# Description
This PR fixes a bug in _recursive_set_additional_properties_false used
in function_calling.convert_to_openai_function.
Previously, schemas with "additionalProperties=True" were not correctly
overridden when strict validation was expected, which could lead to
invalid OpenAI function schemas.
The updated implementation ensures that:
- Any schema with "additionalProperties" already set will now be forced
to False under strict mode.
- Recursive traversal of properties, items, and anyOf is preserved.
- Function signature remains unchanged for backward compatibility.
# Issue
When using tool calling in OpenAI structured output strict mode
(strict=True), 400: "Invalid schema for response_format XXXXX
'additionalProperties' is required to be supplied and to be false" error
raises for the parameter that contains dict type. OpenAI requires
additionalProperties to be set to False.
Some PRs try to resolved the issue.
- PR #25169 introduced _recursive_set_additional_properties_false to
recursively set additionalProperties=False.
- PR #26287 fixed handling of empty parameter tools for OpenAI function
generation.
- PR #30971 added support for Union type arguments in strict mode of
OpenAI function calling / structured output.
Despite these improvements, since Pydantic 2.11, it will always add
`additionalProperties: True` for arbitrary dictionary schemas dict or
Any (https://pydantic.dev/articles/pydantic-v2-11-release#changes).
Schemas that already had additionalProperties=True in such cases were
not being overridden, which this PR addresses to ensure strict mode
behaves correctly in all cases.
# Dependencies
No Changes
---------
Co-authored-by: Zhong, Yu <yzhong@freewheel.com>
This PR adds a new cookbook demonstrating how to build a RAG pipeline
with LangChain and track + evaluate it using MLflow.
Currently not much documentation on LangChain MLflow integration, hope
this can help folks trying to monitor and evaluate their LangChain
applications.
- ArXiv document loader
- In Memory vector store
- LCEL rag pipeline
- MLflow tracing
- MLflow evaluation
Issue:
N/A
Dependencies:
N/A
**Description:**
Updates the Confident AI integration documentation to use modern
patterns and improve code quality. This change:
- Replaces deprecated `DeepEvalCallbackHandler` with the new
`CallbackHandler` from `deepeval.integrations.langchain`
- Updates installation and authentication instructions to match current
best practices
- Adds modern integration examples using LangChain's latest patterns
- Removes deprecated metrics and outdated code examples
- Updates code samples to follow current best practices
The changes make the documentation more maintainable and ensure users
follow the recommended integration patterns.
**Issue:** Fixes#32444
**Dependencies:**
- deepeval
- langchain
- langchain-openai
**Twitter handle:** @Muwinuddin
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Description:
Added "Method Two: Quick Setup (Linux)" section to prerequisites,
providing a curl-based installation method for deploying JaguarDB
without Docker. Retained original Docker setup instructions for
flexibility.
Thank you for contributing to LangChain! Follow these steps to mark your
pull request as ready for review. **If any of these steps are not
completed, your PR will not be considered for review.**
- **Description:** Aerospike Vector Store has been retired. It is no
longer supported so It should no longer be documented on the Langchain
site.
- **Add tests and docs**: Removes docs for retired Aerospike vector
store.
- **Lint and test**: NA
Added a short section to the Weaviate integration docs showing how to
connect to an existing collection (reuse an index) with
`WeaviateVectorStore`. This helps clarify required parameters
(`index_name`, `text_key`) when loading a pre-existing store, which was
previously missing.
Thank you for contributing to LangChain! Follow these steps to mark your
pull request as ready for review. **If any of these steps are not
completed, your PR will not be considered for review.**
### Description
Added a short section to the Weaviate integration docs showing how to
connect to an existing collection (reuse an index) with
`WeaviateVectorStore`. This helps clarify required parameters
(`index_name`, `text_key`) when loading a pre-existing store, which was
previously missing.
### Issue
Fixeslangchain-ai/langchain-weaviate#197
### Dependencies
None
Thank you for contributing to LangChain! Follow these steps to mark your
pull request as ready for review. **If any of these steps are not
completed, your PR will not be considered for review.**
- [x] **PR title**: Follows the format: {TYPE}({SCOPE}): {DESCRIPTION}
- Examples:
- feat(core): add multi-tenant support
- fix(cli): resolve flag parsing error
- docs(openai): update API usage examples
- Allowed `{TYPE}` values:
- feat, fix, docs, style, refactor, perf, test, build, ci, chore,
revert, release
- Allowed `{SCOPE}` values (optional):
- core, cli, langchain, standard-tests, docs, anthropic, chroma,
deepseek, exa, fireworks, groq, huggingface, mistralai, nomic, ollama,
openai, perplexity, prompty, qdrant, xai
- Note: the `{DESCRIPTION}` must not start with an uppercase letter.
- Once you've written the title, please delete this checklist item; do
not include it in the PR.
- [x] **PR message**:
- **Description:** Fixing the import path for `WatsonxToolkit` in
examples after releasing `lnagchain-ibm==0.3.17`
- [ ] **Add tests and docs**: If you're adding a new integration, you
must 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. **We will not consider
a PR unless these three are passing in CI.** See [contribution
guidelines](https://python.langchain.com/docs/contributing/) for more.
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.
### Description
This PR is primarily aimed at updating some usage methods in the
`modelscope.mdx` file.
Specifically, it changes from `ModelScopeLLM` to `ModelScopeEndpoint`.
### Relevant PR
The relevant PR link is:
https://github.com/langchain-ai/langchain/pull/28941
**Description:**
Raise a more descriptive OutputParserException when JSON parsing results
in a non-dict type. This improves debugging and aligns behavior with
expectations when using expected_keys.
**Issue:**
Fixes#32233
**Twitter handle:**
@yashvtobre
**Testing:**
- Ran make format and make lint from the root directory; both passed
cleanly.
- Attempted make test but no such target exists in the root Makefile.
- Executed tests directly via pytest targeting the relevant test file,
confirming all tests pass except for unrelated async test failures
outside the scope of this change.
**Notes:**
- No additional dependencies introduced.
- Changes are backward compatible and isolated within the output parser
module.
---------
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
- **Description:** Currently,
`langchain_core.runnables.graph_mermaid.py` is hardcoded to use
mermaid.ink to render graph diagrams. It would be nice to allow users to
specify a custom URL, e.g. for self-hosted instances of the Mermaid
server.
- **Issue:** [Langchain Forum: allow custom mermaid API
URL](https://forum.langchain.com/t/feature-request-allow-custom-mermaid-api-url/1472)
- **Dependencies:** None
- [X] **Add tests and docs**: Added unit tests using mock requests.
- [X] **Lint and test**: Run `make format`, `make lint` and `make test`.
Minimal example using the feature:
```python
import os
import operator
from pathlib import Path
from typing import Any, Annotated, TypedDict
from langgraph.graph import StateGraph
class State(TypedDict):
messages: Annotated[list[dict[str, Any]], operator.add]
def hello_node(state: State) -> State:
return {"messages": [{"role": "assistant", "content": "pong!"}]}
builder = StateGraph(State)
builder.add_node("hello_node", hello_node)
builder.add_edge("__start__", "hello_node")
builder.add_edge("hello_node", "__end__")
graph = builder.compile()
# Run graph
output = graph.invoke({"messages": [{"role": "user", "content": "ping?"}]})
# Draw graph
Path("graph.png").write_bytes(graph.get_graph().draw_mermaid_png(base_url="https://custom-mermaid.ink"))
```
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
- Beta isn't needed for search result tests anymore
- Add TODO for other tests to come back when generally available
- Regenerate remote MCP snapshot after some testing (now the same, but
fresher)
- Bump deps
This pull request introduces a failing unit test to reproduce the bug
reported in issue #32028.
