Resolve merge conflicts in French translations

.dockerignore

@@ -10,7 +10,8 @@ node_modules
 vite.config.js.timestamp-*
 vite.config.ts.timestamp-*
 __pycache__
-.env
+.idea
+venv
 _old
 uploads
 .ipynb_checkpoints

.gitignore

@@ -306,3 +306,4 @@ dist
 # cypress artifacts
 cypress/videos
 cypress/screenshots
+.vscode/settings.json

backend/apps/audio/main.py

@@ -14,7 +14,6 @@ from fastapi import (
 from fastapi.responses import StreamingResponse, JSONResponse, FileResponse
 
 from fastapi.middleware.cors import CORSMiddleware
-from faster_whisper import WhisperModel
 from pydantic import BaseModel
 
 import uuid
@@ -277,6 +276,8 @@ def transcribe(
             f.close()
 
         if app.state.config.STT_ENGINE == "":
+            from faster_whisper import WhisperModel
+
             whisper_kwargs = {
                 "model_size_or_path": WHISPER_MODEL,
                 "device": whisper_device_type,

backend/apps/images/main.py

@@ -12,7 +12,6 @@ from fastapi import (
     Form,
 )
 from fastapi.middleware.cors import CORSMiddleware
-from faster_whisper import WhisperModel
 
 from constants import ERROR_MESSAGES
 from utils.utils import (

backend/apps/openai/main.py

@@ -25,6 +25,7 @@ from utils.task import prompt_template
 from config import (
     SRC_LOG_LEVELS,
     ENABLE_OPENAI_API,
+    AIOHTTP_CLIENT_TIMEOUT,
     OPENAI_API_BASE_URLS,
     OPENAI_API_KEYS,
     CACHE_DIR,
@@ -463,7 +464,9 @@ async def generate_chat_completion(
     streaming = False
 
     try:
-        session = aiohttp.ClientSession(trust_env=True)
+        session = aiohttp.ClientSession(
+            trust_env=True, timeout=aiohttp.ClientTimeout(total=AIOHTTP_CLIENT_TIMEOUT)
+        )
         r = await session.request(
             method="POST",
             url=f"{url}/chat/completions",

backend/apps/rag/main.py

@@ -48,8 +48,6 @@ import mimetypes
 import uuid
 import json
 
-import sentence_transformers
-
 from apps.webui.models.documents import (
     Documents,
     DocumentForm,
@@ -93,6 +91,8 @@ from config import (
     SRC_LOG_LEVELS,
     UPLOAD_DIR,
     DOCS_DIR,
+    CONTENT_EXTRACTION_ENGINE,
+    TIKA_SERVER_URL,
     RAG_TOP_K,
     RAG_RELEVANCE_THRESHOLD,
     RAG_EMBEDDING_ENGINE,
@@ -148,6 +148,9 @@ app.state.config.ENABLE_RAG_WEB_LOADER_SSL_VERIFICATION = (
     ENABLE_RAG_WEB_LOADER_SSL_VERIFICATION
 )
 
+app.state.config.CONTENT_EXTRACTION_ENGINE = CONTENT_EXTRACTION_ENGINE
+app.state.config.TIKA_SERVER_URL = TIKA_SERVER_URL
+
 app.state.config.CHUNK_SIZE = CHUNK_SIZE
 app.state.config.CHUNK_OVERLAP = CHUNK_OVERLAP
 
@@ -190,6 +193,8 @@ def update_embedding_model(
     update_model: bool = False,
 ):
     if embedding_model and app.state.config.RAG_EMBEDDING_ENGINE == "":
+        import sentence_transformers
+
         app.state.sentence_transformer_ef = sentence_transformers.SentenceTransformer(
             get_model_path(embedding_model, update_model),
             device=DEVICE_TYPE,
@@ -204,6 +209,8 @@ def update_reranking_model(
     update_model: bool = False,
 ):
     if reranking_model:
+        import sentence_transformers
+
         app.state.sentence_transformer_rf = sentence_transformers.CrossEncoder(
             get_model_path(reranking_model, update_model),
             device=DEVICE_TYPE,
@@ -388,6 +395,10 @@ async def get_rag_config(user=Depends(get_admin_user)):
     return {
         "status": True,
         "pdf_extract_images": app.state.config.PDF_EXTRACT_IMAGES,
+        "content_extraction": {
+            "engine": app.state.config.CONTENT_EXTRACTION_ENGINE,
+            "tika_server_url": app.state.config.TIKA_SERVER_URL,
+        },
         "chunk": {
             "chunk_size": app.state.config.CHUNK_SIZE,
             "chunk_overlap": app.state.config.CHUNK_OVERLAP,
@@ -417,6 +428,11 @@ async def get_rag_config(user=Depends(get_admin_user)):
     }
 
 
+class ContentExtractionConfig(BaseModel):
+    engine: str = ""
+    tika_server_url: Optional[str] = None
+
+
 class ChunkParamUpdateForm(BaseModel):
     chunk_size: int
     chunk_overlap: int
@@ -450,6 +466,7 @@ class WebConfig(BaseModel):
 
 class ConfigUpdateForm(BaseModel):
     pdf_extract_images: Optional[bool] = None
+    content_extraction: Optional[ContentExtractionConfig] = None
     chunk: Optional[ChunkParamUpdateForm] = None
     youtube: Optional[YoutubeLoaderConfig] = None
     web: Optional[WebConfig] = None
@@ -463,6 +480,11 @@ async def update_rag_config(form_data: ConfigUpdateForm, user=Depends(get_admin_
         else app.state.config.PDF_EXTRACT_IMAGES
     )
 
