Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

truncate text to fitin embedding model #692

Merged
merged 1 commit into from
May 9, 2024
Merged
Show file tree
Hide file tree
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
13 changes: 7 additions & 6 deletions rag/llm/embedding_model.py
Original file line number Diff line number Diff line change
Expand Up @@ -27,8 +27,7 @@
import numpy as np

from api.utils.file_utils import get_project_base_directory, get_home_cache_dir
from rag.utils import num_tokens_from_string

from rag.utils import num_tokens_from_string, truncate

try:
flag_model = FlagModel(os.path.join(get_home_cache_dir(), "bge-large-zh-v1.5"),
Expand Down Expand Up @@ -70,7 +69,7 @@ def __init__(self, *args, **kwargs):
self.model = flag_model

def encode(self, texts: list, batch_size=32):
texts = [t[:2000] for t in texts]
texts = [truncate(t, 2048) for t in texts]
token_count = 0
for t in texts:
token_count += num_tokens_from_string(t)
Expand All @@ -93,12 +92,14 @@ def __init__(self, key, model_name="text-embedding-ada-002",
self.model_name = model_name

def encode(self, texts: list, batch_size=32):
texts = [truncate(t, 8196) for t in texts]
res = self.client.embeddings.create(input=texts,
model=self.model_name)
return np.array([d.embedding for d in res.data]), res.usage.total_tokens
return np.array([d.embedding for d in res.data]
), res.usage.total_tokens

def encode_queries(self, text):
res = self.client.embeddings.create(input=[text],
res = self.client.embeddings.create(input=[truncate(text, 8196)],
model=self.model_name)
return np.array(res.data[0].embedding), res.usage.total_tokens

Expand All @@ -112,7 +113,7 @@ def encode(self, texts: list, batch_size=10):
import dashscope
res = []
token_count = 0
texts = [txt[:2048] for txt in texts]
texts = [truncate(t, 2048) for t in texts]
for i in range(0, len(texts), batch_size):
resp = dashscope.TextEmbedding.call(
model=self.model_name,
Expand Down
4 changes: 4 additions & 0 deletions rag/utils/__init__.py
Original file line number Diff line number Diff line change
Expand Up @@ -63,3 +63,7 @@ def num_tokens_from_string(string: str) -> int:
num_tokens = len(encoder.encode(string))
return num_tokens


def truncate(string: str, max_len: int) -> int:
"""Returns truncated text if the length of text exceed max_len."""
return encoder.decode(encoder.encode(string)[:max_len])