The test asserts the expected behavior: `BaseCallbackManager.merge()`
should combine `handlers` and `inheritable_handlers` independently,
without mixing them. This test will fail on the current codebase and is
intended to guide the fix and prevent future regressions.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Mason Daugherty <mason@langchain.dev>
The Ollama chat model adapter does not support all of the possible
message content formats. That leads to Ollama model adapter crashing on
some messages from different models (e.g. Gemini 2.5 Flash).
These changes should fix one known scenario - when `content` is a list
containing a string.
This allows to use PEP604 syntax for `ToolNode` error handlers
```python
def error_handler(e: ValueError | ToolException) -> str:
return "error"
ToolNode(my_tool, handle_tool_errors=error_handler).invoke(...)
```
Without this change, this fails with `AttributeError: 'types.UnionType'
object has no attribute '__mro__'`
This is better than using a subclass as returning a `property` works
with `ClassWithBetaMethods.beta_property.__doc__`
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Added an id field to the Document passed to filter for
InMemoryVectorStore similarity search. This allows filtering by Document
id and brings the input to the filter in line with the result returned
by the vector similarity search.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
- stars badge redundant (look at the top of the page)
- remove version badge since we have many pkgs (and it was only showing
core) -- also, just look at the releases tab to the right of the readme
- **Description:** The vectorstore standard-test mistakenly assumes that
the store's `get_by_ids` respects the order of the provided `ids`. This
is not the case (as the base class docstring states). This PR fixes
those tests that would fail otherwise (see issue #32820 for details,
repro and all). Fixes#32820
- **Issue:** Fixes#32820
- **Dependencies:** none
Co-authored-by: Stefano Lottini <stefano.lottini@ibm.com>
## Overview
Adding new `AgentMiddleware` primitive that supports `before_model`,
`after_model`, and `prepare_model_request` hooks.
This is very exciting! It makes our `create_agent` prebuilt much more
extensible + capable. Still in alpha and subject to change.
This is different than the initial
[implementation](https://github.com/langchain-ai/langgraph/tree/nc/25aug/agent)
in that it:
* Fills in gaps w/ missing features, for ex -- new structured output,
optionality of tools + system prompt, sync and async model requests,
provider builtin tools
* Exposes private state extensions for middleware, enabling things like
model call tracking, etc
* Middleware can register tools
* Uses a `TypedDict` for `AgentState` -- dataclass subclassing is tricky
w/ required values + required decorators
* Addition of `model_settings` to `ModelRequest` so that we can pass
through things to bind (like cache kwargs for anthropic middleware)
## TODOs
### top prio
- [x] add middleware support to existing agent
- [x] top prio middlewares
- [x] summarization node
- [x] HITL
- [x] prompt caching
other ones
- [x] model call limits
- [x] tool calling limits
- [ ] usage (requires output state)
### secondary prio
- [x] improve typing for state updates from middleware (not working
right now w/ simple `AgentUpdate` and `AgentJump`, at least in Python)
- [ ] add support for public state (input / output modifications via
pregel channel mods) -- to be tackled in another PR
- [x] testing!
### docs
See https://github.com/langchain-ai/docs/pull/390
- [x] high level docs about middleware
- [x] summarization node
- [x] HITL
- [x] prompt caching
## open questions
Lots of open questions right now, many of them inlined as comments for
the short term, will catalog some more significant ones here.
---------
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
**Description:**
Remove a character in tool_calling.ipynb that causes a grammatical error
Verification: Local docs build passed after fix
**Issue:**
None (direct hotfix for rendering issue identified during documentation
review)
**Dependencies:**
None
**Description:** This PR fixes the broken Anthropic model example in the
documentation introduction page and adds a comment field to display
model version warnings in code blocks. The changes ensure that users can
successfully run the example code and are reminded to check for the
latest model versions.
**Issue:** https://github.com/langchain-ai/langchain/issues/32806
**Changes made:**
- Update Anthropic model from broken "claude-3-5-sonnet-latest" to
working "claude-3-7-sonnet-20250219"
- Add comment field to display model version warnings in code blocks
- Improve user experience by providing working examples and version
guidance
**Dependencies:** None required
Fixes#32747
SpaCy integration test fixture was trying to use pip to download the
SpaCy language model (`en_core_web_sm`), but uv-managed environments
don't include pip by default. Fail test if not installed as opposed to
downloading.
Removed a period in bulleted list for consistency
Thank you for contributing to LangChain! Follow these steps to mark your
pull request as ready for review. **If any of these steps are not
completed, your PR will not be considered for review.**
- [ ] **PR title**: Follows the format: {TYPE}({SCOPE}): {DESCRIPTION}
- Examples:
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- fix(cli): resolve flag parsing error
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- Once you've written the title, please delete this checklist item; do
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- **Description:** a description of the change. Include a [closing
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if applicable to a relevant issue.
- **Issue:** the issue # it fixes, if applicable (e.g. Fixes#123)
- **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, you
must 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. **We will not consider
a PR unless these three are passing in CI.** See [contribution
guidelines](https://python.langchain.com/docs/contributing/) for more.
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.
Completed the sentence by adding a period ".", in sync with other points
>> Click "Propose changes"
to
>> Click "Propose changes".
Thank you for contributing to LangChain! Follow these steps to mark your
pull request as ready for review. **If any of these steps are not
completed, your PR will not be considered for review.**
- [ ] **PR title**: Follows the format: {TYPE}({SCOPE}): {DESCRIPTION}
- Examples:
- feat(core): add multi-tenant support
- fix(cli): resolve flag parsing error
- docs(openai): update API usage examples
- Allowed `{TYPE}` values:
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- Note: the `{DESCRIPTION}` must not start with an uppercase letter.
- Once you've written the title, please delete this checklist item; do
not include it in the PR.
- [ ] **PR message**: ***Delete this entire checklist*** and replace
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- **Description:** a description of the change. Include a [closing
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if applicable to a relevant issue.
- **Issue:** the issue # it fixes, if applicable (e.g. Fixes#123)
- **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, you
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1. A test for the integration, preferably unit tests that do not rely on
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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. **We will not consider
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Additional guidelines:
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- 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.
Update `langchain-core` dependency min from `>=0.3.63` to `>=0.3.75`.
### Motivation
- We located the `langchain-core` package locally in the monorepo and
need to align `langchain-tests` with the new minimum version.
### Overview
Preparing the `1.0.0a1` release of `langchain-tests` to align with
`langchain-core` version `1.0.0a1`.
### Changes
- Bump package version to `1.0.0a1`
- Relax `langchain-core` requirement from `<1.0.0,>=0.3.63` to
`<2.0.0,>=0.3.63`
### Motivation
All main LangChain packages are now publishing `1.0.0a` prereleases.
`langchain-tests` needs a matching prerelease so downstreams can install
tests alongside the 1.0 series without conflicts.
### Tests
- Verified installation and tests against both `0.3.75` and `1.0.0a1`.
Description:
Added the content= keyword when creating SystemMessage and HumanMessage
in the messages list, making it consistent with the API reference.
### Summary
This PR updates the sentence on the "How-to guides" landing page to
replace smart (curly) quotes with straight quotes in the phrase:
> "How do I...?"
### Why This Change?
- Ensures formatting consistency across documentation
- Avoids encoding or rendering issues with smart quotes
- Matches standard Markdown and inline code formatting
This is a small change, but improves clarity and polish on a key landing
page.
Change "Linkedin" to "LinkedIn" to be consistent with LinkedIn's
spelling.
