mirror of
https://github.com/BerriAI/litellm.git
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5099aac1a5
* feat(databricks/chat/): add anthropic w/ reasoning content support via databricks
Allows user to call claude-3-7-sonnet with thinking via databricks
* refactor: refactor choices transformation + add unit testing
* fix(databricks/chat/transformation.py): support thinking blocks on databricks response streaming
* feat(databricks/chat/transformation.py): support response_format for claude models
* fix(databricks/chat/transformation.py): correctly handle response_format={"type": "text"}
* feat(databricks/chat/transformation.py): support 'reasoning_effort' param mapping for anthropic
* fix: fix ruff errors
* fix: fix linting error
* test: update test
* fix(databricks/chat/transformation.py): handle json mode output parsing
* fix(databricks/chat/transformation.py): handle json mode on streaming
* test: update test
* test: update dbrx testing
* test: update testing
* fix(base_model_iterator.py): handle non-json chunk
* test: update tests
* fix: fix ruff check
* fix: fix databricks config import
* fix: handle _tool = none
* test: skip invalid test
812 lines
31 KiB
Python
812 lines
31 KiB
Python
#### What this tests ####
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# This tests if prompts are being correctly formatted
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import os
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import sys
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import pytest
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sys.path.insert(0, os.path.abspath("../.."))
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from typing import Union
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# from litellm.litellm_core_utils.prompt_templates.factory import prompt_factory
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import litellm
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from litellm import completion
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from litellm.litellm_core_utils.prompt_templates.factory import (
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_bedrock_tools_pt,
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anthropic_messages_pt,
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anthropic_pt,
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claude_2_1_pt,
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convert_to_anthropic_image_obj,
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convert_url_to_base64,
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llama_2_chat_pt,
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prompt_factory,
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)
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from litellm.litellm_core_utils.prompt_templates.common_utils import (
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get_completion_messages,
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)
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from litellm.llms.vertex_ai.gemini.transformation import (
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_gemini_convert_messages_with_history,
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)
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from unittest.mock import AsyncMock, MagicMock, patch
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def test_llama_3_prompt():
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messages = [
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{"role": "system", "content": "You are a good bot"},
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{"role": "user", "content": "Hey, how's it going?"},
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]
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received_prompt = prompt_factory(
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model="meta-llama/Meta-Llama-3-8B-Instruct", messages=messages
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)
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print(f"received_prompt: {received_prompt}")
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expected_prompt = """<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are a good bot<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHey, how's it going?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"""
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assert received_prompt == expected_prompt
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def test_codellama_prompt_format():
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messages = [
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{"role": "system", "content": "You are a good bot"},
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{"role": "user", "content": "Hey, how's it going?"},
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]
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expected_prompt = "<s>[INST] <<SYS>>\nYou are a good bot\n<</SYS>>\n [/INST]\n[INST] Hey, how's it going? [/INST]\n"
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assert llama_2_chat_pt(messages) == expected_prompt
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def test_claude_2_1_pt_formatting():
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# Test case: User only, should add Assistant
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messages = [{"role": "user", "content": "Hello"}]
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expected_prompt = "\n\nHuman: Hello\n\nAssistant: "
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assert claude_2_1_pt(messages) == expected_prompt
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# Test case: System, User, and Assistant "pre-fill" sequence,
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# Should return pre-fill
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": 'Please return "Hello World" as a JSON object.'},
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{"role": "assistant", "content": "{"},
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]
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expected_prompt = 'You are a helpful assistant.\n\nHuman: Please return "Hello World" as a JSON object.\n\nAssistant: {'
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assert claude_2_1_pt(messages) == expected_prompt
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# Test case: System, Assistant sequence, should insert blank Human message
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# before Assistant pre-fill
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messages = [
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{"role": "system", "content": "You are a storyteller."},
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{"role": "assistant", "content": "Once upon a time, there "},
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]
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expected_prompt = (
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"You are a storyteller.\n\nHuman: \n\nAssistant: Once upon a time, there "
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)
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assert claude_2_1_pt(messages) == expected_prompt
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# Test case: System, User sequence
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messages = [
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{"role": "system", "content": "System reboot"},
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{"role": "user", "content": "Is everything okay?"},
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]
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expected_prompt = "System reboot\n\nHuman: Is everything okay?\n\nAssistant: "
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assert claude_2_1_pt(messages) == expected_prompt
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def test_anthropic_pt_formatting():