+    if form_data.content_extraction is not None:
+        log.info(f"Updating text settings: {form_data.content_extraction}")
+        app.state.config.CONTENT_EXTRACTION_ENGINE = form_data.content_extraction.engine
+        app.state.config.TIKA_SERVER_URL = form_data.content_extraction.tika_server_url
+
     if form_data.chunk is not None:
         app.state.config.CHUNK_SIZE = form_data.chunk.chunk_size
         app.state.config.CHUNK_OVERLAP = form_data.chunk.chunk_overlap
@@ -499,6 +521,10 @@ async def update_rag_config(form_data: ConfigUpdateForm, user=Depends(get_admin_
     return {
         "status": True,
         "pdf_extract_images": app.state.config.PDF_EXTRACT_IMAGES,
+        "content_extraction": {
+            "engine": app.state.config.CONTENT_EXTRACTION_ENGINE,
+            "tika_server_url": app.state.config.TIKA_SERVER_URL,
+        },
         "chunk": {
             "chunk_size": app.state.config.CHUNK_SIZE,
             "chunk_overlap": app.state.config.CHUNK_OVERLAP,
@@ -985,6 +1011,41 @@ def store_docs_in_vector_db(docs, collection_name, overwrite: bool = False) -> b
         return False
 
 
+class TikaLoader:
+    def __init__(self, file_path, mime_type=None):
+        self.file_path = file_path
+        self.mime_type = mime_type
+
+    def load(self) -> List[Document]:
+        with open(self.file_path, "rb") as f:
+            data = f.read()
+
+        if self.mime_type is not None:
+            headers = {"Content-Type": self.mime_type}
+        else:
+            headers = {}
+
+        endpoint = app.state.config.TIKA_SERVER_URL
+        if not endpoint.endswith("/"):
+            endpoint += "/"
+        endpoint += "tika/text"
+
+        r = requests.put(endpoint, data=data, headers=headers)
+
+        if r.ok:
+            raw_metadata = r.json()
+            text = raw_metadata.get("X-TIKA:content", "<No text content found>")
+
+            if "Content-Type" in raw_metadata:
+                headers["Content-Type"] = raw_metadata["Content-Type"]
+
+            log.info("Tika extracted text: %s", text)
+
+            return [Document(page_content=text, metadata=headers)]
+        else:
+            raise Exception(f"Error calling Tika: {r.reason}")
+
+
 def get_loader(filename: str, file_content_type: str, file_path: str):
     file_ext = filename.split(".")[-1].lower()
     known_type = True
@@ -1035,6 +1096,17 @@ def get_loader(filename: str, file_content_type: str, file_path: str):
         "msg",
     ]
 
+    if (
+        app.state.config.CONTENT_EXTRACTION_ENGINE == "tika"
+        and app.state.config.TIKA_SERVER_URL
+    ):
+        if file_ext in known_source_ext or (
+            file_content_type and file_content_type.find("text/") >= 0
+        ):
+            loader = TextLoader(file_path, autodetect_encoding=True)
+        else:
+            loader = TikaLoader(file_path, file_content_type)
+    else:
         if file_ext == "pdf":
             loader = PyPDFLoader(
                 file_path, extract_images=app.state.config.PDF_EXTRACT_IMAGES

backend/apps/rag/utils.py

@@ -294,15 +294,17 @@ def get_rag_context(
 
         extracted_collections.extend(collection_names)
 
-    context_string = ""
-
+    contexts = []
     citations = []
+
     for context in relevant_contexts:
         try:
             if "documents" in context:
-                context_string += "\n\n".join(
+                contexts.append(
+                    "\n\n".join(
                         [text for text in context["documents"][0] if text is not None]
                     )
+                )
 
                 if "metadatas" in context:
                     citations.append(
@@ -315,9 +317,7 @@ def get_rag_context(
         except Exception as e:
             log.exception(e)
 
-    context_string = context_string.strip()
-
-    return context_string, citations
+    return contexts, citations
 
 
 def get_model_path(model: str, update_model: bool = False):
@@ -442,8 +442,6 @@ from langchain_core.documents import BaseDocumentCompressor, Document
 from langchain_core.callbacks import Callbacks
 from langchain_core.pydantic_v1 import Extra
 
-from sentence_transformers import util
-
 
 class RerankCompressor(BaseDocumentCompressor):
     embedding_function: Any
@@ -468,6 +466,8 @@ class RerankCompressor(BaseDocumentCompressor):
                 [(query, doc.page_content) for doc in documents]
             )
         else:
+            from sentence_transformers import util
+
             query_embedding = self.embedding_function(query)
             document_embedding = self.embedding_function(
                 [doc.page_content for doc in documents]

backend/apps/webui/main.py

@@ -259,6 +259,9 @@ async def generate_function_chat_completion(form_data, user):
                         if isinstance(line, BaseModel):
                             line = line.model_dump_json()
                             line = f"data: {line}"
+                        if isinstance(line, dict):
+                            line = f"data: {json.dumps(line)}"
+
                         try:
                             line = line.decode("utf-8")
                         except:

backend/apps/webui/models/functions.py

@@ -214,8 +214,7 @@ class FunctionsTable:
             user_settings["functions"]["valves"][id] = valves
 
