merged from main and added new translation keys
This commit is contained in:
Jannik Streidl
commit
aa3985e879
18
CHANGELOG.md
18
CHANGELOG.md
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@ -5,6 +5,24 @@ All notable changes to this project will be documented in this file.
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The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
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and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
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## [0.1.111] - 2024-03-10
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### Added
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- 🛡️ **Model Whitelisting**: Admins now have the ability to whitelist models for users with the 'user' role.
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- 🔄 **Update All Models**: Added a convenient button to update all models at once.
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- 📄 **Toggle PDF OCR**: Users can now toggle PDF OCR option for improved parsing performance.
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- 🎨 **DALL-E Integration**: Introduced DALL-E integration for image generation alongside automatic1111.
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- 🛠️ **RAG API Refactoring**: Refactored RAG logic and exposed its API, with additional documentation to follow.
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### Fixed
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- 🔒 **Max Token Settings**: Added max token settings for anthropic/claude-3-sonnet-20240229 (Issue #1094).
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- 🔧 **Misalignment Issue**: Corrected misalignment of Edit and Delete Icons when Chat Title is Empty (Issue #1104).
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- 🔄 **Context Loss Fix**: Resolved RAG losing context on model response regeneration with Groq models via API key (Issue #1105).
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- 📁 **File Handling Bug**: Addressed File Not Found Notification when Dropping a Conversation Element (Issue #1098).
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- 🖱️ **Dragged File Styling**: Fixed dragged file layover styling issue.
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## [0.1.110] - 2024-03-06
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### Added
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@ -179,20 +179,26 @@ def merge_models_lists(model_lists):
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async def get_all_models():
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print("get_all_models")
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tasks = [
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fetch_url(f"{url}/models", app.state.OPENAI_API_KEYS[idx])
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for idx, url in enumerate(app.state.OPENAI_API_BASE_URLS)
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]
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responses = await asyncio.gather(*tasks)
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responses = list(filter(lambda x: x is not None and "error" not in x, responses))
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models = {
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"data": merge_models_lists(
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list(map(lambda response: response["data"], responses))
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)
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}
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app.state.MODELS = {model["id"]: model for model in models["data"]}
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return models
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if len(app.state.OPENAI_API_KEYS) == 1 and app.state.OPENAI_API_KEYS[0] == "":
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models = {"data": []}
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else:
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tasks = [
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fetch_url(f"{url}/models", app.state.OPENAI_API_KEYS[idx])
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for idx, url in enumerate(app.state.OPENAI_API_BASE_URLS)
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]
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responses = await asyncio.gather(*tasks)
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responses = list(
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filter(lambda x: x is not None and "error" not in x, responses)
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)
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models = {
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"data": merge_models_lists(
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list(map(lambda response: response["data"], responses))
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)
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}
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app.state.MODELS = {model["id"]: model for model in models["data"]}
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return models
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@app.get("/models")
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@ -77,6 +77,7 @@ from constants import ERROR_MESSAGES
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app = FastAPI()
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app.state.PDF_EXTRACT_IMAGES = False
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app.state.CHUNK_SIZE = CHUNK_SIZE
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app.state.CHUNK_OVERLAP = CHUNK_OVERLAP
