mirror of
https://github.com/open-webui/open-webui
synced 2024-11-22 08:07:55 +00:00
feat: external embeddings support
This commit is contained in:
Timothy J. Baek
parent
8b10b058e5
commit
2952e61167
@ -654,6 +654,55 @@ async def generate_embeddings(
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)
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def generate_ollama_embeddings(
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form_data: GenerateEmbeddingsForm,
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url_idx: Optional[int] = None,
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):
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if url_idx == None:
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model = form_data.model
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if ":" not in model:
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model = f"{model}:latest"
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if model in app.state.MODELS:
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url_idx = random.choice(app.state.MODELS[model]["urls"])
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else:
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raise HTTPException(
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status_code=400,
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detail=ERROR_MESSAGES.MODEL_NOT_FOUND(form_data.model),
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)
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url = app.state.OLLAMA_BASE_URLS[url_idx]
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log.info(f"url: {url}")
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try:
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r = requests.request(
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method="POST",
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url=f"{url}/api/embeddings",
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data=form_data.model_dump_json(exclude_none=True).encode(),
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)
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r.raise_for_status()
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data = r.json()
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if "embedding" in data:
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return data["embedding"]
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else:
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raise "Something went wrong :/"
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except Exception as e:
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log.exception(e)
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error_detail = "Open WebUI: Server Connection Error"
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if r is not None:
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try:
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res = r.json()
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if "error" in res:
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error_detail = f"Ollama: {res['error']}"
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except:
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error_detail = f"Ollama: {e}"
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raise error_detail
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class GenerateCompletionForm(BaseModel):
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model: str
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prompt: str
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@ -39,13 +39,21 @@ import uuid
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import json
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from apps.ollama.main import generate_ollama_embeddings
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from apps.web.models.documents import (
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Documents,
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DocumentForm,
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DocumentResponse,
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)
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from apps.rag.utils import query_doc, query_collection, get_embedding_model_path
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from apps.rag.utils import (
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query_doc,
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query_embeddings_doc,
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query_collection,
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query_embeddings_collection,
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get_embedding_model_path,
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)
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from utils.misc import (
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calculate_sha256,
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@ -58,6 +66,7 @@ from config import (
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SRC_LOG_LEVELS,
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UPLOAD_DIR,
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DOCS_DIR,
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RAG_EMBEDDING_ENGINE,
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RAG_EMBEDDING_MODEL,
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RAG_EMBEDDING_MODEL_AUTO_UPDATE,
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DEVICE_TYPE,
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@ -74,17 +83,20 @@ log.setLevel(SRC_LOG_LEVELS["RAG"])
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app = FastAPI()
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app.state.PDF_EXTRACT_IMAGES = False
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app.state.TOP_K = 4
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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_EMBEDDING_ENGINE = RAG_EMBEDDING_ENGINE
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app.state.RAG_EMBEDDING_MODEL = RAG_EMBEDDING_MODEL
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app.state.RAG_TEMPLATE = RAG_TEMPLATE
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app.state.RAG_EMBEDDING_MODEL = RAG_EMBEDDING_MODEL
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app.state.PDF_EXTRACT_IMAGES = False
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app.state.TOP_K = 4
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app.state.sentence_transformer_ef = (
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embedding_functions.SentenceTransformerEmbeddingFunction(
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model_name=get_embedding_model_path(
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@ -121,6 +133,7 @@ async def get_status():
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"chunk_size": app.state.CHUNK_SIZE,
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"chunk_overlap": app.state.CHUNK_OVERLAP,
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"template": app.state.RAG_TEMPLATE,
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"embedding_engine": app.state.RAG_EMBEDDING_ENGINE,
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"embedding_model": app.state.RAG_EMBEDDING_MODEL,
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}
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@ -252,12 +265,23 @@ def query_doc_handler(
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):
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try:
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return query_doc(
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collection_name=form_data.collection_name,
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query=form_data.query,
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k=form_data.k if form_data.k else app.state.TOP_K,
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embedding_function=app.state.sentence_transformer_ef,
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)
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if app.state.RAG_EMBEDDING_ENGINE == "ollama":
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query_embeddings = generate_ollama_embeddings(
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{"model": app.state.RAG_EMBEDDING_MODEL, "prompt": form_data.query}
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)
