73 Commits (c816d036998ad4a1970c9d90698516e1fbf6812b)

Author	SHA1	Message	Date
Aayush Kataria	71811e0547	community[minor]: Adds a vector store for Azure Cosmos DB for NoSQL (#21676) ... This PR add supports for Azure Cosmos DB for NoSQL vector store. Summary: Description: added vector store integration for Azure Cosmos DB for NoSQL Vector Store, Dependencies: azure-cosmos dependency, Tag maintainer: @hwchase17, @baskaryan @efriis @eyurtsev --------- Co-authored-by: Bagatur <baskaryan@gmail.com> Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>	3 months ago
am-kinetica	ad101adec8	community[patch]: Kinetica Integrations handled error in querying; quotes in table names; updated gpudb API (#22724) ... - [ ] **Miscellaneous updates and fixes**: - **Description:** Handled error in querying; quotes in table names; updated gpudb API - **Issue:** Threw an error with an error message difficult to understand if a query failed or returned no records - **Dependencies:** Updated GPUDB API version to `7.2.0.9` @baskaryan @hwchase17	3 months ago
Erick Friis	a24a9c6427	multiple: get rid of pyproject extras (#22581) ... They cause `poetry lock` to take a ton of time, and `uv pip install` can resolve the constraints from these toml files in trivial time (addressing problem with #19153) This allows us to properly upgrade lockfile dependencies moving forward, which revealed some issues that were either fixed or type-ignored (see file comments)	4 months ago
Stefano Lottini	328d0c99f2	community[minor]: Add support for metadata indexing policy in Cassandra vector store (#22548) ... This PR adds a constructor `metadata_indexing` parameter to the Cassandra vector store to allow optional fine-tuning of which fields of the metadata are to be indexed. This is a feature supported by the underlying CassIO library. Indexing mode of "all", "none" or deny- and allow-list based choices are available. The rationale is, in some cases it's advisable to programmatically exclude some portions of the metadata from the index if one knows in advance they won't ever be used at search-time. this keeps the index more lightweight and performant and avoids limitations on the length of _indexed_ strings. I added a integration test of the feature. I also added the possibility of running the integration test with Cassandra on an arbitrary IP address (e.g. Dockerized), via `CASSANDRA_CONTACT_POINTS=10.1.1.5,10.1.1.6 poetry run pytest [...]` or similar. While I was at it, I added a line to the `.gitignore` since the mypy _test_ cache was not ignored yet. My X (Twitter) handle: @rsprrs.	4 months ago
Ofer Mendelevitch	ad502e8d50	community[minor]: Vectara Integration Update - Streaming, FCS, Chat, updates to documentation and example notebooks (#21334) ... Thank you for contributing to LangChain! **Description:** update to the Vectara / Langchain integration to integrate new Vectara capabilities: - Full RAG implemented as a Runnable with as_rag() - Vectara chat supported with as_chat() - Both support streaming response - Updated documentation and example notebook to reflect all the changes - Updated Vectara templates **Twitter handle:** ofermend **Add tests and docs**: no new tests or docs, but updated both existing tests and existing docs	4 months ago
Bagatur	50186da0a1	infra: rm unused # noqa violations (#22049) ... Updating #21137	4 months ago
SaschaStoll	709664a079	community[patch]: Performant filter columns option for Hanavector (#21971) ... **Description:** Backwards compatible extension of the initialisation interface of HanaDB to allow the user to specify specific_metadata_columns that are used for metadata storage of selected keys which yields increased filter performance. Any not-mentioned metadata remains in the general metadata column as part of a JSON string. Furthermore switched to executemany for batch inserts into HanaDB. **Issue:** N/A **Dependencies:** no new dependencies added **Twitter handle:** @sapopensource --------- Co-authored-by: Martin Kolb <martin.kolb@sap.com> Co-authored-by: Bagatur <baskaryan@gmail.com>	4 months ago
