Description: Added NVIDIA AI Playground Initial support for a selection of models (Llama models, Mistral, etc.)
Dependencies: These models do depend on the AI Playground services in NVIDIA NGC. API keys with a significant amount of trial compute are available (10K queries as of the time of writing).
H/t to @VKudlay
- Add gemini references
- Fix the notebook (ultra isn't generally available; also gemini will
randomly filter out responses, so added a fallback)
---------
Co-authored-by: Leonid Kuligin <lkuligin@yandex.ru>
h/t to @lkuligin
- **Description:** added new models on VertexAI
- **Twitter handle:** @lkuligin
---------
Co-authored-by: Leonid Kuligin <lkuligin@yandex.ru>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
This PR adds an example notebook for the Databricks Vector Search vector
store. It also adds an introduction to the Databricks Vector Search
product on the Databricks's provider page.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** :
I just update the openai functions docs to use the latest model (ex.
gpt-3.5-turbo-1106)
https://python.langchain.com/docs/modules/chains/how_to/openai_functions
The reason is as follow:
After reviewing the OpenAI Function Calling official guide at
https://platform.openai.com/docs/guides/function-calling, the following
information was noted:
> "The latest models (gpt-3.5-turbo-1106 and gpt-4-1106-preview) have
been trained to both detect when a function should be called (depending
on the input) and to respond with JSON that adheres to the function
signature more closely than previous models. With this capability also
comes potential risks. We strongly recommend building in user
confirmation flows before taking actions that impact the world on behalf
of users (sending an email, posting something online, making a purchase,
etc)."
CC: @efriis
**Description:** This PR fixes `HuggingFaceHubEmbeddings` by making the
API token optional (as in the client beneath). Most models don't require
one. I also updated the notebook for TEI (text-embeddings-inference)
accordingly as requested here #14288. In addition, I fixed a mistake in
the POST call parameters.
**Tag maintainers:** @baskaryan
Description: I was following the docs and got an error about missing
tiktoken dependency. Adding it to the comment where the langchain and
docarray libs are.
This patch fixes some typos.
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Signed-off-by: Masanari Iida <standby24x7@gmail.com>
- **Description:** a notebook documenting Yellowbrick as a vector store
usage
---------
Co-authored-by: markcusack <markcusack@markcusacksmac.lan>
Co-authored-by: markcusack <markcusack@Mark-Cusack-sMac.local>
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Fix `from langchain.llms import DatabricksEmbeddings` to `from
langchain.embeddings import DatabricksEmbeddings`.
Signed-off-by: harupy <17039389+harupy@users.noreply.github.com>
Added `presidio` and `OneNote` references to `microsoft.mdx`; added link
and description to the `presidio` notebook
---------
Co-authored-by: Erick Friis <erickfriis@gmail.com>
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-->
Keeping it consistent with everywhere else in the docs and adding the
missing imports to be able to copy paste and run the code example.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Updated the MongoDB Atlas Vector Search docs to indicate the service is
Generally Available, updated the example to use the new index
definition, and added an example that uses metadata pre-filtering for
semantic search
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Updated provider page by adding LLM and ChatLLM references; removed a
content that is duplicate text from the LLM referenced page.
Updated the collback page
Many jupyter notebooks didn't pass linting. List of these files are
presented in the [tool.ruff.lint.per-file-ignores] section of the
pyproject.toml . Addressed these bugs:
- fixed bugs; added missed imports; updated pyproject.toml
Only the `document_loaders/tensorflow_datasets.ipyn`,
`cookbook/gymnasium_agent_simulation.ipynb` are not completely fixed.
I'm not sure about imports.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
The namespaces like `langchain.agents.format_scratchpad` clogging the
API Reference sidebar.
This change removes those 3-level namespaces from sidebar (this issue
was discussed with @efriis )
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Keeping it simple for now.
Still iterating on our docs build in pursuit of making everything mdxv2
compatible for docusaurus 3, and the fewer custom scripts we're reliant
on through that, the less likely the docs will break again.
