Merge branch 'main' into codex/fix-codeql-alert-28

* fix(security): bump regex from 2024.9.11 to 2026.1.15

Address security vulnerability flagged in regex==2024.9.11

* bump mcp from 1.23.1 to 1.26.0

Address security vulnerability flagged in mcp==1.16.0 (resolved to 1.23.3)

lib/crewai/pyproject.toml

@@ -14,7 +14,7 @@ dependencies = [
     "instructor>=1.3.3",
     # Text Processing
     "pdfplumber~=0.11.4",
-    "regex~=2024.9.11",
+    "regex~=2026.1.15",
     # Telemetry and Monitoring
     "opentelemetry-api~=1.34.0",
     "opentelemetry-sdk~=1.34.0",
@@ -36,7 +36,7 @@ dependencies = [
     "json5~=0.10.0",
     "portalocker~=2.7.0",
     "pydantic-settings~=2.10.1",
-    "mcp~=1.23.1",
+    "mcp~=1.26.0",
     "uv~=0.9.13",
     "aiosqlite~=0.21.0",
 ]

lib/crewai/src/crewai/llms/providers/azure/completion.py

@@ -4,6 +4,7 @@ import json
 import logging
 import os
 from typing import TYPE_CHECKING, Any, TypedDict
+from urllib.parse import urlparse
 
 from pydantic import BaseModel
 from typing_extensions import Self
@@ -175,11 +176,51 @@ class AzureCompletion(BaseLLM):
             prefix in model.lower() for prefix in ["gpt-", "o1-", "text-"]
         )
 
-        self.is_azure_openai_endpoint = (
-            "openai.azure.com" in self.endpoint
-            and "/openai/deployments/" in self.endpoint
+        self.is_azure_openai_endpoint = self._is_azure_openai_deployment_endpoint(
+            self.endpoint
         )
 
+    @staticmethod
+    def _parse_endpoint_url(endpoint: str):
+        parsed_endpoint = urlparse(endpoint)
+        if parsed_endpoint.hostname:
+            return parsed_endpoint
+
+        # Support endpoint values without a URL scheme.
+        return urlparse(f"https://{endpoint}")
+
+    @staticmethod
+    def _is_azure_openai_hostname(endpoint: str) -> bool:
+        parsed_endpoint = AzureCompletion._parse_endpoint_url(endpoint)
+        hostname = parsed_endpoint.hostname or ""
+        labels = [label for label in hostname.lower().split(".") if label]
+
+        return len(labels) >= 3 and labels[-3:] == ["openai", "azure", "com"]
+
+    @staticmethod
+    def _get_endpoint_path_segments(endpoint: str) -> list[str]:
+        parsed_endpoint = AzureCompletion._parse_endpoint_url(endpoint)
+        return [segment for segment in parsed_endpoint.path.split("/") if segment]
+
+    @staticmethod
+    def _is_azure_openai_deployment_endpoint(endpoint: str) -> bool:
+        if not AzureCompletion._is_azure_openai_hostname(endpoint):
+            return False
+
+        path_segments = AzureCompletion._get_endpoint_path_segments(endpoint)
+        return len(path_segments) >= 3 and path_segments[:2] == [
+            "openai",
+            "deployments",
+        ]
+
+    @staticmethod
+    def _is_azure_openai_deployments_collection(endpoint: str) -> bool:
+        if not AzureCompletion._is_azure_openai_hostname(endpoint):
+            return False
+
+        path_segments = AzureCompletion._get_endpoint_path_segments(endpoint)
+        return path_segments == ["openai", "deployments"]
+
     @staticmethod
     def _validate_and_fix_endpoint(endpoint: str, model: str) -> str:
         """Validate and fix Azure endpoint URL format.
@@ -194,10 +235,12 @@ class AzureCompletion(BaseLLM):
         Returns:
             Validated and potentially corrected endpoint URL
         """
-        if "openai.azure.com" in endpoint and "/openai/deployments/" not in endpoint:
+        if AzureCompletion._is_azure_openai_hostname(
+            endpoint
+        ) and not AzureCompletion._is_azure_openai_deployment_endpoint(endpoint):
             endpoint = endpoint.rstrip("/")
 