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# Test case: User only, should add Assistant
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messages = [{"role": "user", "content": "Hello"}]
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expected_prompt = "\n\nHuman: Hello\n\nAssistant: "
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assert anthropic_pt(messages) == expected_prompt
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# Test case: System, User, and Assistant "pre-fill" sequence,
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# Should return pre-fill
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": 'Please return "Hello World" as a JSON object.'},
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{"role": "assistant", "content": "{"},
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]
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expected_prompt = '\n\nHuman: <admin>You are a helpful assistant.</admin>\n\nHuman: Please return "Hello World" as a JSON object.\n\nAssistant: {'
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assert anthropic_pt(messages) == expected_prompt
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# Test case: System, Assistant sequence, should NOT insert blank Human message
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# before Assistant pre-fill, because "System" messages are Human
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# messages wrapped with <admin></admin>
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messages = [
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{"role": "system", "content": "You are a storyteller."},
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{"role": "assistant", "content": "Once upon a time, there "},
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]
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expected_prompt = "\n\nHuman: <admin>You are a storyteller.</admin>\n\nAssistant: Once upon a time, there "
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assert anthropic_pt(messages) == expected_prompt
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# Test case: System, User sequence
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messages = [
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{"role": "system", "content": "System reboot"},
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{"role": "user", "content": "Is everything okay?"},
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]
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expected_prompt = "\n\nHuman: <admin>System reboot</admin>\n\nHuman: Is everything okay?\n\nAssistant: "
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assert anthropic_pt(messages) == expected_prompt
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def test_anthropic_messages_nested_pt():
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from litellm.types.llms.anthropic import (
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AnthopicMessagesAssistantMessageParam,
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AnthropicMessagesUserMessageParam,
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)
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messages = [
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{"content": [{"text": "here is a task", "type": "text"}], "role": "user"},
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{
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"content": [{"text": "sure happy to help", "type": "text"}],
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"role": "assistant",
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},
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{
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"content": [
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{
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"text": "Here is a screenshot of the current desktop with the "
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"mouse coordinates (500, 350). Please select an action "
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"from the provided schema.",
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"type": "text",
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}
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],
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"role": "user",
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},
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]
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new_messages = anthropic_messages_pt(
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messages, model="claude-3-sonnet-20240229", llm_provider="anthropic"
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)
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assert isinstance(new_messages[1]["content"][0]["text"], str)
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# codellama_prompt_format()
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def test_bedrock_tool_calling_pt():
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tools = [
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{
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"type": "function",
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"function": {
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"name": "get_current_weather",
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"description": "Get the current weather in a given location",
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"parameters": {
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "The city and state, e.g. San Francisco, CA",
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},
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"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
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},
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"required": ["location"],
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},
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},
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}
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]
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converted_tools = _bedrock_tools_pt(tools=tools)
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print(converted_tools)
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def test_convert_url_to_img():
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response_url = convert_url_to_base64(
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url="https://images.pexels.com/photos/1319515/pexels-photo-1319515.jpeg?auto=compress&cs=tinysrgb&w=1260&h=750&dpr=1"
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)
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assert "image/jpeg" in response_url
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@pytest.mark.parametrize(
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"url, expected_media_type",
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[
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("data:image/jpeg;base64,1234", "image/jpeg"),
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("data:application/pdf;base64,1234", "application/pdf"),
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(r"data:image\/jpeg;base64,1234", "image/jpeg"),
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],
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)
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def test_base64_image_input(url, expected_media_type):
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response = convert_to_anthropic_image_obj(openai_image_url=url, format=None)
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assert response["media_type"] == expected_media_type
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def test_anthropic_messages_tool_call():
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messages = [
