fix: Remove global state (#1216)

* Remove all global settings state

* chore: remove autogenerated class

* chore: cleanup

* chore: merge conflicts

private_gpt/server/chat/chat_router.py

@@ -1,9 +1,8 @@
-from fastapi import APIRouter, Depends
+from fastapi import APIRouter, Depends, Request
 from llama_index.llms import ChatMessage, MessageRole
 from pydantic import BaseModel
 from starlette.responses import StreamingResponse
 
-from private_gpt.di import root_injector
 from private_gpt.open_ai.extensions.context_filter import ContextFilter
 from private_gpt.open_ai.openai_models import (
     OpenAICompletion,
@@ -52,7 +51,9 @@ class ChatBody(BaseModel):
     responses={200: {"model": OpenAICompletion}},
     tags=["Contextual Completions"],
 )
-def chat_completion(body: ChatBody) -> OpenAICompletion | StreamingResponse:
+def chat_completion(
+    request: Request, body: ChatBody
+) -> OpenAICompletion | StreamingResponse:
     """Given a list of messages comprising a conversation, return a response.
 
     If `use_context` is set to `true`, the model will use context coming
@@ -72,7 +73,7 @@ def chat_completion(body: ChatBody) -> OpenAICompletion | StreamingResponse:
     "finish_reason":null}]}
     ```
     """
-    service = root_injector.get(ChatService)
+    service = request.state.injector.get(ChatService)
     all_messages = [
         ChatMessage(content=m.content, role=MessageRole(m.role)) for m in body.messages
     ]

private_gpt/server/chunks/chunks_router.py

@@ -1,9 +1,8 @@
 from typing import Literal
 
-from fastapi import APIRouter, Depends
+from fastapi import APIRouter, Depends, Request
 from pydantic import BaseModel, Field
 
-from private_gpt.di import root_injector
 from private_gpt.open_ai.extensions.context_filter import ContextFilter
 from private_gpt.server.chunks.chunks_service import Chunk, ChunksService
 from private_gpt.server.utils.auth import authenticated
@@ -25,7 +24,7 @@ class ChunksResponse(BaseModel):
 
 
 @chunks_router.post("/chunks", tags=["Context Chunks"])
-def chunks_retrieval(body: ChunksBody) -> ChunksResponse:
+def chunks_retrieval(request: Request, body: ChunksBody) -> ChunksResponse:
     """Given a `text`, returns the most relevant chunks from the ingested documents.
 
     The returned information can be used to generate prompts that can be
@@ -45,7 +44,7 @@ def chunks_retrieval(body: ChunksBody) -> ChunksResponse:
     `/ingest/list` endpoint. If you want all ingested documents to be used,
     remove `context_filter` altogether.
     """
-    service = root_injector.get(ChunksService)
+    service = request.state.injector.get(ChunksService)
     results = service.retrieve_relevant(
         body.text, body.context_filter, body.limit, body.prev_next_chunks
     )

private_gpt/server/completions/completions_router.py

@@ -1,4 +1,4 @@
-from fastapi import APIRouter, Depends
+from fastapi import APIRouter, Depends, Request
 from pydantic import BaseModel
 from starlette.responses import StreamingResponse
 
@@ -41,7 +41,9 @@ class CompletionsBody(BaseModel):
     responses={200: {"model": OpenAICompletion}},
     tags=["Contextual Completions"],
 )
-def prompt_completion(body: CompletionsBody) -> OpenAICompletion | StreamingResponse:
+def prompt_completion(
+    request: Request, body: CompletionsBody
+) -> OpenAICompletion | StreamingResponse:
     """We recommend most users use our Chat completions API.
 
     Given a prompt, the model will return one predicted completion. If `use_context`
@@ -70,4 +72,4 @@ def prompt_completion(body: CompletionsBody) -> OpenAICompletion | StreamingResp
         include_sources=body.include_sources,
         context_filter=body.context_filter,
     )
-    return chat_completion(chat_body)
+    return chat_completion(request, chat_body)

private_gpt/server/embeddings/embeddings_router.py

@@ -1,9 +1,8 @@
 from typing import Literal
 
-from fastapi import APIRouter, Depends
+from fastapi import APIRouter, Depends, Request
 from pydantic import BaseModel
 
-from private_gpt.di import root_injector
 from private_gpt.server.embeddings.embeddings_service import (
     Embedding,
     EmbeddingsService,
@@ -24,13 +23,13 @@ class EmbeddingsResponse(BaseModel):
 
 
 @embeddings_router.post("/embeddings", tags=["Embeddings"])
-def embeddings_generation(body: EmbeddingsBody) -> EmbeddingsResponse:
+def embeddings_generation(request: Request, body: EmbeddingsBody) -> EmbeddingsResponse:
     """Get a vector representation of a given input.
 