Jesse S	fc79b372cb	community[minor]: add aerospike vectorstore integration (#21735) ... Please let me know if you see any possible areas of improvement. I would very much appreciate your constructive criticism if time allows. **Description:** - Added a aerospike vector store integration that utilizes [Aerospike-Vector-Search](https://aerospike.com/products/vector-database-search-llm/) add-on. - Added both unit tests and integration tests - Added a docker compose file for spinning up a test environment - Added a notebook **Dependencies:** any dependencies required for this change - aerospike-vector-search **Twitter handle:** - No twitter, you can use my GitHub handle or LinkedIn if you'd like Thanks! --------- Co-authored-by: Jesse Schumacher <jschumacher@aerospike.com> Co-authored-by: Bagatur <baskaryan@gmail.com>	4 months ago
Eugene Yurtsev	25fbe356b4	community[patch]: upgrade to recent version of mypy (#21616) ... This PR upgrades community to a recent version of mypy. It inserts type: ignore on all existing failures.	4 months ago
Mark Cusack	060987d755	community[minor]: Add indexing via locality sensitive hashing to the Yellowbrick vector store (#20856) ... - **Description:** Add LSH-based indexing to the Yellowbrick vector store module - **Twitter handle:** @markcusack --------- Co-authored-by: markcusack <markcusack@markcusacksmac.lan> Co-authored-by: markcusack <markcusack@Mark-Cusack-sMac.local> Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>	5 months ago
Rohan Aggarwal	8021d2a2ab	community[minor]: Oraclevs integration (#21123) ... Thank you for contributing to LangChain! - Oracle AI Vector Search Oracle AI Vector Search is designed for Artificial Intelligence (AI) workloads that allows you to query data based on semantics, rather than keywords. One of the biggest benefit of Oracle AI Vector Search is that semantic search on unstructured data can be combined with relational search on business data in one single system. This is not only powerful but also significantly more effective because you don't need to add a specialized vector database, eliminating the pain of data fragmentation between multiple systems. - Oracle AI Vector Search is designed for Artificial Intelligence (AI) workloads that allows you to query data based on semantics, rather than keywords. One of the biggest benefit of Oracle AI Vector Search is that semantic search on unstructured data can be combined with relational search on business data in one single system. This is not only powerful but also significantly more effective because you don't need to add a specialized vector database, eliminating the pain of data fragmentation between multiple systems. This Pull Requests Adds the following functionalities Oracle AI Vector Search : Vector Store Oracle AI Vector Search : Document Loader Oracle AI Vector Search : Document Splitter Oracle AI Vector Search : Summary Oracle AI Vector Search : Oracle Embeddings - We have added unit tests and have our own local unit test suite which verifies all the code is correct. We have made sure to add guides for each of the components and one end to end guide that shows how the entire thing runs. - We have made sure that make format and make lint run clean. Additional guidelines: - Make sure optional dependencies are imported within a function. - Please do not add dependencies to pyproject.toml files (even optional ones) unless they are required for unit tests. - Most PRs should not touch more than one package. - Changes should be backwards compatible. - If you are adding something to community, do not re-import it in langchain. If no one reviews your PR within a few days, please @-mention one of baskaryan, efriis, eyurtsev, hwchase17. --------- Co-authored-by: skmishraoracle <shailendra.mishra@oracle.com> Co-authored-by: hroyofc <harichandan.roy@oracle.com> Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com> Co-authored-by: Bagatur <baskaryan@gmail.com>	5 months ago
