Deepseek model does not return reasoning when hosted on openrouter
(Issue [30067](https://github.com/langchain-ai/langchain/issues/30067))
the following code did not return reasoning:
```python
llm = ChatDeepSeek( model = 'deepseek/deepseek-r1:nitro', api_base="https://openrouter.ai/api/v1", api_key=os.getenv("OPENROUTER_API_KEY"))
messages = [
{"role": "system", "content": "You are an assistant."},
{"role": "user", "content": "9.11 and 9.8, which is greater? Explain the reasoning behind this decision."}
]
response = llm.invoke(messages, extra_body={"include_reasoning": True})
print(response.content)
print(f"REASONING: {response.additional_kwargs.get('reasoning_content', '')}")
print(response)
```
The fix is to extract reasoning from
response.choices[0].message["model_extra"] and from
choices[0].delta["reasoning"]. and place in response additional_kwargs.
Change is really just the addition of a couple one-sentence if
statements.
---------
Co-authored-by: andrasfe <andrasf94@gmail.com>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
# Description
This PR adds reasoning model support for `langchain-ollama` by
extracting reasoning token blocks, like those used in deepseek. It was
inspired by
[ollama-deep-researcher](https://github.com/langchain-ai/ollama-deep-researcher),
specifically the parsing of [thinking
blocks](6d1aaf2139/src/assistant/graph.py (L91)):
```python
# TODO: This is a hack to remove the <think> tags w/ Deepseek models
# It appears very challenging to prompt them out of the responses
while "<think>" in running_summary and "</think>" in running_summary:
start = running_summary.find("<think>")
end = running_summary.find("</think>") + len("</think>")
running_summary = running_summary[:start] + running_summary[end:]
```
This notes that it is very hard to remove the reasoning block from
prompting, but we actually want the model to reason in order to increase
model performance. This implementation extracts the thinking block, so
the client can still expect a proper message to be returned by
`ChatOllama` (and use the reasoning content separately when desired).
This implementation takes the same approach as
[ChatDeepseek](5d581ba22c/libs/partners/deepseek/langchain_deepseek/chat_models.py (L215)),
which adds the reasoning content to
chunk.additional_kwargs.reasoning_content;
```python
if hasattr(response.choices[0].message, "reasoning_content"): # type: ignore
rtn.generations[0].message.additional_kwargs["reasoning_content"] = (
response.choices[0].message.reasoning_content # type: ignore
)
```
This should probably be handled upstream in ollama + ollama-python, but
this seems like a reasonably effective solution. This is a standalone
example of what is happening;
```python
async def deepseek_message_astream(
llm: BaseChatModel,
messages: list[BaseMessage],
config: RunnableConfig | None = None,
*,
model_target: str = "deepseek-r1",
**kwargs: Any,
) -> AsyncIterator[BaseMessageChunk]:
"""Stream responses from Deepseek models, filtering out <think> tags.
Args:
llm: The language model to stream from
messages: The messages to send to the model
Yields:
Filtered chunks from the model response
"""
# check if the model is deepseek based
if (llm.name and model_target not in llm.name) or (hasattr(llm, "model") and model_target not in llm.model):
async for chunk in llm.astream(messages, config=config, **kwargs):
yield chunk
return
# Yield with a buffer, upon completing the <think></think> tags, move them to the reasoning content and start over
buffer = ""
async for chunk in llm.astream(messages, config=config, **kwargs):
# start or append
if not buffer:
buffer = chunk.content
else:
buffer += chunk.content if hasattr(chunk, "content") else chunk
# Process buffer to remove <think> tags
if "<think>" in buffer or "</think>" in buffer:
if hasattr(chunk, "tool_calls") and chunk.tool_calls:
raise NotImplementedError("tool calls during reasoning should be removed?")
if "<think>" in chunk.content or "</think>" in chunk.content:
continue
chunk.additional_kwargs["reasoning_content"] = chunk.content
chunk.content = ""
# upon block completion, reset the buffer
if "<think>" in buffer and "</think>" in buffer:
buffer = ""
yield chunk
```
# Issue
Integrating reasoning models (e.g. deepseek-r1) into existing LangChain
based workflows is hard due to the thinking blocks that are included in
the message contents. To avoid this, we could match the `ChatOllama`
integration with `ChatDeepseek` to return the reasoning content inside
`message.additional_arguments.reasoning_content` instead.
# Dependenices
None
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- Test if models support forcing tool calls via `tool_choice`. If they
do, they should support
- `"any"` to specify any tool
- the tool name as a string to force calling a particular tool
- Add `tool_choice` to signature of `BaseChatModel.bind_tools` in core
- Deprecate `tool_choice_value` in standard tests in favor of a boolean
`has_tool_choice`
Will follow up with PRs in external repos (tested in AWS and Google
already).
**Description**
This contribution adds a retriever for the Zotero API.
[Zotero](https://www.zotero.org/) is an open source reference management
for bibliographic data and related research materials. A retriever will
allow langchain applications to retrieve relevant documents from
personal or shared group libraries, which I believe will be helpful for
numerous applications, such as RAG systems, personal research
assistants, etc. Tests and docs were added.
The documentation provided assumes the retriever will be part of the
langchain-community package, as this seemed customary. Please let me
know if this is not the preferred way to do it. I also uploaded the
implementation to PyPI.
**Dependencies**
The retriever requires the `pyzotero` package for API access. This
dependency is stated in the docs, and the retriever will return an error
if the package is not found. However, this dependency is not added to
the langchain package itself.
**Twitter handle**
I'm no longer using Twitter, but I'd appreciate a shoutout on
[Bluesky](https://bsky.app/profile/koenigt.bsky.social) or
[LinkedIn](https://www.linkedin.com/in/dr-tim-k%C3%B6nig-534aa2324/)!
Let me know if there are any issues, I'll gladly try and sort them out!
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
This pull request includes a change to the following
- docs/docs/integrations/tools/tavily_search.ipynb
- docs/docs/integrations/tools/tavily_extract.ipynb
- added docs/docs/integrations/providers/tavily.mdx
---------
Co-authored-by: pulvedu <dustin@tavily.com>
**Description:**
Implements an additional `browser_session` parameter on
PlaywrightURLLoader which can be used to initialize the browser context
by providing a stored playwright context.
**Description:**
This PR fixes a minor typo in the comments within
`libs/partners/openai/langchain_openai/chat_models/base.py`. The word
"ben" has been corrected to "be" for clarity and professionalism.
**Issue:**
N/A
**Dependencies:**
None
**Description:**
Since `ChatLiteLLM` is forwarding most parameters to
`litellm.completion(...)`, there is no reason to set other default
values than the ones defined by `litellm`.
In the case of parameter 'n', it also provokes an issue when trying to
call a serverless endpoint on Azure, as it is considered an extra
parameter. So we need to keep it optional.
We can debate about backward compatibility of this change: in my
opinion, there should not be big issues since from my experience,
calling `litellm.completion()` without these parameters works fine.
**Issue:**
- #29679
**Dependencies:** None
- **Description:** Adding keep_newlines parameter to process_pages
method with page_ids on Confluence document loader
- **Issue:** N/A (This is an enhancement rather than a bug fix)
- **Dependencies:** N/A
- **Twitter handle:** N/A
# Description
Adds documentation on LangChain website for a Dell specific document
loader for on-prem storage devices. Additional details on what the
document loader is described in the PR as well as on our github repo:
[https://github.com/dell/powerscale-rag-connector](https://github.com/dell/powerscale-rag-connector)
This PR also creates a category on the document loader webpage as no
existing category exists for on-prem. This follows the existing pattern
already established as the website has a category for cloud providers.
# Issue:
New release, no issue.
# Dependencies:
None
# Twitter handle:
DellTech
---------
Signed-off-by: Adam Brenner <adam@aeb.io>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
From function calling to Ollama's [dedicated structured output
feature](https://ollama.com/blog/structured-outputs).
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- **Description:** I was testing out `init_chat` and saw that chat
models can now be inferred. Azure OpenAI is currently only supported but
we would like to add support for Azure AI which is a different package.
