**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)
- **Description:** Touch up of the documentation page for Metaphor
Search Tool integration. Removes documentation for old built-in tool
wrapper.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
CC @baskaryan @hwchase17 @jmorganca
Having a bit of trouble importing `langchain_experimental` from a
notebook, will figure it out tomorrow
~Ah and also is blocked by #13226~
---------
Co-authored-by: Lance Martin <lance@langchain.dev>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:**
Added support for a Pandas DataFrame OutputParser with format
instructions, along with unit tests and a demo notebook. Namely, we've
added the ability to request data from a DataFrame, have the LLM parse
the request, and then use that request to retrieve a well-formatted
response.
Within LangChain, it seamlessly integrates with language models like
OpenAI's `text-davinci-003`, facilitating streamlined interaction using
the format instructions (just like the other output parsers).
This parser structures its requests as
`<operation/column/row>[<optional_array_params>]`. The instructions
detail permissible operations, valid columns, and array formats,
ensuring clarity and adherence to the required format.
For example:
- When the LLM receives the input: "Retrieve the mean of `num_legs` from
rows 1 to 3."
- The provided format instructions guide the LLM to structure the
request as: "mean:num_legs[1..3]".
The parser processes this formatted request, leveraging the LLM's
understanding to extract the mean of `num_legs` from rows 1 to 3 within
the Pandas DataFrame.
This integration allows users to communicate requests naturally, with
the LLM transforming these instructions into structured commands
understood by the `PandasDataFrameOutputParser`. The format instructions
act as a bridge between natural language queries and precise DataFrame
operations, optimizing communication and data retrieval.
**Issue:**
- https://github.com/langchain-ai/langchain/issues/11532
**Dependencies:**
No additional dependencies :)
**Tag maintainer:**
@baskaryan
**Twitter handle:**
No need. :)
---------
Co-authored-by: Wasee Alam <waseealam@protonmail.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
**Description:**
When using Vald, only insecure grpc connection was supported, so secure
connection is now supported.
In addition, grpc metadata can be added to Vald requests to enable
authentication with a token.
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant
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- **Twitter handle:** we announce bigger features on Twitter. If your PR
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2. an example notebook showing its use. It lives in `docs/extras`
directory.
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
grammar correction
<!-- Thank you for contributing to LangChain!
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- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
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network access,
2. an example notebook showing its use. It lives in `docs/extras`
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@baskaryan, @eyurtsev, @hwchase17.
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Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
# Description
This PR implements Self-Query Retriever for MongoDB Atlas vector store.
I've implemented the comparators and operators that are supported by
MongoDB Atlas vector store according to the section titled "Atlas Vector
Search Pre-Filter" from
https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-stage/.
Namely:
```
allowed_comparators = [
Comparator.EQ,
Comparator.NE,
Comparator.GT,
Comparator.GTE,
Comparator.LT,
Comparator.LTE,
Comparator.IN,
Comparator.NIN,
]
"""Subset of allowed logical operators."""
allowed_operators = [
Operator.AND,
Operator.OR
]
```
Translations from comparators/operators to MongoDB Atlas filter
operators(you can find the syntax in the "Atlas Vector Search
Pre-Filter" section from the previous link) are done using the following
dictionary:
```
map_dict = {
Operator.AND: "$and",
Operator.OR: "$or",
Comparator.EQ: "$eq",
Comparator.NE: "$ne",
Comparator.GTE: "$gte",
Comparator.LTE: "$lte",
Comparator.LT: "$lt",
Comparator.GT: "$gt",
Comparator.IN: "$in",
Comparator.NIN: "$nin",
}
```
In visit_structured_query() the filters are passed as "pre_filter" and
not "filter" as in the MongoDB link above since langchain's
implementation of MongoDB atlas vector
store(libs\langchain\langchain\vectorstores\mongodb_atlas.py) in
_similarity_search_with_score() sets the "filter" key to have the value
of the "pre_filter" argument.
```
params["filter"] = pre_filter
```
Test cases and documentation have also been added.
# Issue
#11616
# Dependencies
No new dependencies have been added.
# Documentation
I have created the notebook mongodb_atlas_self_query.ipynb outlining the
steps to get the self-query mechanism working.
