- Introduction of dynamic Retrievers & Specialists

- Introduction of dynamic Processors
- Introduction of caching system
- Introduction of a better template manager
- Adaptation of ModelVariables to support dynamic Processors / Retrievers / Specialists
- Start adaptation of chat client

common/langchain/outputs/base.py

@@ -0,0 +1,23 @@
+# Output Schema Management - common/langchain/outputs/base.py
+from typing import Dict, Type, Any
+from pydantic import BaseModel
+
+
+class BaseSpecialistOutput(BaseModel):
+    """Base class for all specialist outputs"""
+    pass
+
+
+class OutputRegistry:
+    """Registry for specialist output schemas"""
+    _schemas: Dict[str, Type[BaseSpecialistOutput]] = {}
+
+    @classmethod
+    def register(cls, specialist_type: str, schema_class: Type[BaseSpecialistOutput]):
+        cls._schemas[specialist_type] = schema_class
+
+    @classmethod
+    def get_schema(cls, specialist_type: str) -> Type[BaseSpecialistOutput]:
+        if specialist_type not in cls._schemas:
+            raise ValueError(f"No output schema registered for {specialist_type}")
+        return cls._schemas[specialist_type]

common/langchain/outputs/rag.py

@@ -0,0 +1,22 @@
+# RAG Specialist Output - common/langchain/outputs/rag.py
+from typing import List
+from pydantic import Field
+from .base import BaseSpecialistOutput
+
+
+class RAGOutput(BaseSpecialistOutput):
+    """Output schema for RAG specialist"""
+    """Default docstring - to be replaced with actual prompt"""
+
+    answer: str = Field(
+        ...,
+        description="The answer to the user question, based on the given sources",
+    )
+    citations: List[int] = Field(
+        ...,
+        description="The integer IDs of the SPECIFIC sources that were used to generate the answer"
+    )
+    insufficient_info: bool = Field(
+        False,  # Default value is set to False
+        description="A boolean indicating whether given sources were sufficient or not to generate the answer"
+    )