117 Commits (f7f3c025855e89aac8849b8d90b0e58018a0c78e)

Author	SHA1	Message	Date
zhanghexian	62fa2bc518	Add Vearch vectorstore (#9846) ... --------- Co-authored-by: zhanghexian1 <zhanghexian1@jd.com> Co-authored-by: Bagatur <baskaryan@gmail.com> Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>	1 year ago
Harrison Chase	f35db9f43e	(WIP) set up experimental (#7959)	1 year ago
Kacper Łukawski	ed6a5532ac	Implement async support in Qdrant local mode (#8001) ... I've extended the support of async API to local Qdrant mode. It is faked but allows prototyping without spinning a container. The tests are improved to test the in-memory case as well. @baskaryan @rlancemartin @eyurtsev @agola11	1 year ago
Adilkhan Sarsen	7bb843477f	Removed kwargs from add_texts (#7595) ... Removing **kwargs argument from add_texts method in DeepLake vectorstore as it confuses users and doesn't fail when user is typing incorrect parameters. Also added small test to ensure the change is applies correctly. Guys could pls take a look: @rlancemartin, @eyurtsev, this is a small PR. Thx so much!	1 year ago
shibuiwilliam	235264a246	Add/test faiss (#7809) ... # What - Add missing test cases to faiss vectore stores	1 year ago
Kacper Łukawski	1ff5b67025	Implement async API for Qdrant vector store (#7704) ... Inspired by #5550, I implemented full async API support in Qdrant. The docs were extended to mention the existence of asynchronous operations in Langchain. I also used that chance to restructure the tests of Qdrant and provided a suite of tests for the async version. Async API requires the GRPC protocol to be enabled. Thus, it doesn't work on local mode yet, but we're considering including the support to be consistent.	1 year ago
Bagatur	c17a80f11c	fix chroma updated upsert interface (#7643) ... new chroma release seems to not support empty dicts for metadata. related to #7633	1 year ago
Bagatur	b08f903755	fix chroma init bug (#7639)	1 year ago
Yaroslav Halchenko	0d92a7f357	codespell: workflow, config + some (quite a few) typos fixed (#6785) ... Probably the most boring PR to review ;) Individual commits might be easier to digest --------- Co-authored-by: Bagatur <baskaryan@gmail.com> Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>	1 year ago
Bagatur	2babe3069f	Revert pinecone v4 support (#7566) ... Revert 9d13dcd	1 year ago
Kacper Łukawski	1f83b5f47e	Reuse the existing collection if configured properly in Qdrant.from_texts (#7530) ... 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.	1 year ago
Hashem Alsaket	1dd4236177	Fix HF endpoint returns blank for text-generation (#7386) ... 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>	1 year ago
Raymond Yuan	5171c3bcca	Refactor vector storage to correctly handle relevancy scores (#6570) ... 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/6526 https://github.com/hwchase17/langchain/issues/6481 https://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>	1 year ago
Stanko Kuveljic	9d13dcd17c	Pinecone: Add V4 support (#7473)	1 year ago
Adilkhan Sarsen	5debd5043e	Added deeplake use case examples of the new features (#6528) ... <!-- 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 - @hwchase17 VectorStores / Retrievers / Memory - @dev2049 --> 1. Added use cases of the new features 2. Done some code refactoring --------- Co-authored-by: Ivo Stranic <istranic@gmail.com>	1 year ago
Paul-Emile Brotons	d2cf0d16b3	adding max_marginal_relevance_search method to MongoDBAtlasVectorSearch (#7310) ... Adding a maximal_marginal_relevance method to the MongoDBAtlasVectorSearch vectorstore enhances the user experience by providing more diverse search results Issue: #7304	1 year ago
Jan Kubica	fed64ae060	Chroma: add vector search with scores (#6864) ... - Description: Adding to Chroma integration the option to run a similarity search by a vector with relevance scores. Fixing two minor typos. - Issue: The "lambda_mult" typo is related to #4861 - Maintainer: @rlancemartin, @eyurtsev	1 year ago
Tom	e533da8bf2	Adding Marqo to vectorstore ecosystem (#7068) ... This PR brings in a vectorstore interface for [Marqo](https://www.marqo.ai/). The Marqo vectorstore exposes some of Marqo's functionality in addition the the VectorStore base class. The Marqo vectorstore also makes the embedding parameter optional because inference for embeddings is an inherent part of Marqo. Docs, notebook examples and integration tests included. Related PR: https://github.com/hwchase17/langchain/pull/2807 --------- Co-authored-by: Tom Hamer <tom@marqo.ai> Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>	1 year ago
