langchain

Commit Graph

Author	SHA1	Message	Date
HenriZuber	e0605b464b	feat: faiss filter from list (#6537 ) ### Feature Using FAISS on a retrievalQA task, I found myself wanting to allow in multiple sources. From what I understood, the filter feature takes in a dict of form {key: value} which then will check in the metadata for the exact value linked to that key. I added some logic to be able to pass a list which will be checked against instead of an exact value. Passing an exact value will also work. Here's an example of how I could then use it in my own project: ``` pdfs_to_filter_in = ["file_A", "file_B"] filter_dict = { "source": [f"source_pdfs/{pdf_name}.pdf" for pdf_name in pdfs_to_filter_in] } retriever = db.as_retriever() retriever.search_kwargs = {"filter": filter_dict} ``` I added an integration test based on the other ones I found in `tests/integration_tests/vectorstores/test_faiss.py` under `test_faiss_with_metadatas_and_list_filter()`. It doesn't feel like this is worthy of its own notebook or doc, but I'm open to suggestions if needed. Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>	1 year ago
Anubhav Bindlish	94c7899257	Integrate Rockset as Vectorstore (#6216 ) This PR adds Rockset as a vectorstore for langchain. [Rockset](https://rockset.com/blog/introducing-vector-search-on-rockset/) is a real time OLAP database which provides a fast and efficient vector search functionality. Further since it is entirely schemaless, it can store metadata in separate columns thereby allowing fast metadata filters during vector similarity search (as opposed to storing the entire metadata in a single JSON column). It currently supports three distance functions: `COSINE_SIMILARITY`, `EUCLIDEAN_DISTANCE`, and `DOT_PRODUCT`. This PR adds `rockset` client as an optional dependency. We would love a twitter shoutout, our handle is https://twitter.com/RocksetCloud --------- Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>	1 year ago
Stefano Lottini	22af93d851	Vector store support for Cassandra (#6426 ) This addresses #6291 adding support for using Cassandra (and compatible databases, such as DataStax Astra DB) as a [Vector Store](https://cwiki.apache.org/confluence/display/CASSANDRA/CEP-30%3A+Approximate+Nearest+Neighbor(ANN)+Vector+Search+via+Storage-Attached+Indexes). A new class `Cassandra` is introduced, which complies with the contract and interface for a vector store, along with the corresponding integration test, a sample notebook and modified dependency toml. Dependencies: the implementation relies on the library `cassio`, which simplifies interacting with Cassandra for ML- and LLM-oriented workloads. CassIO, in turn, uses the `cassandra-driver` low-lever drivers to communicate with the database. The former is added as optional dependency (+ in `extended_testing`), the latter was already in the project. Integration testing relies on a locally-running instance of Cassandra. [Here](https://cassio.org/more_info/#use-a-local-vector-capable-cassandra) a detailed description can be found on how to compile and run it (at the time of writing the feature has not made it yet to a release). During development of the integration tests, I added a new "fake embedding" class for what I consider a more controlled way of testing the MMR search method. Likewise, I had to amend what looked like a glitch in the behaviour of `ConsistentFakeEmbeddings` whereby an `embed_query` call would have bypassed storage of the requested text in the class cache for use in later repeated invocations. @dev2049 might be the right person to tag here for a review. Thank you! --------- Co-authored-by: rlm <pexpresss31@gmail.com>	1 year ago
zhaoshengbo	ab44c24333	Add Alibaba Cloud OpenSearch as a new vector store (#6154 ) Hello Folks, Thanks for creating and maintaining this great project. I'm excited to submit this PR to add Alibaba Cloud OpenSearch as a new vector store. OpenSearch is a one-stop platform to develop intelligent search services. OpenSearch was built based on the large-scale distributed search engine developed by Alibaba. OpenSearch serves more than 500 business cases in Alibaba Group and thousands of Alibaba Cloud customers. OpenSearch helps develop search services in different search scenarios, including e-commerce, O2O, multimedia, the content industry, communities and forums, and big data query in enterprises. OpenSearch provides the vector search feature. In specific scenarios, especially test question search and image search scenarios, you can use the vector search feature together with the multimodal search feature to improve the accuracy of search results. This PR includes: A AlibabaCloudOpenSearch class that can connect to the Alibaba Cloud OpenSearch instance. add embedings and metadata into a opensearch datasource. querying by squared euclidean and metadata. integration tests. ipython notebook and docs. I have read your contributing guidelines. And I have passed the tests below - [x] make format - [x] make lint - [x] make coverage - [x] make test --------- Co-authored-by: zhaoshengbo <shengbo.zsb@alibaba-inc.com>	1 year ago
