**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
**Description:**
Updated documentation for DeepLake init method.
Especially the exec_option docs needed improvement, but did a general
cleanup while I was looking at it.
**Issue:** n/a
**Dependencies:** None
---------
Co-authored-by: Nathan Voxland <nathan@voxland.net>
- **Description:** In order to override the bool value of
"fetch_schema_from_transport" in the GraphQLAPIWrapper, a
"fetch_schema_from_transport" value needed to be added to the
"_EXTRA_OPTIONAL_TOOLS" dictionary in load_tools in the "graphql" key.
The parameter "fetch_schema_from_transport" must also be passed in to
the GraphQLAPIWrapper to allow reading of the value when creating the
client. Passing as an optional parameter is probably best to avoid
breaking changes. This change is necessary to support GraphQL instances
that do not support fetching schema, such as TigerGraph. More info here:
[TigerGraph GraphQL Schema
Docs](https://docs.tigergraph.com/graphql/current/schema)
- **Threads handle:** @zacharytoliver
---------
Co-authored-by: Zachary Toliver <zt10191991@hotmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- Description: Add missing chunk parameter for _stream/_astream for some
chat models, make all chat models in a consistent behaviour.
- Issue: N/A
- Dependencies: N/A
**Description:** Here is a minimal example to illustrate behavior:
```python
from langchain_core.runnables import RunnableLambda
def my_function(*args, **kwargs):
return 3 + kwargs.get("n", 0)
runnable = RunnableLambda(my_function).bind(n=1)
assert 4 == runnable.invoke({})
assert [4] == list(runnable.stream({}))
assert 4 == await runnable.ainvoke({})
assert [4] == [item async for item in runnable.astream({})]
```
Here, `runnable.invoke({})` and `runnable.stream({})` work fine, but
`runnable.ainvoke({})` raises
```
TypeError: RunnableLambda._ainvoke.<locals>.func() got an unexpected keyword argument 'n'
```
and similarly for `runnable.astream({})`:
```
TypeError: RunnableLambda._atransform.<locals>.func() got an unexpected keyword argument 'n'
```
Here we assume that this behavior is undesired and attempt to fix it.
**Issue:** https://github.com/langchain-ai/langchain/issues/17241,
https://github.com/langchain-ai/langchain/discussions/16446
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.
- **Description:**
The existing `RedisCache` implementation lacks proper handling for redis
client failures, such as `ConnectionRefusedError`, leading to subsequent
failures in pipeline components like LLM calls. This pull request aims
to improve error handling for redis client issues, ensuring a more
robust and graceful handling of such errors.
- **Issue:** Fixes#16866
- **Dependencies:** No new dependency
- **Twitter handle:** N/A
Co-authored-by: snsten <>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Sent to LangSmith
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.
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)
Thank you for contributing to LangChain!
- [ ] **PR title**: "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 checklist*** and replace
with
- **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!
- [ ] **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.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
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**: 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>
## Amazon Personalize support on Langchain
This PR is a successor to this PR -
https://github.com/langchain-ai/langchain/pull/13216
This PR introduces an integration with [Amazon
Personalize](https://aws.amazon.com/personalize/) to help you to
retrieve recommendations and use them in your natural language
applications. This integration provides two new components:
1. An `AmazonPersonalize` client, that provides a wrapper around the
Amazon Personalize API.
2. An `AmazonPersonalizeChain`, that provides a chain to pull in
recommendations using the client, and then generating the response in
natural language.
We have added this to langchain_experimental since there was feedback
from the previous PR about having this support in experimental rather
than the core or community extensions.
Here is some sample code to explain the usage.
```python
from langchain_experimental.recommenders import AmazonPersonalize
from langchain_experimental.recommenders import AmazonPersonalizeChain
from langchain.llms.bedrock import Bedrock
recommender_arn = "<insert_arn>"
client=AmazonPersonalize(
credentials_profile_name="default",
region_name="us-west-2",
recommender_arn=recommender_arn
)
bedrock_llm = Bedrock(
model_id="anthropic.claude-v2",
region_name="us-west-2"
)
chain = AmazonPersonalizeChain.from_llm(
llm=bedrock_llm,
client=client
)
response = chain({'user_id': '1'})
```
Reviewer: @3coins
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 !
- OpenLLM was using outdated method to get the final text output from
openllm client invocation which was raising the error. Therefore
corrected that.
- OpenLLM `_identifying_params` was getting the openllm's client
configuration using outdated attributes which was raising error.
- Updated the docstring for OpenLLM.
- Added timeout parameter to be passed to underlying openllm client.
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.
Another PR will be done for the langchain-astradb package.
Note: for future PRs, devs will be done in the partner package only. This one is just to align with the rest of the components in the community package and it fixes a bunch of issues.
- **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:** adds an `exclude` parameter to the DirectoryLoader
class, based on similar behavior in GenericLoader
- **Issue:** discussed in
https://github.com/langchain-ai/langchain/discussions/9059 and I think
in some other issues that I cannot find at the moment 🙇
- **Dependencies:** None
- **Twitter handle:** don't have one sorry! Just https://github/nejch
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
- **Description:** Addresses the bugs described in linked issue where an
import was erroneously removed and the rename of a keyword argument was
missed when migrating from beta --> stable of the azure-search-documents
package
- **Issue:** https://github.com/langchain-ai/langchain/issues/17598
- **Dependencies:** N/A
- **Twitter handle:** N/A
- **Description:** This fixes an issue with working with RecordManager.
RecordManager was generating new hashes on documents because `add_texts`
was modifying the metadata directly. Additionally moved some tests to
unit tests since that was a more appropriate home.
- **Issue:** N/A
- **Dependencies:** N/A
- **Twitter handle:** `@_morgan_adams_`
The current issue:
Most of the deprecation descriptions are duplicated. For example:
`[Deprecated] Chat Agent.[Deprecated] Chat Agent.` for the [ChatAgent
class](https://api.python.langchain.com/en/latest/langchain_api_reference.html#classes)
description.
NOTE: I've tested it only with new ut! I cannot build API Reference
locally :(
**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>
- **Description:** Updates to the Kuzu API had broken this
functionality. These updates resolve those issues and add a new test to
demonstrate the updates.
- **Issue:** #11874
- **Dependencies:** No new dependencies
- **Twitter handle:** @amirk08
Test results:
```
tests/integration_tests/graphs/test_kuzu.py::TestKuzu::test_query_no_params PASSED [ 33%]
tests/integration_tests/graphs/test_kuzu.py::TestKuzu::test_query_params PASSED [ 66%]
tests/integration_tests/graphs/test_kuzu.py::TestKuzu::test_refresh_schema PASSED [100%]
=================================================== slowest 5 durations ===================================================
0.53s call tests/integration_tests/graphs/test_kuzu.py::TestKuzu::test_refresh_schema
0.34s call tests/integration_tests/graphs/test_kuzu.py::TestKuzu::test_query_no_params
0.28s call tests/integration_tests/graphs/test_kuzu.py::TestKuzu::test_query_params
0.03s teardown tests/integration_tests/graphs/test_kuzu.py::TestKuzu::test_refresh_schema
0.02s teardown tests/integration_tests/graphs/test_kuzu.py::TestKuzu::test_query_params
==================================================== 3 passed in 1.27s ====================================================
```
- **Description:** Allow a bool value to be passed to
fetch_schema_from_transport since not all GraphQL instances support this
feature, such as TigerGraph.
- **Threads:** @zacharytoliver
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
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.
- **Description:** Resolving problem in
`langchain_community\document_loaders\pebblo.py` with `import pwd`.
`pwd` is not available on windows. import moved to try catch block
- **Issue:** #17514
Noticed and fixed a few typos in the SmartLLMChain default ideation and
critique prompts
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Adds an optional name param to our base message to support passing names
into LLMs.
OpenAI supports having a name on anything except tool message now
(system, ai, user/human).
https://github.com/langchain-ai/langchain/issues/17525
### Example Code
```python
from langchain_community.document_loaders.athena import AthenaLoader
database_name = "database"
s3_output_path = "s3://bucket-no-prefix"
query="""SELECT
CAST(extract(hour FROM current_timestamp) AS INTEGER) AS current_hour,
CAST(extract(minute FROM current_timestamp) AS INTEGER) AS current_minute,
CAST(extract(second FROM current_timestamp) AS INTEGER) AS current_second;
"""
profile_name = "AdministratorAccess"
loader = AthenaLoader(
query=query,
database=database_name,
s3_output_uri=s3_output_path,
profile_name=profile_name,
)
documents = loader.load()
print(documents)
```
### Error Message and Stack Trace (if applicable)
NoSuchKey: An error occurred (NoSuchKey) when calling the GetObject
operation: The specified key does not exist
### Description
Athena Loader errors when result s3 bucket uri has no prefix. The Loader
instance call results in a "NoSuchKey: An error occurred (NoSuchKey)
when calling the GetObject operation: The specified key does not exist."
error.
If s3_output_path contains a prefix like:
```python
s3_output_path = "s3://bucket-with-prefix/prefix"
```
Execution works without an error.
## Suggested solution
Modify:
```python
key = "/".join(tokens[1:]) + "/" + query_execution_id + ".csv"
```
to
```python
key = "/".join(tokens[1:]) + ("/" if tokens[1:] else "") + query_execution_id + ".csv"
```
9e8a3fc4ff/libs/community/langchain_community/document_loaders/athena.py (L128)
### System Info
System Information
------------------
> OS: Darwin
> OS Version: Darwin Kernel Version 22.6.0: Fri Sep 15 13:41:30 PDT
2023; root:xnu-8796.141.3.700.8~1/RELEASE_ARM64_T8103
> Python Version: 3.9.9 (main, Jan 9 2023, 11:42:03)
[Clang 14.0.0 (clang-1400.0.29.102)]
Package Information
-------------------
> langchain_core: 0.1.23
> langchain: 0.1.7
> langchain_community: 0.0.20
> langsmith: 0.0.87
> langchain_openai: 0.0.6
> langchainhub: 0.1.14
Packages not installed (Not Necessarily a Problem)
--------------------------------------------------
The following packages were not found:
> langgraph
> langserve
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
If the SQLAlchemyMd5Cache is shared among multiple processes, it is
possible to encounter a race condition during the cache update.
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
- **Description:** Support filtering databases in the use case where
devs do not want to query ALL entries within a DB,
- **Issue:** N/A,
- **Dependencies:** N/A,
- **Twitter handle:** I don't have Twitter but feel free to tag my
Github!
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@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.
- **Description:** Callback manager can't catch chain input or output
validation errors because `prepare_input` and `prepare_output` are not
part of the try/raise logic, this PR fixes that logic.
- **Issue:** #15954
- **Description:** Fixes a type annotation issue in the definition of
BedrockBase. This issue was that the annotation for the `config`
attribute includes a ForwardRef to `botocore.client.Config` which is
only imported when `TYPE_CHECKING`. This can cause pydantic to raise an
error like `pydantic.errors.ConfigError: field "config" not yet prepared
so type is still a ForwardRef, ...`.
- **Issue:** N/A
- **Dependencies:** N/A
- **Twitter handle:** `@__nat_n__`
- **Description :**
Fix: Use shallow copy for schema manipulation in get_format_instructions
Prevents side effects on the original schema object by using a
dictionary comprehension for a safer and more controlled manipulation of
schema key-value pairs, enhancing code reliability.
- **Issue:** #17161
- **Dependencies:** None
- **Twitter handle:** None
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>
Users can provide an Elasticsearch connection with custom headers. This
PR makes sure these headers are preserved when adding the langchain user
agent header.
- **Description:** Depending on `token_max` used in
`load_summarize_chain`, it could cause an infinite loop when documents
cannot collapse under `token_max`. This change would not affect the
existing feature, but it also gives an option to users to avoid the
situation.
- **Issue:** https://github.com/langchain-ai/langchain/issues/16251
- **Dependencies:** None
- **Twitter handle:** None
---------
Co-authored-by: Bagatur <baskaryan@gmail.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:
Addresses a problem where the Date type within an Elasticsearch
SelfQueryRetriever would encounter difficulties in generating a valid
query.
Issue: #17042
---------
Co-authored-by: Max Jakob <max.jakob@elastic.co>
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
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.
-->
---------
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>
Adding a new notebook that demonstrates how to use LangChain's standard
chat features while passing the chat messages back and forth via Apache
Kafka.
This goal is to simulate an architecture where the chat front end and
the LLM are running as separate services that need to communicate with
one another over an internal nework.
It's an alternative to typical pattern of requesting a reponse from the
model via a REST API (there's more info on why you would want to do this
at the end of the notebook).
NOTE: Assuming "uses cases" is the right place for this but feel free to
propose another location.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
**Description:**
- The existing code was trying to find a `.embeddings` property on the
`Coroutine` returned by calling `cohere.async_client.embed`.
- Instead, the `.embeddings` property is present on the value returned
by the `Coroutine`.
- Also, it seems that the original cohere client expects a value of
`max_retries` to not be `None`. Hence, setting the default value of
`max_retries` to `3`.
---------
Co-authored-by: Bagatur <baskaryan@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:** Improve test cases for `SQLDatabase` adapter
component, see
[suggestion](https://github.com/langchain-ai/langchain/pull/16655#pullrequestreview-1846749474).
- **Depends on:** GH-16655
- **Addressed to:** @baskaryan, @cbornet, @eyurtsev
_Remark: This PR is stacked upon GH-16655, so that one will need to go
in first._
Edit: Thank you for bringing in GH-17191, @eyurtsev. This is a little
aftermath, improving/streamlining the corresponding test cases.
- **Description:**
[AS-IS] When dealing with a yaml file, the extension must be .yaml.
[TO-BE] In the absence of extension length constraints in the OS, the
extension of the YAML file is yaml, but control over the yml extension
must still be made.
It's as if it's an error because it's a .jpg extension in jpeg support.
- **Issue:** -
- **Dependencies:**
no dependencies required for this change,
- **Description:** The from__xx methods of FAISS class have hardcoded
InMemoryStore implementation and thereby not let users pass a custom
DocStore implementation,
- **Issue:** no referenced issue,
- **Dependencies:** none,
- **Twitter handle:** ksachdeva
**Description:**
Bugfix: Langchain_community's GitHub Api wrapper throws a TypeError when
searching for issues and/or PRs (the `search_issues_and_prs` method).
This is because PyGithub's PageinatedList type does not support the
len() method. See https://github.com/PyGithub/PyGithub/issues/1476
**Dependencies:** None
**Twitter handle**: @ChrisKeoghNZ
I haven't registered an issue as it would take me longer to fill the
template out than to make the fix, but I'm happy to if that's deemed
essential.
I've added a simple integration test to cover this as there were no
existing unit tests and it was going to be tricky to set them up.
Co-authored-by: Chris Keogh <chris.keogh@xero.com>
- **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>
This PR replaces the memory stream implementation used by the
LogStreamCallbackHandler.
This implementation resolves an issue in which streamed logs and
streamed events originating from sync code would arrive only after the
entire sync code would finish execution (rather than arriving in real
time as they're generated).
One example is if trying to stream tokens from an llm within a tool. If
the tool was an async tool, but the llm was invoked via stream (sync
variant) rather than astream (async variant), then the tokens would fail
to stream in real time and would all arrived bunched up after the tool
invocation completed.
- 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:** changed filtering so that failed filter doesn't add
document to results. Currently filtering is entirely broken and all
documents are returned whether or not they pass the filter.
fixes issue introduced in
https://github.com/langchain-ai/langchain/pull/16190
- **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
- **Description:** This PR adds support for `search_types="mmr"` and
`search_type="similarity_score_threshold"` to retrievers using
`DatabricksVectorSearch`,
- **Issue:**
- **Dependencies:**
- **Twitter handle:**
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
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
Ref: https://openai.com/pricing
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Unlike vector results, the LLM has to completely trust the context of a
graph database result, even if it doesn't provide whole context. We
tried with instructions, but it seems that adding a single example is
the way to go to solve this issue.
### This pull request makes the following changes:
* Fixed issue #16913
Fixed the google gen ai chat_models.py code to make sure that the
callback is called before the token is yielded
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@baskaryan, @eyurtsev, @hwchase17.
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---------
Co-authored-by: Erick Friis <erick@langchain.dev>
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Pydantic's `dict()` function raises an error here if you pass in a
generator. We have a more robust serialization function in lagnsmith
that we will use instead.
**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>
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- **Description:** a description of the change,
- **Issue:** the issue # it fixes (if applicable),
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directory.
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@baskaryan, @eyurtsev, @hwchase17.
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- **Description:**
1. Modify LLMs/Anyscale to work with OAI v1
2. Get rid of openai_ prefixed variables in Chat_model/ChatAnyscale
3. Modify `anyscale_api_base` to `anyscale_base_url` to follow OAI name
convention (reverted)
---------
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>
Description: Missing _identifying_params create issues when dealing with
callbacks to get current run model parameters.
All other model partners implementation provide this property and also
provide _default_params. I'm not sure about the default values to
include or if we can re-use the same as for _VertexAICommon(), this
change allows you to access the model parameters correctly.
Issue: Not exactly this issue but could be related
https://github.com/langchain-ai/langchain/issues/14711
Twitter handle:@musicaoriginal2
The streaming API doesn't separate safety_settings from the
generation_config payload. As the result the following error is observed
when using `stream` API. The functionality is correct with `invoke` API.
The fix separates the `safety_settings` from params and sets it as
argument to the `send_message` method.
```
ERROR: Unknown field for GenerationConfig: safety_settings
Traceback (most recent call last):
File "/Users/user/Library/Caches/pypoetry/virtualenvs/chatbot-worker-main-Ju-qIM-X-py3.12/lib/python3.12/site-packages/langchain_core/language_models/chat_models.py", line 250, in stream
raise e
File "/Users/user/Library/Caches/pypoetry/virtualenvs/chatbot-worker-main-Ju-qIM-X-py3.12/lib/python3.12/site-packages/langchain_core/language_models/chat_models.py", line 234, in stream
for chunk in self._stream(
File "/Users/user/Library/Caches/pypoetry/virtualenvs/chatbot-worker-main-Ju-qIM-X-py3.12/lib/python3.12/site-packages/langchain_google_vertexai/chat_models.py", line 501, in _stream
for response in responses:
File "/Users/user/Library/Caches/pypoetry/virtualenvs/chatbot-worker-main-Ju-qIM-X-py3.12/lib/python3.12/site-packages/vertexai/generative_models/_generative_models.py", line 921, in _send_message_streaming
for chunk in stream:
File "/Users/user/Library/Caches/pypoetry/virtualenvs/chatbot-worker-main-Ju-qIM-X-py3.12/lib/python3.12/site-packages/vertexai/generative_models/_generative_models.py", line 514, in _generate_content_streaming
request = self._prepare_request(
^^^^^^^^^^^^^^^^^^^^^^
File "/Users/user/Library/Caches/pypoetry/virtualenvs/chatbot-worker-main-Ju-qIM-X-py3.12/lib/python3.12/site-packages/vertexai/generative_models/_generative_models.py", line 256, in _prepare_request
gapic_generation_config = gapic_content_types.GenerationConfig(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/user/Library/Caches/pypoetry/virtualenvs/chatbot-worker-main-Ju-qIM-X-py3.12/lib/python3.12/site-packages/proto/message.py", line 576, in __init__
raise ValueError(
ValueError: Unknown field for GenerationConfig: safety_settings
```
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
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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.
I noticed that RunnableConfigurableAlternatives which is an important
composition in LCEL has no Docstring. Therefore I added the detailed
Docstring for it.
@baskaryan, @eyurtsev, @hwchase17 please have a look and let me if the
docstring is looking good.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
This PR enables changing the behaviour of huggingface pipeline between
different calls. For example, before this PR there's no way of changing
maximum generation length between different invocations of the chain.
This is desirable in cases, such as when we want to scale the maximum
output size depending on a dynamic prompt size.
Usage example:
```python
from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_id = "gpt2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
hf = HuggingFacePipeline(pipeline=pipe)
hf("Say foo:", pipeline_kwargs={"max_new_tokens": 42})
```
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
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- **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:** Databricks LLM does not support SerDe the
transform_input_fn and transform_output_fn. After saving and loading,
the LLM will be broken. This PR serialize these functions into a hex
string using pickle, and saving the hex string in the yaml file. Using
pickle to serialize a function can be flaky, but this is a simple
workaround that unblocks many use cases. If more sophisticated SerDe is
needed, we can improve it later.
Test:
Added a simple unit test.
I did manual test on Databricks and it works well.
