message types

docs/ramblings/# what happemns duiring losure.py

@@ -14,7 +14,7 @@ def to_str(self):
 test = # [Test object]
 
 import ell.caching
-@ell.lm("gpt-4-turbo", temperature=0.1, max_tokens=5)
+@ell.text("gpt-4-turbo", temperature=0.1, max_tokens=5)
 def write_a_complete_python_class(user_spec : str):
     return [ell.system(f"""You are an expert python programmer capable of interpreting a user's spec and writing a python class to accomidate their request. You should document all your code, and you best practices.
     {CODE_INSTURCTIONS} {test.to_str()}

docs/ramblings/parsing_example.py

@@ -16,7 +16,7 @@ def parse_outputs(result : lstr) -> str:
     return Personality(name, backstory)
 
 
-@ell.lm(model="gpt-4o-mini", temperature=1.0, output_parser=parse_outputs)
+@ell.text(model="gpt-4o-mini", temperature=1.0, output_parser=parse_outputs)
 def create_random_personality():
     """You are backstoryGPT. You come up with a backstory for a character incljuding name. Choose a completely random name from the list. Format as follows.
 
@@ -37,7 +37,7 @@ def parse_outputs(result : lstr) -> str:
     return Personality(name, backstory)
 
 
-@ell.lm(model="gpt-4o-mini", temperature=1.0, output_parser=parse_outputs)
+@ell.text(model="gpt-4o-mini", temperature=1.0, output_parser=parse_outputs)
 def create_random_personality():
     """You are backstoryGPT. You come up with a backstory for a character incljuding name. Choose a completely random name from the list. Format as follows.
 
@@ -61,7 +61,7 @@ def parse_outputs(result : lstr) -> str:
         backstory = result.split(":")[1]
         return Personality(name, backstory)
 
-@ell.lm(model="gpt-4o-mini", temperature=1.0, output_parser=Personality.parse_outputs)
+@ell.text(model="gpt-4o-mini", temperature=1.0, output_parser=Personality.parse_outputs)
 def create_random_personality():
     """You are backstoryGPT. You come up with a backstory for a character incljuding name. Choose a completely random name from the list. Format as follows.
 
@@ -79,7 +79,7 @@ def parse_outputs(result : lstr) -> str:
     return Personality(name, backstory)
 
 @ell.structure(parser=parse_outputs, retries=3)
-@ell.lm(model="gpt-4o-mini", temperature=1.0)
+@ell.text(model="gpt-4o-mini", temperature=1.0)
 def create_random_personality():
     """You are backstoryGPT. You come up with a backstory for a character incljuding name. Choose a completely random name from the list. Format as follows.
 
@@ -134,7 +134,7 @@ def parser(result : lstr) -> OutputFormat:
 
 # 3. Define our LM we can use the format from our schema or something else
 @ell.structures(parserer=parser, retries=3)
-@ell.lm(model="gpt-4o-mini", temperature=1.0)
+@ell.text(model="gpt-4o-mini", temperature=1.0)
 def create_random_personality():
     f"""Answer in the format {OutputFormat.get_format_prompt()}"""
 
@@ -159,7 +159,7 @@ def parser(pstr):
     return name, backstory
 
 @ell.structure(parserer=parser, retries=3)
-@ell.lm(model="gpt-4o-mini", temperature=1.0)
+@ell.text(model="gpt-4o-mini", temperature=1.0)
 def create_random_personality():
     f"""Answer in the format {format}"""
 
@@ -169,7 +169,7 @@ def create_random_personality():
 ############################
 
 
-@ell.lm(model="gpt-4o-mini", temperature=1.0)
+@ell.text(model="gpt-4o-mini", temperature=1.0)
 def create_random_personality_str():
     f"""Answer in the format {format}"""
 
@@ -197,7 +197,7 @@ def parser(pstr):
 
     return name, backstory
 
-@ell.lm(model="gpt-4o-mini", temperature=1.0, parser=parser)
+@ell.text(model="gpt-4o-mini", temperature=1.0, parser=parser)
 def create_random_personality():
     f"""Answer in the format {format}"""
 
@@ -224,7 +224,7 @@ def create_random_personality():
 
 If I have
 """
-@ell.lm(model="gpt-4o-mini", temperature=1.0, parser=parser)
+@ell.text(model="gpt-4o-mini", temperature=1.0, parser=parser)
 def create_random_personality():
     f"""Answer in the format {format}"""
 