Cahid Arda Öz	cc6191cb90	community[minor]: Add support for Upstash Vector (#20824) ... ## Description Adding `UpstashVectorStore` to utilize [Upstash Vector](https://upstash.com/docs/vector/overall/getstarted)! #17012 was opened to add Upstash Vector to langchain but was closed to wait for filtering. Now filtering is added to Upstash vector and we open a new PR. Additionally, [embedding feature](https://upstash.com/docs/vector/features/embeddingmodels) was added and we add this to our vectorstore aswell. ## Dependencies [upstash-vector](https://pypi.org/project/upstash-vector/) should be installed to use `UpstashVectorStore`. Didn't update dependencies because of [this comment in the previous PR](https://github.com/langchain-ai/langchain/pull/17012#pullrequestreview-1876522450). ## Tests Tests are added and they pass. Tests are naturally network bound since Upstash Vector is offered through an API. There was [a discussion in the previous PR about mocking the unittests](https://github.com/langchain-ai/langchain/pull/17012#pullrequestreview-1891820567). We didn't make changes to this end yet. We can update the tests if you can explain how the tests should be mocked. --------- Co-authored-by: ytkimirti <yusuftaha9@gmail.com> Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com> Co-authored-by: Bagatur <baskaryan@gmail.com>	5 months ago
Mayank Solanki	8c085fc697	community[patch]: Added a function `from_existing_collection` in `Qdrant` vector database. (#20779) ... Issue: #20514 The current implementation of `construct_instance` expects a `texts: List[str]` that will call the embedding function. This might not be needed when we already have a client with collection and `path, you don't want to add any text. This PR adds a class method that returns a qdrant instance with an existing client. Here everytime cb6e5e56c2/libs/community/langchain_community/vectorstores/qdrant.py (L1592) `construct_instance` is called, this line sends some text for embedding generation. --------- Co-authored-by: Anush <anushshetty90@gmail.com>	5 months ago
Jingpan Xiong	1202017c56	community[minor]: Add relyt vector database (#20316) ... Co-authored-by: kaka <kaka@zbyte-inc.cloud> Co-authored-by: Bagatur <baskaryan@gmail.com> Co-authored-by: jingsi <jingsi@leadincloud.com> Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>	5 months ago
Andres Algaba	05ae8ca7d4	community[patch]: deprecate persist method in Chroma (#20855) ... Thank you for contributing to LangChain! - [x] **PR title** - [x] **PR message**: - **Description:** Deprecate persist method in Chroma no longer exists in Chroma 0.4.x - **Issue:** #20851 - **Dependencies:** None - **Twitter handle:** AndresAlgaba1 - [x] **Add tests and docs**: If you're adding a new integration, please include 1. a test for the integration, preferably unit tests that do not rely on network access, 2. an example notebook showing its use. It lives in `docs/docs/integrations` directory. - [x] **Lint and test**: Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified. See contribution guidelines for more: https://python.langchain.com/docs/contributing/ --------- Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>	5 months ago
Tomaz Bratanic	520972fd0f	community[patch]: Support passing graph object to Neo4j integrations (#20876) ... For driver connection reusage, we introduce passing the graph object to neo4j integrations	5 months ago
Raghav Dixit	9b7fb381a4	community[patch]: LanceDB integration patch update (#20686) ... Description : - added functionalities - delete, index creation, using existing connection object etc. - updated usage - Added LaceDB cloud OSS support make lint_diff , make test checks done	5 months ago
volodymyr-memsql	493afe4d8d	community[patch]: add hybrid search to singlestoredb vectorstore (#20793) ... Implemented the ability to enable full-text search within the SingleStore vector store, offering users a versatile range of search strategies. This enhancement allows users to seamlessly combine full-text search with vector search, enabling the following search strategies: * Search solely by vector similarity. * Conduct searches exclusively based on text similarity, utilizing Lucene internally. * Filter search results by text similarity score, with the option to specify a threshold, followed by a search based on vector similarity. * Filter results by vector similarity score before conducting a search based on text similarity. * Perform searches using a weighted sum of vector and text similarity scores. Additionally, integration tests have been added to comprehensively cover all scenarios. Updated notebook with examples. CC: @baskaryan, @hwchase17 --------- Co-authored-by: Volodymyr Tkachuk <vtkachuk-ua@singlestore.com> Co-authored-by: Bagatur <baskaryan@gmail.com>	5 months ago