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{
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"role": "user",
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"content": "Would development of a software platform be under ASC 350-40 or ASC 985?",
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},
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{
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"role": "assistant",
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"content": "",
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"tool_call_id": "bc8cb4b6-88c4-4138-8993-3a9d9cd51656",
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"tool_calls": [
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{
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"id": "bc8cb4b6-88c4-4138-8993-3a9d9cd51656",
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"function": {
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"arguments": '{"completed_steps": [], "next_steps": [{"tool_name": "AccountingResearchTool", "description": "Research ASC 350-40 to understand its scope and applicability to software development."}, {"tool_name": "AccountingResearchTool", "description": "Research ASC 985 to understand its scope and applicability to software development."}, {"tool_name": "AccountingResearchTool", "description": "Compare the scopes of ASC 350-40 and ASC 985 to determine which is more applicable to software platform development."}], "learnings": [], "potential_issues": ["The distinction between the two standards might not be clear-cut for all types of software development.", "There might be specific circumstances or details about the software platform that could affect which standard applies."], "missing_info": ["Specific details about the type of software platform being developed (e.g., for internal use or for sale).", "Whether the entity developing the software is also the end-user or if it\'s being developed for external customers."], "done": false, "required_formatting": null}',
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"name": "TaskPlanningTool",
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},
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"type": "function",
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}
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],
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},
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{
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"role": "function",
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"content": '{"completed_steps":[],"next_steps":[{"tool_name":"AccountingResearchTool","description":"Research ASC 350-40 to understand its scope and applicability to software development."},{"tool_name":"AccountingResearchTool","description":"Research ASC 985 to understand its scope and applicability to software development."},{"tool_name":"AccountingResearchTool","description":"Compare the scopes of ASC 350-40 and ASC 985 to determine which is more applicable to software platform development."}],"formatting_step":null}',
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"name": "TaskPlanningTool",
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"tool_call_id": "bc8cb4b6-88c4-4138-8993-3a9d9cd51656",
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},
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]
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translated_messages = anthropic_messages_pt(
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messages, model="claude-3-sonnet-20240229", llm_provider="anthropic"
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)
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print(translated_messages)
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assert (
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translated_messages[-1]["content"][0]["tool_use_id"]
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== "bc8cb4b6-88c4-4138-8993-3a9d9cd51656"
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)
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def test_anthropic_cache_controls_pt():
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"see anthropic docs for this: https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching#continuing-a-multi-turn-conversation"
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messages = [
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# marked for caching with the cache_control parameter, so that this checkpoint can read from the previous cache.
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{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": "What are the key terms and conditions in this agreement?",
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"cache_control": {"type": "ephemeral"},
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}
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],
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},
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{
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"role": "assistant",
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"content": "Certainly! the key terms and conditions are the following: the contract is 1 year long for $10/mo",
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},
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# The final turn is marked with cache-control, for continuing in followups.
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{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": "What are the key terms and conditions in this agreement?",
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"cache_control": {"type": "ephemeral"},
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}
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],
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},
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{
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"role": "assistant",
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"content": "Certainly! the key terms and conditions are the following: the contract is 1 year long for $10/mo",
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"cache_control": {"type": "ephemeral"},
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},
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]
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translated_messages = anthropic_messages_pt(
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messages, model="claude-3-5-sonnet-20240620", llm_provider="anthropic"
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)
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for i, msg in enumerate(translated_messages):
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if i == 0:
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assert msg["content"][0]["cache_control"] == {"type": "ephemeral"}
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elif i == 1:
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assert "cache_controls" not in msg["content"][0]
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elif i == 2:
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assert msg["content"][0]["cache_control"] == {"type": "ephemeral"}
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elif i == 3:
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assert msg["content"][0]["cache_control"] == {"type": "ephemeral"}
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print("translated_messages: ", translated_messages)
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def test_anthropic_cache_controls_tool_calls_pt():
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"""
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Tests that cache_control is properly set in tool_calls when converting messages
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for the Anthropic API.
