**Description**: This PR adds support for using the [LLMLingua project
](https://github.com/microsoft/LLMLingua) especially the LongLLMLingua
(Enhancing Large Language Model Inference via Prompt Compression) as a
document compressor / transformer.
The LLMLingua project is an interesting project that can greatly improve
RAG system by compressing prompts and contexts while keeping their
semantic relevance.
**Issue**: https://github.com/microsoft/LLMLingua/issues/31
**Dependencies**: [llmlingua](https://pypi.org/project/llmlingua/)
@baskaryan
---------
Co-authored-by: Ayodeji Ayibiowu <ayodeji.ayibiowu@getinge.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
### Description
This PR moves the Elasticsearch classes to a partners package.
Note that we will not move (and later remove) `ElasticKnnSearch`. It
were previously deprecated.
`ElasticVectorSearch` is going to stay in the community package since it
is used quite a lot still.
Also note that I left the `ElasticsearchTranslator` for self query
untouched because it resides in main `langchain` package.
### Dependencies
There will be another PR that updates the notebooks (potentially pulling
them into the partners package) and templates and removes the classes
from the community package, see
https://github.com/langchain-ai/langchain/pull/17468
#### Open question
How to make the transition smooth for users? Do we move the import
aliases and require people to install `langchain-elasticsearch`? Or do
we remove the import aliases from the `langchain` package all together?
What has worked well for other partner packages?
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
**Description**
Adding different threshold types to the semantic chunker. I’ve had much
better and predictable performance when using standard deviations
instead of percentiles.
For all the documents I’ve tried, the distribution of distances look
similar to the above: positively skewed normal distribution. All skews
I’ve seen are less than 1 so that explains why standard deviations
perform well, but I’ve included IQR if anyone wants something more
robust.
Also, using the percentile method backwards, you can declare the number
of clusters and use semantic chunking to get an ‘optimal’ splitting.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
**Description:** Update the example fiddler notebook to use community
path, instead of langchain.callback
**Dependencies:** None
**Twitter handle:** @bhalder
Co-authored-by: Barun Halder <barun@fiddler.ai>
I tried to configure MongoDBChatMessageHistory using the code from the
original documentation to store messages based on the passed session_id
in MongoDB. However, this configuration did not take effect, and the
session id in the database remained as 'test_session'. To resolve this
issue, I found that when configuring MongoDBChatMessageHistory, it is
necessary to set session_id=session_id instead of
session_id=test_session.
Issue: DOC: Ineffective Configuration of MongoDBChatMessageHistory for
Custom session_id Storage
previous code：
```python
chain_with_history = RunnableWithMessageHistory(
chain,
lambda session_id: MongoDBChatMessageHistory(
session_id="test_session",
connection_string="mongodb://root:Y181491117cLj@123.56.224.232:27017",
database_name="my_db",
collection_name="chat_histories",
),
input_messages_key="question",
history_messages_key="history",
)
config = {"configurable": {"session_id": "mmm"}}
chain_with_history.invoke({"question": "Hi! I'm bob"}, config)
```
Modified code:
```python
chain_with_history = RunnableWithMessageHistory(
chain,
lambda session_id: MongoDBChatMessageHistory(
session_id=session_id, # here is my modify code
connection_string="mongodb://root:Y181491117cLj@123.56.224.232:27017",
database_name="my_db",
collection_name="chat_histories",
),
input_messages_key="question",
history_messages_key="history",
)
config = {"configurable": {"session_id": "mmm"}}
chain_with_history.invoke({"question": "Hi! I'm bob"}, config)
```
Effect after modification (it works)：
**Description:** Update the azure search notebook to have more
descriptive comments, and an option to choose between OpenAI and
AzureOpenAI Embeddings
---------
Co-authored-by: Matt Gotteiner <[email protected]>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** Callback handler to integrate fiddler with langchain.
This PR adds the following -
1. `FiddlerCallbackHandler` implementation into langchain/community
2. Example notebook `fiddler.ipynb` for usage documentation
[Internal Tracker : FDL-14305]
**Issue:**
NA
**Dependencies:**
- Installation of langchain-community is unaffected.