             # Update the user settings in the database
-            query = Users.update_user_by_id(user_id, {"settings": user_settings})
-            query.execute()
+            Users.update_user_by_id(user_id, {"settings": user_settings})
 
             return user_settings["functions"]["valves"][id]
         except Exception as e:

backend/apps/webui/models/tools.py

@@ -170,8 +170,7 @@ class ToolsTable:
             user_settings["tools"]["valves"][id] = valves
 
             # Update the user settings in the database
-            query = Users.update_user_by_id(user_id, {"settings": user_settings})
-            query.execute()
+            Users.update_user_by_id(user_id, {"settings": user_settings})
 
             return user_settings["tools"]["valves"][id]
         except Exception as e:

backend/config.py

@@ -5,9 +5,8 @@ import importlib.metadata
 import pkgutil
 import chromadb
 from chromadb import Settings
-from base64 import b64encode
 from bs4 import BeautifulSoup
-from typing import TypeVar, Generic, Union
+from typing import TypeVar, Generic
 from pydantic import BaseModel
 from typing import Optional
 
@@ -19,7 +18,6 @@ import markdown
 import requests
 import shutil
 
-from secrets import token_bytes
 from constants import ERROR_MESSAGES
 
 ####################################
@@ -768,12 +766,14 @@ class BannerModel(BaseModel):
     dismissible: bool
     timestamp: int
 
+try:
+    banners = json.loads(os.environ.get("WEBUI_BANNERS", "[]"))
+    banners = [BannerModel(**banner) for banner in banners]
+except Exception as e:
+    print(f"Error loading WEBUI_BANNERS: {e}")
+    banners = []
 
-WEBUI_BANNERS = PersistentConfig(
-    "WEBUI_BANNERS",
-    "ui.banners",
-    [BannerModel(**banner) for banner in json.loads("[]")],
-)
+WEBUI_BANNERS = PersistentConfig("WEBUI_BANNERS", "ui.banners", banners)
 
 
 SHOW_ADMIN_DETAILS = PersistentConfig(
@@ -885,6 +885,22 @@ WEBUI_SESSION_COOKIE_SECURE = os.environ.get(
 if WEBUI_AUTH and WEBUI_SECRET_KEY == "":
     raise ValueError(ERROR_MESSAGES.ENV_VAR_NOT_FOUND)
 
+####################################
+# RAG document content extraction
+####################################
+
+CONTENT_EXTRACTION_ENGINE = PersistentConfig(
+    "CONTENT_EXTRACTION_ENGINE",
+    "rag.CONTENT_EXTRACTION_ENGINE",
+    os.environ.get("CONTENT_EXTRACTION_ENGINE", "").lower(),
+)
+
+TIKA_SERVER_URL = PersistentConfig(
+    "TIKA_SERVER_URL",
+    "rag.tika_server_url",
+    os.getenv("TIKA_SERVER_URL", "http://tika:9998"),  # Default for sidecar deployment
+)
+
 ####################################
 # RAG
 ####################################

backend/main.py

@@ -33,7 +33,7 @@ from starlette.middleware.sessions import SessionMiddleware
 from starlette.responses import StreamingResponse, Response, RedirectResponse
 
 
-from apps.socket.main import app as socket_app
+from apps.socket.main import sio, app as socket_app
 from apps.ollama.main import (
     app as ollama_app,
     OpenAIChatCompletionForm,
@@ -212,8 +212,79 @@ origins = ["*"]
 ##################################
 
 
+async def get_body_and_model_and_user(request):
+    # Read the original request body
+    body = await request.body()
+    body_str = body.decode("utf-8")
+    body = json.loads(body_str) if body_str else {}
+
+    model_id = body["model"]
+    if model_id not in app.state.MODELS:
+        raise "Model not found"
+    model = app.state.MODELS[model_id]
+
+    user = get_current_user(
+        request,
+        get_http_authorization_cred(request.headers.get("Authorization")),
+    )
+
+    return body, model, user
+
+
+def get_task_model_id(default_model_id):
+    # Set the task model
+    task_model_id = default_model_id
+    # Check if the user has a custom task model and use that model
+    if app.state.MODELS[task_model_id]["owned_by"] == "ollama":
+        if (
+            app.state.config.TASK_MODEL
+            and app.state.config.TASK_MODEL in app.state.MODELS
+        ):
+            task_model_id = app.state.config.TASK_MODEL
+    else:
+        if (
+            app.state.config.TASK_MODEL_EXTERNAL
+            and app.state.config.TASK_MODEL_EXTERNAL in app.state.MODELS
+        ):
+            task_model_id = app.state.config.TASK_MODEL_EXTERNAL
+
+    return task_model_id
+
+
+def get_filter_function_ids(model):
+    def get_priority(function_id):
+        function = Functions.get_function_by_id(function_id)
+        if function is not None and hasattr(function, "valves"):
+            return (function.valves if function.valves else {}).get("priority", 0)
+        return 0
+
+    filter_ids = [function.id for function in Functions.get_global_filter_functions()]
+    if "info" in model and "meta" in model["info"]:
+        filter_ids.extend(model["info"]["meta"].get("filterIds", []))
+        filter_ids = list(set(filter_ids))
+
+    enabled_filter_ids = [
+        function.id
+        for function in Functions.get_functions_by_type("filter", active_only=True)
+    ]
+
+    filter_ids = [
+        filter_id for filter_id in filter_ids if filter_id in enabled_filter_ids
+    ]
+
+    filter_ids.sort(key=get_priority)
+    return filter_ids
+
+
 async def get_function_call_response(
-    messages, files, tool_id, template, task_model_id, user
+    messages,
+    files,
+    tool_id,
+    template,
+    task_model_id,
+    user,
+    model,
+    __event_emitter__=None,
 ):
     tool = Tools.get_tool_by_id(tool_id)
     tools_specs = json.dumps(tool.specs, indent=2)
@@ -350,6 +421,13 @@ async def get_function_call_response(
                             "__id__": tool_id,
                         }
 