Martin Kolb	0186e4e633	community[patch]: Advanced filtering for HANA Cloud Vector Engine (#20821) ... - **Description:** This PR adds support for advanced filtering to the integration of HANA Vector Engine. The newly supported filtering operators are: $eq, $ne, $gt, $gte, $lt, $lte, $between, $in, $nin, $like, $and, $or - **Issue:** N/A - **Dependencies:** no new dependencies added Added integration tests to: `libs/community/tests/integration_tests/vectorstores/test_hanavector.py` Description of the new capabilities in notebook: `docs/docs/integrations/vectorstores/hanavector.ipynb`	5 months ago
ccurme	c010ec8b71	patch: deprecate (a)get_relevant_documents (#20477) ... - `.get_relevant_documents(query)` -> `.invoke(query)` - `.get_relevant_documents(query=query)` -> `.invoke(query)` - `.get_relevant_documents(query, callbacks=callbacks)` -> `.invoke(query, config={"callbacks": callbacks})` - `.get_relevant_documents(query, **kwargs)` -> `.invoke(query, **kwargs)` --------- Co-authored-by: Erick Friis <erick@langchain.dev>	5 months ago
Christophe Bornet	c909ae0152	community[minor]: Add async methods to CassandraVectorStore (#20602) ... Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>	5 months ago
Tomaz Bratanic	8c08cf4619	community: Add support for relationship indexes in neo4j vector (#20657) ... Neo4j has added relationship vector indexes. We can't populate them, but we can use existing indexes for retrieval	5 months ago
sdan	a7c5e41443	community[minor]: Added VLite as VectorStore (#20245) ... Support [VLite](https://github.com/sdan/vlite) as a new VectorStore type. **Description**: vlite is a simple and blazing fast vector database(vdb) made with numpy. It abstracts a lot of the functionality around using a vdb in the retrieval augmented generation(RAG) pipeline such as embeddings generation, chunking, and file processing while still giving developers the functionality to change how they're made/stored. **Before submitting**: Added tests [here](c09c2ebd5c/libs/community/tests/integration_tests/vectorstores/test_vlite.py) Added ipython notebook [here](c09c2ebd5c/docs/docs/integrations/vectorstores/vlite.ipynb) Added simple docs on how to use [here](c09c2ebd5c/docs/docs/integrations/providers/vlite.mdx) **Profiles** Maintainers: @sdan Twitter handles: [@sdand](https://x.com/sdand) --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	5 months ago
Benito Geordie	57b226532d	community[minor]: Added integrations for ThirdAI's NeuralDB as a Retriever (#17334) ... **Description:** Adds ThirdAI NeuralDB retriever integration. NeuralDB is a CPU-friendly and fine-tunable text retrieval engine. We previously added a vector store integration but we think that it will be easier for our customers if they can also find us under under langchain-community/retrievers. --------- Co-authored-by: kartikTAI <129414343+kartikTAI@users.noreply.github.com> Co-authored-by: Kartik Sarangmath <kartik@thirdai.com>	5 months ago
Martín Gotelli Ferenaz	b48add4353	community[patch]: Fix pgvector deprecated filter clause usage with OR and AND conditions (#20446) ... **Description**: Support filter by OR and AND for deprecated PGVector version **Issue**: #20445 **Dependencies**: N/A **Twitter** handle: @martinferenaz	5 months ago
Leonid Ganeline	4cb5f4c353	community[patch]: import flattening fix (#20110) ... This PR should make it easier for linters to do type checking and for IDEs to jump to definition of code. See #20050 as a template for this PR. - As a byproduct: Added 3 missed `test_imports`. - Added missed `SolarChat` in to __init___.py Added it into test_import ut. - Added `# type: ignore` to fix linting. It is not clear, why linting errors appear after ^ changes. --------- Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>	5 months ago
