This is a template demonstrating how to utilize Google Sensitive Data
Protection in conjunction with ChatVertexAI(). Tagging you @efriis as
you reviewed my last template. :) Thanks!
Proof of successful execution:
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Co-authored-by: Erick Friis <erick@langchain.dev>
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Adding rag-opensearch template.
---------
Signed-off-by: kalyanr <kalyan.ben10@live.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
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---------
Co-authored-by: Erick Friis <erick@langchain.dev>
…rnative LLMs until used
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---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
The `langchain` repo was being flagged for using vulnerable
dependencies, some of which were in this template's lockfile. Updating
to newer versions should fix that.
Just `poetry lock` and moving `langchain` to the latest version, in case
folks copy this template.
This resolves some vulnerable dependency alerts GitHub code scanning was
flagging.
**Description:** This is like the rag-conversation template in many
ways. What's different is:
- support for a timescale vector store.
- support for time-based filters.
- support for metadata filters.
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---------
Co-authored-by: Erick Friis <erick@langchain.dev>
# Astra DB Vector store integration
- **Description:** This PR adds a `VectorStore` implementation for
DataStax Astra DB using its HTTP API
- **Issue:** (no related issue)
- **Dependencies:** A new required dependency is `astrapy` (`>=0.5.3`)
which was added to pyptoject.toml, optional, as per guidelines
- **Tag maintainer:** I recently mentioned to @baskaryan this
integration was coming
- **Twitter handle:** `@rsprrs` if you want to mention me
This PR introduces the `AstraDB` vector store class, extensive
integration test coverage, a reworking of the documentation which
conflates Cassandra and Astra DB on a single "provider" page and a new,
completely reworked vector-store example notebook (common to the
Cassandra store, since parts of the flow is shared by the two APIs). I
also took care in ensuring docs (and redirects therein) are behaving
correctly.
All style, linting, typechecks and tests pass as far as the `AstraDB`
integration is concerned.
I could build the documentation and check it all right (but ran into
trouble with the `api_docs_build` makefile target which I could not
verify: `Error: Unable to import module
'plan_and_execute.agent_executor' with error: No module named
'langchain_experimental'` was the first of many similar errors)
Thank you for a review!
Stefano
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
- **Description:** Correct naming for package in README
- **Issue:** README wasn't aligned with pyproject.toml, resulting in not
being able to install the rag-supabase package.
- **Tag maintainer:** @gregnr
This PR adds a self-querying template using Qdrant as a vector store.
The template uses an artificial dataset and was implemented in a way
that simplifies passing different components and choosing LLM and
embedding providers.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
# Description
Add a RAG template showcasing Momento Vector Index as a vector store.
Includes a project directory and README.
# **Twitter handle**
Tag the company @momentohq for a mention and @mlonml for the
contribution.
This change adds a new template for simple RAG using the SingleStoreDB
vectorstore.
Twitter: @alexjpeng
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