Thank you for contributing to LangChain! Follow these steps to mark your
pull request as ready for review. **If any of these steps are not
completed, your PR will not be considered for review.**
- [x] **Add tests and docs**: If you're adding a new integration, you
must 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. **We will not consider
a PR unless these three are passing in CI.** See [contribution
guidelines](https://python.langchain.com/docs/contributing/) for more.
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.
Adding `create_react_agent` and introducing `langchain.agents`!
## Enhanced Structured Output
`create_react_agent` supports coercion of outputs to structured data
types like `pydantic` models, dataclasses, typed dicts, or JSON schemas
specifications.
### Structural Changes
In langgraph < 1.0, `create_react_agent` implemented support for
structured output via an additional LLM call to the model after the
standard model / tool calling loop finished. This introduced extra
expense and was unnecessary.
This new version implements structured output support in the main loop,
allowing a model to choose between calling tools or generating
structured output (or both).
The same basic pattern for structured output generation works:
```py
from langchain.agents import create_react_agent
from langchain_core.messages import HumanMessage
from pydantic import BaseModel
class Weather(BaseModel):
temperature: float
condition: str
def weather_tool(city: str) -> str:
"""Get the weather for a city."""
return f"it's sunny and 70 degrees in {city}"
agent = create_react_agent("openai:gpt-4o-mini", tools=[weather_tool], response_format=Weather)
print(repr(result["structured_response"]))
#> Weather(temperature=70.0, condition='sunny')
```
### Advanced Configuration
The new API exposes two ways to configure how structured output is
generated. Under the hood, LangChain will attempt to pick the best
approach if not explicitly specified. That is, if provider native
support is available for a given model, that takes priority over
artificial tool calling.
1. Artificial tool calling (the default for most models)
LangChain generates a tool (or tools) under the hood that match the
schema of your response format. When the model calls those tools,
LangChain coerces the args to the desired format. Note, LangChain does
not validate outputs adhering to JSON schema specifications.
<details>
<summary>Extended example</summary>
```py
from langchain.agents import create_react_agent
from langchain_core.messages import HumanMessage
from langchain.agents.structured_output import ToolStrategy
from pydantic import BaseModel
class Weather(BaseModel):
temperature: float
condition: str
def weather_tool(city: str) -> str:
"""Get the weather for a city."""
return f"it's sunny and 70 degrees in {city}"
agent = create_react_agent(
"openai:gpt-4o-mini",
tools=[weather_tool],
response_format=ToolStrategy(
schema=Weather, tool_message_content="Final Weather result generated"
),
)
result = agent.invoke({"messages": [HumanMessage("What's the weather in Tokyo?")]})
for message in result["messages"]:
message.pretty_print()
"""
================================ Human Message =================================
What's the weather in Tokyo?
================================== Ai Message ==================================
Tool Calls:
weather_tool (call_Gg933BMHMwck50Q39dtBjXm7)
Call ID: call_Gg933BMHMwck50Q39dtBjXm7
Args:
city: Tokyo
================================= Tool Message =================================
Name: weather_tool
it's sunny and 70 degrees in Tokyo
================================== Ai Message ==================================
Tool Calls:
Weather (call_9xOkYUM7PuEXl9DQq9sWGv5l)
Call ID: call_9xOkYUM7PuEXl9DQq9sWGv5l
Args:
temperature: 70
condition: sunny
================================= Tool Message =================================
Name: Weather
Final Weather result generated
"""
print(repr(result["structured_response"]))
#> Weather(temperature=70.0, condition='sunny')
```
</details>
2. Provider implementations (limited to OpenAI, Groq)
Some providers support structured output generating directly. For those
cases, we offer the `ProviderStrategy` hint:
<details>
<summary>Extended example</summary>
```py
from langchain.agents import create_react_agent
from langchain_core.messages import HumanMessage
from langchain.agents.structured_output import ProviderStrategy
from pydantic import BaseModel
class Weather(BaseModel):
temperature: float
condition: str
def weather_tool(city: str) -> str:
"""Get the weather for a city."""
return f"it's sunny and 70 degrees in {city}"
agent = create_react_agent(
"openai:gpt-4o-mini",
tools=[weather_tool],
response_format=ProviderStrategy(Weather),
)
result = agent.invoke({"messages": [HumanMessage("What's the weather in Tokyo?")]})
for message in result["messages"]:
message.pretty_print()
"""
================================ Human Message =================================
What's the weather in Tokyo?
================================== Ai Message ==================================
Tool Calls:
weather_tool (call_OFJq1FngIXS6cvjWv5nfSFZp)
Call ID: call_OFJq1FngIXS6cvjWv5nfSFZp
Args:
city: Tokyo
================================= Tool Message =================================
Name: weather_tool
it's sunny and 70 degrees in Tokyo
================================== Ai Message ==================================
{"temperature":70,"condition":"sunny"}
Weather(temperature=70.0, condition='sunny')
"""
print(repr(result["structured_response"]))
#> Weather(temperature=70.0, condition='sunny')
```
Note! The final tool message has the custom content provided by the dev.
</details>
Prompted output was previously supported and is no longer supported via
the `response_format` argument to `create_react_agent`. If there's
significant demand for this, we'd be happy to engineer a solution.
## Error Handling
`create_react_agent` now exposes an API for managing errors associated
with structured output generation. There are two common problems with
structured output generation (w/ artificial tool calling):
1. **Parsing error** -- the model generates data that doesn't match the
desired structure for the output
2. **Multiple tool calls error** -- the model generates 2 or more tool
calls associated with structured output schemas
A developer can control the desired behavior for this via the
`handle_errors` arg to `ToolStrategy`.
<details>
<summary>Extended example</summary>
```py
from langchain_core.messages import HumanMessage
from pydantic import BaseModel
from langchain.agents import create_react_agent
from langchain.agents.structured_output import StructuredOutputValidationError, ToolStrategy
class Weather(BaseModel):
temperature: float
condition: str
def weather_tool(city: str) -> str:
"""Get the weather for a city."""
return f"it's sunny and 70 degrees in {city}"
def handle_validation_error(error: Exception) -> str:
if isinstance(error, StructuredOutputValidationError):
return (
f"Please call the {error.tool_name} call again with the correct arguments. "
f"Your mistake was: {error.source}"
)
raise error
agent = create_react_agent(
"openai:gpt-5",
tools=[weather_tool],
response_format=ToolStrategy(
schema=Weather,
handle_errors=handle_validation_error,
),
)
```
</details>
## Error Handling for Tool Calling
Tools fail for two main reasons:
1. **Invocation failure** -- the args generated by the model for the
tool are incorrect (missing, incompatible data types, etc)
2. **Execution failure** -- the tool execution itself fails due to a
developer error, network error, or some other exception.
By default, when tool **invocation** fails, the react agent will return
an artificial `ToolMessage` to the model asking it to correct its
mistakes and retry.
Now, when tool **execution** fails, the react agent raises the
`ToolException` by default instead of asking the model to retry. This
helps to avoid looping that should be avoided due to the aforementioned
issues.
Developers can configure their desired behavior for retries / error
handling via the `handle_tool_errors` arg to `ToolNode`.
## Pre-Bound Models
`create_react_agent` no longer supports inputs to `model` that have been
pre-bound w/ tools or other configuration. To properly support
structured output generation, the agent itself needs the power to bind
tools + structured output kwargs.
This also makes the devx cleaner - it's always expected that `model` is
an instance of `BaseChatModel` (or `str` that we coerce into a chat
model instance).
Dynamic model functions can return a pre-bound model **IF** structured
output is not also used. Dynamic model functions can then bind tools /
structured output logic.
