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fix(factory.py): reduce ollama pt LOC < 50
This commit is contained in:
Krrish Dholakia
parent
f18742edbd
commit
d818530265
1 changed files with 85 additions and 150 deletions
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@ -166,173 +166,108 @@ def convert_to_ollama_image(openai_image_url: str):
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)
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def _handle_ollama_system_message(
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messages: list, prompt: str, msg_i: int
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) -> Tuple[str, int]:
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system_content_str = ""
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## MERGE CONSECUTIVE SYSTEM CONTENT ##
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while msg_i < len(messages) and messages[msg_i]["role"] == "system":
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msg_content = convert_content_list_to_str(messages[msg_i])
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system_content_str += msg_content
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msg_i += 1
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return system_content_str, msg_i
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def ollama_pt(
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model, messages
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) -> Union[
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str, OllamaVisionModelObject
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]: # https://github.com/ollama/ollama/blob/af4cf55884ac54b9e637cd71dadfe9b7a5685877/docs/modelfile.md#template
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if "instruct" in model:
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prompt = custom_prompt(
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role_dict={
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"system": {"pre_message": "### System:\n", "post_message": "\n"},
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"user": {
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"pre_message": "### User:\n",
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"post_message": "\n",
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},
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"assistant": {
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"pre_message": "### Response:\n",
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"post_message": "\n",
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},
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},
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final_prompt_value="### Response:",
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messages=messages,
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user_message_types = {"user", "tool", "function"}
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msg_i = 0
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images = []
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prompt = ""
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while msg_i < len(messages):
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init_msg_i = msg_i
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user_content_str = ""
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## MERGE CONSECUTIVE USER CONTENT ##
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while msg_i < len(messages) and messages[msg_i]["role"] in user_message_types:
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msg_content = messages[msg_i].get("content")
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if msg_content:
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if isinstance(msg_content, list):
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for m in msg_content:
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if m.get("type", "") == "image_url":
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if isinstance(m["image_url"], str):
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images.append(m["image_url"])
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elif isinstance(m["image_url"], dict):
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images.append(m["image_url"]["url"])
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elif m.get("type", "") == "text":
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user_content_str += m["text"]
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else:
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# Tool message content will always be a string
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user_content_str += msg_content
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msg_i += 1
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if user_content_str:
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prompt += f"### User:\n{user_content_str}\n\n"
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system_content_str, msg_i = _handle_ollama_system_message(
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messages, prompt, msg_i
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)
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else:
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user_message_types = {"user", "tool", "function"}
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msg_i = 0
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images = []
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prompt = ""
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while msg_i < len(messages):
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init_msg_i = msg_i
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user_content_str = ""
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## MERGE CONSECUTIVE USER CONTENT ##
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while (
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msg_i < len(messages) and messages[msg_i]["role"] in user_message_types
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):
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msg_content = messages[msg_i].get("content")
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if msg_content:
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if isinstance(msg_content, list):
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for m in msg_content:
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if m.get("type", "") == "image_url":
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if isinstance(m["image_url"], str):
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images.append(m["image_url"])
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elif isinstance(m["image_url"], dict):
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images.append(m["image_url"]["url"])
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elif m.get("type", "") == "text":
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user_content_str += m["text"]
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else:
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# Tool message content will always be a string
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user_content_str += msg_content
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if system_content_str:
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prompt += f"### System:\n{system_content_str}\n\n"
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msg_i += 1
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assistant_content_str = ""
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## MERGE CONSECUTIVE ASSISTANT CONTENT ##
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while msg_i < len(messages) and messages[msg_i]["role"] == "assistant":
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assistant_content_str += convert_content_list_to_str(messages[msg_i])
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msg_i += 1
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if user_content_str:
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prompt += f"### User:\n{user_content_str}\n\n"
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tool_calls = messages[msg_i].get("tool_calls")
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ollama_tool_calls = []
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if tool_calls:
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for call in tool_calls:
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call_id: str = call["id"]
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function_name: str = call["function"]["name"]
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arguments = json.loads(call["function"]["arguments"])
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system_content_str = ""
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## MERGE CONSECUTIVE SYSTEM CONTENT ##
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while (
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msg_i < len(messages) and messages[msg_i]["role"] == "system"
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):
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msg_content = messages[msg_i].get("content")
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if msg_content:
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if isinstance(msg_content, list):