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app.state.RAG_TEMPLATE = RAG_TEMPLATE
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@ -184,12 +185,15 @@ async def update_embedding_model(
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}
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@app.get("/chunk")
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async def get_chunk_params(user=Depends(get_admin_user)):
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@app.get("/config")
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async def get_rag_config(user=Depends(get_admin_user)):
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return {
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"status": True,
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"chunk_size": app.state.CHUNK_SIZE,
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"chunk_overlap": app.state.CHUNK_OVERLAP,
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"pdf_extract_images": app.state.PDF_EXTRACT_IMAGES,
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"chunk": {
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"chunk_size": app.state.CHUNK_SIZE,
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"chunk_overlap": app.state.CHUNK_OVERLAP,
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},
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}
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@ -198,17 +202,24 @@ class ChunkParamUpdateForm(BaseModel):
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chunk_overlap: int
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@app.post("/chunk/update")
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async def update_chunk_params(
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form_data: ChunkParamUpdateForm, user=Depends(get_admin_user)
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):
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app.state.CHUNK_SIZE = form_data.chunk_size
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app.state.CHUNK_OVERLAP = form_data.chunk_overlap
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class ConfigUpdateForm(BaseModel):
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pdf_extract_images: bool
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chunk: ChunkParamUpdateForm
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@app.post("/config/update")
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async def update_rag_config(form_data: ConfigUpdateForm, user=Depends(get_admin_user)):
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app.state.PDF_EXTRACT_IMAGES = form_data.pdf_extract_images
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app.state.CHUNK_SIZE = form_data.chunk.chunk_size
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app.state.CHUNK_OVERLAP = form_data.chunk.chunk_overlap
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return {
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"status": True,
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"chunk_size": app.state.CHUNK_SIZE,
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"chunk_overlap": app.state.CHUNK_OVERLAP,
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"pdf_extract_images": app.state.PDF_EXTRACT_IMAGES,
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"chunk": {
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"chunk_size": app.state.CHUNK_SIZE,
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"chunk_overlap": app.state.CHUNK_OVERLAP,
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},
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}
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@ -364,7 +375,7 @@ def get_loader(filename: str, file_content_type: str, file_path: str):
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]
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if file_ext == "pdf":
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loader = PyPDFLoader(file_path, extract_images=True)
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loader = PyPDFLoader(file_path, extract_images=app.state.PDF_EXTRACT_IMAGES)
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elif file_ext == "csv":
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loader = CSVLoader(file_path)
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elif file_ext == "rst":
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@ -95,3 +95,89 @@ def rag_template(template: str, context: str, query: str):
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template = re.sub(r"\[query\]", query, template)
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return template
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def rag_messages(docs, messages, template, k, embedding_function):
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print(docs)
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last_user_message_idx = None
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for i in range(len(messages) - 1, -1, -1):
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if messages[i]["role"] == "user":
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last_user_message_idx = i
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break
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user_message = messages[last_user_message_idx]
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if isinstance(user_message["content"], list):
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# Handle list content input
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content_type = "list"
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query = ""
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for content_item in user_message["content"]:
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if content_item["type"] == "text":
