Added home.py file for /chat route

private_gpt/constants.py

@@ -1,3 +1,4 @@
 from pathlib import Path
 
 PROJECT_ROOT_PATH: Path = Path(__file__).parents[1]
+UPLOAD_DIR = rf"F:\LLM\privateGPT\private_gpt\uploads"

private_gpt/home.py

@@ -0,0 +1,218 @@
+"""This file should be imported only and only if you want to run the UI locally."""
+from fastapi import Request
+from fastapi.responses import StreamingResponse
+import itertools
+import logging
+from collections.abc import Iterable
+from pathlib import Path
+from typing import Any, List
+
+from fastapi import APIRouter, Depends, Request, FastAPI, Body
+from fastapi.responses import JSONResponse
+from gradio.themes.utils.colors import slate  # type: ignore
+from injector import inject, singleton
+from llama_index.llms import ChatMessage, ChatResponse, MessageRole
+from pydantic import BaseModel
+
+from private_gpt.constants import PROJECT_ROOT_PATH
+from private_gpt.di import global_injector
+from private_gpt.server.chat.chat_service import ChatService, CompletionGen
+from private_gpt.server.chunks.chunks_service import Chunk, ChunksService
+from private_gpt.server.ingest.ingest_service import IngestService
+from private_gpt.settings.settings import settings
+from private_gpt.ui.images import logo_svg
+from private_gpt.ui.common import Source
+
+logger = logging.getLogger(__name__)
+
+THIS_DIRECTORY_RELATIVE = Path(__file__).parent.relative_to(PROJECT_ROOT_PATH)
+# Should be "private_gpt/ui/avatar-bot.ico"
+AVATAR_BOT = THIS_DIRECTORY_RELATIVE / "avatar-bot.ico"
+
+UI_TAB_TITLE = "My Private GPT"
+
+SOURCES_SEPARATOR = "\n\n Sources: \n"
+
+MODES = ["Query Docs", "Search in Docs", "LLM Chat"]
+home_router = APIRouter(prefix="/v1")
+
+
+
+@singleton
+class Home:
+    @inject
+    def __init__(
+        self,
+        ingest_service: IngestService,
+        chat_service: ChatService,
+        chunks_service: ChunksService,
+    ) -> None:
+        self._ingest_service = ingest_service
+        self._chat_service = chat_service
+        self._chunks_service = chunks_service
+
+        # Initialize system prompt based on default mode
+        self.mode = MODES[0]
+        self._system_prompt = self._get_default_system_prompt(self.mode)
+
+    def _chat(self, message: str, history: list[list[str]], mode: str, *_: Any) -> Any:
+        def yield_deltas(completion_gen: CompletionGen) -> Iterable[str]:
+            full_response: str = ""
+            stream = completion_gen.response
+            for delta in stream:
+                if isinstance(delta, str):
+                    full_response += str(delta)
+                elif isinstance(delta, ChatResponse):
+                    full_response += delta.delta or ""
+                yield full_response
+
+            if completion_gen.sources:
+                full_response += SOURCES_SEPARATOR
+                cur_sources = Source.curate_sources(completion_gen.sources)
+                sources_text = "\n\n\n".join(
+                    f'<a href="{source.page_link}" target="_blank" rel="noopener noreferrer">{index}. {source.file} (page {source.page})</a>'
+                    for index, source in enumerate(cur_sources, start=1)
+                )
+                full_response += sources_text
+                print(full_response)
+            yield full_response
+
+        def build_history() -> list[ChatMessage]:
+            history_messages: list[ChatMessage] = list(
+                itertools.chain(
+                    *[
+                        [
+                            ChatMessage(
+                                content=interaction[0], role=MessageRole.USER),
+                            ChatMessage(
+                                # Remove from history content the Sources information
+                                content=interaction[1].split(
+                                    SOURCES_SEPARATOR)[0],
+                                role=MessageRole.ASSISTANT,
+                            ),
+                        ]
+                        for interaction in history
+                    ]
+                )
+            )
+
+            # max 20 messages to try to avoid context overflow
+            return history_messages[:20]
+
+        new_message = ChatMessage(content=message, role=MessageRole.USER)
+        all_messages = [*build_history(), new_message]
+        # If a system prompt is set, add it as a system message
+        if self._system_prompt:
+            all_messages.insert(
+                0,
+                ChatMessage(
+                    content=self._system_prompt,