This PR edits the `base.py` file to add the chat implementation.
- I don't think this adds any additional dependencies
- Will add a test and lint, but starting an initial draft PR.
cc @santiagxf
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
The default model for `ChatGroq`, `"mixtral-8x7b-32768"`, is being
retired on March 20, 2025. Here we remove the default, such that model
names must be explicitly specified (being explicit is a good practice
here, and avoids the need for breaking changes down the line). This
change will be released in a minor version bump to 0.3.
This follows https://github.com/langchain-ai/langchain/pull/30161
(released in version 0.2.5), where we began generating warnings to this
effect.
OpenAIWhisperParser, OpenAIWhisperParserLocal, YandexSTTParser do not
handle in-memory audio data (loaded via Blob.from_data) correctly. They
require Blob.path to be set and AudioSegment is always read from the
file system. In-memory data is handled correctly only for
FasterWhisperParser so far. I changed OpenAIWhisperParser,
OpenAIWhisperParserLocal, YandexSTTParser accordingly to match
FasterWhisperParser.
Thanks for reviewing the PR!
Co-authored-by: qonnop <qonnop@users.noreply.github.com>
**description:** the ChatModel[Integration]Tests classes are powerful
and helpful, this change allows sub-classes to add additional tests.
for instance,
```
class TestChatMyServiceIntegration(ChatModelIntegrationTests):
...
def test_myservice(self, model: BaseChatModel) -> None:
...
```
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
## Description
This pull request introduces a new text splitter,
`JSFrameworkTextSplitter`, to the Langchain library. The
`JSFrameworkTextSplitter` extends the `RecursiveCharacterTextSplitter`
to handle JavaScript framework code effectively, including React (JSX),
Vue, and Svelte. It identifies and utilizes framework-specific component
tags and syntax elements as splitting points, alongside standard
JavaScript syntax. This ensures that code is divided at natural
boundaries, enhancing the parsing and processing of JavaScript and
framework-specific code.
### Key Features
- Supports React (JSX), Vue, and Svelte frameworks.
- Identifies and uses framework-specific tags and syntax elements as
natural splitting points.
- Extends the existing `RecursiveCharacterTextSplitter` for seamless
integration.
## Issue
No specific issue addressed.
## Dependencies
No additional dependencies required.
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
**Description:**
Added an 'extract' mode to FireCrawlLoader that enables structured data
extraction from web pages. This feature allows users to Extract
structured data from a single URLs, or entire websites using Large
Language Models (LLMs).
You can show more params and usage on [firecrawl
docs](https://docs.firecrawl.dev/features/extract-beta).
You can extract from only one url now.(it depends on firecrawl's extract
method)
**Dependencies:**
No new dependencies required. Uses existing FireCrawl API capabilities.
---------
Co-authored-by: chbae <chbae@gcsc.co.kr>
Co-authored-by: ccurme <chester.curme@gmail.com>
FasterWhisperParser fails on a machine without an NVIDIA GPU: "Requested
float16 compute type, but the target device or backend do not support
efficient float16 computation." This problem arises because the
WhisperModel is called with compute_type="float16", which works only for
NVIDIA GPU.
According to the [CTranslate2
docs](https://opennmt.net/CTranslate2/quantization.html#bit-floating-points-float16)
float16 is supported only on NVIDIA GPUs. Removing the compute_type
parameter solves the problem for CPUs. According to the [CTranslate2
docs](https://opennmt.net/CTranslate2/quantization.html#quantize-on-model-loading)
setting compute_type to "default" (standard when omitting the parameter)
uses the original compute type of the model or performs implicit
conversion for the specific computation device (GPU or CPU). I suggest
to remove compute_type="float16".
@hulitaitai you are the original author of the FasterWhisperParser - is
there a reason for setting the parameter to float16?
Thanks for reviewing the PR!
Co-authored-by: qonnop <qonnop@users.noreply.github.com>
Thank you for contributing to LangChain!
- [x] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core, etc. is
being modified. Use "docs: ..." for purely docs changes, "infra: ..."
for CI changes.
- Example: "community: add foobar LLM"
- **Description:** Do not load non-public dimensions and measures
(public: false) with Cube semantic loader
- **Issue:** Currently, non-public dimensions and measures are loaded by
the Cube document loader which leads to downstream applications using
these which is not allowed by Cube.
- [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, eyurtsev, ccurme, vbarda, hwchase17.
- Support features from recent update:
https://www.anthropic.com/news/token-saving-updates (mostly adding
support for built-in tools in `bind_tools`
- Add documentation around prompt caching, token-efficient tool use, and
built-in tools.
Thank you for contributing to LangChain!
- [x] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core, etc. is
being modified. Use "docs: ..." for purely docs changes, "infra: ..."
for CI changes.
- Example: "community: add foobar LLM"
- **Description:** Fix bad log message on line#56 and replace f-string
logs with format specifiers
- **Issue:** Log messages such as this one
`INFO:langchain_community.document_loaders.cube_semantic:Loading
dimension values for: {dimension_name}...`
- [ ] **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, eyurtsev, ccurme, vbarda, hwchase17.
PR Title:
community: Fix Pass API_KEY as argument
PR Message:
Description:
This PR fixes validation error "Value error, Did not find
tavily_api_key, please add an environment variable `TAVILY_API_KEY`
which contains it, or pass `tavily_api_key` as a named parameter."
Dependencies:
No new dependencies introduced.
---------
Co-authored-by: pulvedu <dustin@tavily.com>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
## Description
The models in DashScope support multiple SystemMessage. Here is the
[Doc](https://bailian.console.aliyun.com/model_experience_center/text#/model-market/detail/qwen-long?tabKey=sdk),
and the example code on the document page:
```python
import os
from openai import OpenAI
client = OpenAI(
api_key=os.getenv("DASHSCOPE_API_KEY"), # 如果您没有配置环境变量，请在此处替换您的API-KEY
base_url="https://dashscope.aliyuncs.com/compatible-mode/v1", # 填写DashScope服务base_url
)
# 初始化messages列表
completion = client.chat.completions.create(
model="qwen-long",
messages=[
{'role': 'system', 'content': 'You are a helpful assistant.'},
# 请将 'file-fe-xxx'替换为您实际对话场景所使用的 file-id。
{'role': 'system', 'content': 'fileid://file-fe-xxx'},
{'role': 'user', 'content': '这篇文章讲了什么？'}
],
stream=True,
stream_options={"include_usage": True}
)
full_content = ""
for chunk in completion:
if chunk.choices and chunk.choices[0].delta.content:
# 拼接输出内容
full_content += chunk.choices[0].delta.content
print(chunk.model_dump())
print({full_content})
```
Tip: The example code is for OpenAI, but the document said that it also
supports the DataScope API, and I tested it, and it works.
```
Is the Dashscope SDK invocation method compatible?
Yes, the Dashscope SDK remains compatible for model invocation. However, file uploads and file-ID retrieval are currently only supported via the OpenAI SDK. The file-ID obtained through this method is also compatible with Dashscope for model invocation.
```
```markdown
**Description:**
This PR integrates Valthera into LangChain, introducing an framework designed to send highly personalized nudges by an LLM agent. This is modeled after Dr. BJ Fogg's Behavior Model. This integration includes:
- Custom data connectors for HubSpot, PostHog, and Snowflake.
- A unified data aggregator that consolidates user data.
- Scoring configurations to compute motivation and ability scores.
- A reasoning engine that determines the appropriate user action.
- A trigger generator to create personalized messages for user engagement.
**Issue:**
N/A
**Dependencies:**
N/A
**Twitter handle:**
- `@vselvarajijay`
**Tests and Docs:**
- `docs/docs/integrations/tools/valthera`
- `https://github.com/valthera/langchain-valthera/tree/main/tests`
```
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Thank you for contributing to LangChain!
**Description:** adds ContextualAI's `langchain-contextual` package's
documentation
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
The OpenAI API requires function names to match the pattern
'^[a-zA-Z0-9_-]+$'. This updates the JIRA toolkit's tool names to use
underscores instead of spaces to comply with this requirement and
prevent BadRequestError when using the tools with OpenAI functions.
