Added LiteLLM to the stack

Development/litellm/tests/test_litellm/llms/bedrock/chat/invoke_transformations/test_bedrock_chat_invoke_transformations_anthropic_claude3_transformation.py

@@ -0,0 +1,22 @@
+import asyncio
+import json
+import os
+import sys
+
+import pytest
+
+# Ensure the project root is on the import path so `litellm` can be imported when
+# tests are executed from any working directory.
+sys.path.insert(0, os.path.abspath("../../../../../.."))
+
+from litellm.llms.bedrock.chat.invoke_transformations.anthropic_claude3_transformation import (
+    AmazonAnthropicClaudeConfig,
+)
+
+
+def test_get_supported_params_thinking():
+    config = AmazonAnthropicClaudeConfig()
+    params = config.get_supported_openai_params(
+        model="anthropic.claude-sonnet-4-20250514-v1:0"
+    )
+    assert "thinking" in params

Development/litellm/tests/test_litellm/llms/bedrock/chat/test_invoke_handler.py

@@ -0,0 +1,169 @@
+import json
+import os
+import sys
+
+import pytest
+from fastapi.testclient import TestClient
+
+sys.path.insert(
+    0, os.path.abspath("../../../../..")
+)  # Adds the parent directory to the system path
+from unittest.mock import MagicMock, patch
+
+from litellm.llms.bedrock.chat.invoke_handler import AWSEventStreamDecoder
+
+
+def test_transform_thinking_blocks_with_redacted_content():
+    thinking_block = {"redactedContent": "This is a redacted content"}
+    decoder = AWSEventStreamDecoder(model="test")
+    transformed_thinking_blocks = decoder.translate_thinking_blocks(thinking_block)
+    assert len(transformed_thinking_blocks) == 1
+    assert transformed_thinking_blocks[0]["type"] == "redacted_thinking"
+    assert transformed_thinking_blocks[0]["data"] == "This is a redacted content"
+
+
+def test_transform_tool_calls_index():
+    chunks = [
+        {
+            "delta": {"text": "Certainly! I can help you with the"},
+            "contentBlockIndex": 0,
+        },
+        {
+            "delta": {"text": " current weather and time in Tokyo."},
+            "contentBlockIndex": 0,
+        },
+        {"delta": {"text": " To get this information, I'll"}, "contentBlockIndex": 0},
+        {"delta": {"text": " need to use two"}, "contentBlockIndex": 0},
+        {"delta": {"text": " different tools: one"}, "contentBlockIndex": 0},
+        {"delta": {"text": " for the weather and one for"}, "contentBlockIndex": 0},
+        {"delta": {"text": " the time. Let me fetch"}, "contentBlockIndex": 0},
+        {"delta": {"text": " that data for you."}, "contentBlockIndex": 0},
+        {
+            "start": {
+                "toolUse": {
+                    "toolUseId": "tooluse_JX1wqyUvRjyTcVSg_6-JwA",
+                    "name": "Weather_Tool",
+                }
+            },
+            "contentBlockIndex": 1,
+        },
+        {"delta": {"toolUse": {"input": ""}}, "contentBlockIndex": 1},
+        {"delta": {"toolUse": {"input": '{"locatio'}}, "contentBlockIndex": 1},
+        {"delta": {"toolUse": {"input": 'n": "Toky'}}, "contentBlockIndex": 1},
+        {"delta": {"toolUse": {"input": 'o"}'}}, "contentBlockIndex": 1},
+        {
+            "start": {
+                "toolUse": {
+                    "toolUseId": "tooluse_rxDBNjDMQ-mqA-YOp9_3cQ",
+                    "name": "Query_Time_Tool",
+                }
+            },
+            "contentBlockIndex": 2,
+        },
+        {"delta": {"toolUse": {"input": ""}}, "contentBlockIndex": 2},
+        {"delta": {"toolUse": {"input": '{"locati'}}, "contentBlockIndex": 2},
+        {"delta": {"toolUse": {"input": 'on"'}}, "contentBlockIndex": 2},
+        {"delta": {"toolUse": {"input": ': "Tokyo"}'}}, "contentBlockIndex": 2},
+        {"stopReason": "tool_use"},
+    ]
+    decoder = AWSEventStreamDecoder(model="test")
+    parsed_chunks = []
+    for chunk in chunks:
+        parsed_chunk = decoder._chunk_parser(chunk)
+        parsed_chunks.append(parsed_chunk)
+    tool_call_chunks1 = parsed_chunks[8:12]
+    tool_call_chunks2 = parsed_chunks[13:17]
+    for tool_call_hunk in tool_call_chunks1:
+        tool_call_hunk_dict = tool_call_hunk.model_dump()
+        for tool_call in tool_call_hunk_dict["choices"][0]["delta"]["tool_calls"]:
+            assert tool_call["index"] == 0
+    for tool_call_hunk in tool_call_chunks2:
+        tool_call_hunk_dict = tool_call_hunk.model_dump()
+        for tool_call in tool_call_hunk_dict["choices"][0]["delta"]["tool_calls"]:
+            assert tool_call["index"] == 1
+
+
+def test_transform_tool_calls_index_with_optional_arg_func():
+    chunks = [
+        {
+            "contentBlockIndex": 0,
+            "delta": {"text": "To"},
+            "p": "abcdefghijklmnopqrstuv",
+        },
+        {
+            "contentBlockIndex": 0,
+            "delta": {"text": " get the current time, I"},
+            "p": "abcdefghijklmnopqrstuvwxyzABCD",
+        },
+        {
+            "contentBlockIndex": 0,
+            "delta": {"text": ' can use the "get_time"'},
+            "p": "abcdefghijkl",
+        },
+        {
+            "contentBlockIndex": 0,
+            "delta": {"text": " function. Since the user"},
+            "p": "abcdefghijkl",
+        },
+        {
+            "contentBlockIndex": 0,
+            "delta": {"text": " didn't specify whether"},
+            "p": "abcdefghijklmnopqrstuvw",
+        },
+        {
+            "contentBlockIndex": 0,
+            "delta": {"text": " they want UTC time or local time,"},
+            "p": "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUV",
+        },
+        {
+            "contentBlockIndex": 0,
+            "delta": {"text": " I'll assume they"},
+            "p": "abcdefghijkl",
+        },
+        {
+            "contentBlockIndex": 0,
+            "delta": {"text": " want the local time. Here's"},
+            "p": "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMN",
+        },
+        {
+            "contentBlockIndex": 0,
+            "delta": {"text": " how I"},
+            "p": "abcdefghijklmnopqrstuvw",
+        },
+        {
+            "contentBlockIndex": 0,
+            "delta": {"text": "'ll make the function call:"},
+            "p": "abcdefghijklmnopqrstuvwxyzAB",
+        },
+        {
+            "contentBlockIndex": 0,
+            "p": "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ",
+        },
+        {
+            "contentBlockIndex": 1,
+            "p": "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNO",
+            "start": {
+                "toolUse": {
+                    "name": "get_time",
+                    "toolUseId": "tooluse_htgmgeJATsKTl4s_LW77sQ",
+                }
+            },
+        },
+        {
+            "contentBlockIndex": 1,
+            "delta": {"toolUse": {"input": ""}},
+            "p": "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUV",
+        },
+        {"contentBlockIndex": 1, "p": "abcdefghijklmnopqrstuvw"},
+        {"p": "abcdefghijklmnopqrstuvwxyzABCDEFGHIJK", "stopReason": "tool_use"},
+    ]
+    decoder = AWSEventStreamDecoder(model="test")
+    parsed_chunks = []
+    for chunk in chunks:
+        parsed_chunk = decoder._chunk_parser(chunk)
+        parsed_chunks.append(parsed_chunk)
+    tool_call_chunks = parsed_chunks[11:14]
+    for tool_call_hunk in tool_call_chunks:
+        tool_call_hunk_dict = tool_call_hunk.model_dump()
+        for tool_call in tool_call_hunk_dict["choices"][0]["delta"]["tool_calls"]:
+            assert tool_call["index"] == 0