Richy Wang	cab7d86f23	Implement delete interface of vector store on AnalyticDB (#7170) ... Hi, there This pull request contains two commit: **1. Implement delete interface with optional ids parameter on AnalyticDB.** **2. Allow customization of database connection behavior by exposing engine_args parameter in interfaces.** - This commit adds the `engine_args` parameter to the interfaces, allowing users to customize the behavior of the database connection. The `engine_args` parameter accepts a dictionary of additional arguments that will be passed to the create_engine function. Users can now modify various aspects of the database connection, such as connection pool size and recycle time. This enhancement provides more flexibility and control to users when interacting with the database through the exposed interfaces. This commit is related to VectorStores @rlancemartin @eyurtsev Thank you for your attention and consideration.	1 year ago
Mike Salvatore	d0c7f7c317	Remove `None` default value for FAISS relevance_score_fn (#7085) ... ## Description The type hint for `FAISS.__init__()`'s `relevance_score_fn` parameter allowed the parameter to be set to `None`. However, a default function is provided by the constructor. This led to an unnecessary check in the code, as well as a test to verify this check. **ASSUMPTION**: There's no reason to ever set `relevance_score_fn` to `None`. This PR changes the type hint and removes the unnecessary code.	1 year ago
Ofer Mendelevitch	153b56d19b	Vectara upd2 (#6506) ... Update to Vectara integration - By user request added "add_files" to take advantage of Vectara capabilities to process files on the backend, without the need for separate loading of documents and chunking in the chain. - Updated vectara.ipynb example notebook to be broader and added testing of add_file() @hwchase17 - project lead --------- Co-authored-by: rlm <pexpresss31@gmail.com>	1 year ago
Stefano Lottini	8d2281a8ca	Second Attempt - Add concurrent insertion of vector rows in the Cassandra Vector Store (#7017) ... Retrying with the same improvements as in #6772, this time trying not to mess up with branches. @rlancemartin doing a fresh new PR from a branch with a new name. This should do. Thank you for your help! --------- Co-authored-by: Jonathan Ellis <jbellis@datastax.com> Co-authored-by: rlm <pexpresss31@gmail.com>	1 year ago
Kacper Łukawski	140ba682f1	Support named vectors in Qdrant (#6871) ... # 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.	1 year ago
Rian Dolphin	2e39ede848	add with score option for max marginal relevance (#6867) ... ### Adding the functionality to return the scores with retrieved documents when using the max marginal relevance - Description: Add the method `max_marginal_relevance_search_with_score_by_vector` to the FAISS wrapper. Functionality operates the same as `similarity_search_with_score_by_vector` except for using the max marginal relevance retrieval framework like is used in the `max_marginal_relevance_search_by_vector` method. - Dependencies: None - Tag maintainer: @rlancemartin @eyurtsev - Twitter handle: @RianDolphin --------- Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>	1 year ago
minhajul-clarifai	6e57306a13	Clarifai integration (#5954) ... # Changes This PR adds [Clarifai](https://www.clarifai.com/) integration to Langchain. Clarifai is an end-to-end AI Platform. Clarifai offers user the ability to use many types of LLM (OpenAI, cohere, ect and other open source models). As well, a clarifai app can be treated as a vector database to upload and retrieve data. The integrations includes: - Clarifai LLM integration: Clarifai supports many types of language model that users can utilize for their application - Clarifai VectorDB: A Clarifai application can hold data and embeddings. You can run semantic search with the embeddings #### Before submitting - [x] Added integration test for LLM - [x] Added integration test for VectorDB - [x] Added notebook for LLM - [x] Added notebook for VectorDB Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>	1 year ago
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