-            if not endpoint.endswith("/openai/deployments"):
+            if not AzureCompletion._is_azure_openai_deployments_collection(endpoint):
                 deployment_name = model.replace("azure/", "")
                 endpoint = f"{endpoint}/openai/deployments/{deployment_name}"
                 logging.info(f"Constructed Azure OpenAI endpoint URL: {endpoint}")

lib/crewai/src/crewai/llms/providers/openai/completion.py

@@ -1116,16 +1116,6 @@ class OpenAICompletion(BaseLLM):
 
             return parsed_result
 
-        if function_calls and not available_functions:
-            self._emit_call_completed_event(
-                response=function_calls,
-                call_type=LLMCallType.TOOL_CALL,
-                from_task=from_task,
-                from_agent=from_agent,
-                messages=params.get("input", []),
-            )
-            return function_calls
-
         if function_calls and available_functions:
             for call in function_calls:
                 function_name = call.get("name", "")
@@ -1254,16 +1244,6 @@ class OpenAICompletion(BaseLLM):
 
             return parsed_result
 
-        if function_calls and not available_functions:
-            self._emit_call_completed_event(
-                response=function_calls,
-                call_type=LLMCallType.TOOL_CALL,
-                from_task=from_task,
-                from_agent=from_agent,
-                messages=params.get("input", []),
-            )
-            return function_calls
-
         if function_calls and available_functions:
             for call in function_calls:
                 function_name = call.get("name", "")
@@ -1842,27 +1822,6 @@ class OpenAICompletion(BaseLLM):
 
         self._track_token_usage_internal(usage_data)
 
-        if tool_calls and not available_functions:
-            formatted_tool_calls = [
-                {
-                    "id": call_data.get("id", f"call_{idx}"),
-                    "type": "function",
-                    "function": {
-                        "name": call_data["name"],
-                        "arguments": call_data["arguments"],
-                    },
-                }
-                for idx, call_data in tool_calls.items()
-            ]
-            self._emit_call_completed_event(
-                response=formatted_tool_calls,
-                call_type=LLMCallType.TOOL_CALL,
-                from_task=from_task,
-                from_agent=from_agent,
-                messages=params["messages"],
-            )
-            return formatted_tool_calls
-
         if tool_calls and available_functions:
             for call_data in tool_calls.values():
                 function_name = call_data["name"]
@@ -2185,27 +2144,6 @@ class OpenAICompletion(BaseLLM):
 
         self._track_token_usage_internal(usage_data)
 
-        if tool_calls and not available_functions:
-            formatted_tool_calls = [
-                {
-                    "id": call_data.get("id", f"call_{idx}"),
-                    "type": "function",
-                    "function": {
-                        "name": call_data["name"],
-                        "arguments": call_data["arguments"],
-                    },
-                }
-                for idx, call_data in tool_calls.items()
-            ]
-            self._emit_call_completed_event(
-                response=formatted_tool_calls,
-                call_type=LLMCallType.TOOL_CALL,
-                from_task=from_task,
-                from_agent=from_agent,
-                messages=params["messages"],
-            )
-            return formatted_tool_calls
-
         if tool_calls and available_functions:
             for call_data in tool_calls.values():
                 function_name = call_data["name"]

lib/crewai/tests/llms/azure/test_azure.py

@@ -958,6 +958,34 @@ def test_azure_endpoint_detection_flags():
         assert llm_other.is_azure_openai_endpoint == False
 
 
+def test_azure_endpoint_detection_ignores_spoofed_urls():
+    """
+    Test that endpoint detection does not trust spoofed host/path substrings
+    """
+    with patch.dict(os.environ, {
+        "AZURE_API_KEY": "test-key",
+        "AZURE_ENDPOINT": (
+            "https://evil.example.com/?redirect="
+            "https://test.openai.azure.com/openai/deployments/gpt-4"
+        ),
+    }):
+        llm_query_spoof = LLM(model="azure/gpt-4")
+        assert llm_query_spoof.is_azure_openai_endpoint == False
+        assert "model" in llm_query_spoof._prepare_completion_params(
+            messages=[{"role": "user", "content": "test"}]
+        )
+
+    with patch.dict(os.environ, {
+        "AZURE_API_KEY": "test-key",
+        "AZURE_ENDPOINT": "https://test.openai.azure.com.evil/openai/deployments/gpt-4",
+    }):
+        llm_host_spoof = LLM(model="azure/gpt-4")
+        assert llm_host_spoof.is_azure_openai_endpoint == False
+        assert "model" in llm_host_spoof._prepare_completion_params(
+            messages=[{"role": "user", "content": "test"}]
+        )
+
+
 def test_azure_improved_error_messages():
     """
     Test that improved error messages are provided for common HTTP errors

lib/crewai/tests/llms/openai/test_openai_streaming_tool_calls_no_available_functions.py