I worked closely with [@Farhan-Faisal](https://github.com/Farhan-Faisal)
on this PR.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** Update the document for drop box loader + made the
messages more verbose when loading pdf file since people were getting
confused
- **Issue:** #13952
- **Tag maintainer:** @baskaryan, @eyurtsev, @hwchase17,
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
- **Description:** Added a tool called RedditSearchRun and an
accompanying API wrapper, which searches Reddit for posts with support
for time filtering, post sorting, query string and subreddit filtering.
- **Issue:** #13891
- **Dependencies:** `praw` module is used to search Reddit
- **Tag maintainer:** @baskaryan , and any of the other maintainers if
needed
- **Twitter handle:** None.
Hello,
This is our first PR and we hope that our changes will be helpful to 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 `praw` package which is already used by
RedditPostsLoader in LangChain. Nonetheless, we have added integration
tests and edited unit tests to test our changes. An example notebook is
also provided. These changes were put together by me, @Anika2000,
@CharlesXu123, and @Jeremy-Cheng-stack
Thank you in advance to the maintainers for their time.
---------
Co-authored-by: What-Is-A-Username <49571870+What-Is-A-Username@users.noreply.github.com>
Co-authored-by: Anika2000 <anika.sultana@mail.utoronto.ca>
Co-authored-by: Jeremy Cheng <81793294+Jeremy-Cheng-stack@users.noreply.github.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:** Added some of the more endpoints supported by serpapi
that are not suported on langchain at the moment, like google trends,
google finance, google jobs, and google lens
- **Issue:** [Add support for many of the querying endpoints with
serpapi #11811](https://github.com/langchain-ai/langchain/issues/11811)
---------
Co-authored-by: zushenglu <58179949+zushenglu@users.noreply.github.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: Ian Xu <ian.xu@mail.utoronto.ca>
Co-authored-by: zushenglu <zushenglu1809@gmail.com>
Co-authored-by: KevinT928 <96837880+KevinT928@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** Volc Engine MaaS serves as an enterprise-grade,
large-model service platform designed for developers. You can visit its
homepage at https://www.volcengine.com/docs/82379/1099455 for details.
This change will facilitate developers to integrate quickly with the
platform.
- **Issue:** None
- **Dependencies:** volcengine
- **Tag maintainer:** @baskaryan
- **Twitter handle:** @he1v3tica
---------
Co-authored-by: lvzhong <lvzhong@bytedance.com>
Instead of using JSON-like syntax to describe node and relationship
properties we changed to a shorter and more concise schema description
Old:
```
Node properties are the following:
[{'properties': [{'property': 'name', 'type': 'STRING'}], 'labels': 'Movie'}, {'properties': [{'property': 'name', 'type': 'STRING'}], 'labels': 'Actor'}]
Relationship properties are the following:
[]
The relationships are the following:
['(:Actor)-[:ACTED_IN]->(:Movie)']
```
New:
```
Node properties are the following:
Movie {name: STRING},Actor {name: STRING}
Relationship properties are the following:
The relationships are the following:
(:Actor)-[:ACTED_IN]->(:Movie)
```
Implements
[#12115](https://github.com/langchain-ai/langchain/issues/12115)
Who can review?
@baskaryan , @eyurtsev , @hwchase17
Integrated Stack Exchange API into Langchain, enabling access to diverse
communities within the platform. This addition enhances Langchain's
capabilities by allowing users to query Stack Exchange for specialized
information and engage in discussions. The integration provides seamless
interaction with Stack Exchange content, offering content from varied
knowledge repositories.
A notebook example and test cases were included to demonstrate the
functionality and reliability of this integration.
- Add StackExchange as a tool.
- Add unit test for the StackExchange wrapper and tool.
- Add documentation for the StackExchange wrapper and tool.
If you have time, could you please review the code and provide any
feedback as necessary! My team is welcome to any suggestions.
---------
Co-authored-by: Yuval Kamani <yuvalkamani@gmail.com>
Co-authored-by: Aryan Thakur <aryanthakur@Aryans-MacBook-Pro.local>
Co-authored-by: Manas1818 <79381912+manas1818@users.noreply.github.com>
Co-authored-by: aryan-thakur <61063777+aryan-thakur@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
<!-- Thank you for contributing to LangChain!
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- **Description:** a description of the change,
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Small fix to _summarization_ example, `reduce_template` should use
`{docs}` variable.
Bug likely introduced as following code suggests using
`hub.pull("rlm/map-prompt")` instead of defined prompt.