- Usage of FiddlerCallbackHandler requires installation of latest
fiddler-client (2.5+)
**Twitter handle:** @fiddlerlabs @behalder
Co-authored-by: Barun Halder <barun@fiddler.ai>
- **Description:** Added the `return_sparql_query` feature to the
`GraphSparqlQAChain` class, allowing users to get the formatted SPARQL
query along with the chain's result.
- **Issue:** NA
- **Dependencies:** None
Note: I've ensured that the PR passes linting and testing by running
make format, make lint, and make test locally.
I have added a test for the integration (which relies on network access)
and I have added an example to the notebook showing its use.
https://github.com/langchain-ai/langchain/issues/17657
Thank you for contributing to LangChain!
Checklist:
- [ ] PR title: Please title your PR "package: description", where
"package" is whichever of langchain, community, core, experimental, etc.
is being modified. Use "docs: ..." for purely docs changes, "templates:
..." for template changes, "infra: ..." for CI changes.
- Example: "community: add foobar LLM"
- [ ] PR message: **Delete this entire template message** and replace it
with the following bulleted list
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] Pass lint and test: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified to check that you're
passing lint and testing. See contribution guidelines for more
information on how to write/run tests, lint, etc:
https://python.langchain.com/docs/contributing/
- [ ] Add tests and docs: 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/docs/integrations` directory.
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
**Description:** Initial pull request for Kinetica LLM wrapper
**Issue:** N/A
**Dependencies:** No new dependencies for unit tests. Integration tests
require gpudb, typeguard, and faker
**Twitter handle:** @chad_juliano
Note: There is another pull request for Kinetica vectorstore. Ultimately
we would like to make a partner package but we are starting with a
community contribution.
- **Description:** Update the Azure Search vector store notebook for the
latest version of the SDK
---------
Co-authored-by: Matt Gotteiner <[email protected]>
**Description:** Clean up Google product names and fix document loader
section
**Issue:** NA
**Dependencies:** None
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** Update IBM watsonx.ai docs and add IBM as a provider
docs
- **Dependencies:**
[ibm-watsonx-ai](https://pypi.org/project/ibm-watsonx-ai/),
- **Tag maintainer:** :
Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` to check this
locally. ✅
**Description:** This PR changes the module import path for SQLDatabase
in the documentation
**Issue:** Updates the documentation to reflect the move of integrations
to langchain-community
- **Description:** The URL in the tigris tutorial was htttps instead of
https, leading to a bad link.
- **Issue:** N/A
- **Dependencies:** N/A
- **Twitter handle:** Speucey
In this pull request, we introduce the add_images method to the
SingleStoreDB vector store class, expanding its capabilities to handle
multi-modal embeddings seamlessly. This method facilitates the
incorporation of image data into the vector store by associating each
image's URI with corresponding document content, metadata, and either
pre-generated embeddings or embeddings computed using the embed_image
method of the provided embedding object.
the change includes integration tests, validating the behavior of the
add_images. Additionally, we provide a notebook showcasing the usage of
this new method.
---------
Co-authored-by: Volodymyr Tkachuk <vtkachuk-ua@singlestore.com>
Issue in the API Reference:
If the `Classes` of `Functions` section is empty, it still shown in API
Reference. Here is an
[example](https://api.python.langchain.com/en/latest/core_api_reference.html#module-langchain_core.agents)
where `Functions` table is empty but still presented.
It happens only if this section has only the "private" members (with
names started with '_'). Those members are not shown but the whole
member section (empty) is shown.
This way we can document APIs in methods signature only where they are
checked by the typing system and we get them also in the param
description without having to duplicate in the docstrings (where they
are unchecked).
Twitter: @cbornet_
Description:
In this PR, I am adding a PolygonTickerNews Tool, which can be used to
get the latest news for a given ticker / stock.
Twitter handle: [@virattt](https://twitter.com/virattt)
**Description**: CogniSwitch focusses on making GenAI usage more
reliable. It abstracts out the complexity & decision making required for
tuning processing, storage & retrieval. Using simple APIs documents /
URLs can be processed into a Knowledge Graph that can then be used to
answer questions.
**Dependencies**: No dependencies. Just network calls & API key required
**Tag maintainer**: @hwchase17
**Twitter handle**: https://github.com/CogniSwitch
**Documentation**: Please check
`docs/docs/integrations/toolkits/cogniswitch.ipynb`
**Tests**: The usual tool & toolkits tests using `test_imports.py`
PR has passed linting and testing before this submission.