Other things to consider in future:
Quarto rewriting in ipynbs:
https://quarto.org/docs/extensions/nbfilter.html (but this won't do
md/mdx files)
Docusaurus plugins for rewriting these paths
Description :
Updated the functions with new Clarifai python SDK.
Enabled initialisation of Clarifai class with model URL.
Updated docs with new functions examples.
<!-- Thank you for contributing to LangChain!
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- **Description:** add gitlab url from env,
- **Issue:** no issue,
- **Dependencies:** no,
- **Tag maintainer:** for a quicker response, tag the relevant
maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR
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---------
Co-authored-by: Erick Friis <erick@langchain.dev>
- **Description:** Added a notebook to illustrate how to use
`text-embeddings-inference` from huggingface. As
`HuggingFaceHubEmbeddings` was using a deprecated client, I made the
most of this PR updating that too.
- **Issue:** #13286
- **Dependencies**: None
- **Tag maintainer:** @baskaryan
### Description
Fixed 3 doc issues:
1. `ConfigurableField ` needs to be imported in
`docs/docs/expression_language/how_to/configure.ipynb`
2. use `error` instead of `RateLimitError()` in
`docs/docs/expression_language/how_to/fallbacks.ipynb`
3. I think it might be better to output the fixed json data(when I
looked at this example, I didn't understand its purpose at first, but
then I suddenly realized):
<img width="1219" alt="Screenshot 2023-12-05 at 10 34 13 PM"
src="https://github.com/langchain-ai/langchain/assets/10000925/7623ba13-7b56-4964-8c98-b7430fabc6de">
- **Description:** Adapt JinaEmbeddings to run with the new Jina AI
Embedding platform
- **Twitter handle:** https://twitter.com/JinaAI_
---------
Co-authored-by: Joan Fontanals Martinez <joan.fontanals.martinez@jina.ai>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:**
Reference library azure-search-documents has been adapted in version
11.4.0:
1. Notebook explaining Azure AI Search updated with most recent info
2. HnswVectorSearchAlgorithmConfiguration --> HnswAlgorithmConfiguration
3. PrioritizedFields(prioritized_content_fields) -->
SemanticPrioritizedFields(content_fields)
4. SemanticSettings --> SemanticSearch
5. VectorSearch(algorithm_configurations) -->
VectorSearch(configurations)
--> Changes now reflected on Langchain: default vector search config
from langchain is now compatible with officially released library from
Azure.
- **Issue:**
Issue creating a new index (due to wrong class used for default vector
search configuration) if using latest version of azure-search-documents
with current langchain version
- **Dependencies:** azure-search-documents>=11.4.0,
- **Tag maintainer:** ,
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
The Github utilities are fantastic, so I'm adding support for deeper
interaction with pull requests. Agents should read "regular" comments
and review comments, and the content of PR files (with summarization or
`ctags` abbreviations).
Progress:
- [x] Add functions to read pull requests and the full content of
modified files.
- [x] Function to use Github's built in code / issues search.
Out of scope:
- Smarter summarization of file contents of large pull requests (`tree`
output, or ctags).
- Smarter functions to checkout PRs and edit the files incrementally
before bulk committing all changes.
- Docs example for creating two agents:
- One watches issues: For every new issue, open a PR with your best
attempt at fixing it.
- The other watches PRs: For every new PR && every new comment on a PR,
check the status and try to finish the job.
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Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
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Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the
same people again.
See contribution guidelines for more information on how to write/run
tests, lint, etc:
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-->
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
The `/docs/integrations/toolkits/vectorstore` page is not the
Integration page. The best place is in `/docs/modules/agents/how_to/`
- Moved the file
- Rerouted the page URL
Allow users to pass a generic `BaseStore[str, bytes]` to
MultiVectorRetriever, removing the need to use the `create_kv_docstore`
method. This encoding will now happen internally.
@rlancemartin @eyurtsev
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Switches to a more maintained solution for building ipynb -> md files
(`quarto`)
Also bumps us down to python3.8 because it's significantly faster in the
vercel build step. Uses default openssl version instead of upgrading as
well.
