**Description:**
This pull request introduces a new feature for LangChain: the
integration with the Rememberizer API through a custom retriever.
This enables LangChain applications to allow users to load and sync
their data from Dropbox, Google Drive, Slack, their hard drive into a
vector database that LangChain can query. Queries involve sending text
chunks generated within LangChain and retrieving a collection of
semantically relevant user data for inclusion in LLM prompts.
User knowledge dramatically improved AI applications.
The Rememberizer integration will also allow users to access general
purpose vectorized data such as Reddit channel discussions and US
patents.
**Issue:**
N/A
**Dependencies:**
N/A
**Twitter handle:**
https://twitter.com/Rememberizer
**Description:** Add tests to check API keys and Active Directory tokens
are masked
**Issue:** Resolves#12165 for OpenAI and Azure OpenAI models
**Dependencies:** None
Also resolves#12473 which may be closed.
Additional contributors @alex4321 (#12473) and @onesolpark (#12542)
- [ ] **PR message**:
- **Description:** Refactored the lazy_load method to use asynchronous
execution for improved performance. The method now initiates scraping of
all URLs simultaneously using asyncio.gather, enhancing data fetching
efficiency. Each Document object is yielded immediately once its content
becomes available, streamlining the entire process.
- **Issue:** N/A
- **Dependencies:** Requires the asyncio library for handling
asynchronous tasks, which should already be part of standard Python
libraries in Python 3.7 and above.
- **Email:** [r73327118@gmail.com](mailto:r73327118@gmail.com)
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Update python.py（experimental：Added code for PythonREPL）
Added code for PythonREPL, defining a static method 'sanitize_input'
that takes the string 'query' as input and returns a sanitizing string.
The purpose of this method is to remove unwanted characters from the
input string, Specifically:
1. Delete the whitespace at the beginning and end of the string (' \s').
2. Remove the quotation marks (`` ` ``) at the beginning and end of the
string.
3. Remove the keyword "python" at the beginning of the string (case
insensitive) because the user may have typed it.
This method uses regular expressions (regex) to implement sanitizing.
It all started with this code：
from langchain.agents import Tool
from langchain_experimental.utilities import PythonREPL
python_repl = PythonREPL()
repl_tool = Tool(
name="python_repl",
description="Remove redundant formatting marks at the beginning and end
of source code from input.Use a Python shell to execute python commands.
If you want to see the output of a value, you should print it out with
`print(...)`.",
func=python_repl.run,
)
When I call the agent to write a piece of code for me and execute it
with the defined code, I must get an error: SyntaxError('invalid
syntax', ('<string>', 1, 1,'In', 1, 2))
After checking, I found that pythonREPL has less formatting of input
code than the soon-to-be deprecated pythonREPL tool, so I added this
step to it, so that no matter what code I ask the agent to write for me,
it can be executed smoothly and get the output result.
I have tried modifying the prompt words to solve this problem before,
but it did not work, and by adding a simple format check, the problem is
well resolved.
<img width="1271" alt="image"
src="https://github.com/langchain-ai/langchain/assets/164149097/c49a685f-d246-4b11-b655-fd952fc2f04c">
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description**
This pull request updates the Bagel Network package name from
"betabageldb" to "bagelML" to align with the latest changes made by the
Bagel Network team.
The following modifications have been made:
- Updated all references to the old package name ("betabageldb") with
the new package name ("bagelML") throughout the codebase.
- Modified the documentation, and any relevant scripts to reflect the
package name change.
- Tested the changes to ensure that the functionality remains intact and
no breaking changes were introduced.
By merging this pull request, our project will stay up to date with the
latest Bagel Network package naming convention, ensuring compatibility
and smooth integration with their updated library.
Please review the changes and provide any feedback or suggestions. Thank
you!
**Description:** Update UpstageLayoutAnalysisParser and Loader and add
upstage loader example in pdf section
**Dependencies:** langchain_community
**Twitter handle:** [@upstageai](https://twitter.com/upstageai)
- [x] **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.
- [x] **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.
**Issue:**
Currently `AzureSearch` vector store does not implement `delete` method.
This PR implements it. This also makes it compatible with LangChain
indexer.
**Dependencies:**
None
**Twitter handle:**
@martintriska1
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Upgrades prompts module to use optional imports.
This code was generated with a migration script, but had to be adjusted
manually a bit.
Testing in preparation for applying this code modification across the
rest of the modules in langchain package to reverse the dependency
between langchain community and langchain.
## Summary
No new diagnostics (given that the set of enabled rules hasn't changed),
but gains access to our new parser (much faster) and reduced false
positives all around.
As shown in #13749 , `RecursiveUrlLoader` has encoding issue. This PR is
to solve this.
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
### Description:
When attempting to download PDF files from arXiv, an unexpected 404
error frequently occurs. This error halts the operation, regardless of
whether there are additional documents to process. As a solution, I
suggest implementing a mechanism to ignore and communicate this error
and continue processing the next document from the list.
Proposed Solution: To address the issue of unexpected 404 errors during
PDF downloads from arXiv, I propose implementing the following solution:
- Error Handling: Implement error handling mechanisms to catch and
handle 404 errors gracefully.
- Communication: Inform the user or logging system about the occurrence
of the 404 error.
- Continued Processing: After encountering a 404 error, continue
processing the remaining documents from the list without interruption.
This solution ensures that the application can handle unexpected errors
without terminating the entire operation. It promotes resilience and
robustness in the face of intermittent issues encountered during PDF
downloads from arXiv.
### Issue:
#20909
### Dependencies:
none
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
## Summary
I ran `ruff check --extend-select RUF100 -n` to identify `# noqa`
comments that weren't having any effect in Ruff, and then `ruff check
--extend-select RUF100 -n --fix` on select files to remove all of the
unnecessary `# noqa: F401` violations. It's possible that these were
needed at some point in the past, but they're not necessary in Ruff
v0.1.15 (used by LangChain) or in the latest release.
Co-authored-by: Erick Friis <erick@langchain.dev>
…/17690
Thank you for contributing to LangChain!
- [x] **Fix Google Lens knowledge graph issue**: "langchain: community"
- Fix for [No "knowledge_graph" property in Google Lens API call from
SerpAPI](https://github.com/langchain-ai/langchain/issues/17690)
- [x] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** handled the existence of keys in the json response of
Google Lens
- **Issue:** [No "knowledge_graph" property in Google Lens API call from
SerpAPI](https://github.com/langchain-ai/langchain/issues/17690)
- [x] **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/
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
## Description
Adding `UpstashVectorStore` to utilize [Upstash
Vector](https://upstash.com/docs/vector/overall/getstarted)!
#17012 was opened to add Upstash Vector to langchain but was closed to
wait for filtering. Now filtering is added to Upstash vector and we open
a new PR. Additionally, [embedding
feature](https://upstash.com/docs/vector/features/embeddingmodels) was
added and we add this to our vectorstore aswell.
## Dependencies
[upstash-vector](https://pypi.org/project/upstash-vector/) should be
installed to use `UpstashVectorStore`. Didn't update dependencies
because of [this comment in the previous
PR](https://github.com/langchain-ai/langchain/pull/17012#pullrequestreview-1876522450).
## Tests
Tests are added and they pass. Tests are naturally network bound since
Upstash Vector is offered through an API.
There was [a discussion in the previous PR about mocking the
unittests](https://github.com/langchain-ai/langchain/pull/17012#pullrequestreview-1891820567).
We didn't make changes to this end yet. We can update the tests if you
can explain how the tests should be mocked.
---------
Co-authored-by: ytkimirti <yusuftaha9@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Proposing to centralize code for handling dynamic imports. This allows treating langchain-community as an optional dependency.
---
The proposal is to scan the code base and to replace all existing imports with dynamic imports using this functionality.
Fixed the error that the model name is never actually put into GigaChat
request payload, always defaulting to `GigaChat-Lite`.
With this fix, model selection through
```python
import os
from langchain.chat_models.gigachat import GigaChat
chat = GigaChat(
name="GigaChat-Pro", # <- HERE!!!!!
...
)
```
should actually work, as intended in
[here](804390ba4b/libs/community/langchain_community/llms/gigachat.py (L36)).
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
**Description**: ToolKit and Tools for accessing data in a Cassandra
Database primarily for Agent integration. Initially, this includes the
following tools:
- `cassandra_db_schema` Gathers all schema information for the connected
database or a specific schema. Critical for the agent when determining
actions.
- `cassandra_db_select_table_data` Selects data from a specific keyspace
and table. The agent can pass paramaters for a predicate and limits on
the number of returned records.
- `cassandra_db_query` Expiriemental alternative to
`cassandra_db_select_table_data` which takes a query string completely
formed by the agent instead of parameters. May be removed in future
versions.
Includes unit test and two notebooks to demonstrate usage.
**Dependencies**: cassio
**Twitter handle**: @PatrickMcFadin
---------
Co-authored-by: Phil Miesle <phil.miesle@datastax.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** This pull request introduces a new feature to community
tools, enhancing its search capabilities by integrating the Mojeek
search engine
**Dependencies:** None
---------
Co-authored-by: Igor Brai <igor@mojeek.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: ccurme <chester.curme@gmail.com>
Removed redundant self/cls from required args of class functions in
_get_python_function_required_args:
```python
class MemberTool:
def search_member(
self,
keyword: str,
*args,
**kwargs,
):
"""Search on members with any keyword like first_name, last_name, email
Args:
keyword: Any keyword of member
"""
headers = dict(authorization=kwargs['token'])
members = []
try:
members = request_(
method='SEARCH',
url=f'{service_url}/apiv1/members',
headers=headers,
json=dict(query=keyword),
)
except Exception as e:
logger.info(e.__doc__)
return members
convert_to_openai_tool(MemberTool.search_member)
```
expected result:
```
{'type': 'function', 'function': {'name': 'search_member', 'description': 'Search on members with any keyword like first_name, last_name, username, email', 'parameters': {'type': 'object', 'properties': {'keyword': {'type': 'string', 'description': 'Any keyword of member'}}, 'required': ['keyword']}}}
```
#20685
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Issue: When the third-party package is not installed, whenever we need
to `pip install <package>` the ImportError is raised.
But sometimes, the `ValueError` or `ModuleNotFoundError` is raised. It
is bad for consistency.
Change: replaced the `ValueError` or `ModuleNotFoundError` with
`ImportError` when we raise an error with the `pip install <package>`
message.
Note: Ideally, we replace all `try: import... except... raise ... `with
helper functions like `import_aim` or just use the existing
[langchain_core.utils.utils.guard_import](https://api.python.langchain.com/en/latest/utils/langchain_core.utils.utils.guard_import.html#langchain_core.utils.utils.guard_import)
But it would be much bigger refactoring. @baskaryan Please, advice on
this.
Implemented bind_tools for OllamaFunctions.
Made OllamaFunctions sub class of ChatOllama.
Implemented with_structured_output for OllamaFunctions.
integration unit test has been updated.
notebook has been updated.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
I can't seem to reproduce, but i got this:
```
SystemError: AST constructor recursion depth mismatch (before=102, after=37)
```
And the operation isn't critical for the actual forward pass so seems
preferable to expand our caught exceptions
**Description**: This update enhances the `extract_sub_links` function
within the `langchain_core/utils/html.py` module to include query
parameters in the extracted URLs.
**Issue**: N/A
**Dependencies**: No additional dependencies required for this change.
**Twitter handle**: N/A
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
This introduces `store_kwargs` which behaves similarly to `graph_kwargs`
on the `RdfGraph` object, which will enable users to pass `headers` and
other arguments to the underlying `SPARQLStore` object. I have also made
a [PR in `rdflib` to support passing
`default_graph`](https://github.com/RDFLib/rdflib/pull/2761).
Example usage:
```python
from langchain_community.graphs import RdfGraph
graph = RdfGraph(
query_endpoint="http://localhost/sparql",
standard="rdf",
store_kwargs=dict(
default_graph="http://example.com/mygraph"
)
)
```
<!--If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.-->
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Description: The PebbloSafeLoader should first check for owner,
full_path and size in metadata before implementing its own logic.
Dependencies: None
Documentation: NA.
Signed-off-by: Rahul Tripathi <rauhl.psit.ec@gmail.com>
Co-authored-by: Rahul Tripathi <rauhl.psit.ec@gmail.com>
Issue: #20514
The current implementation of `construct_instance` expects a `texts:
List[str]` that will call the embedding function. This might not be
needed when we already have a client with collection and `path, you
don't want to add any text.
This PR adds a class method that returns a qdrant instance with an
existing client.
Here everytime
cb6e5e56c2/libs/community/langchain_community/vectorstores/qdrant.py (L1592)
`construct_instance` is called, this line sends some text for embedding
generation.
---------
Co-authored-by: Anush <anushshetty90@gmail.com>
* Groundedness Check takes `str` or `list[Document]` as input.
* Deprecate `GroundednessCheck` due to its naming.
* Added `UpstageGroundednessCheck`.
* Hotfix for Groundedness Check parameter.
The name `query` was misleading and it should be `answer` instead.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
This auto generates partner migrations.
At the moment the migration is from community -> partner.
So one would need to run the migration script twice to go from langchain to partner.
Add script to help generate migrations.
This works well for partner packages. Migrations are generated based on run time rather than static analysis (much simpler to get the correct migrations implemented).
The script for generating migrations from langchain to community still needs work.
`langchain_pinecone.Pinecone` is deprecated in favor of
`PineconeVectorStore`, and is currently a subclass of
`PineconeVectorStore`.
