# 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
Cohere released the new embedding API (Embed v3:
https://txt.cohere.com/introducing-embed-v3/) that treats document and
query embeddings differently. This PR updated the `CohereEmbeddings` to
use them appropriately. It also works with the old models.
Description: This PR masks API key secrets for the Nebula model from
Symbl.ai
Issue: #12165
Maintainer: @eyurtsev
---------
Co-authored-by: Praveen Venkateswaran <praveen.venkateswaran@ibm.com>
* ChatAnyscale was missing coercion to SecretStr for anyscale api key
* The model inherits from ChatOpenAI so it should not force the openai
api key to be secret str until openai model has the same changes
https://github.com/langchain-ai/langchain/issues/12841
- **Description:** Remove text "LangChain currently does not support"
which appears to be vestigial leftovers from a previous change.
- **Issue:** N/A
- **Dependencies:** N/A
- **Tag maintainer:** @baskaryan, @eyurtsev
- **Twitter handle:** thezanke
- **Description:** Noticed that the Hugging Face Pipeline documentation
was a bit out of date.
Updated with information about passing in a pipeline directly
(consistent with docstring) and a recent contribution of mine on adding
support for multi-gpu specifications with Accelerate in
21eeba075c
Qdrant was incorrectly calculating the cosine similarity and returning
`0.0` for the best match, instead of `1.0`. Internally Qdrant returns a
cosine score from `-1.0` (worst match) to `1.0` (best match), and the
current formula reflects it.
Possibility to pass on_artifacts to a conversation. It can be then
achieved by adding this way:
```python
result = agent.run(
input=message.text,
metadata={
"on_artifact": CALLBACK_FUNCTION
},
)
```
The line removed is not required as there are no other alternative
solutions above than that.
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This patch fixes a spelling typo in message
within wikibase_agent.ipynb.
Signed-off-by: Masanari Iida <standby24x7@gmail.com>
Signed-off-by: Masanari Iida <standby24x7@gmail.com>
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>
