_urlopen
+
+
+def test_valid_oauth2_authentication(oauth2_client, jwt_token_valid, mock_jwks_urlopen):
     response = oauth2_client.get("/test", headers={"Authorization": f"Bearer {jwt_token_valid}"})
     assert response.status_code == 200
     assert response.json() == {"message": "Authentication successful"}
@@ -447,8 +470,7 @@ def test_oauth2_with_jwks_token_expected(oauth2_client, jwt_token_valid):
     assert response.status_code == 401
 
 
-@patch("httpx.AsyncClient.get", new=mock_auth_jwks_response)
-def test_oauth2_with_jwks_token_configured(oauth2_client_with_jwks_token, jwt_token_valid):
+def test_oauth2_with_jwks_token_configured(oauth2_client_with_jwks_token, jwt_token_valid, mock_jwks_urlopen):
     response = oauth2_client_with_jwks_token.get("/test", headers={"Authorization": f"Bearer {jwt_token_valid}"})
     assert response.status_code == 200
     assert response.json() == {"message": "Authentication successful"}
diff --git a/uv.lock b/uv.lock
index 0fcb02768..747e82aaa 100644
--- a/uv.lock
+++ b/uv.lock
@@ -874,18 +874,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/b0/0d/9feae160378a3553fa9a339b0e9c1a048e147a4127210e286ef18b730f03/durationpy-0.10-py3-none-any.whl", hash = "sha256:3b41e1b601234296b4fb368338fdcd3e13e0b4fb5b67345948f4f2bf9868b286", size = 3922, upload-time = "2025-05-17T13:52:36.463Z" },
 ]
 
-[[package]]
-name = "ecdsa"
-version = "0.19.1"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "six" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/c0/1f/924e3caae75f471eae4b26bd13b698f6af2c44279f67af317439c2f4c46a/ecdsa-0.19.1.tar.gz", hash = "sha256:478cba7b62555866fcb3bb3fe985e06decbdb68ef55713c4e5ab98c57d508e61", size = 201793, upload-time = "2025-03-13T11:52:43.25Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/cb/a3/460c57f094a4a165c84a1341c373b0a4f5ec6ac244b998d5021aade89b77/ecdsa-0.19.1-py2.py3-none-any.whl", hash = "sha256:30638e27cf77b7e15c4c4cc1973720149e1033827cfd00661ca5c8cc0cdb24c3", size = 150607, upload-time = "2025-03-13T11:52:41.757Z" },
-]
-
 [[package]]
 name = "eval-type-backport"
 version = "0.2.2"
@@ -1787,8 +1775,8 @@ dependencies = [
     { name = "pillow" },
     { name = "prompt-toolkit" },
     { name = "pydantic" },
+    { name = "pyjwt", extra = ["crypto"] },
     { name = "python-dotenv" },
-    { name = "python-jose", extra = ["cryptography"] },
     { name = "python-multipart" },
     { name = "rich" },
     { name = "sqlalchemy", extra = ["asyncio"] },
@@ -1910,8 +1898,8 @@ requires-dist = [
     { name = "pillow" },
     { name = "prompt-toolkit" },
     { name = "pydantic", specifier = ">=2.11.9" },
+    { name = "pyjwt", extras = ["crypto"], specifier = ">=2.10.0" },
     { name = "python-dotenv" },
-    { name = "python-jose", extras = ["cryptography"] },
     { name = "python-multipart", specifier = ">=0.0.20" },
     { name = "rich" },
     { name = "sqlalchemy", extras = ["asyncio"], specifier = ">=2.0.41" },
@@ -3558,6 +3546,20 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/c7/21/705964c7812476f378728bdf590ca4b771ec72385c533964653c68e86bdc/pygments-2.19.2-py3-none-any.whl", hash = "sha256:86540386c03d588bb81d44bc3928634ff26449851e99741617ecb9037ee5ec0b", size = 1225217, upload-time = "2025-06-21T13:39:07.939Z" },
 ]
 
+[[package]]
+name = "pyjwt"
+version = "2.10.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/e7/46/bd74733ff231675599650d3e47f361794b22ef3e3770998dda30d3b63726/pyjwt-2.10.1.tar.gz", hash = "sha256:3cc5772eb20009233caf06e9d8a0577824723b44e6648ee0a2aedb6cf9381953", size = 87785, upload-time = "2024-11-28T03:43:29.933Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/61/ad/689f02752eeec26aed679477e80e632ef1b682313be70793d798c1d5fc8f/PyJWT-2.10.1-py3-none-any.whl", hash = "sha256:dcdd193e30abefd5debf142f9adfcdd2b58004e644f25406ffaebd50bd98dacb", size = 22997, upload-time = "2024-11-28T03:43:27.893Z" },
+]
+
+[package.optional-dependencies]
+crypto = [
+    { name = "cryptography" },
+]
+
 [[package]]
 name = "pymilvus"
 version = "2.6.1"
@@ -3747,25 +3749,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/0c/fa/df59acedf7bbb937f69174d00f921a7b93aa5a5f5c17d05296c814fff6fc/python_engineio-4.12.2-py3-none-any.whl", hash = "sha256:8218ab66950e179dfec4b4bbb30aecf3f5d86f5e58e6fc1aa7fde2c698b2804f", size = 59536, upload-time = "2025-06-04T19:22:16.916Z" },
 ]
 
-[[package]]
-name = "python-jose"
-version = "3.5.0"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "ecdsa" },
-    { name = "pyasn1" },
-    { name = "rsa" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/c6/77/3a1c9039db7124eb039772b935f2244fbb73fc8ee65b9acf2375da1c07bf/python_jose-3.5.0.tar.gz", hash = "sha256:fb4eaa44dbeb1c26dcc69e4bd7ec54a1cb8dd64d3b4d81ef08d90ff453f2b01b", size = 92726, upload-time = "2025-05-28T17:31:54.288Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/d9/c3/0bd11992072e6a1c513b16500a5d07f91a24017c5909b02c72c62d7ad024/python_jose-3.5.0-py2.py3-none-any.whl", hash = "sha256:abd1202f23d34dfad2c3d28cb8617b90acf34132c7afd60abd0b0b7d3cb55771", size = 34624, upload-time = "2025-05-28T17:31:52.802Z" },
-]
-
-[package.optional-dependencies]
-cryptography = [
-    { name = "cryptography" },
-]
-
 [[package]]
 name = "python-multipart"
 version = "0.0.20"