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refac: embedding prefix var naming

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This commit is contained in:
Timothy Jaeryang Baek 2025-03-30 21:55:15 -07:00
parent 433b5bddc1
commit 4b75966401
2 changed files with 63 additions and 41 deletions
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12
backend/open_webui/config.py
View File

@ -1783,16 +1783,12 @@ RAG_EMBEDDING_BATCH_SIZE = PersistentConfig(
),
)
RAG_EMBEDDING_QUERY_PREFIX = (
os.environ.get("RAG_EMBEDDING_QUERY_PREFIX", None)
)
RAG_EMBEDDING_QUERY_PREFIX = os.environ.get("RAG_EMBEDDING_QUERY_PREFIX", None)
RAG_EMBEDDING_PASSAGE_PREFIX = (
os.environ.get("RAG_EMBEDDING_PASSAGE_PREFIX", None)
)
RAG_EMBEDDING_CONTENT_PREFIX = os.environ.get("RAG_EMBEDDING_CONTENT_PREFIX", None)
RAG_EMBEDDING_PREFIX_FIELD_NAME = (
os.environ.get("RAG_EMBEDDING_PREFIX_FIELD_NAME", None)
RAG_EMBEDDING_PREFIX_FIELD_NAME = os.environ.get(
"RAG_EMBEDDING_PREFIX_FIELD_NAME", None
)
RAG_RERANKING_MODEL = PersistentConfig(

92
backend/open_webui/retrieval/utils.py
View File

@ -25,9 +25,9 @@ from open_webui.env import (
ENABLE_FORWARD_USER_INFO_HEADERS,
)
from open_webui.config import (
RAG_EMBEDDING_QUERY_PREFIX,
RAG_EMBEDDING_PASSAGE_PREFIX,
RAG_EMBEDDING_PREFIX_FIELD_NAME
RAG_EMBEDDING_QUERY_PREFIX,
RAG_EMBEDDING_CONTENT_PREFIX,
RAG_EMBEDDING_PREFIX_FIELD_NAME,
)
log = logging.getLogger(__name__)
@ -53,7 +53,7 @@ class VectorSearchRetriever(BaseRetriever):
) -> list[Document]:
result = VECTOR_DB_CLIENT.search(
collection_name=self.collection_name,
vectors=[self.embedding_function(query,RAG_EMBEDDING_QUERY_PREFIX)],
vectors=[self.embedding_function(query, RAG_EMBEDDING_QUERY_PREFIX)],
limit=self.top_k,
)
@ -334,7 +334,9 @@ def get_embedding_function(
embedding_batch_size,
):
if embedding_engine == "":
return lambda query, prefix, user=None: embedding_function.encode(query, prompt = prefix if prefix else None).tolist()
return lambda query, prefix, user=None: embedding_function.encode(
query, prompt=prefix if prefix else None
).tolist()
elif embedding_engine in ["ollama", "openai"]:
func = lambda query, prefix, user=None: generate_embeddings(
engine=embedding_engine,
@ -345,22 +347,29 @@ def get_embedding_function(
key=key,
user=user,
)
def generate_multiple(query, prefix, user, func):
if isinstance(query, list):
embeddings = []
for i in range(0, len(query), embedding_batch_size):
embeddings.extend(
func(query[i : i + embedding_batch_size], prefix=prefix, user=user)
func(
query[i : i + embedding_batch_size],
prefix=prefix,
user=user,
)
)
return embeddings
else:
return func(query, prefix, user)
return lambda query, prefix, user=None: generate_multiple(query, prefix, user, func)
return lambda query, prefix, user=None: generate_multiple(
query, prefix, user, func
)
else:
raise ValueError(f"Unknown embedding engine: {embedding_engine}")
def get_sources_from_files(
request,
files,
@ -579,14 +588,11 @@ def generate_openai_batch_embeddings(
url: str = "https://api.openai.com/v1",
key: str = "",
prefix: str = None,
user: UserModel = None
user: UserModel = None,
) -> Optional[list[list[float]]]:
try:
json_data = {
"input": texts,
"model": model
}
if isinstance(RAG_EMBEDDING_PREFIX_FIELD_NAME,str) and isinstance(prefix,str):
json_data = {"input": texts, "model": model}
if isinstance(RAG_EMBEDDING_PREFIX_FIELD_NAME, str) and isinstance(prefix, str):
json_data[RAG_EMBEDDING_PREFIX_FIELD_NAME] = prefix
r = requests.post(
@ -619,21 +625,18 @@ def generate_openai_batch_embeddings(
def generate_ollama_batch_embeddings(
model: str,
model: str,
texts: list[str],
url: str,
key: str = "",
prefix: str = None,
user: UserModel = None
key: str = "",
prefix: str = None,
user: UserModel = None,
) -> Optional[list[list[float]]]:
try:
json_data = {
"input": texts,
"model": model
}
if isinstance(RAG_EMBEDDING_PREFIX_FIELD_NAME,str) and isinstance(prefix,str):
json_data = {"input": texts, "model": model}
if isinstance(RAG_EMBEDDING_PREFIX_FIELD_NAME, str) and isinstance(prefix, str):
json_data[RAG_EMBEDDING_PREFIX_FIELD_NAME] = prefix
r = requests.post(
f"{url}/api/embed",
headers={
@ -664,32 +667,56 @@ def generate_ollama_batch_embeddings(
return None
def generate_embeddings(engine: str, model: str, text: Union[str, list[str]], prefix: Union[str , None] = None, **kwargs):
def generate_embeddings(
engine: str,
model: str,
text: Union[str, list[str]],
prefix: Union[str, None] = None,
**kwargs,
):
url = kwargs.get("url", "")
key = kwargs.get("key", "")
user = kwargs.get("user")
if prefix is not None and RAG_EMBEDDING_PREFIX_FIELD_NAME is None:
if isinstance(text, list):
text = [f'{prefix}{text_element}' for text_element in text]
text = [f"{prefix}{text_element}" for text_element in text]
else:
text = f'{prefix}{text}'
text = f"{prefix}{text}"
if engine == "ollama":
if isinstance(text, list):
embeddings = generate_ollama_batch_embeddings(
**{"model": model, "texts": text, "url": url, "key": key, "prefix": prefix, "user": user}
**{
"model": model,
"texts": text,
"url": url,
"key": key,
"prefix": prefix,
"user": user,
}
)
else:
embeddings = generate_ollama_batch_embeddings(
**{"model": model, "texts": [text], "url": url, "key": key, "prefix": prefix, "user": user}
**{
"model": model,
"texts": [text],
"url": url,
"key": key,
"prefix": prefix,
"user": user,
}
)
return embeddings[0] if isinstance(text, str) else embeddings
elif engine == "openai":
if isinstance(text, list):
embeddings = generate_openai_batch_embeddings(model, text, url, key, prefix, user)
embeddings = generate_openai_batch_embeddings(
model, text, url, key, prefix, user
)
else:
embeddings = generate_openai_batch_embeddings(model, [text], url, key, prefix, user)
embeddings = generate_openai_batch_embeddings(
model, [text], url, key, prefix, user
)
return embeddings[0] if isinstance(text, str) else embeddings
@ -727,8 +754,7 @@ class RerankCompressor(BaseDocumentCompressor):
query_embedding = self.embedding_function(query, RAG_EMBEDDING_QUERY_PREFIX)
document_embedding = self.embedding_function(
[doc.page_content for doc in documents],
RAG_EMBEDDING_PASSAGE_PREFIX
[doc.page_content for doc in documents], RAG_EMBEDDING_CONTENT_PREFIX
)
scores = util.cos_sim(query_embedding, document_embedding)[0]