forked from open-webui/open-webui
feat: openai embeddings support
This commit is contained in:
Timothy J. Baek
parent
36ce157907
commit
b48e73fa43
2 changed files with 127 additions and 54 deletions
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@ -53,6 +53,7 @@ from apps.rag.utils import (
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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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generate_openai_embeddings,
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)
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from utils.misc import (
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@ -93,6 +94,8 @@ 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_OPENAI_API_BASE_URL = "https://api.openai.com"
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app.state.RAG_OPENAI_API_KEY = ""
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app.state.PDF_EXTRACT_IMAGES = False
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@ -144,10 +147,20 @@ async def get_embedding_config(user=Depends(get_admin_user)):
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"status": True,
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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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"openai_config": {
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"url": app.state.RAG_OPENAI_API_BASE_URL,
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"key": app.state.RAG_OPENAI_API_KEY,
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},
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}
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class OpenAIConfigForm(BaseModel):
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url: str
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key: str
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class EmbeddingModelUpdateForm(BaseModel):
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openai_config: Optional[OpenAIConfigForm] = None
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embedding_engine: str
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embedding_model: str
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@ -156,17 +169,19 @@ class EmbeddingModelUpdateForm(BaseModel):
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async def update_embedding_config(
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form_data: EmbeddingModelUpdateForm, user=Depends(get_admin_user)
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):
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log.info(
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f"Updating embedding model: {app.state.RAG_EMBEDDING_MODEL} to {form_data.embedding_model}"
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)
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try:
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app.state.RAG_EMBEDDING_ENGINE = form_data.embedding_engine
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if app.state.RAG_EMBEDDING_ENGINE == "ollama":
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if app.state.RAG_EMBEDDING_ENGINE in ["ollama", "openai"]:
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app.state.RAG_EMBEDDING_MODEL = form_data.embedding_model
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app.state.sentence_transformer_ef = None
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if form_data.openai_config != None:
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app.state.RAG_OPENAI_API_BASE_URL = form_data.openai_config.url
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app.state.RAG_OPENAI_API_KEY = form_data.openai_config.key
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else:
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sentence_transformer_ef = (
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embedding_functions.SentenceTransformerEmbeddingFunction(
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@ -183,6 +198,10 @@ async def update_embedding_config(
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"status": True,
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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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"openai_config": {
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"url": app.state.RAG_OPENAI_API_BASE_URL,
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"key": app.state.RAG_OPENAI_API_KEY,
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},
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}
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except Exception as e:
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@ -275,6 +294,14 @@ def query_doc_handler(
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):
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try:
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if app.state.RAG_EMBEDDING_ENGINE == "":
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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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else:
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if app.state.RAG_EMBEDDING_ENGINE == "ollama":
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query_embeddings = generate_ollama_embeddings(
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GenerateEmbeddingsForm(
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@ -284,19 +311,20 @@ def query_doc_handler(
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}
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)
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)
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elif app.state.RAG_EMBEDDING_ENGINE == "openai":
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query_embeddings = generate_openai_embeddings(
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model=app.state.RAG_EMBEDDING_MODEL,
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text=form_data.query,
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key=app.state.RAG_OPENAI_API_KEY,
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url=app.state.RAG_OPENAI_API_BASE_URL,
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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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@ -317,6 +345,15 @@ def query_collection_handler(
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user=Depends(get_current_user),
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):
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try:
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if app.state.RAG_EMBEDDING_ENGINE == "":
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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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else:
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if app.state.RAG_EMBEDDING_ENGINE == "ollama":
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query_embeddings = generate_ollama_embeddings(
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GenerateEmbeddingsForm(
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@ -326,19 +363,20 @@ def query_collection_handler(
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}
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)
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)
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elif app.state.RAG_EMBEDDING_ENGINE == "openai":
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query_embeddings = generate_openai_embeddings(
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model=app.state.RAG_EMBEDDING_MODEL,
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text=form_data.query,
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key=app.state.RAG_OPENAI_API_KEY,
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url=app.state.RAG_OPENAI_API_BASE_URL,
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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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@ -414,24 +452,7 @@ 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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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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embeddings=[
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generate_ollama_embeddings(
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GenerateEmbeddingsForm(
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**{"model": RAG_EMBEDDING_MODEL, "prompt": text}
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)
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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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if app.state.RAG_EMBEDDING_ENGINE == "":
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collection = CHROMA_CLIENT.create_collection(
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name=collection_name,
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@ -446,6 +467,35 @@ def store_docs_in_vector_db(docs, collection_name, overwrite: bool = False) -> b
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):
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collection.add(*batch)
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else:
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if app.state.RAG_EMBEDDING_ENGINE == "ollama":
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embeddings = [
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generate_ollama_embeddings(
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GenerateEmbeddingsForm(
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**{"model": app.state.RAG_EMBEDDING_MODEL, "prompt": text}
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)
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)
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for text in texts
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]
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elif app.state.RAG_EMBEDDING_ENGINE == "openai":
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embeddings = [
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generate_openai_embeddings(
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model=app.state.RAG_EMBEDDING_MODEL,
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text=text,
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key=app.state.RAG_OPENAI_API_KEY,
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url=app.state.RAG_OPENAI_API_BASE_URL,
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)
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for text in texts
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]
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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=embeddings,
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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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@ -269,3 +269,26 @@ def get_embedding_model_path(
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except Exception as e:
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log.exception(f"Cannot determine embedding model snapshot path: {e}")
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return embedding_model
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def generate_openai_embeddings(
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model: str, text: str, key: str, url: str = "https://api.openai.com"
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):
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try:
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r = requests.post(
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f"{url}/v1/embeddings",
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headers={
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"Content-Type": "application/json",
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"Authorization": f"Bearer {key}",
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},
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json={"input": text, "model": model},
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)
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r.raise_for_status()
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data = r.json()
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if "data" in data:
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return data["data"][0]["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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print(e)
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return None
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