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"""
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messages = [
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{
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"role": "user",
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"content": "Can you help me get the weather?",
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},
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{
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"role": "assistant",
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"content": "",
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"tool_calls": [
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{
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"id": "weather-tool-id-123",
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"function": {
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"arguments": '{"location": "San Francisco"}',
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"name": "get_weather",
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},
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"type": "function",
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}
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],
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"cache_control": {"type": "ephemeral"},
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},
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{
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"role": "function",
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"content": '{"temperature": 72, "unit": "fahrenheit", "description": "sunny"}',
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"name": "get_weather",
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"tool_call_id": "weather-tool-id-123",
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"cache_control": {"type": "ephemeral"},
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},
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]
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translated_messages = anthropic_messages_pt(
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messages, model="claude-3-sonnet-20240229", llm_provider="anthropic"
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)
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print("Translated tool call messages:", translated_messages)
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assert translated_messages[0]["role"] == "user"
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assert translated_messages[1]["role"] == "assistant"
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for content_item in translated_messages[1]["content"]:
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if content_item["type"] == "tool_use":
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assert "cache_control" not in content_item
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assert content_item["name"] == "get_weather"
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assert translated_messages[2]["role"] == "user"
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for content_item in translated_messages[2]["content"]:
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if content_item["type"] == "tool_result":
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assert content_item["cache_control"] == {"type": "ephemeral"}
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@pytest.mark.parametrize("provider", ["bedrock", "anthropic"])
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def test_bedrock_parallel_tool_calling_pt(provider):
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"""
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Make sure parallel tool call blocks are merged correctly - https://github.com/BerriAI/litellm/issues/5277
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"""
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from litellm.litellm_core_utils.prompt_templates.factory import (
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_bedrock_converse_messages_pt,
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)
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from litellm.types.utils import ChatCompletionMessageToolCall, Function, Message
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messages = [
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{
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"role": "user",
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"content": "What's the weather like in San Francisco, Tokyo, and Paris? - give me 3 responses",
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},
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Message(
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content="Here are the current weather conditions for San Francisco, Tokyo, and Paris:",
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role="assistant",
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tool_calls=[
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ChatCompletionMessageToolCall(
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index=1,
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function=Function(
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arguments='{"city": "New York"}',
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name="get_current_weather",
