(20241212) Reorganizing code to perform core actions in one place.

models/core/ai/llm.py

@@ -0,0 +1,255 @@
+"""
+
+    AUTHOR:
+
+        Khushal P Soonderji
+
+    DATE:
+
+        Thursday, 5th Dec., 2024.
+
+    OBJECTIVE:
+
+        To provide a structure to normalize input to and output from a standardized LLM wrapper.
+
+    REFERENCES:
+
+        N/A
+
+    DOWNLOADS:
+
+        N/A
+
+"""
+
+
+#   *****************************************************************************************************************
+#  *****                                                                                                          ****
+# ***                                                   IMPORT                                                      ***
+#  *****                                                                                                          ****
+#   *****************************************************************************************************************
+
+
+# To make sibling directories accessible for imports:
+import sys
+sys.path.append(".")
+sys.path.append("..")
+
+# For making data behaviour_models:
+from pydantic import BaseModel, Field, field_validator, PastDatetime, AwareDatetime
+from typing import Optional, Literal, Union, List, Any
+
+# My utils:
+from utils_v2.string import regex
+from utils_v2.date_time import date_time
+
+# To work with date and time:
+import datetime
+
+
+#   *****************************************************************************************************************
+#  *****                                                                                                          ****
+# ***                                           MACROS / ONE-TIME INIT                                              ***
+#  *****                                                                                                          ****
+#   *****************************************************************************************************************
+
+
+# RegEx Patterns:
+REGEX_SESSION_TOKEN = r"^[a-f0-9]{8}-[a-f0-9]{4}-[1-5][a-f0-9]{3}-[89ab][a-f0-9]{3}-[a-f0-9]{12}$"
+
+
+#   *****************************************************************************************************************
+#  *****                                                                                                          ****
+# ***                                                VARIABLES                                                      ***
+#  *****                                                                                                          ****
+#   *****************************************************************************************************************
+
+
+# --- Nothing Yet
+
+
+#   *****************************************************************************************************************
+#  *****                                                                                                          ****
+# ***                                                  FUNCTIONS                                                    ***
+#  *****                                                                                                          ****
+#   *****************************************************************************************************************
+
+
+class LLMInputMessage(BaseModel):
+
+    role: Literal["system", "ai", "human"] = Field(
+        description = "the role of this message",
+        frozen = True
+    )
+
+    content: str = Field(
+        description = "the message sent by the 'role'",
+        frozen = True
+    )
+
+    # ┏┓    ┏•
+    # ┃ ┏┓┏┓╋┓┏┓
+    # ┗┛┗┛┛┗┛┗┗┫
+    #          ┛
+
+    class Config:
+        extra = "forbid"
+
+
+# ---------------------------------------------------------------------------------------------------------------------
+
+
+class LLMInput(BaseModel):
+
+    messages: List[LLMInputMessage]
+
+    # ┏┓    ┏•
+    # ┃ ┏┓┏┓╋┓┏┓
+    # ┗┛┗┛┛┗┛┗┗┫
+    #          ┛
+
+    class Config:
+        extra = "forbid"
+
+    # ┓┏  ┓• ┓   •
+    # ┃┃┏┓┃┓┏┫┏┓╋┓┏┓┏┓
+    # ┗┛┗┻┗┗┗┻┗┻┗┗┗┛┛┗
+
+    @field_validator("messages")
+    def validate_messages(cls, value):
+
+        # Maintain counter(s):
+        system_message_index = -1
+        system_message_count = 0
+
+        # Loop through the messages and check them:
+        for index, message in enumerate(value):
+
+            # For 'system' messages:
+            if message.role == "system":
+                system_message_index = index
+                system_message_count += 1
+
+        # Verify that there is AT MOST ONE 'system' message,
+        # and verify that the 'system' message is the first message:
+        if system_message_count > 1: raise ValueError(f"there can be at most 1 'system' message, found {system_message_count}")
+        if system_message_index > 0: raise ValueError(f"'system' message must always be at index 0, found it at index {system_message_index}")
+
+        # Done here:
+        return value
+
+
+# ---------------------------------------------------------------------------------------------------------------------
+
+
+class LLMUsageTokens(BaseModel):
+
+    input: int = Field(
+        description = "how many tokens were given in the input",
+        frozen = True
+    )
+
+    output: int = Field(
+        description = "how many tokens were generated as the output",
+        frozen = True
+    )
+
+    total: int = Field(
+        description = "the sum of the input and output tokens",
+        frozen = True
+    )
+
+    # ┏┓    ┏•
+    # ┃ ┏┓┏┓╋┓┏┓
+    # ┗┛┗┛┛┗┛┗┗┫
+    #          ┛
+
+    class Config:
+        extra = "forbid"
+
+
+# ---------------------------------------------------------------------------------------------------------------------
+
+
+class LLMOutput(BaseModel):
+
+    ts: AwareDatetime = Field(
+        description = "the time at which the llm was invoked",
+        default_factory = date_time.get_current_utc_date_time,
+        frozen = True
+    )
+
+    messages: List[LLMInputMessage] = Field(
+        description = "the messages that came in that invoked the llm",
+        frozen = True
+    )
+
+    output: str | None = Field(
+        description = "what the llm generated",
+        default = None,
+        frozen = True
+    )
+
+    client: Literal["openai"] = Field(
+        description = "the co./brand that was used to use an llm",
+        frozen = True
+    )
+
+    model: str = Field(
+        description = "to know which model used in the process",
+        frozen = True
+    )
+
+    tokens: LLMUsageTokens = Field(
+        description = "to know how many tokens were used in the process",
+        default = LLMUsageTokens(input = 0, output = 0, total = 0),
+        frozen = True
+    )
+
+    invocationId: Any | None = Field(
+        description = "the id of the document that notes this invocation; useful for reconciliation",
+        frozen = False,
+        default = None
+    )
+
+    # ┏┓    ┏•
+    # ┃ ┏┓┏┓╋┓┏┓
+    # ┗┛┗┛┛┗┛┗┗┫
+    #          ┛
+
+    class Config:
+        extra = "forbid"
+
+
+#   *****************************************************************************************************************
+#  *****                                                                                                          ****
+# ***                                               MAIN PROGRAM                                                    ***
+#  *****                                                                                                          ****
+#   *****************************************************************************************************************
+
+
+if __name__ == "__main__":
+
+    # llm_messages = [
+    #     {
+    #         "role": "system",
+    #         "content": "You are an office assistant."
+    #     },
+    #     {
+    #         "role": "ai",
+    #         "content": "Hello, sir. How may I help you today?"
+    #     },
+    #     {
+    #         "role": "human",
+    #         "content": "Please summarize this mail for me..."
+    #     }
+    # ]
+    #
+    # llm_input = LLMInput(messages = llm_messages)
+    # print(llm_input.model_dump_json(indent = 4))
+
+    llm_output = LLMOutput(
+        messages=[LLMInputMessage(role='system', content="You are an office assistant. It's Christmas, so definitley respond like Santa Claus."), LLMInputMessage(role='ai', content='Hello, sir. How may I help you today?'), LLMInputMessage(role='human', content='Please summarize this mail for me...')],
+        client = "openai",
+        model = "o1"
+    )