## Import Changes
Users should now use `create_react_agent` from `langchain.agents`
instead of `langgraph.prebuilts`.
Other imports have a similar migration path, `ToolNode` and `AgentState`
for example.
* `chat_agent_executor.py` -> `react_agent.py`
Some notes:
1. Disabled blockbuster + some linting in `langchain/agents` -- beyond
ideal, but necessary to get this across the line for the alpha. We
should re-enable before official release.
- **Description:** Updated Docker command to use ClickHouse 25.7 (has
`vector_similarity` index support). Added `CLICKHOUSE_SKIP_USER_SETUP=1`
env param to [bypass default user
setup](https://clickhouse.com/docs/install/docker#managing-default-user)
and allow external network access. There was also a bug where if you try
to access results using `similarity_search_with_relevance_scores`, they
need to unpacked first.
- **Issue:** Fixes#32094 if someone following tutorial with default
Clickhouse configurations.
# Description
Updated documentation to reflect Microsoft’s rebranding of Azure AI
Studio to Azure AI Foundry. This ensures consistency with current Azure
terminology across the docs.
# Issue
N/A
# Dependencies
None
The async version of the test should use the `ayield_keys` method
instead of `yield_keys`.
Otherwise tools such as `blockbuster` may trigger on a blocking call.
**Description:**
Fixed corrupted text in the code cell output of the documentation
notebook. The code cell itself was correct, but the saved output
contained garbage text.
**Issue:**
The saved output in the documentation notebook contained garbage/typo
text in the table name.
**Dependencies:**
None
Having vercel attempt to deploy on each commit (even if unrelated to
docs) was getting annoying. Options:
- `[skip-preview]`
- `[no-preview]`
- `[skip-deploy]`
Full example: `fix(core): resolve memory leak [no-preview]`
* Create usage metadata on
[`message_delta`](https://docs.anthropic.com/en/docs/build-with-claude/streaming#event-types)
instead of at the beginning. Consequently, token counts are not included
during streaming but instead at the end. This allows for accurate
reporting of server-side tool usage (important for billing)
* Add some clarifying comments
* Fix some outstanding Pylance warnings
* Remove unnecessary `text` popping in thinking blocks
* Also now correctly reports `input_cache_read`/`input_cache_creation`
as a result
When citations are returned from streaming, they include a `file_id:
null` field in their `content_block_location` structure.
When these citations are passed back to the API in subsequent messages,
the API rejects them with "Extra inputs are not permitted" for the
`file_id` field.
**Description:**
Corrected LangGraph documentation link (changed to “guides”), and added
a link to LangGraph JS how-to guides for clarity.
**Issue:**
N/A
**Dependencies:**
None
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
The appropriate `ToolNode` attribute for error handling is called
`handle_tool_errors` instead of `handle_tool_error`.
For further info see [ToolNode source code in
LangGraph](https://github.com/langchain-ai/langgraph/blob/main/libs/prebuilt/langgraph/prebuilt/tool_node.py#L255)
**Twitter handle:** gitaroktato
- [x] **Add tests and docs**: If you're adding a new integration, you
must 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. **We will not consider
a PR unless these three are passing in CI.** See [contribution
guidelines](https://python.langchain.com/docs/contributing/) for more.
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.
## Description
This PR adds support for custom header patterns in
`MarkdownHeaderTextSplitter`, allowing users to define non-standard
Markdown header formats (like `**Header**`) and specify their hierarchy
levels.
**Issue:** Fixes#22738
**Dependencies:** None - this change has no new dependencies
**Key Changes:**
- Added optional `custom_header_patterns` parameter to support
non-standard header formats
- Enable splitting on patterns like `**Header**` and `***Header***`
- Maintain full backward compatibility with existing usage
- Added comprehensive tests for custom and mixed header scenarios
## Example Usage
```python
from langchain_text_splitters import MarkdownHeaderTextSplitter
headers_to_split_on = [
("**", "Chapter"),
("***", "Section"),
]
custom_header_patterns = {
"**": 1, # Level 1 headers
"***": 2, # Level 2 headers
}
splitter = MarkdownHeaderTextSplitter(
headers_to_split_on=headers_to_split_on,
custom_header_patterns=custom_header_patterns,
)
# Now **Chapter 1** is treated as a level 1 header
# And ***Section 1.1*** is treated as a level 2 header
```
## Testing
- ✅ Added unit tests for custom header patterns
- ✅ Added tests for mixed standard and custom headers
- ✅ All existing tests pass (backward compatibility maintained)
- ✅ Linting and formatting checks pass
---
The implementation provides a flexible solution while maintaining the
simplicity of the existing API. Users can continue using the splitter
exactly as before, with the new functionality being entirely opt-in
through the `custom_header_patterns` parameter.
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Co-authored-by: Claude <noreply@anthropic.com>
Supersedes #32461
Fixed incorrect input token reporting during streaming when tools are
used. Previously, input tokens were counted at `message_start` before
tool execution, leading to inaccurate counts. Now input tokens are
properly deferred until `message_delta` (completion), aligning with
Anthropic's billing model and SDK expectations.
**Before Fix:**
- Streaming with tools: Input tokens = 0 ❌
- Non-streaming with tools: Input tokens = 472 ✅
**After Fix:**
- Streaming with tools: Input tokens = 472 ✅
- Non-streaming with tools: Input tokens = 472 ✅
Aligns with Anthropic's SDK expectations. The SDK handles input token
updates in `message_delta` events:
```python
# https://github.com/anthropics/anthropic-sdk-python/blob/main/src/anthropic/lib/streaming/_messages.py
if event.usage.input_tokens is not None:
current_snapshot.usage.input_tokens = event.usage.input_tokens
```
Supersedes #32544
Changes to the `trimmer` behavior resulted in the call `"What math
problem was asked?"` to no longer see the relevant query due to the
number of the queries' tokens. Adjusted to not trigger trimming the
relevant part of the message history. Also, add print to the trimmer to
increase observability on what is leaving the context window.
Add note to trimming tut & format links as inline
Thank you for contributing to LangChain! Follow these steps to mark your
pull request as ready for review. **If any of these steps are not
completed, your PR will not be considered for review.**
- [x] **PR title**: Follows the format: {TYPE}({SCOPE}): {DESCRIPTION}
- Examples:
- feat(core): add multi-tenant support
- fix(cli): resolve flag parsing error
- docs(openai): update API usage examples
- Allowed `{TYPE}` values:
- feat, fix, docs, style, refactor, perf, test, build, ci, chore,
revert, release
- Allowed `{SCOPE}` values (optional):
- core, cli, langchain, standard-tests, docs, anthropic, chroma,
deepseek, exa, fireworks, groq, huggingface, mistralai, nomic, ollama,
openai, perplexity, prompty, qdrant, xai
- Note: the `{DESCRIPTION}` must not start with an uppercase letter.
- Once you've written the title, please delete this checklist item; do
not include it in the PR.
- [x] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change. Include a [closing
keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/using-issues/linking-a-pull-request-to-an-issue#linking-a-pull-request-to-an-issue-using-a-keyword)
if applicable to a relevant issue.
- **Issue:** the issue # it fixes, if applicable (e.g. Fixes#123)
- **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!
- [x] **Add tests and docs**: If you're adding a new integration, you
must 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. **We will not consider
a PR unless these three are passing in CI.** See [contribution
guidelines](https://python.langchain.com/docs/contributing/) for more.
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.
