inner monologue airo parser
This commit is contained in:
Charles Packer
@@ -5,12 +5,13 @@ import requests
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import json
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from .webui.api import get_webui_completion
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from .llm_chat_completion_wrappers import airoboros
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from .llm_chat_completion_wrappers import airoboros, dolphin
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from .utils import DotDict
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HOST = os.getenv("OPENAI_API_BASE")
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HOST_TYPE = os.getenv("BACKEND_TYPE") # default None == ChatCompletion
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DEBUG = False
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# DEBUG = False
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DEBUG = True
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async def get_chat_completion(
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@@ -22,8 +23,11 @@ async def get_chat_completion(
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if function_call != "auto":
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raise ValueError(f"function_call == {function_call} not supported (auto only)")
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if model == "airoboros_v2.1":
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llm_wrapper = airoboros.Airoboros21Wrapper()
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if model == "airoboros-l2-70b-2.1":
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# llm_wrapper = airoboros.Airoboros21Wrapper()
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llm_wrapper = airoboros.Airoboros21InnerMonologueWrapper()
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elif model == "dolphin-2.1-mistral-7b":
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llm_wrapper = dolphin.Dolphin21MistralWrapper()
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else:
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# Warn the user that we're using the fallback
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print(
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@@ -150,6 +150,7 @@ class Airoboros21Wrapper(LLMChatCompletionWrapper):
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if self.include_opening_brance_in_prefix:
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prompt += "\n{"
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print(prompt)
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return prompt
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def clean_function_args(self, function_name, function_args):
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@@ -202,3 +203,215 @@ class Airoboros21Wrapper(LLMChatCompletionWrapper):
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},
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}
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return message
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class Airoboros21InnerMonologueWrapper(Airoboros21Wrapper):
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"""Still expect only JSON outputs from model, but add inner monologue as a field"""
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def __init__(
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self,
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simplify_json_content=True,
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clean_function_args=True,
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include_assistant_prefix=True,
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include_opening_brace_in_prefix=True,
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include_section_separators=True,
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):
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self.simplify_json_content = simplify_json_content
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self.clean_func_args = clean_function_args
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self.include_assistant_prefix = include_assistant_prefix
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self.include_opening_brance_in_prefix = include_opening_brace_in_prefix
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self.include_section_separators = include_section_separators
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def chat_completion_to_prompt(self, messages, functions):
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"""Example for airoboros: https://huggingface.co/jondurbin/airoboros-l2-70b-2.1#prompt-format
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A chat.
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USER: {prompt}
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ASSISTANT:
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Functions support: https://huggingface.co/jondurbin/airoboros-l2-70b-2.1#agentfunction-calling
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As an AI assistant, please select the most suitable function and parameters from the list of available functions below, based on the user's input. Provide your response in JSON format.
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Input: I want to know how many times 'Python' is mentioned in my text file.
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Available functions:
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file_analytics:
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description: This tool performs various operations on a text file.
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params:
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action: The operation we want to perform on the data, such as "count_occurrences", "find_line", etc.
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filters:
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keyword: The word or phrase we want to search for.
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OpenAI functions schema style:
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{
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"name": "send_message",
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"description": "Sends a message to the human user",
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"parameters": {
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"type": "object",
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"properties": {
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# https://json-schema.org/understanding-json-schema/reference/array.html
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"message": {
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"type": "string",
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"description": "Message contents. All unicode (including emojis) are supported.",
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},
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},
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"required": ["message"],
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}
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},
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"""
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prompt = ""
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# System insturctions go first
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assert messages[0]["role"] == "system"
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prompt += messages[0]["content"]
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# Next is the functions preamble
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def create_function_description(schema, add_inner_thoughts=True):
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# airorobos style
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func_str = ""
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func_str += f"{schema['name']}:"
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func_str += f"\n description: {schema['description']}"
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func_str += f"\n params:"
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if add_inner_thoughts:
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func_str += (
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f"\n inner_thoughts: Deep inner monologue private to you only."
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)
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for param_k, param_v in schema["parameters"]["properties"].items():
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# TODO we're ignoring type
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func_str += f"\n {param_k}: {param_v['description']}"
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# TODO we're ignoring schema['parameters']['required']
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return func_str
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# prompt += f"\nPlease select the most suitable function and parameters from the list of available functions below, based on the user's input. Provide your response in JSON format."