Development/litellm/tests/test_litellm/llms/bedrock/chat/test_mistral_config.py

@@ -0,0 +1,32 @@
+from litellm.llms.bedrock.chat.invoke_transformations.amazon_mistral_transformation import (
+    AmazonMistralConfig,
+)
+from litellm.types.utils import ModelResponse
+
+
+def test_mistral_get_outputText():
+    # Set initial model response with arbitrary finish reason
+    model_response = ModelResponse()
+    model_response.choices[0].finish_reason = "None"
+
+    # Models like pixtral will return a completion with the openai format.
+    mock_json_with_choices = {
+        "choices": [{"message": {"content": "Hello!"}, "finish_reason": "stop"}]
+    }
+
+    outputText = AmazonMistralConfig.get_outputText(
+        completion_response=mock_json_with_choices, model_response=model_response
+    )
+
+    assert outputText == "Hello!"
+    assert model_response.choices[0].finish_reason == "stop"
+
+    # Other models might return a completion behind "outputs"
+    mock_json_with_output = {"outputs": [{"text": "Hi!", "stop_reason": "finish"}]}
+
+    outputText = AmazonMistralConfig.get_outputText(
+        completion_response=mock_json_with_output, model_response=model_response
+    )
+
+    assert outputText == "Hi!"
+    assert model_response.choices[0].finish_reason == "finish"