Added support for new Exa API features. Updated Exa docs and python
package (langchain-exa).
Description
Added support for new Exa API features in the langchain-exa package:
- Added max_characters option for text content
- Added support for summary and custom summary prompts
- Added livecrawl option with "always", "fallback", "never" settings
- Added "auto" option for search type
- Updated documentation and tests
Dependencies
- No new dependencies required. Using existing features from exa-py.
twitter: @theishangoswami
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
**Description:** ConversationChain has been deprecated, and the
documentation says to use RunnableWithMessageHistory in its place, but
the link at the top of the page to RunnableWithMessageHistory is broken
(it's rendering as "html()"). See here at the top of the page:
https://python.langchain.com/api_reference/langchain/chains/langchain.chains.conversation.base.ConversationChain.html.
This PR fixes the link.
**Issue**: N/A
**Dependencies**: N/A
**Twitter handle:**: If you're on Bluesky, I'm @vikramsaraph.com
Scheduled testing started failing today because the Responses API
stopped raising `BadRequestError` for a schema that was previously
invalid when `strict=True`.
Although docs still say that [some type-specific keywords are not yet
supported](https://platform.openai.com/docs/guides/structured-outputs#some-type-specific-keywords-are-not-yet-supported)
(including `minimum` and `maximum` for numbers), the below appears to
run and correctly respect the constraints:
```python
import json
import openai
maximums = list(range(1, 11))
arg_values = []
for maximum in maximums:
tool = {
"type": "function",
"name": "magic_function",
"description": "Applies a magic function to an input.",
"parameters": {
"properties": {
"input": {"maximum": maximum, "minimum": 0, "type": "integer"}
},
"required": ["input"],
"type": "object",
"additionalProperties": False
},
"strict": True
}
client = openai.OpenAI()
response = client.responses.create(
model="gpt-4.1",
input=[{"role": "user", "content": "What is the value of magic_function(3)? Use the tool."}],
tools=[tool],
)
function_call = next(item for item in response.output if item.type == "function_call")
args = json.loads(function_call.arguments)
arg_values.append(args["input"])
print(maximums)
print(arg_values)
# [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
# [1, 2, 3, 3, 3, 3, 3, 3, 3, 3]
```
Until yesterday this raised BadRequestError.
The same is not true of Chat Completions, which appears to still raise
BadRequestError
```python
tool = {
"type": "function",
"function": {
"name": "magic_function",
"description": "Applies a magic function to an input.",
"parameters": {
"properties": {
"input": {"maximum": 5, "minimum": 0, "type": "integer"}
},
"required": ["input"],
"type": "object",
"additionalProperties": False
},
"strict": True
}
}
response = client.chat.completions.create(
model="gpt-4.1",
messages=[{"role": "user", "content": "What is the value of magic_function(3)? Use the tool."}],
tools=[tool],
)
response # raises BadRequestError
```
Here we update tests accordingly.
Update docs to add Featherless.ai Provider & Chat Model
- **Description:** Adding Featherless.ai as provider in teh
documentations giving access to over 4300+ open-source models
- **Twitter handle:** https://x.com/FeatherlessAI
* It is possible to chain a `Runnable` with an `AsyncIterator` as seen
in `test_runnable.py`.
* Iterator and AsyncIterator Input/Output of Callables must be put
before `Callable[[Other], Any]` otherwise the pattern matching picks the
latter.
**PR message**: Not sure if I put the check at the right spot, but I
thought throwing the error before the loop made sense to me.
**Description:** Checks if there are only system messages using
AnthropicChat model and throws an error if it's the case. Check Issue
for more details
**Issue:** #30764
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
**Issue:**[
#309070](https://github.com/langchain-ai/langchain/issues/30970)
**Cause**
Arg type in python code
```
arg: Union[SubSchema1, SubSchema2]
```
is translated to `anyOf` in **json schema**
```
"anyOf" : [{sub schema 1 ...}, {sub schema 1 ...}]
```
The value of anyOf is a list sub schemas.
