Description: Update vector store tab inits to match either the docs or
api_ref (whichever was more comprehensive)
List of changes per vector stores:
- In-memory
- no change
- AstraDB
- match to docs - docs/api_refs match (excluding embeddings)
- Chroma
- match to docs - api_refs is less descriptive
- FAISS
- match to docs - docs/api_refs match (excluding embeddings)
- Milvus
- match to docs to use Milvus Lite with Flat index - api_refs does not
have index_param for generalization
- MongoDB
- match to docs - api_refs are sparser
- PGVector
- match to api_ref
- changed to include docker cmd directly in code
- docs/api_ref has comment to view docker command in separate code block
- Pinecone
- match to api_refs - docs have code dispersed
- Qdrant
- match to api_ref - docs has size=3072, api_ref has size=1536
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
**Description:**
a third party package not listed in the default valid namespaces cannot
pass test_serdes because the load() does not allow for extending the
valid_namespaces.
test_serdes will fail with -
ValueError: Invalid namespace: {'lc': 1, 'type': 'constructor', 'id':
['langchain_other', 'chat_models', 'ChatOther'], 'kwargs':
{'model_name': '...', 'api_key': '...'}, 'name': 'ChatOther'}
this change has test_serdes automatically extend valid_namespaces based
off the ChatModel under test's namespace.
this_row_id previously used UUID v1. However, since UUID v1 can be
predicted if the MAC address and timestamp are known, it poses a
potential security risk. Therefore, it has been changed to UUID v4.
added warning when duckdb is used as a vectorstore without pandas being
installed (currently used for similarity search result processing)
Thank you for contributing to LangChain!
- [ ] **PR title**: "community: added warning when duckdb is used as a
vectorstore without pandas"
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** displays a warning when using duckdb as a vector
store without pandas being installed, as it is used by the
`similarity_search` function
- **Issue:** #29933
- **Dependencies:** None
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
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>
Fix several typos in docs/docs/how_to/split_html.ipynb
* `structered` should be `structured`
* `signifcant` should be `significant`
* `seperator` should be `separator`
# 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 PR updates the [MLflow
integration](https://python.langchain.com/docs/integrations/providers/mlflow_tracking/)
docs. This PR is based on feedback and suggestions from @efriis on
#29612 . This proposed revision is much shorter, does not contain
images, and links out to the MLflow docs rather than providing lengthy
descriptions directly within these docs. Thank you for taking another
look!
- **Issue:** NA
- **Dependencies:** NA
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
**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