volodymyr-memsql	d2e9b621ab	Update SinglStoreDB vectorstore (#6423 ) 1. Introduced new distance strategies support: DOT_PRODUCT and EUCLIDEAN_DISTANCE for enhanced flexibility. 2. Implemented a feature to filter results based on metadata fields. 3. Incorporated connection attributes specifying "langchain python sdk" usage for enhanced traceability and debugging. 4. Expanded the suite of integration tests for improved code reliability. 5. Updated the existing notebook with the usage example @dev2049 --------- Co-authored-by: Volodymyr Tkachuk <vtkachuk-ua@singlestore.com> Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>	1 year ago
hp0404	6aa7b04f79	Fix integration tests for Faiss vector store (#6281 ) Fixes #5807 (issue) #### Who can review? Tag maintainers/contributors who might be interested: @dev2049 <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @hwchase17 VectorStores / Retrievers / Memory - @dev2049 -->	1 year ago
Harrison Chase	9bf5b0defa	Harrison/myscale self query (#6376 ) Co-authored-by: Fangrui Liu <fangruil@moqi.ai> Co-authored-by: 刘方瑞 <fangrui.liu@outlook.com> Co-authored-by: Fangrui.Liu <fangrui.liu@ubc.ca>	1 year ago
Slawomir Gonet	eef62bf4e9	qdrant: search by vector (#6043 ) <!-- Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution. Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change. After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost. Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle! --> <!-- Remove if not applicable --> Added support to `search_by_vector` to Qdrant Vector store. <!-- If you're adding a new integration, please include: 1. a test for the integration - favor unit tests that does not rely on network access. 2. an example notebook showing its use See contribution guidelines for more information on how to write tests, lint etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md --> ### Who can review VectorStores / Retrievers / Memory - @dev2049 <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @hwchase17 -->	1 year ago
Richy Wang	444ca3f669	Improve AnalyticDB Vector Store implementation without affecting user (#6086 ) Hi there: As I implement the AnalyticDB VectorStore use two table to store the document before. It seems just use one table is a better way. So this commit is try to improve AnalyticDB VectorStore implementation without affecting user behavior: 1. Streamline the `post_init `behavior by creating a single table with vector indexing. 2. Update the `add_texts` API for document insertion. 3. Optimize `similarity_search_with_score_by_vector` to retrieve results directly from the table. 4. Implement `_similarity_search_with_relevance_scores`. 5. Add `embedding_dimension` parameter to support different dimension embedding functions. Users can continue using the API as before. Test cases added before is enough to meet this commit.	1 year ago
Harrison Chase	af18413d97	Harrison/deeplake new features (#6263 ) Co-authored-by: adilkhan <adilkhan.sarsen@nu.edu.kz> Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>	1 year ago
Harrison Chase	d1561b74eb	Harrison/cognitive search (#6011 ) Co-authored-by: Fabrizio Ruocco <ruoccofabrizio@gmail.com>	1 year ago
Harrison Chase	e05997c25e	Harrison/hologres (#6012 ) Co-authored-by: Changgeng Zhao <changgeng@nyu.edu> Co-authored-by: Changgeng Zhao <zhaochanggeng.zcg@alibaba-inc.com>	1 year ago
Akhil Vempali	d7d629911b	feat: ✨ Added filtering option to FAISS vectorstore (#5966 ) Inspired by the filtering capability available in ChromaDB, added the same functionality to the FAISS vectorestore as well. Since FAISS does not have an inbuilt method of filtering used the approach suggested in this [thread](https://github.com/facebookresearch/faiss/issues/1079) Langchain Issue inspiration: https://github.com/hwchase17/langchain/issues/4572 - [x] Added filtering capability to semantic similarly and MMR - [x] Added test cases for filtering in `tests/integration_tests/vectorstores/test_faiss.py` #### Who can review? Tag maintainers/contributors who might be interested: VectorStores / Retrievers / Memory - @dev2049 - @hwchase17	1 year ago