---------
Co-authored-by: Saicharan Sridhara <145636106+saiCogniswitch@users.noreply.github.com>
Hi, I'm from the LanceDB team.
Improves LanceDB integration by making it easier to use - now you aren't
required to create tables manually and pass them in the constructor,
although that is still backward compatible.
Bug fix - pandas was being used even though it's not a dependency for
LanceDB or langchain
PS - this issue was raised a few months ago but lost traction. It is a
feature improvement for our users kindly review this , Thanks !
This PR replaces the imports of the Astra DB vector store with the
newly-released partner package, in compliance with the deprecation
notice now attached to the community "legacy" store.
- **Description:** Update documentation for RunnableWithMessageHistory
- **Issue:** https://github.com/langchain-ai/langchain/issues/16642
I don't have access to an Anthropic API key so I updated things to use
OpenAI. Let me know if you'd prefer another provider.
**Description:** This PR introduces a new "Astra DB" Partner Package.
So far only the vector store class is _duplicated_ there, all others
following once this is validated and established.
Along with the move to separate package, incidentally, the class name
will change `AstraDB` => `AstraDBVectorStore`.
The strategy has been to duplicate the module (with prospected removal
from community at LangChain 0.2). Until then, the code will be kept in
sync with minimal, known differences (there is a makefile target to
automate drift control. Out of convenience with this check, the
community package has a class `AstraDBVectorStore` aliased to `AstraDB`
at the end of the module).
With this PR several bugfixes and improvement come to the vector store,
as well as a reshuffling of the doc pages/notebooks (Astra and
Cassandra) to align with the move to a separate package.
**Dependencies:** A brand new pyproject.toml in the new package, no
changes otherwise.
**Twitter handle:** `@rsprrs`
---------
Co-authored-by: Christophe Bornet <cbornet@hotmail.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
A few minor changes for contribution:
1) Updating link to say "Contributing" rather than "Developer's guide"
2) Minor changes after going through the contributing documentation
page.
This PR is adding support for NVIDIA NeMo embeddings issue #16095.
---------
Co-authored-by: Praveen Nakshatrala <pnakshatrala@gmail.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
1. integrate with
[`Yuan2.0`](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/README-EN.md)
2. update `langchain.llms`
3. add a new doc for [Yuan2.0
integration](docs/docs/integrations/llms/yuan2.ipynb)
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
This pull request introduces support for various Approximate Nearest
Neighbor (ANN) vector index algorithms in the VectorStore class,
starting from version 8.5 of SingleStore DB. Leveraging this enhancement
enables users to harness the power of vector indexing, significantly
boosting search speed, particularly when handling large sets of vectors.
---------
Co-authored-by: Volodymyr Tkachuk <vtkachuk-ua@singlestore.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:**
1. Added _clear_edges()_ and _get_number_of_nodes()_ functions in
NetworkxEntityGraph class.
2. Added the above two function in graph_networkx_qa.ipynb
documentation.
Thank you for contributing to LangChain!
Checklist:
- **PR title**: docs: add & update docs for Oracle Cloud Infrastructure
(OCI) integrations
- **Description**: adding and updating documentation for two
integrations - OCI Generative AI & OCI Data Science
(1) adding integration page for OCI Generative AI embeddings (@baskaryan
request,
docs/docs/integrations/text_embedding/oci_generative_ai.ipynb)
(2) updating integration page for OCI Generative AI llms
(docs/docs/integrations/llms/oci_generative_ai.ipynb)
(3) adding platform documentation for OCI (@baskaryan request,
docs/docs/integrations/platforms/oci.mdx). this combines the
integrations of OCI Generative AI & OCI Data Science
(4) if possible, requesting to be added to 'Featured Community
Providers' so supplying a modified
docs/docs/integrations/platforms/index.mdx to reflect the addition
- **Issue:** none
- **Dependencies:** no new dependencies
- **Twitter handle:**
---------
Co-authored-by: MING KANG <ming.kang@oracle.com>
1. integrate chat models with
[`Yuan2.0`](https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/README-EN.md)
2. add a new doc for [Yuan2.0
integration](docs/docs/integrations/llms/yuan2.ipynb)
Yuan2.0 is a new generation Fundamental Large Language Model developed
by IEIT System. We have published all three models, Yuan 2.0-102B, Yuan
2.0-51B, and Yuan 2.0-2B.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
## Description
I am submitting this for a school project as part of a team of 5. Other
team members are @LeilaChr, @maazh10, @Megabear137, @jelalalamy. This PR
also has contributions from community members @Harrolee and @Mario928.