+                    if "__event_emitter__" in sig.parameters:
+                        # Call the function with the '__event_emitter__' parameter included
+                        params = {
+                            **params,
+                            "__event_emitter__": __event_emitter__,
+                        }
+
                     if inspect.iscoroutinefunction(function):
                         function_result = await function(**params)
                     else:
@@ -373,68 +451,10 @@ async def get_function_call_response(
     return None, None, False
 
 
-class ChatCompletionMiddleware(BaseHTTPMiddleware):
-    async def dispatch(self, request: Request, call_next):
-        data_items = []
-
-        show_citations = False
-        citations = []
-
-        if request.method == "POST" and any(
-            endpoint in request.url.path
-            for endpoint in ["/ollama/api/chat", "/chat/completions"]
-        ):
-            log.debug(f"request.url.path: {request.url.path}")
-
-            # Read the original request body
-            body = await request.body()
-            body_str = body.decode("utf-8")
-            data = json.loads(body_str) if body_str else {}
-
-            user = get_current_user(
-                request,
-                get_http_authorization_cred(request.headers.get("Authorization")),
-            )
-            # Flag to skip RAG completions if file_handler is present in tools/functions
-            skip_files = False
-            if data.get("citations"):
-                show_citations = True
-                del data["citations"]
-
-            model_id = data["model"]
-            if model_id not in app.state.MODELS:
-                raise HTTPException(
-                    status_code=status.HTTP_404_NOT_FOUND,
-                    detail="Model not found",
-                )
-            model = app.state.MODELS[model_id]
-
-            def get_priority(function_id):
-                function = Functions.get_function_by_id(function_id)
-                if function is not None and hasattr(function, "valves"):
-                    return (function.valves if function.valves else {}).get(
-                        "priority", 0
-                    )
-                return 0
-
-            filter_ids = [
-                function.id for function in Functions.get_global_filter_functions()
-            ]
-            if "info" in model and "meta" in model["info"]:
-                filter_ids.extend(model["info"]["meta"].get("filterIds", []))
-                filter_ids = list(set(filter_ids))
-
-            enabled_filter_ids = [
-                function.id
-                for function in Functions.get_functions_by_type(
-                    "filter", active_only=True
-                )
-            ]
-            filter_ids = [
-                filter_id for filter_id in filter_ids if filter_id in enabled_filter_ids
-            ]
+async def chat_completion_functions_handler(body, model, user, __event_emitter__):
+    skip_files = None
 
-            filter_ids.sort(key=get_priority)
+    filter_ids = get_filter_function_ids(model)
     for filter_id in filter_ids:
         filter = Functions.get_function_by_id(filter_id)
         if filter:
@@ -464,7 +484,7 @@ class ChatCompletionMiddleware(BaseHTTPMiddleware):
 
                     # Get the signature of the function
                     sig = inspect.signature(inlet)
-                            params = {"body": data}
+                    params = {"body": body}
 
                     if "__user__" in sig.parameters:
                         __user__ = {
@@ -492,108 +512,206 @@ class ChatCompletionMiddleware(BaseHTTPMiddleware):
                             "__id__": filter_id,
                         }
 
+                    if "__model__" in sig.parameters:
+                        params = {
+                            **params,
+                            "__model__": model,
+                        }
+                    if "__event_emitter__" in sig.parameters:
+                        params = {
+                            **params,
+                            "__event_emitter__": __event_emitter__,
+                        }
+
                     if inspect.iscoroutinefunction(inlet):
-                                data = await inlet(**params)
+                        body = await inlet(**params)
                     else:
-                                data = inlet(**params)
+                        body = inlet(**params)
 
             except Exception as e:
                 print(f"Error: {e}")
-                        return JSONResponse(
-                            status_code=status.HTTP_400_BAD_REQUEST,
-                            content={"detail": str(e)},
-                        )
+                raise e
+
+    if skip_files:
+        if "files" in body:
+            del body["files"]
+
+    return body, {}
 
-            # Set the task model
-            task_model_id = data["model"]
-            # Check if the user has a custom task model and use that model
-            if app.state.MODELS[task_model_id]["owned_by"] == "ollama":
-                if (
-                    app.state.config.TASK_MODEL
-                    and app.state.config.TASK_MODEL in app.state.MODELS
-                ):
-                    task_model_id = app.state.config.TASK_MODEL
-            else:
-                if (
-                    app.state.config.TASK_MODEL_EXTERNAL
-                    and app.state.config.TASK_MODEL_EXTERNAL in app.state.MODELS
-                ):
-                    task_model_id = app.state.config.TASK_MODEL_EXTERNAL
 