Error fixed:
```
File "langgraph-bug-fix/.venv/lib/python3.13/site-packages/openai/_base_client.py", line 1023, in _request
raise self._make_status_error_from_response(err.response) from None
openai.BadRequestError: Error code: 400 - {'error': {'message': "Invalid 'tools[0].function.name': string does not match pattern. Expected a string that matches the pattern '^[a-zA-Z0-9_-]+$'.", 'type': 'invalid_request_error', 'param': 'tools[0].function.name', 'code': 'invalid_value'}}
During task with name 'agent' and id 'aedd7537-e8d5-6678-d0c5-98129586d3ac'
```
Issue:#30182
Thank you for contributing to LangChain!
- [ ] **PR title**: "community: chinese doc extracting"
- [ ] **PR message**:
- **Description:** add jieba_link_extractor.py for chinese doc
extracting
- **Dependencies:** jieba
- [ ] **Add tests and docs**: If you're adding a new integration, please
include
/doc/doc/integrations/providers/jieba.md
/doc/doc/integrations/vectorstores/jieba_link_extractor.ipynb
/libs/packages.yml
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Groq is retiring `mixtral-8x7b-32768`, which is currently the default
model for ChatGroq, on March 20. Here we emit a warning if the model is
not specified explicitly.
A version 0.3.0 will be released ahead of March 20 that removes the
default altogether.
docs: New integration for LangChain - ads4gpts-langchain
Description: Tools and Toolkit for Agentic integration natively within
LangChain with ADS4GPTs, in order to help applications monetize with
advertising.
Twitter handle: @ads4gpts
Co-authored-by: knitlydevaccount <loom+github@knitly.app>
- **Description: a notebook showing langchain and langraph agents using
the new langchain_tableau tool
- **Twitter handle: @joe_constantin0
---------
Co-authored-by: Joe Constantino <joe@constantino.com>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- Support thinking blocks in core's `convert_to_openai_messages` (pass
through instead of error)
- Ignore thinking blocks in ChatOpenAI (instead of error)
- Support Anthropic-style image blocks in ChatOpenAI
---
Standard integration tests include a `supports_anthropic_inputs`
property which is currently enabled only for tests on `ChatAnthropic`.
This test enforces compatibility with message histories of the form:
```
- system message
- human message
- AI message with tool calls specified only through `tool_use` content blocks
- human message containing `tool_result` and an additional `text` block
```
It additionally checks support for Anthropic-style image inputs if
`supports_image_inputs` is enabled.
Here we change this test, such that if you enable
`supports_anthropic_inputs`:
- You support AI messages with text and `tool_use` content blocks
- You support Anthropic-style image inputs (if `supports_image_inputs`
is enabled)
- You support thinking content blocks.
That is, we add a test case for thinking content blocks, but we also
remove the requirement of handling tool results within HumanMessages
(motivated by existing agent abstractions, which should all return
ToolMessage). We move that requirement to a ChatAnthropic-specific test.
**Description:**
This PR adds a call to `guard_import()` to fix an AttributeError raised
when creating LanceDB vectorstore instance with an existing LanceDB
table.
**Issue:**
This PR fixes issue #30124.
**Dependencies:**
No additional dependencies.
**Twitter handle:**
[@metadaddy](https://x.com/metadaddy), but I spend more time at
[@metadaddy.net](https://bsky.app/profile/metadaddy.net) these days.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
## Description
make DashScope models support Partial Mode for text continuation.
For text continuation in ChatTongYi, it supports text continuation with
a prefix by adding a "partial" argument in AIMessage. The document is
[Partial Mode
](https://help.aliyun.com/zh/model-studio/user-guide/partial-mode?spm=a2c4g.11186623.help-menu-2400256.d_1_0_0_8.211e5b77KMH5Pn&scm=20140722.H_2862210._.OR_help-T_cn~zh-V_1).
The API example is:
```py
import os
import dashscope
messages = [{
"role": "user",
"content": "请对“春天来了，大地”这句话进行续写，来表达春天的美好和作者的喜悦之情"
},
{
"role": "assistant",
"content": "春天来了，大地",
"partial": True
}]
response = dashscope.Generation.call(
api_key=os.getenv("DASHSCOPE_API_KEY"),
model='qwen-plus',
messages=messages,
result_format='message',
)
print(response.output.choices[0].message.content)
```
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- **Description**: Added the request_id field to the check_response
function to improve request tracking and debugging, applicable for the
Tongyi model.
- **Issue**: None
- **Dependencies**: None
- **Twitter handle**: None
- **Add tests and docs**: None
- **Lint and test**: Ran `make format`, `make lint`, and `make test` to
ensure the code meets formatting and testing requirements.
### **Description**
Converts the boolean `jira_cloud` parameter in the Jira API Wrapper to a
string before initializing the Jira Client. Also adds tests for the
same.
### **Issue**
[Jira API Wrapper
Bug](8abb65e138/libs/community/langchain_community/utilities/jira.py (L47))
```python
jira_cloud_str = get_from_dict_or_env(values, "jira_cloud", "JIRA_CLOUD")
jira_cloud = jira_cloud_str.lower() == "true"
```
The above code has a bug where the value of `"jira_cloud"` is a boolean.
If it is passed, calling `.lower()` on a boolean raises an error.
Additionally, `False` cannot be passed explicitly since
`get_from_dict_or_env` falls back to environment variables.
Relevant code in `langchain_core`:
[Source](https://github.com/thesmallstar/langchain/blob/master/.venv/lib/python3.13/site-packages/langchain_core/utils/env.py#L46)
```python
if isinstance(key, str) and key in data and data[key]: # Here, data[key] is False
```
This PR fixes both issues.
### **Twitter Handle**
[Manthan Surkar](https://x.com/manthan_surkar)
This PR adds documentation for the langchain-taiga Tool integration,
including an example notebook at
'docs/docs/integrations/tools/taiga.ipynb' and updates to
'libs/packages.yml' to track the new package.
Issue:
N/A
Dependencies:
None
Twitter handle:
N/A
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
PR Title:
langchain: add attachments support in OpenAIAssistantRunnable
PR Description:
This PR fixes an issue with the "retrieval" tool (internally named
"file_search") in the OpenAI Assistant by adding support for the
"attachments" parameter in the invoke method. This change allows files
to be linked to messages when they are inserted into threads, which is
essential for utilizing OpenAI's Retrieval Augmented Generation (RAG)
feature.
Issue:
N/A
Dependencies:
None
Twitter handle:
N/A
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- **Description:** Fix typo in code samples for max_tokens_for_prompt.
Code blocks had singular "token" but the method has plural "tokens".
- **Issue:** N/A
- **Dependencies:** N/A
- **Twitter handle:** N/A
**Description:**
5 fix of example from function with_alisteners() in
libs/core/langchain_core/runnables/base.py
Replace incoherent example output with workable example's output.
1. SyntaxError: unterminated string literal
print(f"on start callback starts at {format_t(time.time())}
correct as
print(f"on start callback starts at {format_t(time.time())}")
2. SyntaxError: unterminated string literal
print(f"on end callback starts at {format_t(time.time())}
correct as
print(f"on end callback starts at {format_t(time.time())}")
3. NameError: name 'Runnable' is not defined
Fix as
from langchain_core.runnables import Runnable
4. NameError: name 'asyncio' is not defined
Fix as
import asyncio
5. NameError: name 'format_t' is not defined.
Implement format_t() as
from datetime import datetime, timezone
def format_t(timestamp: float) -> str:
return datetime.fromtimestamp(timestamp, tz=timezone.utc).isoformat()
add batch_size to fix oom when embed large amount texts
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core, etc. is
being modified. Use "docs: ..." for purely docs 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, ccurme, vbarda, hwchase17.
Structured output will currently always raise a BadRequestError when
Claude 3.7 Sonnet's `thinking` is enabled, because we rely on forced
tool use for structured output and this feature is not supported when
`thinking` is enabled.
Here we:
- Emit a warning if `with_structured_output` is called when `thinking`
is enabled.
