Merge branch 'main' into refactor/llm-code

# Conflicts:
#	app/llm/basic_request_handler.py
#	app/llm/request_handler_interface.py
#	requirements.txt

app/llm/langchain/__init__.py

@@ -0,0 +1,3 @@
+from llm.langchain.iris_langchain_completion_model import IrisLangchainCompletionModel
+from llm.langchain.iris_langchain_chat_model import IrisLangchainChatModel
+from llm.langchain.iris_langchain_embedding import IrisLangchainEmbeddingModel

app/llm/langchain/iris_langchain_chat_model.py

@@ -0,0 +1,60 @@
+from typing import List, Optional, Any
+
+from langchain_core.callbacks import CallbackManagerForLLMRun
+from langchain_core.language_models.chat_models import (
+    BaseChatModel,
+)
+from langchain_core.messages import BaseMessage
+from langchain_core.outputs import ChatResult
+from langchain_core.outputs.chat_generation import ChatGeneration
+
+from domain import IrisMessage, IrisMessageRole
+from llm import RequestHandlerInterface, CompletionArguments
+
+
+def convert_iris_message_to_base_message(iris_message: IrisMessage) -> BaseMessage:
+    role_map = {
+        IrisMessageRole.USER: "human",
+        IrisMessageRole.ASSISTANT: "ai",
+        IrisMessageRole.SYSTEM: "system",
+    }
+    return BaseMessage(content=iris_message.text, type=role_map[iris_message.role])
+
+
+def convert_base_message_to_iris_message(base_message: BaseMessage) -> IrisMessage:
+    role_map = {
+        "human": IrisMessageRole.USER,
+        "ai": IrisMessageRole.ASSISTANT,
+        "system": IrisMessageRole.SYSTEM,
+    }
+    return IrisMessage(
+        text=base_message.content, role=IrisMessageRole(role_map[base_message.type])
+    )
+
+
+class IrisLangchainChatModel(BaseChatModel):
+    """Custom langchain chat model for our own request handler"""
+
+    request_handler: RequestHandlerInterface
+
+    def __init__(self, request_handler: RequestHandlerInterface, **kwargs: Any) -> None:
+        super().__init__(request_handler=request_handler, **kwargs)
+
+    def _generate(
+        self,
+        messages: List[BaseMessage],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any
+    ) -> ChatResult:
+        iris_messages = [convert_base_message_to_iris_message(m) for m in messages]
+        iris_message = self.request_handler.chat_completion(
+            iris_messages, CompletionArguments(stop=stop)
+        )
+        base_message = convert_iris_message_to_base_message(iris_message)
+        chat_generation = ChatGeneration(message=base_message)
+        return ChatResult(generations=[chat_generation])
+
+    @property
+    def _llm_type(self) -> str:
+        return "Iris"

app/llm/langchain/iris_langchain_completion_model.py

@@ -0,0 +1,37 @@
+from typing import List, Optional, Any
+
+from langchain_core.callbacks import CallbackManagerForLLMRun
+from langchain_core.language_models.llms import BaseLLM
+from langchain_core.outputs import LLMResult
+from langchain_core.outputs.generation import Generation
+
+from llm import RequestHandlerInterface, CompletionArguments
+
+
+class IrisLangchainCompletionModel(BaseLLM):
+    """Custom langchain chat model for our own request handler"""
+
+    request_handler: RequestHandlerInterface
+
+    def __init__(self, request_handler: RequestHandlerInterface, **kwargs: Any) -> None:
+        super().__init__(request_handler=request_handler, **kwargs)
+
+    def _generate(
+        self,
+        prompts: List[str],
+        stop: Optional[List[str]] = None,
+        run_manager: Optional[CallbackManagerForLLMRun] = None,
+        **kwargs: Any
+    ) -> LLMResult:
+        generations = []
+        args = CompletionArguments(stop=stop)
+        for prompt in prompts:
+            completion = self.request_handler.completion(
+                prompt=prompt, arguments=args, **kwargs
+            )
+            generations.append([Generation(text=completion)])
+        return LLMResult(generations=generations)
+
+    @property
+    def _llm_type(self) -> str:
+        return "Iris"

app/llm/langchain/iris_langchain_embedding.py

@@ -0,0 +1,20 @@
+from typing import List, Any
+
+from langchain_core.embeddings import Embeddings
+
+from llm import RequestHandlerInterface
+
+
+class IrisLangchainEmbeddingModel(Embeddings):
+    """Custom langchain embedding for our own request handler"""
+
+    request_handler: RequestHandlerInterface
+
+    def __init__(self, request_handler: RequestHandlerInterface, **kwargs: Any) -> None:
+        super().__init__(request_handler=request_handler, **kwargs)
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        return [self.embed_query(text) for text in texts]
+
+    def embed_query(self, text: str) -> List[float]:
+        return self.request_handler.create_embedding(text)

app/llm/request_handler_interface.py

@@ -21,7 +21,7 @@ def complete(self, prompt: str, arguments: CompletionArguments) -> str:
         raise NotImplementedError
 
     @abstractmethod
-    def chat(self, messages: list[any], arguments: CompletionArguments) -> [IrisMessage]:
+    def chat(self, messages: list[any], arguments: CompletionArguments) -> IrisMessage:
         """Create a completion from the chat messages"""
         raise NotImplementedError
 

requirements.txt

@@ -7,3 +7,4 @@ flake8~=7.0.0
 pre-commit~=3.6.1
 pydantic~=2.6.1
 PyYAML~=6.0.1
+langchain~=0.1.6