Tomaz Bratanic	df25829f33	community[minor]: Add metadata filtering support for neo4j vector (#20001)	6 months ago
Tomaz Bratanic	09a0ecd000	langchain[minor]: Tests update metadata filtering examples of documents (#19963) ... Removing metadata properties that are dicts as some databases don't support that, and those properties aren't used in tests anyhow..	6 months ago
morgana	074ad5095f	community[patch]: mmr search for Rockset vectorstore integration (#16908) ... - **Description:** Adding support for mmr search in the Rockset vectorstore integration. - **Issue:** N/A - **Dependencies:** N/A - **Twitter handle:** `@_morgan_adams_` --------- Co-authored-by: Rockset API Bot <admin@rockset.io> Co-authored-by: Bagatur <baskaryan@gmail.com> Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>	6 months ago
Tomaz Bratanic	dec00d3050	community[patch]: Add the ability to pass maps to neo4j retrieval query (#19758) ... Makes it easier to flatten complex values to text, so you don't have to use a lot of Cypher to do it.	6 months ago
Chaunte W. Lacewell	a31f692f4e	community[minor]: Add VDMS vectorstore (#19551) ... - **Description:** Add support for Intel Lab's [Visual Data Management System (VDMS)](https://github.com/IntelLabs/vdms) as a vector store - **Dependencies:** `vdms` library which requires protobuf = "4.24.2". There is a conflict with dashvector in `langchain` package but conflict is resolved in `community`. - **Contribution maintainer:** [@cwlacewe](https://github.com/cwlacewe) - **Added tests:** libs/community/tests/integration_tests/vectorstores/test_vdms.py - **Added docs:** docs/docs/integrations/vectorstores/vdms.ipynb - **Added cookbook:** cookbook/multi_modal_RAG_vdms.ipynb --------- Co-authored-by: Eugene Yurtsev <eugene@langchain.dev> Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com> Co-authored-by: Bagatur <baskaryan@gmail.com>	6 months ago
yongheng.liu	7e29b6061f	community[minor]: integrate China Mobile Ecloud vector search (#15298) ... - **Description:** integrate China Mobile Ecloud vector search, - **Dependencies:** elasticsearch==7.10.1 Co-authored-by: liuyongheng <liuyongheng@cmss.chinamobile.com> Co-authored-by: Bagatur <baskaryan@gmail.com>	6 months ago
CaroFG	cf96060ab7	community[patch]: update for compatibility with latest Meilisearch version (#18970) ... - **Description:** Updates Meilisearch vectorstore for compatibility with v1.6 and above. Adds embedders settings and embedder_name which are now required. --------- Co-authored-by: Bagatur <baskaryan@gmail.com>	6 months ago
Adrian Valente	2763d8cbe5	community: add len() implementation to Chroma (#19419) ... Thank you for contributing to LangChain! - [x] **Add len() implementation to Chroma**: "package: community" - [x] **PR message**: - **Description:** add an implementation of the __len__() method for the Chroma vectostore, for convenience. - **Issue:** no exposed method to know the size of a Chroma vectorstore - **Dependencies:** None - **Twitter handle:** lowrank_adrian - [x] **Add tests and docs** - [x] **Lint and test** --------- Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>	6 months ago
Aayush Kataria	03c38005cb	community[patch]: Fixing some caching issues for AzureCosmosDBSemanticCache (#18884) ... Fixing some issues for AzureCosmosDBSemanticCache - Added the entry for "AzureCosmosDBSemanticCache" which was missing in langchain/cache.py - Added application name when creating the MongoClient for the AzureCosmosDBVectorSearch, for tracking purposes. @baskaryan, can you please review this PR, we need this to go in asap. These are just small fixes which we found today in our testing.	6 months ago
Martin Kolb	e5bdb26f76	community[patch]: More flexible handling for entity names in vector store "HANA Cloud" (#19523) ... - **Description:** Added support for lower-case and mixed-case names The names for tables and columns previouly had to be UPPER_CASE. With this enhancement, also lower_case and MixedCase are supported, - **Issue:** N/A - **Dependencies:** no new dependecies added - **Twitter handle:** @sapopensource	6 months ago
