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### Description This PR adds a wrapper which adds support for the OpenSearch vector database. Using opensearch-py client we are ingesting the embeddings of given text into opensearch cluster using Bulk API. We can perform the `similarity_search` on the index using the 3 popular searching methods of OpenSearch k-NN plugin: - `Approximate k-NN Search` use approximate nearest neighbor (ANN) algorithms from the [nmslib](https://github.com/nmslib/nmslib), [faiss](https://github.com/facebookresearch/faiss), and [Lucene](https://lucene.apache.org/) libraries to power k-NN search. - `Script Scoring` extends OpenSearch’s script scoring functionality to execute a brute force, exact k-NN search. - `Painless Scripting` adds the distance functions as painless extensions that can be used in more complex combinations. Also, supports brute force, exact k-NN search like Script Scoring. ### Issues Resolved https://github.com/hwchase17/langchain/issues/1054 --------- Signed-off-by: Naveen Tatikonda <navtat@amazon.com>
22 lines
833 B
Markdown
22 lines
833 B
Markdown
# OpenSearch
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This page covers how to use the OpenSearch ecosystem within LangChain.
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It is broken into two parts: installation and setup, and then references to specific OpenSearch wrappers.
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## Installation and Setup
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- Install the Python package with `pip install opensearch-py`
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## Wrappers
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### VectorStore
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There exists a wrapper around OpenSearch vector databases, allowing you to use it as a vectorstore
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for semantic search using approximate vector search powered by lucene, nmslib and faiss engines
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or using painless scripting and script scoring functions for bruteforce vector search.
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To import this vectorstore:
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```python
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from langchain.vectorstores import OpenSearchVectorSearch
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```
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For a more detailed walkthrough of the OpenSearch wrapper, see [this notebook](../modules/indexes/vectorstore_examples/opensearch.ipynb)
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