The saved yaml looks like:
```
llm:
_type: databricks
cluster_driver_port: null
cluster_id: null
databricks_uri: databricks
endpoint_name: databricks-mixtral-8x7b-instruct
extra_params: {}
host: e2-dogfood.staging.cloud.databricks.com
max_tokens: null
model_kwargs: null
n: 1
stop: null
task: null
temperature: 0.0
transform_input_fn: 80049520000000000000008c085f5f6d61696e5f5f948c0f7472616e73666f726d5f696e7075749493942e
transform_output_fn: null
```
@baskaryan
```python
from langchain_community.embeddings import DatabricksEmbeddings
from langchain_community.llms import Databricks
from langchain.chains import RetrievalQA
from langchain.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
import mlflow
embeddings = DatabricksEmbeddings(endpoint="databricks-bge-large-en")
def transform_input(**request):
request["messages"] = [
{
"role": "user",
"content": request["prompt"]
}
]
del request["prompt"]
return request
llm = Databricks(endpoint_name="databricks-mixtral-8x7b-instruct", transform_input_fn=transform_input)
persist_dir = "faiss_databricks_embedding"
# Create the vector db, persist the db to a local fs folder
loader = TextLoader("state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
db = FAISS.from_documents(docs, embeddings)
db.save_local(persist_dir)
def load_retriever(persist_directory):
embeddings = DatabricksEmbeddings(endpoint="databricks-bge-large-en")
vectorstore = FAISS.load_local(persist_directory, embeddings)
return vectorstore.as_retriever()
retriever = load_retriever(persist_dir)
retrievalQA = RetrievalQA.from_llm(llm=llm, retriever=retriever)
with mlflow.start_run() as run:
logged_model = mlflow.langchain.log_model(
retrievalQA,
artifact_path="retrieval_qa",
loader_fn=load_retriever,
persist_dir=persist_dir,
)
# Load the retrievalQA chain
loaded_model = mlflow.pyfunc.load_model(logged_model.model_uri)
print(loaded_model.predict([{"query": "What did the president say about Ketanji Brown Jackson"}]))
```
- **Description:**
Embedding field name was hard-coded named "embedding".
So I suggest that change `res["embedding"]` into
`res[self._embedding_key]`.
- **Issue:** #17177,
- **Twitter handle:**
[@bagcheoljun17](https://twitter.com/bagcheoljun17)
- **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
**Description:**
Implemented unique ID validation in the FAISS component to ensure all
document IDs are distinct. This update resolves issues related to
non-unique IDs, such as inconsistent behavior during deletion processes.
**Description:** enable _parse_response_candidate to support complex
structure format.
**Issue:**
currently, if Gemini response complex args format, people will get
"TypeError: Object of type RepeatedComposite is not JSON serializable"
error from _parse_response_candidate.
response candidate example
```
content {
role: "model"
parts {
function_call {
name: "Information"
args {
fields {
key: "people"
value {
list_value {
values {
string_value: "Joe is 30, his mom is Martha"
}
}
}
}
}
}
}
}
finish_reason: STOP
safety_ratings {
category: HARM_CATEGORY_HARASSMENT
probability: NEGLIGIBLE
}
safety_ratings {
category: HARM_CATEGORY_HATE_SPEECH
probability: NEGLIGIBLE
}
safety_ratings {
category: HARM_CATEGORY_SEXUALLY_EXPLICIT
probability: NEGLIGIBLE
}
safety_ratings {
category: HARM_CATEGORY_DANGEROUS_CONTENT
probability: NEGLIGIBLE
}
```
error msg:
```
Traceback (most recent call last):
File "/home/jupyter/user/abehsu/gemini_langchain_tools/example2.py", line 36, in <module>
print(tagging_chain.invoke({"input": "Joe is 30, his mom is Martha"}))
File "/opt/conda/envs/gemini_langchain_tools/lib/python3.10/site-packages/langchain_core/runnables/base.py", line 2053, in invoke
input = step.invoke(
File "/opt/conda/envs/gemini_langchain_tools/lib/python3.10/site-packages/langchain_core/runnables/base.py", line 3887, in invoke
return self.bound.invoke(
File "/opt/conda/envs/gemini_langchain_tools/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py", line 165, in invoke
self.generate_prompt(
File "/opt/conda/envs/gemini_langchain_tools/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py", line 543, in generate_prompt
return self.generate(prompt_messages, stop=stop, callbacks=callbacks, **kwargs)
File "/opt/conda/envs/gemini_langchain_tools/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py", line 407, in generate
raise e
File "/opt/conda/envs/gemini_langchain_tools/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py", line 397, in generate
self._generate_with_cache(
File "/opt/conda/envs/gemini_langchain_tools/lib/python3.10/site-packages/langchain_core/language_models/chat_models.py", line 576, in _generate_with_cache
return self._generate(
File "/opt/conda/envs/gemini_langchain_tools/lib/python3.10/site-packages/langchain_google_vertexai/chat_models.py", line 406, in _generate
generations = [
File "/opt/conda/envs/gemini_langchain_tools/lib/python3.10/site-packages/langchain_google_vertexai/chat_models.py", line 408, in <listcomp>
message=_parse_response_candidate(c),
File "/opt/conda/envs/gemini_langchain_tools/lib/python3.10/site-packages/langchain_google_vertexai/chat_models.py", line 280, in _parse_response_candidate
function_call["arguments"] = json.dumps(
File "/opt/conda/envs/gemini_langchain_tools/lib/python3.10/json/__init__.py", line 231, in dumps
return _default_encoder.encode(obj)
File "/opt/conda/envs/gemini_langchain_tools/lib/python3.10/json/encoder.py", line 199, in encode
chunks = self.iterencode(o, _one_shot=True)
File "/opt/conda/envs/gemini_langchain_tools/lib/python3.10/json/encoder.py", line 257, in iterencode
return _iterencode(o, 0)
File "/opt/conda/envs/gemini_langchain_tools/lib/python3.10/json/encoder.py", line 179, in default
raise TypeError(f'Object of type {o.__class__.__name__} '
TypeError: Object of type RepeatedComposite is not JSON serializable
```
**Twitter handle:** @abehsu1992626
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description: added logic to override get_num_tokens_from_messages()
for ChatVertexAI. Currently ChatVertexAI was inheriting
get_num_tokens_from_messages() from BaseChatModel which in-turn was
calling GPT-2 tokenizer
- **Issue: NA
- **Dependencies: NA
- **Twitter handle:@aditya_rane
@lkuligin for review
---------
Co-authored-by: adityarane@google.com <adityarane@google.com>
Co-authored-by: Leonid Kuligin <lkuligin@yandex.ru>
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
@baskaryan, @eyurtsev, @hwchase17.
-->
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`
- **Description:**
Actually the test named `test_openai_apredict` isn't testing the
apredict method from ChatOpenAI.
- **Twitter handle:**
https://twitter.com/OAlmofadas
* This PR adds async methods to the LLM cache.
* Adds an implementation using Redis called AsyncRedisCache.
* Adds a docker compose file at the /docker to help spin up docker
* Updates redis tests to use a context manager so flushing always happens by default
<!-- 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
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2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.
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@baskaryan, @eyurtsev, @hwchase17.
-->
- **Description:**
This PR standardizes the `output_parser.py` file across all agent types
to ensure a uniform parsing mechanism is implemented. It introduces a
cohesive structure and common interface for output parsing, facilitating
easier modifications and extensions by users. The standardized approach
enhances maintainability and scalability of the codebase by providing a
consistent pattern for output parsing, which can be easily understood
and utilized across different agent types.
This PR builds upon the foundation set by a previously merged PR, which
focused exclusively on standardizing the `output_parser.py` for the
`conversational_agent` ([PR
#16945](https://github.com/langchain-ai/langchain/pull/16945)). With
this new update, I extend the standardization efforts to encompass
`output_parser.py` files across all agent types. This enhancement not
only unifies the parsing mechanism across the board but also introduces
the flexibility for users to incorporate custom `FORMAT_INSTRUCTIONS`.
- **Issue:**
https://github.com/langchain-ai/langchain/issues/10721https://github.com/langchain-ai/langchain/issues/4044
- **Dependencies:**
No new dependencies required for this change
- **Twitter handle:**
With my github user is enough. Thanks
I hope you accept my PR.
Based on my experiments, the newline isn't always there, so we can make
the regex slightly more robust by allowing an optional newline after the
bacticks
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).
<!-- 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!
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submitting. Run `make format`, `make lint` and `make test` from the root
of the package you've modified to check this locally.
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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.
-->
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:**
before the change I've got
1. propagate InferenceClientException to the caller.
2. stop grpc receiver thread on exception
```
for token in result_queue:
> result_str += token
E TypeError: can only concatenate str (not "InferenceServerException") to str
../../langchain_nvidia_trt/llms.py:207: TypeError
```
And stream thread keeps running.
after the change request thread stops correctly and caller got a root
cause exception:
```
E tritonclient.utils.InferenceServerException: [request id: 4529729] expected number of inputs between 2 and 3 but got 10 inputs for model 'vllm_model'
../../langchain_nvidia_trt/llms.py:205: InferenceServerException
```
- **Issue:** the issue # it fixes if applicable,
- **Dependencies:** any dependencies required for this change,
- **Twitter handle:** [t.me/mkhl_spb](https://t.me/mkhl_spb)
I'm not sure about test coverage. Should I setup deep mocks or there's a
kind of triton stub via testcontainers or so.
**Description:**
With this modification, users can customize the `FORMAT_INSTRUCTIONS`
template, allowing them to create their own prompts
As it is happening in
[this](https://github.com/langchain-ai/langchain/issues/10721) issue,
the `FORMAT_INSTRUCTIONS` is not customizable for the output parser,
unless you create your own class `ConvoOutputParser`. To avoid this, a
modification was done, creating a `format_instruction` variable that
users can customize with ease after initialize the agent.
For example:
```
agent = initialize_agent(
agent = AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION,
tools = tools,
llm = llm_agent,
verbose = True,
max_iterations = 3,
early_stopping_method = 'generate',
memory = b_w_memory,
handle_parsing_errors = True,
agent_kwargs={
'system_message':PREFIX,
'human_message':SUFFIX,
'template_tool_response':TEMPLATE_TOOL_RESPONSE,
}
)
agent.agent.output_parser.format_instructions = "MY CUSTOM FORMAT INSTRUCTIONS"
print(agent.agent.output_parser.get_format_instructions())
MY CUSTOM FORMAT INSTRUCTIONS
```
Other parameters like `system_message`, `human_message`, or
`template_tool_response` are already customizable and with this PR, the
last parameter `FORMAT_INSTRUCTIONS` in
`langchain.agents.conversational_chat.prompt` can be modified.
**Issue:**
https://github.com/langchain-ai/langchain/issues/10721
**Dependencies:**
No new dependencies required for this change
**Twitter handle:**
With my github user is enough. Thanks
I hope you accept my PR.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Please tag this issue with `nvidia_genai`**
- **Description:** Added new Runnables for integration NVIDIA Riva into
LCEL chains for Automatic Speech Recognition (ASR) and Text To Speech
(TTS).
- **Issue:** N/A
- **Dependencies:** To use these runnables, the NVIDIA Riva client
libraries are required. It they are not installed, an error will be
raised instructing how to install them. The Runnables can be safely
imported without the riva client libraries.
- **Twitter handle:** N/A
All of the Riva Runnables are inside a single folder in the Utilities
module. In this folder are four files:
- common.py - Contains all code that is common to both TTS and ASR
- stream.py - Contains a class representing an audio stream that allows
the end user to put data into the stream like a queue.
- asr.py - Contains the RivaASR runnable
- tts.py - Contains the RivaTTS runnable
The following Python function is an example of creating a chain that
makes use of both of these Runnables:
```python
def create(
config: Configuration,
audio_encoding: RivaAudioEncoding,
sample_rate: int,
audio_channels: int = 1,
) -> Runnable[ASRInputType, TTSOutputType]:
"""Create a new instance of the chain."""
_LOGGER.info("Instantiating the chain.")
# create the riva asr client
riva_asr = RivaASR(
url=str(config.riva_asr.service.url),
ssl_cert=config.riva_asr.service.ssl_cert,
encoding=audio_encoding,
audio_channel_count=audio_channels,
sample_rate_hertz=sample_rate,
profanity_filter=config.riva_asr.profanity_filter,
enable_automatic_punctuation=config.riva_asr.enable_automatic_punctuation,
language_code=config.riva_asr.language_code,
)
# create the prompt template
prompt = PromptTemplate.from_template("{user_input}")
# model = ChatOpenAI()
model = ChatNVIDIA(model="mixtral_8x7b") # type: ignore
# create the riva tts client
riva_tts = RivaTTS(
url=str(config.riva_asr.service.url),
ssl_cert=config.riva_asr.service.ssl_cert,
output_directory=config.riva_tts.output_directory,
language_code=config.riva_tts.language_code,
voice_name=config.riva_tts.voice_name,
)
# construct and return the chain
return {"user_input": riva_asr} | prompt | model | riva_tts # type: ignore
```
The following code is an example of creating a new audio stream for
Riva:
```python
input_stream = AudioStream(maxsize=1000)
# Send bytes into the stream
for chunk in audio_chunks:
await input_stream.aput(chunk)
input_stream.close()
```
The following code is an example of how to execute the chain with
RivaASR and RivaTTS
```python
output_stream = asyncio.Queue()
while not input_stream.complete:
async for chunk in chain.astream(input_stream):
output_stream.put(chunk)
```
Everything should be async safe and thread safe. Audio data can be put
into the input stream while the chain is running without interruptions.
---------
Co-authored-by: Hayden Wolff <hwolff@nvidia.com>
Co-authored-by: Hayden Wolff <hwolff@Haydens-Laptop.local>
Co-authored-by: Hayden Wolff <haydenwolff99@gmail.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
**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:** Ensure the `LlamaGrammar` custom type is always
available when instantiating a `LlamaCpp` LLM
- **Issue:** #16994
- **Dependencies:** None
- **Twitter handle:** @fpaupier
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
As described in issue #17060, in the case in which text has only one
sentence the following function fails. Checking for that and adding a
return case fixed the issue.
```python
def split_text(self, text: str) -> List[str]:
"""Split text into multiple components."""
# Splitting the essay on '.', '?', and '!'
single_sentences_list = re.split(r"(?<=[.?!])\s+", text)
sentences = [
{"sentence": x, "index": i} for i, x in enumerate(single_sentences_list)
]
sentences = combine_sentences(sentences)
embeddings = self.embeddings.embed_documents(
[x["combined_sentence"] for x in sentences]
)
for i, sentence in enumerate(sentences):
sentence["combined_sentence_embedding"] = embeddings[i]
distances, sentences = calculate_cosine_distances(sentences)
start_index = 0
# Create a list to hold the grouped sentences
chunks = []
breakpoint_percentile_threshold = 95
breakpoint_distance_threshold = np.percentile(
distances, breakpoint_percentile_threshold
) # If you want more chunks, lower the percentile cutoff
indices_above_thresh = [
i for i, x in enumerate(distances) if x > breakpoint_distance_threshold
] # The indices of those breakpoints on your list
# Iterate through the breakpoints to slice the sentences
for index in indices_above_thresh:
# The end index is the current breakpoint
end_index = index
# Slice the sentence_dicts from the current start index to the end index
group = sentences[start_index : end_index + 1]
combined_text = " ".join([d["sentence"] for d in group])
chunks.append(combined_text)
# Update the start index for the next group
start_index = index + 1
# The last group, if any sentences remain
if start_index < len(sentences):
combined_text = " ".join([d["sentence"] for d in sentences[start_index:]])
chunks.append(combined_text)
return chunks
```
Co-authored-by: Giulio Zani <salamanderxing@Giulios-MBP.homenet.telecomitalia.it>
- **Description:** Add relevant type annotations for relevant session
and query objects to resolve mypy errors when `# type: ignore` comments
are removed.
- **Issue:** #17048
- **Dependencies:** None,
- **Twitter handle:** [clesiemo3](https://twitter.com/clesiemo3)
I attempted to solve the `UpsertionRecord` ignore but it would require
added a deprecated plugin or moving completely to sqlalchemy 2.0+ from
my understanding. I'm assuming this is not something desired at this
point in time.
- **Description:** "load HTML **form** web URLs" should be "load HTML
**from** web URLs"? 🤔
- **Issue:** Typo
- **Dependencies:** Nope
- **Twitter handle:** n0vad3v
- **Description:** Adds a function parameter to HuggingFaceEmbeddings
called `show_progress` that enables a `tqdm` progress bar if enabled.
Does not function if `multi_process = True`.
- **Issue:** n/a
- **Dependencies:** n/a
- **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>
This is a PR about #16334
The Stop sequenes isn't meanful in `json_chat` because it depends json
to work, not completions
<!-- Thank you for contributing to LangChain!
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If you're adding a new integration, please include:
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2. an example notebook showing its use. It lives in
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@baskaryan, @eyurtsev, @hwchase17.
-->
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.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!
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whichever of langchain, community, core, experimental, etc. is being
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Replace this entire comment with:
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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.
-->
<!-- Thank you for contributing to LangChain!
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If you're adding a new integration, please include:
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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.
-->
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- Initial commit oss-tool-retrieval-agent
- README update
- lint
- lock
- format imports
- Rename to retrieval-agent-fireworks
- cr
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---------
Co-authored-by: Taqi Jaffri <tjaffri@docugami.com>
Previously, if this did not find a mypy cache then it wouldnt run
this makes it always run
adding mypy ignore comments with existing uncaught issues to unblock other prs
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
- **Description**: We discovered a bug converting dictionaries to
messages where the ChatMessageChunk message type isn't handled. This PR
adds support for that message type.
- **Issue**: #17022
- **Dependencies**: None
- **Twitter handle**: None
- **Description:** Updating one line code sample for Ollama with new
**langchain_community** package
- **Issue:**
- **Dependencies:** none
- **Twitter handle:** @picsoung
## Description
In #16608, the calling `collection_name` was wrong.
I made a fix for it.
Sorry for the inconvenience!
## Issue
https://github.com/langchain-ai/langchain/issues/16962
## Dependencies
N/A
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---------
Co-authored-by: Kumar Shivendu <kshivendu1@gmail.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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primary problem in pydantic still exists, where `Optional[str]` gets
turned to `string` in the jsonschema `.schema()`
Also fixes the `SchemaSchema` naming issue
---------
Co-authored-by: William Fu-Hinthorn <13333726+hinthornw@users.noreply.github.com>
We didn't override the namespace of the ImagePromptTemplate, so it is
listed as being in langchain.schema
This updates the mapping to let the loader deserialize.
Alternatively, we could make a slight breaking change and update the
namespace of the ImagePromptTemplate since we haven't broadly
publicized/documented it yet..
All models should be calling the callback for new token prior to
yielding the token.
Not doing this can cause callbacks for downstream steps to be called
prior to the callback for the new token; causing issues in
astream_events APIs and other things that depend in callback ordering
being correct.
We need to make this change for all chat models.
The `langchain.prompts.example_selector` [still holds several
artifacts](https://api.python.langchain.com/en/latest/langchain_api_reference.html#module-langchain.prompts)
that belongs to `community`. If they moved to
`langchain_community.example_selectors`, the `langchain.prompts`
namespace would be effectively removed which is great.
- moved a class and afunction to `langchain_community`
Note:
- Previously, the `langchain.prompts.example_selector` artifacts were
moved into the `langchain_core.exampe_selectors`. See the flattened
namespace (`.prompts` was removed)!
Similar flattening was implemented for the `langchain_core` as the
`langchain_core.exampe_selectors`.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
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* Adds `AstraDBEnvironment` class and use it in `AstraDBLoader`,
`AstraDBCache`, `AstraDBSemanticCache`, `AstraDBBaseStore` and
`AstraDBChatMessageHistory`
* Create an `AsyncAstraDB` if we only have an `AstraDB` and vice-versa
so:
* we always have an instance of `AstraDB`
* we always have an instance of `AsyncAstraDB` for recent versions of
astrapy
* Create collection if not exists in `AstraDBBaseStore`
* Some typing improvements
Note: `AstraDB` `VectorStore` not using `AstraDBEnvironment` at the
moment. This will be done after the `langchain-astradb` package is out.
Added notification about limited preview status of Guardrails for Amazon
Bedrock feature to code example.
---------
Co-authored-by: Piyush Jain <piyushjain@duck.com>
- **Description:**
The BaseStore methods are currently blocking. Some implementations
(AstraDBStore, RedisStore) would benefit from having async methods.
Also once we have async methods for BaseStore, we can implement the
async `aembed_documents` in CacheBackedEmbeddings to cache the
embeddings asynchronously.
* adds async methods amget, amset, amedelete and ayield_keys to
BaseStore
* implements the async methods for InMemoryStore
* adds tests for InMemoryStore async methods
- **Twitter handle:** cbornet_
* Add bulk add_messages method to the interface.