- Raise `OutputParserException` if no tool calls are generated.
This is arguably preferable to raising an error in all cases.
```python
from langchain_anthropic import ChatAnthropic
from pydantic import BaseModel
class Person(BaseModel):
name: str
age: int
llm = ChatAnthropic(
model="claude-3-7-sonnet-latest",
max_tokens=5_000,
thinking={"type": "enabled", "budget_tokens": 2_000},
)
structured_llm = llm.with_structured_output(Person) # <-- this generates a warning
```
```python
structured_llm.invoke("Alice is 30.") # <-- works
```
```python
structured_llm.invoke("Hello!") # <-- raises OutputParserException
```
Took a "census" of models supported by init_chat_model-- of those that
return model names in response metadata, these were the only two that
had it keyed under `"model"` instead of `"model_name"`.
- [ ] **PR title**: [langchain_community.llms.xinference]: Add
asynchronous generate interface
- [ ] **PR message**: The asynchronous generate interface support stream
data and non-stream data.
chain = prompt | llm
async for chunk in chain.astream(input=user_input):
yield chunk
- [ ] **Add tests and docs**:
from langchain_community.llms import Xinference
from langchain.prompts import PromptTemplate
llm = Xinference(
server_url="http://0.0.0.0:9997", # replace your xinference server url
model_uid={model_uid} # replace model_uid with the model UID return from
launching the model
stream = True
)
prompt = PromptTemplate(input=['country'], template="Q: where can we
visit in the capital of {country}? A:")
chain = prompt | llm
async for chunk in chain.astream(input=user_input):
yield chunk
Thank you for contributing to LangChain!
- **Implementing the MMR algorithm for OLAP vector storage**:
- Support Apache Doris and StarRocks OLAP database.
- Example: "vectorstore.as_retriever(search_type="mmr",
search_kwargs={"k": 10})"
- **Implementing the MMR algorithm for OLAP vector storage**:
- **Apache Doris
- **StarRocks
- **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**:
- Example: "vectorstore.as_retriever(search_type="mmr",
search_kwargs={"k": 10})"
- [ ] **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, ccurme, vbarda, hwchase17.
---------
Co-authored-by: fakzhao <fakzhao@cisco.com>
This pull request includes a change to the `TavilySearchResults` class
in the `tool.py` file, which updates the code block format in the
documentation.
Documentation update:
*
[`libs/community/langchain_community/tools/tavily_search/tool.py`](diffhunk://#diff-e3b6a980979268b639c6a86e9b182756b0f7c7e9e5605e613bc0a72ea6aa5301L54-R59):
Changed the code block format from Python to JSON in the example
provided in the docstring.Thank you for contributing to LangChain!
## **Description:**
When using the Tavily retriever with include_raw_content=True, the
retriever occasionally fails with a Pydantic ValidationError because
raw_content can be None.
The Document model in langchain_core/documents/base.py requires
page_content to be a non-None value, but the Tavily API sometimes
returns None for raw_content.
This PR fixes the issue by ensuring that even when raw_content is None,
an empty string is used instead:
```python
page_content=result.get("content", "")
if not self.include_raw_content
else (result.get("raw_content") or ""),
This pull request includes updates to the
`libs/community/langchain_community/callbacks/bedrock_anthropic_callback.py`
file to add a new model version to the list of supported models.
Updates to supported models:
* Added support for the `anthropic.claude-3-7-sonnet-20250219-v1:0`
model with a rate of `0.003` for 1000 input tokens.
* Added support for the `anthropic.claude-3-7-sonnet-20250219-v1:0`
model with a rate of `0.015` for 1000 output tokens.
AWS Bedrock pricing reference : https://aws.amazon.com/bedrock/pricing
## PyMuPDF4LLM integration to LangChain for PDF content extraction in
Markdown format
### Description
[PyMuPDF4LLM](https://github.com/pymupdf/RAG) makes it easier to extract
PDF content in Markdown format, needed for LLM & RAG applications.
(License: GNU Affero General Public License v3.0)
[langchain-pymupdf4llm](https://github.com/lakinduboteju/langchain-pymupdf4llm)
integrates PyMuPDF4LLM to LangChain as a Document Loader.
(License: MIT License)
This pull request introduces the integration of
[PyMuPDF4LLM](https://pymupdf.readthedocs.io/en/latest/pymupdf4llm) into
the LangChain project as an integration package:
[`langchain-pymupdf4llm`](https://github.com/lakinduboteju/langchain-pymupdf4llm).
The most important changes include adding new Jupyter notebooks to
document the integration and updating the package configuration file to
include the new package.
### Documentation:
* `docs/docs/integrations/providers/pymupdf4llm.ipynb`: Added a new
Jupyter notebook to document the integration of `PyMuPDF4LLM` with
LangChain, including installation instructions and class imports.
* `docs/docs/integrations/document_loaders/pymupdf4llm.ipynb`: Added a
new Jupyter notebook to document the usage of `langchain-pymupdf4llm` as
a LangChain integration package in detail.
### Package registration:
* `libs/packages.yml`: Updated the package configuration file to include
the `langchain-pymupdf4llm` package.
### Additional information
* Related to: https://github.com/langchain-ai/langchain/pull/29848
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- **Description:** Same changes as #26593 but for FileCallbackHandler
- **Issue:** Fixes#29941
- **Dependencies:** None
- **Twitter handle:** None
- [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/
See https://docs.astral.sh/ruff/rules/#flake8-type-checking-tc
Some fixes done for TC001,TC002 and TC003 but these rules are excluded
since they don't play well with Pydantic.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
**Issue**: This trigger can only be used by the first table created.
Cannot create additional triggers for other tables.
**fixed**: Update the trigger name so that it can be used for new
tables.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
**Description:**
Tavily search results returned from API include useful information like
title, score and (optionally) raw_content that is missed in wrapper
although it's documented there properly. Add this data to the result
structure.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Resolves https://github.com/langchain-ai/langchain/issues/29951
Was able to reproduce the issue with Anthropic installing from pydantic
`main` and correct it with the fix recommended in the issue.
Thanks very much @Viicos for finding the bug and the detailed writeup!
Resolves https://github.com/langchain-ai/langchain/issues/29003,
https://github.com/langchain-ai/langchain/issues/27264
Related: https://github.com/langchain-ai/langchain-redis/issues/52
```python
from langchain.chat_models import init_chat_model
from langchain.globals import set_llm_cache
from langchain_community.cache import SQLiteCache
from pydantic import BaseModel
cache = SQLiteCache()
set_llm_cache(cache)
class Temperature(BaseModel):
value: int
city: str
llm = init_chat_model("openai:gpt-4o-mini")
structured_llm = llm.with_structured_output(Temperature)
```
```python
# 681 ms
response = structured_llm.invoke("What is the average temperature of Rome in May?")
```
```python
# 6.98 ms
response = structured_llm.invoke("What is the average temperature of Rome in May?")
```
Some o-series models will raise a 400 error for `"role": "system"`
(`o1-mini` and `o1-preview` will raise, `o1` and `o3-mini` will not).
Here we update `ChatOpenAI` to update the role to `"developer"` for all
model names matching `^o\d`.
We only make this change on the ChatOpenAI class (not BaseChatOpenAI).
For Context please check #29626
The Deepseek is using langchain_openai. The error happens that it show
`json decode error`.
I added a handler for this to give a more sensible error message which
is DeepSeek API returned empty/invalid json.
Reproducing the issue is a bit challenging as it is inconsistent,
sometimes DeepSeek returns valid data and in other times it returns
invalid data which triggers the JSON Decode Error.
This PR is an exception handling, but not an ultimate fix for the issue.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
**Description:** As commented on the commit
[41b6a86](41b6a86bbe)
it introduced a bug for when we do an embedding request and the model
returns a non-nested list. Typically it's the case for model
**_nomic-embed-text_**.
- I added the unit test, and ran `make format`, `make lint` and `make
test` from the `community` package.