+                    role=MessageRole.SYSTEM,
+                ),
+            )
+        match mode:
+            case "Query Docs":
+                query_stream = self._chat_service.stream_chat(
+                    messages=all_messages,
+                    use_context=True,
+                )
+                yield from yield_deltas(query_stream)
+            case "LLM Chat":
+                llm_stream = self._chat_service.stream_chat(
+                    messages=all_messages,
+                    use_context=False,
+                )
+                yield from yield_deltas(llm_stream)
+
+            case "Search in Docs":
+                response = self._chunks_service.retrieve_relevant(
+                    text=message, limit=4, prev_next_chunks=0
+                )
+
+                sources = Source.curate_sources(response)
+
+                yield "\n\n\n".join(
+                    f"{index}. **{source.file} (page {source.page})**\n"
+                    f" (link: [{source.page_link}]({source.page_link}))\n{source.text}"
+                    for index, source in enumerate(sources, start=1)
+                )
+
+    # On initialization and on mode change, this function set the system prompt
+    # to the default prompt based on the mode (and user settings).
+    @staticmethod
+    def _get_default_system_prompt(mode: str) -> str:
+        p = ""
+        match mode:
+            # For query chat mode, obtain default system prompt from settings
+            case "Query Docs":
+                p = settings().ui.default_query_system_prompt
+            # For chat mode, obtain default system prompt from settings
+            case "LLM Chat":
+                p = settings().ui.default_chat_system_prompt
+            # For any other mode, clear the system prompt
+            case _:
+                p = ""
+        return p
+
+    def _set_system_prompt(self, system_prompt_input: str) -> None:
+        logger.info(f"Setting system prompt to: {system_prompt_input}")
+        self._system_prompt = system_prompt_input
+
+    def _set_current_mode(self, mode: str) -> Any:
+        self.mode = mode
+        self._set_system_prompt(self._get_default_system_prompt(mode))
+    
+    def _list_ingested_files(self) -> list[list[str]]:
+        files = set()
+        for ingested_document in self._ingest_service.list_ingested():
+            if ingested_document.doc_metadata is None:
+                # Skipping documents without metadata
+                continue
+            file_name = ingested_document.doc_metadata.get(
+                "file_name", "[FILE NAME MISSING]"
+            )
+            files.add(file_name)
+        return [[row] for row in files]
+
+    def _upload_file(self, files: list[str]) -> None:
+        logger.debug("Loading count=%s files", len(files))
+        paths = [Path(file) for file in files]
+        self._ingest_service.bulk_ingest(
+            [(str(path.name), path) for path in paths])
+
+
+
+import json
+
+DEFAULT_MODE = MODES[0]
+@home_router.post("/chat")
+async def chat_endpoint(request: Request, message: str = Body(...), mode: str = Body(DEFAULT_MODE)):
+    home_instance = request.state.injector.get(Home)
+    history = []
+    print("The message is: ", message)
+    print("The mode is: ", mode)
+    responses = home_instance._chat(message, history, mode)
+    return StreamingResponse(content=responses, media_type='text/event-stream')
+
+
+
+
+
+    # text = (
+    #     "To run the Celery worker based on the provided context, you can follow these steps: "
+    #     "1. First, make sure you have Celery installed in your project."
+    #     "2. Create a Celery instance and configure it with your settings."
+    #     "3. Define Celery tasks that will be executed by the worker."
+    #     "4. Start the Celery worker using the configured Celery instance."
+    #     "5. Your Celery worker is now running and ready to process tasks."
+    # )
+    # import time
+    # async def generate_stream():
+    #     for i in range(len(text)):
+    #         yield text[:i+1]  # Sending part of the text in each iteration
+    #         time.sleep(0.1)  # Simulating some processing time
+
+    # Return the responses as a StreamingResponse
+    # return StreamingResponse(content=responses, media_type="application/json")
+
+

private_gpt/launcher.py

@@ -14,7 +14,7 @@ from private_gpt.server.ingest.ingest_router import ingest_router
 from private_gpt.users.api.v1.api import api_router
 
 from private_gpt.settings.settings import Settings
-
+from private_gpt.home import home_router
 logger = logging.getLogger(__name__)
 
