- **Description:**
Add missing apostrophe in `user's` in stuff_prompt's system_template.
The first sentence in the system template went from:
> Use the following pieces of context to answer the users question.
to
> Use the following pieces of context to answer the user's question.
- **Issue:**
- **Dependencies:** none
- **Tag maintainer:** @baskaryan
- **Twitter handle:** ojohnnyo
- This is used internally to gather aggregate usage metrics for the
LangChain integrations
- Note: This cannot be added to some of the Vertex AI integrations at
this time because the SDK doesn't allow overriding the
[`ClientInfo`](https://googleapis.dev/python/google-api-core/latest/client_info.html#module-google.api_core.client_info)
- Added to:
- BigQuery
- Google Cloud Storage
- Document AI
- Vertex AI Model Garden
- Document AI Warehouse
- Vertex AI Search
- Vertex AI Matching Engine (Cloud Storage Client)
@baskaryan, @eyurtsev, @hwchase17
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
code of conduct.md file is missing it is generally present in good repos
which have large community
Replace this entire comment with:
- **Description:** Added a `code_of_conduct.md` file to the repository
to establish community standards and guidelines for contributors.
- **Issue:** N/A
- **Dependencies:** N/A
- **Tag maintainer:** N/A
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
- **Description:** In the max_marginal_relevance_search function of the
ElasticsearchStore vector store, the name of the field corresponding to
the vector embedding of the document is hard coded in the delete
statement that drops the field from the document metadata. This results
in an exception if the vector embedding field is customized. This PR
changes the hard-coded "vector" into the vector_query_field variable.
- **Issue:** None
- **Dependencies:** None
- **Tag maintainer:** @hwchase17
Co-authored-by: Shilong Dai <sdai@viperfish.net>
**Description: Allow to inject boto3 client for Cross account access
type of scenarios in using SagemakerEndpointEmbeddings and also updated
the documentation for same in the sample notebook**
**Issue:SagemakerEndpointEmbeddings cross account capability #10634
#10184**
Dependencies: None
Tag maintainer:
Twitter handle:lethargicoder
Co-authored-by: Vikram(VS) <vssht@amazon.com>
- **Description:** sqlalchemy create_engine() does not take into account
connect_args which are mandatory for managed PGSQL instances on cloud
providers (ssl_context for example).
Also re-enabled create_vector_extension at post_init for using pgvector
class seamlessly
- **Tag maintainer:** @baskaryan, @eyurtsev, @hwchase17.
---------
Co-authored-by: Sami Bargaoui <bargaoui.sam@gmail.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
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If non-pickleable objects (like locks) get passed to the tracing
callback, they'll fail in the deepcopy. Fallback to a shallow copy in
these instances .
We don't use any of the new functionality at the moment. Just making
sure we don't fall back on versions and fail to benefit from new
patches. This is an easy upgrade and it's always harder to upgrade
across multiple major versions at once.
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Adding Tavily Search API as a tool. I will be the maintainer and
assaf_elovic is the twitter handler.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Current ChatTongyi is not compatible with DashScope API, which will
cause error when passing api key to chat model directly.
- **Description:** Update tongyi.py to be compatible with DashScope API.
Specifically, update parameter name "dashscope_api_key" to "api_key".
- **Issue:** None.
- **Dependencies:** Nothing new, Tongyi would require DashScope as
before.
- **Description:**
- Replace Telegram with Whatsapp in whatsapp.ipynb
- Add # to mark the telegram as heading in telegram.ipynb
- **Issue:** None
- **Dependencies:** None
- **Description:** Implementing the Google Scholar Tool as requested in
PR #11505. The tool will be using the [serpapi python
package](https://serpapi.com/integrations/python#search-google-scholar).
The main idea of the tool will be to return the results from a Google
Scholar search given a query as an input to the tool.
- **Tag maintainer:** @baskaryan, @eyurtsev, @hwchase17
Replace this entire comment with:
- **Description:** Fix superfluous [Auto-fixing
parser](https://python.langchain.com/docs/modules/model_io/output_parsers/output_fixing_parser)
docs. Also switching to `langchain.pydantic_v1` from the direct
reference to `pydantic`,
- **Issue:** N/A,
- **Dependencies:** N/A,
- **Tag maintainer:** for a quicker response, tag the relevant
maintainer (see below),
- **Twitter handle:** @dosuken123
Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` to check this
locally.
See contribution guidelines for more information on how to write/run
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If you're adding a new integration, please include:
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2. an example notebook showing its use. It lives in `docs/extras`
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@baskaryan, @eyurtsev, @hwchase17.
- Fixes error:
```
ValueError: "GoogleVertexAISearchRetriever" object has no field "_serving_config"
```
Introduced in #11736
@baskaryan, @eyurtsev, @hwchase17 if you could review and merge quickly,
that would be appreciated :)