- No new dependency.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- [x] **PR title**: docs: (community) update ChatLiteLLM
- [x] **PR message**:
- **Description:** updated description of model_kwargs parameter which
was wrongly describing for temperature.
- **Issue:** #29862
- **Dependencies:** N/A
- [x] **Add tests and docs**: N/A
- [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: Chester Curme <chester.curme@gmail.com>
See https://docs.astral.sh/ruff/rules/#flake8-annotations-ann
The interest compared to only mypy is that ruff is very fast at
detecting missing annotations.
ANN101 and ANN102 are deprecated so we ignore them
ANN401 (no Any type) ignored to be in sync with mypy config
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
## Which area of LangChain is being modified?
- This PR adds a new "Permit" integration to the `docs/integrations/`
folder.
- Introduces two new Tools (`LangchainJWTValidationTool` and
`LangchainPermissionsCheckTool`)
- Introduces two new Retrievers (`PermitSelfQueryRetriever` and
`PermitEnsembleRetriever`)
- Adds demo scripts in `examples/` showcasing usage.
## Description of Changes
- Created `langchain_permit/tools.py` for JWT validation and permission
checks with Permit.
- Created `langchain_permit/retrievers.py` for custom Permit-based
retrievers.
- Added documentation in `docs/integrations/providers/permit.ipynb` (or
`.mdx`) to explain setup, usage, and examples.
- Provided sample scripts in `examples/demo_scripts/` to illustrate
usage of these tools and retrievers.
- Ensured all code is linted and tested locally.
Thank you again for reviewing!
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- **Description:**
Since mlx_lm 0.20, all calls to mlx crash due to deprecation of the way
parameters are passed to methods generate and generate_step.
Parameters top_p, temp, repetition_penalty and repetition_context_size
are not passed directly to those method anymore but wrapped into
"sampler" and "logit_processor".
- **Dependencies:** mlx_lm (optional)
- **Tests:**
I've had a new test to existing test file:
tests/integration_tests/llms/test_mlx_pipeline.py
---------
Co-authored-by: Jean-Philippe Dournel <jp@insightkeeper.io>
# community: Fix AttributeError in RankLLMRerank (`list` object has no
attribute `candidates`)
## **Description**
This PR fixes an issue in `RankLLMRerank` where reranking fails with the
following error:
```
AttributeError: 'list' object has no attribute 'candidates'
```
The issue arises because `rerank_batch()` returns a `List[Result]`
instead of an object containing `.candidates`.
### **Changes Introduced**
- Adjusted `compress_documents()` to support both:
- Old API format: `rerank_results.candidates`
- New API format: `rerank_results` as a list
- Also fix wrong .txt location parsing while I was at it.
---
## **Issue**
Fixes **AttributeError** in `RankLLMRerank` when using
`compression_retriever.invoke()`. The issue is observed when
`rerank_batch()` returns a list instead of an object with `.candidates`.
**Relevant log:**
```
AttributeError: 'list' object has no attribute 'candidates'
```
## **Dependencies**
- No additional dependencies introduced.
---
## **Checklist**
- [x] **Backward compatible** with previous API versions
- [x] **Tested** locally with different RankLLM models
- [x] **No new dependencies introduced**
- [x] **Linted** with `make format && make lint`
- [x] **Ready for review**
---
## **Testing**
- Ran `compression_retriever.invoke(query)`
## **Reviewers**
If no review within a few days, please **@mention** one of:
- @baskaryan
- @efriis
- @eyurtsev
- @ccurme
- @vbarda
- @hwchase17
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core, etc. is
being modified. Use "docs: ..." for purely docs changes, "infra: ..."
for CI changes.
- Example: "community: add foobar LLM"
- [ ] **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, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
This PR adds a new cognee integration, knowledge graph based retrieval
enabling developers to ingest documents into cognee’s knowledge graph,
process them, and then retrieve context via CogneeRetriever.
It includes:
- langchain_cognee package with a CogneeRetriever class
- a test for the integration, demonstrating how to create, process, and
retrieve with cognee
- an example notebook showing its use. It lives in
`docs/docs/integrations` directory.
Followed 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.
Thank you for the review!
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
**Description:** Two small changes have been proposed here:
(1)
Previous code assumes that every issue has a priority field. If an issue
lacks this field, the code will raise a KeyError.
Now, the code checks if priority exists before accessing it. If priority
is missing, it assigns None instead of crashing. This prevents runtime
errors when processing issues without a priority.
(2)
Also If the "style" field is missing, the code throws a KeyError.
`.get("style", None)` safely retrieves the value if present.
**Issue:** #29875
**Dependencies:** N/A
Thank you for contributing to LangChain!
- [ ] **Handled query records properly**: "community:
vectorstores/kinetica"
- [ ] **Bugfix for empty query results handling**:
- **Description:** checked for the number of records returned by a query
before processing further
- **Issue:** resulted in an `AttributeError` earlier which has now been
fixed
@efriis
This PR adds documentation for the Azure AI package in Langchain to the
main mono-repo
No issue connected or updated dependencies.
Utilises existing tests and makes updates to the docs
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
**Description:** Update docstring for `reasoning_effort` argument to
specify that it applies to reasoning models only (e.g., OpenAI o1 and
o3-mini), clarifying its supported models.
**Issue:** None
**Dependencies:** None
Adds a `attachment_filter_func` parameter to the ConfluenceLoader class
which can be used to determine which files are indexed. This is useful
if you are interested in excluding files based on their media type or
other metadata.
https://docs.x.ai/docs/guides/structured-outputs
Interface appears identical to OpenAI's.
```python
from langchain.chat_models import init_chat_model
from pydantic import BaseModel
class Joke(BaseModel):
setup: str
punchline: str
llm = init_chat_model("xai:grok-2").with_structured_output(
Joke, method="json_schema"
)
llm.invoke("Tell me a joke about cats.")
```
- **Description:** add deprecation warning when using weaviate from
langchain_community
- **Issue:** NA
- **Dependencies:** NA
- **Twitter handle:** NA
---------
Signed-off-by: hsm207 <hsm207@users.noreply.github.com>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Add `model` properties for OpenAIWhisperParser. Defaulted to `whisper-1`
(previous value).
Please help me update the docs and other related components of this
repo.
**Description:**
This PR adds a Jupyter notebook that explains the features,
installation, and usage of the
[`langchain-salesforce`](https://github.com/colesmcintosh/langchain-salesforce)
package. The notebook includes:
- Setup instructions for configuring Salesforce credentials
- Example code demonstrating common operations such as querying,
describing objects, creating, updating, and deleting records
**Issue:**
N/A
**Dependencies:**
No new dependencies are required.
**Tests and Docs:**
- Added an example notebook demonstrating the usage of the
`langchain-salesforce` package, located in `docs/docs/integrations`.
**Lint and Test:**
- Ran `make format`, `make lint`, and `make test` successfully.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Thank you for contributing to LangChain!
- [X] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core, etc. is
being modified. Use "docs: ..." for purely docs changes, "infra: ..."
for CI changes.
- Example: "community: add foobar LLM"
- [x] **PR message**:
This PR adds top_k as a param to the Needle Retriever. By default we use
top 10.
- [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, ccurme, vbarda, hwchase17.
- ** Description**: I have added a new operator in the operator map with
key `$in` and value `IN`, so that you can define filters using lists as
values. This was already contemplated but as IN operator was not in the
map they cannot be used.
- **Issue**: Fixes#29804.
- **Dependencies**: No extra.
This PR adds documentation for the `langchain-discord-shikenso`
integration, including an example notebook at
`docs/docs/integrations/tools/discord.ipynb` and updates to
`libs/packages.yml` to track the new package.
**Issue:**
N/A
**Dependencies:**
None
**Twitter handle:**
N/A
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- [ ] **PR title**: langchain_community: add image support to
DuckDuckGoSearchAPIWrapper
- **Description:** This PR enhances the DuckDuckGoSearchAPIWrapper
within the langchain_community package by introducing support for image
searches. The enhancement includes:
- Adding a new method _ddgs_images to handle image search queries.
- Updating the run and results methods to process and return image
search results appropriately.