Ofer Mendelevitch	f8cf09a230	Update to Vectara integration (#5950 ) This PR updates the Vectara integration (@hwchase17 ): * Adds reuse of requests.session to imrpove efficiency and speed. * Utilizes Vectara's low-level API (instead of standard API) to better match user's specific chunking with LangChain * Now add_texts puts all the texts into a single Vectara document so indexing is much faster. * updated variables names from alpha to lambda_val (to be consistent with Vectara docs) and added n_context_sentence so it's available to use if needed. * Updates to documentation and tests --------- Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>	1 year ago
Harrison Chase	9218684759	Add a new vector store - AwaDB (#5971 ) (#5992 ) Added AwaDB vector store, which is a wrapper over the AwaDB, that can be used as a vector storage and has an efficient similarity search. Added integration tests for the vector store Added jupyter notebook with the example Delete a unneeded empty file and resolve the conflict(https://github.com/hwchase17/langchain/pull/5886) Please check, Thanks! @dev2049 @hwchase17 --------- <!-- Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution. Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change. After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost. Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle! --> <!-- Remove if not applicable --> Fixes # (issue) #### Before submitting <!-- If you're adding a new integration, please include: 1. a test for the integration - favor unit tests that does not rely on network access. 2. an example notebook showing its use See contribution guidelines for more information on how to write tests, lint etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md --> #### Who can review? Tag maintainers/contributors who might be interested: <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @vowelparrot VectorStores / Retrievers / Memory - @dev2049 --> --------- Co-authored-by: ljeagle <vincent_jieli@yeah.net> Co-authored-by: vincent <awadb.vincent@gmail.com>	1 year ago
volodymyr-memsql	a1549901ce	Added SingleStoreDB Vector Store (#5619 ) - Added `SingleStoreDB` vector store, which is a wrapper over the SingleStore DB database, that can be used as a vector storage and has an efficient similarity search. - Added integration tests for the vector store - Added jupyter notebook with the example @dev2049 --------- Co-authored-by: Volodymyr Tkachuk <vtkachuk-ua@singlestore.com> Co-authored-by: Dev 2049 <dev.dev2049@gmail.com> Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>	1 year ago
bnassivet	9355e3f5f5	qdrant vector store - search with relevancy scores (#5781 ) 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	1 year ago
bnassivet	062c3c00a2	fixed faiss integ tests (#5808 ) Fixes # 5807 Realigned tests with implementation. Also reinforced folder unicity for the test_faiss_local_save_load test using date-time suffix #### Before submitting - Integration test updated - formatting and linting ok (locally) #### Who can review? Tag maintainers/contributors who might be interested: @hwchase17 - project lead VectorStores / Retrievers / Memory -@dev2049	1 year ago
Hao Chen	a4c9053d40	Integrate Clickhouse as Vector Store (#5650 ) <!-- Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution. Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change. After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost. Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle! --> #### Description This PR is mainly to integrate open source version of ClickHouse as Vector Store as it is easy for both local development and adoption of LangChain for enterprises who already have large scale clickhouse deployment. ClickHouse is a open source real-time OLAP database with full SQL support and a wide range of functions to assist users in writing analytical queries. Some of these functions and data structures perform distance operations between vectors, [enabling ClickHouse to be used as a vector database](https://clickhouse.com/blog/vector-search-clickhouse-p1). Recently added ClickHouse capabilities like [Approximate Nearest Neighbour (ANN) indices](https://clickhouse.com/docs/en/engines/table-engines/mergetree-family/annindexes) support faster approximate matching of vectors and provide a promising development aimed to further