---------
Co-authored-by: Mason Daugherty <github@mdrxy.com>
Enhance the integrations table by adding the `js:
'@langchain/community'` reference for several packages and updating the
titles of specific integrations to avoid improper capitalization
Supersedes #32408
Description:
This PR ensures that tool calls without explicitly provided `args` will
default to an empty dictionary (`{}`), allowing tools with no parameters
(e.g. `def foo() -> str`) to be registered and invoked without
validation errors. This change improves compatibility with agent
frameworks that may omit the `args` field when generating tool calls.
Issue:
See
[langgraph#5722](https://github.com/langchain-ai/langgraph/issues/5722)
–
LangGraph currently emits tool calls without `args`, which leads to
validation errors
when tools with no parameters are invoked. This PR ensures compatibility
by defaulting
`args` to `{}` when missing.
Dependencies:
None
---------
Thank you for contributing to LangChain! Follow these steps to mark your
pull request as ready for review. **If any of these steps are not
completed, your PR will not be considered for review.**
- [ ] **PR title**: Follows the format: {TYPE}({SCOPE}): {DESCRIPTION}
- Examples:
- feat(core): add multi-tenant support
- fix(cli): resolve flag parsing error
- docs(openai): update API usage examples
- Allowed `{TYPE}` values:
- feat, fix, docs, style, refactor, perf, test, build, ci, chore,
revert, release
- Allowed `{SCOPE}` values (optional):
- core, cli, langchain, standard-tests, docs, anthropic, chroma,
deepseek, exa, fireworks, groq, huggingface, mistralai, nomic, ollama,
openai, perplexity, prompty, qdrant, xai
- Note: the `{DESCRIPTION}` must not start with an uppercase letter.
- Once you've written the title, please delete this checklist item; do
not include it in the PR.
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change. Include a [closing
keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/using-issues/linking-a-pull-request-to-an-issue#linking-a-pull-request-to-an-issue-using-a-keyword)
if applicable to a relevant issue.
- **Issue:** the issue # it fixes, if applicable (e.g. Fixes#123)
- **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, you
must 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. **We will not consider
a PR unless these three are passing in CI.** See [contribution
guidelines](https://python.langchain.com/docs/contributing/) for more.
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.
---------
Signed-off-by: jitokim <pigberger70@gmail.com>
Co-authored-by: jito <pigberger70@gmail.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
**Description**
Corrected a typo in the Ollama chatbot example output in
`docs/docs/integrations/chat/ollama.ipynb` where `"got-oss"` was
mistakenly used instead of `"gpt-oss"`.
No functional changes to code; documentation-only update.
All notebook outputs were cleared to keep the diff minimal.
**Issue**
N/A
**Dependencies**
None
**Twitter handle**
N/A
Thank you for contributing to LangChain! Follow these steps to mark your
pull request as ready for review. **If any of these steps are not
completed, your PR will not be considered for review.**
- [x] **PR title**: Follows the format: {TYPE}({SCOPE}): {DESCRIPTION}
- [x] **PR message**: ***Delete this entire checklist*** and replace
with
fix#30146
- [x] **Add tests and docs**: If you're adding a new integration, you
must include:
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. **We will not consider
a PR unless these three are passing in CI.** See [contribution
guidelines](https://python.langchain.com/docs/contributing/) for more.
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.
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
```python
from langchain_anthropic import ChatAnthropic
llm = ChatAnthropic(model="claude-3-5-haiku-latest")
caching_llm = llm.bind(cache_control={"type": "ephemeral"})
caching_llm.invoke(
[
HumanMessage("..."),
AIMessage("..."),
HumanMessage("..."), # <-- final message / content block gets cache annotation
]
)
```
Potentially useful given's Anthropic's [incremental
caching](https://docs.anthropic.com/en/docs/build-with-claude/prompt-caching#continuing-a-multi-turn-conversation)
capabilities:
> During each turn, we mark the final block of the final message with
cache_control so the conversation can be incrementally cached. The
system will automatically lookup and use the longest previously cached
prefix for follow-up messages.
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
This commit removes redundant integration info from details page,
additionally, changing reference from "DigitalOcean GradientAI" to
"DigitalOcean Gradient™ AI" and updating the setup instructions
accordingly.
**Description:**
Two broken links were reported by another LangChain employee. This PR
fixes those links.
Fixed and tested locally.
**Dependencies:**
None
This PR adds documentation for integrating [TrueFoundry’s AI
Gateway](https://www.truefoundry.com/ai-gateway) with Langfuse using the
Langraph OpenAI SDK.
The integration sends requests through TrueFoundry’s AI Gateway for
unified governance, observability, and routing, while Langraph runs on
the client side to capture execution traces and telemetry.
- Issue: N/A
- Dependencies: None
- Twitter - https://x.com/truefoundry
tests - Not applicable
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.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 Scrapeless package to enable Langchain
users to seamlessly incorporate Scrapeless into their agents.
- **Dependencies:** None
- **Twitter handle:** [Scrapelessteam](https://x.com/Scrapelessteam)
- [x] **Add tests and docs**: If you're adding a new integration, you
must 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](https://python.langchain.com/docs/contributing/) for more.
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.
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
# Description
This PR updates the docs for the
[langchain-anchorbrowser](https://pypi.org/project/langchain-anchorbrowser/)
package. It adds a few tools
[Anchor Browser](https://anchorbrowser.io/?utm=langchain) is the
platform for AI Agentic browser automation, which solves the challenge
of automating workflows for web applications that lack APIs or have
limited API coverage. It simplifies the creation, deployment, and
management of browser-based automations, transforming complex web
interactions into simple API endpoints.
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
This PR introduces a new Google partner guide for MCP Toolbox. The
primary goal of this new documentation is to enhance the discoverability
of MCP Toolbox for developers working within the Google ecosystem,
providing them with a clear and direct path to using our tools.
> [!IMPORTANT]
> This PR contains link to a page which is added in #32344. This will
cause deployment failure until that PR is merged.
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
This PR introduces a new integration guide for MCP Toolbox. The primary
goal of this new documentation is to enhance the discoverability of MCP
Toolbox for developers working within the LangChain ecosystem, providing
them with a clear and direct path to using our tools.
This approach was chosen to provide users with a practical, hands-on
example that they can easily follow.
> [!NOTE]
> The page added in this PR is linked to from a section in Google
partners page added in #32356.
---------
Co-authored-by: Lauren Hirata Singh <lauren@langchain.dev>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
Co-authored-by: Mason Daugherty <mason@langchain.dev>
In [Rag Part 1
Tutorial](https://python.langchain.com/docs/tutorials/rag/), when QDrant
vector store is selected, the sample code does not work
It fails with error `ValueError: Collection test not found`
So, this fix is creating that collection and ensuring its dimension size
is matching the selection the embedding size of the selected LLM Model
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
```messages_to_pass = [
HumanMessage(content="What's the capital of France?"),
AIMessage(content="The capital of France is Paris."),
HumanMessage(content="And what about Germany?")
]
formatted_prompt = prompt_template.invoke({"msgs": messages_to_pass})
print(formatted_prompt)```
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Co-authored-by: Mason Daugherty <github@mdrxy.com>
**Description:**
I've added a small clarification to the chatbot tutorial. The tutorial
mentions setting the `LANGSMITH_API_KEY`, but doesn't explain how a new
user can get the key from the website. This change adds a brief note to
guide them to the Settings page.
P.S. This is my first pull request, so I'm excited to learn and
contribute!
**Issue:**
N/A
**Dependencies:**
N/A
**Twitter handle:**
@sohamactive
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Closes#32320
This PR updates the `langgraph_agentic_rag.ipynb` notebook to clarify
that LangGraph does not automatically prepend a `SystemMessage`. A
markdown note and an inline Python comment have been added to guide
users to explicitly include a `SystemMessage` when needed.
