# Scores in Vectorestores' Docs Are Explained
Following vectorestores can return scores with similar documents by
using `similarity_search_with_score`:
- chroma
- docarray_hnsw
- docarray_in_memory
- faiss
- myscale
- qdrant
- supabase
- vectara
- weaviate
However, in documents, these scores were either not explained at all or
explained in a way that could lead to misunderstandings (e.g., FAISS).
For instance in FAISS document: if we consider the score returned by the
function as a similarity score, we understand that a document returning
a higher score is more similar to the source document. However, since
the scores returned by the function are distance scores, we should
understand that smaller scores correspond to more similar documents.
For the libraries other than Vectara, I wrote the scores they use by
investigating from the source libraries. Since I couldn't be certain
about the score metric used by Vectara, I didn't make any changes in its
documentation. The links mentioned in Vectara's documentation became
broken due to updates, so I replaced them with working ones.
VectorStores / Retrievers / Memory
- @dev2049
my twitter: [berkedilekoglu](https://twitter.com/berkedilekoglu)
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
# Support Qdrant filters
Qdrant has an [extensive filtering
system](https://qdrant.tech/documentation/concepts/filtering/) with rich
type support. This PR makes it possible to use the filters in Langchain
by passing an additional param to both the
`similarity_search_with_score` and `similarity_search` methods.
## Who can review?
@dev2049 @hwchase17
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
- Added links to the vectorstore providers
- Added installation code (it is not clear that we have to go to the
`LangChan Ecosystem` page to get installation instructions.)
This PR updates Qdrant to 1.1.1 and introduces local mode, so there is
no need to spin up the Qdrant server. By that occasion, the Qdrant
example notebooks also got updated, covering more cases and answering
some commonly asked questions. All the Qdrant's integration tests were
switched to local mode, so no Docker container is required to launch
them.