enhance the vector matching capabilities of ClickHouse. In LangChain, some ClickHouse based commercial variant vector stores like [Chroma](https://github.com/hwchase17/langchain/blob/master/langchain/vectorstores/chroma.py) and [MyScale](https://github.com/hwchase17/langchain/blob/master/langchain/vectorstores/myscale.py), etc are already integrated, but for some enterprises with large scale Clickhouse clusters deployment, it will be more straightforward to upgrade existing clickhouse infra instead of moving to another similar vector store solution, so we believe it's a valid requirement to integrate open source version of ClickHouse as vector store. As `clickhouse-connect` is already included by other integrations, this PR won't include any new dependencies. #### Before submitting <!-- If you're adding a new integration, please include: 1. Added a test for the integration: https://github.com/haoch/langchain/blob/clickhouse/tests/integration_tests/vectorstores/test_clickhouse.py 2. Added an example notebook and document showing its use: * Notebook: https://github.com/haoch/langchain/blob/clickhouse/docs/modules/indexes/vectorstores/examples/clickhouse.ipynb * Doc: https://github.com/haoch/langchain/blob/clickhouse/docs/integrations/clickhouse.md See contribution guidelines for more information on how to write tests, lint etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md --> 1. Added a test for the integration: https://github.com/haoch/langchain/blob/clickhouse/tests/integration_tests/vectorstores/test_clickhouse.py 2. Added an example notebook and document showing its use: * Notebook: https://github.com/haoch/langchain/blob/clickhouse/docs/modules/indexes/vectorstores/examples/clickhouse.ipynb * Doc: https://github.com/haoch/langchain/blob/clickhouse/docs/integrations/clickhouse.md #### Who can review? Tag maintainers/contributors who might be interested: <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @vowelparrot VectorStores / Retrievers / Memory - @dev2049 --> @hwchase17 @dev2049 Could you please help review? --------- Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>	1 year ago
Paul-Emile Brotons	92f218207b	removing client+namespace in favor of collection (#5610 ) removing client+namespace in favor of collection for an easier instantiation and to be similar to the typescript library @dev2049	1 year ago
Caleb Ellington	c5a7a85a4e	fix chroma update_document to embed entire documents, fixes a characer-wise embedding bug (#5584 ) # Chroma update_document full document embeddings bugfix Chroma update_document takes a single document, but treats the page_content sting of that document as a list when getting the new document embedding. This is a two-fold problem, where the resulting embedding for the updated document is incorrect (it's only an embedding of the first character in the new page_content) and it calls the embedding function for every character in the new page_content string, using many tokens in the process. Fixes #5582 Co-authored-by: Caleb Ellington <calebellington@Calebs-MBP.hsd1.ca.comcast.net>	1 year ago
Kacper Łukawski	71a7c16ee0	Fix: Qdrant ids (#5515 ) # 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>	1 year ago
Sheng Han Lim	3bae595182	Add texts with embeddings to PGVector wrapper (#5500 ) Similar to #1813 for faiss, this PR is to extend functionality to pass text and its vector pair to initialize and add embeddings to the PGVector wrapper. Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested: - @dev2049	1 year ago
Kacper Łukawski	8bcaca435a	Feature: Qdrant filters supports (#5446 ) # 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>	1 year ago
Kacper Łukawski	f93d256190	Feat: Add batching to Qdrant (#5443 ) # 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.	1 year ago
Paul-Emile Brotons	a61b7f7e7c	adding MongoDBAtlasVectorSearch (#5338 ) # Add MongoDBAtlasVectorSearch for the python library Fixes #5337 --------- Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>	1 year ago