- Modifying the source parameter to accept "images" as a valid option,
alongside "text" and "news".
- **Dependencies:** No additional dependencies are required for this
change.
- **Description:** Add the new introduction about checking `store` in
in_memory.py, It’s necessary and useful for beginners.
```python
Check Documents:
.. code-block:: python
for doc in vector_store.store.values():
print(doc)
```
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
**Description:** Fixed and updated Apify integration documentation to
use the new [langchain-apify](https://github.com/apify/langchain-apify)
package.
**Twitter handle:** @apify
- **Description:** Small fix in `add_texts` to make embedding
nullability is checked properly.
- **Issue:** #29765
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core, etc. is
being modified. Use "docs: ..." for purely docs 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, ccurme, vbarda, hwchase17.
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core, etc. is
being modified. Use "docs: ..." for purely docs 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, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
This fix ensures that the chunk size is correctly determined when
processing text embeddings. Previously, the code did not properly handle
cases where chunk_size was None, potentially leading to incorrect
chunking behavior.
Now, chunk_size_ is explicitly set to either the provided chunk_size or
the default self.chunk_size, ensuring consistent chunking. This update
improves reliability when processing large text inputs in batches and
prevents unintended behavior when chunk_size is not specified.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core, etc. is
being modified. Use "docs: ..." for purely docs 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, ccurme, vbarda, hwchase17.
Add the documentation for the community package `langchain-abso`. It
provides a new Chat Model class, that uses https://abso.ai
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
- **Description:** Adding Structured Support for ChatPerplexity
- **Issue:** #29357
- This is implemented as per the Perplexity official docs:
https://docs.perplexity.ai/guides/structured-outputs
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
**Description:** Updated init_chat_model to support Granite models
deployed on IBM WatsonX
**Dependencies:**
[langchain-ibm](https://github.com/langchain-ai/langchain-ibm)
Tagging @baskaryan @efriis for review when you get a chance.
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core, etc. is
being modified. Use "docs: ..." for purely docs changes, "infra: ..."
for CI changes.
- Example: "community: add foobar LLM"
community: langchain_community/vectorstore/oraclevs.py
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** Refactored code to allow a connection or a connection
pool.
- **Issue:** Normally an idel connection is terminated by the server
side listener at timeout. A user thus has to re-instantiate the vector
store. The timeout in case of connection is not configurable. The
solution is to use a connection pool where a user can specify a user
defined timeout and the connections are managed by the pool.
- **Dependencies:** None
- **Twitter handle:**
- [ ] **Add tests and docs**: This is not a new integration. A user can
pass either a connection or a connection pool. The determination of what
is passed is made at run time. Everything should work as before.
- [ ] **Lint and test**: Already done.
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, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
1. Make `_convert_chunk_to_generation_chunk` an instance method on
BaseChatOpenAI
2. Override on ChatDeepSeek to add `"reasoning_content"` to message
additional_kwargs.
Resolves https://github.com/langchain-ai/langchain/issues/29513
**Description:**
According to the [wikidata
documentation](https://www.wikidata.org/wiki/Wikidata_talk:REST_API),
Wikibase REST API version 1 (stable) is released from November 11, 2024.
Their guide is to use the new v1 API and, it just requires replacing v0
in the routes with v1 in almost all cases.
So I replaced WIKIDATA_REST_API_URL from v0 to v1 for stable usage.
Co-authored-by: ccurme <chester.curme@gmail.com>
**issue**
In Langchain, the original content is generally stored under the `text`
key. However, the `PineconeHybridSearchRetriever` searches the `context`
field in the metadata and cannot change this key. To address this, I
have modified the code to allow changing the key to something other than
context.
In my opinion, following Langchain's conventions, the `text` key seems
more appropriate than `context`. However, since I wasn't sure about the
author's intent, I have left the default value as `context`.
- Description: Adding getattr methods and set default value 500 to
cls.bulk_size, it can prevent the error below:
Error: type object 'OpenSearchVectorSearch' has no attribute 'bulk_size'
- Issue: https://github.com/langchain-ai/langchain/issues/29071
Description:
This PR fixes handling of null action_input in
[langchain.agents.output_parser]. Previously, passing null to
action_input could cause OutputParserException with unclear error
message which cause LLM don't know how to modify the action. The changes
include:
Added null-check validation before processing action_input
Implemented proper fallback behavior with default values
Maintained backward compatibility with existing implementations
Error Examples:
```
{
"action":"some action",
"action_input":null
}
```
Issue:
None
Dependencies:
None
This is one part of a larger Pull Request (PR) that is too large to be
submitted all at once. This specific part focuses on updating the
PyPDFium2 parser.
For more details, see
https://github.com/langchain-ai/langchain/pull/28970.
Same as https://github.com/langchain-ai/langchain/pull/29043 for the
langchain package.
**Dependencies:**
- blockbuster (test)
**Twitter handle:** cbornet_
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
- **Description:** Add tests for respecting max_concurrency and
implement it for abatch_as_completed so that test passes
- **Issue:** #29425
- **Dependencies:** none
- **Twitter handle:** keenanpepper
Description:
The change allows you to use the overloaded `+` operator correctly when
`+`ing two BaseMessageChunk subclasses. Without this you *must*
instantiate a subclass for it to work.
Which feels... wrong. Base classes should be decoupled from sub classes
and should have in no way a dependency on them.
Issue:
You can't `+` a BaseMessageChunk with a BaseMessageChunk
e.g. this will explode
```py
from langchain_core.outputs import (
ChatGenerationChunk,
)
from langchain_core.messages import BaseMessageChunk
chunk1 = ChatGenerationChunk(
message=BaseMessageChunk(
type="customChunk",
content="HI",
),
)
chunk2 = ChatGenerationChunk(
message=BaseMessageChunk(
type="customChunk",
content="HI",
),
)
# this will throw
new_chunk = chunk1 + chunk2
```
In case anyone ran into this issue themselves, it's probably best to use
the AIMessageChunk:
a la
```py
from langchain_core.outputs import (
ChatGenerationChunk,
)
from langchain_core.messages import AIMessageChunk
chunk1 = ChatGenerationChunk(
message=AIMessageChunk(
content="HI",
),
)
chunk2 = ChatGenerationChunk(
message=AIMessageChunk(
content="HI",
),
)
# No explosion!
new_chunk = chunk1 + chunk2
```
Dependencies:
None!
Twitter handle:
`aaron_vogler`
Keeping these for later if need be:
```
baskaryan
efriis
eyurtsev
ccurme
vbarda
hwchase17
baskaryan
efriis
```
Co-authored-by: Erick Friis <erick@langchain.dev>
- This pull request includes various changes to add a `user_agent`
parameter to Azure OpenAI, Azure Search and Whisper in the Community and
Partner packages. This helps in identifying the source of API requests
so we can better track usage and help support the community better. I
will also be adding the user_agent to the new `langchain-azure` repo as
well.
- No issue connected or updated dependencies.
- Utilises existing tests and docs
---------
Co-authored-by: Erick Friis <erick@langchain.dev>
ONNX and OpenVINO models are available by specifying the `backend`
argument (the model is loaded using `optimum`
https://github.com/huggingface/optimum)
```python
from langchain_huggingface import HuggingFaceEmbeddings
embedding = HuggingFaceEmbeddings(
model_name=model_id,
model_kwargs={"backend": "onnx"},
)
```
With this PR we also enable the IPEX backend
```python
from langchain_huggingface import HuggingFaceEmbeddings
embedding = HuggingFaceEmbeddings(
model_name=model_id,
model_kwargs={"backend": "ipex"},
)
```
**Description**
Currently, when parsing a partial JSON, if a string ends with the escape
character, the whole key/value is removed. For example:
```
>>> from langchain_core.utils.json import parse_partial_json
>>> my_str = '{"foo": "bar", "baz": "qux\\'
>>>
>>> parse_partial_json(my_str)
{'foo': 'bar'}
```
My expectation (and with this fix) would be for `parse_partial_json()`
to return:
```
>>> from langchain_core.utils.json import parse_partial_json
>>>
>>> my_str = '{"foo": "bar", "baz": "qux\\'
>>> parse_partial_json(my_str)
{'foo': 'bar', 'baz': 'qux'}
```
Notes:
1. It could be argued that current behavior is still desired.
2. I have experienced this issue when the streaming output from an LLM
and the chunk happens to end with `\\`
3. I haven't included tests. Will do if change is accepted.
4. This is specially troublesome when this function is used by
187131c55c/libs/core/langchain_core/output_parsers/transform.py (L111)
since what happens is that, for example, if the received sequence of
chunks are: `{"foo": "b` , `ar\\` :
Then, the result of calling `self.parse_result()` is:
```
{"foo": "b"}
```
and the second time:
```
{}
```
Co-authored-by: Erick Friis <erick@langchain.dev>
- **Description:** Before sending a completion chunk at the end of an
OpenAI stream, removing the tool_calls as those have already been sent
as chunks.
