Anthropic updated how they report token counts during streaming today.
See changes to `MessageDeltaUsage` in [this
commit](2da00f26c5 (diff-1a396eba0cd9cd8952dcdb58049d3b13f6b7768ead1411888d66e28211f7bfc5)).
It's clean and simple to grab these fields from the final
`message_delta` event. However, some of them are typed as Optional, and
language
[here](e42451ab3f/src/anthropic/lib/streaming/_messages.py (L462))
suggests they may not always be present. So here we take the required
field from the `message_delta` event as we were doing previously, and
ignore the rest.
partners: (langchain-openai) total_tokens should not add 'Nonetype' t…
# PR Description
## Description
Fixed an issue in `langchain-openai` where `total_tokens` was
incorrectly adding `None` to an integer, causing a TypeError. The fix
ensures proper type checking before adding token counts.
## Issue
Fixes the TypeError traceback shown in the image where `'NoneType'`
cannot be added to an integer.
## Dependencies
None
## Twitter handle
None
Co-authored-by: qiulijie <qiulijie@yuaiweiwu.com>
**Library Repo Path Update **: "langchain-cloudflare"
We recently changed our `langchain-cloudflare` repo to allow for future
libraries.
Created a `libs` folder to hold `langchain-cloudflare` python package.
https://github.com/cloudflare/langchain-cloudflare/tree/main/libs/langchain-cloudflare
On `langchain`, updating `packages.yaml` to point to new
`libs/langchain-cloudflare` library folder.
This PR brings several improvements and modernizations to the
documentation around the Astra DB partner package.
- language alignment for better matching with the terms used in the
Astra DB docs
- updated several links to pages on said documentation
- for the `AstraDBVectorStore`, added mentions of the new features in
the overall `astra.mdx`
- for the vector store, rewritten/upgraded most of the usage example
notebook for a more straightforward experience able to highlight the
main usage patterns (including new ones such as the newly-introduced
"autodetect feature")
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
**What does this PR do?**
This PR replaces deprecated usages of ```.dict()``` with
```.model_dump()``` to ensure compatibility with Pydantic v2 and prepare
for v3, addressing the deprecation warning
```PydanticDeprecatedSince20``` as required in [Issue#
31103](https://github.com/langchain-ai/langchain/issues/31103).
**Changes made:**
* Replaced ```.dict()``` with ```.model_dump()``` in multiple locations
* Ensured consistency with Pydantic v2 migration guidelines
* Verified compatibility across affected modules
**Notes**
* This is a code maintenance and compatibility update
* Tested locally with Pydantic v2.11
* No functional logic changes; only internal method replacements to
prevent deprecation issues
When aggregating AIMessageChunks in a stream, core prefers the leftmost
non-null ID. This is problematic because:
- Core assigns IDs when they are null to `f"run-{run_manager.run_id}"`
- The desired meaningful ID might not be available until midway through
the stream, as is the case for the OpenAI Responses API.
For the OpenAI Responses API, we assign message IDs to the top-level
`AIMessage.id`. This works in `.(a)invoke`, but during `.(a)stream` the
IDs get overwritten by the defaults assigned in langchain-core. These
IDs
[must](https://community.openai.com/t/how-to-solve-badrequesterror-400-item-rs-of-type-reasoning-was-provided-without-its-required-following-item-error-in-responses-api/1151686/9)
be available on the AIMessage object to support passing reasoning items
back to the API (e.g., if not using OpenAI's `previous_response_id`
feature). We could add them elsewhere, but seeing as we've already made
the decision to store them in `.id` during `.(a)invoke`, addressing the
issue in core lets us fix the problem with no interface changes.
- **Description:** `ChatAnthropic.get_num_tokens_from_messages` does not
currently receive `kwargs` and pass those on to
`self._client.beta.messages.count_tokens`. This is a problem if you need
to pass specific options to `count_tokens`, such as the `thinking`
option. This PR fixes that.
- **Issue:** N/A
- **Dependencies:** None
- **Twitter handle:** @bengladwell
Co-authored-by: ccurme <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"
**Description**:
* Starting to put together some PR's to fix the typing around
`langchain-chroma` `filter` and `where_document` query filtering, as
mentioned:
https://github.com/langchain-ai/langchain/issues/30879https://github.com/langchain-ai/langchain/issues/30507
The typing of `dict[str, str]` is on the one hand too restrictive (marks
valid filter expressions as ill-typed) and also too permissive (allows
illegal filter expressions). That's not what this PR addresses though.
This PR just removes from the documentation some examples of filters
that are illegal, and also syntactically incorrect: (a) dictionaries
with keys like `$contains` but the key is missing quotation marks; (b)
dictionaries with multiple entries - this is illegal in Chroma filter
syntax and will raise an exception. (`{"foo": "bar", "qux": "baz"}`).
Filter dictionaries in Chroma must have one and one key only. Again this
is just the documentation issue, which is the lowest hanging fruit. I
also think we need to update the types for `filter` and `where_document`
to be (at the very least `dict[str, Any]`), or, since we have access to
Chroma's types, they should be `Where` and `WhereDocument` types. This
has a wider blast radius though, so I'm starting small.