Martin Holecek	44b48d9518	Fix update_document function, add test and documentation. (#5359 ) # Fix for `update_document` Function in Chroma ## Summary This pull request addresses an issue with the `update_document` function in the Chroma class, as described in [#5031](https://github.com/hwchase17/langchain/issues/5031#issuecomment-1562577947). The issue was identified as an `AttributeError` raised when calling `update_document` due to a missing corresponding method in the `Collection` object. This fix refactors the `update_document` method in `Chroma` to correctly interact with the `Collection` object. ## Changes 1. Fixed the `update_document` method in the `Chroma` class to correctly call methods on the `Collection` object. 2. Added the corresponding test `test_chroma_update_document` in `tests/integration_tests/vectorstores/test_chroma.py` to reflect the updated method call. 3. Added an example and explanation of how to use the `update_document` function in the Jupyter notebook tutorial for Chroma. ## Test Plan All existing tests pass after this change. In addition, the `test_chroma_update_document` test case now correctly checks the functionality of `update_document`, ensuring that the function works as expected and updates the content of documents correctly. ## Reviewers @dev2049 This fix will ensure that users are able to use the `update_document` function as expected, without encountering the previous `AttributeError`. This will enhance the usability and reliability of the Chroma class for all users. Thank you for considering this pull request. I look forward to your feedback and suggestions.	1 year ago
Ofer Mendelevitch	c81fb88035	Vectara (#5069 ) # Vectara Integration This PR provides integration with Vectara. Implemented here are: * langchain/vectorstore/vectara.py * tests/integration_tests/vectorstores/test_vectara.py * langchain/retrievers/vectara_retriever.py And two IPYNB notebooks to do more testing: * docs/modules/chains/index_examples/vectara_text_generation.ipynb * docs/modules/indexes/vectorstores/examples/vectara.ipynb --------- Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>	1 year ago
Jettro Coenradie	b950022894	Fixes issue #5072 - adds additional support to Weaviate (#5085 ) Implementation is similar to search_distance and where_filter # adds 'additional' support to Weaviate queries Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>	1 year ago
Donger	039f8f1abb	Add the usage of SSL certificates for Elasticsearch and user password authentication (#5058 ) Enhance the code to support SSL authentication for Elasticsearch when using the VectorStore module, as previous versions did not provide this capability. @dev2049 --------- Co-authored-by: caidong <zhucaidong1992@gmail.com> Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>	1 year ago
Eugene Yurtsev	0ff59569dc	Adds 'IN' metadata filter for pgvector for checking set presence (#4982 ) # Adds "IN" metadata filter for pgvector to all checking for set presence PGVector currently supports metadata filters of the form: ``` {"filter": {"key": "value"}} ``` which will return documents where the "key" metadata field is equal to "value". This PR adds support for metadata filters of the form: ``` {"filter": {"key": { "IN" : ["list", "of", "values"]}}} ``` Other vector stores support this via an "$in" syntax. I chose to use "IN" to match postgres' syntax, though happy to switch. Tested locally with PGVector and ChatVectorDBChain. @dev2049 --------- Co-authored-by: jade@spanninglabs.com <jade@spanninglabs.com>	1 year ago
Davis Chase	55baa0d153	Update redis integration tests (#4937 )	1 year ago
yujiosaka	6561efebb7	Accept uuids kwargs for weaviate (#4800 ) # Accept uuids kwargs for weaviate Fixes #4791	1 year ago
Magnus Friberg	d126276693	Specify which data to return from chromadb (#4393 ) # Improve the Chroma get() method by adding the optional "include" parameter. The Chroma get() method excludes embeddings by default. You can customize the response by specifying the "include" parameter to selectively retrieve the desired data from the collection. --------- Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>	1 year ago
Anirudh Suresh	03ac39368f	Fixing DeepLake Overwrite Flag (#4683 ) # Fix DeepLake Overwrite Flag Issue Fixes Issue #4682: essentially, setting overwrite to False in the DeepLake constructor still triggers an overwrite, because the logic is just checking for the presence of "overwrite" in kwargs. The fix is simple--just add some checks to inspect if "overwrite" in kwargs AND kwargs["overwrite"]==True. Added a new test in tests/integration_tests/vectorstores/test_deeplake.py to reflect the desired behavior. Co-authored-by: Anirudh Suresh <ani@Anirudhs-MBP.cable.rcn.com> Co-authored-by: Anirudh Suresh <ani@Anirudhs-MacBook-Pro.local> Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>	1 year ago