-            prompt = get_last_user_message(data["messages"])
-            context = ""
+async def chat_completion_tools_handler(body, model, user, __event_emitter__):
+    skip_files = None
+
+    contexts = []
+    citations = None
+
+    task_model_id = get_task_model_id(body["model"])
 
     # If tool_ids field is present, call the functions
-            if "tool_ids" in data:
-                print(data["tool_ids"])
-                for tool_id in data["tool_ids"]:
+    if "tool_ids" in body:
+        print(body["tool_ids"])
+        for tool_id in body["tool_ids"]:
             print(tool_id)
             try:
-                        response, citation, file_handler = (
-                            await get_function_call_response(
-                                messages=data["messages"],
-                                files=data.get("files", []),
+                response, citation, file_handler = await get_function_call_response(
+                    messages=body["messages"],
+                    files=body.get("files", []),
                     tool_id=tool_id,
                     template=app.state.config.TOOLS_FUNCTION_CALLING_PROMPT_TEMPLATE,
                     task_model_id=task_model_id,
                     user=user,
-                            )
+                    model=model,
+                    __event_emitter__=__event_emitter__,
                 )
 
                 print(file_handler)
                 if isinstance(response, str):
-                            context += ("\n" if context != "" else "") + response
+                    contexts.append(response)
 
                 if citation:
+                    if citations is None:
+                        citations = [citation]
+                    else:
                         citations.append(citation)
-                            show_citations = True
 
                 if file_handler:
                     skip_files = True
 
             except Exception as e:
                 print(f"Error: {e}")
-                del data["tool_ids"]
-
-                print(f"tool_context: {context}")
-
-            # If files field is present, generate RAG completions
-            # If skip_files is True, skip the RAG completions
-            if "files" in data:
-                if not skip_files:
-                    data = {**data}
-                    rag_context, rag_citations = get_rag_context(
-                        files=data["files"],
-                        messages=data["messages"],
+        del body["tool_ids"]
+        print(f"tool_contexts: {contexts}")
+
+    if skip_files:
+        if "files" in body:
+            del body["files"]
+
+    return body, {
+        **({"contexts": contexts} if contexts is not None else {}),
+        **({"citations": citations} if citations is not None else {}),
+    }
+
+
+async def chat_completion_files_handler(body):
+    contexts = []
+    citations = None
+
+    if "files" in body:
+        files = body["files"]
+        del body["files"]
+
+        contexts, citations = get_rag_context(
+            files=files,
+            messages=body["messages"],
             embedding_function=rag_app.state.EMBEDDING_FUNCTION,
             k=rag_app.state.config.TOP_K,
             reranking_function=rag_app.state.sentence_transformer_rf,
             r=rag_app.state.config.RELEVANCE_THRESHOLD,
             hybrid_search=rag_app.state.config.ENABLE_RAG_HYBRID_SEARCH,
         )
-                    if rag_context:
-                        context += ("\n" if context != "" else "") + rag_context
 
-                    log.debug(f"rag_context: {rag_context}, citations: {citations}")
+        log.debug(f"rag_contexts: {contexts}, citations: {citations}")
 
-                    if rag_citations:
-                        citations.extend(rag_citations)
+    return body, {
+        **({"contexts": contexts} if contexts is not None else {}),
+        **({"citations": citations} if citations is not None else {}),
+    }
 
-                del data["files"]
 
-            if show_citations and len(citations) > 0:
-                data_items.append({"citations": citations})
+class ChatCompletionMiddleware(BaseHTTPMiddleware):
+    async def dispatch(self, request: Request, call_next):
+        if request.method == "POST" and any(
+            endpoint in request.url.path
+            for endpoint in ["/ollama/api/chat", "/chat/completions"]
+        ):
+            log.debug(f"request.url.path: {request.url.path}")
+
+            try:
+                body, model, user = await get_body_and_model_and_user(request)
+            except Exception as e:
+                return JSONResponse(
+                    status_code=status.HTTP_400_BAD_REQUEST,
+                    content={"detail": str(e)},
+                )
+
+            # Extract session_id, chat_id and message_id from the request body
+            session_id = None
+            if "session_id" in body:
+                session_id = body["session_id"]
+                del body["session_id"]
+            chat_id = None
+            if "chat_id" in body:
+                chat_id = body["chat_id"]
+                del body["chat_id"]
+            message_id = None
+            if "id" in body:
+                message_id = body["id"]
+                del body["id"]
+
+            async def __event_emitter__(data):
+                await sio.emit(
+                    "chat-events",
+                    {
+                        "chat_id": chat_id,
+                        "message_id": message_id,
+                        "data": data,
+                    },
+                    to=session_id,
+                )
+
+            # Initialize data_items to store additional data to be sent to the client
+            data_items = []
+
+            # Initialize context, and citations
+            contexts = []
+            citations = []
 
-            if context != "":
-                system_prompt = rag_template(
-                    rag_app.state.config.RAG_TEMPLATE, context, prompt
+            try:
+                body, flags = await chat_completion_functions_handler(
+                    body, model, user, __event_emitter__
                 )
-                print(system_prompt)
-                data["messages"] = add_or_update_system_message(
-                    system_prompt, data["messages"]
+            except Exception as e:
+                return JSONResponse(
+                    status_code=status.HTTP_400_BAD_REQUEST,
+                    content={"detail": str(e)},
                 )
 
