- [x] **PR title**: "community: add indexname to other functions in
opensearch"
- [x] **PR message**:
- **Description:** add ability to over-ride index-name if provided in
the kwargs of sub-functions. When used in WSGI application it's crucial
to be able to dynamically change parameters.
- [ ] **Add tests and docs**:
- [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/
Chat models currently implement support for:
- images in OpenAI Chat Completions format
- other multimodal types (e.g., PDF and audio) in a cross-provider
[standard
format](https://python.langchain.com/docs/how_to/multimodal_inputs/)
Here we update core to extend support to PDF and audio input in Chat
Completions format. **If an OAI-format PDF or audio content block is
passed into any chat model, it will be transformed to the LangChain
standard format**. We assume that any chat model supporting OAI-format
PDF or audio has implemented support for the standard format.
`mixtral-8x-7b-instruct` was recently retired from Fireworks Serverless.
Here we remove the default model altogether, so that the model must be
explicitly specified on init:
```python
ChatFireworks(model="accounts/fireworks/models/llama-v3p1-70b-instruct") # for example
```
We also set a null default for `temperature`, which previously defaulted
to 0.0. This parameter will no longer be included in request payloads
unless it is explicitly provided.
**langchain_openai: Support of reasoning summary streaming**
**Description:**
OpenAI API now supports streaming reasoning summaries for reasoning
models (o1, o3, o3-mini, o4-mini). More info about it:
https://platform.openai.com/docs/guides/reasoning#reasoning-summaries
It is supported only in Responses API (not Completion API), so you need
to create LangChain Open AI model as follows to support reasoning
summaries streaming:
```
llm = ChatOpenAI(
model="o4-mini", # also o1, o3, o3-mini support reasoning streaming
use_responses_api=True, # reasoning streaming works only with responses api, not completion api
model_kwargs={
"reasoning": {
"effort": "high", # also "low" and "medium" supported
"summary": "auto" # some models support "concise" summary, some "detailed", but auto will always work
}
}
)
```
Now, if you stream events from llm:
```
async for event in llm.astream_events(prompt, version="v2"):
print(event)
```
or
```
for chunk in llm.stream(prompt):
print (chunk)
```
OpenAI API will send you new types of events:
`response.reasoning_summary_text.added`
`response.reasoning_summary_text.delta`
`response.reasoning_summary_text.done`
These events are new, so they were ignored. So I have added support of
these events in function `_convert_responses_chunk_to_generation_chunk`,
so reasoning chunks or full reasoning added to the chunk
additional_kwargs.
Example of how this reasoning summary may be printed:
```
async for event in llm.astream_events(prompt, version="v2"):
if event["event"] == "on_chat_model_stream":
chunk: AIMessageChunk = event["data"]["chunk"]
if "reasoning_summary_chunk" in chunk.additional_kwargs:
print(chunk.additional_kwargs["reasoning_summary_chunk"], end="")
elif "reasoning_summary" in chunk.additional_kwargs:
print("\n\nFull reasoning step summary:", chunk.additional_kwargs["reasoning_summary"])
elif chunk.content and chunk.content[0]["type"] == "text":
print(chunk.content[0]["text"], end="")
```
or
```
for chunk in llm.stream(prompt):
if "reasoning_summary_chunk" in chunk.additional_kwargs:
print(chunk.additional_kwargs["reasoning_summary_chunk"], end="")
elif "reasoning_summary" in chunk.additional_kwargs:
print("\n\nFull reasoning step summary:", chunk.additional_kwargs["reasoning_summary"])
elif chunk.content and chunk.content[0]["type"] == "text":
print(chunk.content[0]["text"], end="")
```
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
PR title:
docs: add Valyu integration documentation
Description:
This PR adds documentation and example notebooks for the Valyu
integration, including retriever and tool usage.
Issue:
N/A
Dependencies:
No new dependencies.
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
PR Summary
This change adds a fallback in ChatAnthropic.with_structured_output() to
handle Pydantic models that don’t include a docstring. Without it,
calling:
```py
from pydantic import BaseModel
from langchain_anthropic import ChatAnthropic
class SampleModel(BaseModel):
sample_field: str
llm = ChatAnthropic(
model="claude-3-7-sonnet-latest"
).with_structured_output(SampleModel.model_json_schema())
llm.invoke("test")
```
will raise a
```
KeyError: 'description'
```
because Pydantic omits the description field when no docstring is
present.
This issue doesn’t occur when using ChatOpenAI or if you add a docstring
to the model:
```py
from pydantic import BaseModel
from langchain_openai import ChatOpenAI
class SampleModel(BaseModel):
"""Schema for sample_field output."""
sample_field: str
llm = ChatOpenAI(
model="gpt-4o-mini"
).with_structured_output(SampleModel.model_json_schema())
llm.invoke("test")
```
---------
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, eyurtsev, ccurme, vbarda, hwchase17.
Addresses #30158
When using the output parser—either in a chain or standalone—hitting
max_tokens triggers a misleading “missing variable” error instead of
indicating the output was truncated. This subtle bug often surfaces with
Anthropic models.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
PR title:
Community: Add bind variable support for oracle adb docloader
Description:
This PR adds support of using bind variable to oracle adb doc loader
class, including minor document change.