Initial context is in the issue we opened (#11229).
This pull request adds:
- Generic framework for expanding the languages that `LanguageParser`
can handle, using the
[tree-sitter](https://github.com/tree-sitter/py-tree-sitter#py-tree-sitter)
parsing library and existing language-specific parsers written for it
- Support for the following additional languages in `LanguageParser`:
- C
- C++
- C#
- Go
- Java (contributed by @Mario928
https://github.com/ThatsJustCheesy/langchain/pull/2)
- Kotlin
- Lua
- Perl
- Ruby
- Rust
- Scala
- TypeScript (contributed by @Harrolee
https://github.com/ThatsJustCheesy/langchain/pull/1)
Here is the [design
document](https://docs.google.com/document/d/17dB14cKCWAaiTeSeBtxHpoVPGKrsPye8W0o_WClz2kk)
if curious, but no need to read it.
## Issues
- Closes#11229
- Closes#10996
- Closes#8405
## Dependencies
`tree_sitter` and `tree_sitter_languages` on PyPI. We have tried to add
these as optional dependencies.
## Documentation
We have updated the list of supported languages, and also added a
section to `source_code.ipynb` detailing how to add support for
additional languages using our framework.
## Maintainer
- @hwchase17 (previously reviewed
https://github.com/langchain-ai/langchain/pull/6486)
Thanks!!
## Git commits
We will gladly squash any/all of our commits (esp merge commits) if
necessary. Let us know if this is desirable, or if you will be
squash-merging anyway.
<!-- 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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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: Maaz Hashmi <mhashmi373@gmail.com>
Co-authored-by: LeilaChr <87657694+LeilaChr@users.noreply.github.com>
Co-authored-by: Jeremy La <jeremylai511@gmail.com>
Co-authored-by: Megabear137 <zubair.alnoor27@gmail.com>
Co-authored-by: Lee Harrold <lhharrold@sep.com>
Co-authored-by: Mario928 <88029051+Mario928@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:** Pebblo opensource project enables developers to
safely load data to their Gen AI apps. It identifies semantic topics and
entities found in the loaded data and summarizes them in a
developer-friendly report.
- **Dependencies:** none
- **Twitter handle:** srics
@hwchase17
**Description**: This PR adds a chain for Amazon Neptune graph database
RDF format. It complements the existing Neptune Cypher chain. The PR
also includes a Neptune RDF graph class to connect to, introspect, and
query a Neptune RDF graph database from the chain. A sample notebook is
provided under docs that demonstrates the overall effect: invoking the
chain to make natural language queries against Neptune using an LLM.
**Issue**: This is a new feature
**Dependencies**: The RDF graph class depends on the AWS boto3 library
if using IAM authentication to connect to the Neptune database.
---------
Co-authored-by: Piyush Jain <piyushjain@duck.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** This PR adds support for
[flashrank](https://github.com/PrithivirajDamodaran/FlashRank) for
reranking as alternative to Cohere.
I'm not sure `libs/langchain` is the right place for this change. At
first, I wanted to put it under `libs/community`. All the compressors
were under `libs/langchain/retrievers/document_compressors` though. Hope
this makes sense!
- **Description:** This adds a delete method so that rocksetdb can be
used with `RecordManager`.
- **Issue:** N/A
- **Dependencies:** N/A
- **Twitter handle:** `@_morgan_adams_`
---------
Co-authored-by: Rockset API Bot <admin@rockset.io>
- Reordered sections
- Applied consistent formatting
- Fixed headers (there were 2 H1 headers; this breaks CoT)
- Added `Settings` header and moved all related sections under it
Description: Updated doc for integrations/chat/anthropic_functions with
new functions: invoke. Changed structure of the document to match the
required one.
Issue: https://github.com/langchain-ai/langchain/issues/15664
Dependencies: None
Twitter handle: None
---------
Co-authored-by: NaveenMaltesh <naveen@onmeta.in>
- **Description:** Adds the document loader for [AWS
Athena](https://aws.amazon.com/athena/), a serverless and interactive
analytics service.
