refactor: integrate ConfigService for model management and add configuration update endpoint

app/api/endpoints.py

@@ -6,7 +6,13 @@ from fastapi import APIRouter, BackgroundTasks, Depends, File, HTTPException, Up
 from fastapi.responses import StreamingResponse
 from structlog import get_logger
 
-from app.schemas.models import IngestResponse, QueryRequest, QueryResponse
+from app.schemas.models import (
+    ConfigUpdateRequest,
+    IngestResponse,
+    QueryRequest,
+    QueryResponse,
+)
+from app.services.config_service import ConfigService
 from app.services.rag_service import RAGService
 
 logger = get_logger()
@@ -21,6 +27,12 @@ def get_rag_service():
     return app_state["rag_service"]
 
 
+def get_config_service():
+    from app.main import app_state
+
+    return app_state["config_service"]
+
+
 @router.post("/ingest", response_model=IngestResponse)
 async def ingest_file(
     background_tasks: BackgroundTasks,
@@ -79,3 +91,23 @@ async def query_index_stream(
     except Exception:
         logger.exception("Failed to answer query")
         raise HTTPException(status_code=500, detail="Internal server error")
+
+
+@router.post("/config")
+async def update_configuration(
+    request: ConfigUpdateRequest,
+    config_service: Annotated[ConfigService, Depends(get_config_service)],
+):
+    responses = []
+    if request.embedding_model:
+        response = await config_service.set_embedding_model(request.embedding_model)
+        responses.append(response)
+    if request.reranker_model:
+        response = await config_service.set_reranker_model(request.reranker_model)
+        responses.append(response)
+    # Add similar logic for LLM models and providers when implemented
+
+    if not responses:
+        return {"message": "No configuration changes requested."}
+
+    return {"message": " ".join(responses)}

app/core/registry.py

@@ -0,0 +1,48 @@
+from collections.abc import Callable
+from typing import Any, Generic, TypeVar
+
+T = TypeVar("T")
+
+
+class Registry(Generic[T]):
+    """A generic registry to store and retrieve objects by name."""
+
+    def __init__(self):
+        self._items: dict[str, T] = {}
+
+    def register(self, name: str, item: T):
+        """Registers an item with a given name."""
+        if not isinstance(name, str) or not name:
+            raise ValueError("Name must be a non-empty string.")
+        if name in self._items:
+            raise ValueError(f"Item with name '{name}' already registered.")
+        self._items[name] = item
+
+    def get(self, name: str) -> T:
+        """Retrieves an item by its name."""
+        if name not in self._items:
+            raise KeyError(f"Item with name '{name}' not found in registry.")
+        return self._items[name]
+
+    def unregister(self, name: str):
+        """Unregisters an item by its name."""
+        if name not in self._items:
+            raise KeyError(f"Item with name '{name}' not found in registry.")
+        del self._items[name]
+
+    def list_available(self) -> list[str]:
+        """Lists all available item names in the registry."""
+        return list(self._items.keys())
+
+
+class EmbeddingModelRegistry(Registry[Callable[..., Any]]):
+    """Registry specifically for embedding model constructors."""
+
+
+class RerankerRegistry(Registry[Callable[..., Any]]):
+    """Registry specifically for reranker constructors."""
+
+
+# Global instances of the registries
+embedding_model_registry = EmbeddingModelRegistry()
+reranker_registry = RerankerRegistry()

app/main.py

@@ -5,7 +5,7 @@ from fastapi import FastAPI
 from structlog import get_logger
 
 from app.api import endpoints
-from app.services.embedding_providers import MiniLMEmbeddingModel
+from app.services.config_service import ConfigService
 from app.services.rag_service import RAGService
 from app.services.vector_stores import PGVectorStore
 
@@ -20,14 +20,17 @@ app_state = {}
 
 @asynccontextmanager
 async def lifespan(app: FastAPI):
-    embedding_provider = MiniLMEmbeddingModel()
-    vector_store_provider = PGVectorStore()
+    config_service = ConfigService()
+    await config_service.initialize_models()
+    app_state["config_service"] = config_service
 