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query = content_item["text"]
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break
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elif isinstance(user_message["content"], str):
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# Handle text content input
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content_type = "text"
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query = user_message["content"]
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else:
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# Fallback in case the input does not match expected types
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content_type = None
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query = ""
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relevant_contexts = []
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for doc in docs:
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context = None
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try:
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if doc["type"] == "collection":
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context = query_collection(
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collection_names=doc["collection_names"],
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query=query,
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k=k,
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embedding_function=embedding_function,
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)
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else:
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context = query_doc(
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collection_name=doc["collection_name"],
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query=query,
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k=k,
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embedding_function=embedding_function,
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)
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except Exception as e:
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print(e)
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context = None
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relevant_contexts.append(context)
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context_string = ""
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for context in relevant_contexts:
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if context:
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context_string += " ".join(context["documents"][0]) + "\n"
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ra_content = rag_template(
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template=template,
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context=context_string,
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query=query,
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)
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if content_type == "list":
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new_content = []
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for content_item in user_message["content"]:
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if content_item["type"] == "text":
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# Update the text item's content with ra_content
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new_content.append({"type": "text", "text": ra_content})
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else:
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# Keep other types of content as they are
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new_content.append(content_item)
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new_user_message = {**user_message, "content": new_content}
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else:
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new_user_message = {
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**user_message,
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"content": ra_content,
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}
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messages[last_user_message_idx] = new_user_message
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return messages
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@ -209,10 +209,6 @@ OLLAMA_API_BASE_URL = os.environ.get(
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OLLAMA_BASE_URL = os.environ.get("OLLAMA_BASE_URL", "")
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if ENV == "prod":
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if OLLAMA_BASE_URL == "/ollama":
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OLLAMA_BASE_URL = "http://host.docker.internal:11434"
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if OLLAMA_BASE_URL == "" and OLLAMA_API_BASE_URL != "":
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OLLAMA_BASE_URL = (
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@ -221,6 +217,11 @@ if OLLAMA_BASE_URL == "" and OLLAMA_API_BASE_URL != "":
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else OLLAMA_API_BASE_URL
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)
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if ENV == "prod":
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if OLLAMA_BASE_URL == "/ollama":
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OLLAMA_BASE_URL = "http://host.docker.internal:11434"
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OLLAMA_BASE_URLS = os.environ.get("OLLAMA_BASE_URLS", "")