- **Issue:** -
- **Dependencies:** -
- **Twitter handle:** -
@ccurme as mentioned in another PR
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Added `similarity_search_with_score_by_vector()` function to the
`QdrantVectorStore` class.
It is required when we want to query multiple time with the same
embeddings. It was present in the now deprecated original `Qdrant`
vectorstore implementation, but was absent from the new one. It is also
implemented in a number of others `VectorStore` implementations
I have added tests for this new function
Note that I also argued in this discussion that it should be part of the
general `VectorStore`
https://github.com/langchain-ai/langchain/discussions/29638
Co-authored-by: Erick Friis <erick@langchain.dev>
Thank you for contributing to LangChain!
- [x] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core, etc. is
being modified. Use "docs: ..." for purely docs 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, ccurme, vbarda, hwchase17.
I made a change to how was implemented the support for GPU in
`FastEmbedEmbeddings` to be more consistent with the existing
implementation `langchain-qdrant` sparse embeddings implementation
It is directly enabling to provide the list of ONNX execution providers:
https://github.com/langchain-ai/langchain/blob/master/libs/partners/qdrant/langchain_qdrant/fastembed_sparse.py#L15
It is a bit less clear to a user that just wants to enable GPU, but
gives more capabilities to work with other execution providers that are
not the `CUDAExecutionProvider`, and is more future proof
Sorry for the disturbance @ccurme
> Nice to see you just moved to `uv`! It is so much nicer to run
format/lint/test! No need to manually rerun the `poetry install` with
all required extras now
These are set in Github workflows, but forgot to add them to most
makefiles for convenience when developing locally.
`uv run` will automatically sync the lock file. Because many of our
development dependencies are local installs, it will pick up version
changes and update the lock file. Passing `--frozen` or setting this
environment variable disables the behavior.
Motivation: dedicated structured output features are becoming more
common, such that integrations can support structured output without
supporting tool calling.
Here we make two changes:
1. Update the `has_structured_output` method to default to True if a
model supports tool calling (in addition to defaulting to True if
`with_structured_output` is overridden).
2. Update structured output tests to engage if `has_structured_output`
is True.
Deep Lake recently released version 4, which introduces significant
architectural changes, including a new on-disk storage format, enhanced
indexing mechanisms, and improved concurrency. However, LangChain's
vector store integration currently does not support Deep Lake v4 due to
breaking API changes.
Previously, the installation command was:
`pip install deeplake[enterprise]`
This installs the latest available version, which now defaults to Deep
Lake v4. Since LangChain's vector store integration is still dependent
on v3, this can lead to compatibility issues when using Deep Lake as a
vector database within LangChain.
To ensure compatibility, the installation command has been updated to:
`pip install deeplake[enterprise]<4.0.0`
This constraint ensures that pip installs the latest available version
of Deep Lake within the v3 series while avoiding the incompatible v4
update.
- **Description:** add a `gpu: bool = False` field to the
`FastEmbedEmbeddings` class which enables to use GPU (through ONNX CUDA
provider) when generating embeddings with any fastembed model. It just
requires the user to install a different dependency and we use a
different provider when instantiating `fastembed.TextEmbedding`
- **Issue:** when generating embeddings for a really large amount of
documents this drastically increase performance (honestly that is a must
have in some situations, you can't just use CPU it is way too slow)
- **Dependencies:** no direct change to dependencies, but internally the
users will need to install `fastembed-gpu` instead of `fastembed`, I
made all the changes to the init function to properly let the user know
which dependency they should install depending on if they enabled `gpu`
or not
cf. fastembed docs about GPU for more details:
https://qdrant.github.io/fastembed/examples/FastEmbed_GPU/
I did not added test because it would require access to a GPU in the
testing environment
### PR Title:
**community: add latest OpenAI models pricing**
### Description:
This PR updates the OpenAI model cost calculation mapping by adding the
latest OpenAI models, **o1 (non-preview)** and **o3-mini**, based on the
pricing listed on the [OpenAI pricing
page](https://platform.openai.com/docs/pricing).
### Changes:
- Added pricing for `o1`, `o1-2024-12-17`, `o1-cached`, and
`o1-2024-12-17-cached` for input tokens.
- Added pricing for `o1-completion` and `o1-2024-12-17-completion` for
output tokens.
- Added pricing for `o3-mini`, `o3-mini-2025-01-31`, `o3-mini-cached`,
and `o3-mini-2025-01-31-cached` for input tokens.
- Added pricing for `o3-mini-completion` and
`o3-mini-2025-01-31-completion` for output tokens.
### Issue:
N/A
### Dependencies:
None
### Testing & Validation:
- No functional changes outside of updating the cost mapping.
- No tests were added or modified.
**Description:**
The response from `tool.invoke()` is always a ToolMessage, with content
and artifact fields, not a tuple.
The tuple is converted to a ToolMessage here
b6ae7ca91d/libs/core/langchain_core/tools/base.py (L726)
**Issue:**
Currently `ToolsIntegrationTests` requires `invoke()` to return a tuple
and so standard tests fail for "content_and_artifact" tools. This fixes
that to check the returned ToolMessage.
This PR also adds a test that now passes.
Description: Fixes PreFilter value handling in Azure Cosmos DB NoSQL
vectorstore. The current implementation fails to handle numeric values
in filter conditions, causing an undefined value variable error. This PR
adds support for numeric, boolean, and NULL values while maintaining the
existing string and list handling.
Changes:
Added handling for numeric types (int/float)
Added boolean value support
Added NULL value handling
Added type validation for unsupported values
Fixed scope of value variable initialization
Issue:
Fixes#29610
Implementation Notes:
No changes to public API
Backwards compatible
Maintains consistent behavior with existing MongoDB-style filtering
Preserves SQL injection prevention through proper value handling
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
This is one part of a larger Pull Request (PR) that is too large to be
submitted all at once. This specific part focuses on updating the XXX
parser.
For more details, see [PR
28970](https://github.com/langchain-ai/langchain/pull/28970).
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Failing with:
> ValueError: Provider page not found for databricks-langchain. Please
add one at docs/integrations/providers/databricks-langchain.{mdx,ipynb}
**PR title**: "community: Option to pass auth_file_location for
oci_generative_ai"
**Description:** Option to pass auth_file_location, to overwrite config
file default location "~/.oci/config" where profile name configs
present. This is not fixing any issues. Just added optional parameter
called "auth_file_location", which internally supported by any OCI
client including GenerativeAiInferenceClient.
- **Description:** Add to check pad_token_id and eos_token_id of model
config. It seems that this is the same bug as the HuggingFace TGI bug.
It's same bug as #29434
- **Issue:** #29431
- **Dependencies:** none
- **Twitter handle:** tell14
Example code is followings:
```python
from langchain_huggingface.llms import HuggingFacePipeline
hf = HuggingFacePipeline.from_model_id(
model_id="meta-llama/Llama-3.2-3B-Instruct",
task="text-generation",
pipeline_kwargs={"max_new_tokens": 10},
)
from langchain_core.prompts import PromptTemplate
template = """Question: {question}
Answer: Let's think step by step."""
prompt = PromptTemplate.from_template(template)
chain = prompt | hf
question = "What is electroencephalography?"
print(chain.invoke({"question": question}))
```
## Description:
This PR addresses issue #29429 by fixing the _wrap_query method in
langchain_community/graphs/age_graph.py. The method now correctly
handles Cypher queries with UNION and EXCEPT operators, ensuring that
the fields in the SQL query are ordered as they appear in the Cypher
query. Additionally, the method now properly handles cases where RETURN
* is not supported.