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),
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id="tooluse_XcqEBfm8R-2YVaPhDUHsPQ",
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type="function",
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),
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ChatCompletionMessageToolCall(
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index=2,
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function=Function(
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arguments='{"city": "London"}',
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name="get_current_weather",
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),
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id="tooluse_VB9nk7UGRniVzGcaj6xrAQ",
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type="function",
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),
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],
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function_call=None,
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),
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{
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"tool_call_id": "tooluse_XcqEBfm8R-2YVaPhDUHsPQ",
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"role": "tool",
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"name": "get_current_weather",
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"content": "25 degrees celsius.",
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},
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{
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"tool_call_id": "tooluse_VB9nk7UGRniVzGcaj6xrAQ",
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"role": "tool",
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"name": "get_current_weather",
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"content": "28 degrees celsius.",
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},
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]
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if provider == "bedrock":
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translated_messages = _bedrock_converse_messages_pt(
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messages=messages,
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model="anthropic.claude-3-sonnet-20240229-v1:0",
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llm_provider="bedrock",
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)
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else:
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translated_messages = anthropic_messages_pt(
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messages=messages,
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model="claude-3-sonnet-20240229-v1:0",
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llm_provider=provider,
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)
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print(translated_messages)
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number_of_messages = len(translated_messages)
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# assert last 2 messages are not the same role
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assert (
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translated_messages[number_of_messages - 1]["role"]
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!= translated_messages[number_of_messages - 2]["role"]
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)
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def test_vertex_only_image_user_message():
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base64_image = "/9j/2wCEAAgGBgcGBQ"
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messages = [
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{
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"role": "user",
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"content": [
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{
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"type": "image_url",
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"image_url": {"url": f"data:image/jpeg;base64,{base64_image}"},
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},
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],
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},
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]
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response = _gemini_convert_messages_with_history(messages=messages)
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expected_response = [
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{
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"role": "user",
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"parts": [
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{
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"inline_data": {
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"data": "/9j/2wCEAAgGBgcGBQ",
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"mime_type": "image/jpeg",
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}
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},
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{"text": " "},
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],