 
@@ -25,28 +25,25 @@ def create_app(root_injector: Injector) -> FastAPI:
         request.state.injector = root_injector
 
     app = FastAPI(dependencies=[Depends(bind_injector_to_request)])
-
-    # app.include_router(completions_router)
-    # app.include_router(chat_router)
-    # app.include_router(chunks_router)
-    # app.include_router(ingest_router)
-    # app.include_router(embeddings_router)
-    # app.include_router(health_router)
+    app.include_router(completions_router)
+    app.include_router(chat_router)
+    app.include_router(chunks_router)
+    app.include_router(ingest_router)
+    app.include_router(embeddings_router)
+    app.include_router(health_router)
     
     app.include_router(api_router)
-
-
+    app.include_router(home_router)
     settings = root_injector.get(Settings)
-    if settings.server.cors.enabled:
-        logger.debug("Setting up CORS middleware")
-        app.add_middleware(
-            CORSMiddleware,
-            allow_credentials=settings.server.cors.allow_credentials,
-            allow_origins=settings.server.cors.allow_origins,
-            allow_origin_regex=settings.server.cors.allow_origin_regex,
-            allow_methods=settings.server.cors.allow_methods,
-            allow_headers=settings.server.cors.allow_headers,
-        )
+    # if settings.server.cors.enabled/:
+    logger.debug("Setting up CORS middleware")
+    app.add_middleware(
+        CORSMiddleware,
+        allow_credentials=True,
+        allow_origins=["http://localhost:5173", "http://localhost:8001"],
+        allow_methods=["DELETE", "GET", "POST", "PUT", "OPTIONS"],
+        allow_headers=["*"],
+    )
 
     # if settings.ui.enabled:
     #     logger.debug("Importing the UI module")

private_gpt/server/chat/chat_router.py

@@ -106,3 +106,4 @@ def chat_completion(
         return to_openai_response(
             completion.response, completion.sources if body.include_sources else None
         )
+    

private_gpt/server/ingest/ingest_router.py

@@ -1,15 +1,17 @@
 from typing import Literal
 
-from fastapi import APIRouter, Depends, HTTPException, Request, UploadFile
+from fastapi import APIRouter, Depends, HTTPException, Request, UploadFile, File
+from fastapi.responses import JSONResponse
 from pydantic import BaseModel, Field
 
 from private_gpt.server.ingest.ingest_service import IngestService
 from private_gpt.server.ingest.model import IngestedDoc
 from private_gpt.server.utils.auth import authenticated
+from private_gpt.constants import UPLOAD_DIR
+from pathlib import Path
 
 ingest_router = APIRouter(prefix="/v1", dependencies=[Depends(authenticated)])
 
-
 class IngestTextBody(BaseModel):
     file_name: str = Field(examples=["Avatar: The Last Airbender"])
     text: str = Field(
@@ -38,7 +40,7 @@ def ingest(request: Request, file: UploadFile) -> IngestResponse:
 
 
 @ingest_router.post("/ingest/file", tags=["Ingestion"])
-def ingest_file(request: Request, file: UploadFile) -> IngestResponse:
+def ingest_file(request: Request, file: UploadFile = File(...)) -> IngestResponse:
     """Ingests and processes a file, storing its chunks to be used as context.
 