```python
@deprecated(since="0.0.3", removal="0.2.0", alternative="PineconeVectorStore")
class Pinecone(PineconeVectorStore):
"""Deprecated. Use PineconeVectorStore instead."""
pass
```
**Description:** AzureSearch vector store has no tests. This PR adds
initial tests to validate the code can be imported and used.
**Issue:** N/A
**Dependencies:** azure-search-documents and azure-identity are added as
optional dependencies for testing
---------
Co-authored-by: Matt Gotteiner <[email protected]>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description**:
_PebbloSafeLoader_: Add support for pebblo server and client version
**Documentation:** NA
**Unit test:** NA
**Issue:** NA
**Dependencies:** None
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
- [ ] **Kinetica Document Loader**: "community: a class to load
Documents from Kinetica"
- [ ] **Kinetica Document Loader**:
- **Description:** implemented KineticaLoader in `kinetica_loader.py`
- **Dependencies:** install the Kinetica API using `pip install
gpudb==7.2.0.1 `
**Description:** Fixes a bug in the HuggingGPT task execution logic
here:
except Exception as e:
self.status = "failed"
self.message = str(e)
self.status = "completed"
self.save_product()
where a caught exception effectively just sets `self.message` and can
then throw an exception if, e.g., `self.product` is not defined.
**Issue:** None that I'm aware of.
**Dependencies:** None
**Twitter handle:** https://twitter.com/michaeljschock
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
- **Description:** Changes
`lanchain_core.output_parsers.CommaSeparatedListOutputParser` to handle
`,` as a delimiter alongside the previous implementation which used `, `
as delimiter.
- **Issue:** Started noticing that some results returned by LLMs were
not getting parsed correctly when the output contained `,` instead of `,
`.
- **Dependencies:** No
- **Twitter handle:** not active on twitter.
<!---
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
-->
- **Description**:
- **add support for more data types**: by default `IpexLLM` will load
the model in int4 format. This PR adds more data types support such as
`sym_in5`, `sym_int8`, etc. Data formats like NF3, NF4, FP4 and FP8 are
only supported on GPU and will be added in future PR.
- Fix a small issue in saving/loading, update api docs
- **Dependencies**: `ipex-llm` library
- **Document**: In `docs/docs/integrations/llms/ipex_llm.ipynb`, added
instructions for saving/loading low-bit model.
- **Tests**: added new test cases to
`libs/community/tests/integration_tests/llms/test_ipex_llm.py`, added
config params.
- **Contribution maintainer**: @shane-huang
Description: Add support for Semantic topics and entities.
Classification done by pebblo-server is not used to enhance metadata of
Documents loaded by document loaders.
Dependencies: None
Documentation: Updated.
Signed-off-by: Rahul Tripathi <rauhl.psit.ec@gmail.com>
Co-authored-by: Rahul Tripathi <rauhl.psit.ec@gmail.com>
Thank you for contributing to LangChain!
- [x] **PR title**
- [x] **PR message**:
- **Description:** Deprecate persist method in Chroma no longer exists
in Chroma 0.4.x
- **Issue:** #20851
- **Dependencies:** None
- **Twitter handle:** AndresAlgaba1
- [x] **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.
- [x] **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/
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
**Description:**
The RecursiveUrlLoader loader offers a link_regex parameter that can
filter out URLs. However, this filtering capability is limited, and if
the internal links of the website change, unexpected resources may be
loaded. These resources, such as font files, can cause problems in
subsequent embedding processing.
>
https://blog.langchain.dev/assets/fonts/source-sans-pro-v21-latin-ext_latin-regular.woff2?v=0312715cbf
We can add the Content-Type in the HTTP response headers to the document
metadata so developers can choose which resources to use. This allows
developers to make their own choices.
For example, the following may be a good choice for text knowledge.
- text/plain - simple text file
- text/html - HTML web page
- text/xml - XML format file
- text/json - JSON format data
- application/pdf - PDF file
- application/msword - Word document
and ignore the following
- text/css - CSS stylesheet
- text/javascript - JavaScript script
- application/octet-stream - binary data
- image/jpeg - JPEG image
- image/png - PNG image
- image/gif - GIF image
- image/svg+xml - SVG image
- audio/mpeg - MPEG audio files
- video/mp4 - MP4 video file
- application/font-woff - WOFF font file
- application/font-ttf - TTF font file
- application/zip - ZIP compressed file
- application/octet-stream - binary data
**Twitter handle:** @coolbeevip
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** Adapt JinaEmbeddings to run with the new Jina AI
Rerank API
- **Twitter handle:** https://twitter.com/JinaAI_
- [ ] **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/
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Add the remove_unwanted_classnames method to the
BeautifulSoupTransformer class, which can filter more effectively.
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
OpenAI API compatible server may not support `safe_len_embedding`，
use `disable_safe_len_embeddings=True` to disable it.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
* Updating the provider docs page.
The RAG example was meant to be moved to cookbook, but was merged by
mistake.
* Fix bug in Groundedness Check
---------
Co-authored-by: JuHyung-Son <sonju0427@gmail.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
Currently, when a new dev container is created, poetry does not work in
it with the error "No module named 'rapidfuzz'".
Install Poetry outside the project venv so that poetry and project
dependencies do not get mixed. Use pipx to install poetry securely in
its own isolated environment.
Issue: #12237
Twitter handle: https://twitter.com/ibratoev
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
- **Description:** Currently, the regex is static (`r"(?<=[.?!])\s+"`),
which is only useful for certain use cases. The current change only
moves this to be a parameter of split_text(). Which adds flexibility
without making it more complex (as the default regex is still the same).
- **Issue:** Not applicable (I searched, no one seems to have created
this issue yet).
- **Dependencies:** None.
_If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17._
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Description: MarkdownHeaderTextSplitter Fails to Parse Headers with
non-printable characters. more #20643
The following is the official test case. Just replacing `# Foo\n\n` with
`\ufeff# Foo\n\n` will cause the test case to fail.
chunk metadata is empty
```python
def test_md_header_text_splitter_1() -> None:
"""Test markdown splitter by header: Case 1."""
markdown_document = (
"\ufeff# Foo\n\n"
" ## Bar\n\n"
"Hi this is Jim\n\n"
"Hi this is Joe\n\n"
" ## Baz\n\n"
" Hi this is Molly"
)
headers_to_split_on = [
("#", "Header 1"),
("##", "Header 2"),
]
markdown_splitter = MarkdownHeaderTextSplitter(
headers_to_split_on=headers_to_split_on,
)
output = markdown_splitter.split_text(markdown_document)
expected_output = [
Document(
page_content="Hi this is Jim \nHi this is Joe",
metadata={"Header 1": "Foo", "Header 2": "Bar"},
),
Document(
page_content="Hi this is Molly",
metadata={"Header 1": "Foo", "Header 2": "Baz"},
),
]
assert output == expected_output
```
twitter: @coolbeevip
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Description :
- added functionalities - delete, index creation, using existing
connection object etc.
- updated usage
- Added LaceDB cloud OSS support
make lint_diff , make test checks done
- **Description:** fix a bug in the agent_token_buffer_memory
- **Issue:** agent_token_buffer_memory was not working with openai tools
- **Dependencies:** None
- **Twitter handle:** @pokidyshef
## Description
Add `aprep_output` method to `langchain/chains/base.py`. Some downstream
`ChatMessageHistory` objects that use async connections require an async
way to append to the context.
It turned out that `ainvoke()` was calling `prep_output` which is
synchronous.
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
# Proxy Fix for Groq Class 🐛🚀
## Description
This PR fixes a bug related to proxy settings in the `Groq` class,
allowing users to connect to LangChain services via a proxy.
## Changes Made
- ✅ FIX support for specifying proxy settings in the `Groq` class.
- ✅ Resolved the bug causing issues with proxy settings.
- ❌ Did not include unit tests and documentation updates.
- ❌ Did not run make format, make lint, and make test to ensure code
quality and functionality because I couldn't get it to run, so I don't
program in Python and couldn't run `ruff`.
- ❔ Ensured that the changes are backwards compatible.
- ✅ No additional dependencies were added to `pyproject.toml`.
### Error Before Fix
```python
Traceback (most recent call last):
File "/home/bg/Documents/code/github.com/back2nix/test/groq/main.py", line 9, in <module>
chat = ChatGroq(
^^^^^^^^^
File "/home/bg/Documents/code/github.com/back2nix/test/groq/venv310/lib/python3.11/site-packages/langchain_core/load/serializable.py", line 120, in __init__
super().__init__(**kwargs)
File "/home/bg/Documents/code/github.com/back2nix/test/groq/venv310/lib/python3.11/site-packages/pydantic/v1/main.py", line 341, in __init__
raise validation_error
pydantic.v1.error_wrappers.ValidationError: 1 validation error for ChatGroq
__root__
Invalid `http_client` argument; Expected an instance of `httpx.AsyncClient` but got <class 'httpx.Client'> (type=type_error)
```
### Example usage after fix
```python3
import os
import httpx
from langchain_core.prompts import ChatPromptTemplate
from langchain_groq import ChatGroq
chat = ChatGroq(
temperature=0,
groq_api_key=os.environ.get("GROQ_API_KEY"),
model_name="mixtral-8x7b-32768",
http_client=httpx.Client(
proxies="socks5://127.0.0.1:1080",
transport=httpx.HTTPTransport(local_address="0.0.0.0"),
),
http_async_client=httpx.AsyncClient(
proxies="socks5://127.0.0.1:1080",
transport=httpx.HTTPTransport(local_address="0.0.0.0"),
),
)
system = "You are a helpful assistant."
human = "{text}"
prompt = ChatPromptTemplate.from_messages([("system", system), ("human", human)])
chain = prompt | chat
out = chain.invoke({"text": "Explain the importance of low latency LLMs"})
print(out)
```
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Implemented the ability to enable full-text search within the
SingleStore vector store, offering users a versatile range of search
strategies. This enhancement allows users to seamlessly combine
full-text search with vector search, enabling the following search
strategies:
* Search solely by vector similarity.
* Conduct searches exclusively based on text similarity, utilizing
Lucene internally.
* Filter search results by text similarity score, with the option to
specify a threshold, followed by a search based on vector similarity.
* Filter results by vector similarity score before conducting a search
based on text similarity.
* Perform searches using a weighted sum of vector and text similarity
scores.
Additionally, integration tests have been added to comprehensively cover
all scenarios.
Updated notebook with examples.
CC: @baskaryan, @hwchase17
---------
Co-authored-by: Volodymyr Tkachuk <vtkachuk-ua@singlestore.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- added guard on the `pyTigerGraph` import
- added a missed example page in the `docs/integrations/graphs/`
- formatted the `docs/integrations/providers/` page to the consistent
format. Added links.
- **Description:**
This PR adds support for advanced filtering to the integration of HANA
Vector Engine.
The newly supported filtering operators are: $eq, $ne, $gt, $gte, $lt,
$lte, $between, $in, $nin, $like, $and, $or
- **Issue:** N/A
- **Dependencies:** no new dependencies added
Added integration tests to:
`libs/community/tests/integration_tests/vectorstores/test_hanavector.py`
Description of the new capabilities in notebook:
`docs/docs/integrations/vectorstores/hanavector.ipynb`
Thank you for contributing to LangChain!
community:perplexity[patch]: standardize init args
updated pplx_api_key and request_timeout so that aliased to api_key, and
timeout respectively. Added test that both continue to set the same
underlying attributes.
Related to
[20085](https://github.com/langchain-ai/langchain/issues/20085)
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
This PR moves the interface and the logic to core.
The following changes to namespaces:
`indexes` -> `indexing`
`indexes._api` -> `indexing.api`
Testing code is intentionally duplicated for now since it's testing
different
implementations of the record manager (in-memory vs. SQL).
Common logic will need to be pulled out into the test client.
A follow up PR will move the SQL based implementation outside of
LangChain.
**Description:**
This PR fixes an issue in message formatting function for Anthropic
models on Amazon Bedrock.
Currently, LangChain BedrockChat model will crash if it uses Anthropic
models and the model return a message in the following type:
- `AIMessageChunk`
Moreover, when use BedrockChat with for building Agent, the following
message types will trigger the same issue too:
- `HumanMessageChunk`
- `FunctionMessage`
**Issue:**
https://github.com/langchain-ai/langchain/issues/18831
**Dependencies:**
No.
**Testing:**
Manually tested. The following code was failing before the patch and
works after.
```
@tool
def square_root(x: str):
"Useful when you need to calculate the square root of a number"
return math.sqrt(int(x))
llm = ChatBedrock(
model_id="anthropic.claude-3-sonnet-20240229-v1:0",
model_kwargs={ "temperature": 0.0 },
)
prompt = ChatPromptTemplate.from_messages(
[
("system", FUNCTION_CALL_PROMPT),
("human", "Question: {user_input}"),
MessagesPlaceholder(variable_name="agent_scratchpad"),
]
)
tools = [square_root]
tools_string = format_tool_to_anthropic_function(square_root)
agent = (
RunnablePassthrough.assign(
user_input=lambda x: x['user_input'],
agent_scratchpad=lambda x: format_to_openai_function_messages(
x["intermediate_steps"]
)
)
| prompt
| llm
| AnthropicFunctionsAgentOutputParser()
)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True, return_intermediate_steps=True)
output = agent_executor.invoke({
"user_input": "What is the square root of 2?",
"tools_string": tools_string,
})
```
List of messages returned from Bedrock:
```
<SystemMessage> content='You are a helpful assistant.'
<HumanMessage> content='Question: What is the square root of 2?'
