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Oraclevs integration #21123
Oraclevs integration #21123
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Hi @hwchase17: (Escalating this to you would greatly appreciate any response) We are a group in Oracle responsible for integration of langchain with OracleDBAI (the latest release of Oracle Database). The code is in a branch of langchain-ai/langchain. We requested that after reviews our branch be allowed to merge to main. @baskaryan is our reviewer. We have sent a no. of notes requesting status of the reviews & if we can help him reviewing the code. It appears that he is busy with some other errands. Hence escalating this to you. The following is a note that we send to him: Hi @baskaryan: |
please make sure all imports of optional dependencies like oracledb are conditional (happen inside a function). see updated OracleVS implementation for an example |
Head branch was pushed to by a user without write access
@baskaryan I have addressed your comments - now the make test on my machine runs fine the way it runs in the langchain CI/CD environment. I think now this PR is good to merge. Can you please take a look. @efriis can you please approve the workflow. |
Thank you for the contribution @rohanaggarwal7997 @shailendrah @skmishraoracle! Will be included in the next langchain-community release. |
from langchain.vectorstores import OracleVS |
@shahn33: Use from langchain_community.vectorstores import OracleVS |
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 <[email protected]> Co-authored-by: hroyofc <[email protected]> Co-authored-by: Bagatur <[email protected]> Co-authored-by: Bagatur <[email protected]>
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:
If no one reviews your PR within a few days, please @-mention one of baskaryan, efriis, eyurtsev, hwchase17.