- **Dependencies:** Added boto3 as a dependency
This PR updates the `TF-IDF.ipynb` documentation to reflect the new
import path for TFIDFRetriever in the langchain-community package. The
previous path, `from langchain.retrievers import TFIDFRetriever`, has
been updated to `from langchain_community.retrievers import
TFIDFRetriever` to align with the latest changes in the langchain
library.
according to https://youtu.be/rZus0JtRqXE?si=aFo1JTDnu5kSEiEN&t=678 by
@efriis
- **Description:** Seems the requirements for tool names have changed
and spaces are no longer allowed. Changed the tool name from Google
Search to google_search in the notebook
- **Issue:** n/a
- **Dependencies:** none
- **Twitter handle:** @mesirii
**Description**
Make some functions work with Milvus:
1. get_ids: Get primary keys by field in the metadata
2. delete: Delete one or more entities by ids
3. upsert: Update/Insert one or more entities
**Issue**
None
**Dependencies**
None
**Tag maintainer:**
@hwchase17
**Twitter handle:**
None
---------
Co-authored-by: HoaNQ9 <hoanq.1811@gmail.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
## Summary
This PR upgrades LangChain's Ruff configuration in preparation for
Ruff's v0.2.0 release. (The changes are compatible with Ruff v0.1.5,
which LangChain uses today.) Specifically, we're now warning when
linter-only options are specified under `[tool.ruff]` instead of
`[tool.ruff.lint]`.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Issue:** Issue with model argument support (been there for a while
actually):
- Non-specially-handled arguments like temperature don't work when
passed through constructor.
- Such arguments DO work quite well with `bind`, but also do not abide
by field requirements.
- Since initial push, server-side error messages have gotten better and
v0.0.2 raises better exceptions. So maybe it's better to let server-side
handle such issues?
- **Description:**
- Removed ChatNVIDIA's argument fields in favor of
`model_kwargs`/`model_kws` arguments which aggregates constructor kwargs
(from constructor pathway) and merges them with call kwargs (bind
pathway).
- Shuffled a few functions from `_NVIDIAClient` to `ChatNVIDIA` to
streamline construction for future integrations.
- Minor/Optional: Old services didn't have stop support, so client-side
stopping was implemented. Now do both.
- **Any Breaking Changes:** Minor breaking changes if you strongly rely
on chat_model.temperature, etc. This is captured by
chat_model.model_kwargs.
PR passes tests and example notebooks and example testing. Still gonna
chat with some people, so leaving as draft for now.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
The Integrations `Toolkits` menu was named as [`Agents and
toolkits`](https://python.langchain.com/docs/integrations/toolkits).
This name has a historical reason that is not correct anymore. Now this
menu is all about community `Toolkits`. There is a separate menu for
[Agents](https://python.langchain.com/docs/modules/agents/). Also Agents
are officially not part of Integrations (Community package) but part of
LangChain package.
<!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is
whichever of langchain, community, core, experimental, etc. is being
modified.
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,
- **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` from the root
of the package you've modified to check this locally.
See contribution guidelines for more information on how to write/run
tests, lint, etc: https://python.langchain.com/docs/contributing/
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/docs/integrations` directory.
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
- **Description: changes to you.com files**
- general cleanup
- adds community/utilities/you.py, moving bulk of code from retriever ->
utility
- removes `snippet` as endpoint
- adds `news` as endpoint
- adds more tests
<s>**Description: update community MAKE file**
- adds `integration_tests`
- adds `coverage`</s>
- **Issue:** the issue # it fixes if applicable,
- [For New Contributors: Update Integration
Documentation](https://github.com/langchain-ai/langchain/issues/15664#issuecomment-1920099868)
- **Dependencies:** n/a
- **Twitter handle:** @scottnath
- **Mastodon handle:** scottnath@mastodon.social
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** This adds a recursive json splitter class to the
existing text_splitters as well as unit tests
- **Issue:** splitting text from structured data can cause issues if you
have a large nested json object and you split it as regular text you may
end up losing the structure of the json. To mitigate against this you
can split the nested json into large chunks and overlap them, but this
causes unnecessary text processing and there will still be times where
the nested json is so big that the chunks get separated from the parent
keys.