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return query_embeddings_doc(
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collection_name=form_data.collection_name,
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query_embeddings=query_embeddings,
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k=form_data.k if form_data.k else app.state.TOP_K,
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)
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else:
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return query_doc(
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collection_name=form_data.collection_name,
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query=form_data.query,
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k=form_data.k if form_data.k else app.state.TOP_K,
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embedding_function=app.state.sentence_transformer_ef,
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)
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except Exception as e:
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log.exception(e)
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raise HTTPException(
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@ -277,12 +301,30 @@ def query_collection_handler(
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form_data: QueryCollectionsForm,
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user=Depends(get_current_user),
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):
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return query_collection(
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collection_names=form_data.collection_names,
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query=form_data.query,
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k=form_data.k if form_data.k else app.state.TOP_K,
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embedding_function=app.state.sentence_transformer_ef,
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)
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try:
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if app.state.RAG_EMBEDDING_ENGINE == "ollama":
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query_embeddings = generate_ollama_embeddings(
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{"model": app.state.RAG_EMBEDDING_MODEL, "prompt": form_data.query}
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)
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return query_embeddings_collection(
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collection_names=form_data.collection_names,
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query_embeddings=query_embeddings,
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k=form_data.k if form_data.k else app.state.TOP_K,
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)
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else:
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return query_collection(
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collection_names=form_data.collection_names,
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query=form_data.query,
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k=form_data.k if form_data.k else app.state.TOP_K,
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embedding_function=app.state.sentence_transformer_ef,
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)
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except Exception as e:
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log.exception(e)
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raise HTTPException(
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status_code=status.HTTP_400_BAD_REQUEST,
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detail=ERROR_MESSAGES.DEFAULT(e),
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)
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@app.post("/web")
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@ -317,6 +359,7 @@ def store_data_in_vector_db(data, collection_name, overwrite: bool = False) -> b
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chunk_overlap=app.state.CHUNK_OVERLAP,
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add_start_index=True,
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)
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docs = text_splitter.split_documents(data)
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if len(docs) > 0:
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@ -337,7 +380,9 @@ def store_text_in_vector_db(
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return store_docs_in_vector_db(docs, collection_name, overwrite)
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def store_docs_in_vector_db(docs, collection_name, overwrite: bool = False) -> bool:
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async def store_docs_in_vector_db(
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docs, collection_name, overwrite: bool = False
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) -> bool:
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texts = [doc.page_content for doc in docs]
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metadatas = [doc.metadata for doc in docs]
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@ -349,20 +394,36 @@ def store_docs_in_vector_db(docs, collection_name, overwrite: bool = False) -> b
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log.info(f"deleting existing collection {collection_name}")
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CHROMA_CLIENT.delete_collection(name=collection_name)
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collection = CHROMA_CLIENT.create_collection(
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name=collection_name,
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embedding_function=app.state.sentence_transformer_ef,
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)
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if app.state.RAG_EMBEDDING_ENGINE == "ollama":
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collection = CHROMA_CLIENT.create_collection(name=collection_name)
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for batch in create_batches(
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api=CHROMA_CLIENT,
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ids=[str(uuid.uuid1()) for _ in texts],
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metadatas=metadatas,
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documents=texts,
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):
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collection.add(*batch)
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for batch in create_batches(
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api=CHROMA_CLIENT,
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ids=[str(uuid.uuid1()) for _ in texts],
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metadatas=metadatas,
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embeddings=[
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generate_ollama_embeddings(
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{"model": RAG_EMBEDDING_MODEL, "prompt": text}
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)
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for text in texts
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],
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):
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collection.add(*batch)
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else:
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collection = CHROMA_CLIENT.create_collection(
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name=collection_name,
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embedding_function=app.state.sentence_transformer_ef,
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)
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return True
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for batch in create_batches(
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api=CHROMA_CLIENT,
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ids=[str(uuid.uuid1()) for _ in texts],
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metadatas=metadatas,
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documents=texts,
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):
