… (#14723)
- **Description:** Minor updates per marketing requests. Namely, name
decisions (AI Foundation Models / AI Playground)
- **Tag maintainer:** @hinthornw
Do want to pass around the PR for a bit and ask a few more marketing
questions before merge, but just want to make sure I'm not working in a
vacuum. No major changes to code functionality intended; the PR should
be for documentation and only minor tweaks.
Note: QA model is a bit borked across staging/prod right now. Relevant
teams have been informed and are looking into it, and I'm placeholdered
the response to that of a working version in the notebook.
Co-authored-by: Vadim Kudlay <32310964+VKudlay@users.noreply.github.com>
Replace this entire comment with:
- **Description:** added support for new Google GenerativeAI models
- **Twitter handle:** lkuligin
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
hi! just a simple typo fix in the local LLM python docs
- **Description:** removing a trailing "\`" character in a `!pip install
...` command
- **Issue:** n/a
- **Dependencies:** n/a
- **Tag maintainer:** n/a
- **Twitter handle:** n/a
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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2. an example notebook showing its use. It lives in `docs/extras`
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@baskaryan, @eyurtsev, @hwchase17.
-->
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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- **Issue:** the issue # it fixes (if applicable),
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1. a test for the integration, preferably unit tests that do not rely on
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2. an example notebook showing its use. It lives in `docs/extras`
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@baskaryan, @eyurtsev, @hwchase17.
-->
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!
Replace this entire comment with:
- **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
gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` to check this
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See contribution guidelines for more information on how to write/run
tests, lint, etc:
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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. It lives in `docs/extras`
directory.
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
---------
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.
<!-- Thank you for contributing to LangChain!
Replace this 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 maintainer
(see below),
- Twitter handle: we announce bigger features on Twitter. If your PR
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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.
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:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
---------
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)
- **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
maintainer (see below),
- **Twitter handle:** we announce bigger features on Twitter. If your PR
gets announced, and you'd like a mention, we'll gladly shout you out!
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
tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
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. 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!
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,
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2. an example notebook showing its use. It lives in `docs/extras`
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@baskaryan, @eyurtsev, @hwchase17.
-->
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!
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
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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.
### Description:
Hey 👋🏽 this is a small docs example fix. Hoping it helps future developers who are working with Langchain.
### Problem:
Take a look at the original example code. You were not able to get the `dialogue_turn[0]` while it was a tuple.
Original code:
```python
def _format_chat_history(chat_history: List[Tuple]) -> str:
buffer = ""
for dialogue_turn in chat_history:
human = "Human: " + dialogue_turn[0]
ai = "Assistant: " + dialogue_turn[1]
buffer += "\n" + "\n".join([human, ai])
return buffer
```
In the original code you were getting this error:
```bash
human = "Human: " + dialogue_turn[0].content
~~~~~~~~~~~~~^^^
TypeError: 'HumanMessage' object is not subscriptable
```
### Solution:
The fix is to just for loop over the chat history and look to see if its a human or ai message and add it to the buffer.
The `integrations/vectorstores/matchingengine.ipynb` example has the
"Google Vertex AI Vector Search" title. This place this Title in the
wrong order in the ToC (it is sorted by the file name).
- Renamed `integrations/vectorstores/matchingengine.ipynb` into
`integrations/vectorstores/google_vertex_ai_vector_search.ipynb`.
- Updated a correspondent comment in docstring
- Rerouted old URL to a new URL
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Addressed this issue with the top menu: It allocates too much space. If the screen is small, then the top menu items are split into two lines and look unreadable.
Another issue is with several top menu items: "Chat our docs" and "Also by LangChain". They are compound of several words which also hurts readability. The top menu items should be 1-word size.
Updates:
- "Chat our docs" -> "Chat" (the meaning is clean after clicking/opening the item)
- "Also by LangChain" -> "🦜️🔗"
- "🦜️🔗" moved before "Chat" item. This new item is partially copied from the first left item, the "🦜️🔗 LangChain". This design (with two 🦜️🔗 elements, visually splits the top menu into two parts. The first item in each part holds the 🦜️🔗 symbols and, when we click the second 🦜️🔗 item, it opens the drop-down menu. So, we've got two visually similar parts, which visually split the top menu on the right side: the LangChain Docs (and Doc-related items) and the lift side: other LangChain.ai (company) products/docs.