As an example you wouldn't want the following to be split in half:
```shell
{'val0': 'DFWeNdWhapbR',
'val1': {'val10': 'QdJo',
'val11': 'FWSDVFHClW',
'val12': 'bkVnXMMlTiQh',
'val13': 'tdDMKRrOY',
'val14': 'zybPALvL',
'val15': 'JMzGMNH',
'val16': {'val160': 'qLuLKusFw',
'val161': 'DGuotLh',
'val162': 'KztlcSBropT',
-----------------------------------------------------------------------split-----
'val163': 'YlHHDrN',
'val164': 'CtzsxlGBZKf',
'val165': 'bXzhcrWLmBFp',
'val166': 'zZAqC',
'val167': 'ZtyWno',
'val168': 'nQQZRsLnaBhb',
'val169': 'gSpMbJwA'},
'val17': 'JhgiyF',
'val18': 'aJaqjUSFFrI',
'val19': 'glqNSvoyxdg'}}
```
Any llm processing the second chunk of text may not have the context of
val1, and val16 reducing accuracy. Embeddings will also lack this
context and this makes retrieval less accurate.
Instead you want it to be split into chunks that retain the json
structure.
```shell
{'val0': 'DFWeNdWhapbR',
'val1': {'val10': 'QdJo',
'val11': 'FWSDVFHClW',
'val12': 'bkVnXMMlTiQh',
'val13': 'tdDMKRrOY',
'val14': 'zybPALvL',
'val15': 'JMzGMNH',
'val16': {'val160': 'qLuLKusFw',
'val161': 'DGuotLh',
'val162': 'KztlcSBropT',
'val163': 'YlHHDrN',
'val164': 'CtzsxlGBZKf'}}}
```
and
```shell
{'val1':{'val16':{
'val165': 'bXzhcrWLmBFp',
'val166': 'zZAqC',
'val167': 'ZtyWno',
'val168': 'nQQZRsLnaBhb',
'val169': 'gSpMbJwA'},
'val17': 'JhgiyF',
'val18': 'aJaqjUSFFrI',
'val19': 'glqNSvoyxdg'}}
```
This recursive json text splitter does this. Values that contain a list
can be converted to dict first by using split(... convert_lists=True)
otherwise long lists will not be split and you may end up with chunks
larger than the max chunk.
In my testing large json objects could be split into small chunks with
✅ Increased question answering accuracy
✅ The ability to split into smaller chunks meant retrieval queries can
use fewer tokens
- **Dependencies:** json import added to text_splitter.py, and random
added to the unit test
- **Twitter handle:** @joelsprunger
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
**Description:**: Fix 422 error in example with LangServe client code
httpx.HTTPStatusError: Client error '422 Unprocessable Entity' for url
'http://localhost:8000/agent/invoke'
- **Description:** Fixes in the Ontotext GraphDB Graph and QA Chain
related to the error handling in case of invalid SPARQL queries, for
which `prepareQuery` doesn't throw an exception, but the server returns
400 and the query is indeed invalid
- **Issue:** N/A
- **Dependencies:** N/A
- **Twitter handle:** @OntotextGraphDB
Ran
```python
import glob
import re
def update_prompt(x):
return re.sub(
r"(?P<start>\b)PromptTemplate\(template=(?P<template>.*), input_variables=(?:.*)\)",
"\g<start>PromptTemplate.from_template(\g<template>)",
x
)
for fn in glob.glob("docs/**/*", recursive=True):
try:
content = open(fn).readlines()
except:
continue
content = [update_prompt(l) for l in content]
with open(fn, "w") as f:
f.write("".join(content))
```
Replace this entire comment with:
- **Description:** Added missing link for Quickstart in Model IO
documentation,
- **Issue:** N/A,
- **Dependencies:** N/A,
- **Twitter handle:** N/A
<!--
If no one reviews your PR within a few days, please @-mention one of
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Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Several notebooks have Title != file name. That results in corrupted
sorting in Navbar (ToC).
- Fixed titles and file names.
- Changed text formats to the consistent form
- Redirected renamed files in the `Vercel.json`
This PR is opinionated.
- Moved `Embedding models` item to place after `LLMs` and `Chat model`,
so all items with models are together.