     The context obtained from files is later used in
@@ -54,12 +56,22 @@ def ingest_file(request: Request, file: UploadFile) -> IngestResponse:
     can be used to filter the context used to create responses in
     `/chat/completions`, `/completions`, and `/chunks` APIs.
     """
+    # try:
     service = request.state.injector.get(IngestService)
     if file.filename is None:
         raise HTTPException(400, "No file name provided")
-    ingested_documents = service.ingest_bin_data(file.filename, file.file)
+    upload_path = Path(f"{UPLOAD_DIR}/{file.filename}")
+    try:
+        with open(upload_path, "wb") as f:
+            f.write(file.file.read())
+        with open(upload_path, "rb") as f:
+            ingested_documents = service.ingest_bin_data(file.filename, f)
+    except Exception as e:
+        return {"message": f"There was an error uploading the file(s)\n {e}"}
+    finally:
+        file.file.close()
     return IngestResponse(object="list", model="private-gpt", data=ingested_documents)
-
+    
 
 @ingest_router.post("/ingest/text", tags=["Ingestion"])
 def ingest_text(request: Request, body: IngestTextBody) -> IngestResponse:
@@ -102,3 +114,14 @@ def delete_ingested(request: Request, doc_id: str) -> None:
     """
     service = request.state.injector.get(IngestService)
     service.delete(doc_id)
+
+
+@ingest_router.delete("/ingest", tags=["Ingestion"])
+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 = request.state.injector.get(IngestService)
+    service.delete(doc_id)

private_gpt/server/ingest/ingest_service.py

@@ -130,3 +130,4 @@ class IngestService:
             "Deleting the ingested document=%s in the doc and index store", doc_id
         )
         self.ingest_component.delete(doc_id)
+

private_gpt/server/ingest/model.py

@@ -1,8 +1,9 @@
 from typing import Any, Literal
-
+import os
 from llama_index import Document
 from pydantic import BaseModel, Field
-
+from private_gpt.constants import UPLOAD_DIR
+from pathlib import Path
 
 class IngestedDoc(BaseModel):
     object: Literal["ingest.document"]
@@ -30,3 +31,4 @@ class IngestedDoc(BaseModel):
             doc_id=document.doc_id,
             doc_metadata=IngestedDoc.curate_metadata(document.metadata),
         )
+

private_gpt/ui/admin_ui.py

@@ -33,6 +33,8 @@ SOURCES_SEPARATOR = "\n\n Sources: \n"
 
 MODES = ["Query Docs", "Search in Docs", "LLM Chat"]
 
+
+# generate 
 @singleton
 class PrivateAdminGptUi:
     @inject
@@ -67,11 +69,16 @@ class PrivateAdminGptUi:
             if completion_gen.sources:
                 full_response += SOURCES_SEPARATOR
                 cur_sources = Source.curate_sources(completion_gen.sources)
+                # sources_text = "\n\n\n".join(
+                #     f"{index}. {source.file} (page {source.page}) (page_link {source.page_link})"
+                #     for index, source in enumerate(cur_sources, start=1)
+                # )
                 sources_text = "\n\n\n".join(
-                    f"{index}. {source.file} (page {source.page})"
+                    f'<a href="#" target="_blank" rel="noopener noreferrer">{index}. {source.file} (page {source.page})</a>'
                     for index, source in enumerate(cur_sources, start=1)
                 )
                 full_response += sources_text
+                print(full_response)
             yield full_response
 
         def build_history() -> list[ChatMessage]:
@@ -125,11 +132,10 @@ class PrivateAdminGptUi:
                 )
 
                 sources = Source.curate_sources(response)
-
+                
                 yield "\n\n\n".join(
-                    f"{index}. **{source.file} "
-                    f"(page {source.page})**\n "
-                    f"{source.text}"
+                    f"{index}. **{source.file} (page {source.page})**\n"
+                    f" (link: [{source.page_link}]({source.page_link}))\n{source.text}"
                     for index, source in enumerate(sources, start=1)
                 )
 

private_gpt/ui/common.py

@@ -1,10 +1,12 @@
 from pydantic import BaseModel
 from private_gpt.server.chunks.chunks_service import Chunk, ChunksService
-
+from private_gpt.constants import UPLOAD_DIR
+from pathlib import Path
 class Source(BaseModel):
     file: str
     page: str
     text: str
+    page_link: str
 
     class Config:
         frozen = True
@@ -18,8 +20,9 @@ class Source(BaseModel):
 
             file_name = doc_metadata.get("file_name", "-") if doc_metadata else "-"
             page_label = doc_metadata.get("page_label", "-") if doc_metadata else "-"
+            page_link = str(Path(f"{UPLOAD_DIR}/{file_name}#page={page_label}"))
 