This PR does not fix the issues mentioned above, it's just starting to
get the ball rolling, and cleaning up the documentation.
- [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.
---------
Co-authored-by: Really Him <hesereallyhim@proton.me>
This PR includes the following documentation fixes for the SAP HANA
Cloud vector store integration:
- Removed stale output from the `%pip install` code cell.
- Replaced an unrelated vectorstore documentation link on the provider
overview page.
- Renamed the provider from "SAP HANA" to "SAP HANA Cloud"
Hi there, I'm Célina from 🤗,
This PR introduces support for Hugging Face's serverless Inference
Providers (documentation
[here](https://huggingface.co/docs/inference-providers/index)), allowing
users to specify different providers for chat completion and text
generation tasks.
This PR also removes the usage of `InferenceClient.post()` method in
`HuggingFaceEndpoint`, in favor of the task-specific `text_generation`
method. `InferenceClient.post()` is deprecated and will be removed in
`huggingface_hub v0.31.0`.
---
## Changes made
- bumped the minimum required version of the `huggingface-hub` package
to ensure compatibility with the latest API usage.
- added a `provider` field to `HuggingFaceEndpoint`, enabling users to
select the inference provider (e.g., 'cerebras', 'together',
'fireworks-ai'). Defaults to `hf-inference` (HF Inference API).
- replaced the deprecated `InferenceClient.post()` call in
`HuggingFaceEndpoint` with the task-specific `text_generation` method
for future-proofing, `post()` will be removed in huggingface-hub
v0.31.0.
- updated the `ChatHuggingFace` component:
- added async and streaming support.
- added support for tool calling.
- exposed underlying chat completion parameters for more granular
control.
- Added integration tests for `ChatHuggingFace` and updated the
corresponding unit tests.
✅ All changes are backward compatible.
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
Follow up to https://github.com/langchain-ai/langsmith-sdk/pull/1696,
I've bumped the `langsmith` version where applicable in `uv.lock`.
Type checking problems here because deps have been updated in
`pyproject.toml` and `uv lock` hasn't been run - we should enforce that
in the future - goes with the other dependabot todos :).
## Description
Added support for retrieving column comments in the SQL Database
utility. This feature allows users to see comments associated with
database columns when querying table information. Column comments
provide valuable metadata that helps LLMs better understand the
semantics and purpose of database columns.
A new optional parameter `get_col_comments` was added to the
`get_table_info` method, defaulting to `False` for backward
compatibility. When set to `True`, it retrieves and formats column
comments for each table.
Currently, this feature is supported on PostgreSQL, MySQL, and Oracle
databases.
## Implementation
You should create Table with column comments before.
```python
db = SQLDatabase.from_uri("YOUR_DB_URI")
print(db.get_table_info(get_col_comments=True))
```
## Result
```
CREATE TABLE test_table (
name VARCHAR
school VARCHAR)
/*
Column Comments: {'name': person name, 'school":school_name}
*/
/*
3 rows from test_table:
name
a
b
c
*/
```
## Benefits
1. Enhances LLM's understanding of database schema semantics
2. Preserves valuable domain knowledge embedded in database design
3. Improves accuracy of SQL query generation
4. Provides more context for data interpretation
Tests are available in
`langchain/libs/community/tests/test_sql_get_table_info.py`.
---------
Co-authored-by: chbae <chbae@gcsc.co.kr>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- **Description:**
This PR marks the `HanaDB` vector store (and related utilities) in
`langchain_community` as deprecated using the `@deprecated` annotation.
- Set `since="0.1.0"` and `removal="1.0"`
- Added a clear migration path and a link to the SAP-maintained
replacement in the
[`langchain_hana`](https://github.com/SAP/langchain-integration-for-sap-hana-cloud)
package.
Additionally, the example notebook has been updated to use the new
`HanaDB` class from `langchain_hana`, ensuring users follow the
recommended integration moving forward.
- **Issue:** None
- **Dependencies:** None
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
The `_chunk_size` has not changed by method `self._tokenize`, So i think
these is duplicate code.
Signed-off-by: zhanluxianshen <zhanluxianshen@163.com>
This is a PR to return the message attachments in _get_response, as when
files are generated these attachments are not returned thus generated
files cannot be retrieved
Fixes issue: https://github.com/langchain-ai/langchain/issues/30851
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: fix browserbase integration
docs: update docs
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** Updated BrowserbaseLoader to use the new python sdk.
- **Issue:** update browserbase integration with langchain
- **Dependencies:** n/a
- **Twitter handle:** @kylejeong21
- [ ] **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/
Following https://github.com/langchain-ai/langchain/pull/30909: need to
retain "empty" reasoning output when streaming, e.g.,
```python
{'id': 'rs_...', 'summary': [], 'type': 'reasoning'}
```
Tested by existing integration tests, which are currently failing.