-            modified_body_bytes = json.dumps(data).encode("utf-8")
+            try:
+                body, flags = await chat_completion_tools_handler(
+                    body, model, user, __event_emitter__
+                )
+
+                contexts.extend(flags.get("contexts", []))
+                citations.extend(flags.get("citations", []))
+            except Exception as e:
+                print(e)
+                pass
+
+            try:
+                body, flags = await chat_completion_files_handler(body)
+
+                contexts.extend(flags.get("contexts", []))
+                citations.extend(flags.get("citations", []))
+            except Exception as e:
+                print(e)
+                pass
+
+            # If context is not empty, insert it into the messages
+            if len(contexts) > 0:
+                context_string = "/n".join(contexts).strip()
+                prompt = get_last_user_message(body["messages"])
+                body["messages"] = add_or_update_system_message(
+                    rag_template(
+                        rag_app.state.config.RAG_TEMPLATE, context_string, prompt
+                    ),
+                    body["messages"],
+                )
+
+            # If there are citations, add them to the data_items
+            if len(citations) > 0:
+                data_items.append({"citations": citations})
+
+            modified_body_bytes = json.dumps(body).encode("utf-8")
             # Replace the request body with the modified one
             request._body = modified_body_bytes
             # Set custom header to ensure content-length matches new body length
@@ -715,9 +833,6 @@ def filter_pipeline(payload, user):
                 pass
 
     if "pipeline" not in app.state.MODELS[model_id]:
-        if "chat_id" in payload:
-            del payload["chat_id"]
-
         if "title" in payload:
             del payload["title"]
 
@@ -1008,6 +1123,17 @@ async def chat_completed(form_data: dict, user=Depends(get_verified_user)):
             else:
                 pass
 
+    async def __event_emitter__(data):
+        await sio.emit(
+            "chat-events",
+            {
+                "chat_id": data["chat_id"],
+                "message_id": data["id"],
+                "data": data,
+            },
+            to=data["session_id"],
+        )
+
     def get_priority(function_id):
         function = Functions.get_function_by_id(function_id)
         if function is not None and hasattr(function, "valves"):
@@ -1083,6 +1209,18 @@ async def chat_completed(form_data: dict, user=Depends(get_verified_user)):
                             "__id__": filter_id,
                         }
 
+                    if "__model__" in sig.parameters:
+                        params = {
+                            **params,
+                            "__model__": model,
+                        }
+
+                    if "__event_emitter__" in sig.parameters:
+                        params = {
+                            **params,
+                            "__event_emitter__": __event_emitter__,
+                        }
+
                     if inspect.iscoroutinefunction(outlet):
                         data = await outlet(**params)
                     else:
@@ -1213,6 +1351,9 @@ async def generate_title(form_data: dict, user=Depends(get_verified_user)):
             content={"detail": e.args[1]},
         )
 
+    if "chat_id" in payload:
+        del payload["chat_id"]
+
     return await generate_chat_completions(form_data=payload, user=user)
 
 
@@ -1273,6 +1414,9 @@ async def generate_search_query(form_data: dict, user=Depends(get_verified_user)
             content={"detail": e.args[1]},
         )
 
+    if "chat_id" in payload:
+        del payload["chat_id"]
+
     return await generate_chat_completions(form_data=payload, user=user)
 
 
@@ -1337,6 +1481,9 @@ Message: """{{prompt}}"""
             content={"detail": e.args[1]},
         )
 
+    if "chat_id" in payload:
+        del payload["chat_id"]
+
     return await generate_chat_completions(form_data=payload, user=user)
 
 

backend/requirements.txt

@@ -10,7 +10,7 @@ python-socketio==5.11.3
 python-jose==3.3.0
 passlib[bcrypt]==1.7.4
 
-requests==2.32.2
+requests==2.32.3
 aiohttp==3.9.5
 peewee==3.17.5
 peewee-migrate==1.12.2
@@ -30,21 +30,21 @@ openai
 anthropic
 google-generativeai==0.5.4
 
-langchain==0.2.0
-langchain-community==0.2.0
+langchain==0.2.6
+langchain-community==0.2.6
 langchain-chroma==0.1.2
 
 fake-useragent==1.5.1
 chromadb==0.5.3
-sentence-transformers==2.7.0
+sentence-transformers==3.0.1
 pypdf==4.2.0
 docx2txt==0.8
 python-pptx==0.6.23
-unstructured==0.14.0
+unstructured==0.14.9
 Markdown==3.6
 pypandoc==1.13
 pandas==2.2.2
-openpyxl==3.1.2
+openpyxl==3.1.5
 pyxlsb==1.0.10
 xlrd==2.0.1
 validators==0.28.1
@@ -61,7 +61,7 @@ PyJWT[crypto]==2.8.0
 authlib==1.3.1
 
 black==24.4.2
-langfuse==2.33.0
+langfuse==2.36.2
 youtube-transcript-api==0.6.2
 pytube==15.0.0
 