Issue:
N/A
Dependencies:
No new dependencies.
This is a follow-on PR to go with the identical changes that were made
in parters/openai.
Previous PR: https://github.com/langchain-ai/langchain/pull/30757
When calling embed_documents and providing a chunk_size argument, that
argument is ignored when OpenAIEmbeddings is instantiated with its
default configuration (where check_embedding_ctx_length=True).
_get_len_safe_embeddings specifies a chunk_size parameter but it's not
being passed through in embed_documents, which is its only caller. This
appears to be an oversight, especially given that the
_get_len_safe_embeddings docstring states it should respect "the set
embedding context length and chunk size."
Developers typically expect method parameters to take effect (also, take
precedence) when explicitly provided, especially when instantiating
using defaults. I was confused as to why my API calls were being
rejected regardless of the chunk size I provided.
When calling `embed_documents` and providing a `chunk_size` argument,
that argument is ignored when `OpenAIEmbeddings` is instantiated with
its default configuration (where `check_embedding_ctx_length=True`).
`_get_len_safe_embeddings` specifies a `chunk_size` parameter but it's
not being passed through in `embed_documents`, which is its only caller.
This appears to be an oversight, especially given that the
`_get_len_safe_embeddings` docstring states it should respect "the set
embedding context length and chunk size."
Developers typically expect method parameters to take effect (also, take
precedence) when explicitly provided, especially when instantiating
using defaults. I was confused as to why my API calls were being
rejected regardless of the chunk size I provided.
This bug also exists in langchain_community package. I can add that to
this PR if requested otherwise I will create a new one once this passes.
**Description:**
partners-anthropic: ChatAnthropic supports b64 and urls in the
part[image_url][url] message variable
**Issue**:
ChatAnthropic right now only supports b64 encoded images in the
part[image_url][url] message variable. This PR enables ChatAnthropic to
also accept image urls in said variable and makes it compatible with
OpenAI messages to make model switching easier.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
SingleStore integration now has its package `langchain-singlestore', so
the community implementation will no longer be maintained.
Added `deprecated` decorator to `SingleStoreDBChatMessageHistory`,
`SingleStoreDBSemanticCache`, and `SingleStoreDB` classes in the
community package.
**Dependencies:** https://github.com/langchain-ai/langchain/pull/30841
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
Support "usage_metadata" for LiteLLM streaming calls.
This is a follow-up to
https://github.com/langchain-ai/langchain/pull/30625, which tackled
non-streaming calls.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, eyurtsev, ccurme, vbarda, hwchase17.
- [ ] **PR message**:
- **Description:** including metadata_field in
max_marginal_relevance_search() would result in error, changed the logic
to be similar to how it's handled in similarity_search, where it can be
any field or simply a "*" to include every field
Replacement for PR #30191 (@ccurme)
**Description**: currently, ChatOllama [will raise a value error if a
ChatMessage is passed to
it](https://github.com/langchain-ai/langchain/blob/master/libs/partners/ollama/langchain_ollama/chat_models.py#L514),
as described
https://github.com/langchain-ai/langchain/pull/30147#issuecomment-2708932481.
Furthermore, ollama-python is removing the limitations on valid roles
that can be passed through chat messages to a model in ollama -
https://github.com/ollama/ollama-python/pull/462#event-16917810634.
This PR removes the role limitations imposed by langchain and enables
passing langchain ChatMessages with arbitrary 'role' values through the
langchain ChatOllama class to the underlying ollama-python Client.
As this PR relies on [merged but unreleased functionality in
ollama-python](
https://github.com/ollama/ollama-python/pull/462#event-16917810634), I
have temporarily pointed the ollama package source to the main branch of
the ollama-python github repo.
Format, lint, and tests of new functionality passing. Need to resolve
issue with recently added ChatOllama tests. (Now resolved)
**Issue**: resolves#30122 (related to ollama issue
https://github.com/ollama/ollama/issues/8955)
**Dependencies**: no new dependencies
[x] PR title
[x] PR message
[x] Lint and test: format, lint, and test all running successfully and
passing
---------
Co-authored-by: Ryan Stewart <ryanstewart@Ryans-MacBook-Pro.local>
Co-authored-by: Chester Curme <chester.curme@gmail.com>
* Remove unused ignores
* Add type ignore codes
* Add mypy rule `warn_unused_ignores`
* Add ruff rule PGH003
NB: some `type: ignore[unused-ignore]` are added because the ignores are
needed when `extended_testing_deps.txt` deps are installed.
We only need to rebuild model schemas if type annotation information
isn't available during declaration - that shouldn't be the case for
these types corrected here.
Need to do more thorough testing to make sure these structures have
complete schemas, but hopefully this boosts startup / import time.
- [ ] **PR title**: "docs: adding Smabbler's Galaxia integration"
- [ ] **PR message**: **Twitter handle:** @Galaxia_graph
I'm adding docs here + added the package to the packages.yml. I didn't
add a unit test, because this integration is just a thin wrapper on top
of our API. There isn't much left to test if you mock it away.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