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OLLAMA_BASE_URLS = OLLAMA_BASE_URLS if OLLAMA_BASE_URLS != "" else OLLAMA_BASE_URL
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@ -234,8 +235,6 @@ OLLAMA_BASE_URLS = [url.strip() for url in OLLAMA_BASE_URLS.split(";")]
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OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY", "")
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OPENAI_API_BASE_URL = os.environ.get("OPENAI_API_BASE_URL", "")
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if OPENAI_API_KEY == "":
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OPENAI_API_KEY = "none"
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if OPENAI_API_BASE_URL == "":
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OPENAI_API_BASE_URL = "https://api.openai.com/v1"
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|
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@ -1,4 +1,5 @@
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{
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"version": "0.0.1",
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"ui": {
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"prompt_suggestions": [
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{
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|
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132
backend/main.py
132
backend/main.py
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@ -28,7 +28,7 @@ from typing import List
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from utils.utils import get_admin_user
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from apps.rag.utils import query_doc, query_collection, rag_template
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from apps.rag.utils import rag_messages
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from config import (
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WEBUI_NAME,
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@ -60,19 +60,6 @@ app.state.MODEL_FILTER_LIST = MODEL_FILTER_LIST
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origins = ["*"]
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app.add_middleware(
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CORSMiddleware,
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allow_origins=origins,
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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@app.on_event("startup")
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async def on_startup():
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await litellm_app_startup()
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class RAGMiddleware(BaseHTTPMiddleware):
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async def dispatch(self, request: Request, call_next):
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@ -91,98 +78,33 @@ class RAGMiddleware(BaseHTTPMiddleware):
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# Example: Add a new key-value pair or modify existing ones
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# data["modified"] = True # Example modification
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if "docs" in data:
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docs = data["docs"]
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print(docs)
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last_user_message_idx = None
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for i in range(len(data["messages"]) - 1, -1, -1):
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if data["messages"][i]["role"] == "user":
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last_user_message_idx = i
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break
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user_message = data["messages"][last_user_message_idx]
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|
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if isinstance(user_message["content"], list):
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# Handle list content input
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content_type = "list"
|
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query = ""
|
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for content_item in user_message["content"]:
|
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if content_item["type"] == "text":
|
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query = content_item["text"]
|
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break
|
||||
elif isinstance(user_message["content"], str):
|
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# Handle text content input
|
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content_type = "text"
|
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query = user_message["content"]
|
||||
else:
|
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# Fallback in case the input does not match expected types
|
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content_type = None
|
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query = ""
|
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|
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relevant_contexts = []
|
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|
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for doc in docs:
|
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context = None
|
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|
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try:
|
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if doc["type"] == "collection":
|
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context = query_collection(
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collection_names=doc["collection_names"],