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collection.add(*batch)
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return True
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except Exception as e:
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log.exception(e)
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if e.__class__.__name__ == "UniqueConstraintError":
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@ -2,6 +2,9 @@ import os
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import re
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import logging
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from typing import List
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import requests
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from huggingface_hub import snapshot_download
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from config import SRC_LOG_LEVELS, CHROMA_CLIENT
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@ -26,6 +29,21 @@ def query_doc(collection_name: str, query: str, k: int, embedding_function):
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raise e
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def query_embeddings_doc(collection_name: str, query_embeddings, k: int):
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try:
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# if you use docker use the model from the environment variable
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collection = CHROMA_CLIENT.get_collection(
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name=collection_name,
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)
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result = collection.query(
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query_embeddings=[query_embeddings],
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n_results=k,
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)
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return result
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except Exception as e:
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raise e
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def merge_and_sort_query_results(query_results, k):
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# Initialize lists to store combined data
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combined_ids = []
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@ -96,6 +114,24 @@ def query_collection(
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return merge_and_sort_query_results(results, k)
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def query_embeddings_collection(collection_names: List[str], query_embeddings, k: int):
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results = []
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for collection_name in collection_names:
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try:
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collection = CHROMA_CLIENT.get_collection(name=collection_name)
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result = collection.query(
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query_embeddings=[query_embeddings],
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n_results=k,
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)
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results.append(result)
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except:
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pass
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return merge_and_sort_query_results(results, k)
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def rag_template(template: str, context: str, query: str):
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template = template.replace("[context]", context)
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template = template.replace("[query]", query)
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@ -405,6 +405,9 @@ if WEBUI_AUTH and WEBUI_SECRET_KEY == "":
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CHROMA_DATA_PATH = f"{DATA_DIR}/vector_db"
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# this uses the model defined in the Dockerfile ENV variable. If you dont use docker or docker based deployments such as k8s, the default embedding model will be used (all-MiniLM-L6-v2)
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RAG_EMBEDDING_ENGINE = os.environ.get("RAG_EMBEDDING_ENGINE", "")
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RAG_EMBEDDING_MODEL = os.environ.get("RAG_EMBEDDING_MODEL", "all-MiniLM-L6-v2")
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log.info(f"Embedding model set: {RAG_EMBEDDING_MODEL}"),
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@ -220,6 +220,32 @@ export const generatePrompt = async (token: string = '', model: string, conversa
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return res;
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};
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export const generateEmbeddings = async (token: string = '', model: string, text: string) => {
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let error = null;
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const res = await fetch(`${OLLAMA_API_BASE_URL}/api/embeddings`, {
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method: 'POST',
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headers: {
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Accept: 'application/json',
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'Content-Type': 'application/json',
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Authorization: `Bearer ${token}`
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},
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body: JSON.stringify({
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model: model,
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prompt: text
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})
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}).catch((err) => {
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error = err;
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return null;
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});
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if (error) {
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throw error;
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}
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return res;
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};
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export const generateTextCompletion = async (token: string = '', model: string, text: string) => {
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let error = null;
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@ -26,6 +26,7 @@
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let showResetConfirm = false;
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let embeddingEngine = '';
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let chunkSize = 0;
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let chunkOverlap = 0;
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let pdfExtractImages = true;
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@ -118,81 +119,119 @@
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<div>
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<div class=" mb-2 text-sm font-medium">{$i18n.t('General Settings')}</div>
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<div class=" flex w-full justify-between">
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<div class=" self-center text-xs font-medium">
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{$i18n.t('Scan for documents from {{path}}', { path: '/data/docs' })}
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<div class=" flex w-full justify-between">
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<div class=" self-center text-xs font-medium">{$i18n.t('Embedding Engine')}</div>
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<div class="flex items-center relative">
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<select
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class="dark:bg-gray-900 w-fit pr-8 rounded px-2 p-1 text-xs bg-transparent outline-none text-right"
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bind:value={embeddingEngine}