@@ -238,7 +238,7 @@ def parse_to_my_fucked_up_unserializable_format(pstr):
     return MyFuckedUpObject(pstr)
 
 @ell.structure(parser=parse_to_my_fucked_up_unserializable_format, retries=3)
-@ell.lm(model="gpt-4o-mini", temperature=1.0)
+@ell.text(model="gpt-4o-mini", temperature=1.0)
 def create_random_personality():
     f"""Answer in the format {format}"""
 
@@ -255,7 +255,7 @@ def create_random_personality():
 
 This is equivalent to the following
 """
-@ell.lm(model="gpt-4o-mini", temperature=1.0)
+@ell.text(model="gpt-4o-mini", temperature=1.0)
 def create_random_personality():
     return "Come up with a backstory about " + random.choice(names_list)
 
@@ -307,13 +307,13 @@ def converted_lm_func():
             return new_system_prompt, user_prompt
 
         return retry(schema.parse(
-            ell.lm(**lm_kwargs)(converted_lm_func)), tries=3)
+            ell.text(**lm_kwargs)(converted_lm_func)), tries=3)
 
     return decorator
 
 # This does this:
 
-@ell.lm(model="gpt-4o-mini", temperature=1.0)
+@ell.text(model="gpt-4o-mini", temperature=1.0)
 def internal_make_a_rpg_character(name : str):
     return [
         ell.system("You are a rpg character creator. You create rpg characters. You must respond only in JSON in the following format: " + {RPGSchema.get_format_prompt()}),

examples/bv.py

@@ -20,7 +20,7 @@ def get_lmp(z = 10):
     y = 13
     y = z
 
-    @ell.lm("gpt-4o-mini", temperature=0.1, max_tokens=6)
+    @ell.text("gpt-4o-mini", temperature=0.1, max_tokens=6)
     def write_a_complete_python_class(user_spec : str):
         return [ell.system(f"""You are an mid-tier python programmer capable of interpreting a user's spec and writing a python class to accomidate their request. You should document all your code, and you best practices.
         {CODE_INSTURCTIONS} {z} {y} {test} {another_serializeable_global}

examples/calculator_structured.py

@@ -39,7 +39,7 @@ class Div:
 CalcOp = Union[Add, Sub, Mul, Div]
 
 
-@ell.lm(model="gpt-4o", temperature=0.1)
+@ell.text(model="gpt-4o", temperature=0.1)
 def parse_json(task: str, type: Type[Any]):
     return [
         ell.system(

examples/chatbot.py

@@ -16,12 +16,12 @@ def approve_claim(claim_id : str):
     """Approve a claim"""
     pass
 
-@ell.multimodal(model="gpt-4o", tools=[create_claim_draft, approve_claim], temperature=0.1)
+@ell.multimodal(model="gpt-4o", tools=[create_claim_draft, approve_claim], temperature=0.1, exempt_from_tracking=True)
 def insurance_claim_chatbot(message_history: List[str]):
     return [
         ell.system( """You are a an insurance adjuster AI. You are given a dialogue with a user and have access to various tools to effectuate the insurance claim adjustment process. Ask question until you have enough information to create a claim draft. Then ask for approval."""),
     ] + [
-        ell.Message(role="user" if i % 2 == 0 else "assistant", content=message)
+        ell.message(role="user" if i % 2 == 0 else "assistant", content=message)
         for i, message in enumerate(message_history)
     ] 
 
@@ -42,225 +42,3 @@ def insurance_claim_chatbot(message_history: List[str]):
         message_history.append(response)
 