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query=query,
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k=rag_app.state.TOP_K,
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embedding_function=rag_app.state.sentence_transformer_ef,
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)
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else:
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context = query_doc(
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collection_name=doc["collection_name"],
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query=query,
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k=rag_app.state.TOP_K,
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embedding_function=rag_app.state.sentence_transformer_ef,
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)
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except Exception as e:
|
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print(e)
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context = None
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|
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relevant_contexts.append(context)
|
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|
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context_string = ""
|
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for context in relevant_contexts:
|
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if context:
|
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context_string += " ".join(context["documents"][0]) + "\n"
|
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|
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ra_content = rag_template(
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template=rag_app.state.RAG_TEMPLATE,
|
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context=context_string,
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query=query,
|
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data = {**data}
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data["messages"] = rag_messages(
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data["docs"],
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data["messages"],
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rag_app.state.RAG_TEMPLATE,
|
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rag_app.state.TOP_K,
|
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rag_app.state.sentence_transformer_ef,
|
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)
|
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|
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if content_type == "list":
|
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new_content = []
|
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for content_item in user_message["content"]:
|
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if content_item["type"] == "text":
|
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# Update the text item's content with ra_content
|
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new_content.append({"type": "text", "text": ra_content})
|
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else:
|
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# Keep other types of content as they are
|
||||
new_content.append(content_item)
|
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new_user_message = {**user_message, "content": new_content}
|
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else:
|
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new_user_message = {
|
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**user_message,
|
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"content": ra_content,
|
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}
|
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|
||||
data["messages"][last_user_message_idx] = new_user_message
|
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del data["docs"]
|
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|
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print(data["messages"])
|
||||
|
||||
modified_body_bytes = json.dumps(data).encode("utf-8")
|
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|
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# Create a new request with the modified body
|
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scope = request.scope
|
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scope["body"] = modified_body_bytes
|
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request = Request(scope, receive=lambda: self._receive(modified_body_bytes))
|
||||
# Replace the request body with the modified one
|
||||
request._body = modified_body_bytes
|
||||
|
||||
# Set custom header to ensure content-length matches new body length
|
||||
request.headers.__dict__["_list"] = [
|
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(b"content-length", str(len(modified_body_bytes)).encode("utf-8")),
|
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*[
|
||||
(k, v)
|
||||
for k, v in request.headers.raw
|
||||
if k.lower() != b"content-length"
|
||||
],
|
||||
]
|
||||
|
||||
response = await call_next(request)
|
||||
return response
|
||||
|
|
@ -194,6 +116,15 @@ class RAGMiddleware(BaseHTTPMiddleware):
|
|||
app.add_middleware(RAGMiddleware)
|
||||
|
||||
|
||||
app.add_middleware(
|
||||
CORSMiddleware,
|
||||
allow_origins=origins,
|
||||
allow_credentials=True,
|
||||
allow_methods=["*"],
|
||||
allow_headers=["*"],
|
||||
)
|
||||
|
||||
|
||||
@app.middleware("http")
|
||||
async def check_url(request: Request, call_next):
|
||||
start_time = int(time.time())
|
||||
|
|
@ -204,6 +135,11 @@ async def check_url(request: Request, call_next):
|
|||
return response
|
||||
|