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placeholder="Select an embedding engine"
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>
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<option value="">{$i18n.t('Default (SentenceTransformer)')}</option>
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<option value="ollama">{$i18n.t('Ollama')}</option>
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</select>
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</div>
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<button
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class=" self-center text-xs p-1 px-3 bg-gray-100 dark:bg-gray-800 dark:hover:bg-gray-700 rounded-lg flex flex-row space-x-1 items-center {scanDirLoading
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? ' cursor-not-allowed'
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: ''}"
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on:click={() => {
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scanHandler();
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console.log('check');
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}}
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type="button"
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disabled={scanDirLoading}
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>
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<div class="self-center font-medium">{$i18n.t('Scan')}</div>
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{#if scanDirLoading}
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<div class="ml-3 self-center">
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<svg
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class=" w-3 h-3"
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viewBox="0 0 24 24"
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fill="currentColor"
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xmlns="http://www.w3.org/2000/svg"
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><style>
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.spinner_ajPY {
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transform-origin: center;
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animation: spinner_AtaB 0.75s infinite linear;
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}
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@keyframes spinner_AtaB {
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100% {
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transform: rotate(360deg);
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}
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}
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</style><path
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d="M12,1A11,11,0,1,0,23,12,11,11,0,0,0,12,1Zm0,19a8,8,0,1,1,8-8A8,8,0,0,1,12,20Z"
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opacity=".25"
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/><path
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d="M10.14,1.16a11,11,0,0,0-9,8.92A1.59,1.59,0,0,0,2.46,12,1.52,1.52,0,0,0,4.11,10.7a8,8,0,0,1,6.66-6.61A1.42,1.42,0,0,0,12,2.69h0A1.57,1.57,0,0,0,10.14,1.16Z"
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class="spinner_ajPY"
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/></svg
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>
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</div>
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{/if}
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</button>
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</div>
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</div>
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<hr class=" dark:border-gray-700" />
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<div class="space-y-2">
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<div>
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<div class=" mb-2 text-sm font-medium">{$i18n.t('Update Embedding Model')}</div>
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<div class="flex w-full">
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<div class="flex-1 mr-2">
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<input
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class="w-full rounded-lg py-2 px-4 text-sm dark:text-gray-300 dark:bg-gray-850 outline-none"
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placeholder={$i18n.t('Update embedding model (e.g. {{model}})', {
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model: embeddingModel.slice(-40)
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})}
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bind:value={embeddingModel}
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/>
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</div>
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<button
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class="px-2.5 bg-gray-100 hover:bg-gray-200 text-gray-800 dark:bg-gray-850 dark:hover:bg-gray-800 dark:text-gray-100 rounded-lg transition"
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on:click={() => {
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embeddingModelUpdateHandler();
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}}
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disabled={updateEmbeddingModelLoading}
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>
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{#if updateEmbeddingModelLoading}
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<div class="self-center">
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{#if embeddingEngine === 'ollama'}
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<div>da</div>
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{:else}
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<div class=" mb-2 text-sm font-medium">{$i18n.t('Update Embedding Model')}</div>
|
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<div class="flex w-full">
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<div class="flex-1 mr-2">
|
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<input
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class="w-full rounded-lg py-2 px-4 text-sm dark:text-gray-300 dark:bg-gray-850 outline-none"
|
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placeholder={$i18n.t('Update embedding model (e.g. {{model}})', {
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model: embeddingModel.slice(-40)
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})}
|
||||
bind:value={embeddingModel}
|
||||
/>
|
||||
</div>
|
||||
<button
|
||||
class="px-2.5 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={() => {
|
||||
embeddingModelUpdateHandler();
|
||||
}}
|
||||
disabled={updateEmbeddingModelLoading}
|
||||
>
|
||||
{#if updateEmbeddingModelLoading}
|
||||
<div class="self-center">
|
||||
<svg
|
||||
class=" w-4 h-4"
|
||||
viewBox="0 0 24 24"
|
||||
fill="currentColor"
|
||||
xmlns="http://www.w3.org/2000/svg"
|
||||
><style>
|
||||
.spinner_ajPY {
|
||||
transform-origin: center;
|
||||
animation: spinner_AtaB 0.75s infinite linear;
|
||||
}
|
||||
@keyframes spinner_AtaB {
|
||||
100% {
|
||||
transform: rotate(360deg);
|
||||
}
|
||||
}
|
||||
</style><path
|
||||
d="M12,1A11,11,0,1,0,23,12,11,11,0,0,0,12,1Zm0,19a8,8,0,1,1,8-8A8,8,0,0,1,12,20Z"
|
||||
opacity=".25"
|
||||
/><path
|
||||
d="M10.14,1.16a11,11,0,0,0-9,8.92A1.59,1.59,0,0,0,2.46,12,1.52,1.52,0,0,0,4.11,10.7a8,8,0,0,1,6.66-6.61A1.42,1.42,0,0,0,12,2.69h0A1.57,1.57,0,0,0,10.14,1.16Z"
|
||||
class="spinner_ajPY"
|
||||
/></svg
|
||||
>
|
||||
</div>
|
||||
{:else}
|
||||
<svg
|
||||
class=" w-4 h-4"
|
||||
xmlns="http://www.w3.org/2000/svg"
|
||||
viewBox="0 0 16 16"
|
||||
fill="currentColor"
|
||||
class="w-4 h-4"
|
||||
>
|
||||
<path
|
||||
d="M8.75 2.75a.75.75 0 0 0-1.5 0v5.69L5.03 6.22a.75.75 0 0 0-1.06 1.06l3.5 3.5a.75.75 0 0 0 1.06 0l3.5-3.5a.75.75 0 0 0-1.06-1.06L8.75 8.44V2.75Z"
|
||||
/>
|
||||
<path
|
||||
d="M3.5 9.75a.75.75 0 0 0-1.5 0v1.5A2.75 2.75 0 0 0 4.75 14h6.5A2.75 2.75 0 0 0 14 11.25v-1.5a.75.75 0 0 0-1.5 0v1.5c0 .69-.56 1.25-1.25 1.25h-6.5c-.69 0-1.25-.56-1.25-1.25v-1.5Z"
|
||||
/>
|
||||
</svg>
|
||||
{/if}
|
||||
</button>
|
||||
</div>
|
||||
|
||||
<div class="mt-2 mb-1 text-xs text-gray-400 dark:text-gray-500">
|
||||
{$i18n.t(
|
||||
'Warning: If you update or change your embedding model, you will need to re-import all documents.'