This improves documentation for developers working with LangGraph-based
agents and avoids confusion about system-level behavior not being
applied.
---------
Co-authored-by: Mason Daugherty <mason@langchain.dev>
Bumps
[actions/download-artifact](https://github.com/actions/download-artifact)
from 4 to 5.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/actions/download-artifact/releases">actions/download-artifact's
releases</a>.</em></p>
<blockquote>
<h2>v5.0.0</h2>
<h2>What's Changed</h2>
<ul>
<li>Update README.md by <a
href="https://github.com/nebuk89"><code>@nebuk89</code></a> in <a
href="https://redirect.github.com/actions/download-artifact/pull/407">actions/download-artifact#407</a></li>
<li>BREAKING fix: inconsistent path behavior for single artifact
downloads by ID by <a
href="https://github.com/GrantBirki"><code>@GrantBirki</code></a> in <a
href="https://redirect.github.com/actions/download-artifact/pull/416">actions/download-artifact#416</a></li>
</ul>
<h2>v5.0.0</h2>
<h3>🚨 Breaking Change</h3>
<p>This release fixes an inconsistency in path behavior for single
artifact downloads by ID. <strong>If you're downloading single artifacts
by ID, the output path may change.</strong></p>
<h4>What Changed</h4>
<p>Previously, <strong>single artifact downloads</strong> behaved
differently depending on how you specified the artifact:</p>
<ul>
<li><strong>By name</strong>: <code>name: my-artifact</code> → extracted
to <code>path/</code> (direct)</li>
<li><strong>By ID</strong>: <code>artifact-ids: 12345</code> → extracted
to <code>path/my-artifact/</code> (nested)</li>
</ul>
<p>Now both methods are consistent:</p>
<ul>
<li><strong>By name</strong>: <code>name: my-artifact</code> → extracted
to <code>path/</code> (unchanged)</li>
<li><strong>By ID</strong>: <code>artifact-ids: 12345</code> → extracted
to <code>path/</code> (fixed - now direct)</li>
</ul>
<h4>Migration Guide</h4>
<h5>✅ No Action Needed If:</h5>
<ul>
<li>You download artifacts by <strong>name</strong></li>
<li>You download <strong>multiple</strong> artifacts by ID</li>
<li>You already use <code>merge-multiple: true</code> as a
workaround</li>
</ul>
<h5>⚠️ Action Required If:</h5>
<p>You download <strong>single artifacts by ID</strong> and your
workflows expect the nested directory structure.</p>
<p><strong>Before v5 (nested structure):</strong></p>
<pre lang="yaml"><code>- uses: actions/download-artifact@v4
with:
artifact-ids: 12345
path: dist
# Files were in: dist/my-artifact/
</code></pre>
<blockquote>
<p>Where <code>my-artifact</code> is the name of the artifact you
previously uploaded</p>
</blockquote>
<p><strong>To maintain old behavior (if needed):</strong></p>
<pre lang="yaml"><code></tr></table>
</code></pre>
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="634f93cb29"><code>634f93c</code></a>
Merge pull request <a
href="https://redirect.github.com/actions/download-artifact/issues/416">#416</a>
from actions/single-artifact-id-download-path</li>
<li><a
href="b19ff43027"><code>b19ff43</code></a>
refactor: resolve download path correctly in artifact download tests
(mainly ...</li>
<li><a
href="e262cbee4a"><code>e262cbe</code></a>
bundle dist</li>
<li><a
href="bff23f9308"><code>bff23f9</code></a>
update docs</li>
<li><a
href="fff8c148a8"><code>fff8c14</code></a>
fix download path logic when downloading a single artifact by id</li>
<li><a
href="448e3f862a"><code>448e3f8</code></a>
Merge pull request <a
href="https://redirect.github.com/actions/download-artifact/issues/407">#407</a>
from actions/nebuk89-patch-1</li>
<li><a
href="47225c44b3"><code>47225c4</code></a>
Update README.md</li>
<li>See full diff in <a
href="https://github.com/actions/download-artifact/compare/v4...v5">compare
view</a></li>
</ul>
</details>
<br />
[](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores)
Dependabot will resolve any conflicts with this PR as long as you don't
alter it yourself. You can also trigger a rebase manually by commenting
`@dependabot rebase`.
[//]: # (dependabot-automerge-start)
[//]: # (dependabot-automerge-end)
---
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**Description:**
In the `docs/docs/how_to/structured_output.ipynb` notebook, an
`AIMessage` within the tool-calling few-shot example was missing the
`name="example_assistant"` parameter. This was inconsistent with the
other `AIMessage` instances in the same list.
This change adds the missing `name` parameter to ensure all examples in
the section are consistent, improving the clarity and correctness of the
documentation.
**Issue:** N/A
**Dependencies:** N/A
While trying the line People.schema got a warning.
```The `schema` method is deprecated; use `model_json_schema` instead```
So made the changes and now working file.
Thank you for contributing to LangChain! Follow these steps to mark your pull request as ready for review. **If any of these steps are not completed, your PR will not be considered for review.**
- [ ] **PR title**: Follows the format: {TYPE}({SCOPE}): {DESCRIPTION}
- Examples:
- feat(core): add multi-tenant support
- fix(cli): resolve flag parsing error
- docs(openai): update API usage examples
- Allowed `{TYPE}` values:
- feat, fix, docs, style, refactor, perf, test, build, ci, chore, revert, release
- Allowed `{SCOPE}` values (optional):
- core, cli, langchain, standard-tests, docs, anthropic, chroma, deepseek, exa, fireworks, groq, huggingface, mistralai, nomic, ollama, openai, perplexity, prompty, qdrant, xai
- Note: the `{DESCRIPTION}` must not start with an uppercase letter.
- Once you've written the title, please delete this checklist item; do not include it in the PR.
- [ ] **PR message**: ***Delete this entire checklist*** and replace with
- **Description:** a description of the change. Include a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/using-issues/linking-a-pull-request-to-an-issue#linking-a-pull-request-to-an-issue-using-a-keyword) if applicable to a relevant issue.
- **Issue:** the issue # it fixes, if applicable (e.g. Fixes#123)
- **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, you must 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. **We will not consider a PR unless these three are passing in CI.** See [contribution guidelines](https://python.langchain.com/docs/contributing/) for more.
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.
Description:
Corrected the guide title from "How deal with high cardinality
categoricals" to "How to deal with high-cardinality categoricals".
- Added missing "to" for grammatical correctness.
- Hyphenated "high-cardinality" for standard compound adjective usage.
Issue:
N/A
Dependencies:
None
Twitter handle:
https://x.com/mishraravibhush
@@ -15,12 +15,12 @@ You may use the button above, or follow these steps to open this repo in a Codes
1. Click **Create codespace on master**.
For more info, check out the [GitHub documentation](https://docs.github.com/en/free-pro-team@latest/github/developing-online-with-codespaces/creating-a-codespace#creating-a-codespace).
## VS Code Dev Containers
[](https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/langchain-ai/langchain)
> [!NOTE]
> [!NOTE]
> If you click the link above you will open the main repo (`langchain-ai/langchain`) and *not* your local cloned repo. This is fine if you only want to run and test the library, but if you want to contribute you can use the link below and replace with your username and cloned repo name:
@@ -7,4 +7,4 @@ To learn how to contribute to LangChain, please follow the [contribution guide h
## New features
For new features, please start a new [discussion](https://forum.langchain.com/), where the maintainers will help with scoping out the necessary changes.
For new features, please start a new [discussion on our forum](https://forum.langchain.com/), where the maintainers will help with scoping out the necessary changes.
Thank you for taking the time to file a bug report.
