- Description: Solving, anthropic packaging version issue by clearing
the mixup from package.version that is being confused with version from
- importlib.metadata.version.
- Issue: it fixes the issue #7283
- Maintainer: @hwchase17
The following change has been explained in the comment -
https://github.com/hwchase17/langchain/issues/7283#issuecomment-1624328978
- Description: pydantic's `ModelField.type_` only exposes the native
data type but not complex type hints like `List`. Thus, generating a
Tool with `from_function` through function signature produces incorrect
argument schemas (e.g., `str` instead of `List[str]`)
- Issue: N/A
- Dependencies: N/A
- Tag maintainer: @hinthornw
- Twitter handle: `mapped`
All the unittest (with an additional one in this PR) passed, though I
didn't try integration tests...
- Description: Sometimes there are csv attachments with the media type
"application/vnd.ms-excel". These files failed to be loaded via the xlrd
library. It throws a corrupted file error. I fixed it by separately
processing excel files using pandas. Excel files will be processed just
like before.
- Dependencies: pandas, os, io
---------
Co-authored-by: Chathura <chathurar@yaalalabs.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
In some cases, the OpenAI response is missing the `finish_reason`
attribute. It seems to happen when using Ada or Babbage and
`stream=true`, but I can't always reproduce it. This change just
gracefully handles the missing key.
Introduction of newest function calling feature doesn't work properly
with PromptLayerChatOpenAI model since on the `_generate` method,
functions argument are not even getting passed to the `ChatOpenAI` base
class which results in empty `ai_message.additional_kwargs`
Fixes #6365
updated `tutorials.mdx`:
- added a link to new `Deeplearning AI` course on LangChain
- added links to other tutorial videos
- fixed format
@baskaryan, @hwchase17
#### Description
refactor BedrockEmbeddings class to clean code as below:
1. inline content type and accept
2. rewrite input_body as a dictionary literal
3. no need to declare embeddings variable, so remove it
- Description: Adding to Chroma integration the option to run a
similarity search by a vector with relevance scores. Fixing two minor
typos.
- Issue: The "lambda_mult" typo is related to #4861
- Maintainer: @rlancemartin, @eyurtsev
Based on user feedback, we have improved the Alibaba Cloud OpenSearch
vector store documentation.
Co-authored-by: zhaoshengbo <shengbo.zsb@alibaba-inc.com>
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Several updates for the PowerBI tools:
- Handle 0 records returned by requesting redo with different filtering
- Handle too large results by optionally tokenizing the result and
comparing against a max (change in signature, non-breaking)
- Implemented LLMChain with Chat for chat models for the tools.
- Updates to the main prompt including tables
- Update to Tool prompt with TOPN function
- Split the tool prompt to allow the LLMChain with ChatPromptTemplate
Smaller fixes for stability.
For visibility: @hinthornw
Replace this comment with:
- Description: added documentation for a template repo that helps
dockerizing and deploying a LangChain using a Cloud Build CI/CD pipeline
to Google Cloud build serverless
- Issue: None,
- Dependencies: None,
- Tag maintainer: @baskaryan,
- Twitter handle: EdenEmarco177
If you're adding a new integration, please include:
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use.
- Description:
- When `keep_separator` is `True` the `_split_text_with_regex()` method
in `text_splitter` uses regex to split, but when `keep_separator` is
`False` it uses `str.split()`. This causes problems when the separator
is a special regex character like `.` or `*`. This PR fixes that by
using `re.split()` in both cases.
- Issue: #7262
- Tag maintainer: @baskaryan
**Description**
In the following page, "Wikipedia" tool is explained.
https://python.langchain.com/docs/modules/agents/tools/integrations/wikipedia
However, the WikipediaAPIWrapper being used is not a tool. This PR
updated the documentation to use a tool WikipediaQueryRun.
**Issue**
None
**Tag maintainer**
Agents / Tools / Toolkits: @hinthornw
- Description: This is a chat model equivalent of HumanInputLLM. An
example notebook is also added.
- Tag maintainer: @hwchase17, @baskaryan
- Twitter handle: N/A
- Description: I have added a `show_progress_bar` parameter (defaults.to
`False`) to the `OpenAIEmbeddings`. If the user sets `show_progress_bar`
to `True`, a progress bar will be displayed.
- Issue: #7246
- Dependencies: N/A
- Tag maintainer: @hwchase17, @baskaryan
- Twitter handle: N/A
Description: `flan-t5-xl` hangs, updated to `flan-t5-xxl`. Tested all
stabilityai LLMs- all hang so removed from tutorial. Temperature > 0 to
prevent unintended determinism.
Issue: #3275
Tag maintainer: @baskaryan
Fix for bug in SitemapLoader
`aiohttp` `get` does not accept `verify` argument, and currently throws
error, so SitemapLoader is not working
This PR fixes it by removing `verify` param for `get` function call
Fixes#6107
#### Who can review?
Tag maintainers/contributors who might be interested:
@eyurtsev
---------
Co-authored-by: techcenary <127699216+techcenary@users.noreply.github.com>
Add an "eval" tag to traced evaluation runs
Most of this PR is actually
https://github.com/hwchase17/langchain/pull/7207 but I can't diff off
two separate PRs
---------
Co-authored-by: Ankush Gola <9536492+agola11@users.noreply.github.com>
### Description
This pull request introduces the "Cube Semantic Layer" document loader,
which demonstrates the retrieval of Cube's data model metadata in a
format suitable for passing to LLMs as embeddings. This enhancement aims
to provide contextual information and improve the understanding of data.
Twitter handle:
@the_cube_dev
---------
Co-authored-by: rlm <pexpresss31@gmail.com>
This PR brings in a vectorstore interface for
[Marqo](https://www.marqo.ai/).
The Marqo vectorstore exposes some of Marqo's functionality in addition
the the VectorStore base class. The Marqo vectorstore also makes the
embedding parameter optional because inference for embeddings is an
inherent part of Marqo.
Docs, notebook examples and integration tests included.
Related PR:
https://github.com/hwchase17/langchain/pull/2807
---------
Co-authored-by: Tom Hamer <tom@marqo.ai>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