* Update documentation for add_ai_message and add_human_message to
denote them as being marked for deprecation. We should stop using them
as they create more incorrect (inefficient) ways of doing things
Adds:
* methods `aload()` and `alazy_load()` to interface `BaseLoader`
* implementation for class `MergedDataLoader `
* support for class `BaseLoader` in async function `aindex()` with unit
tests
Note: this is compatible with existing `aload()` methods that some
loaders already had.
**Twitter handle:** @cbornet_
---------
Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>
- **Description:** the existing AssemblyAI API allows to pass a path or
an url to transcribe an audio file and turn in into Langchain Documents,
this PR allows to get existing transcript by their transcript id and
turn them into Documents.
- **Issue:** not related to an existing issue
- **Dependencies:** requests
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
The current implementation leaves it up to the particular file loader
implementation to report the file on which an error was encountered - in
my case pdfminer was simply saying it could not parse a file as a PDF,
but I didn't know which of my hundreds of files it was failing on.
No reason not to log the particular item on which an error was
encountered, and it should be an immense debugging assistant.
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Description: Added the parameter for a possibility to change a language
model in SpacyEmbeddings. The default value is still the same:
"en_core_web_sm", so it shouldn't affect a code which previously did not
specify this parameter, but it is not hard-coded anymore and easy to
change in case you want to use it with other languages or models.
Issue: At Barcelona Supercomputing Center in Aina project
(https://github.com/projecte-aina), a project for Catalan Language
Models and Resources, we would like to use Langchain for one of our
current projects and we would like to comment that Langchain, while
being a very powerful and useful open-source tool, is pretty much
focused on English language. We would like to contribute to make it a
bit more adaptable for using with other languages.
Dependencies: This change requires the Spacy library and a language
model, specified in the model parameter.
Tag maintainer: @dev2049
Twitter handle: @projecte_aina
---------
Co-authored-by: Marina Pliusnina <marina.pliusnina@bsc.es>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description**: fully async versions are available for astrapy 0.7+.
For older astrapy versions or if the user provides a sync client without
an async one, the async methods will call the sync ones wrapped in
`run_in_executor`
- **Twitter handle:** cbornet_
Replace this entire comment with:
- **Description:** Add Baichuan LLM to integration/llm, also updated
related docs.
Co-authored-by: BaiChuanHelper <wintergyc@WinterGYCs-MacBook-Pro.local>
- **Description:**
Filtering in a FAISS vectorstores is very inflexible and doesn't allow
that many use case. I think supporting callable like this enables a lot:
regular expressions, condition on multiple keys etc. **Note** I had to
manually alter a test. I don't understand if it was falty to begin with
or if there is something funky going on.
- **Issue:** None
- **Dependencies:** None
- **Twitter handle:** None
Signed-off-by: thiswillbeyourgithub <26625900+thiswillbeyourgithub@users.noreply.github.com>
Adjusted deprecate decorator to make sure decorated async functions are
still recognized as "coroutinefunction" by inspect
Addresses #16402
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---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
## Description
The PR is to return the ID and collection name from qdrant client to
metadata field in `Document` class.
## Issue
The motivation is almost same to
[11592](https://github.com/langchain-ai/langchain/issues/11592)
Returning ID is useful to update existing records in a vector store, but
we cannot know them if we use some retrievers.
In order to avoid any conflicts, breaking changes, the new fields in
metadata have a prefix `_`
## Dependencies
N/A
## Twitter handle
@kill_in_sun
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Use the real "history" provided by the original program instead of
putting "None" in the history.
- **Description:** I change one line in the code to make it return the
"history" of the chat model.
- **Issue:** At the moment it returns only the answers of the chat
model. However the chat model himself provides a history more complet
with the questions of the user.
- **Dependencies:** no dependencies required for this change,
This PR includes updates for OctoAI integrations:
- The LLM class was updated to fix a bug that occurs with multiple
sequential calls
- The Embedding class was updated to support the new GTE-Large endpoint
released on OctoAI lately
- The documentation jupyter notebook was updated to reflect using the
new LLM sdk
Thank you!
Description: One too many set of triple-ticks in a sample code block in
the QuickStart doc was causing "\`\`\`shell" to appear in the shell
command that was being demonstrated. I just deleted the extra "```".
Issue: Didn't see one
Dependencies: None
## Summary
This PR implements the "Connery Action Tool" and "Connery Toolkit".
Using them, you can integrate Connery actions into your LangChain agents
and chains.
Connery is an open-source plugin infrastructure for AI.
With Connery, you can easily create a custom plugin with a set of
actions and seamlessly integrate them into your LangChain agents and
chains. Connery will handle the rest: runtime, authorization, secret
management, access management, audit logs, and other vital features.
Additionally, Connery and our community offer a wide range of
ready-to-use open-source plugins for your convenience.
Learn more about Connery:
- GitHub: https://github.com/connery-io/connery-platform
- Documentation: https://docs.connery.io
- Twitter: https://twitter.com/connery_io
## TODOs
- [x] API wrapper
- [x] Integration tests
- [x] Connery Action Tool
- [x] Docs
- [x] Example
- [x] Integration tests
- [x] Connery Toolkit
- [x] Docs
- [x] Example
- [x] Formatting (`make format`)
- [x] Linting (`make lint`)
- [x] Testing (`make test`)
- **Description:** To adapt more parameters related to
MemorySearchPayload for the search method of ZepChatMessageHistory,
- **Issue:** None,
- **Dependencies:** None,
- **Twitter handle:** None
**Description:**
Updated the retry.ipynb notebook, it contains the illustrations of
RetryOutputParser in LangChain. But the notebook lacks to explain the
compatibility of RetryOutputParser with existing chains. This changes
adds some code to illustrate the workflow of using RetryOutputParser
with the user chain.
Changes:
1. Changed RetryWithErrorOutputParser with RetryOutputParser, as the
markdown text says so.
2. Added code at the last of the notebook to define a chain which passes
the LLM completions to the retry parser, which can be customised for
user needs.
**Issue:**
Since RetryOutputParser/RetryWithErrorOutputParser does not implement
the parse function it cannot be used with LLMChain directly like
[this](https://python.langchain.com/docs/expression_language/cookbook/prompt_llm_parser#prompttemplate-llm-outputparser).
This also raised various issues #15133#12175#11719 still open, instead
of adding new features/code changes its best to explain the "how to
integrate LLMChain with retry parsers" clearly with an example in the
corresponding notebook.
Inspired from:
https://github.com/langchain-ai/langchain/issues/15133#issuecomment-1868972580
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Add missing async similarity_distance_threshold handling in
RedisVectorStoreRetriever
- **Description:** added method `_aget_relevant_documents` to
`RedisVectorStoreRetriever` that overrides parent method to add support
of `similarity_distance_threshold` in async mode (as for sync mode)
- **Issue:** #16099
- **Dependencies:** N/A
- **Twitter handle:** N/A
- **Description:** This is a template for creating shopping assistant
chat bots
- **Issue:** Example for creating a shopping assistant with OpenAI Tools
Agent
- **Dependencies:** Ionic
https://github.com/ioniccommerce/ionic_langchain
- **Twitter handle:** @ioniccommerce
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
- **Description:** Presidio-based anonymizers are not working because
`_remove_conflicts_and_get_text_manipulation_data` was being called
without a conflict resolution strategy. This PR fixes this issue. In
addition, it removes some mutable default arguments (antipattern).
To reproduce the issue, just run the very first cell of this
[notebook](https://python.langchain.com/docs/guides/privacy/2/) from
langchain's documentation.
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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.
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**Description** : This PR updates the documentation for installing
llama-cpp-python on Windows.
- Updates install command to support pyproject.toml
- Makes CPU/GPU install instructions clearer
- Adds reinstall with GPU support command
**Issue**: Existing
[documentation](https://python.langchain.com/docs/integrations/llms/llamacpp#compiling-and-installing)
lists the following commands for installing llama-cpp-python
```
python setup.py clean
python setup.py install
````
The current version of the repo does not include a `setup.py` and uses a
`pyproject.toml` instead.
This can be replaced with
```
python -m pip install -e .
```
As explained in
https://github.com/abetlen/llama-cpp-python/issues/965#issuecomment-1837268339
**Dependencies**: None
**Twitter handle**: None
---------
Co-authored-by: blacksmithop <angstycoder101@gmaii.com>
- **Description:** The current pubmed tool documentation is referencing
the path to langchain core not the path to the tool in community. The
old tool redirects anyways, but for efficiency of using the more direct
path, just adding this documentation so it references the new path
- **Issue:** doesn't fix an issue
- **Dependencies:** no dependencies
- **Twitter handle:** rooftopzen
* Description: Fixed schema discrepancy in **from_texts** function for
weaviate vectorstore which created a redundant property "key" inside a
class.
* Issue: Fixed: https://github.com/langchain-ai/langchain/issues/16692
* Twitter handle: @pashvamehta1
- **Description:** Syntax correction according to langchain version
update in 'Retry Parser' tutorial example,
- **Issue:** #16698
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:** Adds Wikidata support to langchain. Can read out
documents from Wikidata.
- **Issue:** N/A
- **Dependencies:** Adds implicit dependencies for
`wikibase-rest-api-client` (for turning items into docs) and
`mediawikiapi` (for hitting the search endpoint)
- **Twitter handle:** @derenrich
You can see an example of this tool used in a chain
[here](https://nbviewer.org/urls/d.erenrich.net/upload/Wikidata_Langchain.ipynb)
or
[here](https://nbviewer.org/urls/d.erenrich.net/upload/Wikidata_Lars_Kai_Hansen.ipynb)
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URL : https://python.langchain.com/docs/use_cases/extraction
Desc:
<b> While the following statement executes successfully, it throws an
error which is described below when we use the imported packages</b>
```py
from pydantic import BaseModel, Field, validator
```
Code:
```python
from langchain.output_parsers import PydanticOutputParser
from langchain.prompts import (
PromptTemplate,
)
from langchain_openai import OpenAI
from pydantic import BaseModel, Field, validator
# Define your desired data structure.
class Joke(BaseModel):
setup: str = Field(description="question to set up a joke")
punchline: str = Field(description="answer to resolve the joke")
# You can add custom validation logic easily with Pydantic.
@validator("setup")
def question_ends_with_question_mark(cls, field):
if field[-1] != "?":
raise ValueError("Badly formed question!")
return field
```
Error:
```md
PydanticUserError: The `field` and `config` parameters are not available
in Pydantic V2, please use the `info` parameter instead.
For further information visit
https://errors.pydantic.dev/2.5/u/validator-field-config-info
```
Solution:
Instead of doing:
```py
from pydantic import BaseModel, Field, validator
```
We should do:
```py
from langchain_core.pydantic_v1 import BaseModel, Field, validator
```
Thanks.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** This update ensures that the user-defined embedding
function specified during vector store creation is applied during
queries. Previously, even if a custom embedding function was defined at
the time of store creation, Bagel DB would default to using the standard
embedding function during query execution. This pull request addresses
this issue by consistently using the user-defined embedding function for
queries if one has been specified earlier.
- **Description:** This change allows the `_fetch` method in the
`WebBaseLoader` class to utilize cookies from an existing
`requests.Session`. It ensures that when the `fetch` method is used, any
cookies in the provided session are included in the request. This
enhancement maintains compatibility with existing functionality while
extending the utility of the `fetch` method for scenarios where cookie
persistence is necessary.
- **Issue:** Not applicable (new feature),
- **Dependencies:** Requires `aiohttp` and `requests` libraries (no new
dependencies introduced),
- **Twitter handle:** N/A
Co-authored-by: Joao Almeida <joao.almeida@mercedes-benz.io>
We can't use `json.dumps` by default as many types returned by the
cassandra driver are not serializable. It's safer to use `str` and let
users define their own custom `page_content_mapper` if needed.
if eg. the stream iterator is interrupted then adding more events to the
send_stream will raise an exception that we should catch (and handle
where appropriate)
<!-- 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,
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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
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2. an example notebook showing its use. It lives in
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- **Description**: YoutubeLoader right now returns one document that
contains the entire transcript. I think it would be useful to add an
option to return multiple documents, where each document would contain
one line of transcript with the start time and duration in the metadata.
For example,
[AssemblyAIAudioTranscriptLoader](https://github.com/langchain-ai/langchain/blob/master/libs/community/langchain_community/document_loaders/assemblyai.py)
is implemented in a similar way, it allows you to choose between the
format to use for the document loader.
- **Description:** This PR adds [EdenAI](https://edenai.co/) for the
chat model (already available in LLM & Embeddings). It supports all
[ChatModel] functionality: generate, async generate, stream, astream and
batch. A detailed notebook was added.
- **Dependencies**: No dependencies are added as we call a rest API.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
… converters
One way to convert anything to an OAI function:
convert_to_openai_function
One way to convert anything to an OAI tool: convert_to_openai_tool
Corresponding bind functions on OAI models: bind_functions, bind_tools
community:
- **Description:**
- Add new ChatLiteLLMRouter class that allows a client to use a LiteLLM
Router as a LangChain chat model.
- Note: The existing ChatLiteLLM integration did not cover the LiteLLM
Router class.
- Add tests and Jupyter notebook.
- **Issue:** None
- **Dependencies:** Relies on existing ChatLiteLLM integration
- **Twitter handle:** @bburgin_0
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:**
The parameters for user and assistant in Anthropic should be 'ai ->
assistant,' but they are reversed to 'assistant -> ai.'
Below is error code.
```python
anthropic.BadRequestError: Error code: 400 - {'type': 'error', 'error': {'type': 'invalid_request_error', 'message': 'messages: Unexpected role "ai". Allowed roles are "user" or "assistant"'}}
```
[anthropic](7177f3a71f/src/anthropic/types/beta/message_param.py (L13))
- **Issue:** : #16561
- **Dependencies:** : None
- **Twitter handle:** : None
<!-- 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:** Adding Oracle Cloud Infrastructure Generative AI
integration. Oracle Cloud Infrastructure (OCI) Generative AI is a fully
managed service that provides a set of state-of-the-art, customizable
large language models (LLMs) that cover a wide range of use cases, and
which is available through a single API. Using the OCI Generative AI
service you can access ready-to-use pretrained models, or create and
host your own fine-tuned custom models based on your own data on
dedicated AI clusters.
https://docs.oracle.com/en-us/iaas/Content/generative-ai/home.htm
- **Issue:** None,
- **Dependencies:** OCI Python SDK,
- **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.
Passed
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.
we provide unit tests. However, we cannot provide integration tests due
to Oracle policies that prohibit public sharing of api keys.
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
---------
Co-authored-by: Arthur Cheng <arthur.cheng@oracle.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Added support for optionally supplying 'Guardrails for Amazon Bedrock'
on both types of model invocations (batch/regular and streaming) and for
all models supported by the Amazon Bedrock service.
@baskaryan @hwchase17
```python
llm = Bedrock(model_id="<model_id>", client=bedrock,
model_kwargs={},
guardrails={"id": " <guardrail_id>",
"version": "<guardrail_version>",
"trace": True}, callbacks=[BedrockAsyncCallbackHandler()])
class BedrockAsyncCallbackHandler(AsyncCallbackHandler):
"""Async callback handler that can be used to handle callbacks from langchain."""
async def on_llm_error(
self,
error: BaseException,
**kwargs: Any,
) -> Any:
reason = kwargs.get("reason")
if reason == "GUARDRAIL_INTERVENED":
# kwargs contains additional trace information sent by 'Guardrails for Bedrock' service.
print(f"""Guardrails: {kwargs}""")
# streaming
llm = Bedrock(model_id="<model_id>", client=bedrock,
model_kwargs={},
streaming=True,
guardrails={"id": "<guardrail_id>",
"version": "<guardrail_version>"})
```
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:**
This PR adds a VectorStore integration for SAP HANA Cloud Vector Engine,
which is an upcoming feature in the SAP HANA Cloud database
(https://blogs.sap.com/2023/11/02/sap-hana-clouds-vector-engine-announcement/).
- **Issue:** N/A
- **Dependencies:** [SAP HANA Python
Client](https://pypi.org/project/hdbcli/)
- **Twitter handle:** @sapopensource
Implementation of the integration:
`libs/community/langchain_community/vectorstores/hanavector.py`
Unit tests:
`libs/community/tests/unit_tests/vectorstores/test_hanavector.py`
Integration tests:
`libs/community/tests/integration_tests/vectorstores/test_hanavector.py`
Example notebook:
`docs/docs/integrations/vectorstores/hanavector.ipynb`
Access credentials for execution of the integration tests can be
provided to the maintainers.
---------
Co-authored-by: sascha <sascha.stoll@sap.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Description:
- checked that the doc chat/google_vertex_ai_palm is using new
functions: invoke, stream etc.
- added Gemini example
- fixed wrong output in Sanskrit example
Issue: https://github.com/langchain-ai/langchain/issues/15664
Dependencies: None
Twitter handle: None
Flushing out the `mypy` config in `langchain-google-vertexai` to show
error codes and other warnings
This PR also bumps `mypy` to above version 1's stable release
**Description:**
Handle unsupported languages in same way as when none is provided
**Issue:**
The following line will throw a KeyError if the language is not
supported.
```python
self.Segmenter = LANGUAGE_SEGMENTERS[language]
```
E.g. when using `Language.CPP` we would get `KeyError: <Language.CPP:
'cpp'>`
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
<!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is
whichever of langchain, community, core, experimental, etc. is being
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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:** added the conversational task to hugginFace endpoint
in order to use models designed for chatbot programming.
- **Dependencies:** None
---------
Co-authored-by: Alessio Serra (ext.) <alessio.serra@partner.bmw.de>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** Updated `_get_elements()` function of
`UnstructuredFileLoader `class to check if the argument self.file_path
is a file or list of files. If it is a list of files then it iterates
over the list of file paths, calls the partition function for each one,
and appends the results to the elements list. If self.file_path is not a
list, it calls the partition function as before.
- **Issue:** Fixed#15607,
- **Dependencies:** NA
- **Twitter handle:** NA
Co-authored-by: H161961 <Raunak.Raunak@Honeywell.com>
- **Description:** This PR enables LangChain to access the iFlyTek's
Spark LLM via the chat_models wrapper.
- **Dependencies:** websocket-client ^1.6.1
- **Tag maintainer:** @baskaryan
### SparkLLM chat model usage
Get SparkLLM's app_id, api_key and api_secret from [iFlyTek SparkLLM API
Console](https://console.xfyun.cn/services/bm3) (for more info, see
[iFlyTek SparkLLM Intro](https://xinghuo.xfyun.cn/sparkapi) ), then set
environment variables `IFLYTEK_SPARK_APP_ID`, `IFLYTEK_SPARK_API_KEY`
and `IFLYTEK_SPARK_API_SECRET` or pass parameters when using it like the
demo below:
```python3
from langchain.chat_models.sparkllm import ChatSparkLLM
client = ChatSparkLLM(
spark_app_id="<app_id>",
spark_api_key="<api_key>",
spark_api_secret="<api_secret>"
)
```
- **Description:**
This PR aims to enhance the `langchain` library by enabling the support
for passing `custom_headers` in the `GraphQLAPIWrapper` usage within
`langchain/agents/load_tools.py`.
While the `GraphQLAPIWrapper` from the `langchain_community` module is
inherently capable of handling `custom_headers`, its current invocation
in `load_tools.py` does not facilitate this functionality.
This limitation restricts the use of the `graphql` tool with databases
or APIs that require token-based authentication.
The absence of support for `custom_headers` in this context also leads
to a lack of error messages when attempting to interact with secured
GraphQL endpoints, making debugging and troubleshooting more
challenging.
This update modifies the `load_tools` function to correctly handle
`custom_headers`, thereby allowing secure and authenticated access to
GraphQL services requiring tokens.
Example usage after the proposed change:
```python
tools = load_tools(
["graphql"],
graphql_endpoint="https://your-graphql-endpoint.com/graphql",
custom_headers={"Authorization": f"Token {api_token}"},
)
```
- **Issue:** None,
- **Dependencies:** None,
- **Twitter handle:** None
- **Description:** This addresses the issue tagged below where if you
try to pass your own client when creating an OpenAI assistant, a
pydantic error is raised:
Example code:
```python
import openai
from langchain.agents.openai_assistant import OpenAIAssistantRunnable
client = openai.OpenAI()
interpreter_assistant = OpenAIAssistantRunnable.create_assistant(
name="langchain assistant",
instructions="You are a personal math tutor. Write and run code to answer math questions.",
tools=[{"type": "code_interpreter"}],
model="gpt-4-1106-preview",
client=client
)
```
Error:
`pydantic.v1.errors.ConfigError: field "client" not yet prepared, so the
type is still a ForwardRef. You might need to call
OpenAIAssistantRunnable.update_forward_refs()`
It additionally updates type hints and docstrings to indicate that an
AzureOpenAI client is permissible as well.