     That vector representation can be easily consumed
     by machine learning models and algorithms.
     """
-    service = root_injector.get(EmbeddingsService)
+    service = request.state.injector.get(EmbeddingsService)
     input_texts = body.input if isinstance(body.input, list) else [body.input]
     embeddings = service.texts_embeddings(input_texts)
     return EmbeddingsResponse(object="list", model="private-gpt", data=embeddings)

private_gpt/server/ingest/ingest_router.py

@@ -1,9 +1,8 @@
 from typing import Literal
 
-from fastapi import APIRouter, Depends, HTTPException, UploadFile
+from fastapi import APIRouter, Depends, HTTPException, Request, UploadFile
 from pydantic import BaseModel
 
-from private_gpt.di import root_injector
 from private_gpt.server.ingest.ingest_service import IngestedDoc, IngestService
 from private_gpt.server.utils.auth import authenticated
 
@@ -17,7 +16,7 @@ class IngestResponse(BaseModel):
 
 
 @ingest_router.post("/ingest", tags=["Ingestion"])
-def ingest(file: UploadFile) -> IngestResponse:
+def ingest(request: Request, file: UploadFile) -> IngestResponse:
     """Ingests and processes a file, storing its chunks to be used as context.
 
     The context obtained from files is later used in
@@ -33,7 +32,7 @@ def ingest(file: UploadFile) -> IngestResponse:
     can be used to filter the context used to create responses in
     `/chat/completions`, `/completions`, and `/chunks` APIs.
     """
-    service = root_injector.get(IngestService)
+    service = request.state.injector.get(IngestService)
     if file.filename is None:
         raise HTTPException(400, "No file name provided")
     ingested_documents = service.ingest(file.filename, file.file.read())
@@ -41,23 +40,23 @@ def ingest(file: UploadFile) -> IngestResponse:
 
 
 @ingest_router.get("/ingest/list", tags=["Ingestion"])
-def list_ingested() -> IngestResponse:
+def list_ingested(request: Request) -> IngestResponse:
     """Lists already ingested Documents including their Document ID and metadata.
 
     Those IDs can be used to filter the context used to create responses
     in `/chat/completions`, `/completions`, and `/chunks` APIs.
     """
-    service = root_injector.get(IngestService)
+    service = request.state.injector.get(IngestService)
     ingested_documents = service.list_ingested()
     return IngestResponse(object="list", model="private-gpt", data=ingested_documents)
 
 
 @ingest_router.delete("/ingest/{doc_id}", tags=["Ingestion"])
-def delete_ingested(doc_id: str) -> None:
+def delete_ingested(request: Request, doc_id: str) -> None:
     """Delete the specified ingested Document.
 
     The `doc_id` can be obtained from the `GET /ingest/list` endpoint.
     The document will be effectively deleted from your storage context.
     """
-    service = root_injector.get(IngestService)
+    service = request.state.injector.get(IngestService)
     service.delete(doc_id)

private_gpt/server/utils/auth.py

@@ -38,13 +38,13 @@ logger = logging.getLogger(__name__)
 
 def _simple_authentication(authorization: Annotated[str, Header()] = "") -> bool:
     """Check if the request is authenticated."""
-    if not secrets.compare_digest(authorization, settings.server.auth.secret):
+    if not secrets.compare_digest(authorization, settings().server.auth.secret):
         # If the "Authorization" header is not the expected one, raise an exception.
         raise NOT_AUTHENTICATED
     return True
 
 
-if not settings.server.auth.enabled:
+if not settings().server.auth.enabled:
     logger.debug(
         "Defining a dummy authentication mechanism for fastapi, always authenticating requests"
     )
@@ -62,7 +62,7 @@ else:
         _simple_authentication: Annotated[bool, Depends(_simple_authentication)]
     ) -> bool:
         """Check if the request is authenticated."""
-        assert settings.server.auth.enabled
+        assert settings().server.auth.enabled
         if not _simple_authentication:
             raise NOT_AUTHENTICATED
         return True