<AIMessageChunk> content="Okay, let's calculate the square root of 2.<scratchpad>\nTo calculate the square root of a number, I can use the square_root tool:\n\n<function_calls>\n <invoke>\n <tool_name>square_root</tool_name>\n <parameters>\n <__arg1>2</__arg1>\n </parameters>\n </invoke>\n</function_calls>\n</scratchpad>\n\n<function_results>\n<search_result>\nThe square root of 2 is approximately 1.414213562373095\n</search_result>\n</function_results>\n\n<answer>\nThe square root of 2 is approximately 1.414213562373095\n</answer>" id='run-92363df7-eff6-4849-bbba-fa16a1b2988c'"
<FunctionMessage> content='1.4142135623730951' name='square_root'
```
Hi! My name is Alex, I'm an SDK engineer from
[Comet](https://www.comet.com/site/)
This PR updates the `CometTracer` class.
Fixed an issue when `CometTracer` failed while logging the data to Comet
because this data is not JSON-encodable.
The problem was in some of the `Run` attributes that could contain
non-default types inside, now these attributes are taken not from the
run instance, but from the `run.dict()` return value.
Causes an issue for this code
```python
from langchain.chat_models.openai import ChatOpenAI
from langchain.output_parsers.openai_tools import JsonOutputToolsParser
from langchain.schema import SystemMessage
prompt = SystemMessage(content="You are a nice assistant.") + "{question}"
llm = ChatOpenAI(
model_kwargs={
"tools": [
{
"type": "function",
"function": {
"name": "web_search",
"description": "Searches the web for the answer to the question.",
"parameters": {
"type": "object",
"properties": {
"query": {
"type": "string",
"description": "The question to search for.",
},
},
},
},
}
],
},
streaming=True,
)
parser = JsonOutputToolsParser(first_tool_only=True)
llm_chain = prompt | llm | parser | (lambda x: x)
for chunk in llm_chain.stream({"question": "tell me more about turtles"}):
print(chunk)
# message = llm_chain.invoke({"question": "tell me more about turtles"})
# print(message)
```
Instead by definition, we'll assume that RunnableLambdas consume the
entire stream and that if the stream isn't addable then it's the last
message of the stream that's in the usable format.
---
If users want to use addable dicts, they can wrap the dict in an
AddableDict class.
---
Likely, need to follow up with the same change for other places in the
code that do the upgrade
- **Description:** In January, Laiyer.ai became part of ProtectAI, which
means the model became owned by ProtectAI. In addition to that,
yesterday, we released a new version of the model addressing issues the
Langchain's community and others mentioned to us about false-positives.
The new model has a better accuracy compared to the previous version,
and we thought the Langchain community would benefit from using the
[latest version of the
model](https://huggingface.co/protectai/deberta-v3-base-prompt-injection-v2).
- **Issue:** N/A
- **Dependencies:** N/A
- **Twitter handle:** @alex_yaremchuk
This PR moves the implementations for chat history to core. So it's
easier to determine which dependencies need to be broken / add
deprecation warnings
Vector indexes in ClickHouse are experimental at the moment and can
sometimes break/change behaviour. So this PR makes it possible to say
that you don't want to specify an index type.
Any queries against the embedding column will be brute force/linear
scan, but that gives reasonable performance for small-medium dataset
sizes.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
**Description:** implemented GraphStore class for Apache Age graph db
**Dependencies:** depends on psycopg2
Unit and integration tests included. Formatting and linting have been
run.
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
This pull request corrects a mistake in the variable name within the
example code. The variable doc_schema has been changed to dog_schema to
fix the error.
Description: you don't need to pass a version for Replicate official
models. That was broken on LangChain until now!
You can now run:
```
llm = Replicate(
model="meta/meta-llama-3-8b-instruct",
model_kwargs={"temperature": 0.75, "max_length": 500, "top_p": 1},
)
prompt = """
User: Answer the following yes/no question by reasoning step by step. Can a dog drive a car?
Assistant:
"""
llm(prompt)
```
I've updated the replicate.ipynb to reflect that.
twitter: @charliebholtz
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
ZhipuAI API only accepts `temperature` parameter between `(0, 1)` open
interval, and if `0` is passed, it responds with status code `400`.
However, 0 and 1 is often accepted by other APIs, for example, OpenAI
allows `[0, 2]` for temperature closed range.
This PR truncates temperature parameter passed to `[0.01, 0.99]` to
improve the compatibility between langchain's ecosystem's and ZhipuAI
(e.g., ragas `evaluate` often generates temperature 0, which results in
a lot of 400 invalid responses). The PR also truncates `top_p` parameter
since it has the same restriction.
Reference: [glm-4 doc](https://open.bigmodel.cn/dev/api#glm-4) (which
unfortunately is in Chinese though).
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
faster-whisper is a reimplementation of OpenAI's Whisper model using
CTranslate2, which is up to 4 times faster than enai/whisper for the
same accuracy while using less memory. The efficiency can be further
improved with 8-bit quantization on both CPU and GPU.
It can automatically detect the following 14 languages and transcribe
the text into their respective languages: en, zh, fr, de, ja, ko, ru,
es, th, it, pt, vi, ar, tr.
The gitbub repository for faster-whisper is :
https://github.com/SYSTRAN/faster-whisper
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Replaced `from langchain.prompts` with `from langchain_core.prompts`
where it is appropriate.
Most of the changes go to `langchain_experimental`
Similar to #20348
…gFaceTextGenInference)
- [x] **PR title**: community[patch]: Invoke callback prior to yielding
token fix for [HuggingFaceTextGenInference]
- [x] **PR message**:
- **Description:** Invoke callback prior to yielding token in stream
method in [HuggingFaceTextGenInference]
- **Issue:** https://github.com/langchain-ai/langchain/issues/16913
- **Dependencies:** None
- **Twitter handle:** @bolun_zhang
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
fix timeout issue
fix zhipuai usecase notebookbook
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.
@rgupta2508 I believe this change is necessary following
https://github.com/langchain-ai/langchain/pull/20318 because of how
Milvus handles defaults:
59bf5e811a/pymilvus/client/prepare.py (L82-L85)
```python
num_shards = kwargs[next(iter(same_key))]
if not isinstance(num_shards, int):
msg = f"invalid num_shards type, got {type(num_shards)}, expected int"
raise ParamError(message=msg)
req.shards_num = num_shards
```
this way lets Milvus control the default value (instead of maintaining a
separate default in Langchain).
Let me know if I've got this wrong or you feel it's unnecessary. Thanks.
To support number of the shards for the collection to create in milvus
vvectorstores.
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:** Move `FileCallbackHandler` from community to core
**Issue:** #20493
**Dependencies:** None
(imo) `FileCallbackHandler` is a built-in LangChain callback handler
like `StdOutCallbackHandler` and should properly be in in core.
- **Description:** added the headless parameter as optional argument to
the langchain_community.document_loaders AsyncChromiumLoader class
- **Dependencies:** None
- **Twitter handle:** @perinim_98
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- would happen when user's code tries to access attritbute that doesnt
exist, we prefer to let this crash in the user's code, rather than here
- also catch more cases where a runnable is invoked/streamed inside a
lambda. before we weren't seeing these as deps
**Description:** currently, the `DirectoryLoader` progress-bar maximum value is based on an incorrect number of files to process
In langchain_community/document_loaders/directory.py:127:
```python
paths = p.rglob(self.glob) if self.recursive else p.glob(self.glob)
items = [
path
for path in paths
if not (self.exclude and any(path.match(glob) for glob in self.exclude))
]
```
`paths` returns both files and directories. `items` is later used to determine the maximum value of the progress-bar which gives an incorrect progress indication.
- Add functions (_stream, _astream)
- Connect to _generate and _agenerate
Thank you for contributing to LangChain!
- [x] **PR title**: "community: Add streaming logic in ChatHuggingFace"
- [x] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** Addition functions (_stream, _astream) and connection
to _generate and _agenerate
- **Issue:** #18782
- **Dependencies:** none
- **Twitter handle:** @lunara_x
**Community: Unify Titan Takeoff Integrations and Adding Embedding
Support**
**Description:**
Titan Takeoff no longer reflects this either of the integrations in the
community folder. The two integrations (TitanTakeoffPro and
TitanTakeoff) where causing confusion with clients, so have moved code
into one place and created an alias for backwards compatibility. Added
Takeoff Client python package to do the bulk of the work with the
requests, this is because this package is actively updated with new
versions of Takeoff. So this integration will be far more robust and
will not degrade as badly over time.
**Issue:**
Fixes bugs in the old Titan integrations and unified the code with added
unit test converge to avoid future problems.
**Dependencies:**
Added optional dependency takeoff-client, all imports still work without
dependency including the Titan Takeoff classes but just will fail on
initialisation if not pip installed takeoff-client
**Twitter**
@MeryemArik9
Thanks all :)
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Description: Add support for authorized identities in PebbloSafeLoader.
Now with this change, PebbloSafeLoader will extract
authorized_identities from metadata and send it to pebblo server
Dependencies: None
Documentation: None
Signed-off-by: Rahul Tripathi <rauhl.psit.ec@gmail.com>
Co-authored-by: Rahul Tripathi <rauhl.psit.ec@gmail.com>
From `langchain_community 0.0.30`, there's a bug that cannot send a
file-like object via `file` parameter instead of `file path` due to
casting the `file_path` to str type even if `file_path` is None.
which means that when I call the `partition_via_api()`, exactly one of
`filename` and `file` must be specified by the following error message.
however, from `langchain_community 0.0.30`, `file_path` is casted into
`str` type even `file_path` is None in `get_elements_from_api()` and got
an error at `exactly_one(filename=filename, file=file)`.
here's an error message
```
---> 51 exactly_one(filename=filename, file=file)
53 if metadata_filename and file_filename:
54 raise ValueError(
55 "Only one of metadata_filename and file_filename is specified. "
56 "metadata_filename is preferred. file_filename is marked for deprecation.",
57 )
File /opt/homebrew/lib/python3.11/site-packages/unstructured/partition/common.py:441, in exactly_one(**kwargs)
439 else:
440 message = f"{names[0]} must be specified."
--> 441 raise ValueError(message)
ValueError: Exactly one of filename and file must be specified.
```
So, I simply made a change that casting to str type when `file_path` is
not None.
I use `UnstructuredAPIFileLoader` like below.
```
from langchain_community.document_loaders.unstructured import UnstructuredAPIFileLoader
documents: list = UnstructuredAPIFileLoader(
file_path=None,
file=file, # file-like object, io.BytesIO type
mode='elements',
url='http://127.0.0.1:8000/general/v0/general',
content_type='application/pdf',
metadata_filename='asdf.pdf',
).load_and_split()
```
- [x] **PR title**: "community: improve kuzu cypher generation prompt"
- [x] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** Improves the Kùzu Cypher generation prompt to be more
robust to open source LLM outputs
- **Issue:** N/A
- **Dependencies:** N/A
- **Twitter handle:** @kuzudb
- [x] **Add tests and docs**: If you're adding a new integration, please
include
No new tests (non-breaking. change)
- [x] **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/
## Description:
The PR introduces 3 changes:
1. added `recursive` property to `O365BaseLoader`. (To keep the behavior
unchanged, by default is set to `False`). When `recursive=True`,
`_load_from_folder()` also recursively loads all nested folders.
2. added `folder_id` to SharePointLoader.(similar to (this
PR)[https://github.com/langchain-ai/langchain/pull/10780] ) This
provides an alternative to `folder_path` that doesn't seem to reliably
work.
3. when none of `document_ids`, `folder_id`, `folder_path` is provided,
the loader fetches documets from root folder. Combined with
`recursive=True` this provides an easy way of loading all compatible
documents from SharePoint.
The PR contains the same logic as [this stale
PR](https://github.com/langchain-ai/langchain/pull/10780) by
@WaleedAlfaris. I'd like to ask his blessing for moving forward with
this one.
## Issue:
- As described in https://github.com/langchain-ai/langchain/issues/19938
and https://github.com/langchain-ai/langchain/pull/10780 the sharepoint
loader often does not seem to work with folder_path.
- Recursive loading of subfolders is a missing functionality
## Dependecies: None
Twitter handle:
@martintriska1 @WRhetoric
This is my first PR here, please be gentle :-)
Please review @baskaryan
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
This PR updates OctoAIEndpoint LLM to subclass BaseOpenAI as OctoAI is
an OpenAI-compatible service. The documentation and tests have also been
updated.
**Description:** Adds ThirdAI NeuralDB retriever integration. NeuralDB
is a CPU-friendly and fine-tunable text retrieval engine. We previously
added a vector store integration but we think that it will be easier for
our customers if they can also find us under under
langchain-community/retrievers.
---------
Co-authored-by: kartikTAI <129414343+kartikTAI@users.noreply.github.com>
Co-authored-by: Kartik Sarangmath <kartik@thirdai.com>
**Description:** Make ChatDatabricks model supports stream
**Issue:** N/A
**Dependencies:** MLflow nightly build version (we will release next
MLflow version soon)
**Twitter handle:** N/A
Manually test:
(Before testing, please install `pip install
git+https://github.com/mlflow/mlflow.git`)
```python
# Test Databricks Foundation LLM model
from langchain.chat_models import ChatDatabricks
chat_model = ChatDatabricks(
endpoint="databricks-llama-2-70b-chat",
max_tokens=500
)
from langchain_core.messages import AIMessageChunk
for chunk in chat_model.stream("What is mlflow?"):
print(chunk.content, end="|")
```
- [x] **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.
- [x] **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.