### Issue: #29429
### Dependencies: None
### Add tests and docs:
Added unit tests in tests/unit_tests/graphs/test_age_graph.py to
validate the changes.
No new integrations were added, so no example notebook is necessary.
Lint and test:
Ran make format, make lint, and make test to ensure code quality and
functionality.
Thank you for contributing to LangChain!
- [x] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core, etc. is
being modified. Use "docs: ..." for purely docs changes, "infra: ..."
for CI changes.
- Example: "community: add foobar LLM"
- [x] **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!
- [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, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
This PR uses the [blockbuster](https://github.com/cbornet/blockbuster)
library in langchain-core to detect blocking calls made in the asyncio
event loop during unit tests.
Avoiding blocking calls is hard as these can be deeply buried in the
code or made in 3rd party libraries.
Blockbuster makes it easier to detect them by raising an exception when
a call is made to a known blocking function (eg: `time.sleep`).
Adding blockbuster allowed to find a blocking call in
`aconfig_with_context` (it ends up calling `get_function_nonlocals`
which loads function code).
**Dependencies:**
- blockbuster (test)
**Twitter handle:** cbornet_
This is one part of a larger Pull Request (PR) that is too large to be
submitted all at once.
This specific part focuses on updating the PyPDF parser.
For more details, see [PR
28970](https://github.com/langchain-ai/langchain/pull/28970).
*Description:**
Updates the YahooFinanceNewsTool to handle the current yfinance news
data structure. The tool was failing with a KeyError due to changes in
the yfinance API's response format. This PR updates the code to
correctly extract news URLs from the new structure.
**Issue:** #29495
**Dependencies:**
No new dependencies required. Works with existing yfinance package.
The changes maintain backwards compatibility while fixing the KeyError
that users were experiencing.
The modified code properly handles the new data structure where:
- News type is now at `content.contentType`
- News URL is now at `content.canonicalUrl.url`
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
This pull request addresses an issue with import statements in the
langchain_core/retrievers.py file. The following changes have been made:
Corrected the import for Document from langchain_core.documents.base.
Corrected the import for BaseRetriever from langchain_core.retrievers.
These changes ensure that the SimpleRetriever class can correctly
reference the Document and BaseRetriever classes, improving code
reliability and maintainability.
---------
Co-authored-by: Matheus Torquato <mtorquat@jaguarlandrover.com>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Description
This PR adds support for MongoDB-style $in operator filtering in the
Supabase vectorstore implementation. Currently, filtering with $in
operators returns no results, even when matching documents exist. This
change properly translates MongoDB-style filters to PostgreSQL syntax,
enabling efficient multi-document filtering.
Changes
Modified similarity_search_by_vector_with_relevance_scores to handle
MongoDB-style $in operators
Added automatic conversion of $in filters to PostgreSQL IN clauses
Preserved original vector type handling and numpy array conversion
Maintained compatibility with existing postgrest filters
Added support for the same filtering in
similarity_search_by_vector_returning_embeddings
Issue
Closes#27932
Implementation Notes
No changes to public API or function signatures
Backwards compatible - behavior unchanged for non-$in filters
More efficient than multiple individual queries for multi-ID searches
Preserves all existing functionality including numpy array conversion
for vector types
Dependencies
None
Additional Notes
The implementation handles proper SQL escaping for filter values
Maintains consistent behavior with other vectorstore implementations
that support MongoDB-style operators
Future extensions could support additional MongoDB-style operators ($gt,
$lt, etc.)
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
allow any credential type in AzureAIDocumentInteligence, not only
`api_key`.
This allows to use any of the credentials types integrated with AD.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Description: Fix TypeError in AzureSearch similarity_search_with_score
by removing search_type from kwargs before passing to underlying
requests.
This resolves issue #29407 where search_type was being incorrectly
passed through to Session.request().
Issue: #29407
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Thank you for contributing to LangChain!
- [x] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core, etc. is
being modified. Use "docs: ..." for purely docs changes, "infra: ..."
for CI changes.
- Example: "community: add foobar LLM"
- [x] **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!
- [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/
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, ccurme, vbarda, hwchase17.
- **Description:** Add to check pad_token_id and eos_token_id of model
config. It seems that this is the same bug as the HuggingFace TGI bug.
In addition, the source code of
libs/partners/huggingface/langchain_huggingface/llms/huggingface_pipeline.py
also requires similar changes.
- **Issue:** #29431
- **Dependencies:** none
- **Twitter handle:** tell14
TRY004 ("use TypeError rather than ValueError") existing errors are
marked as ignore to preserve backward compatibility.
LMK if you prefer to fix some of them.
Co-authored-by: Erick Friis <erick@langchain.dev>
We currently return string (and therefore no content blocks / citations)
if the response is of the form
```
[
{"text": "a claim", "citations": [...]},
]
```
There are other cases where we do return citations as-is:
```
[
{"text": "a claim", "citations": [...]},
{"text": "some other text"},
{"text": "another claim", "citations": [...]},
]
```
Here we update to return content blocks including citations in the first
case as well.
`RunnableLambda`'s `__repr__` may do costly OS operation by calling
`get_lambda_source`.
So it's better to cache it.
See #29043
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
This pull request removes the now-unused html_chunks_with_headers.xslt
file from the codebase. In a previous update ([PR
#27678](https://github.com/langchain-ai/langchain/pull/27678)), the
HTMLHeaderTextSplitter class was refactored to utilize BeautifulSoup
instead of lxml and XSLT for HTML processing. As a result, the
html_chunks_with_headers.xslt file is no longer necessary and can be
safely deleted to maintain code cleanliness and reduce potential
confusion.
Issue: N/A
Dependencies: N/A
- **Description:** the `delete` function of
AzureCosmosDBNoSqlVectorSearch is using
`self._container.delete_item(document_id)` which miss a mandatory
parameter `partition_key`
We use the class function `delete_document_by_id` to provide a default
`partition_key`
- **Issue:** #29372
- **Dependencies:** None
- **Twitter handle:** None
Co-authored-by: Loris Alexandre <loris.alexandre@boursorama.fr>
This pr, expands the gitlab url so it can also be set in a constructor,
instead of only through env variables.
This allows to do something like this.
```
# Create the GitLab API wrapper
gitlab_api = GitLabAPIWrapper(
gitlab_url=self.gitlab_url,
gitlab_personal_access_token=self.gitlab_personal_access_token,
gitlab_repository=self.gitlab_repository,
gitlab_branch=self.gitlab_branch,
gitlab_base_branch=self.gitlab_base_branch,
)
```
Where before you could not set the url in the constructor.
Co-authored-by: Tim Mallezie <tim.mallezie@dropsolid.com>
Why not Ollama ?
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core, etc. is
being modified. Use "docs: ..." for purely docs 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, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
…ore.py
Thank you for contributing to LangChain!
- [x] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core, etc. is
being modified. Use "docs: ..." for purely docs changes, "infra: ..."
for CI changes.
- Example: "community: add foobar LLM"
Added docstring linting in the vectorstore.py file relating to issue
#25154
- [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, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Siddhant Jain <sjain35@buffalo.edu>
Co-authored-by: Erick Friis <erick@langchain.dev>
Description: Added pipeshift integration. This integrates pipeshift LLM
and ChatModels APIs with langchain
Dependencies: none
Unit Tests & Integration tests are added
Documentation is added as well
This PR is w.r.t
[#27390](https://github.com/langchain-ai/langchain/pull/27390) and as
per request, a freshly minted `langchain-pipeshift` package is uploaded
to PYPI. Only changes to the docs & packages.yml are made in langchain
master branch
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Co-authored-by: Erick Friis <erick@langchain.dev>
