apunkt/trustgraph
1
0
Fork 0
mirror of https://github.com/trustgraph-ai/trustgraph.git synced 2026-07-24 20:51:02 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

Logging strategy updates

Browse source
This commit is contained in:
Cyber MacGeddon 2025-07-30 22:52:03 +01:00
parent 793d2bc77a
commit e0ba70dcf3
1 changed files with 6 additions and 2 deletions
Download patch file Download diff file Expand all files Collapse all files

8
trustgraph-embeddings-hf/trustgraph/embeddings/hf/hf.py
View file

@ -4,10 +4,14 @@ Embeddings service, applies an embeddings model selected from HuggingFace.
Input is text, output is embeddings vector. Input is text, output is embeddings vector.
""" """
import logging
from ... base import EmbeddingsService from ... base import EmbeddingsService
from langchain_huggingface import HuggingFaceEmbeddings from langchain_huggingface import HuggingFaceEmbeddings
# Module logger
logger = logging.getLogger(__name__)
default_ident = "embeddings" default_ident = "embeddings"
default_model="all-MiniLM-L6-v2" default_model="all-MiniLM-L6-v2"
@ -22,13 +26,13 @@ class Processor(EmbeddingsService):
**params | { "model": model } **params | { "model": model }
) )
print("Get model...", flush=True) logger.info(f"Loading HuggingFace embeddings model: {model}")
self.embeddings = HuggingFaceEmbeddings(model_name=model) self.embeddings = HuggingFaceEmbeddings(model_name=model)
async def on_embeddings(self, text): async def on_embeddings(self, text):
embeds = self.embeddings.embed_documents([text]) embeds = self.embeddings.embed_documents([text])
print("Done.", flush=True) logger.debug("Embeddings generation complete")
return embeds return embeds
@staticmethod @staticmethod