There are the following main changes in this PR:
1. Rewrite of the DocugamiLoader to not do any XML parsing of the DGML
format internally, and instead use the `dgml-utils` library we are
separately working on. This is a very lightweight dependency.
2. Added MMR search type as an option to multi-vector retriever, similar
to other retrievers. MMR is especially useful when using Docugami for
RAG since we deal with large sets of documents within which a few might
be duplicates and straight similarity based search doesn't give great
results in many cases.
We are @docugami on twitter, and I am @tjaffri
---------
Co-authored-by: Taqi Jaffri <tjaffri@docugami.com>
- **Description:** Adds a retriever implementation for [Knowledge Bases
for Amazon Bedrock](https://aws.amazon.com/bedrock/knowledge-bases/), a
new service announced at AWS re:Invent, shortly before this PR was
opened. This depends on the `bedrock-agent-runtime` service, which will
be included in a future version of `boto3` and of `botocore`. We will
open a follow-up PR documenting the minimum required versions of `boto3`
and `botocore` after that information is available.
- **Issue:** N/A
- **Dependencies:** `boto3>=1.33.2, botocore>=1.33.2`
- **Tag maintainer:** @baskaryan
- **Twitter handles:** `@pjain7` `@dead_letter_q`
This PR includes a documentation notebook under
`docs/docs/integrations/retrievers`, which I (@dlqqq) have verified
independently.
EDIT: `bedrock-agent-runtime` service is now included in
`boto3>=1.33.2`:
5cf793f493
---------
Co-authored-by: Piyush Jain <piyushjain@duck.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** dead link replacement
- **Issue:** no open issue
**Note:**
Hi langchain team,
Sorry to open a PR for this concern but we realized that one of the
links present in the documentation booklet was broken 😄
- **Description:** Reduce image asset file size used in documentation by
running them via lossless image optimization
([tinypng](https://www.npmjs.com/package/tinypng-cli) was used in this
case). Images wider than 1916px (the maximum width of an image displayed
in documentation) where downsized.
- **Issue:** No issue is created for this, but the large image file
assets caused slow documentation load times
- **Dependencies:** No dependencies affected
- **Description:** Existing model used for Prompt Injection is quite
outdated but we fine-tuned and open-source a new model based on the same
model deberta-v3-base from Microsoft -
[laiyer/deberta-v3-base-prompt-injection](https://huggingface.co/laiyer/deberta-v3-base-prompt-injection).
It supports more up-to-date injections and less prone to
false-positives.
- **Dependencies:** No
- **Tag maintainer:** -
- **Twitter handle:** @alex_yaremchuk
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Current docs for adapters are in the `Guides/Adapters which is not a
good place.
- moved Adapters into `Integratons/Components/Adapters/
- simplified the OpenAI adapter notebook
- rerouted the old OpenAI adapter page URL to a new one.
**Description:**
This PR adds Databricks Vector Search as a new vector store in
LangChain.
- [x] Add `DatabricksVectorSearch` in `langchain/vectorstores/`
- [x] Unit tests
- [x] Add
[`databricks-vectorsearch`](https://pypi.org/project/databricks-vectorsearch/)
as a new optional dependency
We ran the following checks:
- `make format` passed ✅
- `make lint` failed but the failures were caused by other files
+ Files touched by this PR passed the linter ✅
- `make test` passed ✅
- `make coverage` failed but the failures were caused by other files.
Tests added by or related to this PR all passed
+ langchain/vectorstores/databricks_vector_search.py test coverage 94% ✅
- `make spell_check` passed ✅
The example notebook and updates to the [provider's documentation
page](https://github.com/langchain-ai/langchain/blob/master/docs/docs/integrations/providers/databricks.md)
will be added later in a separate PR.