backend/utils/misc.py

@@ -8,9 +8,17 @@ import uuid
 import time
 
 
-def get_last_user_message(messages: List[dict]) -> str:
+def get_last_user_message_item(messages: List[dict]) -> str:
     for message in reversed(messages):
         if message["role"] == "user":
+            return message
+    return None
+
+
+def get_last_user_message(messages: List[dict]) -> str:
+    message = get_last_user_message_item(messages)
+
+    if message is not None:
         if isinstance(message["content"], list):
             for item in message["content"]:
                 if item["type"] == "text":

package-lock.json

 {
 	"name": "open-webui",
-	"version": "0.3.7",
+	"version": "0.3.8",
 	"lockfileVersion": 3,
 	"requires": true,
 	"packages": {
 		"": {
 			"name": "open-webui",
-			"version": "0.3.7",
+			"version": "0.3.8",
 			"dependencies": {
 				"@codemirror/lang-javascript": "^6.2.2",
 				"@codemirror/lang-python": "^6.1.6",

package.json

 {
 	"name": "open-webui",
-	"version": "0.3.7",
+	"version": "0.3.8",
 	"private": true,
 	"scripts": {
 		"dev": "npm run pyodide:fetch && vite dev --host",

src/lib/apis/rag/index.ts

@@ -32,6 +32,11 @@ type ChunkConfigForm = {
 	chunk_overlap: number;
 };
 
+type ContentExtractConfigForm = {
+	engine: string;
+	tika_server_url: string | null;
+};
+
 type YoutubeConfigForm = {
 	language: string[];
 	translation?: string | null;
@@ -40,6 +45,7 @@ type YoutubeConfigForm = {
 type RAGConfigForm = {
 	pdf_extract_images?: boolean;
 	chunk?: ChunkConfigForm;
+	content_extraction?: ContentExtractConfigForm;
 	web_loader_ssl_verification?: boolean;
 	youtube?: YoutubeConfigForm;
 };

src/lib/components/admin/Settings/Documents.svelte

@@ -37,6 +37,10 @@
 	let embeddingModel = '';
 	let rerankingModel = '';
 
+	let contentExtractionEngine = 'default';
+	let tikaServerUrl = '';
+	let showTikaServerUrl = false;
+
 	let chunkSize = 0;
 	let chunkOverlap = 0;
 	let pdfExtractImages = true;
@@ -163,11 +167,20 @@
 			rerankingModelUpdateHandler();
 		}
 
+		if (contentExtractionEngine === 'tika' && tikaServerUrl === '') {
+			toast.error($i18n.t('Tika Server URL required.'));
+			return;
+		}
+
 		const res = await updateRAGConfig(localStorage.token, {
 			pdf_extract_images: pdfExtractImages,
 			chunk: {
 				chunk_overlap: chunkOverlap,
 				chunk_size: chunkSize
+			},
+			content_extraction: {
+				engine: contentExtractionEngine,
+				tika_server_url: tikaServerUrl
 			}
 		});
 
@@ -213,6 +226,10 @@
 
 			chunkSize = res.chunk.chunk_size;
 			chunkOverlap = res.chunk.chunk_overlap;
+
+			contentExtractionEngine = res.content_extraction.engine;
+			tikaServerUrl = res.content_extraction.tika_server_url;
+			showTikaServerUrl = contentExtractionEngine === 'tika';
 		}
 	});
 </script>
@@ -388,7 +405,7 @@
 			</div>
 		</div>
 
-		<hr class=" dark:border-gray-850 my-1" />
+		<hr class="dark:border-gray-850" />
 
 		<div class="space-y-2" />
 		<div>
@@ -562,6 +579,39 @@
 
 		<hr class=" dark:border-gray-850" />
 
+		<div class="">
+			<div class="text-sm font-medium">{$i18n.t('Content Extraction')}</div>
+
+			<div class="flex w-full justify-between mt-2">
+				<div class="self-center text-xs font-medium">{$i18n.t('Engine')}</div>
+				<div class="flex items-center relative">
+					<select
+						class="dark:bg-gray-900 w-fit pr-8 rounded px-2 p-1 text-xs bg-transparent outline-none text-right"
+						bind:value={contentExtractionEngine}
+						on:change={(e) => {
+							showTikaServerUrl = e.target.value === 'tika';
+						}}
+					>
+						<option value="">{$i18n.t('Default')} </option>
+						<option value="tika">{$i18n.t('Tika')}</option>
+					</select>
+				</div>
+			</div>
+
+			{#if showTikaServerUrl}
+				<div class="flex w-full mt-2">
+					<div class="flex-1 mr-2">
+						<input
+							class="w-full rounded-lg py-2 px-4 text-sm dark:text-gray-300 dark:bg-gray-850 outline-none"
+							placeholder={$i18n.t('Enter Tika Server URL')}
+							bind:value={tikaServerUrl}
+						/>
+					</div>
+				</div>
+			{/if}
+		</div>
+		<hr class=" dark:border-gray-850" />
+
 		<div class=" ">
 			<div class=" text-sm font-medium">{$i18n.t('Query Params')}</div>
 

src/lib/components/admin/Settings/Pipelines.svelte

@@ -19,6 +19,7 @@
 	} from '$lib/apis';
 
 	import Spinner from '$lib/components/common/Spinner.svelte';
+	import Switch from '$lib/components/common/Switch.svelte';
 
 	const i18n: Writable<i18nType> = getContext('i18n');
 
