Define data models for LLM interactions

neon_data_models/models/__init__.py

@@ -23,3 +23,9 @@
 # LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
 # NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
 # SOFTWARE,  EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+from neon_data_models.models.user import *
+User.rebuild_model()
+
+from neon_data_models.models.client import *
+from neon_data_models.models.api import *

neon_data_models/models/api/__init__.py

@@ -25,5 +25,6 @@
 # SOFTWARE,  EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
 from neon_data_models.models.api.node_v1 import *
-from neon_data_models.models.api.mq import *
 from neon_data_models.models.api.jwt import *
+from neon_data_models.models.api.llm import *
+from neon_data_models.models.api.mq import *

neon_data_models/models/api/llm.py

@@ -0,0 +1,186 @@
+# NEON AI (TM) SOFTWARE, Software Development Kit & Application Development System
+# All trademark and other rights reserved by their respective owners
+# Copyright 2008-2024 Neongecko.com Inc.
+# BSD-3
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+# 1. Redistributions of source code must retain the above copyright notice,
+#    this list of conditions and the following disclaimer.
+# 2. Redistributions in binary form must reproduce the above copyright notice,
+#    this list of conditions and the following disclaimer in the documentation
+#    and/or other materials provided with the distribution.
+# 3. Neither the name of the copyright holder nor the names of its
+#    contributors may be used to endorse or promote products derived from this
+#    software without specific prior written permission.
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
+# THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
+# CONTRIBUTORS  BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA,
+# OR PROFITS;  OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+# LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+# SOFTWARE,  EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+from typing import List, Tuple, Optional, Literal
+from pydantic import Field, model_validator, computed_field
+
+from neon_data_models.models.base import BaseModel
+
+
+_DEFAULT_MQ_TO_ROLE = {"user": "user", "llm": "assistant"}
+
+
+class LLMPersona(BaseModel):
+    name: str = Field(description="Unique name for this persona")
+    description: Optional[str] = Field(
+        None, description="Human-readable description of this persona")
+    system_prompt: str = Field(
+        None, description="System prompt associated with this persona. "
+                          "If None, `description` will be used.")
+    enabled: bool = Field(
+        True, description="Flag used to mark a defined persona as "
+                          "available for use.")
+    user_id: Optional[str] = Field(
+        None, description="`user_id` of the user who created this persona.")
+
+    @model_validator(mode='after')
+    def validate_request(self):
+        assert any((self.description, self.system_prompt))
+        if self.system_prompt is None:
+            self.system_prompt = self.description
+        return self
+
+    @computed_field
+    @property
+    def id(self) -> str:
+        persona_id = self.name
+        if self.user_id:
+            persona_id += f"_{self.user_id}"
+        return persona_id
+
+
+class LLMRequest(BaseModel):
+    query: str = Field(description="Incoming user prompt")
+    # TODO: History may support more options in the future
+    history: List[Tuple[Literal["user", "llm"], str]] = Field(
+        description="OpenAI-formatted chat history (excluding system prompt)")
+    persona: LLMPersona = Field(
+        description="Requested persona to respond to this message")
+    model: str = Field(description="Model to request")
+    max_tokens: int = Field(
+        default=512, ge=64, le=2048,
+        description="Maximum number of tokens to include in the response")
+    temperature: float = Field(
+        default=0.0, ge=0.0, le=1.0,
+        description="Temperature of response. 0 guarantees reproducibility, "
+                    "higher values increase variability.")
+    repetition_penalty: float = Field(
+        default=1.0, ge=1.0, le=2.0,
+        description="Repetition penalty. Higher values limit repeated "
+                    "information in responses")
+    stream: bool = Field(
+        default=None, description="Enable streaming responses. "
