This PR changes the behavior of `Qdrant.from_texts` so the collection is
reused if not requested to recreate it. Previously, calling
`Qdrant.from_texts` or `Qdrant.from_documents` resulted in removing the
old data which was confusing for many.
Description: Current `_call` function in the
`langchain.llms.HuggingFaceEndpoint` class truncates response when
`task=text-generation`. Same error discussed a few days ago on Hugging
Face: https://huggingface.co/tiiuae/falcon-40b-instruct/discussions/51
Issue: Fixes#7353
Tag maintainer: @hwchase17 @baskaryan @hinthornw
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Description: This pull request aims to support generating the correct
generic relevancy scores for different vector stores by refactoring the
relevance score functions and their selection in the base class and
subclasses of VectorStore. This is especially relevant with VectorStores
that require a distance metric upon initialization. Note many of the
current implenetations of `_similarity_search_with_relevance_scores` are
not technically correct, as they just return
`self.similarity_search_with_score(query, k, **kwargs)` without applying
the relevant score function
Also includes changes associated with:
https://github.com/hwchase17/langchain/pull/6564 and
https://github.com/hwchase17/langchain/pull/6494
See more indepth discussion in thread in #6494
Issue:
https://github.com/hwchase17/langchain/issues/6526https://github.com/hwchase17/langchain/issues/6481https://github.com/hwchase17/langchain/issues/6346
Dependencies: None
The changes include:
- Properly handling score thresholding in FAISS
`similarity_search_with_score_by_vector` for the corresponding distance
metric.
- Refactoring the `_similarity_search_with_relevance_scores` method in
the base class and removing it from the subclasses for incorrectly
implemented subclasses.
- Adding a `_select_relevance_score_fn` method in the base class and
implementing it in the subclasses to select the appropriate relevance
score function based on the distance strategy.
- Updating the `__init__` methods of the subclasses to set the
`relevance_score_fn` attribute.
- Removing the `_default_relevance_score_fn` function from the FAISS
class and using the base class's `_euclidean_relevance_score_fn`
instead.
- Adding the `DistanceStrategy` enum to the `utils.py` file and updating
the imports in the vector store classes.
- Updating the tests to import the `DistanceStrategy` enum from the
`utils.py` file.
---------
Co-authored-by: Hanit <37485638+hanit-com@users.noreply.github.com>
# 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.
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Added support to `search_by_vector` to Qdrant Vector store.
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### Who can review
VectorStores / Retrievers / Memory
- @dev2049
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Implementation of similarity_search_with_relevance_scores for quadrant
vector store.
As implemented the method is also compatible with other capacities such
as filtering.
Integration tests updated.
#### Who can review?
Tag maintainers/contributors who might be interested:
VectorStores / Retrievers / Memory
- @dev2049
# Fix Qdrant ids creation
There has been a bug in how the ids were created in the Qdrant vector
store. They were previously calculated based on the texts. However,
there are some scenarios in which two documents may have the same piece
of text but different metadata, and that's a valid case. Deduplication
should be done outside of insertion.
It has been fixed and covered with the integration tests.
---------
Co-authored-by: Dev 2049 <dev.dev2049@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>
# Add batching to Qdrant
Several people requested a batching mechanism while uploading data to
Qdrant. It is important, as there are some limits for the maximum size
of the request payload, and without batching implemented in Langchain,
users need to implement it on their own. This PR exposes a new optional
`batch_size` parameter, so all the documents/texts are loaded in batches
of the expected size (64, by default).
The integration tests of Qdrant are extended to cover two cases:
1. Documents are sent in separate batches.
2. All the documents are sent in a single request.
# Add support for Qdrant nested filter
This extends the filter functionality for the Qdrant vectorstore. The
current filter implementation is limited to a single-level metadata
structure; however, Qdrant supports nested metadata filtering. This
extends the functionality for users to maximize the filter functionality
when using Qdrant as the vectorstore.
Reference: https://qdrant.tech/documentation/filtering/#nested-key
---------
Signed-off-by: Aivin V. Solatorio <avsolatorio@gmail.com>
Hello
1) Passing `embedding_function` as a callable seems to be outdated and
the common interface is to pass `Embeddings` instance
2) At the moment `Qdrant.add_texts` is designed to be used with
`embeddings.embed_query`, which is 1) slow 2) causes ambiguity due to 1.
It should be used with `embeddings.embed_documents`
This PR solves both problems and also provides some new tests
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.
This PR implements a basic metadata filtering mechanism similar to the
ones in Chroma and Pinecone. It still cannot express complex conditions,
as there are no operators, but some users requested to have that feature
available.
This PR:
- Increases `qdrant-client` version to 1.0.4
- Introduces custom content and metadata keys (as requested in #1087)
- Moves all the `QdrantClient` parameters into the method parameters to
simplify code completion
Kacper Łukawski