- **Issue:** https://github.com/langchain-ai/langchain/issues/15948
- **Dependencies:** N/A
Description:
- Added output and environment variables
- Updated the documentation for chat/anthropic, changing references from
`langchain.schema` to `langchain_core.prompts`.
Issue: https://github.com/langchain-ai/langchain/issues/15664
Dependencies: None
Twitter handle: None
Since this is my first open-source PR, please feel free to point out any
mistakes, and I'll be eager to make corrections.
This PR introduces update to Konko Integration with LangChain.
1. **New Endpoint Addition**: Integration of a new endpoint to utilize
completion models hosted on Konko.
2. **Chat Model Updates for Backward Compatibility**: We have updated
the chat models to ensure backward compatibility with previous OpenAI
versions.
4. **Updated Documentation**: Comprehensive documentation has been
updated to reflect these new changes, providing clear guidance on
utilizing the new features and ensuring seamless integration.
Thank you to the LangChain team for their exceptional work and for
considering this PR. Please let me know if any additional information is
needed.
---------
Co-authored-by: Shivani Modi <shivanimodi@Shivanis-MacBook-Pro.local>
Co-authored-by: Shivani Modi <shivanimodi@Shivanis-MBP.lan>
- **Description:** extreact the _aperform_agent_action in the
AgentExecutor class to allow for easier overriding. Extracted logic from
_iter_next_step into a new method _perform_agent_action for consistency
and easier overriding.
- **Issue:** #15706Closes#15706
- **Description:** The HTMLHeaderTextSplitter Class now explicitly
specifies utf-8 encoding in the part of the split_text_from_file method
that calls the HTMLParser.
- **Issue:** Prevent garbled characters due to differences in encoding
of html files (except for English in particular, I noticed that problem
with Japanese).
- **Dependencies:** No dependencies,
- **Twitter handle:** @i_w__a
Adds the ability to return similarity scores when using
`RetrievalQA.from_chain_type` with `MongoDBAtlasVectorSearch`. Requires
that `return_source_documents=True` is set.
Example use:
```
vector_search = MongoDBAtlasVectorSearch.from_documents(...)
qa = RetrievalQA.from_chain_type(
llm=OpenAI(),
chain_type="stuff",
retriever=vector_search.as_retriever(search_kwargs={"additional": ["similarity_score"]}),
return_source_documents=True
)
...
docs = qa({"query": "..."})
docs["source_documents"][0].metadata["score"] # score will be here
```
I've tested this feature locally, using a MongoDB Atlas Cluster with a
vector search index.
- **Description:** Allow passing run_id to MLflowCallbackHandler to
resume a run instead of creating a new run. Support recording retriever
relevant metrics. Refactor the code to fix some bugs.
---------
Signed-off-by: Serena Ruan <serena.rxy@gmail.com>
In this PR I added a post-processing function to normalize the
embeddings. This happens only if the new `normalize` flag is `True`.
---------
Co-authored-by: taamedag <Davide.Menini@swisscom.com>
- **Description:** Baichuan Chat (with both Baichuan-Turbo and
Baichuan-Turbo-192K models) has updated their APIs. There are breaking
changes. For example, BAICHUAN_SECRET_KEY is removed in the latest API
but is still required in Langchain. Baichuan's Langchain integration
needs to be updated to the latest version.
- **Issue:** #15206
- **Dependencies:** None,
- **Twitter handle:** None
@hwchase17.
Co-authored-by: BaiChuanHelper <wintergyc@WinterGYCs-MacBook-Pro.local>
**Description:**
- Implement `SQLStrStore` and `SQLDocStore` classes that inherits from
`BaseStore` to allow to persist data remotely on a SQL server.
- SQL is widely used and sometimes we do not want to install a caching
solution like Redis.
- Multiple issues/comments complain that there is no easy remote and
persistent solution that are not in memory (users want to replace
InMemoryStore), e.g.,
https://github.com/langchain-ai/langchain/issues/14267,
https://github.com/langchain-ai/langchain/issues/15633,
https://github.com/langchain-ai/langchain/issues/14643,
https://stackoverflow.com/questions/77385587/persist-parentdocumentretriever-of-langchain
- This is particularly painful when wanting to use
`ParentDocumentRetriever `
- This implementation is particularly useful when:
* it's expensive to construct an InMemoryDocstore/dict
* you want to retrieve documents from remote sources
* you just want to reuse existing objects
- This implementation integrates well with PGVector, indeed, when using
PGVector, you already have a SQL instance running. `SQLDocStore` is a
convenient way of using this instance to store documents associated to
vectors. An integration example with ParentDocumentRetriever and
PGVector is provided in docs/docs/integrations/stores/sql.ipynb or
[here](https://github.com/gcheron/langchain/blob/sql-store/docs/docs/integrations/stores/sql.ipynb).
- It persists `str` and `Document` objects but can be easily extended.
**Issue:**
Provide an easy SQL alternative to `InMemoryStore`.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:** this PR upgrades the `HuggingFaceHub` LLM:
* support more tasks (`translation` and `conversational`)
* replaced the deprecated `InferenceApi` with `InferenceClient`
* adjusted the overall logic to use the "recommended" model for each
task when no model is provided, and vice-versa.
- **Tag mainter(s)**: @baskaryan @hwchase17
For tracing, if a validation error occurs, currently it is attributed to
the previous step of the chain. It would be nice to have the on_start
and on_error callbacks called for tools when there is a validation error
that occurs to more easily attribute the root-cause
**Description** : New documents loader for visio files (with extension
.vsdx)
A [visio file](https://fr.wikipedia.org/wiki/Microsoft_Visio) (with
extension .vsdx) is associated with Microsoft Visio, a diagram creation
software. It stores information about the structure, layout, and
graphical elements of a diagram. This format facilitates the creation
and sharing of visualizations in areas such as business, engineering,
and computer science.
A Visio file can contain multiple pages. Some of them may serve as the
background for others, and this can occur across multiple layers. This
loader extracts the textual content from each page and its associated
pages, enabling the extraction of all visible text from each page,
similar to what an OCR algorithm would do.
**Dependencies** : xmltodict package
- **Description:** Updated the Chat/Ollama docs notebook with LCEL chain
examples
- **Issue:** #15664 I'm a new contributor 😊
- **Dependencies:** No dependencies
- **Twitter handle:**
Comments:
- How do I truncate the output of the stream in the notebook if and or
when it goes on and on and on for even the basic of prompts?
Edit:
Looking forward to feedback @baskaryan
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
## Problem
Spent several hours trying to figure out how to pass
`RedisChatMessageHistory` as a `GetSessionHistoryCallable` with a
different REDIS hostname. This example kept connecting to
`redis://localhost:6379`, but I wanted to connect to a server not hosted
locally.
## Cause
Assumption the user knows how to implement `BaseChatMessageHistory` and
`GetSessionHistoryCallable`
## Solution
Update documentation to show how to explicitly set the REDIS hostname
using a lambda function much like the MongoDB and SQLite examples.
After merging [PR
#16304](https://github.com/langchain-ai/langchain/pull/16304), I
realized that our notebook example for integrating TiDB with LangChain
was too basic. To make it more useful and user-friendly, I plan to
create a detailed example. This will show how to use TiDB for saving
history messages in LangChain, offering a clearer, more practical guide
for our users
I also added LANGCHAIN_COMET_TRACING to enable the CometLLM tracing
integration similar to other tracing integrations. This is easier for
end-users to enable it rather than importing the callback and pass it
manually.
(This is the same content as
https://github.com/langchain-ai/langchain/pull/14650 but rebased and
squashed as something seems to confuse Github Action).
- **Description:** At the moment it's not possible to include in the
same project langchain-google-vertexai and boto3 (e.g. use bedrock and
vertex in the same application) because of the dependency resolutions
conflict. boto3 is still using urllib3 1.x, meanwhile
langchain-google-vertexai -> types-requests depends on urllib3 2.x. [the
last version of types-requests that allows urllib3 1.x is
2.31.0.6](https://pypi.org/project/types-requests/#description).
In this PR I allow the vertexai package to get that version also.
- **Twitter handle:** nicoloboschi
Description: Added support for asynchronous streaming in the Bedrock
class and corresponding tests.
Primarily:
async def aprepare_output_stream
async def _aprepare_input_and_invoke_stream
async def _astream
async def _acall
I've ensured that the code adheres to the project's linting and
formatting standards by running make format, make lint, and make test.
Issue: #12054, #11589
Dependencies: None
Tag maintainer: @baskaryan
Twitter handle: @dominic_lovric
---------
Co-authored-by: Piyush Jain <piyushjain@duck.com>
Replace this entire comment with:
- **Description:** allow user to define tVector length in PGVector when
creating the embedding store, this allows for later indexing
- **Issue:** #16132
- **Dependencies:** None
**Description:** Add support for querying TigerGraph databases through
the InquiryAI service.
**Issue**: N/A
**Dependencies:** N/A
**Twitter handle:** @TigerGraphDB
there is a case where "coords" does not exist in the "sentence"
therefore, the "split(";")" will lead to error.
we can fix that by adding "if sentence.get("coords") is not None:"
the resulting empty "sbboxes" from this scenario will raise error at
"sbboxes[0]["page"]" because sbboxes are empty.
the PDF from https://pubmed.ncbi.nlm.nih.gov/23970373/ can replicate
those errors.
This pull request integrates the TiDB database into LangChain for
storing message history, marking one of several steps towards a
comprehensive integration of TiDB with LangChain.
A simple usage
```python
from datetime import datetime
from langchain_community.chat_message_histories import TiDBChatMessageHistory
history = TiDBChatMessageHistory(
connection_string="mysql+pymysql://<host>:<PASSWORD>@<host>:4000/<db>?ssl_ca=/etc/ssl/cert.pem&ssl_verify_cert=true&ssl_verify_identity=true",
session_id="code_gen",
earliest_time=datetime.utcnow(), # Optional to set earliest_time to load messages after this time point.
)
history.add_user_message("hi! How's feature going?")
history.add_ai_message("It's almot done")
```
The callbacks get started demo code was updated , replacing the
chain.run() command ( which is now depricated) ,with the updated
chain.invoke() command.
Solving the following issue : #16379
Twitter/X : @Hazxhx
- **Description:** add support for kwargs in`MlflowEmbeddings`
`embed_document()` and `embed_query()` so that all the arguments
required by Cohere API (and others?) can be passed down to the server.
- **Issue:** #15234
- **Dependencies:** MLflow with MLflow Deployments (`pip install
mlflow[genai]`)
**Tests**
Now this code [adapted from the
docs](https://python.langchain.com/docs/integrations/providers/mlflow#embeddings-example)
for the Cohere API works locally.
```python
"""
Setup
-----
export COHERE_API_KEY=...
mlflow deployments start-server --config-path examples/deployments/cohere/config.yaml
Run
---
python /path/to/this/file.py
"""
embeddings = MlflowCohereEmbeddings(target_uri="http://127.0.0.1:5000", endpoint="embeddings")
print(embeddings.embed_query("hello")[:3])
print(embeddings.embed_documents(["hello", "world"])[0][:3])
```
Output
```
[0.060455322, 0.028793335, -0.025848389]
[0.031707764, 0.021057129, -0.009361267]
```
Titan Express model was not supported as a chat model because LangChain
messages were not "translated" to a text prompt.
Co-authored-by: Guillem Orellana Trullols <guillem.orellana_trullols@siemens.com>
Adjusted `deprecate` decorator to make sure decorated async functions
are still recognized as "coroutinefunction" by `inspect`.
Before change, functions such as `LLMChain.acall` which are decorated as
deprecated are not recognized as coroutine functions. After the change,
they are recognized:
```python
import inspect
from langchain import LLMChain
# Is false before change but true after.
inspect.iscoroutinefunction(LLMChain.acall)
```
- **Description:** I removed two queries to the database and left just
one whose results were formatted afterward into other type of schema
(avoided two calls to DB)
- **Issue:** /
- **Dependencies:** /
- **Twitter handle:** @supe_katarina
- **Description:** Some code sources have been moved from `langchain` to
`langchain_community` and so the documentation is not yet up-to-date.
This is specifically true for `StreamlitCallbackHandler` which returns a
`warning` message if not loaded from `langchain_community`.,
- **Issue:** I don't see a # issue that could address this problem but
perhaps #10744,
- **Dependencies:** Since it's a documentation change no dependencies
are required
- **Description:** update documentation on jaguar vector store:
Instruction for setting up jaguar server and usage of text_tag.
- **Issue:**
- **Dependencies:**
- **Twitter handle:**
---------
Co-authored-by: JY <jyjy@jaguardb>
Implement similarity function selector for ElasticsearchStore. The
scores coming back from Elasticsearch are already similarities (not
distances) and they are already normalized (see
[docs](https://www.elastic.co/guide/en/elasticsearch/reference/current/dense-vector.html#dense-vector-params)).
Hence we leave the scores untouched and just forward them.
This fixes#11539.
However, in hybrid mode (when keyword search and vector search are
involved) Elasticsearch currently returns no scores. This PR adds an
error message around this fact. We need to think a bit more to come up
with a solution for this case.
This PR also corrects a small error in the Elasticsearch integration
test.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
- **Issue:** This is a PR about #16340
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Co-authored-by: yuhei.tsunoda <yuhei.tsunoda@brainpad.co.jp>
- **Description:** Updating documentation of IBM
[watsonx.ai](https://www.ibm.com/products/watsonx-ai) LLM with using
`invoke` instead of `__call__`
- **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. ✅
The following warning information show when i use `run` and `__call__`
method:
```
LangChainDeprecationWarning: The function `__call__` was deprecated in LangChain 0.1.7 and will be removed in 0.2.0. Use invoke instead.
warn_deprecated(
```
We need to update documentation for using `invoke` method
The following warning information will be displayed when i use
`llm(PROMPT)`:
```python
/Users/169/llama.cpp/venv/lib/python3.11/site-packages/langchain_core/_api/deprecation.py:117: LangChainDeprecationWarning: The function `__call__` was deprecated in LangChain 0.1.7 and will be removed in 0.2.0. Use invoke instead.
warn_deprecated(
```
So I changed to standard usage.
**Description:**
In this PR, I am adding a `PolygonLastQuote` Tool, which can be used to
get the latest price quote for a given ticker / stock.
Additionally, I've added a Polygon Toolkit, which we can use to
encapsulate future tools that we build for Polygon.
**Twitter handle:** [@virattt](https://twitter.com/virattt)
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- Used to be None, now is just the last chunk
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fixed multi-query template for Vectara
added self-query template for Vectara
Also added prompt_name parameter to summarization
CC @efriis
**Twitter handle:** @ofermend
Add a version parameter while the method is in beta phase.
The idea is to make it possible to minimize making breaking changes for users while we're iterating on schema.
Once the API is stable we can assign a default version requirement.
- **Description:** Adds a text splitter based on
[Konlpy](https://konlpy.org/en/latest/#start) which is a Python package
for natural language processing (NLP) of the Korean language. (It is
like Spacy or NLTK for Korean)
- **Dependencies:** Konlpy would have to be installed before this
splitter is used,
- **Twitter handle:** @untilhamza
- **Description:** Fixes a few issues in NVIDIAcanonical RAG template's
README, and adds a notebook for the template
- **Dependencies:** Adds the pypdf dependency which is needed for
ingestion, and updates the lock file
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Add privileged version for issue creation.
This adds a version of issue creation which is unstructured by design to
make it easier for maintainers to create issues.
Maintainers are expected to write / describe issues clearly.
- **Description:** Some text-generation models on huggingface repeat the
prompt in their generated response, but not all do! The tests use "gpt2"
which DOES repeat the prompt and as such, the HuggingFaceHub class is
hardcoded to remove the first few characters of the response (to match
the len(prompt)). However, if you are using a model (such as the very
popular "meta-llama/Llama-2-7b-chat-hf") that DOES NOT repeat the prompt
in it's generated text, then the beginning of the generated text will be
cut off. This code change fixes that bug by first checking whether the
prompt is repeated in the generated response and removing it
conditionally.
- **Issue:** #16232
- **Dependencies:** N/A
- **Twitter handle:** N/A
This PR adds `astream_events` method to Runnables to make it easier to
stream data from arbitrary chains.
* Streaming only works properly in async right now
* One should use `astream()` with if mixing in imperative code as might
be done with tool implementations
* Astream_log has been modified with minimal additive changes, so no
breaking changes are expected
* Underlying callback code / tracing code should be refactored at some
point to handle things more consistently (OK for now)
- ~~[ ] verify event for on_retry~~ does not work until we implement
streaming for retry
- ~~[ ] Any rrenaming? Should we rename "event" to "hook"?~~
- [ ] Any other feedback from community?
- [x] throw NotImplementedError for `RunnableEach` for now
## Example
See this [Example
Notebook](dbbc7fa0d6/docs/docs/modules/agents/how_to/streaming_events.ipynb)
for an example with streaming in the context of an Agent
## Event Hooks Reference
Here is a reference table that shows some events that might be emitted
by the various Runnable objects.
Definitions for some of the Runnable are included after the table.
| event | name | chunk | input | output |
|----------------------|------------------|---------------------------------|-----------------------------------------------|-------------------------------------------------|
| on_chat_model_start | [model name] | | {"messages": [[SystemMessage,
HumanMessage]]} | |
| on_chat_model_stream | [model name] | AIMessageChunk(content="hello")
| | |
| on_chat_model_end | [model name] | | {"messages": [[SystemMessage,
HumanMessage]]} | {"generations": [...], "llm_output": None, ...} |
| on_llm_start | [model name] | | {'input': 'hello'} | |
| on_llm_stream | [model name] | 'Hello' | | |
| on_llm_end | [model name] | | 'Hello human!' |
| on_chain_start | format_docs | | | |
| on_chain_stream | format_docs | "hello world!, goodbye world!" | | |
| on_chain_end | format_docs | | [Document(...)] | "hello world!,
goodbye world!" |
| on_tool_start | some_tool | | {"x": 1, "y": "2"} | |
| on_tool_stream | some_tool | {"x": 1, "y": "2"} | | |
| on_tool_end | some_tool | | | {"x": 1, "y": "2"} |
| on_retriever_start | [retriever name] | | {"query": "hello"} | |
| on_retriever_chunk | [retriever name] | {documents: [...]} | | |
| on_retriever_end | [retriever name] | | {"query": "hello"} |
{documents: [...]} |
| on_prompt_start | [template_name] | | {"question": "hello"} | |
| on_prompt_end | [template_name] | | {"question": "hello"} |
ChatPromptValue(messages: [SystemMessage, ...]) |
Here are declarations associated with the events shown above:
`format_docs`:
```python
def format_docs(docs: List[Document]) -> str:
'''Format the docs.'''
return ", ".join([doc.page_content for doc in docs])
format_docs = RunnableLambda(format_docs)
```
`some_tool`:
```python
@tool
def some_tool(x: int, y: str) -> dict:
'''Some_tool.'''
return {"x": x, "y": y}
```
`prompt`:
```python
template = ChatPromptTemplate.from_messages(
[("system", "You are Cat Agent 007"), ("human", "{question}")]
).with_config({"run_name": "my_template", "tags": ["my_template"]})
```
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- **Description:** In Google Vertex AI, Gemini Chat models currently
doesn't have a support for SystemMessage. This PR adds support for it
only if a user provides additional convert_system_message_to_human flag
during model initialization (in this case, SystemMessage would be
prepended to the first HumanMessage). **NOTE:** The implementation is
similar to #14824
- **Twitter handle:** rajesh_thallam
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
- **Description**: Updated doc for llm/google_vertex_ai_palm with new
functions: `invoke`, `stream`... Changed structure of the document to
match the required one.
- **Issue**: #15664
- **Dependencies**: None
- **Twitter handle**: None
---------
Co-authored-by: Jorge Zaldívar <jzaldivar@google.com>
**Description:** Gemini model has quite annoying default safety_settings
settings. In addition, current VertexAI class doesn't provide a property
to override such settings.
So, this PR aims to
- add safety_settings property to VertexAI
- fix issue with incorrect LLM output parsing when LLM responds with
appropriate 'blocked' response
- fix issue with incorrect parsing LLM output when Gemini API blocks
prompt itself as inappropriate
- add safety_settings related tests
I'm not enough familiar with langchain code base and guidelines. So, any
comments and/or suggestions are very welcome.
**Issue:** it will likely fix#14841
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
* Removed some env vars not used in langchain package IT
* Added Astra DB env vars in langchain package, used for cache tests
* Added conftest.py to load env vars in langchain_community IT
* Added .env.example in langchain_community IT
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The timeout function comes in handy when you want to kill longrunning
queries.