Thank you for taking the time to file a bug report.
Use this to report BUGS in LangChain. For usage questions, feature requests and general design questions, please use the [LangChain Forum](https://forum.langchain.com/).
@@ -50,7 +50,7 @@ body:
If a maintainer can copy it, run it, and see it right away, there's a much higher chance that you'll be able to get help.
**Important!**
**Important!**
* Avoid screenshots when possible, as they are hard to read and (more importantly) don't allow others to copy-and-paste your code.
* Reduce your code to the minimum required to reproduce the issue if possible. This makes it much easier for others to help you.
@@ -58,14 +58,14 @@ body:
* INCLUDE the language label (e.g. `python`) after the first three backticks to enable syntax highlighting. (e.g., ```python rather than ```).
placeholder:|
The following code:
The following code:
```python
from langchain_core.runnables import RunnableLambda
If you are not a LangChain maintainer or were not asked directly by a maintainer to create an issue, then please start the conversation on the [LangChain Forum](https://forum.langchain.com/) instead.
You are a LangChain maintainer if you maintain any of the packages inside of the LangChain repository
You are a LangChain maintainer if you maintain any of the packages inside of the LangChain repository
or are a regular contributor to LangChain with previous merged pull requests.
Thank you for contributing to LangChain! Follow these steps to mark your pull request as ready for review. **If any of these steps are not completed, your PR will not be considered for review.**
- [ ]**PR title**: Follows the format: {TYPE}({SCOPE}): {DESCRIPTION}
@@ -9,14 +11,13 @@ Thank you for contributing to LangChain! Follow these steps to mark your pull re
- Note: the `{DESCRIPTION}` must not start with an uppercase letter.
-*Note:* the `{DESCRIPTION}` must not start with an uppercase letter.
- Once you've written the title, please delete this checklist item; do not include it in the PR.
- [ ]**PR message**: ***Delete this entire checklist*** and replace with
- **Description:** a description of the change. Include a [closing keyword](https://docs.github.com/en/issues/tracking-your-work-with-issues/using-issues/linking-a-pull-request-to-an-issue#linking-a-pull-request-to-an-issue-using-a-keyword) if applicable to a relevant issue.
- **Issue:** the issue # it fixes, if applicable (e.g. Fixes #123)
- **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, you must include:
1. A test for the integration, preferably unit tests that do not rely on network access,
@@ -26,7 +27,7 @@ Thank you for contributing to LangChain! Follow these steps to mark your pull re
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.
- Please do not add dependencies to `pyproject.toml` files (even optional ones) unless they are **required** for unit tests.
- Changes should be backwards compatible.
- Make sure optional dependencies are imported within a function.
[](https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/langchain-ai/langchain)
[<img src="https://github.com/codespaces/badge.svg" alt="Open in Github Codespace" title="Open in Github Codespace" width="150" height="20">](https://codespaces.new/langchain-ai/langchain)
and scale agents effortlessly with a purpose-built deployment platform for long-running, stateful workflows. Discover, reuse, configure, and share agents across
teams — and iterate quickly with visual prototyping in
@@ -4,9 +4,9 @@ LangChain has a large ecosystem of integrations with various external resources
## Best practices
When building such applications developers should remember to follow good security practices:
When building such applications, developers should remember to follow good security practices:
* [**Limit Permissions**](https://en.wikipedia.org/wiki/Principle_of_least_privilege): Scope permissions specifically to the application's need. Granting broad or excessive permissions can introduce significant security vulnerabilities. To avoid such vulnerabilities, consider using read-only credentials, disallowing access to sensitive resources, using sandboxing techniques (such as running inside a container), specifying proxy configurations to control external requests, etc. as appropriate for your application.
* [**Limit Permissions**](https://en.wikipedia.org/wiki/Principle_of_least_privilege): Scope permissions specifically to the application's need. Granting broad or excessive permissions can introduce significant security vulnerabilities. To avoid such vulnerabilities, consider using read-only credentials, disallowing access to sensitive resources, using sandboxing techniques (such as running inside a container), specifying proxy configurations to control external requests, etc., as appropriate for your application.
* **Anticipate Potential Misuse**: Just as humans can err, so can Large Language Models (LLMs). Always assume that any system access or credentials may be used in any way allowed by the permissions they are assigned. For example, if a pair of database credentials allows deleting data, it's safest to assume that any LLM able to use those credentials may in fact delete data.
* [**Defense in Depth**](https://en.wikipedia.org/wiki/Defense_in_depth_(computing)): No security technique is perfect. Fine-tuning and good chain design can reduce, but not eliminate, the odds that a Large Language Model (LLM) may make a mistake. It's best to combine multiple layered security approaches rather than relying on any single layer of defense to ensure security. For example: use both read-only permissions and sandboxing to ensure that LLMs are only able to access data that is explicitly meant for them to use.
@@ -67,8 +67,7 @@ All out of scope targets defined by huntr as well as:
for more details, but generally tools interact with the real world. Developers are
expected to understand the security implications of their code and are responsible
for the security of their tools.
* Code documented with security notices. This will be decided on a case by
case basis, but likely will not be eligible for a bounty as the code is already
* Code documented with security notices. This will be decided on a case-by-case basis, but likely will not be eligible for a bounty as the code is already
documented with guidelines for developers that should be followed for making their
application secure.
* Any LangSmith related repositories or APIs (see [Reporting LangSmith Vulnerabilities](#reporting-langsmith-vulnerabilities)).
[rag-locally-on-intel-cpu.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/rag-locally-on-intel-cpu.ipynb) | Perform Retrieval-Augmented-Generation (RAG) on locally downloaded open-source models using langchain and open source tools and execute it on Intel Xeon CPU. We showed an example of how to apply RAG on Llama 2 model and enable it to answer the queries related to Intel Q1 2024 earnings release.
[visual_RAG_vdms.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/visual_RAG_vdms.ipynb) | Performs Visual Retrieval-Augmented-Generation (RAG) using videos and scene descriptions generated by open source models.
[contextual_rag.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/contextual_rag.ipynb) | Performs contextual retrieval-augmented generation (RAG) prepending chunk-specific explanatory context to each chunk before embedding.
[rag-agents-locally-on-intel-cpu.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/local_rag_agents_intel_cpu.ipynb) | Build a RAG agent locally with open source models that routes questions through one of two paths to find answers. The agent generates answers based on documents retrieved from either the vector database or retrieved from web search. If the vector database lacks relevant information, the agent opts for web search. Open-source models for LLM and embeddings are used locally on an Intel Xeon CPU to execute this pipeline.
[rag-agents-locally-on-intel-cpu.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/local_rag_agents_intel_cpu.ipynb) | Build a RAG agent locally with open source models that routes questions through one of two paths to find answers. The agent generates answers based on documents retrieved from either the vector database or retrieved from web search. If the vector database lacks relevant information, the agent opts for web search. Open-source models for LLM and embeddings are used locally on an Intel Xeon CPU to execute this pipeline.
[rag_mlflow_tracking_evaluation.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/rag_mlflow_tracking_evaluation.ipynb) | Guide on how to create a RAG pipeline and track + evaluate it with MLflow.