+                                  "Mutually exclusive with `beam_search`.")
+    best_of: int = Field(
+        default=1,
+        description="Number of beams to use if `beam_search` is enabled.")
+    beam_search: bool = Field(
+        default=None, description="Enable beam search. "
+                                  "Mutually exclusive with `stream`.")
+    max_history: int = Field(
+        default=2, description="Maximum number of user/assistant "
+                               "message pairs to include in history context.")
+
+    @model_validator(mode='before')
+    @classmethod
+    def validate_inputs(cls, values):
+        # Neon modules previously defined `user` and `llm` keys, but Open AI
+        # specifies `assistant` in place of `llm` and is the de-facto standard
+        for idx, itm in enumerate(values.get('history', [])):
+            if itm[0] == "assistant":
+                values['history'][idx] = ("llm", itm[1])
+        return values
+
+    @model_validator(mode='after')
+    def validate_request(self):
+        # If beams are specified, make sure valid `stream` and `beam_search`
+        # values are specified
+        if self.best_of > 1:
+            if self.stream is True:
+                raise ValueError("Cannot stream with a `best_of` value "
+                                 "greater than 1")
+            if self.beam_search is False:
+                raise ValueError("Cannot have a `best_of` value other than 1 "
+                                 "if `beam_search` is False")
+            self.stream = False
+            self.beam_search = True
+        # If streaming, beam_search must be False
+        if self.stream is True:
+            if self.beam_search is True:
+                raise ValueError("Cannot enable both `stream` and "
+                                 "`beam_search`")
+            self.beam_search = False
+        # If beam search is enabled, streaming must be False
+        if self.beam_search is True:
+            if self.stream is True:
+                raise ValueError("Cannot enable both `stream` and "
+                                 "`beam_search`")
+            self.stream = False
+        if self.stream is None and self.beam_search is None:
+            self.stream = True
+            self.beam_search = False
+        assert isinstance(self.stream, bool)
+        assert isinstance(self.beam_search, bool)
+        return self
+
+    @property
+    def messages(self) -> List[dict]:
+        """
+        Get chat history as a list of dict messages
+        """
+        return [{"role": m[0], "content": m[1]} for m in self.history]
+
+    def to_completion_kwargs(self, mq2role: dict = None) -> dict:
+        """
+        Get kwargs to pass to an OpenAI completion request.
+        @param mq2role: dict mapping `llm` and `user` keys to `role` values to
+            use in message history.
+        """
+        mq2role = mq2role or _DEFAULT_MQ_TO_ROLE
+        history = self.messages[-2*self.max_history:]
+        for msg in history:
+            msg["role"] = mq2role.get(msg["role"]) or msg["role"]
+        history.insert(0, {"role": "system",
+                           "content": self.persona.system_prompt})
+        return {"model": self.model,
+                "messages": history,
+                "max_tokens": self.max_tokens,
+                "temperature": self.temperature,
+                "stream": self.stream,
+                "extra_body": {"add_special_tokens": True,
+                               "repetition_penalty": self.repetition_penalty,
+                               "use_beam_search": self.beam_search,
+                               "best_of": self.best_of}}
+
+
+class LLMResponse(BaseModel):
+    response: str = Field(description="LLM Response to the input query")
+    history: List[Tuple[Literal["user", "llm"], str]] = Field(
+        description="List of (role, content) tuples in chronological order "
+                    "(`response` is in the last list element)")
+
+    @model_validator(mode='before')
+    @classmethod
+    def validate_inputs(cls, values):
+        # Neon modules previously defined `user` and `llm` keys, but Open AI
+        # specifies `assistant` in place of `llm` and is the de-facto standard
+        for idx, itm in enumerate(values.get('history', [])):
+            if itm[0] == "assistant":
+                values['history'][idx] = ("llm", itm[1])
+        return values
+
+
+__all__ = [LLMPersona.__name__, LLMRequest.__name__, LLMResponse.__name__]