||||
|
||||
@app.on_event("startup")
|
||||
async def on_startup():
|
||||
await litellm_app_startup()
|
||||
|
||||
|
||||
app.mount("/api/v1", webui_app)
|
||||
app.mount("/litellm/api", litellm_app)
|
||||
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
{
|
||||
"name": "open-webui",
|
||||
"version": "0.1.110",
|
||||
"version": "0.1.111",
|
||||
"private": true,
|
||||
"scripts": {
|
||||
"dev": "vite dev --host",
|
||||
|
|
|
|||
|
|
@ -1,9 +1,9 @@
|
|||
import { RAG_API_BASE_URL } from '$lib/constants';
|
||||
|
||||
export const getChunkParams = async (token: string) => {
|
||||
export const getRAGConfig = async (token: string) => {
|
||||
let error = null;
|
||||
|
||||
const res = await fetch(`${RAG_API_BASE_URL}/chunk`, {
|
||||
const res = await fetch(`${RAG_API_BASE_URL}/config`, {
|
||||
method: 'GET',
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
|
|
@ -27,18 +27,27 @@ export const getChunkParams = async (token: string) => {
|
|||
return res;
|
||||
};
|
||||
|
||||
export const updateChunkParams = async (token: string, size: number, overlap: number) => {
|
||||
type ChunkConfigForm = {
|
||||
chunk_size: number;
|
||||
chunk_overlap: number;
|
||||
};
|
||||
|
||||
type RAGConfigForm = {
|
||||
pdf_extract_images: boolean;
|
||||
chunk: ChunkConfigForm;
|
||||
};
|
||||
|
||||
export const updateRAGConfig = async (token: string, payload: RAGConfigForm) => {
|
||||
let error = null;
|
||||
|
||||
const res = await fetch(`${RAG_API_BASE_URL}/chunk/update`, {
|
||||
const res = await fetch(`${RAG_API_BASE_URL}/config/update`, {
|
||||
method: 'POST',
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
Authorization: `Bearer ${token}`
|
||||
},
|
||||
body: JSON.stringify({
|
||||
chunk_size: size,
|
||||
chunk_overlap: overlap
|
||||
...payload
|
||||
})
|
||||
})
|
||||
.then(async (res) => {
|
||||
|
|
|
|||
|
|
@ -14,6 +14,7 @@
|
|||
import { splitStream } from '$lib/utils';
|
||||
import { onMount, getContext } from 'svelte';
|
||||
import { addLiteLLMModel, deleteLiteLLMModel, getLiteLLMModelInfo } from '$lib/apis/litellm';
|
||||
import Tooltip from '$lib/components/common/Tooltip.svelte';
|
||||
|
||||
const i18n = getContext('i18n');
|
||||
|
||||
|
|
@ -39,6 +40,10 @@
|
|||
|
||||
let OLLAMA_URLS = [];
|
||||
let selectedOllamaUrlIdx: string | null = null;
|
||||
|
||||
let updateModelId = null;
|
||||
let updateProgress = null;
|
||||
|
||||
let showExperimentalOllama = false;
|
||||
let ollamaVersion = '';
|
||||
const MAX_PARALLEL_DOWNLOADS = 3;
|
||||
|
|
@ -63,6 +68,71 @@
|
|||
|
||||
let deleteModelTag = '';
|
||||
|
||||
const updateModelsHandler = async () => {
|
||||
for (const model of $models.filter(
|
||||
(m) =>
|
||||
m.size != null &&
|
||||
(selectedOllamaUrlIdx === null ? true : (m?.urls ?? []).includes(selectedOllamaUrlIdx))
|
||||
)) {
|
||||
console.log(model);
|
||||
|
||||
updateModelId = model.id;
|
||||
const res = await pullModel(localStorage.token, model.id, selectedOllamaUrlIdx).catch(
|
||||
(error) => {
|
||||
toast.error(error);
|
||||
return null;
|
||||
}
|
||||
);
|
||||
|
||||
if (res) {
|
||||
const reader = res.body
|
||||
.pipeThrough(new TextDecoderStream())
|
||||
.pipeThrough(splitStream('\n'))
|
||||
.getReader();
|
||||
|
||||
while (true) {
|
||||
try {
|
||||
const { value, done } = await reader.read();
|
||||
if (done) break;
|
||||
|
||||
let lines = value.split('\n');
|
||||
|
||||
for (const line of lines) {
|
||||
if (line !== '') {
|
||||
let data = JSON.parse(line);
|
||||
|
||||
console.log(data);
|
||||
if (data.error) {
|
||||
throw data.error;
|
||||
}
|
||||
if (data.detail) {
|
||||
throw data.detail;
|
||||
}
|
||||
if (data.status) {
|
||||
if (data.digest) {
|
||||
updateProgress = 0;
|
||||
if (data.completed) {
|
||||
updateProgress = Math.round((data.completed / data.total) * 1000) / 10;
|
||||
} else {
|
||||
updateProgress = 100;
|
||||
}
|
||||
} else {
|
||||
toast.success(data.status);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
} catch (error) {
|
||||
console.log(error);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
updateModelId = null;
|
||||
updateProgress = null;
|
||||
};
|
||||
|
||||
const pullModelHandler = async () => {
|
||||
const sanitizedModelTag = modelTag.trim();
|
||||
if (modelDownloadStatus[sanitizedModelTag]) {
|
||||
|
|
@ -389,7 +459,7 @@
|
|||
return [];
|
||||
});
|
||||
|
||||
if (OLLAMA_URLS.length > 1) {
|
||||
if (OLLAMA_URLS.length > 0) {
|
||||
selectedOllamaUrlIdx = 0;
|
||||
}
|
||||
|
||||
|
|
@ -404,18 +474,51 @@
|
|||
<div class="space-y-2 pr-1.5">
|
||||
<div class="text-sm font-medium">{$i18n.t('Manage Ollama Models')}</div>
|
||||
|
||||
{#if OLLAMA_URLS.length > 1}
|
||||
<div class="flex-1 pb-1">
|
||||
<select
|
||||
class="w-full rounded-lg py-2 px-4 text-sm dark:text-gray-300 dark:bg-gray-850 outline-none"
|
||||
bind:value={selectedOllamaUrlIdx}
|
||||
placeholder={$i18n.t('Select an Ollama instance')}
|
||||
>
|
||||
{#each OLLAMA_URLS as url, idx}
|
||||
<option value={idx} class="bg-gray-100 dark:bg-gray-700">{url}</option>
|
||||
{/each}
|
||||
</select>
|
||||
{#if OLLAMA_URLS.length > 0}
|
||||
<div class="flex gap-2">
|
||||
<div class="flex-1 pb-1">
|
||||
<select
|
||||
class="w-full rounded-lg py-2 px-4 text-sm dark:text-gray-300 dark:bg-gray-850 outline-none"
|
||||
bind:value={selectedOllamaUrlIdx}
|
||||
placeholder="Select an Ollama instance"
|
||||
>
|
||||
{#each OLLAMA_URLS as url, idx}
|
||||
<option value={idx} class="bg-gray-100 dark:bg-gray-700">{url}</option>
|
||||
{/each}
|
||||
</select>
|
||||
</div>
|
||||
|
||||
<div>
|
||||
<div class="flex w-full justify-end">
|
||||
<Tooltip content="Update All Models" placement="top">
|
||||
<button
|
||||
class="p-2.5 flex gap-2 items-center bg-gray-100 hover:bg-gray-200 text-gray-800 dark:bg-gray-850 dark:hover:bg-gray-800 dark:text-gray-100 rounded-lg transition"