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}
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]
|
|
|
|
assert len(response) == len(expected_response)
|
|
for idx, content in enumerate(response):
|
|
assert (
|
|
content == expected_response[idx]
|
|
), "Invalid gemini input. Got={}, Expected={}".format(
|
|
content, expected_response[idx]
|
|
)
|
|
|
|
|
|
def test_convert_url():
|
|
convert_url_to_base64("https://picsum.photos/id/237/200/300")
|
|
|
|
|
|
def test_azure_tool_call_invoke_helper():
|
|
messages = [
|
|
{"role": "system", "content": "You are a helpful assistant."},
|
|
{"role": "user", "content": "What is the weather in Copenhagen?"},
|
|
{"role": "assistant", "function_call": {"name": "get_weather"}},
|
|
]
|
|
|
|
transformed_messages = litellm.AzureOpenAIConfig().transform_request(
|
|
model="gpt-4o",
|
|
messages=messages,
|
|
optional_params={},
|
|
litellm_params={},
|
|
headers={},
|
|
)
|
|
|
|
assert transformed_messages["messages"] == [
|
|
{"role": "system", "content": "You are a helpful assistant."},
|
|
{"role": "user", "content": "What is the weather in Copenhagen?"},
|
|
{
|
|
"role": "assistant",
|
|
"function_call": {"name": "get_weather", "arguments": ""},
|
|
},
|
|
]
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"messages, expected_messages, user_continue_message, assistant_continue_message",
|
|
[
|
|
(
|
|
[
|
|
{"role": "user", "content": "Hello!"},
|
|
{"role": "assistant", "content": "Hello! How can I assist you today?"},
|
|
{"role": "user", "content": "What is Databricks?"},
|
|
{"role": "user", "content": "What is Azure?"},
|
|
{"role": "assistant", "content": "I don't know anyything, do you?"},
|
|
],
|
|
[
|
|
{"role": "user", "content": "Hello!"},
|
|
{
|
|
"role": "assistant",
|
|
"content": "Hello! How can I assist you today?",
|
|
},
|
|
{"role": "user", "content": "What is Databricks?"},
|
|
{
|
|
"role": "assistant",
|
|
"content": "Please continue.",
|
|
},
|
|
{"role": "user", "content": "What is Azure?"},
|
|
{
|
|
"role": "assistant",
|
|
"content": "I don't know anyything, do you?",
|
|
},
|
|
{
|
|
"role": "user",
|
|
"content": "Please continue.",
|
|
},
|
|
],
|
|
None,
|
|
None,
|
|
),
|
|
(
|
|
[
|
|
{"role": "user", "content": "Hello!"},
|
|
],
|
|
[
|
|
{"role": "user", "content": "Hello!"},
|
|
],
|
|
None,
|
|
None,
|
|
),
|
|
(
|
|
[
|
|
{"role": "user", "content": "Hello!"},
|
|
{"role": "user", "content": "What is Databricks?"},
|
|
],
|
|
[
|
|
{"role": "user", "content": "Hello!"},
|
|
{"role": "assistant", "content": "Please continue."},
|
|
{"role": "user", "content": "What is Databricks?"},
|
|
],
|
|
None,
|
|
None,
|
|
),
|
|
(
|
|
[
|
|
{"role": "user", "content": "Hello!"},
|
|
{"role": "user", "content": "What is Databricks?"},
|
|
{"role": "user", "content": "What is Azure?"},
|
|
],
|
|
[
|
|
{"role": "user", "content": "Hello!"},
|
|
{"role": "assistant", "content": "Please continue."},
|
|
{"role": "user", "content": "What is Databricks?"},
|
|
{
|
|
"role": "assistant",
|
|
"content": "Please continue.",
|
|
},
|
|
{"role": "user", "content": "What is Azure?"},
|
|
],
|
|
None,
|
|
None,
|
|
),
|
|
(
|
|
[
|
|
{"role": "user", "content": "Hello!"},
|
|
{
|
|
"role": "assistant",
|
|
"content": "Hello! How can I assist you today?",
|
|
},
|
|
{"role": "user", "content": "What is Databricks?"},
|
|
{"role": "user", "content": "What is Azure?"},
|
|
{"role": "assistant", "content": "I don't know anyything, do you?"},
|
|
{"role": "assistant", "content": "I can't repeat sentences."},
|
|
],
|
|
[
|
|
{"role": "user", "content": "Hello!"},
|
|
{
|
|
"role": "assistant",
|
|
"content": "Hello! How can I assist you today?",
|
|
},
|
|
{"role": "user", "content": "What is Databricks?"},
|
|
{
|
|
"role": "assistant",
|
|
"content": "Please continue",
|
|
},
|
|
{"role": "user", "content": "What is Azure?"},
|
|
{
|
|
"role": "assistant",
|
|
"content": "I don't know anyything, do you?",
|
|
},
|
|
{
|
|
"role": "user",
|
|
"content": "Ok",
|
|
},
|
|
{
|
|
"role": "assistant",
|
|
"content": "I can't repeat sentences.",
|
|
},
|
|
{"role": "user", "content": "Ok"},
|
|
],
|
|
{
|
|
"role": "user",
|
|
"content": "Ok",
|
|
},
|
|
{
|
|
"role": "assistant",
|
|
"content": "Please continue",
|
|
},
|
|
),
|
|
],
|
|
)
|
|
def test_ensure_alternating_roles(
|
|
messages, expected_messages, user_continue_message, assistant_continue_message
|
|
):
|
|
|
|
messages = get_completion_messages(
|
|
messages=messages,
|
|
assistant_continue_message=assistant_continue_message,
|
|
user_continue_message=user_continue_message,
|
|
ensure_alternating_roles=True,
|
|
)
|
|
|
|
print(messages)
|
|
|
|
assert messages == expected_messages
|
|
|
|
|
|
def test_alternating_roles_e2e():
|
|
from litellm.llms.custom_httpx.http_handler import HTTPHandler
|
|
import json
|
|
|
|
litellm.set_verbose = True
|
|
http_handler = HTTPHandler()
|
|
|
|
with patch.object(http_handler, "post", new=MagicMock()) as mock_post:
|
|
try:
|
|
response = litellm.completion(
|
|
**{
|
|
"model": "databricks/databricks-meta-llama-3-1-70b-instruct",
|
|
"messages": [
|
|
{"role": "user", "content": "Hello!"},
|
|
{
|
|
"role": "assistant",
|
|
"content": "Hello! How can I assist you today?",
|
|
},
|
|
{"role": "user", "content": "What is Databricks?"},
|
|
{"role": "user", "content": "What is Azure?"},
|
|
{"role": "assistant", "content": "I don't know anyything, do you?"},
|
|
{"role": "assistant", "content": "I can't repeat sentences."},