Hugoberry	96dc180883	community[minor]: Add `DuckDB` as a vectorstore (#18916) ... DuckDB has a cosine similarity function along list and array data types, which can be used as a vector store. - **Description:** The latest version of DuckDB features a cosine similarity function, which can be used with its support for list or array column types. This PR surfaces this functionality to langchain. - **Dependencies:** duckdb 0.10.0 - **Twitter handle:** @igocrite --------- Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com> Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>	6 months ago
Nithish Raghunandanan	7ad0a3f2a7	community: add Couchbase Vector Store (#18994) ... - **Description:** Added support for Couchbase Vector Search to LangChain. - **Dependencies:** couchbase>=4.1.12 - **Twitter handle:** @nithishr --------- Co-authored-by: Nithish Raghunandanan <nithishr@users.noreply.github.com>	6 months ago
Vittorio Rigamonti	9b2f9ee952	community: VectorStore Infinispan, adding autoconfiguration (#18967) ... **Description**: this PR enable VectorStore autoconfiguration for Infinispan: if metadatas are only of basic types, protobuf config will be automatically generated for the user.	6 months ago
Eugene Yurtsev	6cdca4355d	community[minor]: Revamp PGVector Filtering (#18992) ... This PR makes the following updates in the pgvector database: 1. Use JSONB field for metadata instead of JSON 2. Update operator syntax to include required `$` prefix before the operators (otherwise there will be name collisions with fields) 3. The change is non-breaking, old functionality is still the default, but it will emit a deprecation warning 4. Previous functionality has bugs associated with comparisons due to casting to text (so lexical ordering is used incorrectly for numeric fields) 5. Adds an a GIN index on the JSONB field for more efficient querying	6 months ago
Eugene Yurtsev	1f50274df7	community[patch]: Add pgvector to docker compose and update settings used in integration test (#18815)	7 months ago
Ian	390ef6abe3	community[minor]: Add Initial Support for TiDB Vector Store (#15796) ... This pull request introduces initial support for the TiDB vector store. The current version is basic, laying the foundation for the vector store integration. While this implementation provides the essential features, we plan to expand and improve the TiDB vector store support with additional enhancements in future updates. Upcoming Enhancements: * Support for Vector Index Creation: To enhance the efficiency and performance of the vector store. * Support for max marginal relevance search. * Customized Table Structure Support: Recognizing the need for flexibility, we plan for more tailored and efficient data store solutions. Simple use case exmaple ```python from typing import List, Tuple from langchain.docstore.document import Document from langchain_community.vectorstores import TiDBVectorStore from langchain_openai import OpenAIEmbeddings db = TiDBVectorStore.from_texts( embedding=embeddings, texts=['Andrew like eating oranges', 'Alexandra is from England', 'Ketanji Brown Jackson is a judge'], table_name="tidb_vector_langchain", connection_string=tidb_connection_url, distance_strategy="cosine", ) query = "Can you tell me about Alexandra?" docs_with_score: List[Tuple[Document, float]] = db.similarity_search_with_score(query) for doc, score in docs_with_score: print("-" * 80) print("Score: ", score) print(doc.page_content) print("-" * 80) ```	7 months ago
Sam Khano	1b4dcf22f3	community[minor]: Add DocumentDBVectorSearch VectorStore (#17757) ... **Description:** - Added Amazon DocumentDB Vector Search integration (HNSW index) - Added integration tests - Updated AWS documentation with DocumentDB Vector Search instructions - Added notebook for DocumentDB integration with example usage --------- Co-authored-by: EC2 Default User <ec2-user@ip-172-31-95-226.ec2.internal>	7 months ago
Vittorio Rigamonti	51f3902bc4	community[minor]: Adding support for Infinispan as VectorStore (#17861) ... **Description:** This integrates Infinispan as a vectorstore. Infinispan is an open-source key-value data grid, it can work as single node as well as distributed. Vector search is supported since release 15.x For more: [Infinispan Home](https://infinispan.org) Integration tests are provided as well as a demo notebook	7 months ago