-            source = Source(file=file_name, page=page_label, text=chunk.text)
+            source = Source(file=file_name, page=page_label, text=chunk.text, page_link=page_link)
             curated_sources.add(source)
 
         return curated_sources

private_gpt/ui/ui.py

@@ -69,7 +69,7 @@ class PrivateGptUi:
                 full_response += SOURCES_SEPARATOR
                 cur_sources = Source.curate_sources(completion_gen.sources)
                 sources_text = "\n\n\n".join(
-                    f"{index}. {source.file} (page {source.page})"
+                    f"{index}. {source.file} (page {source.page}) (page_link {source.page_link})"
                     for index, source in enumerate(cur_sources, start=1)
                 )
                 full_response += sources_text
@@ -130,6 +130,7 @@ class PrivateGptUi:
                 yield "\n\n\n".join(
                     f"{index}. **{source.file} "
                     f"(page {source.page})**\n "
+                    f"(link {source.page_link})**\n "
                     f"{source.text}"
                     for index, source in enumerate(sources, start=1)
                 )

private_gpt/ui/users_ui.py

@@ -1,141 +0,0 @@
-"""This file should be imported only and only if you want to run the UI locally."""
-import itertools
-import logging
-from collections.abc import Iterable
-from pathlib import Path
-from typing import Any
-
-import gradio as gr  # type: ignore
-from fastapi import FastAPI
-from gradio.themes.utils.colors import slate  # type: ignore
-from injector import inject, singleton
-from llama_index.llms import ChatMessage, ChatResponse, MessageRole
-from pydantic import BaseModel
-
-from private_gpt.constants import PROJECT_ROOT_PATH
-from private_gpt.di import global_injector
-from private_gpt.server.chat.chat_service import ChatService, CompletionGen
-from private_gpt.server.chunks.chunks_service import Chunk, ChunksService
-from private_gpt.server.ingest.ingest_service import IngestService
-from private_gpt.settings.settings import settings
-from private_gpt.ui.images import logo_svg
-
-logger = logging.getLogger(__name__)
-
-THIS_DIRECTORY_RELATIVE = Path(__file__).parent.relative_to(PROJECT_ROOT_PATH)
-# Should be "private_gpt/ui/avatar-bot.ico"
-AVATAR_BOT = THIS_DIRECTORY_RELATIVE / "avatar-bot.ico"
-
-UI_TAB_TITLE = "My Private GPT"
-
-SOURCES_SEPARATOR = "\n\n Sources: \n"
-
-MODES = ["Query Docs", "Search in Docs", "LLM Chat"]
-
-from private_gpt.ui.common import PrivateGpt
-
-@singleton
-class UsersUI(PrivateGpt):
-
-    def __init__(
-        self,
-        ingest_service: IngestService,
-        chat_service: ChatService,
-        chunks_service: ChunksService,
-    ) -> None:
-        super().__init__(ingest_service, chat_service, chunks_service)
-
-    def _build_ui_blocks(self) -> gr.Blocks:
-        logger.debug("Creating the UI blocks")
-        with gr.Blocks(
-            title=UI_TAB_TITLE,
-            theme=gr.themes.Soft(primary_hue=slate),
-            css=".logo { "
-            "display:flex;"
-            "background-color: #C7BAFF;"
-            "height: 80px;"
-            "border-radius: 8px;"
-            "align-content: center;"
-            "justify-content: center;"
-            "align-items: center;"
-            "}"
-            ".logo img { height: 25% }"
-            ".contain { display: flex !important; flex-direction: column !important; }"
-            "#component-0, #component-3, #component-10, #component-8  { height: 100% !important; }"
-            "#chatbot { flex-grow: 1 !important; overflow: auto !important;}"
-            "#col { height: calc(100vh - 112px - 16px) !important; }",
-        ) as users:
-            # with gr.Row():
-            #     gr.HTML(f"<div class='logo'/><img src={logo_svg} alt=PrivateGPT></div")
-
-            with gr.Row(equal_height=False):
-                with gr.Column(scale=3):
-                    mode = gr.Radio(
-                        MODES,
-                        label="Mode",