@@ -1,449 +0,0 @@
-"""Tests for streaming tool call handling when available_functions is None.
-
-Covers the fix for GitHub issue #4442: async streaming fails with tool/function calls
-when available_functions is not provided (i.e., when the executor handles tool execution
-instead of the LLM provider).
-
-The fix ensures that streaming methods return accumulated tool calls in the correct
-format instead of falling through and returning None/empty.
-"""
-
-import asyncio
-from typing import Any
-from unittest.mock import AsyncMock, MagicMock, patch
-
-import pytest
-
-from crewai.llms.providers.openai.completion import OpenAICompletion
-
-
-def _make_completion_chunk(
-    tool_call_index: int = 0,
-    tool_call_id: str | None = None,
-    function_name: str | None = None,
-    function_arguments: str | None = None,
-    content: str | None = None,
-    has_usage: bool = False,
-) -> MagicMock:
-    """Create a mock ChatCompletionChunk for streaming."""
-    chunk = MagicMock()
-    chunk.id = "chatcmpl-test123"
-
-    if has_usage:
-        chunk.usage = MagicMock(prompt_tokens=10, completion_tokens=5, total_tokens=15)
-        chunk.choices = []
-        return chunk
-
-    chunk.usage = None
-
-    choice = MagicMock()
-    delta = MagicMock()
-
-    delta.content = content
-
-    if function_name is not None or function_arguments is not None or tool_call_id is not None:
-        tc = MagicMock()
-        tc.index = tool_call_index
-        tc.id = tool_call_id
-        tc.function = MagicMock()
-        tc.function.name = function_name
-        tc.function.arguments = function_arguments
-        delta.tool_calls = [tc]
-    else:
-        delta.tool_calls = None
-
-    choice.delta = delta
-    chunk.choices = [choice]
-    return chunk
-
-
-def _make_responses_events(
-    function_name: str = "get_temperature",
-    function_args: str = '{"city": "Paris"}',
-    call_id: str = "call_abc123",
-) -> list[MagicMock]:
-    """Create mock Responses API streaming events with a function call."""
-    created_event = MagicMock()
-    created_event.type = "response.created"
-    created_event.response = MagicMock(id="resp_test123")
-
-    args_delta_event = MagicMock()
-    args_delta_event.type = "response.function_call_arguments.delta"
-
-    item_done_event = MagicMock()
-    item_done_event.type = "response.output_item.done"
-    item_done_event.item = MagicMock()
-    item_done_event.item.type = "function_call"
-    item_done_event.item.call_id = call_id
-    item_done_event.item.name = function_name
-    item_done_event.item.arguments = function_args
-
-    completed_event = MagicMock()
-    completed_event.type = "response.completed"
-    completed_event.response = MagicMock()
-    completed_event.response.id = "resp_test123"
-    completed_event.response.usage = MagicMock(
-        input_tokens=10, output_tokens=5, total_tokens=15
-    )
-
-    return [created_event, args_delta_event, item_done_event, completed_event]
-
-
-class TestStreamingCompletionToolCallsNoAvailableFunctions:
-    """Tests for _handle_streaming_completion returning tool calls when available_functions is None."""
-
-    def test_streaming_completion_returns_tool_calls_when_no_available_functions(self):
-        """When streaming with tool calls and available_functions=None,
-        the method should return formatted tool calls list."""
-        llm = OpenAICompletion(model="gpt-4o")
-
-        chunks = [
-            _make_completion_chunk(
-                tool_call_index=0,
-                tool_call_id="call_abc123",
-                function_name="get_temperature",
-                function_arguments="",
-            ),
-            _make_completion_chunk(
-                tool_call_index=0,
-                function_arguments='{"city":',
-            ),
-            _make_completion_chunk(
-                tool_call_index=0,
-                function_arguments=' "Paris"}',
-            ),
-            _make_completion_chunk(has_usage=True),
-        ]
-
-        mock_stream = MagicMock()
-        mock_stream.__iter__ = MagicMock(return_value=iter(chunks))
-
-        with patch.object(
-            llm.client.chat.completions, "create", return_value=mock_stream
-        ):
-            result = llm._handle_streaming_completion(
-                params={"messages": [{"role": "user", "content": "test"}], "stream": True},
-                available_functions=None,
-            )
-
-        assert isinstance(result, list)
-        assert len(result) == 1
-        assert result[0]["type"] == "function"
-        assert result[0]["function"]["name"] == "get_temperature"
-        assert result[0]["function"]["arguments"] == '{"city": "Paris"}'
-        assert result[0]["id"] == "call_abc123"
-
-    def test_streaming_completion_multiple_tool_calls_no_available_functions(self):
-        """When streaming with multiple tool calls and available_functions=None,
-        all tool calls should be returned."""
-        llm = OpenAICompletion(model="gpt-4o")
-
-        chunks = [
-            _make_completion_chunk(
-                tool_call_index=0,
-                tool_call_id="call_1",
-                function_name="get_temperature",
-                function_arguments='{"city": "Paris"}',
-            ),
-            _make_completion_chunk(
-                tool_call_index=1,
-                tool_call_id="call_2",
-                function_name="get_temperature",
-                function_arguments='{"city": "London"}',
-            ),
-            _make_completion_chunk(has_usage=True),
-        ]
-
-        mock_stream = MagicMock()
-        mock_stream.__iter__ = MagicMock(return_value=iter(chunks))
-
-        with patch.object(
-            llm.client.chat.completions, "create", return_value=mock_stream
-        ):
-            result = llm._handle_streaming_completion(
-                params={"messages": [{"role": "user", "content": "test"}], "stream": True},
-                available_functions=None,
-            )
-
-        assert isinstance(result, list)
-        assert len(result) == 2
-        assert result[0]["function"]["name"] == "get_temperature"
-        assert result[0]["function"]["arguments"] == '{"city": "Paris"}'
-        assert result[1]["function"]["name"] == "get_temperature"
-        assert result[1]["function"]["arguments"] == '{"city": "London"}'
-
-    def test_streaming_completion_with_available_functions_still_executes(self):
-        """When available_functions IS provided, tool should be executed as before."""
-        llm = OpenAICompletion(model="gpt-4o")
-
-        chunks = [
-            _make_completion_chunk(
-                tool_call_index=0,
-                tool_call_id="call_abc",
-                function_name="get_temperature",
-                function_arguments='{"city": "Paris"}',
-            ),
-            _make_completion_chunk(has_usage=True),
-        ]
-
-        mock_stream = MagicMock()
-        mock_stream.__iter__ = MagicMock(return_value=iter(chunks))
-
-        with patch.object(
-            llm.client.chat.completions, "create", return_value=mock_stream
-        ), patch.object(
-            llm, "_handle_tool_execution", return_value="72F in Paris"
-        ) as mock_exec:
-            result = llm._handle_streaming_completion(
-                params={"messages": [{"role": "user", "content": "test"}], "stream": True},
-                available_functions={"get_temperature": lambda city: f"72F in {city}"},
-            )
-
-        assert result == "72F in Paris"
-        mock_exec.assert_called_once()
-
-
-class TestAsyncStreamingCompletionToolCallsNoAvailableFunctions:
-    """Tests for _ahandle_streaming_completion returning tool calls when available_functions is None."""
-
-    @pytest.mark.asyncio
-    async def test_async_streaming_completion_returns_tool_calls_when_no_available_functions(self):
-        """When async streaming with tool calls and available_functions=None,
-        the method should return formatted tool calls list."""
-        llm = OpenAICompletion(model="gpt-4o")
-
-        chunks = [
-            _make_completion_chunk(
-                tool_call_index=0,
-                tool_call_id="call_abc123",
-                function_name="get_temperature",
-                function_arguments="",
-            ),
-            _make_completion_chunk(
-                tool_call_index=0,
-                function_arguments='{"city":',
-            ),
-            _make_completion_chunk(
-                tool_call_index=0,
-                function_arguments=' "Paris"}',
-            ),
-            _make_completion_chunk(has_usage=True),
-        ]
-
-        async def mock_aiter():
-            for c in chunks:
-                yield c
-
-        with patch.object(
-            llm.async_client.chat.completions,
-            "create",
-            new_callable=AsyncMock,
-            return_value=mock_aiter(),
-        ):
-            result = await llm._ahandle_streaming_completion(
-                params={"messages": [{"role": "user", "content": "test"}], "stream": True},
-                available_functions=None,
-            )
-
-        assert isinstance(result, list)
-        assert len(result) == 1
-        assert result[0]["type"] == "function"
-        assert result[0]["function"]["name"] == "get_temperature"
-        assert result[0]["function"]["arguments"] == '{"city": "Paris"}'
-        assert result[0]["id"] == "call_abc123"
-
-    @pytest.mark.asyncio
-    async def test_async_streaming_completion_multiple_tool_calls_no_available_functions(self):
-        """When async streaming with multiple tool calls and available_functions=None,
-        all tool calls should be returned."""
-        llm = OpenAICompletion(model="gpt-4o")
-
-        chunks = [
-            _make_completion_chunk(
-                tool_call_index=0,
-                tool_call_id="call_1",
-                function_name="get_temperature",
-                function_arguments='{"city": "Paris"}',
-            ),
-            _make_completion_chunk(
-                tool_call_index=1,
-                tool_call_id="call_2",
-                function_name="get_temperature",
-                function_arguments='{"city": "London"}',
-            ),
-            _make_completion_chunk(has_usage=True),
-        ]
-
-        async def mock_aiter():
-            for c in chunks:
-                yield c
-
-        with patch.object(
-            llm.async_client.chat.completions,
-            "create",
-            new_callable=AsyncMock,
-            return_value=mock_aiter(),
-        ):
-            result = await llm._ahandle_streaming_completion(
-                params={"messages": [{"role": "user", "content": "test"}], "stream": True},
-                available_functions=None,
-            )
-
-        assert isinstance(result, list)
-        assert len(result) == 2
-        assert result[0]["function"]["name"] == "get_temperature"
-        assert result[1]["function"]["name"] == "get_temperature"
-        assert result[0]["function"]["arguments"] == '{"city": "Paris"}'
-        assert result[1]["function"]["arguments"] == '{"city": "London"}'
-
-
-class TestStreamingResponsesToolCallsNoAvailableFunctions:
-    """Tests for _handle_streaming_responses returning function calls when available_functions is None."""
-
-    def test_streaming_responses_returns_function_calls_when_no_available_functions(self):
-        """When streaming Responses API with function calls and available_functions=None,
-        the method should return function_calls list."""
-        llm = OpenAICompletion(model="gpt-4o", use_responses_api=True)
-
-        events = _make_responses_events(
-            function_name="get_temperature",
-            function_args='{"city": "Paris"}',
-            call_id="call_abc123",
-        )
-
-        mock_stream = MagicMock()
-        mock_stream.__iter__ = MagicMock(return_value=iter(events))
-
-        with patch.object(
-            llm.client.responses, "create", return_value=mock_stream
-        ):
-            result = llm._handle_streaming_responses(
-                params={"input": [{"role": "user", "content": "test"}], "model": "gpt-4o", "stream": True},
-                available_functions=None,
-            )
-
-        assert isinstance(result, list)
-        assert len(result) == 1
-        assert result[0]["name"] == "get_temperature"
-        assert result[0]["arguments"] == '{"city": "Paris"}'
-        assert result[0]["id"] == "call_abc123"
-
-    def test_streaming_responses_with_available_functions_still_executes(self):
-        """When available_functions IS provided, tool should be executed as before."""
-        llm = OpenAICompletion(model="gpt-4o", use_responses_api=True)
-
-        events = _make_responses_events(
-            function_name="get_temperature",
-            function_args='{"city": "Paris"}',
-        )
-
-        mock_stream = MagicMock()
-        mock_stream.__iter__ = MagicMock(return_value=iter(events))
-
-        with patch.object(
-            llm.client.responses, "create", return_value=mock_stream
-        ), patch.object(
-            llm, "_handle_tool_execution", return_value="72F in Paris"
-        ) as mock_exec:
-            result = llm._handle_streaming_responses(
-                params={"input": [{"role": "user", "content": "test"}], "model": "gpt-4o", "stream": True},
-                available_functions={"get_temperature": lambda city: f"72F in {city}"},
-            )
-
-        assert result == "72F in Paris"
-        mock_exec.assert_called_once()
-
-
-class TestAsyncStreamingResponsesToolCallsNoAvailableFunctions:
-    """Tests for _ahandle_streaming_responses returning function calls when available_functions is None."""
-
-    @pytest.mark.asyncio
-    async def test_async_streaming_responses_returns_function_calls_when_no_available_functions(self):
-        """When async streaming Responses API with function calls and available_functions=None,
-        the method should return function_calls list."""
-        llm = OpenAICompletion(model="gpt-4o", use_responses_api=True)
-
-        events = _make_responses_events(
-            function_name="get_temperature",
-            function_args='{"city": "Paris"}',
-            call_id="call_abc123",
-        )
-
-        async def mock_aiter():
-            for e in events:
-                yield e
-
-        with patch.object(
-            llm.async_client.responses,
-            "create",
-            new_callable=AsyncMock,
-            return_value=mock_aiter(),
-        ):
-            result = await llm._ahandle_streaming_responses(
-                params={"input": [{"role": "user", "content": "test"}], "model": "gpt-4o", "stream": True},
-                available_functions=None,
-            )
-
-        assert isinstance(result, list)
-        assert len(result) == 1
-        assert result[0]["name"] == "get_temperature"
-        assert result[0]["arguments"] == '{"city": "Paris"}'
-        assert result[0]["id"] == "call_abc123"
-
-    @pytest.mark.asyncio
-    async def test_async_streaming_responses_multiple_function_calls_no_available_functions(self):
-        """When async streaming Responses API with multiple function calls and available_functions=None,
-        all function calls should be returned."""
-        llm = OpenAICompletion(model="gpt-4o", use_responses_api=True)
-
-        created_event = MagicMock()
-        created_event.type = "response.created"
-        created_event.response = MagicMock(id="resp_test")
-
-        item1 = MagicMock()
-        item1.type = "response.output_item.done"
-        item1.item = MagicMock()
-        item1.item.type = "function_call"
-        item1.item.call_id = "call_1"
-        item1.item.name = "get_temperature"
-        item1.item.arguments = '{"city": "Paris"}'
-
-        item2 = MagicMock()
-        item2.type = "response.output_item.done"
-        item2.item = MagicMock()
-        item2.item.type = "function_call"
-        item2.item.call_id = "call_2"
-        item2.item.name = "get_temperature"
-        item2.item.arguments = '{"city": "London"}'
-
-        completed = MagicMock()
-        completed.type = "response.completed"
-        completed.response = MagicMock()
-        completed.response.id = "resp_test"
-        completed.response.usage = MagicMock(
-            input_tokens=10, output_tokens=5, total_tokens=15
-        )
-
-        events = [created_event, item1, item2, completed]
-
-        async def mock_aiter():
-            for e in events:
-                yield e
-
-        with patch.object(
-            llm.async_client.responses,
-            "create",
-            new_callable=AsyncMock,
-            return_value=mock_aiter(),
-        ):
-            result = await llm._ahandle_streaming_responses(
-                params={"input": [{"role": "user", "content": "test"}], "model": "gpt-4o", "stream": True},
-                available_functions=None,
-            )
-
-        assert isinstance(result, list)
-        assert len(result) == 2
-        assert result[0]["name"] == "get_temperature"
-        assert result[0]["arguments"] == '{"city": "Paris"}'
-        assert result[1]["name"] == "get_temperature"
-        assert result[1]["arguments"] == '{"city": "London"}'

uv.lock

@@ -1295,7 +1295,7 @@ requires-dist = [
     { name = "json5", specifier = "~=0.10.0" },
     { name = "jsonref", specifier = "~=1.1.0" },
     { name = "litellm", marker = "extra == 'litellm'", specifier = ">=1.74.9,<3" },
-    { name = "mcp", specifier = "~=1.23.1" },
+    { name = "mcp", specifier = "~=1.26.0" },
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     { name = "openai", specifier = ">=1.83.0,<3" },
     { name = "openpyxl", specifier = "~=3.1.5" },
@@ -1311,7 +1311,7 @@ requires-dist = [
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     { name = "qdrant-client", extras = ["fastembed"], marker = "extra == 'qdrant'", specifier = "~=1.14.3" },
-    { name = "regex", specifier = "~=2024.9.11" },
+    { name = "regex", specifier = "~=2026.1.15" },
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     { name = "tokenizers", specifier = "~=0.20.3" },
     { name = "tomli", specifier = "~=2.0.2" },
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 dependencies = [
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