---------
Signed-off-by: Weichen Xu <weichen.xu@databricks.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- Add conditional: bool property to json representation of the graphs
- Add option to generate mermaid graph stripped of styles (useful as a
text representation of graph)
…s arg too
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:**
This PR adds a callback handler for UpTrain. It performs evaluations in
the RAG pipeline to check the quality of retrieved documents, generated
queries and responses.
- **Dependencies:**
- The UpTrainCallbackHandler requires the uptrain package
---------
Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>
enviroment variable ANTHROPIC_API_URL will not work if anthropic_api_url
has default value
---------
Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>
**Description**: Support filter by OR and AND for deprecated PGVector
version
**Issue**: #20445
**Dependencies**: N/A
**Twitter** handle: @martinferenaz
Description: For simplicity, migrate the logic of excluding intermediate
nodes in the .get_graph() of langgraph package
(https://github.com/langchain-ai/langgraph/pull/310) at graph creation
time instead of graph rendering time.
Note: #20381 needs to be approved first
---------
Co-authored-by: Angel Igareta <angel.igareta@klarna.com>
Co-authored-by: Nuno Campos <nuno@langchain.dev>
Co-authored-by: Nuno Campos <nuno@boringbits.io>
Description of features on mermaid graph renderer:
- Fixing CDN to use official Mermaid JS CDN:
https://www.jsdelivr.com/package/npm/mermaid?tab=files
- Add device_scale_factor to allow increasing quality of resulting PNG.
- [x] **PR title**: community[patch]: Invoke callback prior to yielding
token fix for [DeepInfra]
- [x] **PR message**:
- **Description:** Invoke callback prior to yielding token in stream
method in [DeepInfra]
- **Issue:** https://github.com/langchain-ai/langchain/issues/16913
- **Dependencies:** None
- **Twitter handle:** @bolun_zhang
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
Description: This update refines the documentation for
`RunnablePassthrough` by removing an unnecessary import and correcting a
minor syntactical error in the example provided. This change enhances
the clarity and correctness of the documentation, ensuring that users
have a more accurate guide to follow.
Issue: N/A
Dependencies: None
This PR focuses solely on documentation improvements, specifically
targeting the `RunnablePassthrough` class within the `langchain_core`
module. By clarifying the example provided in the docstring, users are
offered a more straightforward and error-free guide to utilizing the
`RunnablePassthrough` class effectively.
As this is a documentation update, it does not include changes that
require new integrations, tests, or modifications to dependencies. It
adheres to the guidelines of minimal package interference and backward
compatibility, ensuring that the overall integrity and functionality of
the LangChain package remain unaffected.
Thank you for considering this documentation refinement for inclusion in
the LangChain project.
Fix of YandexGPT embeddings.
The current version uses a single `model_name` for queries and
documents, essentially making the `embed_documents` and `embed_query`
methods the same. Yandex has a different endpoint (`model_uri`) for
encoding documents, see
[this](https://yandex.cloud/en/docs/yandexgpt/concepts/embeddings). The
bug may impact retrievers built with `YandexGPTEmbeddings` (for instance
FAISS database as retriever) since they use both `embed_documents` and
`embed_query`.
A simple snippet to test the behaviour:
```python
from langchain_community.embeddings.yandex import YandexGPTEmbeddings
embeddings = YandexGPTEmbeddings()
q_emb = embeddings.embed_query('hello world')
doc_emb = embeddings.embed_documents(['hello world', 'hello world'])
q_emb == doc_emb[0]
```
The response is `True` with the current version and `False` with the
changes I made.
Twitter: @egor_krash
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:**
`_ListSQLDatabaseToolInput` raise error if model returns `{}`.
For example, gpt-4-turbo returns `{}` with SQL Agent initialized by
`create_sql_agent`.
So, I set default value `""` for `_ListSQLDatabaseToolInput` tool_input.
This is actually a gpt-4-turbo issue, not a LangChain issue, but I
thought it would be helpful to set a default value `""`.
This problem is discussed in detail in the following Issue.
**Issue:** https://github.com/langchain-ai/langchain/issues/20405
**Dependencies:** none
Sorry, I did not add or change the test code, as tests for this
components was not exist .
However, I have tested the following code based on the [SQL Agent
Document](https://python.langchain.com/docs/use_cases/sql/agents/), to
make sure it works.
```
from langchain_community.agent_toolkits.sql.base import create_sql_agent
from langchain_community.utilities.sql_database import SQLDatabase
from langchain_openai import ChatOpenAI
db = SQLDatabase.from_uri("sqlite:///Chinook.db")
llm = ChatOpenAI(model="gpt-4-turbo", temperature=0)
agent_executor = create_sql_agent(llm, db=db, agent_type="openai-tools", verbose=True)
result = agent_executor.invoke("List the total sales per country. Which country's customers spent the most?")
print(result["output"])
```
- **Description:** Complete the support for Lua code in
langchain.text_splitter module.
- **Dependencies:** No
- **Twitter handle:** @saberuster
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
```python
from langchain.agents import AgentExecutor, create_tool_calling_agent, tool
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_groq import ChatGroq
prompt = ChatPromptTemplate.from_messages(
[
("system", "You are a helpful assistant"),
("human", "{input}"),
MessagesPlaceholder("agent_scratchpad"),
]
)
model = ChatGroq(model_name="mixtral-8x7b-32768", temperature=0)
@tool
def magic_function(input: int) -> int:
"""Applies a magic function to an input."""
return input + 2
tools = [magic_function]
agent = create_tool_calling_agent(model, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
agent_executor.invoke({"input": "what is the value of magic_function(3)?"})
```
```
> Entering new AgentExecutor chain...
Invoking: `magic_function` with `{'input': 3}`
5The value of magic\_function(3) is 5.
> Finished chain.
{'input': 'what is the value of magic_function(3)?',
'output': 'The value of magic\\_function(3) is 5.'}
```
**Description:** Masking of the API key for AI21 models
**Issue:** Fixes#12165 for AI21
**Dependencies:** None
Note: This fix came in originally through #12418 but was possibly missed
in the refactor to the AI21 partner package
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Replaced all `from langchain.callbacks` into `from
langchain_core.callbacks` .
Changes in the `langchain` and `langchain_experimental`
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
- [x] **PR title**: community[patch]: Invoke callback prior to yielding
token fix for Llamafile
- [x] **PR message**:
- **Description:** Invoke callback prior to yielding token in stream
method in community llamafile.py
- **Issue:** https://github.com/langchain-ai/langchain/issues/16913
- **Dependencies:** None
- **Twitter handle:** @bolun_zhang
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
- [x] **PR title**: community[patch]: Invoke callback prior to yielding
token fix for HuggingFaceEndpoint
- [x] **PR message**:
- **Description:** Invoke callback prior to yielding token in stream
method in community HuggingFaceEndpoint
- **Issue:** https://github.com/langchain-ai/langchain/issues/16913
- **Dependencies:** None
- **Twitter handle:** @bolun_zhang
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Added the [FireCrawl](https://firecrawl.dev) document loader. Firecrawl
crawls and convert any website into LLM-ready data. It crawls all
accessible subpages and give you clean markdown for each.
- **Description:** Adds FireCrawl data loader
- **Dependencies:** firecrawl-py
- **Twitter handle:** @mendableai
ccing contributors: (@ericciarla @nickscamara)
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
LLMs might sometimes return invalid response for LLM graph transformer.
Instead of failing due to pydantic validation, we skip it and manually
check and optionally fix error where we can, so that more information
gets extracted
Mistral gives us one ID per response, no individual IDs for tool calls.
```python
from langchain.agents import AgentExecutor, create_tool_calling_agent, tool
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_mistralai import ChatMistralAI
prompt = ChatPromptTemplate.from_messages(
[
("system", "You are a helpful assistant"),
("human", "{input}"),
MessagesPlaceholder("agent_scratchpad"),
]
)
model = ChatMistralAI(model="mistral-large-latest", temperature=0)
@tool
def magic_function(input: int) -> int:
"""Applies a magic function to an input."""
return input + 2
tools = [magic_function]
agent = create_tool_calling_agent(model, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
agent_executor.invoke({"input": "what is the value of magic_function(3)?"})
```
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
**Description:** Adds chroma to the partners package. Tests & code
mirror those in the community package.
**Dependencies:** None
**Twitter handle:** @akiradev0x
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
This PR should make it easier for linters to do type checking and for IDEs to jump to definition of code.
See #20050 as a template for this PR.
- As a byproduct: Added 3 missed `test_imports`.
- Added missed `SolarChat` in to __init___.py Added it into test_import
ut.
- Added `# type: ignore` to fix linting. It is not clear, why linting
errors appear after ^ changes.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
```python
from langchain.agents import AgentExecutor, create_tool_calling_agent, tool
from langchain_anthropic import ChatAnthropic
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
prompt = ChatPromptTemplate.from_messages(
[
("system", "You are a helpful assistant"),
MessagesPlaceholder("chat_history", optional=True),
("human", "{input}"),
MessagesPlaceholder("agent_scratchpad"),
]
)
model = ChatAnthropic(model="claude-3-opus-20240229")
@tool
def magic_function(input: int) -> int:
"""Applies a magic function to an input."""
return input + 2
tools = [magic_function]
agent = create_tool_calling_agent(model, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
agent_executor.invoke({"input": "what is the value of magic_function(3)?"})
```
```
> Entering new AgentExecutor chain...
Invoking: `magic_function` with `{'input': 3}`
responded: [{'text': '<thinking>\nThe user has asked for the value of magic_function applied to the input 3. Looking at the available tools, magic_function is the relevant one to use here, as it takes an integer input and returns an integer output.\n\nThe magic_function has one required parameter:\n- input (integer)\n\nThe user has directly provided the value 3 for the input parameter. Since the required parameter is present, we can proceed with calling the function.\n</thinking>', 'type': 'text'}, {'id': 'toolu_01HsTheJPA5mcipuFDBbJ1CW', 'input': {'input': 3}, 'name': 'magic_function', 'type': 'tool_use'}]
5
Therefore, the value of magic_function(3) is 5.
> Finished chain.
{'input': 'what is the value of magic_function(3)?',
'output': 'Therefore, the value of magic_function(3) is 5.'}
```
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
core[minor], langchain[patch], openai[minor], anthropic[minor], fireworks[minor], groq[minor], mistralai[minor]
```python
class ToolCall(TypedDict):
name: str
args: Dict[str, Any]
id: Optional[str]
class InvalidToolCall(TypedDict):
name: Optional[str]
args: Optional[str]
id: Optional[str]
error: Optional[str]
class ToolCallChunk(TypedDict):
name: Optional[str]
args: Optional[str]
id: Optional[str]
index: Optional[int]
class AIMessage(BaseMessage):
...
tool_calls: List[ToolCall] = []
invalid_tool_calls: List[InvalidToolCall] = []
...
class AIMessageChunk(AIMessage, BaseMessageChunk):
...
tool_call_chunks: Optional[List[ToolCallChunk]] = None
...
```
Important considerations:
- Parsing logic occurs within different providers;
- ~Changing output type is a breaking change for anyone doing explicit
type checking;~
- ~Langsmith rendering will need to be updated:
https://github.com/langchain-ai/langchainplus/pull/3561~
- ~Langserve will need to be updated~
- Adding chunks:
- ~AIMessage + ToolCallsMessage = ToolCallsMessage if either has
non-null .tool_calls.~
- Tool call chunks are appended, merging when having equal values of
`index`.
- additional_kwargs accumulate the normal way.
- During streaming:
- ~Messages can change types (e.g., from AIMessageChunk to
AIToolCallsMessageChunk)~
- Output parsers parse additional_kwargs (during .invoke they read off
tool calls).
Packages outside of `partners/`:
- https://github.com/langchain-ai/langchain-cohere/pull/7
- https://github.com/langchain-ai/langchain-google/pull/123/files
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Description: When multithreading is set to True and using the
DirectoryLoader, there was a bug that caused the return type to be a
double nested list. This resulted in other places upstream not being
able to utilize the from_documents method as it was no longer a
`List[Documents]` it was a `List[List[Documents]]`. The change made was
to just loop through the `future.result()` and yield every item.
Issue: #20093
Dependencies: N/A
Twitter handle: N/A
This unit test fails likely validation by the openai client.
Newer openai library seems to be doing more validation so the existing
test fails since http_client needs to be of httpx instance
- **Description**: fixes BooleanOutputParser detecting sub-words ("NOW
this is likely (YES)" -> `True`, not `AmbiguousError`)
- **Issue(s)**: fixes#11408 (follow-up to #17810)
- **Dependencies**: None
- **GitHub handle**: @casperdcl
<!-- if unreviewd after a few days, @-mention one of baskaryan, efriis,
eyurtsev, hwchase17 -->
- [x] **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/
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
**Description:**
Use the `Stream` context managers in `ChatOpenAi` `stream` and `astream`
method.
Using the context manager returned by the OpenAI client makes it
possible to terminate the stream early since the response connection
will be closed when the context manager exists.
**Issue:** #5340
**Twitter handle:** @snopoke
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
- **Description:** Bug fix. Removed extra line in `GCSDirectoryLoader`
to allow catching Exceptions. Now also logs the file path if Exception
is raised for easier debugging.
- **Issue:** #20198 Bug since langchain-community==0.0.31
- **Dependencies:** No change
- **Twitter handle:** timothywong731
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- make Tencent Cloud VectorDB support metadata filtering.
- implement delete function for Tencent Cloud VectorDB.
- support both Langchain Embedding model and Tencent Cloud VDB embedding
model.
- Tencent Cloud VectorDB support filter search keyword, compatible with
langchain filtering syntax.