The value sanitization removes all lists that are larger than 128
elements. The idea here is to remove embedding properties from results.
- **Description:** As Shell tool is very versatile, while integrating it
into applications as openai functions, developers have no clue about
what command is being executed using the ShellTool. All one can see is:
Summarising my feature request:
1. There's no visibility about what command was executed.
2. There's no mechanism to prevent a command to be executed using
ShellTool, like a y/n human input which can be accepted from user to
proceed with executing the command.,
- **Issue:** the issue #15931 it fixes if applicable,
- **Dependencies:** There isn't any dependancy,
- **Twitter handle:** @krishnashed
- **Description:** Made a small fix for the `SQLDatabase` highlighted in
an issue. The issue pertains to switching schema for different SQL
engines.
- **Issue:** #16023
@baskaryan
- **Description:** Support IN and LIKE comparators in Milvus
self-querying retriever, based on [Boolean Expression
Rules](https://milvus.io/docs/boolean.md)
- **Issue:** No
- **Dependencies:** No
- **Twitter handle:** No
Signed-off-by: ChengZi <chen.zhang@zilliz.com>
**Description**: This PR fixes an error in the documentation for Azure
Cosmos DB Integration.
**Issue**: The correct way to import `AzureCosmosDBVectorSearch` is
```python
from langchain_community.vectorstores.azure_cosmos_db import (
AzureCosmosDBVectorSearch,
)
```
While the
[documentation](https://python.langchain.com/docs/integrations/vectorstores/azure_cosmos_db)
states it to be
```python
from langchain_community.vectorstores.azure_cosmos_db_vector_search import (
AzureCosmosDBVectorSearch,
CosmosDBSimilarityType,
)
```
As you can see in
[azure_cosmos_db.py](c323742f4f/libs/langchain/langchain/vectorstores/azure_cosmos_db.py (L1C45-L2))
**Dependencies:**: None
**Twitter handle**: None
- **Description:** This handles the cohere response when documents
aren't included in the response
- **Issue:** N/A
- **Dependencies:** N/A
- **Twitter handle:** N/A
- bumps package post versions for packages without current unreleased
updates
- will bump package version in release prs associated with packages that
do have changes (mistral, vertex)
- **Description:** Adds MistralAIEmbeddings class for embeddings, using
the new official API.
- **Dependencies:** mistralai
- **Tag maintainer**: @efriis, @hwchase17
- **Twitter handle:** @LMS_David_RS
Create `integrations/text_embedding/mistralai.ipynb`: an example
notebook for MistralAIEmbeddings class
Modify `embeddings/__init__.py`: Import the class
Create `embeddings/mistralai.py`: The embedding class
Create `integration_tests/embeddings/test_mistralai.py`: The test file.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
**Description:**
Implement `adelete` function from `VectorStore` in `Qdrant` to support
other asynchronous flows such as async indexing (`aindex`) which
requires `adelete` to be implemented. Since `Qdrant` can be passed an
async qdrant client, this can be supported easily.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
This PR addresses an issue in OpenAIWhisperParserLocal where requesting
CUDA without availability leads to an AttributeError #15143
Changes:
- Refactored Logic for CUDA Availability: The initialization now
includes a check for CUDA availability. If CUDA is not available, the
code falls back to using the CPU. This ensures seamless operation
without manual intervention.
- Parameterizing Batch Size and Chunk Size: The batch_size and
chunk_size are now configurable parameters, offering greater flexibility
and optimization options based on the specific requirements of the use
case.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
**Description:** This new feature enhances the flexibility of pipeline
integration, particularly when working with RESTful APIs.
``JsonRequestsWrapper`` allows for the decoding of JSON output, instead
of the only option for text output.
---------
Co-authored-by: Zhichao HAN <hanzhichao2000@hotmail.com>
- **Description:** Adds documentation for the
`FirestoreChatMessageHistory` integration and lists integration in
Google's documentation
- **Issue:** NA
- **Dependencies:** No
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Fixed the issue mentioned in #15698 for SlackGetChannel Tool.
@baskaryan.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** add deprecated warning for ErnieBotChat and
ErnieEmbeddings.
- These two classes **lack maintenance** and do not use the sdk provided
by qianfan, which means hard to implement some key feature like
streaming.
- The alternative `langchain_community.chat_models.QianfanChatEndpoint`
and `langchain_community.embeddings.QianfanEmbeddingsEndpoint` can
completely replace these two classes, only need to change configuration
items.
- **Issue:** None,
- **Dependencies:** None,
- **Twitter handle:** None
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description**: `zip` is iterator that will only produce result once,
so the previous code will cause the `embeddings` to be an empty list.
**Issue**: I could not find a related issue.
**Dependencies**: this PR does not introduce or affect dependencies.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** docs update following the changes introduced in
#15879
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BigQuery vector search lets you use GoogleSQL to do semantic search,
using vector indexes for fast but approximate results, or using brute
force for exact results.
This PR:
1. Add `metadata[_job_ib]` in Document returned by any similarity search
2. Add `explore_job_stats` to enable users to explore job statistics and
better the debuggability
3. Set the minimum row limit for running create vector index.
## Description
In this update, I addressed the missing implementation for
atransform_document, which is the asynchronous counterpart of
transform_document in Doctran.
### Usage Example:
```py
# Instantiate DoctranPropertyExtractor with specified properties
property_extractor = DoctranPropertyExtractor(properties=properties)
# Asynchronously extract properties from a list of documents
extracted_document = await property_extractor.atransform_documents(
documents, properties=properties
)
# Display metadata of the first extracted document
print(json.dumps(extracted_document[0].metadata, indent=2))
```
## Issue
- Pull request #14525 has caused a break in the aforementioned code.
Instead of removing an asynchronous implementation of a function,
consider implementing a synchronous version alongside it.
- **Description:** Added parenthesis in return statement of
aembed_query() funtion to fix 'coroutine' object is not subscriptable
error.
- **Dependencies:** NA
Co-authored-by: H161961 <Raunak.Raunak@Honeywell.com>
## Feature
- Follow parameter structure as per official documentation
- top level parameters (e.g. model, system, template) will be passed as
top level parameters
- other parameters will be sent in options unless options is provided
## Tests
- Test if top level parameters handled properly
- Test if parameters that are not top level parameters are handled as
options
- Test if options is provided, it will be passed as is
**Description:** Added the new gpt-3.5-turbo-1106 for **finetuned** cost
calculation,
**Issue:** no issue found open
By the information in OpenAI the pricing is the same as the older model
(0613)
- vertex chat
- google
- some pip openai
- percent and openai
- all percent
- more
- pip
- fmt
- docs: google vertex partner docs
- fmt
- docs: more pip installs
- **Description:** Added a `PolygonAPIWrapper` and an initial
`get_last_quote` endpoint, which allows us to get the last price quote
for a given `ticker`. Once merged, I can add a Polygon tool in `tools/`
for agents to use.
- **Twitter handle:** [@virattt](https://twitter.com/virattt)
The Polygon.io Stocks API provides REST endpoints that let you query the
latest market data from all US stock exchanges.
Support [Lantern](https://github.com/lanterndata/lantern) as a new
VectorStore type.
- Added Lantern as VectorStore.
It will support 3 distance functions `l2 squared`, `cosine` and
`hamming` and will use `HNSW` index.
- Added tests
- Added example notebook
**Description**: the "page" mode in the
AzureAIDocumentIntelligenceParser is not accessible due to a wrong
membership test. The mode argument can only be a string (also see the
assertion in the `__init__`: `assert self.mode in ["single", "page",
"object", "markdown"]`, so the check `elif self.mode == ["page"]:`
always fails.
As a result, effectively the "object" mode is used when selecting the
"page" mode, which may lead to errors.
The docstring of the `AzureAIDocumentIntelligenceLoader` also ommitted
the `mode` parameter alltogether, so I added it.
**Issue**: I could not find a related issue (this class is only 3 weeks
old anyways)
**Dependencies**: this PR does not introduce or affect dependencies.
The current demo notebook and examples are not affected because they all
use the default markdown mode.
- **Description:** Azure Cognitive Search vector DB store performs slow
embedding as it does not utilize the batch embedding functionality. This
PR provide a fix to improve the performance of Azure Search class when
adding documents to the vector search,
- **Issue:** #11313 ,
- **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:**
Remove section on how to install Action Server and direct the users t o
the instructions on Robocorp repository.
**Reason:**
Robocorp Action Server has moved from a pip installation to a standalone
cli application and is due for changes. Because of that, leaving only
LangChain integration relevant part in the documentation.
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whichever of langchain, community, core, experimental, etc. is being
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- **Description:** Milvus's partition key is an important feature. It
can support multi-tenancy. We hope to introduce this feature.
https://milvus.io/docs/partition_key.md
- **Issue:** No
- **Dependencies:** No
- **Twitter handle:** No
---------
Signed-off-by: ChengZi <chen.zhang@zilliz.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Add support for end_point and transport parameters to the Gemini API
---------
Co-authored-by: yangenfeng <yangenfeng@xiaoniangao.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
**Description:**
Added aembed_documents() and aembed_query() async functions in
HuggingFaceHubEmbeddings class in
langchain_community\embeddings\huggingface_hub.py file. It will support
to make async calls to HuggingFaceHub's
embedding endpoint and generate embeddings asynchronously.
Test Cases: Added test_huggingfacehub_embedding_async_documents() and
test_huggingfacehub_embedding_async_query()
functions in test_huggingface_hub.py file to test the two async
functions created in HuggingFaceHubEmbeddings class.
Documentation: Updated huggingfacehub.ipynb with steps to install
huggingface_hub package and use
HuggingFaceHubEmbeddings.
**Dependencies:** None,
**Twitter handle:** I do not have a Twitter account
---------
Co-authored-by: H161961 <Raunak.Raunak@Honeywell.com>
- **Description:** This PR defines the output parser of
OpenAIFunctionsAgent as an attribute, enabling customization and
subclassing of the parser logic.
- **Issue:** Subclassing is currently impossible as the
`OpenAIFunctionsAgentOutputParser` class is hard coded into the `plan`
and `aplan` methods
- **Dependencies:** None
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---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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## Feature
- Set additional headers in constructor
- Headers will be sent in post request
This feature is useful if deploying Ollama on a cloud service such as
hugging face, which requires authentication tokens to be passed in the
request header.
## Tests
- Test if header is passed
- Test if header is not passed
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Major changes:
- Rename `wasm_chat.py` to `llama_edge.py`
- Rename the `WasmChatService` class to `ChatService`
- Implement the `stream` interface for `ChatService`
- Add `test_chat_wasm_service_streaming` in the integration test
- Update `llama_edge.ipynb`
---------
Signed-off-by: Xin Liu <sam@secondstate.io>
- **Description:** `AmadeusToolkit` and `AmadeusClosestAirport`
contained a hardcoded call to `ChatOpenAI`. This PR makes it
LLM-independent, while guaranteeing backward compatibility.
- **Issue:** #15847
- **Dependencies:** None
@baskaryan
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**Description:**
Fixes OutputParserException thrown by the output_parser when 'query' is
'Null'.
Replace this entire comment with:
- **Description:** Current implentation of output_parser throws
OutputParserException if the response from the LLM contains `query:
null`. This unfortunately happens for my use case. And since there is no
way to modify the prompt used in SelfQueryRetriever, then we have to fix
it here, so it doesn't crash.
- **Issue:** https://github.com/langchain-ai/langchain/issues/15914
Didn't run tests. `make test` is not working. There is no `test` rule in
the `Makefile`.
Co-authored-by: Jan Horcicka <jhorcick@amazon.com>
- **Description:** The pinecone docstring instructs to pass the
embedding query text causing the warning below. It should be the
embeddings object.
warning message: UserWarning: Passing in `embedding` as a Callable is
deprecated. Please pass in an Embeddings object instead.
- **Issue:** NA
- **Dependencies:** None
@baskaryan
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---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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Community : Modified doc strings and example notebook for Clarifai
Description:
1. Modified doc strings inside clarifai vectorstore class and
embeddings.
2. Modified notebook examples.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
- **Description:** Adds a RAG template that uses NVIDIA AI playground
and embedding models, along with Milvus vector store
- **Dependencies:** This template depends on the AI playground service
in NVIDIA NGC. API keys with a significant trial compute are available
(10k queries at the time of writing). This template also depends on the
Milvus Vector store which is publicly available.
Note: [A quick link to get a
key](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/ai-foundation/models/codellama-13b/api)
when you have an NGC account. Generate Key button at the top right of
the code window.
---------
Co-authored-by: Sagar B Manjunath <sbogadimanju@nvidia.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
This PR fixes an issue where AgentExecutor with RunnableAgent does not allow users to see individual llm tokens if streaming=True is not set explicitly on the underlying chat model.
The majority of this PR is testing code:
1. Create a test chat model that makes it easier to test streaming and
supports AIMessages that include function invocation information.
2. Tests for the chat model
3. Tests for RunnableAgent (previously untested)
4. Tests for openai agent (previously untested)
- **Description:**
`QianfanChatEndpoint` extends `BaseChatModel` as a super class, which
has a default stream implement might concat the MessageChunk with
`__add__`. When call stream(), a ValueError for duplicated key will be
raise.
- **Issues:**
* #13546
* #13548
* merge two single test file related to qianfan.
- **Dependencies:** no
- **Tag maintainer:**
---------
Co-authored-by: root <liujun45@baidu.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
experimental relies on `from langchain_core.runnables.config import
run_in_executor` which was introduced in core 0.1.5.
Updated pyproject dependency as well as minimum version test.
Now the SQL used to delete vector doc from myscale is as follow:
```sql
DELETE FROM collection WHERE id = '1' AND id = '2' AND id = '3'
```
But the expected one should be
```sql
DELETE FROM collection WHERE id IN ('1', '2', '3')
```
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**Description:** Fixes the word "iteratively" in the use-cases
documentation
**Twitter handle:** @untilhamza
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This change fixes the AstraDB logical operator filtering (`$and,`
`$or`).
The `metadata` prefix must not be added if the key is `$and` or `$or`.
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See preview :
https://langchain-git-fork-cbornet-astra-loader-doc-langchain.vercel.app/docs/integrations/document_loaders/astradb
This means that users of astream_log() now get streamed output of
virtually all requested runs, whereas before the only streamed output
would be for the root run and raw llm runs
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- **Description:** Add missing import of 'ConfigurableField' in 'Full
code comparison' example in LCEL
- **Issue:** Example code not running
- **Dependencies:** None
- **Twitter handle:** @heyyoshan
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- **Description:** This update rectifies an error in the notebook by
changing the input variable from `zhipu_api_key` to `api_key`. It also
includes revisions to comments to improve program readability.
- **Issue:** The input variable in the notebook example should be
`api_key` instead of `zhipu_api_key`.
- **Dependencies:** No additional dependencies are required for this
change.
To ensure quality and standards, we have performed extensive linting and
testing. Commands such as make format, make lint, and make test have
been run from the root of the modified package to ensure compliance with
LangChain's coding standards.
- ArgillaCallbackHandler does not properly set the default values while
initializing. This PR corrects the line.
- Issue: #15531
- Dependencies: Argilla
- Also corrected some dead links.
fix of #14905
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---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Improving documentation
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- **Description:** Adding resource for Curie model
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---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:** The `delete_collection` method deletes an entire
collection regardless of custom ID. The `delete` method deletes
everything with the provided custom IDs regardless of collection. It can
be useful to restrict deletion to both the collection and a set of
custom IDs. This change adds support for that by allowing you to
optionally specify that `delete` should be restricted to the collection
defined on the `PGVector` instance.
- **Description:** Includes the PDF ID in the MathPix document metadata.
This is useful in case you need to re-request a processed PDF from the
MathPix API later.
- **Description:** The `error_info['id']` can be cross-referenced with
the MathPix API documentation to get very specific information about why
an error occurred.
- **Description:** This PR is to fix a bug of "system message check" in
langchain_community/ chat_models/tongyi.py
- **Issue:** In term of current logic, if there's no system message in
the chat messages, an error of "System message can only be the first
message." will be wrongly raised.
- **Dependencies:** No.
- **Twitter handle:** I don't have a Twitter account.
- **Description:** This PR is to fix a bug in
semantic_hybrid_search_with_score_and_rerank() function in
langchain_community/vectorstores/azuresearch.py. The hardcoded
"metadata" name is replaced with FIELDS_METADATA variable with an if
block to check if the metadata column exists or not.
- **Issue:** Fixed#15581
- **Dependencies:** No
- **Twitter handle:** None
Co-authored-by: H161961 <Raunak.Raunak@Honeywell.com>
Updates docs and cookbooks to import ChatOpenAI, OpenAI, and OpenAI
Embeddings from `langchain_openai`
There are likely more
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Todo
- [x] copy over integration tests
- [x] update docs with new instructions in #15513
- [x] add linear ticket to bump core -> community, community->langchain,
and core->openai deps
- [ ] (optional): add `pip install langchain-openai` command to each
notebook using it
- [x] Update docstrings to not need `openai` install
- [x] Add serialization
- [x] deprecate old models
Contributor steps:
- [x] Add secret names to manual integrations workflow in
.github/workflows/_integration_test.yml
- [x] Add secrets to release workflow (for pre-release testing) in
.github/workflows/_release.yml
Maintainer steps (Contributors should not do these):
- [x] set up pypi and test pypi projects
- [x] add credential secrets to Github Actions
- [ ] add package to conda-forge
Functional changes to existing classes:
- now relies on openai client v1 (1.6.1) via concrete dep in
langchain-openai package
Codebase organization
- some function calling stuff moved to
`langchain_core.utils.function_calling` in order to be used in both
community and langchain-openai
removed the deprecated model from text embedding page of openai notebook
and added the suggested model from openai page
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Removes unused `Params` in `libs/langchain/langchain/llms/mlflow.py`.
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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The example code for `llms.Mlflow` is outdated.
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:** `MarkdownHeaderTextSplitter` currently strips header
lines from chunked content. Many applications require these header lines
are preserved. This adds an optional parameter to preserve those headers
in the chunked content.
- **Issue:** #2836 (relevant)
- **Dependencies:** -
- **Tag maintainer:** @baskaryan
- **Twitter handle:** @finnless
Unit tests and new examples in notebook included.
cc @rlancemartin
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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Adds `WasmChat` integration. `WasmChat` runs GGUF models locally or via
chat service in lightweight and secure WebAssembly containers. In this
PR, `WasmChatService` is introduced as the first step of the
integration. `WasmChatService` is driven by
[llama-api-server](https://github.com/second-state/llama-utils) and
[WasmEdge Runtime](https://wasmedge.org/).
---------
Signed-off-by: Xin Liu <sam@secondstate.io>
Follow up on https://github.com/langchain-ai/langchain/pull/13048.
This PR intends to simplify the Qdrant async implementation by replacing
the internal GRPC methods with the `QdrantAsyncClient` methods.
This is a backward compatible change with no additional steps required
after merge.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Fixes#14347
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- **Description:** Added the traceback of the previous error to keep the
initial error type,
- **Issue:** #14347 ,
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---------
Co-authored-by: Julien Raffy <julien.raffy@emeria.eu>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:** the ability to add all extra parameter of vectorstore
and using them SemanticSimilarityExampleSelector.
- **Issue:** #14583
- **Dependencies:** no dependensies
- **Tag maintainer:**
- **Twitter handle:** @AmirMalekiz
---------
Co-authored-by: Amir Maleki <amaleki@fb.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Description: Add support for setting the `score_threshold` for
similarity search in SupabaseVectoreStore.
This pull request addresses issue #14438
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:** changed json.py to handle additional cases of partial
json string to be parsed, basically by dropping the last character in
the string until a valid json string is found or the string is empty.
Also added additional test cases.
- **Issue:** function parse_partial_json could not parse cases where the
key is present but the value is not.
---------
Co-authored-by: Nuno Campos <nuno@langchain.dev>
Because Milvus' collection_name doesn't support UFT8 characters in other
languages, I want the `collection_descriotion`.
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**Description:** Fix for processing for serpapi response for Google Maps
API
**Issue:** Due to the fact corresponding
[api](https://serpapi.com/google-maps-api) returns 'local_results' as
list, and old version requested `res["local_results"].keys()` of the
list. As the result we got exception: ```AttributeError: 'list' object
has no attribute 'keys'```.