|
||||
)}
|
||||
</div>
|
||||
{/if}
|
||||
|
||||
<hr class=" dark:border-gray-700 my-3" />
|
||||
|
||||
<div class=" flex w-full justify-between">
|
||||
<div class=" self-center text-xs font-medium">
|
||||
{$i18n.t('Scan for documents from {{path}}', { path: '/data/docs' })}
|
||||
</div>
|
||||
|
||||
<button
|
||||
class=" self-center text-xs p-1 px-3 bg-gray-100 dark:bg-gray-800 dark:hover:bg-gray-700 rounded-lg flex flex-row space-x-1 items-center {scanDirLoading
|
||||
? ' cursor-not-allowed'
|
||||
: ''}"
|
||||
on:click={() => {
|
||||
scanHandler();
|
||||
console.log('check');
|
||||
}}
|
||||
type="button"
|
||||
disabled={scanDirLoading}
|
||||
>
|
||||
<div class="self-center font-medium">{$i18n.t('Scan')}</div>
|
||||
|
||||
{#if scanDirLoading}
|
||||
<div class="ml-3 self-center">
|
||||
<svg
|
||||
class=" w-3 h-3"
|
||||
viewBox="0 0 24 24"
|
||||
fill="currentColor"
|
||||
xmlns="http://www.w3.org/2000/svg"
|
||||
@ -215,30 +254,10 @@
|
||||
/></svg
|
||||
>
|
||||
</div>
|
||||
{:else}
|
||||
<svg
|
||||
xmlns="http://www.w3.org/2000/svg"
|
||||
viewBox="0 0 16 16"
|
||||
fill="currentColor"
|
||||
class="w-4 h-4"
|
||||
>
|
||||
<path
|
||||
d="M8.75 2.75a.75.75 0 0 0-1.5 0v5.69L5.03 6.22a.75.75 0 0 0-1.06 1.06l3.5 3.5a.75.75 0 0 0 1.06 0l3.5-3.5a.75.75 0 0 0-1.06-1.06L8.75 8.44V2.75Z"
|
||||
/>
|
||||
<path
|
||||
d="M3.5 9.75a.75.75 0 0 0-1.5 0v1.5A2.75 2.75 0 0 0 4.75 14h6.5A2.75 2.75 0 0 0 14 11.25v-1.5a.75.75 0 0 0-1.5 0v1.5c0 .69-.56 1.25-1.25 1.25h-6.5c-.69 0-1.25-.56-1.25-1.25v-1.5Z"
|
||||
/>
|
||||
</svg>
|
||||
{/if}
|
||||
</button>
|
||||
</div>
|
||||
|
||||
<div class="mt-2 mb-1 text-xs text-gray-400 dark:text-gray-500">
|
||||
{$i18n.t(
|
||||
'Warning: If you update or change your embedding model, you will need to re-import all documents.'
|
||||
)}
|
||||
</div>
|
||||
|
||||
<hr class=" dark:border-gray-700 my-3" />
|
||||
|
||||
<div class=" ">
|
||||
|
||||
Loading…
Reference in New Issue
Block a user