|
|
],
|
|
"user_continue_message": {
|
|
"role": "user",
|
|
"content": "Ok",
|
|
},
|
|
"assistant_continue_message": {
|
|
"role": "assistant",
|
|
"content": "Please continue",
|
|
},
|
|
"ensure_alternating_roles": True,
|
|
},
|
|
client=http_handler,
|
|
)
|
|
except Exception as e:
|
|
print(f"error: {e}")
|
|
|
|
assert mock_post.call_args.kwargs["data"] == json.dumps(
|
|
{
|
|
"model": "databricks-meta-llama-3-1-70b-instruct",
|
|
"messages": [
|
|
{"role": "user", "content": "Hello!"},
|
|
{
|
|
"role": "assistant",
|
|
"content": "Hello! How can I assist you today?",
|
|
},
|
|
{"role": "user", "content": "What is Databricks?"},
|
|
{
|
|
"role": "assistant",
|
|
"content": "Please continue",
|
|
},
|
|
{"role": "user", "content": "What is Azure?"},
|
|
{
|
|
"role": "assistant",
|
|
"content": "I don't know anyything, do you?",
|
|
},
|
|
{
|
|
"role": "user",
|
|
"content": "Ok",
|
|
},
|
|
{
|
|
"role": "assistant",
|
|
"content": "I can't repeat sentences.",
|
|
},
|
|
{
|
|
"role": "user",
|
|
"content": "Ok",
|
|
},
|
|
]
|
|
}
|
|
)
|
|
|
|
|
|
def test_just_system_message():
|
|
from litellm.litellm_core_utils.prompt_templates.factory import (
|
|
_bedrock_converse_messages_pt,
|
|
)
|
|
|
|
with pytest.raises(litellm.BadRequestError) as e:
|
|
_bedrock_converse_messages_pt(
|
|
messages=[],
|
|
model="anthropic.claude-3-sonnet-20240229-v1:0",
|
|
llm_provider="bedrock",
|
|
)
|
|
assert "bedrock requires at least one non-system message" in str(e.value)
|
|
|
|
|
|
def test_convert_generic_image_chunk_to_openai_image_obj():
|
|
from litellm.litellm_core_utils.prompt_templates.factory import (
|
|
convert_generic_image_chunk_to_openai_image_obj,
|
|
convert_to_anthropic_image_obj,
|
|
)
|
|
|
|
url = "https://i.pinimg.com/736x/b4/b1/be/b4b1becad04d03a9071db2817fc9fe77.jpg"
|
|
image_obj = convert_to_anthropic_image_obj(url, format=None)
|
|
url_str = convert_generic_image_chunk_to_openai_image_obj(image_obj)
|
|
image_obj = convert_to_anthropic_image_obj(url_str, format=None)
|
|
print(image_obj)
|
|
|
|
|
|
def test_hf_chat_template():
|
|
from litellm.litellm_core_utils.prompt_templates.factory import (
|
|
hf_chat_template,
|
|
)
|
|
|
|
model = "llama/arn:aws:bedrock:us-east-1:1234:imported-model/45d34re"
|
|
litellm.register_prompt_template(
|
|
model=model,
|
|
tokenizer_config={
|
|
"add_bos_token": True,
|
|
"add_eos_token": False,
|
|
"bos_token": {
|
|
"__type": "AddedToken",
|
|
"content": "",
|
|
"lstrip": False,
|
|
"normalized": True,
|
|
"rstrip": False,
|
|
"single_word": False,
|
|
},
|
|
"clean_up_tokenization_spaces": False,
|
|
"eos_token": {
|
|
"__type": "AddedToken",
|
|
"content": "",
|
|
"lstrip": False,
|
|
"normalized": True,
|
|
"rstrip": False,
|
|
"single_word": False,
|
|
},
|
|
"legacy": True,
|
|
"model_max_length": 16384,
|
|
"pad_token": {
|
|
"__type": "AddedToken",
|
|
"content": "",
|
|
"lstrip": False,
|
|
"normalized": True,
|
|
"rstrip": False,
|
|
"single_word": False,
|
|
},
|
|
"sp_model_kwargs": {},
|
|
"unk_token": None,
|
|
"tokenizer_class": "LlamaTokenizerFast",
|
|
"chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set ns = namespace(is_first=false, is_tool=false, is_output_first=true, system_prompt='') %}{%- for message in messages %}{%- if message['role'] == 'system' %}{% set ns.system_prompt = message['content'] %}{%- endif %}{%- endfor %}{{bos_token}}{{ns.system_prompt}}{%- for message in messages %}{%- if message['role'] == 'user' %}{%- set ns.is_tool = false -%}{{' ' + message['content']}}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is none %}{%- set ns.is_tool = false -%}{%- for tool in message['tool_calls']%}{%- if not ns.is_first %}{{' ' + tool['type'] + ' ' + tool['function']['name'] + '\n' + '```json' + '\n' + tool['function']['arguments'] + '\n' + '```' + ' '}}{%- set ns.is_first = true -%}{%- else %}{{' ' + tool['type'] + ' ' + tool['function']['name'] + '\n' + '```json' + '\n' + tool['function']['arguments'] + '\n' + '```' + ' '}}{{' '}}{%- endif %}{%- endfor %}{%- endif %}{%- if message['role'] == 'assistant' and message['content'] is not none %}{%- if ns.is_tool %}{{' ' + message['content'] + ' '}}{%- set ns.is_tool = false -%}{%- else %}{% set content = message['content'] %}{% if '</think>' in content %}{% set content = content.split('</think>')[-1] %}{% endif %}{{' ' + content + ' '}}{%- endif %}{%- endif %}{%- if message['role'] == 'tool' %}{%- set ns.is_tool = true -%}{%- if ns.is_output_first %}{{' ' + message['content'] + ' '}}{%- set ns.is_output_first = false %}{%- else %}{{' ' + message['content'] + ' '}}{%- endif %}{%- endif %}{%- endfor -%}{% if ns.is_tool %}{{' '}}{% endif %}{% if add_generation_prompt and not ns.is_tool %}{{' '}}{% endif %}",
|
|
},
|
|
)
|
|
|
|
messages = [
|
|
{"role": "system", "content": "You are a helpful assistant."},
|
|
{"role": "user", "content": "What is the weather in Copenhagen?"},
|
|
]
|
|
chat_template = hf_chat_template(model=model, messages=messages)
|
|
print(chat_template)
|
|
assert (
|
|
chat_template.rstrip()
|
|
== "You are a helpful assistant. What is the weather in Copenhagen?"
|
|
)
|
|
|
|
|
|
def test_ollama_pt():
|
|
from litellm.litellm_core_utils.prompt_templates.factory import ollama_pt
|
|
|
|
messages = [
|
|
{"role": "system", "content": "You are a helpful assistant."},
|
|
{"role": "user", "content": "Hello!"},
|
|
]
|
|
prompt = ollama_pt(model="ollama/llama3.1", messages=messages)
|
|
print(prompt)
|