-                        value="Query Docs",
-                    )
-                    ingested_dataset = gr.List(
-                        self._list_ingested_files,
-                        headers=["File name"],
-                        label="Ingested Files",
-                        interactive=False,
-                        render=False,  # Rendered under the button
-                    )
-                    ingested_dataset.change(
-                        self._list_ingested_files,
-                        outputs=ingested_dataset,
-                    )
-                    ingested_dataset.render()
-                    system_prompt_input = gr.Textbox(
-                        placeholder=self._system_prompt,
-                        label="System Prompt",
-                        lines=2,
-                        interactive=True,
-                        render=False,
-                    )
-                    # When mode changes, set default system prompt
-                    mode.change(
-                        self._set_current_mode, inputs=mode, outputs=system_prompt_input
-                    )
-                    # On blur, set system prompt to use in queries
-                    system_prompt_input.blur(
-                        self._set_system_prompt,
-                        inputs=system_prompt_input,
-                    )
-
-                with gr.Column(scale=7, elem_id="col"):
-                    _ = gr.ChatInterface(
-                        self._chat,
-                        chatbot=gr.Chatbot(
-                            label=f"LLM: {settings().llm.mode}",
-                            show_copy_button=True,
-                            elem_id="chatbot",
-                            render=False,
-                            # avatar_images=(
-                            #     None,
-                            #     AVATAR_BOT,
-                            # ),
-                        ),
-                        additional_inputs=[mode, system_prompt_input],
-                    )
-        return users
-
-    def get_ui_blocks(self) -> gr.Blocks:
-        if self._ui_block is None:
-            self._ui_block = self._build_ui_blocks()
-        return self._ui_block
-
-    def mount_in_app(self, app: FastAPI, path: str) -> None:
-        logger.info("PATH---------------------------->:%s", path)
-        blocks = self.get_ui_blocks()
-        blocks.queue()
-        logger.info("Mounting the regular gradio UI at path=%s", path)
-        gr.mount_gradio_app(app, blocks, path=path)
-    
-if __name__ == "__main__":
-    ui = global_injector.get(UsersUI)
-    _blocks = ui.get_ui_blocks()
-    _blocks.queue()
-    _blocks.launch(debug=False, show_api=False)
-        

private_gpt/users/api/v1/routers/auth.py

@@ -54,10 +54,12 @@ def create_token_payload(user: models.User, user_role: models.UserRole) -> dict:
     """
     return {
         "id": str(user.id),
+        "email": str(user.email),
         "role": user_role.role.name,
         "company_id": user_role.company.id if user_role.company else None,
     }
 
+
 @router.post("/login", response_model=schemas.TokenSchema)
 def login_access_token(
     db: Session = Depends(deps.get_db),
@@ -97,6 +99,7 @@ def login_access_token(
 
     token_payload = {
         "id": str(user.id),
+        "email": str(user.email),
         "role": role,
         "company_id": company_id,
     }

private_gpt/users/api/v1/routers/companies.py

@@ -24,7 +24,7 @@ def list_companies(
     ),
 ) -> List[schemas.Company]:
     """
-    List companies
+    Retrieve a list of companies with pagination support.
     """
     companies = crud.company.get_multi(db, skip=skip, limit=limit)
     return companies

settings.yaml

@@ -5,8 +5,9 @@ server:
   env_name: ${APP_ENV:prod}
   port: ${PORT:8001}
   cors:
-    enabled: false
-    allow_origins: ["*"]
+    enabled: true
+    allow_credentials: true
+    allow_origins: ["http://localhost:5173/"]
     allow_methods: ["*"]
     allow_headers: ["*"]
   auth: