Thank you for contributing to LangChain!
- **Adding documentation for PGVectorStore**:
docs: Adding documentation for the new PGVectorStore as a part of
langchain-postgres
- **Add docs**: The notebook for PGVectorStore is now added to the
directory `docs/docs/integrations`.
As a part of this change, we've also updated the VectorStore features
table and VectorStoreTabs
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
**Description:**
Fixes a bug in the file callback test where ANSI escape codes were
causing test failures. The improved test now properly handles ANSI
escape sequences by:
- Using exact string comparison instead of substring checking
- Applying the `strip_ansi` function consistently to all file contents
- Adding descriptive assertion messages
- Maintaining test coverage and backward compatibility
The changes ensure tests pass reliably even when terminal control
sequences are present in the output
**Issue:** Fixes#32150
**Dependencies:** None required - uses existing dependencies only.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
This PR addresses the common issue where users struggle to pass custom
parameters to OpenAI-compatible APIs like LM Studio, vLLM, and others.
The problem occurs when users try to use `model_kwargs` for custom
parameters, which causes API errors.
## Problem
Users attempting to pass custom parameters (like LM Studio's `ttl`
parameter) were getting errors:
```python
# ❌ This approach fails
llm = ChatOpenAI(
base_url="http://localhost:1234/v1",
model="mlx-community/QwQ-32B-4bit",
model_kwargs={"ttl": 5} # Causes TypeError: unexpected keyword argument 'ttl'
)
```
## Solution
The `extra_body` parameter is the correct way to pass custom parameters
to OpenAI-compatible APIs:
```python
# ✅ This approach works correctly
llm = ChatOpenAI(
base_url="http://localhost:1234/v1",
model="mlx-community/QwQ-32B-4bit",
extra_body={"ttl": 5} # Custom parameters go in extra_body
)
```
## Changes Made
1. **Enhanced Documentation**: Updated the `extra_body` parameter
docstring with comprehensive examples for LM Studio, vLLM, and other
providers
2. **Added Documentation Section**: Created a new "OpenAI-compatible
APIs" section in the main class docstring with practical examples
3. **Unit Tests**: Added tests to verify `extra_body` functionality
works correctly:
- `test_extra_body_parameter()`: Verifies custom parameters are included
in request payload
- `test_extra_body_with_model_kwargs()`: Ensures `extra_body` and
`model_kwargs` work together
4. **Clear Guidance**: Documented when to use `extra_body` vs
`model_kwargs`
## Examples Added
**LM Studio with TTL (auto-eviction):**
```python
ChatOpenAI(
base_url="http://localhost:1234/v1",
api_key="lm-studio",
model="mlx-community/QwQ-32B-4bit",
extra_body={"ttl": 300} # Auto-evict after 5 minutes
)
```
**vLLM with custom sampling:**
```python
ChatOpenAI(
base_url="http://localhost:8000/v1",
api_key="EMPTY",
model="meta-llama/Llama-2-7b-chat-hf",
extra_body={
"use_beam_search": True,
"best_of": 4
}
)
```
## Why This Works
- `model_kwargs` parameters are passed directly to the OpenAI client's
`create()` method, causing errors for non-standard parameters
- `extra_body` parameters are included in the HTTP request body, which
is exactly what OpenAI-compatible APIs expect for custom parameters
Fixes#32115.
<!-- START COPILOT CODING AGENT TIPS -->
---
💬 Share your feedback on Copilot coding agent for the chance to win a
$200 gift card! Click
[here](https://survey.alchemer.com/s3/8343779/Copilot-Coding-agent) to
start the survey.
---------
Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: mdrxy <61371264+mdrxy@users.noreply.github.com>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Further clean up of namespace:
- Removed prompts (we'll re-add in a separate commit)
- Remove LocalFileStore until we can review whether all the
implementation details are necessary
- Remove message processing logic from memory (we'll figure out where to
expose it)
- Remove `Tool` primitive (should be sufficient to use `BaseTool` for
typing purposes)
- Remove utilities to create kv stores. Unclear if they've had much
usage outside MultiparentRetriever
This PR adds scaffolding for langchain 1.0 entry package.
Most contents have been removed.
Currently remaining entrypoints for:
* chat models
* embedding models
* memory -> trimming messages, filtering messages and counting tokens
[we may remove this]
* prompts -> we may remove some prompts
* storage: primarily to support cache backed embeddings, may remove the
kv store
* tools -> report tool primitives
Things to be added:
* Selected agent implementations
* Selected workflows
* Common primitives: messages, Document
* Primitives for type hinting: BaseChatModel, BaseEmbeddings
* Selected retrievers
* Selected text splitters
Things to be removed:
* Globals needs to be removed (needs an update in langchain core)
Todos:
* TBD indexing api (requires sqlalchemy which we don't want as a
dependency)
* Be explicit about public/private interfaces (e.g., likely rename
chat_models.base.py to something more internal)
* Remove dockerfiles
* Update module doc-strings and README.md
The `_dereference_refs_helper` in `langchain_core.utils.json_schema`
incorrectly handled objects with a reference and other fields.
**Issue**: #32170
# Description
We change the check so that it accepts other keys in the object.
## Summary
- Fixed redundant word "done" in SECURITY.md line 69
- Fixed grammar errors in Fireworks README.md line 77: "how it fares
compares" → "how it compares" and "in terms just" → "in terms of"
## Test plan
- [x] Verified changes improve readability and correct grammar
- [x] No functional changes, documentation only
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-authored-by: Claude <claude@anthropic.com>
Co-authored-by: Claude <noreply@anthropic.com>
Multiple models were
[retired](https://docs.anthropic.com/en/docs/about-claude/model-deprecations#model-status)
yesterday.
Tests remain broken until we figure out what to do with the legacy
Anthropic LLM integration— currently uses their (legacy) text
completions API, for which there appear to be no remaining supported
models.
* Adding support for more Chroma client options (`HttpClient` and
`CloundClient`). This includes adding arguments necessary for
instantiating these clients.
* Adding support for Chroma's new persisted collection configuration (we
moved index configuration into this new construct).
* Delegate `Settings` configuration to Chroma's client constructors.
## Problem
When using `ChatOllama` with `create_react_agent`, agents would
sometimes terminate prematurely with empty responses when Ollama
returned `done_reason: 'load'` responses with no content. This caused
agents to return empty `AIMessage` objects instead of actual generated
text.
```python
from langchain_ollama import ChatOllama
from langgraph.prebuilt import create_react_agent
from langchain_core.messages import HumanMessage
llm = ChatOllama(model='qwen2.5:7b', temperature=0)
agent = create_react_agent(model=llm, tools=[])
result = agent.invoke(HumanMessage('Hello'), {"configurable": {"thread_id": "1"}})
# Before fix: AIMessage(content='', response_metadata={'done_reason': 'load'})
# Expected: AIMessage with actual generated content
```
## Root Cause
The `_iterate_over_stream` and `_aiterate_over_stream` methods treated
any response with `done: True` as final, regardless of `done_reason`.
When Ollama returns `done_reason: 'load'` with empty content, it
indicates the model was loaded but no actual generation occurred - this
should not be considered a complete response.
## Solution
Modified the streaming logic to skip responses when:
- `done: True`
- `done_reason: 'load'`
- Content is empty or contains only whitespace
This ensures agents only receive actual generated content while
preserving backward compatibility for load responses that do contain
content.
## Changes
- **`_iterate_over_stream`**: Skip empty load responses instead of
yielding them
- **`_aiterate_over_stream`**: Apply same fix to async streaming
- **Tests**: Added comprehensive test cases covering all edge cases
## Testing
All scenarios now work correctly:
- ✅ Empty load responses are skipped (fixes original issue)
- ✅ Load responses with actual content are preserved (backward
compatibility)
- ✅ Normal stop responses work unchanged
- ✅ Streaming behavior preserved
- ✅ `create_react_agent` integration fixed
Fixes#31482.
<!-- START COPILOT CODING AGENT TIPS -->
---
💡 You can make Copilot smarter by setting up custom instructions,
customizing its development environment and configuring Model Context
Protocol (MCP) servers. Learn more [Copilot coding agent
tips](https://gh.io/copilot-coding-agent-tips) in the docs.
---------
Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: mdrxy <61371264+mdrxy@users.noreply.github.com>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
## **Description:**
This PR updates the internal documentation link for the RAG tutorials to
reflect the updated path. Previously, the link pointed to the root
`/docs/tutorials/`, which was generic. It now correctly routes to the
RAG-specific tutorial page for the following text-embedding models.
1. DatabricksEmbeddings
2. IBM watsonx.ai
3. OpenAIEmbeddings
4. NomicEmbeddings
5. CohereEmbeddings
6. MistralAIEmbeddings
7. FireworksEmbeddings
8. TogetherEmbeddings
9. LindormAIEmbeddings
10. ModelScopeEmbeddings
11. ClovaXEmbeddings
12. NetmindEmbeddings
13. SambaNovaCloudEmbeddings
14. SambaStudioEmbeddings
15. ZhipuAIEmbeddings
## **Issue:** N/A
## **Dependencies:** None
## **Twitter handle:** N/A