- add Tencent Cloud VectorDB TranslationVisitor, now work with self
query retriever.
- more documentations.
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Issue `langchain_community.cross_encoders` didn't have flattening
namespace code in the __init__.py file.
Changes:
- added code to flattening namespaces (used #20050 as a template)
- added ut for a change
- added missed `test_imports` for `chat_loaders` and
`chat_message_histories` modules
This PR make `request_timeout` and `max_retries` configurable for
ChatAnthropic.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
Issue:
When async_req is the default value True, pinecone client return the
multiprocessing AsyncResult object.
When async_req is set to False, pinecone client return the result
directly. `[{'upserted_count': 1}]` . Calling get() method will throw an
error in this case.
- [x] **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"
- [x] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** Langchain-Predibase integration was failing, because
it was not current with the Predibase SDK; in addition, Predibase
integration tests were instantiating the Langchain Community `Predibase`
class with one required argument (`model`) missing. This change updates
the Predibase SDK usage and fixes the integration tests.
- **Twitter handle:** `@alexsherstinsky`
- [x] **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.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
Last year Microsoft [changed the
name](https://learn.microsoft.com/en-us/azure/search/search-what-is-azure-search)
of Azure Cognitive Search to Azure AI Search. This PR updates the
Langchain Azure Retriever API and it's associated docs to reflect this
change. It may be confusing for users to see the name Cognitive here and
AI in the Microsoft documentation which is why this is needed. I've also
added a more detailed example to the Azure retriever doc page.
There are more places that need a similar update but I'm breaking it up
so the PRs are not too big 😄 Fixing my errors from the previous PR.
Twitter: @marlene_zw
Two new tests added to test backward compatibility in
`libs/community/tests/integration_tests/retrievers/test_azure_cognitive_search.py`
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
After this PR it will be possible to pass a cache instance directly to a
language model. This is useful to allow different language models to use
different caches if needed.
- **Issue:** close#19276
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
**Description:**
The `LocalFileStore` class can be used to create an on-disk
`CacheBackedEmbeddings` cache. However, the default `umask` settings
gives file/directory write permissions only to the original user. Once
the cache directory is created by the first user, other users cannot
write their own cache entries into the directory.
To make the cache usable by multiple users, this pull request updates
the `LocalFileStore` constructor to allow the permissions for newly
created directories and files to be specified. The specified permissions
override the default `umask` values.
For example, when configured as follows:
```python
file_store = LocalFileStore(temp_dir, chmod_dir=0o770, chmod_file=0o660)
```
then "user" and "group" (but not "other") have permissions to access the
store, which means:
* Anyone in our group could contribute embeddings to the cache.
* If we implement cache cleanup/eviction in the future, anyone in our
group could perform the cleanup.
The default values for the `chmod_dir` and `chmod_file` parameters is
`None`, which retains the original behavior of using the default `umask`
settings.
**Issue:**
Implements enhancement #18075.
**Testing:**
I updated the `LocalFileStore` unit tests to test the permissions.
---------
Signed-off-by: chrispy <chrispy@synopsys.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
- **Description:** Adds async variants of afrom_texts and
afrom_embeddings into `OpenSearchVectorSearch`, which allows for
`afrom_documents` to be called.
- **Issue:** I implemented this because my use case involves an async
scraper generating documents as and when they're ready to be ingested by
Embedding/OpenSearch
- **Dependencies:** None that I'm aware
Co-authored-by: Ben Mitchell <b.mitchell@reply.com>
This PR supports using Pydantic v2 objects to generate the schema for
the JSONOutputParser (#19441). This also adds a `json_schema` parameter
to allow users to pass any JSON schema to validate with, not just
pydantic.
core/langchain_core/_api[Patch]: mypy ignore fixes#17048
Related to #17048
Applied mypy fixes to below two files:
libs/core/langchain_core/_api/deprecation.py
libs/core/langchain_core/_api/beta_decorator.py
Summary of Fixes:
**Issue 1**
class _deprecated_property(type(obj)): # type: ignore
error: Unsupported dynamic base class "type" [misc]
Fix:
1. Added an __init__ method to _deprecated_property to initialize the
fget, fset, fdel, and __doc__ attributes.
2. In the __get__, __set__, and __delete__ methods, we now use the
self.fget, self.fset, and self.fdel attributes to call the original
methods after emitting the warning.
3. The finalize function now creates an instance of _deprecated_property
with the fget, fset, fdel, and doc attributes from the original obj
property.
**Issue 2**
def finalize( # type: ignore
wrapper: Callable[..., Any], new_doc: str
) -> T:
error: All conditional function variants must have identical
signatures
Fix: Ensured that both definitions of the finalize function have the
same signature
Twitter Handle -
https://x.com/gupteutkarsha?s=11&t=uwHe4C3PPpGRvoO5Qpm1aA
**Description:** Citations are the main addition in this PR. We now emit
them from the multihop agent! Additionally the agent is now more
flexible with observations (`Any` is now accepted), and the Cohere SDK
version is bumped to fix an issue with the most recent version of
pydantic v1 (1.10.15)
- **Description:** In order to use index and aindex in
libs/langchain/langchain/indexes/_api.py, I implemented delete method
and all async methods in opensearch_vector_search
- **Dependencies:** No changes
- **Description:** Improvement for #19599: fixing missing return of
graph.draw_mermaid_png and improve it to make the saving of the rendered
image optional
Co-authored-by: Angel Igareta <angel.igareta@klarna.com>
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.
Thank you for contributing to LangChain!
- [ ] **PR title**: "community: deprecating integrations moved to
langchain_google_community"
- [ ] **PR message**: deprecating integrations moved to
langchain_google_community
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
Removes required usage of `requests` from `langchain-core`, all of which
has been deprecated.
- removes Tracer V1 implementations
- removes old `try_load_from_hub` github-based hub implementations
Removal done in a way where imports will still succeed, and usage will
fail with a `RuntimeError`.
**Description**: Improves the stability of all Cohere partner package
integration tests. Fixes a bug with document parsing (both dicts and
Documents are handled).
**Description**: This PR simplifies an integration test within the
Cohere partner package:
* It no longer relies on exact model answers
* It no longer relies on a third party tool
cohere: update imports and installs to langchain_cohere
---------
Co-authored-by: Harry M <127103098+harry-cohere@users.noreply.github.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
**Description**: Adds an agent that uses Cohere with multiple hops and
multiple tools.
This PR is a continuation of
https://github.com/langchain-ai/langchain/pull/19650 - which was
previously approved. Conceptually nothing has changed, but this PR has
extra fixes, documentation and testing.
---------
Co-authored-by: BeatrixCohere <128378696+BeatrixCohere@users.noreply.github.com>
Co-authored-by: Erick Friis <erickfriis@gmail.com>
This PR completes work for PR #18798 to expose raw tool output in
on_tool_end.
Affected APIs:
* astream_log
* astream_events
* callbacks sent to langsmith via langsmith-sdk
* Any other code that relies on BaseTracer!
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- This ensures ids are stable across streamed chunks
- Multiple messages in batch call get separate ids
- Also fix ids being dropped when combining message chunks
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:** add `remove_comments` option (default: True): do not
extract html _comments_,
- **Issue:** None,
- **Dependencies:** None,
- **Tag maintainer:** @nfcampos ,
- **Twitter handle:** peter_v
I ran `make format`, `make lint` and `make test`.
Discussion: I my use case, I prefer to not have the comments in the
extracted text:
* e.g. from a Google tag that is added in the html as comment
* e.g. content that the authors have temporarily hidden to make it non
visible to the regular reader
Removing the comments makes the extracted text more alike the intended
text to be seen by the reader.
**Choice to make:** do we prefer to make the default for this
`remove_comments` option to be True or False?
I have changed it to True in a second commit, since that is how I would
prefer to use it by default. Have the
cleaned text (without technical Google tags etc.) and also closer to the
actually visible and intended content.
I am not sure what is best aligned with the conventions of langchain in
general ...
INITIAL VERSION (new version above):
~**Choice to make:** do we prefer to make the default for this
`ignore_comments` option to be True or False?
I have set it to False now to be backwards compatible. On the other
hand, I would use it mostly with True.
I am not sure what is best aligned with the conventions of langchain in
general ...~
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** adds integration with [Layerup
Security](https://uselayerup.com). Docs can be found
[here](https://docs.uselayerup.com). Integrates directly with our Python
SDK.
**Dependencies:**
[LayerupSecurity](https://pypi.org/project/LayerupSecurity/)
**Note**: all methods for our product require a paid API key, so I only
included 1 test which checks for an invalid API key response. I have
tested extensively locally.
**Twitter handle**: [@layerup_](https://twitter.com/layerup_)
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
As in #19346, this PR exposes `request_timeout` in `BaseCohere`, while
`max_retires` is no longer a parameter of the beneath client
(`cohere.Client`) and it is already configured in
`langchain_cohere.llms.Cohere`.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** the layout of html pages can be variant based on the
bootstrap framework or the styles of the pages. So we need to have a
splitter to transform the html tags to a proper layout and then split
the html content based on the provided list of tags to determine its
html sections. We are using BS4 library along with xslt structure to
split the html content using an section aware approach.
- **Dependencies:** No new dependencies
- **Twitter handle:** @m_setayesh
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.
-->
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
[Dria](https://dria.co/) is a hub of public RAG models for developers to
both contribute and utilize a shared embedding lake. This PR adds a
retriever that can retrieve documents from Dria.
Thank you for contributing to LangChain!
- [x] **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"
- [x] **PR message**:
- **Description:** Fix argument translation from OpenAPI spec to OpenAI
function call (and similar)
- **Issue:** OpenGPTs failures with calling Action Server based actions.
- **Dependencies:** None
- **Twitter handle:** mikkorpela
- [x] **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.~
- [x] **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: Update `ChatZhipuAI` to support the latest `glm-4` model.
Issue: N/A
Dependencies: httpx, httpx-sse, PyJWT
The previous `ChatZhipuAI` implementation requires the `zhipuai`
package, and cannot call the latest GLM model. This is because
- The old version `zhipuai==1.*` doesn't support the latest model.
- `zhipuai==2.*` requires `pydantic V2`, which is incompatible with
'langchain-community'.
This re-implementation invokes the GLM model by sending HTTP requests to
[open.bigmodel.cn](https://open.bigmodel.cn/dev/api) via the `httpx`
package, and uses the `httpx-sse` package to handle stream events.
---------
Co-authored-by: zR <2448370773@qq.com>
- **Description:** Add functionality to generate Mermaid syntax and
render flowcharts from graph data. This includes support for custom node
colors and edge curve styles, as well as the ability to export the
generated graphs to PNG images using either the Mermaid.INK API or
Pyppeteer for local rendering.
- **Dependencies:** Optional dependencies are `pyppeteer` if rendering
wants to be done using Pypeteer and Javascript code.
---------
Co-authored-by: Angel Igareta <angel.igareta@klarna.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
* Replace `source_documents` with `documents`
* Pass `documents` as a named arg vs keyword
* Make `parsed_docs` more robust
* Fix edge case of doc page_content being `None`
- **Updating Together.ai Endpoint**: "langchain_together: Updated
Deprecated endpoint for partner package"
- Description: The inference API of together is deprecates, do replaced
with completions and made corresponding changes.
- Twitter handle: @dev_yashmathur
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** Add attribution_token within
GoogleVertexAISearchRetriever so user can provide this information to
Google support team or product team during debug session.
Reference:
https://cloud.google.com/generative-ai-app-builder/docs/view-analytics#user-events
Attribution tokens. Attribution tokens are unique IDs generated by
Vertex AI Search and returned with each search request. Make sure to
include that attribution token as UserEvent.attributionToken with any
user events resulting from a search. This is needed to identify if a
search is served by the API. Only user events with a Google-generated
attribution token are used to compute metrics.
- **Issue:** No
- **Dependencies:** No
- **Twitter handle:** abehsu1992626
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:** Support reranking based on cross encoder models
available from HuggingFace.
- Added `CrossEncoder` schema
- Implemented `HuggingFaceCrossEncoder` and
`SagemakerEndpointCrossEncoder`
- Implemented `CrossEncoderReranker` that performs similar functionality
to `CohereRerank`
- Added `cross-encoder-reranker.ipynb` to demonstrate how to use it.
Please let me know if anything else needs to be done to make it visible
on the table-of-contents navigation bar on the left, or on the card list
on [retrievers documentation
page](https://python.langchain.com/docs/integrations/retrievers).
- **Issue:** N/A
- **Dependencies:** None other than the existing ones.
---------
Co-authored-by: Kenny Choe <kchoe@amazon.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Description: Video imagery to text (Closed Captioning)
This pull request introduces the VideoCaptioningChain, a tool for
automated video captioning. It processes audio and video to generate
subtitles and closed captions, merging them into a single SRT output.
Issue: https://github.com/langchain-ai/langchain/issues/11770
Dependencies: opencv-python, ffmpeg-python, assemblyai, transformers,
pillow, torch, openai
Tag maintainer:
@baskaryan
@hwchase17
Hello! We are a group of students from the University of Toronto
(@LunarECL, @TomSadan, @nicoledroi1, @A2113S) that want to make a
contribution to the LangChain community! We have ran make format, make
lint and make test locally before submitting the PR. To our knowledge,
our changes do not introduce any new errors.
Thank you for taking the time to review our PR!
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
### Description
This implementation adds functionality from the AlphaVantage API,
renowned for its comprehensive financial data. The class encapsulates
various methods, each dedicated to fetching specific types of financial
information from the API.
### Implemented Functions
- **`search_symbols`**:
- Searches the AlphaVantage API for financial symbols using the provided
keywords.