The bug is caused since the sub schemas inside `anyOf` list is not taken
care of.
The location where the issue happens is `convert_to_openai_function`
function -> `_recursive_set_additional_properties_false` function, that
recursively adds `"additionalProperties": false` to json schema which is
[required by OpenAI's strict function
calling](https://platform.openai.com/docs/guides/structured-outputs?api-mode=responses#additionalproperties-false-must-always-be-set-in-objects)
**Solution:**
This PR fixes this issue by iterating each sub schema inside `anyOf`
list.
A unit test is added.
**Twitter handle:** shengboma
If no one reviews your PR within a few days, please @-mention one of
baskaryan, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
`aindex` function should check not only `adelete` method, but `delete`
method too
**PR title**: "core: fix async indexing issue with adelete/delete
checking"
**PR message**: Currently `langchain.indexes.aindex` checks if vector
store has overrided adelete method. But due to `adelete` default
implementation store can have just `delete` overrided to make `adelete`
working.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
**Description**:
Add a `async_client_kwargs` field to ollama chat/llm/embeddings adapters
that is passed to async httpx client constructor.
**Motivation:**
In my use-case:
- chat/embedding model adapters may be created frequently, sometimes to
be called just once or to never be called at all
- they may be used in bots sunc and async mode (not known at the moment
they are created)
So, I want to keep a static transport instance maintaining connection
pool, so model adapters can be created and destroyed freely. But that
doesn't work when both sync and async functions are in use as I can only
pass one transport instance for both sync and async client, while
transport types must be different for them. So I can't make both sync
and async calls use shared transport with current model adapter
interfaces.
In this PR I add a separate `async_client_kwargs` that gets passed to
async client constructor, so it will be possible to pass a separate
transport instance. For sake of backwards compatibility, it is merged
with `client_kwargs`, so nothing changes when it is not set.
I am unable to run linter right now, but the changes look ok.
Updates dependencies to Chroma to integrate the major release of Chroma
with improved performance, and to fix issues users have been seeing
using the latest chroma docker image with langchain-chroma
https://github.com/langchain-ai/langchain/issues/31047#issuecomment-2850790841
Updates chromadb dependency to >=1.0.9
This also removes the dependency of chroma-hnswlib, meaning it can run
against python 3.13 runners for tests as well.
Tested this by pulling the latest Chroma docker image, running
langchain-chroma using client mode
```
httpClient = chromadb.HttpClient(host="localhost", port=8000)
vector_store = Chroma(
client=httpClient,
collection_name="test",
embedding_function=embeddings,
)
```
* Remove unnecessary cast of id -> str (can do with a field setting)
* Remove unnecessary `set_text` model validator (can be done with a
computed field - though we had to make some changes to the `Generation`
class to make this possible
Before: ~2.4s
Blue circles represent time spent in custom validators :(
<img width="1337" alt="Screenshot 2025-05-14 at 10 10 12 AM"
src="https://github.com/user-attachments/assets/bb4f477f-4ee3-4870-ae93-14ca7f197d55"
/>
After: ~2.2s
<img width="1344" alt="Screenshot 2025-05-14 at 10 11 03 AM"
src="https://github.com/user-attachments/assets/99f97d80-49de-462f-856f-9e7e8662adbc"
/>
We still want to optimize the backwards compatible tool calls model
validator, though I think this might involve breaking changes, so wanted
to separate that into a different PR. This is circled in green.
**Description:** Before this commit, if one record is batched in more
than 32k rows for sqlite3 >= 3.32 or more than 999 rows for sqlite3 <
3.31, the `record_manager.delete_keys()` will fail, as we are creating a
query with too many variables.
This commit ensures that we are batching the delete operation leveraging
the `cleanup_batch_size` as it is already done for `full` cleanup.
Added unit tests for incremental mode as well on different deleting
batch size.