- Renamed `Text embedding models` to `Embedding models`. Now, it is
shorter and easier to read. `Text` is obvious from context. The same as
the `Text LLMs` vs. `LLMs` (we also have multi-modal LLMs).
The `Partner libs` menu is not sorted. Now it is long enough, and items
should be sorted to simplify a package search.
- Sorted items in the `Partner libs` menu
### Description
support load any github file content based on file extension.
Why not use [git
loader](https://python.langchain.com/docs/integrations/document_loaders/git#load-existing-repository-from-disk)
?
git loader clones the whole repo even only interested part of files,
that's too heavy. This GithubFileLoader only downloads that you are
interested files.
### Twitter handle
my twitter: @shufanhaotop
---------
Co-authored-by: Hao Fan <h_fan@apple.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** Link to the Brave Website added to the
`brave-search.ipynb` notebook.
This notebook is shown in the docs as an example for the brave tool.
**Issue:** There was to reference on where / how to get an api key
**Dependencies:** none
**Twitter handle:** not for this one :)
- **Description:** docs: update StreamlitCallbackHandler example.
- **Issue:** None
- **Dependencies:** None
I have updated the example for StreamlitCallbackHandler in the
documentation bellow.
https://python.langchain.com/docs/integrations/callbacks/streamlit
Previously, the example used `initialize_agent`, which has been
deprecated, so I've updated it to use `create_react_agent` instead. Many
langchain users are likely searching examples of combining
`create_react_agent` or `openai_tools_agent_chain` with
StreamlitCallbackHandler. I'm sure this update will be really helpful
for them!
Unfortunately, writing unit tests for this example is difficult, so I
have not written any tests. I have run this code in a standalone Python
script file and ensured it runs correctly.
- **Description:** "load HTML **form** web URLs" should be "load HTML
**from** web URLs"? 🤔
- **Issue:** Typo
- **Dependencies:** Nope
- **Twitter handle:** n0vad3v
- **Description:** Adds an additional class variable to `BedrockBase`
called `provider` that allows sending a model provider such as amazon,
cohere, ai21, etc.
Up until now, the model provider is extracted from the `model_id` using
the first part before the `.`, such as `amazon` for
`amazon.titan-text-express-v1` (see [supported list of Bedrock model IDs
here](https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids-arns.html)).
But for custom Bedrock models where the ARN of the provisioned
throughput must be supplied, the `model_id` is like
`arn:aws:bedrock:...` so the `model_id` cannot be extracted from this. A
model `provider` is required by the LangChain Bedrock class to perform
model-based processing. To allow the same processing to be performed for
custom-models of a specific base model type, passing this `provider`
argument can help solve the issues.
The alternative considered here was the use of
`provider.arn:aws:bedrock:...` which then requires ARN to be extracted
and passed separately when invoking the model. The proposed solution
here is simpler and also does not cause issues for current models
already using the Bedrock class.
- **Issue:** N/A
- **Dependencies:** N/A
---------
Co-authored-by: Piyush Jain <piyushjain@duck.com>
- **Description:** Several meta/usability updates, including User-Agent.
- **Issue:**
- User-Agent metadata for tracking connector engagement. @milesial
please check and advise.
- Better error messages. Tries harder to find a request ID. @milesial
requested.
- Client-side image resizing for multimodal models. Hope to upgrade to
Assets API solution in around a month.
- `client.payload_fn` allows you to modify payload before network
request. Use-case shown in doc notebook for kosmos_2.
- `client.last_inputs` put back in to allow for advanced
support/debugging.
- **Dependencies:**
- Attempts to pull in PIL for image resizing. If not installed, prints
out "please install" message, warns it might fail, and then tries
without resizing. We are waiting on a more permanent solution.
For LC viz: @hinthornw
For NV viz: @fciannella @milesial @vinaybagade
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
<!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is
whichever of langchain, community, core, experimental, etc. is being
modified.
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,
- **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` from the root
of the package you've modified to check this locally.
See contribution guidelines for more information on how to write/run
tests, lint, etc: https://python.langchain.com/docs/contributing/
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/docs/integrations` directory.
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
- **Description:** Updating one line code sample for Ollama with new
**langchain_community** package
- **Issue:**
- **Dependencies:** none
- **Twitter handle:** @picsoung