Way to reproduce wrong behaviour:
```
params = {
"engine": "google_maps",
"type": "search",
"google_domain": "google.de",
"ll": "@51.1917,10.525,14z",
"hl": "de",
"gl": "de",
}
search = SerpAPIWrapper(params=params)
results = search.run("cafe")
```
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Ran <rccalman@gmail.com>
Because Milvus doesn't support nullable fields, but document metadata is
very rich, so it makes more sense to store it as json.
https://github.com/milvus-io/pymilvus/issues/1705#issuecomment-1731112372
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---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
BigQuery vector search lets you use GoogleSQL to do semantic search,
using vector indexes for fast but approximate results, or using brute
force for exact results.
This PR integrates LangChain vectorstore with BigQuery Vector Search.
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---------
Co-authored-by: Vlad Kolesnikov <vladkol@google.com>
- **Description:** replace score_threshold with args
- **Issue:** needs a way to pass more options to similarity search
- **Dependencies:** None
- **Twitter handle:** @workbot
---------
Co-authored-by: JY <jyjy@jaguardb>
- **Description:** Tool now supports querying over 200 million
scientific articles, vastly expanding its reach beyond the 2 million
articles accessible through Arxiv. This update significantly broadens
access to the entire scope of scientific literature.
- **Dependencies:** semantischolar
https://github.com/danielnsilva/semanticscholar
- **Twitter handle:** @shauryr
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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…tch]: import models from community
ran
```bash
git grep -l 'from langchain\.chat_models' | xargs -L 1 sed -i '' "s/from\ langchain\.chat_models/from\ langchain_community.chat_models/g"
git grep -l 'from langchain\.llms' | xargs -L 1 sed -i '' "s/from\ langchain\.llms/from\ langchain_community.llms/g"
git grep -l 'from langchain\.embeddings' | xargs -L 1 sed -i '' "s/from\ langchain\.embeddings/from\ langchain_community.embeddings/g"
git checkout master libs/langchain/tests/unit_tests/llms
git checkout master libs/langchain/tests/unit_tests/chat_models
git checkout master libs/langchain/tests/unit_tests/embeddings/test_imports.py
make format
cd libs/langchain; make format
cd ../experimental; make format
cd ../core; make format
```
- easier to write custom logic/loops with automatic tracing
- if you don't want to streaming support write a regular function and
pass to RunnableLambda
- if you do want streaming write a generator and pass it to
RunnableGenerator
```py
import json
from typing import AsyncIterator
from langchain_core.messages import BaseMessage, FunctionMessage, HumanMessage
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.runnables import Runnable, RunnableGenerator, RunnablePassthrough
from langchain_core.tools import BaseTool
from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
from langchain.chat_models import ChatOpenAI
from langchain.tools.render import format_tool_to_openai_function
def _get_tavily():
from langchain.tools.tavily_search import TavilySearchResults
from langchain.utilities.tavily_search import TavilySearchAPIWrapper
tavily_search = TavilySearchAPIWrapper()
return TavilySearchResults(api_wrapper=tavily_search)
async def _agent_executor_generator(
input: AsyncIterator[list[BaseMessage]],
*,
max_iterations: int = 10,
tools: dict[str, BaseTool],
agent: Runnable[list[BaseMessage], BaseMessage],
parser: Runnable[BaseMessage, AgentAction | AgentFinish],
) -> AsyncIterator[BaseMessage]:
messages = [m async for mm in input for m in mm]
for _ in range(max_iterations):
next_message = await agent.ainvoke(messages)
yield next_message
messages.append(next_message)
parsed = await parser.ainvoke(next_message)
if isinstance(parsed, AgentAction):
result = await tools[parsed.tool].ainvoke(parsed.tool_input)
next_message = FunctionMessage(name=parsed.tool, content=json.dumps(result))
yield next_message
messages.append(next_message)
elif isinstance(parsed, AgentFinish):
return
def get_agent_executor(tools: list[BaseTool], system_message: str):
llm = ChatOpenAI(model="gpt-4-1106-preview", temperature=0, streaming=True)
prompt = ChatPromptTemplate.from_messages(
[
("system", system_message),
MessagesPlaceholder(variable_name="messages"),
]
)
llm_with_tools = llm.bind(
functions=[format_tool_to_openai_function(t) for t in tools]
)
agent = {"messages": RunnablePassthrough()} | prompt | llm_with_tools
parser = OpenAIFunctionsAgentOutputParser()
executor = RunnableGenerator(_agent_executor_generator)
return executor.bind(
tools={tool.name for tool in tools}, agent=agent, parser=parser
)
agent = get_agent_executor([_get_tavily()], "You are a very nice agent!")
async def main():
async for message in agent.astream(
[HumanMessage(content="whats the weather in sf tomorrow?")]
):
print(message)
if __name__ == "__main__":
import asyncio
asyncio.run(main())
```
results in this trace
https://smith.langchain.com/public/fa17f05d-9724-4d08-8fa1-750f8fcd051b/r
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- **Description:** SingleFileFacebookMessengerChatLoader did not handle
the case for when messages had stickers and/or photos so fixed that.
- **Issue:** #15356
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:** updates/enhancements to IBM
[watsonx.ai](https://www.ibm.com/products/watsonx-ai) LLM provider
(prompt tuned models and prompt templates deployments support)
- **Dependencies:**
[ibm-watsonx-ai](https://pypi.org/project/ibm-watsonx-ai/),
- **Tag maintainer:** : @hwchase17 , @eyurtsev , @baskaryan
- **Twitter handle:** details in comment below.
Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` to check this
locally. ✅
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
The fix#14221 has broken default gitlab url which is forcing the users
to specify GITLAB_URL for default one. With this fix if GITLAB_URL is
not set, the default gitlab url will be taken.
- **Description:** Add the GITHUB URL instead of None
- **Issue:** the issue #14221 has broken the default github URL
- **Dependencies:** None
- **Tag maintainer:** @hwchase17
- **Twitter handle:** manjunath_shiva
- **Description:** This PR adds `api_base` to `_client_params` in the
`chat_model` of LiteLLM to ensure it's included in API calls.
Previously, `api_base` was set on the client but was not included in the
parameters passed to the completion function. This change ensures that
`api_base` is correctly passed to all API calls.
- **Issue:** #14338
- **Tag maintainer:** @hwchase17 @agola11
- **Twitter handle:** @LMS_David_RS
Sometimes, the tool_schema is like:
` {'action_name': 'search_items', 'action': {'term': 'pizza'}}`
sometimes, specially with gpt3.5 it comes like:
`{'action_name': 'search_items', 'term': 'pizza'}`
and it fails.
This PR is a way to make it work in both scenarios.
issues releated: #6624
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-->
Co-authored-by: Lucca Zenobio <lucca.zenobio@ifood.com.br>
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This change addresses the issue where DashScopeEmbeddingAPI limits
requests to 25 lines of data, and DashScopeEmbeddings did not handle
cases with more than 25 lines, leading to errors. I have implemented a
fix to manage data exceeding this limit efficiently.
---------
Co-authored-by: xuxiang <xuxiang@aliyun.com>
Adding to my previously, already merged PR I made some further
improvements:
* Added documentation to the existing Pydantic Parser notebook, with an
example using LCEL and `with_retry()` on `OutputParserException`.
* Added an additional output example to the prompt
* More lenient parser in terms of LLM output format
* Amended unit test
FYI @hwchase17
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:** Update _retrieve_ref inside json_schema.py to include
an isdigit() check
- **Issue:** This library is used inside dereference_refs inside
langchain_community.agent_toolkits.openapi.spec. When I read in a yaml
file which has references for "400", "401" etc; the line "out =
out[component]" causes a KeyError. The isdigit() check ensures that if
it is an integer like "400" or "401"; it converts it into integer before
using it as a key to prevent the error.
- **Dependencies:** No dependencies
- **Tag maintainer:** @baskaryan
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
# Description: _python-lint_
This agent writes Python code that is formatted and linted using
`black`, `ruff`, and `mypy`, but does not execute the code. It writes
the code to a temporary file and then runs the linters. Once these
checks pass, the code is returned.
# Dependencies
- black
- ruff
- mypy
# Demo
The functionality can be seen here:
https://huggingface.co/spaces/joshuasundance/langchain-streamlit-demo
Added some Headers in steam tool notebook to match consistency with the
other toolkit notebooks
- Dependencies: no new dependencies
- Tag maintainer: @hwchase17, @baskaryan
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
`integrations/document_loaders/` `Excel` and `OneNote` pages in the
navbar were in the wrong sort order. It is because the file names are
not equal to the page titles.
- renamed `excel` and `onenote` file names
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- **Description:** Using PGVector vector store, it was only possible to
filter for values equals, in or not in metadata. Extended this feature
to work with the following keywords : IN, NIN, BETWEEN, GT, LT, NE, EQ,
LIKE, CONTAINS, OR, AND
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
The regex used to match "Action" and "Action Input" in the output parser
has been updated. Previously, the regex did not correctly handle
multi-line inputs for "Action Input". The updated code uses the
're.DOTALL' flag to ensure multi-line inputs are correctly captured.
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---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:**
- This PR introduces a significant enhancement to the LangChain project
by integrating a new chat model powered by the third-generation base
large model, ChatGLM3, via the zhipuai API.
- This advanced model supports functionalities like function calls, code
interpretation, and intelligent Agent capabilities.
- The additions include the chat model itself, comprehensive
documentation in the form of Python notebook docs, and thorough testing
with both unit and integrated tests.
- **Dependencies:** This update relies on the ZhipuAI package as a key
dependency.
- **Twitter handle:** If this PR receives spotlight attention, we would
be honored to receive a mention for our integration of the advanced
ChatGLM3 model via the ZhipuAI API. Kindly tag us at @kaiwu.
To ensure quality and standards, we have performed extensive linting and
testing. Commands such as make format, make lint, and make test have
been run from the root of the modified package to ensure compliance with
LangChain's coding standards.
TO DO: Continue refining and enhancing both the unit tests and
integrated tests.
---------
Co-authored-by: jing <jingguo92@gmail.com>
Co-authored-by: hyy1987 <779003812@qq.com>
Co-authored-by: jianchuanqi <qijianchuan@hotmail.com>
Co-authored-by: lirq <whuclarence@gmail.com>
Co-authored-by: whucalrence <81530213+whucalrence@users.noreply.github.com>
Co-authored-by: Jing Guo <48378126+JaneCrystall@users.noreply.github.com>
Description: Volcano Ark is an enterprise-grade large-model service
platform for developers, providing a full range of functions and
services such as model training, inference, evaluation, fine-tuning. You
can visit its homepage at https://www.volcengine.com/docs/82379/1099455
for details. This change could help developers use the platform for
embedding.
Issue: None
Dependencies: volcengine
Tag maintainer: @baskaryan
Twitter handle: @hinnnnnnnnnnnns
---------
Co-authored-by: lujingxuansc <lujingxuansc@bytedance.com>
Updated prompt input suggestions
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---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
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- **Description:** updated the outdated code in the document that was
generating the error,
- **Issue:** #15086 ,
- **Dependencies:** N/A,
- **Twitter handle:** [@vardhaman722](https://twitter.com/vardhaman722)
**Description:** the MWDumpLoader implementation currently does not
support the lazy_load method, and the files are usually very large. We
are proposing refactoring the load function, extracting two private
functions with the functionality of loading the dump file and parsing a
single page, to reuse the code in the lazy_load implementation.
**Description:**
This PR adds the `**kwargs` parameter to six calls in the `chroma.py`
package. All functions already were able to receive `kwargs` but they
were discarded before.
**Issue:**
When passing `kwargs` to functions in the `chroma.py` package they are
being ignored.
For example:
```
chroma_instance.similarity_search_with_score(
query,
k=100,
include=["metadatas", "documents", "distances", "embeddings"], # this parameter gets ignored
)
```
The `include` parameter does not get passed on to the next function and
does not have any effect.
**Dependencies:**
None
The quickstart doc is missing a few but very simple things that without
them, the code does not work. This PR fixes that by
- Adding commands to install `tiktoken` and `langchainhub`
- Adds a comma between 2 parameters for one of the methods
- **Description:** Fix a few spelling and grammar issues
- **Issue:** NA
- **Dependencies:** NA
- **Twitter handle:** @donovancmuller
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- **Description:** This PR corrects a documentation error in the
`ollama` usage tutorial. Specifically, it fixes a missing `])` in the
`CallbackManager()` example, ensuring that the code snippet is
syntactically correct and can be successfully executed.
- **Issue:** N/A
- **Dependencies:** No additional dependencies are required for this
change.
- **Twitter handle:** My twitter is @yhzhu99
Updated comment for better understanding
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- **Description:**
- support custom kwargs in object initialization. For instantance, QPS
differs from multiple object(chat/completion/embedding with diverse
models), for which global env is not a good choice for configuration.
- **Issue:** no
- **Dependencies:** no
- **Twitter handle:** no
@baskaryan PTAL
These can happen for edge cases not covered by `default` handler (eg.
"strange" keys in dicts)
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- Any direct usage of ThreadPoolExecutor or asyncio.run_in_executor
needs manual handling of context vars
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- **Description:** fix parse issue for AIMessageChunk when using
- **Issue:** https://github.com/langchain-ai/langchain/issues/14511
- **Dependencies:** none
- **Twitter handle:** none
Taken from this fix:
https://github.com/gpt-engineer-org/gpt-engineer/issues/804#issuecomment-1769853850
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---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
removed bad comments
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- **Description:** fixes and upgrades for the Tongyi LLM and ChatTongyi
Model
- Fixed typos; it should be `Tongyi`, not `OpenAI`.
- Fixed a bug in `stream_generate_with_retry`; it's a real stream
generator now.
- Fixed a bug in `validate_environment`; the `dashscope_api_key` should
be properly handled when set by environment variables or initialization
parameters.
- Changed the `dashscope` response to incremental output by setting the
parameter `incremental_output`, which eliminates the need for the
prefix-removal trick.
- Removed some unused parameters, like `n`, `prefix_messages`.
- Added `_stream` method.
- Added async methods support, such as `_astream`, `_agenerate`,
`_abatch`.
- **Dependencies:** No new dependencies.
- **Tag maintainer:** @hwchase17
> PS: Some may be confused about the terms `dashscope`, `tongyi`, and
`Qwen`:
> - `dashscope`: A platform to deploy LLMs and provide APIs to invoke
the LLM.
> - `tongyi`: A brand name or overall term about Alibaba Cloud's LLM/AI.
> - `Qwen`: An LLM that is open-sourced and deployed in `dashscope`.
>
> We use the `dashscope` SDK to interact with the `tongyi`-`Qwen` LLM.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Correcting a small typo ('the' instead of 'then') and changing another
'the' (instead of 'then' too, it was a hard day for the 'n' key :D) to
'also' to match better with what is done in the code
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- **Description:** in the code_understanding.ipynb example, the loader
errors out on the
langchain/libs/community/tests/examples/non-utf8-encoding.py file, so I
updated the loader to exclude that file. Excluding that file allows the
example to run.
- **Issue:** not applicable
- **Dependencies:** none
- do not match text after - in the middle of a sentence
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…parse
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```shell
Python 3.11.6 (main, Nov 2 2023, 04:39:43) [Clang 14.0.3 (clang-1403.0.22.14.1)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> s = {'name': 'gc', 'arguments': '{"prompt":"hi\nbob."}'}
>>> import json
>>> json.loads(s['arguments'])
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/opt/homebrew/Cellar/python@3.11/3.11.6_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/__init__.py", line 346, in loads
return _default_decoder.decode(s)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/Cellar/python@3.11/3.11.6_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/decoder.py", line 337, in decode
obj, end = self.raw_decode(s, idx=_w(s, 0).end())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/Cellar/python@3.11/3.11.6_1/Frameworks/Python.framework/Versions/3.11/lib/python3.11/json/decoder.py", line 353, in raw_decode
obj, end = self.scan_once(s, idx)
^^^^^^^^^^^^^^^^^^^^^^
json.decoder.JSONDecodeError: Invalid control character at: line 1 column 14 (char 13)
>>> json.loads(s['arguments'].replace('\n', '\\n'))
{'prompt': 'hi\nbob.'}
>>>
```
---------
Co-authored-by: Nuno Campos <nuno@langchain.dev>
While using `chain.batch`, the default implementation uses a
`ThreadPoolExecutor` and run the chains in separate threads. An issue
with this approach is that that [the token counting
callback](https://python.langchain.com/docs/modules/callbacks/token_counting)
fails to work as a consequence of the context not being propagated
between threads. This PR adds context propagation to the new threads and
adds some thread synchronization in the OpenAI callback. With this
change, the token counting callback works as intended.
Having the context propagation change would be highly beneficial for
those implementing custom callbacks for similar functionalities as well.
---------
Co-authored-by: Nuno Campos <nuno@langchain.dev>
- Enables strict=False by default
- Uses partial json recovery logic by default
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- **Description:** The Github error prompt is confused because of JWT
enctrypt to somebody not familiar with Github connection method. This PR
is to add some useful error prompt to help users troubleshooting.
- **Issue:**
https://github.com/langchain-ai/langchain/issues/14550#issuecomment-1867445049
- **Dependencies:** None,
- **Twitter handle:** None
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…ableBinding
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…unnableAssign or RunnablePick
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…ching documentation
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- **Description:** Fixed the wrong output and code block comment in
`Upstash Redis` Cache section of LLM Caching documentation,
- **Issue:** #15139 ,
- **Dependencies:** N/A,
- **Twitter handle:** [@vardhaman722](https://twitter.com/vardhaman722)
**Description:**
Adding async methods to booth OllamaLLM and ChatOllama to enable async
streaming and async .on_llm_new_token callbacks.
**Issue:**
ChatOllama is not working in combination with an AsyncCallbackManager
because the .on_llm_new_token method is not awaited.
**Description:** `decouple` is not the correct package, it's
`python-decouple`, and the notebook cell doesn't compile.
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This document uses Oxford comma (A, B, and C), in this list the comma
was missing before "and".
This PR corrects that.
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- Added ensure_ascii property to ElasticsearchChatMessageHistory
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---------
Co-authored-by: Ivan Chetverikov <ivan.chetverikov@raftds.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
**Description**: The parameter chunk_type was being hard coded to
"extractive_answers", so that when "snippet" was being passed, it was
being ignored. This change simply doesn't do that.
Added the call function get_summaries_as_docs inside of Arxivloader
- **Description:** Added a function that returns the documents from
get_summaries_as_docs, as the call signature is present in the parent
file but never used from Arxivloader, this can be used from Arxivloader
itself just like .load() as both the signatures are same.
- **Issue:** Reduces time to load papers as no pdf is processed only
metadata is pulled from Arxiv allowing users for faster load times on
bulk loads. Users can then choose one or more paper and use ID directly
with .load() to load pdf thereby loading all the contents of the paper.
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## Description
Changes the behavior of `add_user_message` and `add_ai_message` to allow
for messages of those types to be passed in. Currently, if you want to
use the `add_user_message` or `add_ai_message` methods, you have to pass
in a string. For `add_message` on `ChatMessageHistory`, however, you
have to pass a `BaseMessage`. This behavior seems a bit inconsistent.
Personally, I'd love to be able to be explicit that I want to
`add_user_message` and pass in a `HumanMessage` without having to grab
the `content` attribute. This PR allows `add_user_message` to accept
`HumanMessage`s or `str`s and `add_ai_message` to accept `AIMessage`s or
`str`s to add that functionality and ensure backwards compatibility.
## Issue
* None
## Dependencies
* None
## Tag maintainer
@hinthornw
@baskaryan
## Note
`make test` results in `make: *** No rule to make target 'test'. Stop.`
- **Description:** `tools.gmail.send_message` implements a
`SendMessageSchema` that is not used anywhere. `GmailSendMessage` also
does not have an `args_schema` attribute (this led to issues when
invoking the tool with an OpenAI functions agent, at least for me). Here
we add the missing attribute and a minimal test for the tool.
- **Issue:** N/A
- **Dependencies:** N/A
- **Twitter handle:** N/A
---------
Co-authored-by: Chester Curme <chestercurme@microsoft.com>
Fixing typos: it's -> its
Fixing grammatical mistakes:
* having to worry -> worrying
* convert -> converts
* few main types -> a few main types
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
add_video_info should be false in the first example
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- **Description:** In response to user feedback, this PR refactors the
Baseten integration with updated model endpoints, as well as updates
relevant documentation. This PR has been tested by end users in
production and works as expected.
- **Issue:** N/A
- **Dependencies:** This PR actually removes the dependency on the
`baseten` package!
- **Twitter handle:** https://twitter.com/basetenco
# Description
This PR adds the ability to pass a `botocore.config.Config` instance to
the boto3 client instantiated by the Bedrock LLM.
