Support named vectors in Qdrant #6871
Conversation
|
The latest updates on your projects. Learn more about Vercel for Git ↗︎ 1 Ignored Deployment
|
|
Your approach over mine allows the following:
My approach in my second PR #5975 allows both scenarios but expands first to allow adding documents to collections that define multiple vector keys. Now, my use case is already fulfilled with your work, because my use of |
langchain/vectorstores/qdrant.py
Outdated
| with_payload=True, | ||
| with_vectors=True, | ||
| limit=fetch_k, | ||
| ) | ||
| embeddings = [result.vector for result in results] | ||
| mmr_selected = maximal_marginal_relevance( | ||
| np.array(embedding), embeddings, k=k, lambda_mult=lambda_mult | ||
| np.array(query_vector), embeddings, k=k, lambda_mult=lambda_mult |
There was a problem hiding this comment.
we probably don't want to pass in vector_name to this, right?
There was a problem hiding this comment.
@dev2049 Thanks for pointing this out! This was not covered well in the tests, so I extended them and fixed the issues. I would appreciate another look!
We should not expose the vector configuration to the Langchain users. If anyone wants to use a custom configuration for their collection, it should be created directly with |
|
lgtm |
# Description
This PR makes it possible to use named vectors from Qdrant in Langchain.
That was requested multiple times, as people want to reuse externally
created collections in Langchain. It doesn't change anything for the
existing applications. The changes were covered with some integration
tests and included in the docs.
## Example
```python
Qdrant.from_documents(
docs,
embeddings,
location=":memory:",
collection_name="my_documents",
vector_name="custom_vector",
)
```
### Issue: #2594
Tagging @rlancemartin & @eyurtsev. I'd appreciate your review.
# Description
This PR makes it possible to use named vectors from Qdrant in Langchain.
That was requested multiple times, as people want to reuse externally
created collections in Langchain. It doesn't change anything for the
existing applications. The changes were covered with some integration
tests and included in the docs.
## Example
```python
Qdrant.from_documents(
docs,
embeddings,
location=":memory:",
collection_name="my_documents",
vector_name="custom_vector",
)
```
### Issue: langchain-ai#2594
Tagging @rlancemartin & @eyurtsev. I'd appreciate your review.
Description
This PR makes it possible to use named vectors from Qdrant in Langchain. That was requested multiple times, as people want to reuse externally created collections in Langchain. It doesn't change anything for the existing applications. The changes were covered with some integration tests and included in the docs.
Example
Issue: #2594
Tagging @rlancemartin & @eyurtsev. I'd appreciate your review.