- **`_get_market_news_sentiment`**:
- Retrieves market news sentiment for a specified stock symbol from the
AlphaVantage API.
- **`_get_time_series_daily`**:
- Fetches daily time series data for a specific symbol from the
AlphaVantage API.
- **`_get_quote_endpoint`**:
- Obtains the latest price and volume information for a given symbol
from the AlphaVantage API.
- **`_get_time_series_weekly`**:
- Gathers weekly time series data for a particular symbol from the
AlphaVantage API.
- **`_get_top_gainers_losers`**:
- Provides details on top gainers, losers, and most actively traded
tickers in the US market from the AlphaVantage API.
### Issue:
- #11994
### Dependencies:
- 'requests' library for HTTP requests. (import requests)
- 'pytest' library for testing. (import pytest)
---------
Co-authored-by: Adam Badar <94140103+adam-badar@users.noreply.github.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- [x] **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"
- [x] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** Langchain-Predibase integration was failing, because
it was not current with the Predibase SDK; in addition, Predibase
integration tests were instantiating the Langchain Community `Predibase`
class with one required argument (`model`) missing. This change updates
the Predibase SDK usage and fixes the integration tests.
- **Twitter handle:** `@alexsherstinsky`
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Thank you for contributing to LangChain!
- [x] **PR title**: "community: added support for llmsherpa library"
- [x] **Add tests and docs**:
1. Integration test:
'docs/docs/integrations/document_loaders/test_llmsherpa.py'.
2. an example notebook:
`docs/docs/integrations/document_loaders/llmsherpa.ipynb`.
- [x] **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.
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
# Description
Implementing `_combine_llm_outputs` to `ChatMistralAI` to override the
default implementation in `BaseChatModel` returning `{}`. The
implementation is inspired by the one in `ChatOpenAI` from package
`langchain-openai`.
# Issue
None
# Dependencies
None
# Twitter handle
None
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** We'd like to support passing additional kwargs in
`with_structured_output`. I believe this is the accepted approach to
enable additional arguments on API calls.
- **Description:** Haskell language support added in text_splitter
module
- **Dependencies:** No
- **Twitter handle:** @nisargtr
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** PR adds support for limiting number of messages
preserved in a session history for DynamoDBChatMessageHistory
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
### Subject: Fix Type Misdeclaration for index_schema in redis/base.py
I noticed a type misdeclaration for the index_schema column in the
redis/base.py file.
When following the instructions outlined in [Redis Custom Metadata
Indexing](https://python.langchain.com/docs/integrations/vectorstores/redis)
to create our own index_schema, it leads to a Pylance type error. <br/>
**The error message indicates that Dict[str, list[Dict[str, str]]] is
incompatible with the type Optional[Union[Dict[str, str], str,
os.PathLike]].**
```
index_schema = {
"tag": [{"name": "credit_score"}],
"text": [{"name": "user"}, {"name": "job"}],
"numeric": [{"name": "age"}],
}
rds, keys = Redis.from_texts_return_keys(
texts,
embeddings,
metadatas=metadata,
redis_url="redis://localhost:6379",
index_name="users_modified",
index_schema=index_schema,
)
```
Therefore, I have created this pull request to rectify the type
declaration problem.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
## 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
Similar to https://github.com/langchain-ai/langchain/pull/15881
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
If `prompt` is passed into `create_sql_agent()`, then
`toolkit.get_context()` shouldn't be executed against the database
unless relevant prompt variables (`table_info` or `table_names`) are
present .
Description: I implemented a tool to use Hugging Face text-to-speech
inference API.
Issue: n/a
Dependencies: n/a
Twitter handle: No Twitter, but do have
[LinkedIn](https://www.linkedin.com/in/robby-horvath/) lol.
---------
Co-authored-by: Robby <h0rv@users.noreply.github.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Thank you for contributing to LangChain!
- [x] **PR title**: "community: Implement DirectoryLoader lazy_load
function"
- [x] **Description**: The `lazy_load` function of the `DirectoryLoader`
yields each document separately. If the given `loader_cls` of the
`DirectoryLoader` also implemented `lazy_load`, it will be used to yield
subdocuments of the file.
- [x] **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:
`libs/community/tests/unit_tests/document_loaders/test_directory_loader.py`
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory:
`docs/docs/integrations/document_loaders/directory.ipynb`
- [x] **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.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
**Description:**
When using the SQLDatabaseChain with Llama2-70b LLM and, SQLite
database. I was getting `Warning: You can only execute one statement at
a time.`.
```
from langchain.sql_database import SQLDatabase
from langchain_experimental.sql import SQLDatabaseChain
sql_database_path = '/dccstor/mmdataretrieval/mm_dataset/swimming_record/rag_data/swimmingdataset.db'
sql_db = get_database(sql_database_path)
db_chain = SQLDatabaseChain.from_llm(mistral, sql_db, verbose=True, callbacks = [callback_obj])
db_chain.invoke({
"query": "What is the best time of Lance Larson in men's 100 meter butterfly competition?"
})
```
Error:
```
Warning Traceback (most recent call last)
Cell In[31], line 3
1 import langchain
2 langchain.debug=False
----> 3 db_chain.invoke({
4 "query": "What is the best time of Lance Larson in men's 100 meter butterfly competition?"
5 })
File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/langchain/chains/base.py:162, in Chain.invoke(self, input, config, **kwargs)
160 except BaseException as e:
161 run_manager.on_chain_error(e)
--> 162 raise e
163 run_manager.on_chain_end(outputs)
164 final_outputs: Dict[str, Any] = self.prep_outputs(
165 inputs, outputs, return_only_outputs
166 )
File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/langchain/chains/base.py:156, in Chain.invoke(self, input, config, **kwargs)
149 run_manager = callback_manager.on_chain_start(
150 dumpd(self),
151 inputs,
152 name=run_name,
153 )
154 try:
155 outputs = (
--> 156 self._call(inputs, run_manager=run_manager)
157 if new_arg_supported
158 else self._call(inputs)
159 )
160 except BaseException as e:
161 run_manager.on_chain_error(e)
File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/langchain_experimental/sql/base.py:198, in SQLDatabaseChain._call(self, inputs, run_manager)
194 except Exception as exc:
195 # Append intermediate steps to exception, to aid in logging and later
196 # improvement of few shot prompt seeds
197 exc.intermediate_steps = intermediate_steps # type: ignore
--> 198 raise exc
File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/langchain_experimental/sql/base.py:143, in SQLDatabaseChain._call(self, inputs, run_manager)
139 intermediate_steps.append(
140 sql_cmd
141 ) # output: sql generation (no checker)
142 intermediate_steps.append({"sql_cmd": sql_cmd}) # input: sql exec
--> 143 result = self.database.run(sql_cmd)
144 intermediate_steps.append(str(result)) # output: sql exec
145 else:
File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/langchain_community/utilities/sql_database.py:436, in SQLDatabase.run(self, command, fetch, include_columns)
425 def run(
426 self,
427 command: str,
428 fetch: Literal["all", "one"] = "all",
429 include_columns: bool = False,
430 ) -> str:
431 """Execute a SQL command and return a string representing the results.
432
433 If the statement returns rows, a string of the results is returned.
434 If the statement returns no rows, an empty string is returned.
435 """
--> 436 result = self._execute(command, fetch)
438 res = [
439 {
440 column: truncate_word(value, length=self._max_string_length)
(...)
443 for r in result
444 ]
446 if not include_columns:
File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/langchain_community/utilities/sql_database.py:413, in SQLDatabase._execute(self, command, fetch)
410 elif self.dialect == "postgresql": # postgresql
411 connection.exec_driver_sql("SET search_path TO %s", (self._schema,))
--> 413 cursor = connection.execute(text(command))
414 if cursor.returns_rows:
415 if fetch == "all":
File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/sqlalchemy/engine/base.py:1416, in Connection.execute(self, statement, parameters, execution_options)
1414 raise exc.ObjectNotExecutableError(statement) from err
1415 else:
-> 1416 return meth(
1417 self,
1418 distilled_parameters,
1419 execution_options or NO_OPTIONS,
1420 )
File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/sqlalchemy/sql/elements.py:516, in ClauseElement._execute_on_connection(self, connection, distilled_params, execution_options)
514 if TYPE_CHECKING:
515 assert isinstance(self, Executable)
--> 516 return connection._execute_clauseelement(
517 self, distilled_params, execution_options
518 )
519 else:
520 raise exc.ObjectNotExecutableError(self)
File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/sqlalchemy/engine/base.py:1639, in Connection._execute_clauseelement(self, elem, distilled_parameters, execution_options)
1627 compiled_cache: Optional[CompiledCacheType] = execution_options.get(
1628 "compiled_cache", self.engine._compiled_cache
1629 )
1631 compiled_sql, extracted_params, cache_hit = elem._compile_w_cache(
1632 dialect=dialect,
1633 compiled_cache=compiled_cache,
(...)
1637 linting=self.dialect.compiler_linting | compiler.WARN_LINTING,
1638 )
-> 1639 ret = self._execute_context(
1640 dialect,
1641 dialect.execution_ctx_cls._init_compiled,
1642 compiled_sql,
1643 distilled_parameters,
1644 execution_options,
1645 compiled_sql,
1646 distilled_parameters,
1647 elem,
1648 extracted_params,
1649 cache_hit=cache_hit,
1650 )
1651 if has_events:
1652 self.dispatch.after_execute(
1653 self,
1654 elem,
(...)
1658 ret,
1659 )
File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/sqlalchemy/engine/base.py:1848, in Connection._execute_context(self, dialect, constructor, statement, parameters, execution_options, *args, **kw)
1843 return self._exec_insertmany_context(
1844 dialect,
1845 context,
1846 )
1847 else:
-> 1848 return self._exec_single_context(
1849 dialect, context, statement, parameters
1850 )
File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/sqlalchemy/engine/base.py:1988, in Connection._exec_single_context(self, dialect, context, statement, parameters)
1985 result = context._setup_result_proxy()
1987 except BaseException as e:
-> 1988 self._handle_dbapi_exception(
1989 e, str_statement, effective_parameters, cursor, context
1990 )
1992 return result
File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/sqlalchemy/engine/base.py:2346, in Connection._handle_dbapi_exception(self, e, statement, parameters, cursor, context, is_sub_exec)
2344 else:
2345 assert exc_info[1] is not None
-> 2346 raise exc_info[1].with_traceback(exc_info[2])
2347 finally:
2348 del self._reentrant_error
File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/sqlalchemy/engine/base.py:1969, in Connection._exec_single_context(self, dialect, context, statement, parameters)
1967 break
1968 if not evt_handled:
-> 1969 self.dialect.do_execute(
1970 cursor, str_statement, effective_parameters, context
1971 )
1973 if self._has_events or self.engine._has_events:
1974 self.dispatch.after_cursor_execute(
1975 self,
1976 cursor,
(...)
1980 context.executemany,
1981 )
File ~/.conda/envs/guardrails1/lib/python3.9/site-packages/sqlalchemy/engine/default.py:922, in DefaultDialect.do_execute(self, cursor, statement, parameters, context)
921 def do_execute(self, cursor, statement, parameters, context=None):
--> 922 cursor.execute(statement, parameters)
Warning: You can only execute one statement at a time.
```
**Issue:**
The Error occurs because when generating the SQLQuery, the llm_input
includes the stop character of "\nSQLResult:", so for this user query
the LLM generated response is **SELECT Time FROM men_butterfly_100m
WHERE Swimmer = 'Lance Larson';\nSQLResult:** it is required to remove
the SQLResult suffix on the llm response before executing it on the
database.
```
llm_inputs = {
"input": input_text,
"top_k": str(self.top_k),
"dialect": self.database.dialect,
"table_info": table_info,
"stop": ["\nSQLResult:"],
}
sql_cmd = self.llm_chain.predict(
callbacks=_run_manager.get_child(),
**llm_inputs,
).strip()
if SQL_RESULT in sql_cmd:
sql_cmd = sql_cmd.split(SQL_RESULT)[0].strip()
result = self.database.run(sql_cmd)
```
<!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is
whichever of langchain, community, core, experimental, etc. is being
modified.
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes if applicable,
- **Dependencies:** any dependencies required for this change,
- **Twitter handle:** we announce bigger features on Twitter. If your PR
gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` from the root
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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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If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Description: Fix xml parser to handle strings that only contain the root
tag
Issue: N/A
Dependencies: None
Twitter handle: N/A
A valid xml text can contain only the root level tag. Example: <body>
Some text here
</body>
The example above is a valid xml string. If parsed with the current
implementation the result is {"body": []}. This fix checks if the root
level text contains any non-whitespace character and if that's the case
it returns {root.tag: root.text}. The result is that the above text is
correctly parsed as {"body": "Some text here"}
@ale-delfino
Thank you for contributing to LangChain!
Checklist:
- [x] 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"
- [x] 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!