     # This code runs on startup
     logger.info("Application starting up...")
     # Initialize the RAG Service and store it in the app_state
     app_state["rag_service"] = RAGService(
-        embedding_model=embedding_provider, vector_db=vector_store_provider
+        embedding_model=config_service.get_current_embedding_model(),
+        vector_db=PGVectorStore(),
+        reranker=config_service.get_current_reranker_model(),
     )
     yield
 

app/schemas/models.py

@@ -1,4 +1,5 @@
-from pydantic import BaseModel
+from pydantic import BaseModel, Field
+from typing import Optional
 
 
 class QueryRequest(BaseModel):
@@ -13,3 +14,16 @@ class QueryResponse(BaseModel):
 class IngestResponse(BaseModel):
     message: str
     filename: str
+
+
+class ConfigUpdateRequest(BaseModel):
+    embedding_model: Optional[str] = Field(
+        None, description="Name of the embedding model to use"
+    )
+    reranker_model: Optional[str] = Field(
+        None, description="Name of the reranker model to use"
+    )
+    llm_model: Optional[str] = Field(None, description="Name of the LLM model to use")
+    llm_provider: Optional[str] = Field(
+        None, description="Name of the LLM provider to use"
+    )

app/services/config_service.py

@@ -0,0 +1,118 @@
+import logging
+
+from app.core.config import settings
+from app.core.interfaces import EmbeddingModel, Reranker
+from app.core.registry import embedding_model_registry, reranker_registry
+from app.services.embedding_providers import MiniLMEmbeddingModel
+from app.services.rerankers import MiniLMReranker
+
+logger = logging.getLogger(__name__)
+
+
+class ConfigService:
+    def __init__(self):
+        self._current_embedding_model: EmbeddingModel | None = None
+        self._current_reranker_model: Reranker | None = None
+        self._loading_status: dict[str, bool] = {
+            "embedding_model": False,
+            "reranker_model": False,
+        }
+
+        # Register available models
+        self._register_models()
+
+    def _register_models(self):
+        # Register embedding models
+        embedding_model_registry.register("MiniLMEmbeddingModel", MiniLMEmbeddingModel)
+        # Register reranker models
+        reranker_registry.register("MiniLMReranker", MiniLMReranker)
+
+    async def initialize_models(self):
+        logger.info("Initializing default models...")
+        # Initialize default embedding model
+        default_embedding_model_name = settings.EMBEDDING_MODEL
+        await self.set_embedding_model(default_embedding_model_name)
+        logger.info(
+            f"Default embedding model initialized: {default_embedding_model_name}"
+        )
+
+        # Initialize default reranker model (if any)
+        # Assuming a default reranker can be set in settings if needed
+        # For now, let's assume MiniLMReranker is the default if not specified
+        default_reranker_model_name = "MiniLMReranker"  # Or from settings
+        await self.set_reranker_model(default_reranker_model_name)
+        logger.info(
+            f"Default reranker model initialized: {default_reranker_model_name}"
+        )
+
+    async def set_embedding_model(self, model_name: str) -> str:
+        if (
+            self._current_embedding_model
+            and self._current_embedding_model.__class__.__name__ == model_name
+        ):
+            return f"Embedding model '{model_name}' is already in use."
+
+        if self._loading_status["embedding_model"]:
+            return "Another embedding model is currently being loaded. Please wait."
+
+        try:
+            self._loading_status["embedding_model"] = True
+            logger.info(f"Attempting to load embedding model: {model_name}")
+            model_constructor = embedding_model_registry.get(model_name)
+            self._current_embedding_model = model_constructor()
+            settings.EMBEDDING_MODEL = model_name  # Update settings
+            logger.info(f"Successfully loaded embedding model: {model_name}")
+            return f"Embedding model set to '{model_name}' successfully."
+        except KeyError:
+            logger.warning(f"Embedding model '{model_name}' not found in registry.")
+            return (
+                f"Embedding model '{model_name}' not available. "
+                f"Current model remains '{self._current_embedding_model.__class__.__name__ if self._current_embedding_model else 'None'}'."
+            )
+        except Exception as e:
+            logger.exception(f"Error loading embedding model {model_name}: {e}")
+            return f"Failed to load embedding model '{model_name}': {e}"
+        finally:
+            self._loading_status["embedding_model"] = False
+
+    async def set_reranker_model(self, model_name: str) -> str:
+        if (
+            self._current_reranker_model
+            and self._current_reranker_model.__class__.__name__ == model_name
+        ):
+            return f"Reranker model '{model_name}' is already in use."
+
+        if self._loading_status["reranker_model"]:
+            return "Another reranker model is currently being loaded. Please wait."
+
+        try:
+            self._loading_status["reranker_model"] = True
+            logger.info(f"Attempting to load reranker model: {model_name}")
+            model_constructor = reranker_registry.get(model_name)
+            self._current_reranker_model = model_constructor()
+            # settings.RERANKER_MODEL = model_name # Add this to settings if you want to persist
+            logger.info(f"Successfully loaded reranker model: {model_name}")
+            return f"Reranker model set to '{model_name}' successfully."
+        except KeyError:
+            logger.warning(f"Reranker model '{model_name}' not found in registry.")
+            return (
+                f"Reranker model '{model_name}' not available. "
+                f"Current model remains '{self._current_reranker_model.__class__.__name__ if self._current_reranker_model else 'None'}'."
+            )
+        except Exception as e:
+            logger.exception(f"Error loading reranker model {model_name}: {e}")
+            return f"Failed to load reranker model '{model_name}': {e}"
+        finally:
+            self._loading_status["reranker_model"] = False
+
+    def get_current_embedding_model(self) -> EmbeddingModel | None:
+        return self._current_embedding_model
+
+    def get_current_reranker_model(self) -> Reranker | None:
+        return self._current_reranker_model
+
+    def get_available_embedding_models(self) -> list[str]:
+        return embedding_model_registry.list_available()
+
+    def get_available_reranker_models(self) -> list[str]:
+        return reranker_registry.list_available()