**Dependencies:**
Optional dependency:
[`databricks-vectorsearch`](https://pypi.org/project/databricks-vectorsearch/)
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** Added a retriever for the Outline API to ask
questions on knowledge base
- **Issue:** resolves#11814
- **Dependencies:** None
- **Tag maintainer:** @baskaryan
- **Description:**
I encountered an issue while running the existing sample code on the
page https://python.langchain.com/docs/modules/agents/how_to/agent_iter
in an environment with Pydantic 2.0 installed. The following error was
triggered:
```python
ValidationError Traceback (most recent call last)
<ipython-input-12-2ffff2c87e76> in <cell line: 43>()
41
42 tools = [
---> 43 Tool(
44 name="GetPrime",
45 func=get_prime,
2 frames
/usr/local/lib/python3.10/dist-packages/pydantic/v1/main.py in __init__(__pydantic_self__, **data)
339 values, fields_set, validation_error = validate_model(__pydantic_self__.__class__, data)
340 if validation_error:
--> 341 raise validation_error
342 try:
343 object_setattr(__pydantic_self__, '__dict__', values)
ValidationError: 1 validation error for Tool
args_schema
subclass of BaseModel expected (type=type_error.subclass; expected_class=BaseModel)
```
I have made modifications to the example code to ensure it functions
correctly in environments with Pydantic 2.0.
This PR provides idiomatic implementations for the exact-match and the
semantic LLM caches using Astra DB as backend through the database's
HTTP JSON API. These caches require the `astrapy` library as dependency.
Comes with integration tests and example usage in the `llm_cache.ipynb`
in the docs.
@baskaryan this is the Astra DB counterpart for the Cassandra classes
you merged some time ago, tagging you for your familiarity with the
topic. Thank you!
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
This PR adds a chat message history component that uses Astra DB for
persistence through the JSON API.
The `astrapy` package is required for this class to work.
I have added tests and a small notebook, and updated the relevant
references in the other docs pages.
(@rlancemartin this is the counterpart of the Cassandra equivalent class
you so helpfully reviewed back at the end of June)
Thank you!
- **Description:** Fix typo in MongoDB memory docs
- **Tag maintainer:** @eyurtsev
<!-- Thank you for contributing to LangChain!
- **Description:** Fix typo in MongoDB memory docs
- **Issue:** the issue # it fixes (if applicable),
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** @baskaryan
- **Twitter handle:** we announce bigger features on Twitter. If your PR
gets announced, and you'd like a mention, we'll gladly shout you out!
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
tests, lint, etc:
https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md
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. It lives in `docs/extras`
directory.
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
- **Description:** This change adds an agent to the Azure Cognitive
Services toolkit for identifying healthcare entities
- **Dependencies:** azure-ai-textanalytics (Optional)
---------
Co-authored-by: James Beck <James.Beck@sa.gov.au>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:**
This commit adds embedchain retriever along with tests and docs.
Embedchain is a RAG framework to create data pipelines.
**Twitter handle:**
- [Taranjeet's twitter](https://twitter.com/taranjeetio) and
[Embedchain's twitter](https://twitter.com/embedchain)
**Reviewer**
@hwchase17
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:**
Enhance the functionality of YoutubeLoader to enable the translation of
available transcripts by refining the existing logic.
**Issue:**
Encountering a problem with YoutubeLoader (#13523) where the translation
feature is not functioning as expected.
Tag maintainers/contributors who might be interested:
@eyurtsev
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
## Update 2023-09-08
This PR now supports further models in addition to Lllama-2 chat models.
See [this comment](#issuecomment-1668988543) for further details. The
title of this PR has been updated accordingly.