**Description:**
Adds the document loader for [Couchbase](http://couchbase.com/), a
distributed NoSQL database.
**Dependencies:**
Added the Couchbase SDK as an optional dependency.
**Twitter handle:** nithishr
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** Our PR is an integration of a Steam API Tool that
makes recommendations on steam games based on user's Steam profile and
provides information on games based on user provided queries.
- **Issue:** the issue # our PR implements:
https://github.com/langchain-ai/langchain/issues/12120
- **Dependencies:** python-steam-api library, steamspypi library and
decouple library
- **Tag maintainer:** @baskaryan, @hwchase17
- **Twitter handle:** N/A
Hello langchain Maintainers,
We are a team of 4 University of Toronto students contributing to
langchain as part of our course [CSCD01 (link to course
page)](https://cscd01.com/work/open-source-project). We hope our changes
help the community. We have run make format, make lint and make test
locally before submitting the PR. To our knowledge, our changes do not
introduce any new errors.
Our PR integrates the python-steam-api, steamspypi and decouple
packages. We have added integration tests to test our python API
integration into langchain and an example notebook is also provided.
Our amazing team that contributed to this PR: @JohnY2002, @shenceyang,
@andrewqian2001 and @muntaqamahmood
Thank you in advance to all the maintainers for reviewing our PR!
---------
Co-authored-by: Shence <ysc1412799032@163.com>
Co-authored-by: JohnY2002 <johnyuan0526@gmail.com>
Co-authored-by: Andrew Qian <andrewqian2001@gmail.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: JohnY <94477598+JohnY2002@users.noreply.github.com>
### Description
Starting from [openai version
1.0.0](17ac677995 (module-level-client)),
the camel case form of `openai.ChatCompletion` is no longer supported
and has been changed to lowercase `openai.chat.completions`. In
addition, the returned object only accepts attribute access instead of
index access:
```python
import openai
# optional; defaults to `os.environ['OPENAI_API_KEY']`
openai.api_key = '...'
# all client options can be configured just like the `OpenAI` instantiation counterpart
openai.base_url = "https://..."
openai.default_headers = {"x-foo": "true"}
completion = openai.chat.completions.create(
model="gpt-4",
messages=[
{
"role": "user",
"content": "How do I output all files in a directory using Python?",
},
],
)
print(completion.choices[0].message.content)
```
So I implemented a compatible adapter that supports both attribute
access and index access:
```python
In [1]: from langchain.adapters import openai as lc_openai
...: messages = [{"role": "user", "content": "hi"}]
In [2]: result = lc_openai.chat.completions.create(
...: messages=messages, model="gpt-3.5-turbo", temperature=0
...: )
In [3]: result.choices[0].message
Out[3]: {'role': 'assistant', 'content': 'Hello! How can I assist you today?'}
In [4]: result["choices"][0]["message"]
Out[4]: {'role': 'assistant', 'content': 'Hello! How can I assist you today?'}
In [5]: result = await lc_openai.chat.completions.acreate(
...: messages=messages, model="gpt-3.5-turbo", temperature=0
...: )
In [6]: result.choices[0].message
Out[6]: {'role': 'assistant', 'content': 'Hello! How can I assist you today?'}
In [7]: result["choices"][0]["message"]
Out[7]: {'role': 'assistant', 'content': 'Hello! How can I assist you today?'}
In [8]: for rs in lc_openai.chat.completions.create(
...: messages=messages, model="gpt-3.5-turbo", temperature=0, stream=True
...: ):
...: print(rs.choices[0].delta)
...: print(rs["choices"][0]["delta"])
...:
{'role': 'assistant', 'content': ''}
{'role': 'assistant', 'content': ''}
{'content': 'Hello'}
{'content': 'Hello'}
{'content': '!'}
{'content': '!'}
In [20]: async for rs in await lc_openai.chat.completions.acreate(
...: messages=messages, model="gpt-3.5-turbo", temperature=0, stream=True
...: ):
...: print(rs.choices[0].delta)
...: print(rs["choices"][0]["delta"])
...:
{'role': 'assistant', 'content': ''}
{'role': 'assistant', 'content': ''}
{'content': 'Hello'}
{'content': 'Hello'}
{'content': '!'}
{'content': '!'}
...