|
||||
on:click={() => {
|
||||
updateModelsHandler();
|
||||
}}
|
||||
>
|
||||
<svg
|
||||
xmlns="http://www.w3.org/2000/svg"
|
||||
viewBox="0 0 16 16"
|
||||
fill="currentColor"
|
||||
class="w-4 h-4"
|
||||
>
|
||||
<path
|
||||
d="M7 1a.75.75 0 0 1 .75.75V6h-1.5V1.75A.75.75 0 0 1 7 1ZM6.25 6v2.94L5.03 7.72a.75.75 0 0 0-1.06 1.06l2.5 2.5a.75.75 0 0 0 1.06 0l2.5-2.5a.75.75 0 1 0-1.06-1.06L7.75 8.94V6H10a2 2 0 0 1 2 2v3a2 2 0 0 1-2 2H4a2 2 0 0 1-2-2V8a2 2 0 0 1 2-2h2.25Z"
|
||||
/>
|
||||
<path
|
||||
d="M4.268 14A2 2 0 0 0 6 15h6a2 2 0 0 0 2-2v-3a2 2 0 0 0-1-1.732V11a3 3 0 0 1-3 3H4.268Z"
|
||||
/>
|
||||
</svg>
|
||||
</button>
|
||||
</Tooltip>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
{#if updateModelId}
|
||||
Updating "{updateModelId}" {updateProgress ? `(${updateProgress}%)` : ''}
|
||||
{/if}
|
||||
{/if}
|
||||
|
||||
<div class="space-y-2">
|
||||
|
|
|
|||
|
|
@ -1,10 +1,10 @@
|
|||
<script lang="ts">
|
||||
import { getDocs } from '$lib/apis/documents';
|
||||
import {
|
||||
getChunkParams,
|
||||
getRAGConfig,
|
||||
updateRAGConfig,
|
||||
getQuerySettings,
|
||||
scanDocs,
|
||||
updateChunkParams,
|
||||
updateQuerySettings
|
||||
} from '$lib/apis/rag';
|
||||
import { documents } from '$lib/stores';
|
||||
|
|
@ -19,6 +19,7 @@
|
|||
|
||||
let chunkSize = 0;
|
||||
let chunkOverlap = 0;
|
||||
let pdfExtractImages = true;
|
||||
|
||||
let querySettings = {
|
||||
template: '',
|
||||
|
|
@ -37,16 +38,24 @@
|
|||
};
|
||||
|
||||
const submitHandler = async () => {
|
||||
const res = await updateChunkParams(localStorage.token, chunkSize, chunkOverlap);
|
||||
const res = await updateRAGConfig(localStorage.token, {
|
||||
pdf_extract_images: pdfExtractImages,
|
||||
chunk: {
|
||||
chunk_overlap: chunkOverlap,
|
||||
chunk_size: chunkSize
|
||||
}
|
||||
});
|
||||
querySettings = await updateQuerySettings(localStorage.token, querySettings);
|
||||
};
|
||||
|
||||
onMount(async () => {
|
||||
const res = await getChunkParams(localStorage.token);
|
||||
const res = await getRAGConfig(localStorage.token);
|
||||
|
||||
if (res) {
|
||||
chunkSize = res.chunk_size;
|
||||
chunkOverlap = res.chunk_overlap;
|
||||
pdfExtractImages = res.pdf_extract_images;
|
||||
|
||||
chunkSize = res.chunk.chunk_size;
|
||||
chunkOverlap = res.chunk.chunk_overlap;
|
||||
}
|
||||
|
||||
querySettings = await getQuerySettings(localStorage.token);
|
||||
|
|
@ -163,6 +172,22 @@
|
|||
</div>
|
||||
</div>
|
||||
|
||||
<div>
|
||||
<div class="flex justify-between items-center text-xs">
|
||||
<div class=" text-xs font-medium">{$i18n.t('PDF Extract Images (OCR)')}</div>
|
||||
|
||||
<button
|
||||
class=" text-xs font-medium text-gray-500"
|
||||
type="button"
|
||||
on:click={() => {
|
||||
pdfExtractImages = !pdfExtractImages;
|
||||
}}>{pdfExtractImages ? $i18n.t('On') : $i18n.t('Off')}</button
|
||||
>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<div>
|
||||
<div class=" text-sm font-medium">{$i18n.t('Query Params')}</div>
|
||||
|
||||
<div class=" flex">
|
||||
|
|
@ -182,19 +207,19 @@
|
|||
</div>
|
||||
|
||||
<!-- <div class="flex w-full">
|
||||
<div class=" self-center text-xs font-medium min-w-fit">Chunk Overlap</div>
|
||||
|
||||
<div class="self-center p-3">
|
||||
<input
|
||||
class="w-full rounded py-1.5 px-4 text-sm dark:text-gray-300 dark:bg-gray-800 outline-none border border-gray-100 dark:border-gray-600"
|
||||
type="number"
|
||||
placeholder="Enter Chunk Overlap"
|
||||
bind:value={chunkOverlap}
|
||||
autocomplete="off"
|
||||
min="0"
|
||||
/>
|
||||
</div>
|
||||
</div> -->
|
||||
<div class=" self-center text-xs font-medium min-w-fit">Chunk Overlap</div>
|
||||
|
||||
<div class="self-center p-3">
|
||||
<input
|
||||
class="w-full rounded py-1.5 px-4 text-sm dark:text-gray-300 dark:bg-gray-800 outline-none border border-gray-100 dark:border-gray-600"
|
||||
type="number"
|
||||
placeholder="Enter Chunk Overlap"
|
||||
bind:value={chunkOverlap}
|
||||
autocomplete="off"
|
||||
min="0"
|
||||
/>
|
||||
</div>
|
||||
</div> -->
|
||||
</div>
|
||||
|
||||
<div>
|
||||
|
|
|
|||
|
|
@ -236,7 +236,7 @@
|
|||
"Prompts": "Prompts",
|
||||
"Pull a model from Ollama.com": "Ein Modell von Ollama.com abrufen",
|
||||
"Pull Progress": "Fortschritt abrufen",
|
||||
"Query Params": "",
|
||||
"Query Params": "Query Parameter",
|
||||
"RAG Template": "RAG-Vorlage",
|
||||
"Raw Format": "Rohformat",
|
||||
"Record voice": "Stimme aufnehmen",
|
||||
|
|
@ -346,5 +346,6 @@
|
|||
"Write a summary in 50 words that summarizes [topic or keyword].": "Schreibe eine kurze Zusammenfassung in 50 Wörtern, die [Thema oder Schlüsselwort] zusammenfasst.",
|
||||
"You": "Du",
|
||||
"You're a helpful assistant.": "Du bist ein hilfreicher Assistent.",
|
||||
"You're now logged in.": "Du bist nun eingeloggt."
|
||||
"You're now logged in.": "Du bist nun eingeloggt.",
|
||||
"PDF Extract Images (OCR)": "Text von Bilder aus PDFs extrahieren (OCR)"
|
||||
}
|
||||
|
|
|
|||
|
|
@ -236,7 +236,7 @@
|
|||
"Prompts": "Prompts",
|
||||
"Pull a model from Ollama.com": "Pull a model from Ollama.com",
|
||||
"Pull Progress": "Pull Progress",
|
||||
"Query Params": "",
|
||||
"Query Params": "Query Params",
|
||||
"RAG Template": "RAG Template",
|
||||
"Raw Format": "Raw Format",
|
||||
"Record voice": "Record voice",
|
||||
|
|
@ -346,5 +346,6 @@
|
|||
"Write a summary in 50 words that summarizes [topic or keyword].": "Write a summary in 50 words that summarizes [topic or keyword].",
|
||||
"You": "You",
|
||||
"You're a helpful assistant.": "You're a helpful assistant.",
|
||||
"You're now logged in.": "You're now logged in."
|
||||
"You're now logged in.": "You're now logged in.",
|
||||
"PDF Extract Images (OCR)": "PDF Extract Images (OCR)"
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in New Issue