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prompt += f"\nPlease select the most suitable function and parameters from the list of available functions below, based on the ongoing conversation. Provide your response in JSON format."
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prompt += f"\nAvailable functions:"
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for function_dict in functions:
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prompt += f"\n{create_function_description(function_dict)}"
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def create_function_call(function_call, inner_thoughts=None):
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"""Go from ChatCompletion to Airoboros style function trace (in prompt)
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ChatCompletion data (inside message['function_call']):
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"function_call": {
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"name": ...
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"arguments": {
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"arg1": val1,
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...
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}
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Airoboros output:
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{
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"function": "send_message",
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"params": {
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"message": "Hello there! I am Sam, an AI developed by Liminal Corp. How can I assist you today?"
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}
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}
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"""
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airo_func_call = {
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"function": function_call["name"],
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"params": {
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"inner_thoughts": inner_thoughts,
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**json.loads(function_call["arguments"]),
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},
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}
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return json.dumps(airo_func_call, indent=2)
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# Add a sep for the conversation
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if self.include_section_separators:
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prompt += "\n### INPUT"
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# Last are the user/assistant messages
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for message in messages[1:]:
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assert message["role"] in ["user", "assistant", "function"], message
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if message["role"] == "user":
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if self.simplify_json_content:
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try:
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content_json = json.loads(message["content"])
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content_simple = content_json["message"]
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prompt += f"\nUSER: {content_simple}"
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except:
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prompt += f"\nUSER: {message['content']}"
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elif message["role"] == "assistant":
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prompt += f"\nASSISTANT:"
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# need to add the function call if there was one
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inner_thoughts = message["content"]
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if message["function_call"]:
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prompt += f"\n{create_function_call(message['function_call'], inner_thoughts=inner_thoughts)}"
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elif message["role"] == "function":
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# TODO find a good way to add this
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# prompt += f"\nASSISTANT: (function return) {message['content']}"
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prompt += f"\nFUNCTION RETURN: {message['content']}"
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continue
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else:
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raise ValueError(message)
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# Add a sep for the response
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if self.include_section_separators:
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prompt += "\n### RESPONSE"
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if self.include_assistant_prefix:
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prompt += f"\nASSISTANT:"
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if self.include_opening_brance_in_prefix:
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prompt += "\n{"
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return prompt
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def clean_function_args(self, function_name, function_args):
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"""Some basic MemGPT-specific cleaning of function args"""
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cleaned_function_name = function_name
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cleaned_function_args = function_args.copy()
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if function_name == "send_message":
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# strip request_heartbeat
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cleaned_function_args.pop("request_heartbeat", None)
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inner_thoughts = None
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if "inner_thoughts" in function_args:
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inner_thoughts = cleaned_function_args.pop("inner_thoughts")
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# TODO more cleaning to fix errors LLM makes
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return inner_thoughts, cleaned_function_name, cleaned_function_args
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def output_to_chat_completion_response(self, raw_llm_output):
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"""Turn raw LLM output into a ChatCompletion style response with:
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"message" = {
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"role": "assistant",
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"content": ...,
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"function_call": {
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"name": ...
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"arguments": {
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"arg1": val1,
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...
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}
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}
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}
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"""
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if self.include_opening_brance_in_prefix and raw_llm_output[0] != "{":
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raw_llm_output = "{" + raw_llm_output
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try:
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function_json_output = json.loads(raw_llm_output)
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except Exception as e:
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raise Exception(f"Failed to decode JSON from LLM output:\n{raw_llm_output}")
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function_name = function_json_output["function"]
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function_parameters = function_json_output["params"]
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if self.clean_func_args:
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(
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inner_thoughts,
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function_name,
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function_parameters,
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) = self.clean_function_args(function_name, function_parameters)
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message = {
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"role": "assistant",
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"content": inner_thoughts,
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"function_call": {
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"name": function_name,
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"arguments": json.dumps(function_parameters),
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},
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}
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return message
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@@ -2,6 +2,7 @@ SIMPLE = {
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"stopping_strings": [
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"\nUSER:",
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"\nASSISTANT:",
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"\nFUNCTION RETURN:",
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# '\n' +
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# '</s>',
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# '<|',
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@@ -394,6 +394,8 @@ async def main(
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).ask_async()
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clear_line()
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user_input = user_input.rstrip()
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if user_input.startswith("!"):
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print(f"Commands for CLI begin with '/' not '!'")
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continue
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