repositories
loading repo index
repositories
loading repo index
repository
loading code, commits, and activity
public Clawd ADK gateway launch mirror
stars
latest
clone command
git clone gitlawb://did:key:z6Mkq5mY...iFZ5/my-project-publ...git clone gitlawb://did:key:z6Mkq5mY.../my-project-publ...2fa351d6docs: add automaton and perps launch sources16d ago| #1 | import logging |
| #2 | from typing import Literal, Optional |
| #3 | |
| #4 | from openai import OpenAI |
| #5 | from sentence_transformers import SentenceTransformer |
| #6 | |
| #7 | from mem0.configs.embeddings.base import BaseEmbedderConfig |
| #8 | from mem0.embeddings.base import EmbeddingBase |
| #9 | |
| #10 | logging.getLogger("transformers").setLevel(logging.WARNING) |
| #11 | logging.getLogger("sentence_transformers").setLevel(logging.WARNING) |
| #12 | logging.getLogger("huggingface_hub").setLevel(logging.WARNING) |
| #13 | |
| #14 | |
| #15 | class HuggingFaceEmbedding(EmbeddingBase): |
| #16 | def __init__(self, config: Optional[BaseEmbedderConfig] = None): |
| #17 | super().__init__(config) |
| #18 | |
| #19 | if config.huggingface_base_url: |
| #20 | self.client = OpenAI(base_url=config.huggingface_base_url) |
| #21 | self.config.model = self.config.model or "tei" |
| #22 | else: |
| #23 | self.config.model = self.config.model or "multi-qa-MiniLM-L6-cos-v1" |
| #24 | |
| #25 | self.model = SentenceTransformer(self.config.model, **self.config.model_kwargs) |
| #26 | |
| #27 | self.config.embedding_dims = self.config.embedding_dims or self.model.get_sentence_embedding_dimension() |
| #28 | |
| #29 | def embed(self, text, memory_action: Optional[Literal["add", "search", "update"]] = None): |
| #30 | """ |
| #31 | Get the embedding for the given text using Hugging Face. |
| #32 | |
| #33 | Args: |
| #34 | text (str): The text to embed. |
| #35 | memory_action (optional): The type of embedding to use. Must be one of "add", "search", or "update". Defaults to None. |
| #36 | Returns: |
| #37 | list: The embedding vector. |
| #38 | """ |
| #39 | if self.config.huggingface_base_url: |
| #40 | return self.client.embeddings.create( |
| #41 | input=text, model=self.config.model, **self.config.model_kwargs |
| #42 | ).data[0].embedding |
| #43 | else: |
| #44 | return self.model.encode(text, convert_to_numpy=True).tolist() |
| #45 |