Evan Jones	f668251948	parameterized distance metrics; lint; format; tests (#4375 ) # Parameterize Redis vectorstore index Redis vectorstore allows for three different distance metrics: `L2` (flat L2), `COSINE`, and `IP` (inner product). Currently, the `Redis._create_index` method hard codes the distance metric to COSINE. I've parameterized this as an argument in the `Redis.from_texts` method -- pretty simple. Fixes #4368 ## Before submitting I've added an integration test showing indexes can be instantiated with all three values in the `REDIS_DISTANCE_METRICS` literal. An example notebook seemed overkill here. Normal API documentation would be more appropriate, but no standards are in place for that yet. ## Who can review? Not sure who's responsible for the vectorstore module... Maybe @eyurtsev / @hwchase17 / @agola11 ?	1 year ago
Davis Chase	46b100ea63	Add DocArray vector stores (#4483 ) Thanks to @anna-charlotte and @jupyterjazz for the contribution! Made few small changes to get it across the finish line --------- Signed-off-by: anna-charlotte <charlotte.gerhaher@jina.ai> Signed-off-by: jupyterjazz <saba.sturua@jina.ai> Co-authored-by: anna-charlotte <charlotte.gerhaher@jina.ai> Co-authored-by: jupyterjazz <saba.sturua@jina.ai> Co-authored-by: Saba Sturua <45267439+jupyterjazz@users.noreply.github.com>	1 year ago
Aivin V. Solatorio	6335cb5b3a	Add support for Qdrant nested filter (#4354 ) # 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>	1 year ago
Naveen Tatikonda	782df1db10	OpenSearch: Add Similarity Search with Score (#4089 ) ### Description Add `similarity_search_with_score` method for OpenSearch to return scores along with documents in the search results Signed-off-by: Naveen Tatikonda <navtat@amazon.com>	1 year ago
George	2324f19c85	Update qdrant interface (#3971 ) 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	1 year ago
Davis Chase	2451310975	Chroma fix mmr (#3897 ) Fixes #3628, thanks @derekmoeller for the issue!	1 year ago
Harrison Chase	0c0f14407c	Harrison/tair (#3770 ) Co-authored-by: Seth Huang <848849+seth-hg@users.noreply.github.com>	1 year ago
Harrison Chase	be7a8e0824	Harrison/redis cache (#3766 ) Co-authored-by: Tyler Hutcherson <tyler.hutcherson@redis.com>	1 year ago
Harrison Chase	a35bbbfa9e	Harrison/lancedb (#3634 ) Co-authored-by: Minh Le <minhle@canva.com>	1 year ago
Harrison Chase	ab749fa1bb	Harrison/opensearch logic (#3631 ) Co-authored-by: engineer-matsuo <95115586+engineer-matsuo@users.noreply.github.com>	1 year ago
cs0lar	440c98e24b	Fix/issue 2695 (#3608 ) ## Background fixes #2695 ## Changes The `add_text` method uses the internal embedding function if one was passes to the `Weaviate` constructor. NOTE: the latest merge on the `Weaviate` class made the specification of a `weaviate_api_key` mandatory which might not be desirable for all users and connection methods (for example weaviate also support Embedded Weaviate which I am happy to add support to here if people think it's desirable). I wrapped the fetching of the api key into a try catch in order to allow the `weaviate_api_key` to be unspecified. Do let me know if this is unsatisfactory. ## Test Plan added test for `add_texts` method.	1 year ago
Harrison Chase	408a0183cd	Harrison/weaviate (#3494 ) Co-authored-by: Nick Rubell <nick@rubell.com>	1 year ago
cs0lar	3033c6b964	fixes #1214 (#3003 ) ### Background Continuing to implement all the interface methods defined by the `VectorStore` class. This PR pertains to implementation of the `max_marginal_relevance_search_by_vector` method. ### Changes - a `max_marginal_relevance_search_by_vector` method implementation has been added in `weaviate.py` - tests have been added to the the new method - vcr cassettes have been added for the weaviate tests ### Test Plan Added tests for the `max_marginal_relevance_search_by_vector` implementation ### Change Safety - [x] I have added tests to cover my changes	1 year ago
Davit Buniatyan	2c0023393b	Deep Lake mini upgrades (#3375 ) Improvements * set default num_workers for ingestion to 0 * upgraded notebooks for avoiding dataset creation ambiguity * added `force_delete_dataset_by_path` * bumped deeplake to 3.3.0 * creds arg passing to deeplake object that would allow custom S3 Notes * please double check if poetry is not messed up (thanks!) Asks * Would be great to create a shared slack channel for quick questions --------- Co-authored-by: Davit Buniatyan <d@activeloop.ai>	1 year ago
Harrison Chase	a6664be79c	Harrison/myscale (#3352 ) Co-authored-by: Fangrui Liu <fangruil@moqi.ai> Co-authored-by: 刘方瑞 <fangrui.liu@outlook.com> Co-authored-by: Fangrui.Liu <fangrui.liu@ubc.ca>	1 year ago

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