## Original PR description
This PR adds a generic `Llama2Chat` model, a wrapper for LLMs able to
serve Llama-2 chat models (like `LlamaCPP`,
`HuggingFaceTextGenInference`, ...). It implements `BaseChatModel`,
converts a list of chat messages into the [required Llama-2 chat prompt
format](https://huggingface.co/blog/llama2#how-to-prompt-llama-2) and
forwards the formatted prompt as `str` to the wrapped `LLM`. Usage
example:
```python
# uses a locally hosted Llama2 chat model
llm = HuggingFaceTextGenInference(
inference_server_url="http://127.0.0.1:8080/",
max_new_tokens=512,
top_k=50,
temperature=0.1,
repetition_penalty=1.03,
)
# Wrap llm to support Llama2 chat prompt format.
# Resulting model is a chat model
model = Llama2Chat(llm=llm)
messages = [
SystemMessage(content="You are a helpful assistant."),
MessagesPlaceholder(variable_name="chat_history"),
HumanMessagePromptTemplate.from_template("{text}"),
]
prompt = ChatPromptTemplate.from_messages(messages)
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
chain = LLMChain(llm=model, prompt=prompt, memory=memory)
# use chat model in a conversation
# ...
```
Also part of this PR are tests and a demo notebook.
- Tag maintainer: @hwchase17
- Twitter handle: `@mrt1nz`
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
The original notebook has the `faiss` title which is duplicated in
the`faiss.jpynb`. As a result, we have two `faiss` items in the
vectorstore ToC. And the first item breaks the searching order (it is
placed between `A...` items).
- I updated title to `Asynchronous Faiss`.
- Fixed titles for two notebooks. They were inconsistent with other
titles and clogged ToC.
- Added `Upstash` description and link
- Moved the authentication text up in the `Elasticsearch` nb, right
after package installation. It was on the end of the page which was a
wrong place.
This PR brings a few minor improvements to the docs, namely class/method
docstrings and the demo notebook.
- A note on how to control concurrency levels to tune performance in
bulk inserts, both in the class docstring and the demo notebook;
- Slightly increased concurrency defaults after careful experimentation
(still on the conservative side even for clients running on
less-than-typical network/hardware specs)
- renamed the DB token variable to the standardized
`ASTRA_DB_APPLICATION_TOKEN` name (used elsewhere, e.g. in the Astra DB
docs)
- added a note and a reference (add_text docstring, demo notebook) on
allowed metadata field names.
Thank you!
The current `integrations/document_loaders/` sidebar has the
`example_data` item, which is a menu with a single item: "Notebook".
It is happening because the `integrations/document_loaders/` folder has
the `example_data/notebook.md` file that is used to autogenerate the
above menu item.
- removed an example_data/notebook.md file. Docusaurus doesn't have
simple ways to fix this problem (to exclude folders/files from an
autogenerated sidebar). Removing this file didn't break any existing
examples, so this fix is safe.
Updated several notebooks:
- fixed titles which are inconsistent or break the ToC sorting order.
- added missed soruce descriptions and links
- fixed formatting
- the `SemaDB` notebook was placed in additional subfolder which breaks
the vectorstore ToC. I moved file up, removed this unnecessary
subfolder; updated the `vercel.json` with rerouting for the new URL
- Added SemaDB description and link
- improved text consistency
- Fixed the title of the notebook. It created an ugly ToC element as
`Activeloop DeepLake's DeepMemory + LangChain + ragas or how to get +27%
on RAG recall.`
- Added Activeloop description
- improved consistency in text
- fixed ToC (it was using HTML tagas that break left-side in-page ToC).
Now in-page ToC works
- Fixed headers (was more then 1 Titles)
- Removed security token value. It was OK to have it, because it is
temporary token, but the automatic security swippers raise warnings on
that.
- Added `ClickUp` service description and link.
The `Integrations` site is hidden now.
I've added it into the `More` menu.
The name is `Integration Cards` otherwise, it is confused with the
`Integrations` menu.
---------
Co-authored-by: Erick Friis <erickfriis@gmail.com>
The new ruff version fixed the blocking bugs, and I was able to fairly
easily us to a passing state: ruff fixed some issues on its own, I fixed
a handful by hand, and I added a list of narrowly-targeted exclusions
for files that are currently failing ruff rules that we probably should
look into eventually.
I went pretty lenient on the docs / cookbooks rules, allowing dead code
and such things. Perhaps in the future we may want to tighten the rules
further, but this is already a good set of checks that found real issues
and will prevent them going forward.