- [x] 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/
- [x] 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.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
When testing Nomic embeddings --
```
from langchain_community.embeddings import LlamaCppEmbeddings
embd_model_path = "/Users/rlm/Desktop/Code/llama.cpp/models/nomic-embd/nomic-embed-text-v1.Q4_K_S.gguf"
embd_lc = LlamaCppEmbeddings(model_path=embd_model_path)
embedding_lc = embd_lc.embed_query(query)
```
We were seeing this error for strings > a certain size --
```
File ~/miniforge3/envs/llama2/lib/python3.9/site-packages/llama_cpp/llama.py:827, in Llama.embed(self, input, normalize, truncate, return_count)
824 s_sizes = []
826 # add to batch
--> 827 self._batch.add_sequence(tokens, len(s_sizes), False)
828 t_batch += n_tokens
829 s_sizes.append(n_tokens)
File ~/miniforge3/envs/llama2/lib/python3.9/site-packages/llama_cpp/_internals.py:542, in _LlamaBatch.add_sequence(self, batch, seq_id, logits_all)
540 self.batch.token[j] = batch[i]
541 self.batch.pos[j] = i
--> 542 self.batch.seq_id[j][0] = seq_id
543 self.batch.n_seq_id[j] = 1
544 self.batch.logits[j] = logits_all
ValueError: NULL pointer access
```
The default `n_batch` of llama-cpp-python's Llama is `512` but we were
explicitly setting it to `8`.
These need to be set to equal for embedding models.
* The embedding.cpp example has an assertion to make sure these are
always equal.
* Apparently this is not being done properly in llama-cpp-python.
With `n_batch` set to 8, if more than 8 tokens are passed the batch runs
out of space and it crashes.
This also explains why the CPU compute buffer size was small:
raw client with default `n_batch=512`
```
llama_new_context_with_model: CPU input buffer size = 3.51 MiB
llama_new_context_with_model: CPU compute buffer size = 21.00 MiB
```
langchain with `n_batch=8`
```
llama_new_context_with_model: CPU input buffer size = 0.04 MiB
llama_new_context_with_model: CPU compute buffer size = 0.33 MiB
```
We can work around this by passing `n_batch=512`, but this will not be
obvious to some users:
```
embedding = LlamaCppEmbeddings(model_path=embd_model_path,
n_batch=512)
```
From discussion w/ @cebtenzzre. Related:
https://github.com/abetlen/llama-cpp-python/issues/1189
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** The base URL for OpenAI is retrieved from the
environment variable "OPENAI_BASE_URL", whereas for langchain it is
obtained from "OPENAI_API_BASE". By adding `base_url =
os.environ.get("OPENAI_API_BASE")`, the OpenAI proxy can execute
correctly.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Thank you for contributing to LangChain!
- **Description:** added unit tests for NotebookLoader. Linked PR:
https://github.com/langchain-ai/langchain/pull/17614
- **Issue:**
[#17614](https://github.com/langchain-ai/langchain/pull/17614)
- **Twitter handle:** @paulodoestech
- [x] 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/
- [x] 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.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
---------
Co-authored-by: lachiewalker <lachiewalker1@hotmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** Created a Langchain Tool for OpenAI DALLE Image
Generation.
**Issue:**
[#15901](https://github.com/langchain-ai/langchain/issues/15901)
**Dependencies:** n/a
**Twitter handle:** @paulodoestech
- [x] **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.
- [x] **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.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:**: adding checking codes for calling AI model get error
in chat_models/base.py and llms/base.py
**Issue**: Sometimes the AI Model calling will get error, we should
raise it.
Otherwise, the next code 'choices.extend(response["choices"])' will
throw a "TypeError: 'NoneType' object is not iterable" error to mask the
true error.
Because 'response["choices"]' is None.
**Dependencies**: None
---------
Co-authored-by: yangkx <yangkx@asiainfo-int.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
## PR message
**Description:** This PR adds a README file for the Together API in the
`libs/partners` folder of this repository. The README includes:
- A brief description of the package
- Installation instructions and class introductions
- Simple usage examples
**Issue:** #17545
This PR only contains document changes.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- **Description:**
1. Fix the BiliBiliLoader that can receive cookie parameters, it
requires 3 other parameters to run. The change is backward compatible.
2. Add test;
3. Add example in docs
- **Issue:** [#14213]
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
- [x] **PR title**: "community: Support streaming in Azure ML and few
naming changes"
- [x] **PR message**:
- **Description:** Added support for streaming for azureml_endpoint.
Also, renamed and AzureMLEndpointApiType.realtime to
AzureMLEndpointApiType.dedicated. Also, added new classes
CustomOpenAIChatContentFormatter and CustomOpenAIContentFormatter and
updated the classes LlamaChatContentFormatter and LlamaContentFormatter
to now show a deprecated warning message when instantiated.
---------
Co-authored-by: Sachin Paryani <saparan@microsoft.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** At times, BaseChatMemory._get_input_output may acquire
some extra keys such as 'intermediate_steps' (agent_executor with
return_intermediate_steps set to True) and 'messages'
(agent_executor.iter with memory). In these instances, _get_input_output
can raise an error due to the presence of multiple keys. The 'output'
field should be used as the default field in these cases.
**Issue:** #16791
- Description: Added missing `from_documents` method to `KNNRetriever`,
providing the ability to supply metadata to LangChain `Document`s, and
to give it parity to the other retrievers, which do have
`from_documents`.
- Issue: None
- Dependencies: None
- Twitter handle: None
Co-authored-by: Victor Adan <vadan@netroadshow.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Relates to #17048
Description : Applied fix to dynamodb and elasticsearch file.
Error was : `Cannot override writeable attribute with read-only
property`
Suggestion:
instead of adding
```
@messages.setter
def messages(self, messages: List[BaseMessage]) -> None:
raise NotImplementedError("Use add_messages instead")
```
we can change base class property
`messages: List[BaseMessage]`
to
```
@property
def messages(self) -> List[BaseMessage]:...
```
then we don't need to add `@messages.setter` in all child classes.
**Description:**
While not technically incorrect, the TypeVar used for the `@beta`
decorator prevented pyright (and thus most vscode users) from correctly
seeing the types of functions/classes decorated with `@beta`.
This is in part due to a small bug in pyright
(https://github.com/microsoft/pyright/issues/7448 ) - however, the
`Type` bound in the typevar `C = TypeVar("C", Type, Callable)` is not
doing anything - classes are `Callables` by default, so by my
understanding binding to `Type` does not actually provide any more
safety - the modified annotation still works correctly for both
functions, properties, and classes.
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** Update to the docstring for class RunnableSerializable,
method configurable_fields
**Issue:** [Add in code documentation to core Runnable methods
#18804](https://github.com/langchain-ai/langchain/issues/18804)
**Dependencies:** None
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
**Description:** Update to the docstring for class RunnableSerializable,
method configurable_alternatives
**Issue:** [Add in code documentation to core Runnable methods
#18804](https://github.com/langchain-ai/langchain/issues/18804)
**Dependencies:** None
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
In this small PR I added the `template_tool_response` arg to the
`create_json_chat` function, so that users can customize this prompt in
case of need.
Thanks for your reviews!
---------
Co-authored-by: taamedag <Davide.Menini@swisscom.com>
Add our solar chat models, available model choices:
* solar-1-mini-chat
* solar-1-mini-translate-enko
* solar-1-mini-translate-koen
More documents and pricing can be found at
https://console.upstage.ai/services/solar.
The references to our solar model can be found at
* https://arxiv.org/abs/2402.17032
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** Adds support for `with_structured_output` to Cohere,
which supports single function calling.
---------
Co-authored-by: BeatrixCohere <128378696+BeatrixCohere@users.noreply.github.com>
This PR allows to calculate token usage for prompts and completion
directly in the generation method of BedrockChat. The token usage
details are then returned together with the generations, so that other
downstream tasks can access them easily.
This allows to define a callback for tokens tracking and cost
calculation, similarly to what happens with OpenAI (see
[OpenAICallbackHandler](https://api.python.langchain.com/en/latest/_modules/langchain_community/callbacks/openai_info.html#OpenAICallbackHandler).
I plan on adding a BedrockCallbackHandler later.
Right now keeping track of tokens in the callback is already possible,
but it requires passing the llm, as done here:
https://how.wtf/how-to-count-amazon-bedrock-anthropic-tokens-with-langchain.html.
However, I find the approach of this PR cleaner.
Thanks for your reviews. FYI @baskaryan, @hwchase17
---------
Co-authored-by: taamedag <Davide.Menini@swisscom.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- [x] **PR title**: "community: fix baidu qianfan missing stop
parameter"
- [x] **PR message**:
- **Description: Baidu Qianfan lost the stop parameter when requesting
service due to extracting it from kwargs. This bug can cause the agent
to receive incorrect results
---------
Co-authored-by: ligang33 <ligang33@baidu.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Bug fixes in this PR:
* allows for other params such as "message" not just the input param to
the prompt for the cohere tools agent
* fixes to documents kwarg from messages
* fixes to tool_calls API call
---------
Co-authored-by: Harry M <127103098+harry-cohere@users.noreply.github.com>
- **Issue:** When passing an empty list to MergerRetriever it fails with
error: ValueError: max() arg is an empty sequence
- **Description:** We have a use case where we dynamically select
retrievers and use MergerRetriever for merging the output of the
retrievers. We faced this issue when the retriever_docs list is empty.
Adding a default 0 for cases when retriever_docs is an empty list to
avoid "ValueError: max() arg is an empty sequence". Also, changed to use
map() which is more than twice as fast compared to the current
implementation.
```
import timeit
# Sample retriever_docs with varying lengths of sublists
retriever_docs = [[i for i in range(j)] for j in range(1, 1000)]
# First code snippet
code1 = '''
max_docs = max(len(docs) for docs in retriever_docs)
'''
# Second code snippet
code2 = '''
max_docs = max(map(len, retriever_docs), default=0)
'''
# Benchmarking
time1 = timeit.timeit(stmt=code1, globals=globals(), number=10000)
time2 = timeit.timeit(stmt=code2, globals=globals(), number=10000)
# Output
print(f"Execution time for code snippet 1: {time1} seconds")
print(f"Execution time for code snippet 2: {time2} seconds")
```
- **Dependencies:** none
The previous version didn't had Voyage rerank in the init file
- [ ] **PR title**: langchain_voyageai reranker is not working
- [ ] **PR message**:
- **Description:** This fix let you run reranker from voyage
- **Issue:** Was not able to run reranker from voyage
@efriis
#### Description
Fixed the following error with `rerank` method from `CohereRerank`:
```
---> [79](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/jjmov99/legal-colombia/~/legal-colombia/.venv/lib/python3.11/site-packages/langchain/retrievers/document_compressors/cohere_rerank.py:79) results = self.client.rerank(
[80](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/jjmov99/legal-colombia/~/legal-colombia/.venv/lib/python3.11/site-packages/langchain/retrievers/document_compressors/cohere_rerank.py:80) query, docs, model, top_n=top_n, max_chunks_per_doc=max_chunks_per_doc
[81](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/jjmov99/legal-colombia/~/legal-colombia/.venv/lib/python3.11/site-packages/langchain/retrievers/document_compressors/cohere_rerank.py:81) )
[82](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/jjmov99/legal-colombia/~/legal-colombia/.venv/lib/python3.11/site-packages/langchain/retrievers/document_compressors/cohere_rerank.py:82) result_dicts = []
[83](https://vscode-remote+wsl-002bubuntu.vscode-resource.vscode-cdn.net/home/jjmov99/legal-colombia/~/legal-colombia/.venv/lib/python3.11/site-packages/langchain/retrievers/document_compressors/cohere_rerank.py:83) for res in results.results:
TypeError: BaseCohere.rerank() takes 1 positional argument but 4 positional arguments (and 2 keyword-only arguments) were given
```
This was easily fixed going from this:
```
def rerank(
self,
documents: Sequence[Union[str, Document, dict]],
query: str,
*,
model: Optional[str] = None,
top_n: Optional[int] = -1,
max_chunks_per_doc: Optional[int] = None,
) -> List[Dict[str, Any]]:
...
if len(documents) == 0: # to avoid empty api call
return []
docs = [
doc.page_content if isinstance(doc, Document) else doc for doc in documents
]
model = model or self.model
top_n = top_n if (top_n is None or top_n > 0) else self.top_n
results = self.client.rerank(
query, docs, model, top_n=top_n, max_chunks_per_doc=max_chunks_per_doc
)
result_dicts = []
for res in results:
result_dicts.append(
{"index": res.index, "relevance_score": res.relevance_score}
)
return result_dicts
```
to this:
```
def rerank(
self,
documents: Sequence[Union[str, Document, dict]],
query: str,
*,
model: Optional[str] = None,
top_n: Optional[int] = -1,
max_chunks_per_doc: Optional[int] = None,
) -> List[Dict[str, Any]]:
...
if len(documents) == 0: # to avoid empty api call
return []
docs = [
doc.page_content if isinstance(doc, Document) else doc for doc in documents
]
model = model or self.model
top_n = top_n if (top_n is None or top_n > 0) else self.top_n
results = self.client.rerank(
query=query, documents=docs, model=model, top_n=top_n, max_chunks_per_doc=max_chunks_per_doc <-------------
)
result_dicts = []
for res in results.results: <-------------
result_dicts.append(
{"index": res.index, "relevance_score": res.relevance_score}
)
return result_dicts
```
#### Unit & Integration tests
I added a unit test to check the behaviour of `rerank`. Also fixed the
original integration test which was failing.
#### Format & Linting
Everything worked properly with `make lint_diff`, `make format_diff` and
`make format`. However I noticed an error coming from other part of the
library when doing `make lint`:
```
(langchain-py3.9) ➜ langchain git:(master) make format
[ "." = "" ] || poetry run ruff format .
1636 files left unchanged
[ "." = "" ] || poetry run ruff --select I --fix .
(langchain-py3.9) ➜ langchain git:(master) make lint
./scripts/check_pydantic.sh .
./scripts/lint_imports.sh
poetry run ruff .
[ "." = "" ] || poetry run ruff format . --diff
1636 files already formatted
[ "." = "" ] || poetry run ruff --select I .
