-
### What is the issue?
I am using the ollama Python library for all the results I am getting.
As I create embeddings using ollama.embed() I get progressively worse embeddings as the batches are …
-
**Is your feature request related to a problem? Please describe.**
I'm fitting a Kmeans model on a 1 billion * 1024 dims dataset using MNMG Kmeans. Cosine distance is preferred since the semantic emb…
-
### Issue
Not able to change distance model when creating a collection with FastEmbed.
### Minimal steps to reproduce
```
from qdrant_client import QdrantClient
from qdrant_client.models import…
-
# Feature Request: Integrate Dimension Insensitive Euclidean Metric (DIEM) into Qdrant
## Background
We have come across a paper that proposes a novel distance metric called **Dimension Insensitiv…
-
Hi, thank you for this excellent work, I am already using it in my project which shows impressive results.
I just want to ask about the training objective:
https://github.com/facebookresearch/Q…
-
@eyurtsev hello. I'd like to ask a follow up question. the `COSINE` distance strategy is resulting in scores >1.. from this [code](https://github.com/langchain-ai/langchain/blob/023cb59e8aaf3dfaad684b…
rnyak updated
2 months ago
-
When I query for cosine distances to return the distance in a new column, I get back unordered results.
Here's a reproducible script for how I'm setting up my dataset:
https://gist.github.com/rso…
-
Hi, thank you for such a nice piece of work.
In Section 4.4 in your paper, it is mentioned that you have calculated the cosine distance between the flattened embedding. If we have 4 images. i.e. f(…
-
Only uses L2 distance for now
-
### What happened?
Hi, I changed the default model to `sup-simcse-roberta-large` from Huggingface transformers and
I got a strange bug in the form of too large a distance than 1. (vector length of…