Currently, the Bedrock LLM doesn't support a way to pass a Config, which
means that some settings (e.g., timeouts and retry configuration)
require instantiating a new boto3 client with a Config and then
replacing the LLM's client:
```python
llm = Bedrock(
region_name='us-west-2',
model_id="anthropic.claude-v2",
model_kwargs={'max_tokens_to_sample': 4096, 'temperature': 0},
)
llm.client = boto_client('bedrock-runtime', region_name='us-west-2', config=Config({'read_timeout': 300}))
```
# Issue
N/A
# Dependencies
N/A
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fix spellings
**seperate -> separate**: found more occurrences, see
https://github.com/langchain-ai/langchain/pull/14602
**initialise -> intialize**: the latter is more common in the repo
**pre-defined > predefined**: adding a comma after a prefix is a
delicate matter, but this is a generally accepted word
also, another word that appears in the repo is "fs" (stands for
filesystem), e.g., in `libs/core/langchain_core/prompts/loading.py`
` """Unified method for loading a prompt from LangChainHub or local
fs."""`
Isn't "filesystem" better?
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---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Ran <rccalman@gmail.com>
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- **Description:** This PR fixes test failures on Windows caused by path
handling differences and unescaped special characters in regex. The
failing tests are:
```
FAILED tests/unit_tests/storage/test_filesystem.py::test_yield_keys - AssertionError: assert ['key1', 'subdir\\key2'] == ['key1', 'subdir/key2']
FAILED tests/unit_tests/test_imports.py::test_importable_all - ModuleNotFoundError: No module named 'langchain_community.langchain_community\\adapters'
FAILED tests/unit_tests/tools/file_management/test_utils.py::test_get_validated_relative_path_errs_on_absolute - re.error: incomplete escape \U at position 53
FAILED tests/unit_tests/tools/file_management/test_utils.py::test_get_validated_relative_path_errs_on_parent_dir - re.error: incomplete escape \U at position 69
FAILED tests/unit_tests/tools/file_management/test_utils.py::test_get_validated_relative_path_errs_for_symlink_outside_root - re.error: incomplete escape \U at position 64
```
- **Issue:** fixes
https://github.com/langchain-ai/langchain/issues/11775 (partially)
- **Dependencies:** none
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Replace this entire comment with:
- **Description:** @kurtisvg has raised a point that it's a good idea to
have a fixed version for embeddings (since otherwise a user might run a
query with one version vs a vectorstore where another version was used).
In order to avoid breaking changes, I'd suggest to give users a warning,
and make a `model_name` a required argument in 1.5 months.
Surrealdb client changes from 0.3.1 to 0.3.2 broke the surrealdb vectore
integration.
This PR updates the code to work with the updated client. The change is
backwards compatible with previous versions of surrealdb client.
Also expanded the vector store implementation to store and retrieve
metadata that's included with the document object.
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- **Description:** Fixed jaguar.py to import JaguarHttpClient with try
and catch
- **Issue:** the issue # Unable to use the JaguarHttpClient at run time
- **Dependencies:** It requires "pip install -U jaguardb-http-client"
- **Twitter handle:** workbot
---------
Co-authored-by: JY <jyjy@jaguardb>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description**
For the Momento Vector Index (MVI) vector store implementation, pass
through `filter_expression` kwarg to the MVI client, if specified. This
change will enable the MVI self query implementation in a future PR.
Also fixes some integration tests.
- **Description:** Fix typo in class Docstring to replace
AZURE_OPENAI_API_ENDPOINT by AZURE_OPENAI_ENDPOINT
- **Issue:** the issue #14901
- **Dependencies:** NA
- **Twitter handle:**
Co-authored-by: Yacine Bouakkaz <Yacine.Bouakkaz@evokegroup.com>
* This PR adds `stream` implementations to Runnable Branch.
* Runnable Branch still does not support `transform` so it'll break streaming if it happens in middle or end of sequence, but will work if happens at beginning of sequence.
* Fixes use the async callback manager for async methods
* Handle BaseException rather than Exception, so more errors could be logged as errors when they are encountered
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Description: Adding Summarization to Vectara, to reflect it provides not
only vector-store type functionality but also can return a summary.
Also added:
MMR capability (in the Vectara platform side)
Updated templates
Updated documentation and IPYNB examples
Tag maintainer: @baskaryan
Twitter handle: @ofermend
---------
Co-authored-by: Ofer Mendelevitch <ofermend@gmail.com>
**What is the reproduce code?**
```python
from langchain.chains import LLMChain, load_chain
from langchain.llms import Databricks
from langchain.prompts import PromptTemplate
def transform_output(response):
# Extract the answer from the responses.
return str(response["candidates"][0]["text"])
def transform_input(**request):
full_prompt = f"""{request["prompt"]}
Be Concise.
"""
request["prompt"] = full_prompt
return request
chat_model = Databricks(
endpoint_name="llama2-13B-chat-Brambles",
transform_input_fn=transform_input,
transform_output_fn=transform_output,
verbose=True,
)
print(f"Test chat model: {chat_model('What is Apache Spark')}") # This works
llm_chain = LLMChain(llm=chat_model, prompt=PromptTemplate.from_template("{chat_input}"))
llm_chain("colorful socks") # this works
llm_chain.save("databricks_llm_chain.yaml") # transform_input_fn and transform_output_fn are not serialized into the model yaml file
loaded_chain = load_chain("databricks_llm_chain.yaml") # The Databricks LLM is recreated with transform_input_fn=None, transform_output_fn=None.
loaded_chain("colorful socks") # Thus this errors. The transform_output_fn is needed to produce the correct output
```
Error:
```
File "/local_disk0/.ephemeral_nfs/envs/pythonEnv-6c34afab-3473-421d-877f-1ef18930ef4d/lib/python3.10/site-packages/pydantic/v1/main.py", line 341, in __init__
raise validation_error
pydantic.v1.error_wrappers.ValidationError: 1 validation error for Generation
text
str type expected (type=type_error.str)
request payload: {'query': 'What is a databricks notebook?'}'}
```
**What does the error mean?**
When the LLM generates an answer, represented by a Generation data
object. The Generation data object takes a str field called text, e.g.
Generation(text=”blah”). However, the Databricks LLM tried to put a
non-str to text, e.g. Generation(text={“candidates”:[{“text”: “blah”}]})
Thus, pydantic errors.
**Why the output format becomes incorrect after saving and loading the
Databricks LLM?**
Databrick LLM does not support serializing transform_input_fn and
transform_output_fn, so they are not serialized into the model yaml
file. When the Databricks LLM is loaded, it is recreated with
transform_input_fn=None, transform_output_fn=None. Without
transform_output_fn, the output text is not unwrapped, thus errors.
Missing transform_output_fn causes this error.
Missing transform_input_fn causes the additional prompt “Be Concise.” to
be lost after saving and loading.
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---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
## Description
This PR intends to add support for Qdrant's new [sparse vector
retrieval](https://qdrant.tech/articles/sparse-vectors/) by introducing
a new retriever class, `QdrantSparseVectorRetriever`.
Necessary usage docs and integration tests have been added for the
retriever.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:**
This PR fixes the issue faces with duplicate input id in Clarifai
vectorstore class when ingesting documents into the vectorstore more
than the batch size.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
## Description
Similar to https://github.com/langchain-ai/langchain/issues/5861, I've
experienced `KeyError`s resulting from unsafe lookups in the
`convert_dict_to_message` function in [this
file](https://github.com/langchain-ai/langchain/blob/master/libs/community/langchain_community/adapters/openai.py).
While that issue focused on `KeyError 'content'`, I've opened another
issue (#14764) about how the problem still exists in the same function
but with `KeyError 'role'`. The fix for #5861 only added a safe lookup
to the specific line that was giving them trouble.. This PR fixes the
unsafe lookup in the rest of the function but the problem still exists
across the repo.
## Issues
* #14764
* #5861
## Dependencies
* None
## Checklist
[x] make format
[x] make lint
[ ] make test - Results in `make: *** No rule to make target 'test'.
Stop.`
## Maintainers
* @hinthornw
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
This PR adds support for PygmalionAI's [Aphrodite
Engine](https://github.com/PygmalionAI/aphrodite-engine), based on
vLLM's attention mechanism. At the moment, this PR does not include
support for the API servers, but they will be added in a later PR.
The only dependency as of now is `aphrodite-engine==0.4.2`. We pin the
version to prevent breakage due to changes in the aphrodite-engine
library.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** Modify community chat model vertexai to handle png
and other image types encoded in base64
- **Dependencies:** added `import re` but no new dependencies.
This addresses a problem where the vertexai method
_parse_chat_history_gemini() was only recognizing image uris in jpeg
format. I made a simple change to cover other extension types.
- **Description:** The Qianfan SDK offers multiple authentication
methods, but in the `QianfanEndpoint` of Langchain, it currently only
supports authentication through AK and SK. In order to accommodate users
who wish to use alternative authentication methods, this pull request
makes AK and SK optional. This change should not impact existing users,
while allowing users to configure other authentication methods as per
the Qianfan SDK documentation.
- **Issue:** /
- **Dependencies:** No
- **Tag maintainer:** No
- **Twitter handle:**
Added Entry ID as a return value inside get_summaries_as_docs
- **Description:** Added the Entry ID as a return, so it's easier to
track the IDs of the papers that are being returned.
With the addition return of the entry ID in functions like
ArxivRetriever, it will be easier to reference the ID of the paper
itself.
- Description: Just a minor add to the documentation to clarify how to
load all files from a folder. I assumed and try to do it specifying it
in the bucket (BUCKET/FOLDER), instead of using the prefix.
- **Description:** Documentation update. The custom tool notebook
documentation is updated to revome the warning caused by directly
instantiating of the LLMMathChain with an llm which is is deprecated.
The from_llm class method is used instead. LLM output results gets
updated as well.
- **Issue:** no applicable
- **Dependencies:** No dependencies
- **Tag maintainer:** @baskaryan
- **Twitter handle:** @ybouakkaz
Co-authored-by: Yacine Bouakkaz <Yacine.Bouakkaz@evokegroup.com>
- **Description:** Going forward, we have a own API `pip install
gradientai`. Therefore gradually removing the self-build packages in
llamaindex, haystack and langchain.
- **Issue:** None.
- **Dependencies:** `pip install gradientai`
- **Tag maintainer:** @michaelfeil
**Description:** Added logic for re-calling the YandexGPT API in case of
an error
---------
Co-authored-by: Dmitry Tyumentsev <dmitry.tyumentsev@raftds.com>
Description: A new vector store Jaguar is being added. Class, test
scripts, and documentation is added.
Issue: None -- This is the first PR contributing to LangChain
Dependencies: This depends on "pip install -U jaguardb-http-client"
client http package
Tag maintainer: @baskaryan, @eyurtsev, @hwchase1
Twitter handle: @workbot
---------
Co-authored-by: JY <jyjy@jaguardb>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Addded missed docstrings. Fixed inconsistency in docstrings.
**Note** CC @efriis
There were PR errors on
`langchain_experimental/prompt_injection_identifier/hugging_face_identifier.py`
But, I didn't touch this file in this PR! Can it be some cache problems?
I fixed this error.
- **Description:** added support for chat_history for Google
GenerativeAI (to actually use the `chat` API) plus since Gemini
currently doesn't have a support for SystemMessage, added support for it
only if a user provides additional `convert_system_message_to_human`
flag during model initialization (in this case, SystemMessage would be
prepanded to the first HumanMessage)
- **Issue:** #14710
- **Dependencies:** any dependencies required for this change,
- **Tag maintainer:** for a quicker response, tag the relevant
maintainer (see below),
- **Twitter handle:** lkuligin
---------
Co-authored-by: William FH <13333726+hinthornw@users.noreply.github.com>
- updated `Tencent` provider page: added a chat model and document
loader references; company description
- updated Chat model and Document loader pages with descriptions, links
- renamed files to consistent formats; redirected file names
Note:
I was getting this linting error on code that **was not changed in my
PR**!
> Error:
docs/docs/guides/safety/hugging_face_prompt_injection.ipynb:1:1: I001
Import block is un-sorted or un-formatted
> make: *** [Makefile:47: lint_package] Error 1
I've fixed this error in the notebook
Replace this entire comment with:
- **Description:** OPENAI_PROXY is not working for openai==1.3.9, The
`proxies` argument is deprecated. The `http_client` argument should be
passed instead,
- **Issue:** OPENAI_PROXY is not working,
- **Dependencies:** None,
- **Tag maintainer:** @hwchase17 ,
- **Twitter handle:** timothy66666
- **Description:** This is addition to [my previous
PR](https://github.com/langchain-ai/langchain/pull/13930) with
improvements to flexibility allowing different models and notebook to
use ONNX runtime for faster speed. Since the last PR, [our
model](https://huggingface.co/laiyer/deberta-v3-base-prompt-injection)
got more than 660k downloads, and with the [public
benchmark](https://huggingface.co/spaces/laiyer/prompt-injection-benchmark)
showed much fewer false-positives than the previous one from deepset.
Additionally, on the ONNX runtime, it can be running 3x faster on the
CPU, which might be handy for builders using Langchain.
**Issue:** N/A
- **Dependencies:** N/A
- **Tag maintainer:** N/A
- **Twitter handle:** `@laiyer_ai`
Fixing issue - https://github.com/langchain-ai/langchain/issues/14494 to
avoid Kendra query ValidationException
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** Update kendra.py to avoid Kendra query
ValidationException,
- **Issue:** the issue
#https://github.com/langchain-ai/langchain/issues/14494,
- **Dependencies:** None,
- **Tag maintainer:** ,
- **Twitter handle:**
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
**Description**
The contributing docs lists a poetry command to install community for
dev work that includes a poetry group called `integration_tests`. This
is a mistake: the poetry group for integration tests is called
`test_integration`, not `integration_tests`. See here:
https://github.com/langchain-ai/langchain/blob/master/libs/community/pyproject.toml#L119
<!-- Thank you for contributing to LangChain!
Replace this entire comment with:
- **Description:** fixed tiktoken link error,
- **Issue:** no,
- **Dependencies:** no,
- **Tag maintainer:** for a quicker response, tag the relevant
maintainer (see below),
- **Twitter handle:** no!
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://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/extras`
directory.
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
- **Description:** fixed tiktoken link error,
- **Issue:** no,
- **Dependencies:** no,
- **Tag maintainer:** @baskaryan,
- **Twitter handle:** SignetCode!
- **Description:**
- Add a break case to `text_splitter.py::split_text_on_tokens()` to
avoid unwanted item at the end of result.
- Add a testcase to enforce the behavior.
- **Issue:**
- #14649
- #5897
- **Dependencies:** n/a,
---
**Quick illustration of change:**
```
text = "foo bar baz 123"
tokenizer = Tokenizer(
chunk_overlap=3,
tokens_per_chunk=7
)
output = split_text_on_tokens(text=text, tokenizer=tokenizer)
```
output before change: `["foo bar", "bar baz", "baz 123", "123"]`
output after change: `["foo bar", "bar baz", "baz 123"]`
This is technically a breaking change because it'll switch out default
models from `text-davinci-003` to `gpt-3.5-turbo-instruct`, but OpenAI
is shutting off those endpoints on 1/4 anyways.
Feels less disruptive to switch out the default instead.
- **Description:** Modification of descriptions for marketing purposes
and transitioning towards `platforms` directory if possible.
- **Issue:** Some marketing opportunities, lodging PR and awaiting later
discussions.
-
This PR is intended to be merged when decisions settle/hopefully after
further considerations. Submitting as Draft for now. Nobody @'d yet.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Gpt-3.5 sometimes calls with empty string arguments instead of `{}`
I'd assume it's because the typescript representation on their backend
makes it a bit ambiguous.
- **Description:** VertexAIEmbeddings performance improvements
- **Twitter handle:** @vladkol
## Improvements
- Dynamic batch size, starting from 250, lowering down to 5. Batch size
varies across regions.
Some regions support larger batches, and it significantly improves
performance.
When running large batches of texts in `us-central1`, performance gain
can be up to 3.5x.
The dynamic batching also makes sure every batch is below 20K token
limit.
- New model parameter `embeddings_type` that translates to `task_type`
parameter of the API. Newer model versions support [different embeddings
task
types](https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-text-embeddings#api_changes_to_models_released_on_or_after_august_2023).
Now that it's supported again for OAI chat models .
Shame this wouldn't include it in the `.invoke()` output though (it's
not included in the message itself). Would need to do a follow-up for
that to be the case
Fixed:
- `_agenerate` return value in the YandexGPT Chat Model
- duplicate line in the documentation
Co-authored-by: Dmitry Tyumentsev <dmitry.tyumentsev@raftds.com>
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---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Builds out a developer documentation section in the docs
- Links it from contributing.md
- Adds an initial guide on how to contribute an integration
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Adds the option for `similarity_score_threshold` when using
`MongoDBAtlasVectorSearch` as a vector store retriever.
Example use:
```
vector_search = MongoDBAtlasVectorSearch.from_documents(...)
qa_retriever = vector_search.as_retriever(
search_type="similarity_score_threshold",
search_kwargs={
"score_threshold": 0.5,
}
)
qa = RetrievalQA.from_chain_type(
llm=OpenAI(),
chain_type="stuff",
retriever=qa_retriever,
)
docs = qa({"query": "..."})
```
I've tested this feature locally, using a MongoDB Atlas Cluster with a
vector search index.
… (#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
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Co-authored-by: fangkeke <3339698829@qq.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- 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>
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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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Add a new ChatGoogleGenerativeAI class in a `langchain-google-genai`
package.
Still todo: add a deprecation warning in PALM
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: Leonid Kuligin <lkuligin@yandex.ru>
Co-authored-by: Bagatur <baskaryan@gmail.com>
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
When using local Chatglm2-6B by changing OPENAI_BASE_URL to localhost,
the token_usage in ChatOpenAI becomes None. This leads to an
AttributeError when trying to access token_usage.items().
This commit adds a check to ensure token_usage is not None before
accessing its items. This change prevents the AttributeError and allows
ChatOpenAI to work seamlessly with a local Chatglm2-6B model, aligning
with the way it operates with the OpenAI API.
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@baskaryan, @eyurtsev, @hwchase17.
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Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
**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.
## Description
New YAML output parser as a drop-in replacement for the Pydantic output
parser. Yaml is a much more token-efficient format than JSON, proving to
be **~35% faster and using the same percentage fewer completion
tokens**.
☑️ Formatted
☑️ Linted
☑️ Tested (analogous to the existing`test_pydantic_parser.py`)
The YAML parser excels in situations where a list of objects is
required, where the root object needs no key:
```python
class Products(BaseModel):
__root__: list[Product]
```
I ran the prompt `Generate 10 healthy, organic products` 10 times on one
chain using the `PydanticOutputParser`, the other one using
the`YamlOutputParser` with `Products` (see below) being the targeted
model to be created.
LLMs used were Fireworks' `lama-v2-34b-code-instruct` and OpenAI
`gpt-3.5-turbo`. All runs succeeded without validation errors.
```python
class Nutrition(BaseModel):
sugar: int = Field(description="Sugar in grams")
fat: float = Field(description="% of daily fat intake")
class Product(BaseModel):
name: str = Field(description="Product name")
stats: Nutrition
class Products(BaseModel):
"""A list of products"""
products: list[Product] # Used `__root__` for the yaml chain
```
Stats after 10 runs reach were as follows:
### JSON
ø time: 7.75s
ø tokens: 380.8
### YAML
ø time: 5.12s
ø tokens: 242.2
Looking forward to feedback, tips and contributions!
This patch fixes some typos.
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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.
-->
Signed-off-by: Masanari Iida <standby24x7@gmail.com>
**Description:**
Fixes to rag-semi-structured template.
- Added required libraries
- pdfminer was causing issues when installing with pip. pdfminer.six
works best
- Changed the pdf name for demo from llama2 to llava
<!-- 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.
-->
- **Description:** There is a bug in RedisNum filter that filter towards
value 0 will be parsed as "*". This is a fix to it.
- **Issue:** NA
- **Dependencies:** NA
- **Tag maintainer:** NA
- **Twitter handle:** NA
seperate -> separate
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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.
-->
**Description:** Update the information in the Docugami cookbook. Fix
broken links and add information on our kg-rag template.
Co-authored-by: Kenzie Mihardja <kenzie@docugami.com>
Hi there! Thank you for even being interested in contributing to LangChain.
As an open-source project in a rapidly developing field, we are extremely open to contributions, whether they involve new features, improved infrastructure, better documentation, or bug fixes.
## 🗺️ Guidelines
### 👩💻 Contributing Code
To contribute to this project, please follow the ["fork and pull request"](https://docs.github.com/en/get-started/quickstart/contributing-to-projects) workflow.
Please do not try to push directly to this repo unless you are a maintainer.
Please follow the checked-in pull request template when opening pull requests. Note related issues and tag relevant
maintainers.