[ "." = "" ] || mkdir -p .mypy_cache && poetry run mypy . --cache-dir .mypy_cache
langchain/agents/openai_assistant/base.py:252: error: Argument "file_ids" to "create" of "Assistants" has incompatible type "Optional[Any]"; expected "Union[list[str], NotGiven]" [arg-type]
langchain/agents/openai_assistant/base.py:374: error: Argument "file_ids" to "create" of "AsyncAssistants" has incompatible type "Optional[Any]"; expected "Union[list[str], NotGiven]" [arg-type]
Found 2 errors in 1 file (checked 1634 source files)
make: *** [Makefile:65: lint] Error 1
```
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Due to changes in the OpenAI SDK, the previous method of setting the
OpenAI proxy in ChatOpenAI no longer works. This PR fixes this issue,
making the previous way of setting the OpenAI proxy in ChatOpenAI
effective again.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
This is a follow up to #18371. These are the changes:
- New **Azure AI Services** toolkit and tools to replace those of
**Azure Cognitive Services**.
- Updated documentation for Microsoft platform.
- The image analysis tool has been rewritten to use the new package
`azure-ai-vision-imageanalysis`, doing a proper replacement of
`azure-ai-vision`.
These changes:
- Update outdated naming from "Azure Cognitive Services" to "Azure AI
Services".
- Update documentation to use non-deprecated methods to create and use
agents.
- Removes need to depend on yanked python package (`azure-ai-vision`)
There is one new dependency that is needed as a replacement to
`azure-ai-vision`:
- `azure-ai-vision-imageanalysis`. This is optional and declared within
a function.
There is a new `azure_ai_services.ipynb` notebook showing usage; Changes
have been linted and formatted.
I am leaving the actions of adding deprecation notices and future
removal of Azure Cognitive Services up to the LangChain team, as I am
not sure what the current practice around this is.
---
If this PR makes it, my handle is @galo@mastodon.social
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: ccurme <chester.curme@gmail.com>
- **Description**: `bigdl-llm` library has been renamed to
[`ipex-llm`](https://github.com/intel-analytics/ipex-llm). This PR
migrates the `bigdl-llm` integration to `ipex-llm` .
- **Issue**: N/A. The original PR of `bigdl-llm` is
https://github.com/langchain-ai/langchain/pull/17953
- **Dependencies**: `ipex-llm` library
- **Contribution maintainer**: @shane-huang
Updated doc: docs/docs/integrations/llms/ipex_llm.ipynb
Updated test:
libs/community/tests/integration_tests/llms/test_ipex_llm.py
- **Description:** Add support for Intel Lab's [Visual Data Management
System (VDMS)](https://github.com/IntelLabs/vdms) as a vector store
- **Dependencies:** `vdms` library which requires protobuf = "4.24.2".
There is a conflict with dashvector in `langchain` package but conflict
is resolved in `community`.
- **Contribution maintainer:** [@cwlacewe](https://github.com/cwlacewe)
- **Added tests:**
libs/community/tests/integration_tests/vectorstores/test_vdms.py
- **Added docs:** docs/docs/integrations/vectorstores/vdms.ipynb
- **Added cookbook:** cookbook/multi_modal_RAG_vdms.ipynb
---------
Co-authored-by: Eugene Yurtsev <eugene@langchain.dev>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
If you use an embedding dist function in an eval loop, you get warned
every time. Would prefer to just check once and forget about it.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- .stream() and .astream() call on_llm_new_token, removing the need for
subclasses to do so. Backwards compatible because now we don't pass
run_manager into ._stream and ._astream
- .generate() and .agenerate() now handle `stream: bool` kwarg for
_generate and _agenerate. Subclasses handle this arg by delegating to
._stream(), now one less thing they need to do. Backwards compat because
this is an optional arg that we now never pass to the subclasses
- .generate() and .agenerate() now inspect callback handlers to decide
on a default value for stream:bool if not passed in. This auto enables
streaming when using astream_events and astream_log
- as a result of these three changes any usage of .astream_events and
.astream_log should now yield chat model stream events
- In future PRs we can update all subclasses to reflect these two things
now handled by base class, but in meantime all will continue to work
* **Description**: add `None` type for `file_path` along with `str` and
`List[str]` types.
* `file_path`/`filename` arguments in `get_elements_from_api()` and
`partition()` can be `None`, however, there's no `None` type hint for
`file_path` in `UnstructuredAPIFileLoader` and `UnstructuredFileLoader`
currently.
* calling the function with `file_path=None` is no problem, but my IDE
annoys me lol.
* **Issue**: N/A
* **Dependencies**: N/A
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
- **Description:** Updates Meilisearch vectorstore for compatibility
with v1.6 and above. Adds embedders settings and embedder_name which are
now required.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:**
This PR adds a slightly more helpful message to a Tool Exception
```
# current state
langchain_core.tools.ToolException: Too many arguments to single-input tool
# proposed state
langchain_core.tools.ToolException: Too many arguments to single-input tool. Consider using a StructuredTool instead.
```
**Issue:** Somewhat discussed here 👉#6197
**Dependencies:** None
**Twitter handle:** N/A
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
As mentioned in #18322, the current PydanticOutputParser won't work for
anyone trying to parse to pydantic v2 models. This PR adds a separate
`PydanticV2OutputParser`, as well as a `langchain_core.pydantic_v2`
namespace that will fail on import to any projects using pydantic<2.
Happy to update the docs for output parsers if this is something we're
interesting in adding.
On a separate note, I also updated `check_pydantic.sh` to detect
pydantic imports with leading whitespace and excluded the internal
namespaces. That change can be separated into its own PR if needed.
---------
Co-authored-by: Jan Nissen <jan23@gmail.com>
Added example to the docstring of the "bind" method of Runnable. This
makes it easier to understand the purpose of the method when reviewing
in code editors. E.g. VS Code below.
<img width="833" alt="Screenshot 2024-03-27 at 16 24 18"
src="https://github.com/langchain-ai/langchain/assets/45722942/ad022d4e-7bc0-4f4b-aa7a-838f1816cc52">
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
**Description:**
PebbloSafeLoader: Add support for non-file-based Document Loaders
This pull request enhances PebbloSafeLoader by introducing support for
several non-file-based Document Loaders. With this update,
PebbloSafeLoader now seamlessly integrates with the following loaders:
- GoogleDriveLoader
- SlackDirectoryLoader
- Unstructured EmailLoader
**Issue:** NA
**Dependencies:** - None
**Twitter handle:** @Raj__725
---------
Co-authored-by: Rahul Tripathi <rauhl.psit.ec@gmail.com>
Patch potential XML vulnerability CVE-2024-1455
This patches a potential XML vulnerability in the XMLOutputParser in
langchain-core. The vulnerability in some situations could lead to a
denial of service attack.
At risk are users that:
1) Running older distributions of python that have older version of
libexpat
2) Are using XMLOutputParser with an agent
3) Accept inputs from untrusted sources with this agent (e.g., endpoint
on the web that allows an untrusted user to interact wiith the parser)
Introduction
[Intel® Extension for
Transformers](https://github.com/intel/intel-extension-for-transformers)
is an innovative toolkit designed to accelerate GenAI/LLM everywhere
with the optimal performance of Transformer-based models on various
Intel platforms
Description
adding ITREX runtime embeddings using intel-extension-for-transformers.
added mdx documentation and example notebooks
added embedding import testing.
---------
Signed-off-by: yuwenzho <yuwen.zhou@intel.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- [x] **PR title**: "experimental: Enhance LLMGraphTransformer with
async processing and improved readability"
- [x] **PR message**:
- **Description:** This pull request refactors the `process_response`
and `convert_to_graph_documents` methods in the LLMGraphTransformer
class to improve code readability and adds async versions of these
methods for concurrent processing.
The main changes include:
- Simplifying list comprehensions and conditional logic in the
process_response method for better readability.
- Adding async versions aprocess_response and
aconvert_to_graph_documents to enable concurrent processing of
documents.
These enhancements aim to improve the overall efficiency and
maintainability of the `LLMGraphTransformer` class.
- **Issue:** N/A
- **Dependencies:** No additional dependencies required.
- **Twitter handle:** @jjovalle99
- [x] **Add tests and docs**: N/A (This PR does not introduce a new
integration)
- [x] **Lint and test**: Ran make format, make lint, and make test from
the root of the modified package(s). All tests pass successfully.
Additional notes:
- The changes made in this PR are backwards compatible and do not
introduce any breaking changes.
- The PR touches only the `LLMGraphTransformer` class within the
experimental package.
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
- **Description:** Update Azure Document Intelligence implementation by
Microsoft team and RAG cookbook with Azure AI Search
---------
Co-authored-by: Lu Zhang (AI) <luzhan@microsoft.com>
Co-authored-by: Yateng Hong <yatengh@microsoft.com>
Co-authored-by: teethache <hongyateng2006@126.com>
Co-authored-by: Lu Zhang <44625949+luzhang06@users.noreply.github.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
- **Description:** Implemented try-except block for
`GCSDirectoryLoader`. Reason: Users processing large number of
unstructured files in a folder may experience many different errors. A
try-exception block is added to capture these errors. A new argument
`use_try_except=True` is added to enable *silent failure* so that error
caused by processing one file does not break the whole function.
- **Issue:** N/A
- **Dependencies:** no new dependencies
- **Twitter handle:** timothywong731
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Eugene Yurtsev
Harrison Chase
East Agile
William FH
aditya thomas
Noah
Liu Xiaodong
Ismail Hossain Polas
Tomaz Bratanic
tianzedavid
junkeon
Bagatur
MacanPN
Erick Friis
Charlie Marsh
Jamsheed Mistri
fubuki8087
Jakub Pawłowski
Christophe Bornet
Kuro Denjiro
Alexander Dicke
davidkgp
高远
Rahul Triptahi
Leonid Ganeline
Rodrigo Nogueira
Cahid Arda Öz
chyroc
Massimiliano Pronesti
Andrei Panferov
Patrick McFadin
Igor Brai
hmn falahi
Pengcheng Liu
Karim Lalani
WilliamEspegren
ccurme
YH
Chip Davis
Guilherme Zanotelli
Jorge Piedrahita Ortiz
Amine Djeghri
Naveen Tatikonda
Mayank Solanki
Leonid Kuligin
Sean
Lei Zhang
Matt
Dristy Srivastava
am-kinetica
Michael Schock
Anish Chakraborty
Shengsheng Huang
Jingpan Xiong
davidefantiniIntel
Andres Algaba
Joan Fontanals
Jason_Chen
YISH
Mish Ushakov
Ivaylo Bratoev
GustavoSept
Raghav Dixit
Nikita Pokidyshev
Alex Lee
back2nix
volodymyr-memsql
Martin Kolb
Alex Sherstinsky
Nuno Campos
JeffKatzy
Nestor Qin
Aliaksandr Kuzmik
Oleksandr Yaremchuk
Mark Needham
Matheus Henrique Raymundo
shumway743
Dmitry Tyumentsev
Mateusz Szewczyk
Souls-R
Sivaudha
Charlie Holtz
Congyu
Lance Martin
hulitaitai
Guangdong Liu
balloonio
Ethan Yang
zR
Rohit Gupta
Marco Perini
Tomer Cagan
kaijietti
Sevin F. Varoglu
Eun Hye Kim
pjb157
sdan
Hyeongchan Kim
Prashanth Rao
Benito Geordie
WeichenXu
Dhruv Chawla
Ravindu Somawansa
Dawson Bauer
Fayfox
Martín Gotelli Ferenaz
Ángel Igareta
Juan Carlos José Camacho
Alexander Smirnov
Egor Krasheninnikov
Yuki Oshima
saberuster
Corey Zumar
Nicolas
P. Taylor Goetz
Isak Nyberg
killind-dev
Yuki Watanabe
Casper da Costa-Luis
seray
Simon Kelly
David Lee
Timothy
jeff kit
Piyush Jain
Shuqian
Prince Canuma
Harry Jiang
david02871
Marlene
Maxime Perrin
Chris Papademetrious
Ben Mitchell
Jan Nissen
Utkarsha Gupte
harry-cohere
Jacob Lee
Graden Rea
happy-go-lucky
Cheng, Penghui
billytrend-cohere
Max Jakob
Mohammad Mohtashim
Peter Vandenabeele
Jamsheed Mistri
Mahdi Setayesh
Anıl Berk Altuner
Mikko Korpela
Chenhui Zhang
Giannis
Yash Mathur
hsuyuming
Kenneth Choe
LunarECL
Kamal Zhang
DrKroll
anshaneel
ethynic
Ahmed Moubtahij
M.Abdulrahman Alnaseer
Pierre Véron
BeatrixCohere
Nisarg Trivedi
Hrvoje Milković
Luka Krapic
Dt22
morgana
shahrin014
Jan Chorowski
Arturs Konfino
高璟琦
Robby
DasDingoCodes
Jialei
Kirushikesh DB
T Cramer
Marcus Virginia
ale-delfino
kYLe
Zijian Han
Paulo Nascimento
Kaixin Yang
Vincent Chen
Jiaming
Sachin Paryani
xiaohuanshu
Victor Adan
Smit Parmar
Luca Dorigo
HuangZiy
wulixuan
Davide Menini
Chaunte W. Lacewell
ligang-super
Nilanjan De
Jaid
Juan Jose Miguel Ovalle Villamil
Christian Galo
yongheng.liu
CaroFG
Kahlil Wehmeyer
Evgenii Zheltonozhskii
Taqi Jaffri
jhicks2306
Rajendra Kadam
yuwenzho
Fabrizio Ruocco
Timothy
fzowl