Eugene Yurtsev	4c25b49229	community[major]: breaking change in some APIs to force users to opt-in for pickling (#18696) ... This is a PR that adds a dangerous load parameter to force users to opt in to use pickle. This is a PR that's meant to raise user awareness that the pickling module is involved.	7 months ago
Aayush Kataria	7c2f3f6f95	community[minor]: Adding Azure Cosmos Mongo vCore Vector DB Cache (#16856) ... Description: This pull request introduces several enhancements for Azure Cosmos Vector DB, primarily focused on improving caching and search capabilities using Azure Cosmos MongoDB vCore Vector DB. Here's a summary of the changes: - **AzureCosmosDBSemanticCache**: Added a new cache implementation called AzureCosmosDBSemanticCache, which utilizes Azure Cosmos MongoDB vCore Vector DB for efficient caching of semantic data. Added comprehensive test cases for AzureCosmosDBSemanticCache to ensure its correctness and robustness. These tests cover various scenarios and edge cases to validate the cache's behavior. - **HNSW Vector Search**: Added HNSW vector search functionality in the CosmosDB Vector Search module. This enhancement enables more efficient and accurate vector searches by utilizing the HNSW (Hierarchical Navigable Small World) algorithm. Added corresponding test cases to validate the HNSW vector search functionality in both AzureCosmosDBSemanticCache and AzureCosmosDBVectorSearch. These tests ensure the correctness and performance of the HNSW search algorithm. - **LLM Caching Notebook** - The notebook now includes a comprehensive example showcasing the usage of the AzureCosmosDBSemanticCache. This example highlights how the cache can be employed to efficiently store and retrieve semantic data. Additionally, the example provides default values for all parameters used within the AzureCosmosDBSemanticCache, ensuring clarity and ease of understanding for users who are new to the cache implementation. @hwchase17,@baskaryan, @eyurtsev,	7 months ago
Tomaz Bratanic	5999c4a240	Add support for parameters in neo4j retrieval query (#18310) ... Sometimes, you want to use various parameters in the retrieval query of Neo4j Vector to personalize/customize results. Before, when there were only predefined chains, it didn't really make sense. Now that it's all about custom chains and LCEL, it is worth adding since users can inject any params they wish at query time. Isn't prone to SQL injection-type attacks since we use parameters and not concatenating strings.	7 months ago
am-kinetica	9b8f6455b1	Langchain vectorstore integration with Kinetica (#18102) ... - **Description:** New vectorstore integration with the Kinetica database - **Issue:** - **Dependencies:** the Kinetica Python API `pip install gpudb==7.2.0.1`, - **Tag maintainer:** @baskaryan, @hwchase17 - **Twitter handle:** --------- Co-authored-by: Chad Juliano <cjuliano@kinetica.com>	7 months ago
ehude	9e54c227f1	community[patch]: Bug Neo4j VectorStore when having multiple indexes the sort is not working and the store that returned is random (#17396) ... Bug fix: when having multiple indexes the sort is not working and the store that returned is random. The following small fix resolves the issue.	7 months ago
volodymyr-memsql	0a9a519a39	community[patch]: Added add_images method to SingleStoreDB vector store (#17871) ... In this pull request, we introduce the add_images method to the SingleStoreDB vector store class, expanding its capabilities to handle multi-modal embeddings seamlessly. This method facilitates the incorporation of image data into the vector store by associating each image's URI with corresponding document content, metadata, and either pre-generated embeddings or embeddings computed using the embed_image method of the provided embedding object. the change includes integration tests, validating the behavior of the add_images. Additionally, we provide a notebook showcasing the usage of this new method. --------- Co-authored-by: Volodymyr Tkachuk <vtkachuk-ua@singlestore.com>	7 months ago