app/services/rag_service.py

@@ -6,7 +6,7 @@ from typing import TypedDict
 import litellm
 from structlog import get_logger
 
-from app.core.interfaces import EmbeddingModel, VectorDB
+from app.core.interfaces import EmbeddingModel, Reranker, VectorDB
 from app.core.utils import RecursiveCharacterTextSplitter
 
 logger = get_logger()
@@ -18,10 +18,16 @@ class AnswerResult(TypedDict):
 
 
 class RAGService:
-    def __init__(self, embedding_model: EmbeddingModel, vector_db: VectorDB):
+    def __init__(
+        self,
+        embedding_model: EmbeddingModel,
+        vector_db: VectorDB,
+        reranker: Reranker | None = None,
+    ):
         self.embedding_model = embedding_model
         self.vector_db = vector_db
-        self.prompt = """Answer the question based on the following context. 
+        self.reranker = reranker
+        self.prompt = """Answer the question based on the following context.
 If you don't know the answer, say you don't know. Don't make up an answer.
 
 Context:
@@ -69,8 +75,12 @@ Answer:"""
     def answer_query(self, question: str) -> AnswerResult:
         query_embedding = self.embedding_model.embed_query(question)
         search_results = self.vector_db.search(query_embedding, top_k=5)
-        sources = list({chunk["source"] for chunk in search_results if chunk["source"]})
 
+        if self.reranker:
+            logger.info("Reranking search results...")
+            search_results = self.reranker.rerank(search_results, question)
+
+        sources = list({chunk["source"] for chunk in search_results if chunk["source"]})
         context_str = "\n\n".join([chunk["content"] for chunk in search_results])
 
         try:
@@ -92,7 +102,7 @@ Answer:"""
                 max_tokens=500,
             )
 
-            answer_text = response.choices[0].message.content.strip()  # type: ignore
+            answer_text = response.choices[0].message.content.strip()
 
             if not answer_text:
                 answer_text = "No answer generated"
@@ -108,6 +118,11 @@ Answer:"""
     def answer_query_stream(self, question: str) -> Generator[str, None, None]:
         query_embedding = self.embedding_model.embed_query(question)
         search_results = self.vector_db.search(query_embedding, top_k=5)
+
+        if self.reranker:
+            logger.info("Reranking search results...")
+            search_results = self.reranker.rerank(search_results, question)
+
         sources = list({chunk["source"] for chunk in search_results if chunk["source"]})
         context_str = "\n\n".join([chunk["content"] for chunk in search_results])
 

app/services/rag_service_v1.py

@@ -175,9 +175,9 @@ Answer:"""
             with self.db_conn.cursor() as cursor:
                 cursor.execute(
                     """
-                    SELECT content, source 
-                    FROM documents 
-                    ORDER BY embedding <-> %s::vector 
+                    SELECT content, source
+                    FROM documents
+                    ORDER BY embedding <-> %s::vector
                     LIMIT %s
                     """,
                     (question_embedding.tolist(), top_k),