 
-
-
-# import openai
-
-
-
-
-# class StrContent(BaseModel):
-#     content: str
-#     type : Literal["text"]
-
-
-# class Message(BaseModel):
-#     content: Union[StrContent, FunctionCall, Image, Audio]
-
-
-# x = my_lm()
-# if x.content.type == "text":
-#     print(x.content.text)
-
-# if x.text:
-#     print(x.text)
-
-
-
-
-
-
-
-
-
-# class Message(BaseModel):
-#     text: str
-#     image: str
-#     tool_result: str
-#     audio: str
-#     function_call  : FunctionCall
-
-# # class FunctionCall(BaseModel):
-# #     name: str
-# #     arguments: BaseModel
-# #     fn : Callable
-# #     def call(self):
-# #         return self.fn(self.arguments)
-
-
-
-# @ell.lm(model="claude-3.5-sonnet")
-# def i_use_different_content_blocks():
-#     return "asdasd"
-
-
-
-# i_use_different_content_blocks()
-# -> 
-# [
-#     {
-#         "type": "text",
-#         "content": "asdasd"
-#     },
-#     {
-#         "type": "function_call",
-#         "content": {
-#             "name": "asdasd",
-#             "arguments": "asdasd"
-#         }
-#     },
-#     {
-#         "type": "file",
-#         "content": "asdasd"
-#     }
-# ]
-
-
-# @ell.multimodal_lm(model="gpt-4o")
-# def i_use_different_content_blocks():
-#     return "asdasd"
-
-
-# class MultimodalMessage(BaseModel):
-#     text: str
-#     image: str
-#     tool_result: str
-#     audio: str
-#     function_call  : FunctionCall
-#     _raw
-
-
-
-
-# @ell.text_lm(model="gpt-4o")
-# def i_use_different_content_blocks():
-#     return "asdasd"
-
-
-
-# # -> str
-
-
-
-
-
-# TOOLS = [
-#     tool1,
-#     tool2,
-#     tool3,
-# ]
-# @ell.lm(model="llama-3-8b-instruct")
-# def i_use_tools(request : str):
-#     return [
-#         ell.system("You are a helpful assistant. You have access to the following tools:" + "\n" + tool.prompt()),
-#         ell.user(request),
-#     ]
-
-
-
-# TOOLS = [
-#     tool1,
-#     tool2,
-#     tool3,
-# ]
-# @ell.lm(model="gpt-4o", tools=TOOLS)
-# def i_use_tools(request : str):
-#     pass
-
-
-
-# # It's our job to abstract different kidns of Foundation Models
-
-# @ell.openai.lm(model="gpt-4o", tools=TOOLS)
-# def i_use_tools(request : str):
-#     pass
-
-
-# # Throws an error: # llama lms do not support explicit tool calling ,you need to prompt this
-# @ell.llama.lm(model="llama-3-8b-instruct", tools=TOOLS) 
-# def i_use_tools(request : str):
-#     pass
-
-# # therefore : ->
-# @ell.llama.lm(model="llama-3-8b-instruct")
-# def i_use_tools(request : str):
-#     return [
-#         ell.system("You are a helpful assistant. You have access to the following tools:" + "\n" + tool.prompt()),
-#         ell.user(request),
-#     ]
-
-
-# @ell.anthropic.lm(model="claude-3.5-sonnet")
-# def i_use_tools(request : str):
-#     return [
-#         ell.system("You are a helpful assistant. You have access to the following tools:" + "\n" + tool.prompt()),
-#         ell.user(request),
-#     ]
-# class AnthropicMessage(BaseModel):
-#     content_blocks : List[ContentBlock]
-
-# class ContentBlock(BaseModel):
-#     type : Literal["text", "function_call", "file"]
-#     content : str
-
-
-
-# #------------------------------- -- Against øur design philosophy
-
-# @ell.lm(model="gpt-4o")
-# def i_use_tools(request : str):
-#     return MessageCreationParams(
-#         tools=tools,
-#         message=[
-#             ell.system("You are a helpful assistant. You have access to the following tools:" + "\n" + tool.prompt()),
-#             ell.user(request),
-#         ]
-#     )
-
-
-# @ell.lm(model="gpt-4o")
-# def i_use_tools(request : str):
-#     """Suystem prompt"""
-#     return "user pomrpt"
-
-
-
-
-# # --------------
-
-
-# @ell.lm # ALLL STRING BASED GET FUCKEd
-# def gn():
-#     return "asdasd"
-
-# gn() # ->  str
-
-
-
-# @ell.omni(tools=TOOLS, structured_outputs=True) # Gives your raw return tpes form the mdoel
-# def fn():
-#     return "asdasd"
-
-# fn() # -> OpenAI.ChatCompletion
-
-# fn().choices[0].message.content 
-
-
-
-
-# class ContnetBlock(BaseModel):
-#     text :lstr
-#     audio : lnumpy
-#     image : lmnumpy
-#     function_call : lFunctionCall
-
-
-# T = TypeVar("ContentBlockType")
-# class ContentBlock(BaseModel):
-#     type: Literal["text", "audio", "image", "function_call"]
-#     content : T
-
-
-# class TextContent(BaseModel):
-#     type: Literal["text"]
-#     content: str