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for m in msg_content:
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if m.get("type", "") == "image_url":
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if isinstance(m["image_url"], str):
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images.append(m["image_url"])
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elif isinstance(m["image_url"], dict):
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images.append(m["image_url"]["url"])
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elif m.get("type", "") == "text":
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system_content_str += m["text"]
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else:
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# Tool message content will always be a string
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system_content_str += msg_content
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msg_i += 1
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if system_content_str:
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prompt += f"### System:\n{system_content_str}\n\n"
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assistant_content_str = ""
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## MERGE CONSECUTIVE ASSISTANT CONTENT ##
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while msg_i < len(messages) and messages[msg_i]["role"] == "assistant":
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msg_content = messages[msg_i].get("content")
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if msg_content:
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if isinstance(msg_content, list):
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for m in msg_content:
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if m.get("type", "") == "text":
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assistant_content_str += m["text"]
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elif isinstance(msg_content, str):
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# Tool message content will always be a string
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assistant_content_str += msg_content
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tool_calls = messages[msg_i].get("tool_calls")
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ollama_tool_calls = []
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if tool_calls:
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for call in tool_calls:
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call_id: str = call["id"]
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function_name: str = call["function"]["name"]
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arguments = json.loads(call["function"]["arguments"])
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ollama_tool_calls.append(
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{
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"id": call_id,
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"type": "function",
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"function": {
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"name": function_name,
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"arguments": arguments,
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},
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}
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)
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if ollama_tool_calls:
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assistant_content_str += (
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f"Tool Calls: {json.dumps(ollama_tool_calls, indent=2)}"
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ollama_tool_calls.append(
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{
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"id": call_id,
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"type": "function",
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"function": {
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"name": function_name,
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"arguments": arguments,
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},
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}
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)
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msg_i += 1
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if assistant_content_str:
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prompt += f"### Assistant:\n{assistant_content_str}\n\n"
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if msg_i == init_msg_i: # prevent infinite loops
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raise litellm.BadRequestError(
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message=BAD_MESSAGE_ERROR_STR + f"passed in {messages[msg_i]}",
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model=model,
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llm_provider="ollama",
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if ollama_tool_calls:
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assistant_content_str += (
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f"Tool Calls: {json.dumps(ollama_tool_calls, indent=2)}"
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)
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# prompt = ""
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# images = []
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# for message in messages:
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# if isinstance(message["content"], str):
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# prompt += message["content"]
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# elif isinstance(message["content"], list):
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# # see https://docs.litellm.ai/docs/providers/openai#openai-vision-models
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# for element in message["content"]:
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# if isinstance(element, dict):
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# if element["type"] == "text":
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# prompt += element["text"]
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# elif element["type"] == "image_url":
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# base64_image = convert_to_ollama_image(
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# element["image_url"]["url"]
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# )
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# images.append(base64_image)
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# if "tool_calls" in message:
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# tool_calls = []
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msg_i += 1
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# for call in message["tool_calls"]:
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# call_id: str = call["id"]
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# function_name: str = call["function"]["name"]
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# arguments = json.loads(call["function"]["arguments"])
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if assistant_content_str:
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prompt += f"### Assistant:\n{assistant_content_str}\n\n"
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# tool_calls.append(
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# {
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# "id": call_id,
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# "type": "function",
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# "function": {"name": function_name, "arguments": arguments},
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# }
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# )
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if msg_i == init_msg_i: # prevent infinite loops
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raise litellm.BadRequestError(
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message=BAD_MESSAGE_ERROR_STR + f"passed in {messages[msg_i]}",
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model=model,
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llm_provider="ollama",
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)
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# prompt += f"### Assistant:\nTool Calls: {json.dumps(tool_calls, indent=2)}\n\n"
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response_dict: OllamaVisionModelObject = {
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"prompt": prompt,
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"images": images,
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}
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# elif "tool_call_id" in message:
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# prompt += f"### User:\n{message['content']}\n\n"
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return {"prompt": prompt, "images": images}
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return prompt
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return response_dict
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def mistral_instruct_pt(messages):
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