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
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- 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 :)
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- **Description:** The return info in the documentation for
similarity_search_by_vector and similarity_search_with_relevance_scores
is wrong
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This reverts commit a46eef64a7.
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- **Description:** Provide a way to use different text for embedding.
- For example, if you are ingesting stack-overflow Q&As for RAG, you
would want to embed the questions and return the answer(s) for the hits.
With this change, the consumer of langchain can implement that easily.
- I noticed the similar function is added on faiss.py with #1912 which
was for performance reason, but I see the same function can be used to
achieve what I thought. So instead of changing Document class to have
embedding_content, I mimicked the implementation of faiss.py.
- The test should provide some guidance on how to use it. It would be
more intuitive if I just pass texts and embedding_texts as separate
arguments, but I chose to use `zip`-ed object for the consistency with
faiss.py implementation.
- I plan to make similar pull request for OpenSearch.
- **Issue:** N/A
- **Dependencies:** None other than the existing ones.
Co-authored-by: Bagatur <baskaryan@gmail.com>
