This PR upgrades langchain-community to pydantic 2.
* Most of this PR was auto-generated using code mods with gritql
(https://github.com/eyurtsev/migrate-pydantic/tree/main)
* Subsequently, some code was fixed manually due to accommodate
differences between pydantic 1 and 2
Breaking Changes:
- Use TEXTEMBED_API_KEY and TEXTEMBEB_API_URL for env variables for text
embed integrations:
cbea780492
Other changes:
- Added pydantic_settings as a required dependency for community. This
may be removed if we have enough time to convert the dependency into an
optional one.
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Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
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>
**Description:**
This pull request introduces a new feature for LangChain: the
integration with the Rememberizer API through a custom retriever.
This enables LangChain applications to allow users to load and sync
their data from Dropbox, Google Drive, Slack, their hard drive into a
vector database that LangChain can query. Queries involve sending text
chunks generated within LangChain and retrieving a collection of
semantically relevant user data for inclusion in LLM prompts.
User knowledge dramatically improved AI applications.
The Rememberizer integration will also allow users to access general
purpose vectorized data such as Reddit channel discussions and US
patents.
**Issue:**
N/A
**Dependencies:**
N/A
**Twitter handle:**
https://twitter.com/Rememberizer