```
### Twitter handle
[lin_bob57617](https://twitter.com/lin_bob57617)
Depends on #13699. Updates the existing mlflow and databricks examples.
---------
Co-authored-by: Ben Wilson <39283302+BenWilson2@users.noreply.github.com>
The `AWS` platform page has many missed integrations.
- added missed integration references to the `AWS` platform page
- added/updated descriptions and links in the referenced notebooks
- renamed two notebook files. They have file names != page Title, which
generate unordered ToC.
- reroute the URLs for renamed files
- fixed `amazon_textract` notebook: removed failed cell outputs
Hi,
I made some code changes on the Hologres vector store to improve the
data insertion performance.
Also, this version of the code uses `hologres-vector` library. This
library is more convenient for us to update, and more efficient in
performance.
The code has passed the format/lint/spell check. I have run the unit
test for Hologres connecting to my own database.
Please check this PR again and tell me if anything needs to change.
Best,
Changgeng,
Developer @ Alibaba Cloud
Co-authored-by: Changgeng Zhao <zhaochanggeng.zcg@alibaba-inc.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
`Hugging Face` is definitely a platform. It includes many integrations
for many modules (LLM, Embedding, DocumentLoader, Tool)
So, a doc page was added that defines Hugging Face as a platform.
- **Description:**
This PR introduces the Slack toolkit to LangChain, which allows users to
read and write to Slack using the Slack API. Specifically, we've added
the following tools.
1. get_channel: Provides a summary of all the channels in a workspace.
2. get_message: Gets the message history of a channel.
3. send_message: Sends a message to a channel.
4. schedule_message: Sends a message to a channel at a specific time and
date.
- **Issue:** This pull request addresses [Add Slack Toolkit
#11747](https://github.com/langchain-ai/langchain/issues/11747)
- **Dependencies:** package`slack_sdk`
Note: For this toolkit to function you will need to add a Slack app to
your workspace. Additional info can be found
[here](https://slack.com/help/articles/202035138-Add-apps-to-your-Slack-workspace).
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: ArianneLavada <ariannelavada@gmail.com>
Co-authored-by: ArianneLavada <84357335+ArianneLavada@users.noreply.github.com>
Co-authored-by: ariannelavada@gmail.com <you@example.com>
- **Description:** : As described in the issue below,
https://python.langchain.com/docs/use_cases/summarization
I've modified the Python code in the above notebook to perform well.
I also modified the OpenAI LLM model to the latest version as shown
below.
`gpt-3.5-turbo-16k --> gpt-3.5-turbo-1106`
This is because it seems to be a bit more responsive.
- **Issue:** : #14066
### Description
The `RateLimitError` initialization method has changed after openai v1,
and the usage of `patch` needs to be changed.
### Twitter handle
[lin_bob57617](https://twitter.com/lin_bob57617)
This PR adds an "Azure AI data" document loader, which allows Azure AI
users to load their registered data assets as a document object in
langchain.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** Change instances of RunnableMap to RunnableParallel,
as that should be the one used going forward. This makes it consistent
across the codebase.
### Description:
Doc addition for LCEL introduction. Adds a more basic starter guide for
using LCEL.
---------
Co-authored-by: Alex Kira <akira@Alexs-MBP.local.tld>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** just a little change of ErnieChatBot class
description, sugguesting user to use more suitable class
- **Issue:** none,
- **Dependencies:** none,
- **Tag maintainer:** @baskaryan ,
- **Twitter handle:** none
### Description
Now if `example` in Message is False, it will not be displayed. Update
the output in this document.
```python
In [22]: m = HumanMessage(content="Text")
In [23]: m
Out[23]: HumanMessage(content='Text')
In [24]: m = HumanMessage(content="Text", example=True)
In [25]: m
Out[25]: HumanMessage(content='Text', example=True)
```
### Twitter handle
[lin_bob57617](https://twitter.com/lin_bob57617)