@@ -476,15 +477,40 @@
 												</div>
 
 												{#if (valves[property] ?? null) !== null}
-													<div class="flex mt-0.5 space-x-2">
+													<!-- {valves[property]} -->
+													<div class="flex mt-0.5 mb-1.5 space-x-2">
 														<div class=" flex-1">
+															{#if valves_spec.properties[property]?.enum ?? null}
+																<select
+																	class="w-full rounded-lg py-2 px-4 text-sm dark:text-gray-300 dark:bg-gray-850 outline-none"
+																	bind:value={valves[property]}
+																>
+																	{#each valves_spec.properties[property].enum as option}
+																		<option value={option} selected={option === valves[property]}>
+																			{option}
+																		</option>
+																	{/each}
+																</select>
+															{:else if (valves_spec.properties[property]?.type ?? null) === 'boolean'}
+																<div class="flex justify-between items-center">
+																	<div class="text-xs text-gray-500">
+																		{valves[property] ? 'Enabled' : 'Disabled'}
+																	</div>
+
+																	<div class=" pr-2">
+																		<Switch bind:state={valves[property]} />
+																	</div>
+																</div>
+															{:else}
 																<input
 																	class="w-full rounded-lg py-2 px-4 text-sm dark:text-gray-300 dark:bg-gray-850 outline-none"
 																	type="text"
 																	placeholder={valves_spec.properties[property].title}
 																	bind:value={valves[property]}
 																	autocomplete="off"
+																	required
 																/>
+															{/if}
 														</div>
 													</div>
 												{/if}

src/lib/components/chat/Chat.svelte

@@ -126,6 +126,27 @@
 		})();
 	}
 
+	const chatEventHandler = async (data) => {
+		if (data.chat_id === $chatId) {
+			await tick();
+			console.log(data);
+			let message = history.messages[data.message_id];
+
+			const status = {
+				done: data?.data?.done ?? null,
+				description: data?.data?.status ?? null
+			};
+
+			if (message.statusHistory) {
+				message.statusHistory.push(status);
+			} else {
+				message.statusHistory = [status];
+			}
+
+			messages = messages;
+		}
+	};
+
 	onMount(async () => {
 		const onMessageHandler = async (event) => {
 			if (event.origin === window.origin) {
@@ -163,6 +184,8 @@
 		};
 		window.addEventListener('message', onMessageHandler);
 
+		$socket.on('chat-events', chatEventHandler);
+
 		if (!$chatId) {
 			chatId.subscribe(async (value) => {
 				if (!value) {
@@ -177,6 +200,8 @@
 
 		return () => {
 			window.removeEventListener('message', onMessageHandler);
+
+			$socket.off('chat-events');
 		};
 	});
 
@@ -302,7 +327,7 @@
 		}
 	};
 
-	const chatCompletedHandler = async (modelId, messages) => {
+	const chatCompletedHandler = async (modelId, responseMessageId, messages) => {
 		await mermaid.run({
 			querySelector: '.mermaid'
 		});
@@ -316,7 +341,9 @@
 				info: m.info ? m.info : undefined,
 				timestamp: m.timestamp
 			})),
-			chat_id: $chatId
+			chat_id: $chatId,
+			session_id: $socket?.id,
+			id: responseMessageId
 		}).catch((error) => {
 			toast.error(error);
 			messages.at(-1).error = { content: error };
@@ -665,6 +692,7 @@
 		await tick();
 
 		const [res, controller] = await generateChatCompletion(localStorage.token, {
+			stream: true,
 			model: model.id,
 			messages: messagesBody,
 			options: {
@@ -682,8 +710,9 @@
 			keep_alive: $settings.keepAlive ?? undefined,
 			tool_ids: selectedToolIds.length > 0 ? selectedToolIds : undefined,
 			files: files.length > 0 ? files : undefined,
-			citations: files.length > 0 ? true : undefined,
-			chat_id: $chatId
+			session_id: $socket?.id,
+			chat_id: $chatId,
+			id: responseMessageId
 		});
 
 		if (res && res.ok) {
@@ -704,7 +733,7 @@
 						controller.abort('User: Stop Response');
 					} else {
 						const messages = createMessagesList(responseMessageId);
-						await chatCompletedHandler(model.id, messages);
+						await chatCompletedHandler(model.id, responseMessageId, messages);
 					}
 
 					_response = responseMessage.content;
@@ -912,8 +941,8 @@
 			const [res, controller] = await generateOpenAIChatCompletion(
 				localStorage.token,
 				{
-					model: model.id,
 					stream: true,
+					model: model.id,
 					stream_options:
 						model.info?.meta?.capabilities?.usage ?? false
 							? {
@@ -983,9 +1012,9 @@
 					max_tokens: $settings?.params?.max_tokens ?? undefined,
 					tool_ids: selectedToolIds.length > 0 ? selectedToolIds : undefined,
 					files: files.length > 0 ? files : undefined,
-					citations: files.length > 0 ? true : undefined,
-
-					chat_id: $chatId
+					session_id: $socket?.id,
+					chat_id: $chatId,
+					id: responseMessageId
 				},
 				`${WEBUI_BASE_URL}/api`
 			);
@@ -1014,7 +1043,7 @@
 						} else {
 							const messages = createMessagesList(responseMessageId);
 
-							await chatCompletedHandler(model.id, messages);
+							await chatCompletedHandler(model.id, responseMessageId, messages);
 						}
 
 						_response = responseMessage.content;