neon_data_models/models/api/mq/__init__.py

@@ -0,0 +1,28 @@
+# NEON AI (TM) SOFTWARE, Software Development Kit & Application Development System
+# All trademark and other rights reserved by their respective owners
+# Copyright 2008-2024 Neongecko.com Inc.
+# BSD-3
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+# 1. Redistributions of source code must retain the above copyright notice,
+#    this list of conditions and the following disclaimer.
+# 2. Redistributions in binary form must reproduce the above copyright notice,
+#    this list of conditions and the following disclaimer in the documentation
+#    and/or other materials provided with the distribution.
+# 3. Neither the name of the copyright holder nor the names of its
+#    contributors may be used to endorse or promote products derived from this
+#    software without specific prior written permission.
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
+# THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
+# CONTRIBUTORS  BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA,
+# OR PROFITS;  OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+# LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+# SOFTWARE,  EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+from neon_data_models.models.api.mq.llm import *
+from neon_data_models.models.api.mq.users import *

neon_data_models/models/api/mq/llm.py

@@ -0,0 +1,71 @@
+# NEON AI (TM) SOFTWARE, Software Development Kit & Application Development System
+# All trademark and other rights reserved by their respective owners
+# Copyright 2008-2024 Neongecko.com Inc.
+# BSD-3
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+# 1. Redistributions of source code must retain the above copyright notice,
+#    this list of conditions and the following disclaimer.
+# 2. Redistributions in binary form must reproduce the above copyright notice,
+#    this list of conditions and the following disclaimer in the documentation
+#    and/or other materials provided with the distribution.
+# 3. Neither the name of the copyright holder nor the names of its
+#    contributors may be used to endorse or promote products derived from this
+#    software without specific prior written permission.
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
+# THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
+# CONTRIBUTORS  BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA,
+# OR PROFITS;  OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+# LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+# SOFTWARE,  EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+from typing import Optional, Dict, List
+from pydantic import Field
+
+from neon_data_models.models.api.llm import LLMRequest, LLMPersona
+from neon_data_models.models.base.contexts import MQContext
+
+
+class LLMProposeRequest(MQContext, LLMRequest):
+    model: Optional[str] = Field(
+        default=None,
+        description="MQ implementation defines `model` as optional because the "
+                    "queue defines the requested model in most cases.")
+    persona: Optional[LLMPersona] = Field(
+        default=None,
+        description="MQ implementation defines `persona` as an optional "
+                    "parameter, with default behavior hard-coded into each "
+                    "LLM module.")
+
+
+class LLMProposeResponse(MQContext):
+    response: str = Field(description="LLM response to the prompt")
+
+
+class LLMDiscussRequest(LLMProposeRequest):
+    options: Dict[str, str] = Field(
+        description="Mapping of participant name to response to be discussed.")
+
+
+class LLMDiscussResponse(MQContext):
+    opinion: str = Field(description="LLM response to the available options.")
+
+
+class LLMVoteRequest(LLMProposeRequest):
+    responses: List[str] = Field(
+        description="List of responses to choose from.")
+
+
+class LLMVoteResponse(MQContext):
+    sorted_answer_indexes: List[int] = Field(
+        description="Indices of `responses` ordered high to low by preference.")
+
+
+__all__ = [LLMProposeRequest.__name__, LLMProposeResponse.__name__,
+           LLMDiscussRequest.__name__, LLMDiscussResponse.__name__,
+           LLMVoteRequest.__name__, LLMVoteResponse.__name__]

neon_data_models/models/api/mq.py → neon_data_models/models/api/mq/users.py

@@ -25,7 +25,6 @@
 # SOFTWARE,  EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
 from typing import Literal, Optional, Annotated, Union
-
 from pydantic import Field, TypeAdapter, model_validator
 
 from neon_data_models.models.api.jwt import HanaToken

neon_data_models/models/user/database.py

@@ -181,11 +181,12 @@ class TokenConfig(BaseModel):
 
 
 class User(BaseModel):
-    def __init__(self, **kwargs):
+
+    @classmethod
+    def rebuild_model(cls):
         # Ensure `HanaToken` is populated from the import space
         from neon_data_models.models.api.jwt import HanaToken
-        self.model_rebuild()
-        BaseModel.__init__(self, **kwargs)
+        cls.model_rebuild()
 
     username: str
     password_hash: Optional[str] = None