1. Removes summation of `ChatGenerationChunk` from hot loops in `stream`
and `astream`
2. Removes run id gen from loop as well (minor impact)
Again, benchmarking on processing ~200k chunks (a poem about broccoli).
Before: ~4.2s
Blue circle is all the time spent adding up gen chunks
<img width="1345" alt="Screenshot 2025-05-14 at 7 48 33 AM"
src="https://github.com/user-attachments/assets/08a59d78-134d-4cd3-9d54-214de689df51"
/>
After: ~2.3s
Blue circle is remaining time spent on adding chunks, which can be
minimized in a future PR by optimizing the `merge_content`,
`merge_dicts`, and `merge_lists` utilities.
<img width="1353" alt="Screenshot 2025-05-14 at 7 50 08 AM"
src="https://github.com/user-attachments/assets/df6b3506-929e-4b6d-b198-7c4e992c6d34"
/>
1. Remove `shielded` decorator from non-end event handlers
2. Exit early with a `self.handlers` check instead of doing unnecessary
asyncio work
Using a benchmark that processes ~200k chunks (a poem about broccoli).
Before: ~15s
Circled in blue is unnecessary event handling time. This is addressed by
point 2 above
<img width="1347" alt="Screenshot 2025-05-14 at 7 37 53 AM"
src="https://github.com/user-attachments/assets/675e0fed-8f37-46c0-90b3-bef3cb9a1e86"
/>
After: ~4.2s
The total time is largely reduced by the removal of the `shielded`
decorator, which holds little significance for non-end handlers.
<img width="1348" alt="Screenshot 2025-05-14 at 7 37 22 AM"
src="https://github.com/user-attachments/assets/54be8a3e-5827-4136-a87b-54b0d40fe331"
/>
Some providers include (legacy) function calls in `additional_kwargs` in
addition to tool calls. We currently unpack both function calls and tool
calls if present, but OpenAI will raise 400 in this case.
This can come up if providers are mixed in a tool-calling loop. Example:
```python
from langchain.chat_models import init_chat_model
from langchain_core.messages import HumanMessage
from langchain_core.tools import tool
@tool
def get_weather(location: str) -> str:
"""Get weather at a location."""
return "It's sunny."
gemini = init_chat_model("google_genai:gemini-2.0-flash-001").bind_tools([get_weather])
openai = init_chat_model("openai:gpt-4.1-mini").bind_tools([get_weather])
input_message = HumanMessage("What's the weather in Boston?")
tool_call_message = gemini.invoke([input_message])
assert len(tool_call_message.tool_calls) == 1
tool_call = tool_call_message.tool_calls[0]
tool_message = get_weather.invoke(tool_call)
response = openai.invoke( # currently raises 400 / BadRequestError
[input_message, tool_call_message, tool_message]
)
```
Here we ignore function calls if tool calls are present.
**Description**: The 'inspect' package in python skips over the aliases
set in the schema of a pydantic model. This is a workound to include the
aliases from the original input.
**issue**: #31035
Cc: @ccurme @eyurtsev
---------
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, core, etc. is being
modified. Use "docs: ..." for purely docs changes, "infra: ..." for CI
changes.
- Example: "core: add foobar LLM"
- **Description:** Integrated the Bright Data package to enable
Langchain users to seamlessly incorporate Bright Data into their agents.
- **Dependencies:** None
- **LinkedIn handle**:[Bright
Data](https://www.linkedin.com/company/bright-data)
- [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 no one reviews your PR within a few days, please @-mention one of
baskaryan, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
**Description:**
Fix the merge logic in `CharacterTextSplitter.split_text` so that when
using a regex lookahead separator (`is_separator_regex=True`) with
`keep_separator=False`, the raw pattern is not re-inserted between
chunks.
**Issue:**
Fixes#31136
**Dependencies:**
None
**Twitter handle:**
None
Since this is my first open-source PR, please feel free to point out any
mistakes, and I'll be eager to make corrections.