Pull requests cannot land without passing the formatting, linting, and testing checks first. See [Testing](#testing) and
[Formatting and Linting](#formatting-and-linting) for how to run these checks locally.
It's essential that we maintain great documentation and testing. If you:
- Fix a bug
- Add a relevant unit or integration test when possible. These live in `tests/unit_tests` and `tests/integration_tests`.
- Make an improvement
- Update any affected example notebooks and documentation. These live in `docs`.
- Update unit and integration tests when relevant.
- Add a feature
- Add a demo notebook in `docs/docs/`.
- Add unit and integration tests.
We are a small, progress-oriented team. If there's something you'd like to add or change, opening a pull request is the
best way to get our attention.
### 🚩GitHub Issues
Our [issues](https://github.com/langchain-ai/langchain/issues) page is kept up to date with bugs, improvements, and feature requests.
There is a taxonomy of labels to help with sorting and discovery of issues of interest. Please use these to help organize issues.
If you start working on an issue, please assign it to yourself.
If you are adding an issue, please try to keep it focused on a single, modular bug/improvement/feature.
If two issues are related, or blocking, please link them rather than combining them.
We will try to keep these issues as up-to-date as possible, though
with the rapid rate of development in this field some may get out of date.
If you notice this happening, please let us know.
### 🙋Getting Help
Our goal is to have the simplest developer setup possible. Should you experience any difficulty getting setup, please
contact a maintainer! Not only do we want to help get you unblocked, but we also want to make sure that the process is
smooth for future contributors.
In a similar vein, we do enforce certain linting, formatting, and documentation standards in the codebase.
If you are finding these difficult (or even just annoying) to work with, feel free to contact a maintainer for help -
we do not want these to get in the way of getting good code into the codebase.
## 🚀 Quick Start
This quick start guide explains how to run the repository locally.
For a [development container](https://containers.dev/), see the [.devcontainer folder](https://github.com/langchain-ai/langchain/tree/master/.devcontainer).
### Dependency Management: Poetry and other env/dependency managers
This project utilizes [Poetry](https://python-poetry.org/) v1.6.1+ as a dependency manager.
❗Note: *Before installing Poetry*, if you use `Conda`, create and activate a new Conda env (e.g. `conda create -n langchain python=3.9`)
Install Poetry: **[documentation on how to install it](https://python-poetry.org/docs/#installation)**.
❗Note: If you use `Conda` or `Pyenv` as your environment/package manager, after installing Poetry,
tell Poetry to use the virtualenv python environment (`poetry config virtualenvs.prefer-active-python true`)
### Different packages
This repository contains multiple packages:
-`langchain-core`: Base interfaces for key abstractions as well as logic for combining them in chains (LangChain Expression Language).
-`langchain-community`: Third-party integrations of various components.
-`langchain`: Chains, agents, and retrieval logic that makes up the cognitive architecture of your applications.
-`langchain-experimental`: Components and chains that are experimental, either in the sense that the techniques are novel and still being tested, or they require giving the LLM more access than would be possible in most production systems.
Each of these has its own development environment. Docs are run from the top-level makefile, but development
is split across separate test & release flows.
For this quickstart, start with langchain:
```bash
cd libs/langchain
```
### Local Development Dependencies
Install langchain development requirements (for running langchain, running examples, linting, formatting, tests, and coverage):
```bash
poetry install --with test
```
Then verify dependency installation:
```bash
make test
```
If the tests don't pass, you may need to pip install additional dependencies, such as `numexpr` and `openapi_schema_pydantic`.
If during installation you receive a `WheelFileValidationError` for `debugpy`, please make sure you are running
Poetry v1.6.1+. This bug was present in older versions of Poetry (e.g. 1.4.1) and has been resolved in newer releases.
If you are still seeing this bug on v1.6.1, you may also try disabling "modern installation"
(`poetry config installer.modern-installation false`) and re-installing requirements.
See [this `debugpy` issue](https://github.com/microsoft/debugpy/issues/1246) for more details.
### Testing
_some test dependencies are optional; see section about optional dependencies_.
Unit tests cover modular logic that does not require calls to outside APIs.
If you add new logic, please add a unit test.
To run unit tests:
```bash
make test
```
To run unit tests in Docker:
```bash
make docker_tests
```
There are also [integration tests and code-coverage](https://github.com/langchain-ai/langchain/tree/master/libs/langchain/tests/README.md) available.
### Only develop langchain_core or langchain_experimental
If you are only developing `langchain_core` or `langchain_experimental`, you can simply install the dependencies for the respective projects and run tests:
```bash
cd libs/core
poetry install --with test
make test
```
Or:
```bash
cd libs/experimental
poetry install --with test
make test
```
### Formatting and Linting
Run these locally before submitting a PR; the CI system will check also.
#### Code Formatting
Formatting for this project is done via [ruff](https://docs.astral.sh/ruff/rules/).
To run formatting for docs, cookbook and templates:
```bash
make format
```
To run formatting for a library, run the same command from the relevant library directory:
```bash
cd libs/{LIBRARY}
make format
```
Additionally, you can run the formatter only on the files that have been modified in your current branch as compared to the master branch using the format_diff command:
```bash
make format_diff
```
This is especially useful when you have made changes to a subset of the project and want to ensure your changes are properly formatted without affecting the rest of the codebase.
#### Linting
Linting for this project is done via a combination of [ruff](https://docs.astral.sh/ruff/rules/) and [mypy](http://mypy-lang.org/).
To run linting for docs, cookbook and templates:
```bash
make lint
```
To run linting for a library, run the same command from the relevant library directory:
```bash
cd libs/{LIBRARY}
make lint
```
In addition, you can run the linter only on the files that have been modified in your current branch as compared to the master branch using the lint_diff command:
```bash
make lint_diff
```
This can be very helpful when you've made changes to only certain parts of the project and want to ensure your changes meet the linting standards without having to check the entire codebase.
We recognize linting can be annoying - if you do not want to do it, please contact a project maintainer, and they can help you with it. We do not want this to be a blocker for good code getting contributed.
#### Spellcheck
Spellchecking for this project is done via [codespell](https://github.com/codespell-project/codespell).
Note that `codespell` finds common typos, so it could have false-positive (correctly spelled but rarely used) and false-negatives (not finding misspelled) words.
To check spelling for this project:
```bash
make spell_check
```
To fix spelling in place:
```bash
make spell_fix
```
If codespell is incorrectly flagging a word, you can skip spellcheck for that word by adding it to the codespell config in the `pyproject.toml` file.
Langchain relies heavily on optional dependencies to keep the Langchain package lightweight.
You only need to add a new dependency if a **unit test** relies on the package.
If your package is only required for **integration tests**, then you can skip these
steps and leave all pyproject.toml and poetry.lock files alone.
If you're adding a new dependency to Langchain, assume that it will be an optional dependency, and
that most users won't have it installed.
Users who do not have the dependency installed should be able to **import** your code without
any side effects (no warnings, no errors, no exceptions).
To introduce the dependency to the pyproject.toml file correctly, please do the following:
1. Add the dependency to the main group as an optional dependency
```bash
poetry add --optional [package_name]
```
2. Open pyproject.toml and add the dependency to the `extended_testing` extra
3. Relock the poetry file to update the extra.
```bash
poetry lock --no-update
```
4. Add a unit test that the very least attempts to import the new code. Ideally, the unit
test makes use of lightweight fixtures to test the logic of the code.
5. Please use the `@pytest.mark.requires(package_name)` decorator for any tests that require the dependency.
## Adding a Jupyter Notebook
If you are adding a Jupyter Notebook example, you'll want to install the optional `dev` dependencies.
To install dev dependencies:
```bash
poetry install --with dev
```
Launch a notebook:
```bash
poetry run jupyter notebook
```
When you run `poetry install`, the `langchain` package is installed as editable in the virtualenv, so your new logic can be imported into the notebook.
## Documentation
While the code is split between `langchain` and `langchain.experimental`, the documentation is one holistic thing.
This covers how to get started contributing to documentation.
From the top-level of this repo, install documentation dependencies:
```bash
poetry install
```
### Contribute Documentation
The docs directory contains Documentation and API Reference.
Documentation is built using [Docusaurus 2](https://docusaurus.io/).
API Reference are largely autogenerated by [sphinx](https://www.sphinx-doc.org/en/master/) from the code.
For that reason, we ask that you add good documentation to all classes and methods.
Similar to linting, we recognize documentation can be annoying. If you do not want to do it, please contact a project maintainer, and they can help you with it. We do not want this to be a blocker for good code getting contributed.
### Build Documentation Locally
In the following commands, the prefix `api_` indicates that those are operations for the API Reference.
Before building the documentation, it is always a good idea to clean the build directory:
```bash
make docs_clean
make api_docs_clean
```
Next, you can build the documentation as outlined below:
```bash
make docs_build
make api_docs_build
```
Finally, run the link checker to ensure all links are valid:
```bash
make docs_linkcheck
make api_docs_linkcheck
```
### Verify Documentation changes
After pushing documentation changes to the repository, you can preview and verify that the changes are
what you wanted by clicking the `View deployment` or `Visit Preview` buttons on the pull request `Conversation` page.
This will take you to a preview of the documentation changes.
This preview is created by [Vercel](https://vercel.com/docs/getting-started-with-vercel).
## 📕 Releases & Versioning
As of now, LangChain has an ad hoc release process: releases are cut with high frequency by
a maintainer and published to [PyPI](https://pypi.org/).
The different packages are versioned slightly differently.
### `langchain-core`
`langchain-core` is currently on version `0.1.x`.
As `langchain-core` contains the base abstractions and runtime for the whole LangChain ecosystem, we will communicate any breaking changes with advance notice and version bumps. The exception for this is anything in `langchain_core.beta`. The reason for `langchain_core.beta` is that given the rate of change of the field, being able to move quickly is still a priority, and this module is our attempt to do so.
Minor version increases will occur for:
- Breaking changes for any public interfaces NOT in `langchain_core.beta`
Patch version increases will occur for:
- Bug fixes
- New features
- Any changes to private interfaces
- Any changes to `langchain_core.beta`
### `langchain`
`langchain` is currently on version `0.0.x`
All changes will be accompanied by a patch version increase. Any changes to public interfaces are nearly always done in a backwards compatible way and will be communicated ahead of time when they are not backwards compatible.
We are targeting January 2024 for a release of `langchain` v0.1, at which point `langchain` will adopt the same versioning policy as `langchain-core`.
### `langchain-community`
`langchain-community` is currently on version `0.0.x`
All changes will be accompanied by a patch version increase.
### `langchain-experimental`
`langchain-experimental` is currently on version `0.0.x`
All changes will be accompanied by a patch version increase.
## 🌟 Recognition
If your contribution has made its way into a release, we will want to give you credit on Twitter (only if you want though)!
If you have a Twitter account you would like us to mention, please let us know in the PR or through another means.
To learn how to contribute to LangChain, please follow the [contribution guide here](https://python.langchain.com/docs/contributing/).
description:Submit a proposal/request for a new LangChain feature
labels:["02 Feature Request"]
labels:[idea]
body:
- type:checkboxes
id:checks
attributes:
label:Checked
description:Please confirm and check all the following options.
options:
- label:I searched existing ideas and did not find a similar one
required:true
- label:I added a very descriptive title
required:true
- label:I've clearly described the feature request and motivation for it
required:true
- type:textarea
id:feature-request
validations:
@@ -10,7 +20,6 @@ body:
label:Feature request
description:|
A clear and concise description of the feature proposal. Please provide links to any relevant GitHub repos, papers, or other resources if relevant.
- type:textarea
id:motivation
validations:
@@ -19,12 +28,11 @@ body:
label:Motivation
description:|
Please outline the motivation for the proposal. Is your feature request related to a problem? e.g., I'm always frustrated when [...]. If this is related to another GitHub issue, please link here too.
- type:textarea
id:contribution
id:proposal
validations:
required:true
required:false
attributes:
label:Your contribution
label:Proposal (If applicable)
description:|
Is there any way that you could help, e.g. by submitting a PR? Make sure to read the CONTRIBUTING.MD [readme](https://github.com/langchain-ai/langchain/blob/master/.github/CONTRIBUTING.md)
If you would like to propose a solution, please describe it here.
description:Please confirm and check all the following options.
options:
- label:I added a very descriptive title to this question.
required:true
- label:I searched the LangChain documentation with the integrated search.
required:true
- label:I used the GitHub search to find a similar question and didn't find it.
required:true
- type:checkboxes
id:help
attributes:
label:Commit to Help
description:|
After submitting this, I commit to one of:
* Read open questions until I find 2 where I can help someone and add a comment to help there.
* I already hit the "watch" button in this repository to receive notifications and I commit to help at least 2 people that ask questions in the future.
* Once my question is answered, I will mark the answer as "accepted".
options:
- label:I commit to help with one of those options 👆
required:true
- type:textarea
id:example
attributes:
label:Example Code
description:|
Please add a self-contained, [minimal, reproducible, example](https://stackoverflow.com/help/minimal-reproducible-example) with your use case.
If a maintainer can copy it, run it, and see it right away, there's a much higher chance that you'll be able to get help.
**Important!**
* Use code tags (e.g., ```python ... ```) to correctly [format your code](https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting).
* INCLUDE the language label (e.g. `python`) after the first three backticks to enable syntax highlighting. (e.g., ```python rather than ```).
* Reduce your code to the minimum required to reproduce the issue if possible. This makes it much easier for others to help you.
* Avoid screenshots when possible, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.
placeholder:|
from langchain_core.runnables import RunnableLambda
def bad_code(inputs) -> int:
raise NotImplementedError('For demo purpose')
chain = RunnableLambda(bad_code)
chain.invoke('Hello!')
render:python
validations:
required:true
- type:textarea
id:description
attributes:
label:Description
description:|
What is the problem, question, or error?
Write a short description explaining what you are doing, what you expect to happen, and what is currently happening.
placeholder:|
* I'm trying to use the `langchain` library to do X.
* I expect to see Y.
* Instead, it does Z.
validations:
required:true
- type:textarea
id:system-info
attributes:
label:System Info
description:|
Please share your system info with us.
"pip freeze | grep langchain"
platform (windows / linux / mac)
python version
OR if you're on a recent version of langchain-core you can paste the output of:
python -m langchain_core.sys_info
placeholder:|
"pip freeze | grep langchain"
platform
python version
Alternatively, if you're on a recent version of langchain-core you can paste the output of:
python -m langchain_core.sys_info
These will only surface LangChain packages, don't forget to include any other relevant
packages you're using (if you're not sure what's relevant, you can paste the entire output of `pip freeze`).
description:Submit a bug report to help us improve LangChain. To report a security issue, please instead use the security option below.
description:Report a bug in LangChain. To report a security issue, please instead use the security option below. For questions, please use the GitHub Discussions.
labels:["02 Bug Report"]
body:
- type:markdown
attributes:
value:>
Thank you for taking the time to file a bug report. Before creating a new
issue, please make sure to take a few moments to check the issue tracker
for existing issues about the bug.
- type:textarea
id:system-info
attributes:
label:System Info
description:Please share your system info with us.
Your issue will be replied to more quickly if you can figure out the right person to tag with @
If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
The core maintainers strive to read all issues, but tagging them will help them prioritize.
Please tag fewer than 3 people.
@hwchase17 - project lead
Tracing / Callbacks
- @agola11
Async
- @agola11
DataLoader Abstractions
- @eyurtsev
LLM/Chat Wrappers
- @hwchase17
- @agola11
Tools / Toolkits
- ...
placeholder:"@Username ..."
Thank you for taking the time to file a bug report.
Use this to report bugs in LangChain.
If you're not certain that your issue is due to a bug in LangChain, please use [GitHub Discussions](https://github.com/langchain-ai/langchain/discussions)
to ask for help with your issue.
Relevant links to check before filing a bug report to see if your issue has already been reported, fixed or
if there's another way to solve your problem:
[LangChain documentation with the integrated search](https://python.langchain.com/docs/get_started/introduction),
- label:I added a very descriptive title to this issue.
required:true
- label:I searched the LangChain documentation with the integrated search.
required:true
- label:I used the GitHub search to find a similar question and didn't find it.
required:true
- label:I am sure that this is a bug in LangChain rather than my code.
required:true
- type:textarea
id:reproduction
validations:
required:true
attributes:
label:Reproduction
label:Example Code
description:|
Please provide a [code sample](https://stackoverflow.com/help/minimal-reproducible-example) that reproduces the problem you ran into. It can be a Colab link or just a code snippet.
If you have code snippets, error messages, stack traces please provide them here as well.
Important! Use code tags to correctly format your code. See https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting
Avoid screenshots when possible, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.
Please add a self-contained, [minimal, reproducible, example](https://stackoverflow.com/help/minimal-reproducible-example) with your use case.
If a maintainer can copy it, run it, and see it right away, there's a much higher chance that you'll be able to get help.
**Important!**
* Use code tags (e.g., ```python ... ```) to correctly [format your code](https://help.github.com/en/github/writing-on-github/creating-and-highlighting-code-blocks#syntax-highlighting).
* INCLUDE the language label (e.g. `python`) after the first three backticks to enable syntax highlighting. (e.g., ```python rather than ```).
* Reduce your code to the minimum required to reproduce the issue if possible. This makes it much easier for others to help you.
* Avoid screenshots when possible, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.
placeholder:|
Steps to reproduce the behavior:
1.
2.
3.
The following code:
```python
from langchain_core.runnables import RunnableLambda
def bad_code(inputs) -> int:
raise NotImplementedError('For demo purpose')
chain = RunnableLambda(bad_code)
chain.invoke('Hello!')
```
- type:textarea
id:expected-behavior
id:error
validations:
required:false
attributes:
label:Error Message and Stack Trace (if applicable)
description:|
If you are reporting an error, please include the full error message and stack trace.
placeholder:|
Exception + full stack trace
- type:textarea
id:description
attributes:
label:Description
description:|
What is the problem, question, or error?
Write a short description telling what you are doing, what you expect to happen, and what is currently happening.
placeholder:|
* I'm trying to use the `langchain` library to do X.
* I expect to see Y.
* Instead, it does Z.
validations:
required:true
- type:textarea
id:system-info
attributes:
label:Expected behavior
description:"A clear and concise description of what you would expect to happen."
label:System Info
description:|
Please share your system info with us.
"pip freeze | grep langchain"
platform (windows / linux / mac)
python version
OR if you're on a recent version of langchain-core you can paste the output of:
python -m langchain_core.sys_info
placeholder:|
"pip freeze | grep langchain"
platform
python version
Alternatively, if you're on a recent version of langchain-core you can paste the output of:
python -m langchain_core.sys_info
These will only surface LangChain packages, don't forget to include any other relevant
packages you're using (if you're not sure what's relevant, you can paste the entire output of `pip freeze`).
description:You are a LangChain maintainer, or was asked directly by a maintainer to create an issue here. If not, check the other options.
body:
- type:markdown
attributes:
value:|
Thanks for your interest in LangChain! 🚀
If you are not a LangChain maintainer or were not asked directly by a maintainer to create an issue, then please start the conversation in a [Question in GitHub Discussions](https://github.com/langchain-ai/langchain/discussions/categories/q-a) instead.
You are a LangChain maintainer if you maintain any of the packages inside of the LangChain repository
or are a regular contributor to LangChain with previous merged merged pull requests.
- type:checkboxes
id:privileged
attributes:
label:Privileged issue
description:Confirm that you are allowed to create an issue here.
options:
- label:I am a LangChain maintainer, or was asked directly by a LangChain maintainer to create an issue here.
- **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!
- [ ]**PR title**: "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"
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:
- [ ]**PR message**: ***Delete this entire checklist*** and replace with
- **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!
If you're adding a new integration, please include:
- [ ]**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/extras` directory.
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.
-->
- [ ]**Lint and test**: Run `make format`, `make lint` and `make test` from the root of the package(s) you've modified. See contribution guidelines for more: https://python.langchain.com/docs/contributing/
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.
Input to this tool is a detailed question about the dataset, output is a result from the dataset. It will try to answer the question using the dataset, and if it cannot, it will ask for clarification.
"LLM chain for QueryPowerBITool must have input variables ['tool_input', 'tables', 'schemas', 'examples'], found %s",# noqa: C0301 E501 # pylint: disable=C0301
logger.info("0 records in result, query was valid.")
return(
None,
"0 rows returned, this might be correct, but please validate if all filter values were correct?",# noqa: E501
)
result=json_to_md(rows)
too_long,length=self._result_too_large(result)
iftoo_long:
return(
f"Result too large, please try to be more specific or use the `TOPN` function. The result is {length} tokens long, the limit is {self.output_token_limit} tokens.",# noqa: E501
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