"# RAG Pipeline with MLflow Tracking, Tracing & Evaluation\n",
"\n",
"This notebook demonstrates how to build a complete Retrieval-Augmented Generation (RAG) pipeline using LangChain and integrate it with MLflow for experiment tracking, tracing, and evaluation.\n",
"\n",
"\n",
"- **RAG Pipeline Construction**: Build a complete RAG system using LangChain components\n",
"- **MLflow Integration**: Track experiments, parameters, and artifacts\n",
" \"system_prompt\": \"You are a helpful assistant. Use the following context to answer the question. Use three sentences maximum and keep the answer concise.\",\n",
" \"llm\": \"gpt-5-nano\",\n",
" \"temperature\": 0,\n",
"}"
]
},
{
"cell_type": "markdown",
"id": "8a2985f1",
"metadata": {},
"source": [
"#### ArXiv Dcoument Loading and Processing"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "1f32aa36",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'Published': '2023-08-02', 'Title': 'Attention Is All You Need', 'Authors': 'Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin', 'Summary': 'The dominant sequence transduction models are based on complex recurrent or\\nconvolutional neural networks in an encoder-decoder configuration. The best\\nperforming models also connect the encoder and decoder through an attention\\nmechanism. We propose a new simple network architecture, the Transformer, based\\nsolely on attention mechanisms, dispensing with recurrence and convolutions\\nentirely. Experiments on two machine translation tasks show these models to be\\nsuperior in quality while being more parallelizable and requiring significantly\\nless time to train. Our model achieves 28.4 BLEU on the WMT 2014\\nEnglish-to-German translation task, improving over the existing best results,\\nincluding ensembles by over 2 BLEU. On the WMT 2014 English-to-French\\ntranslation task, our model establishes a new single-model state-of-the-art\\nBLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction\\nof the training costs of the best models from the literature. We show that the\\nTransformer generalizes well to other tasks by applying it successfully to\\nEnglish constituency parsing both with large and limited training data.'}\n"
]
}
],
"source": [
"# Load documents from ArXiv\n",
"loader = ArxivLoader(\n",
" query=\"1706.03762\",\n",
" load_max_docs=1,\n",
")\n",
"docs = loader.load()\n",
"print(docs[0].metadata)\n",
"\n",
"# Split documents into chunks\n",
"splitter = RecursiveCharacterTextSplitter(\n",
" chunk_size=CONFIG[\"chunk_size\"],\n",
" chunk_overlap=CONFIG[\"chunk_overlap\"],\n",
")\n",
"chunks = splitter.split_documents(docs)\n",
"\n",
"# Join chunks into a single string\n",
"def join_chunks(chunks):\n",
" return \"\\n\\n\".join([chunk.page_content for chunk in chunks])\n"
"Create a prediction function decorated with `@mlflow.trace` to automatically log:\n",
"- Input queries\n",
"- Retrieved documents\n",
"- Generated responses\n",
"- Execution time\n",
"- Chain intermediate steps"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "7b45fc04",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Question: What is the main idea of the paper?\n",
"Response: The main idea is to replace recurrent/convolutional sequence models with a pure attention-based architecture called the Transformer. It uses self-attention to model dependencies between all positions in the input and output, enabling full parallelization and better handling of long-range relations. This approach achieves strong results on translation and can extend to other modalities.\n"
]
}
],
"source": [
"@mlflow.trace\n",
"def predict_fn(question: str) -> str:\n",
" return rag_chain.invoke(question)\n",
"\n",
"# Test the prediction function\n",
"sample_question = \"What is the main idea of the paper?\"\n",
"response = predict_fn(sample_question)\n",
"print(f\"Question: {sample_question}\")\n",
"print(f\"Response: {response}\")"
]
},
{
"cell_type": "markdown",
"id": "421469de",
"metadata": {},
"source": [
"#### Evaluation Dataset and Scoring\n",
"\n",
"Define an evaluation dataset and run systematic evaluation using [MLflow's built-in scorers](https://mlflow.org/docs/latest/genai/eval-monitor/scorers/llm-judge/predefined/#available-scorers):\n",
"\n",
"<u>Evaluation Components:</u>\n",
"- **Dataset**: Questions with expected concepts and facts\n",
"- **Scorers**: \n",
" - `RelevanceToQuery`: Measures how relevant the response is to the question\n",
" - `Correctness`: Evaluates factual accuracy of the response\n",
" - `ExpectationsGuidelines`: Checks that output matches expectation guidelines\n",
"\n",
"<u>Best Practices:</u>\n",
"- Create diverse test cases covering different query types\n",
"- Include expected concepts to guide evaluation\n",
"- Use multiple scoring metrics for comprehensive assessment"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "5c1dc4f2",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2025/08/23 20:14:39 INFO mlflow.models.evaluation.utils.trace: Auto tracing is temporarily enabled during the model evaluation for computing some metrics and debugging. To disable tracing, call `mlflow.autolog(disable=True)`.\n",
"2025/08/23 20:14:39 INFO mlflow.genai.utils.data_validation: Testing model prediction with the first sample in the dataset.\n"
@@ -12,7 +12,7 @@ You are expected to be familiar with asynchronous programming in Python before r
This guide specifically focuses on what you need to know to work with LangChain in an asynchronous context, assuming that you are already familiar with asynchronous programming.
:::
## Langchain asynchronous APIs
## LangChain asynchronous APIs
Many LangChain APIs are designed to be asynchronous, allowing you to build efficient and responsive applications.
@@ -31,7 +31,7 @@ The conceptual guide does not cover step-by-step instructions or specific implem
- **[Vector stores](/docs/concepts/vectorstores)**: Storage of and efficient search over vectors and associated metadata.
- **[Retriever](/docs/concepts/retrievers)**: A component that returns relevant documents from a knowledge base in response to a query.
- **[Retrieval Augmented Generation (RAG)](/docs/concepts/rag)**: A technique that enhances language models by combining them with external knowledge bases.
- **[Agents](/docs/concepts/agents)**: Use a [language model](/docs/concepts/chat_models) to choose a sequence of actions to take. Agents can interact with external resources via [tool](/docs/concepts/tools).
- **[Agents](/docs/concepts/agents)**: Use a [language model](/docs/concepts/chat_models) to choose a sequence of actions to take. Agents can interact with external resources via [tools](/docs/concepts/tools).
- **[Prompt templates](/docs/concepts/prompt_templates)**: Component for factoring out the static parts of a model "prompt" (usually a sequence of messages). Useful for serializing, versioning, and reusing these static parts.
- **[Output parsers](/docs/concepts/output_parsers)**: Responsible for taking the output of a model and transforming it into a more suitable format for downstream tasks. Output parsers were primarily useful prior to the general availability of [tool calling](/docs/concepts/tool_calling) and [structured outputs](/docs/concepts/structured_outputs).
- **[Few-shot prompting](/docs/concepts/few_shot_prompting)**: A technique for improving model performance by providing a few examples of the task to perform in the prompt.
@@ -48,7 +48,7 @@ The conceptual guide does not cover step-by-step instructions or specific implem
- **[AIMessage](/docs/concepts/messages#aimessage)**: Represents a complete response from an AI model.
- **[astream_events](/docs/concepts/chat_models#key-methods)**: Stream granular information from [LCEL](/docs/concepts/lcel) chains.
- **[BaseTool](/docs/concepts/tools/#tool-interface)**: The base class for all tools in LangChain.
- **[batch](/docs/concepts/runnables)**: Use to execute a runnable with batch inputs.
- **[batch](/docs/concepts/runnables)**: Used to execute a runnable with batch inputs.
- **[bind_tools](/docs/concepts/tool_calling/#tool-binding)**: Allows models to interact with tools.
- **[Caching](/docs/concepts/chat_models#caching)**: Storing results to avoid redundant calls to a chat model.
- **[Chat models](/docs/concepts/multimodality/#multimodality-in-chat-models)**: Chat models that handle multiple data modalities.
@@ -147,7 +147,7 @@ An `AIMessage` has the following attributes. The attributes which are **standard
| `tool_calls` | Standardized | Tool calls associated with the message. See [tool calling](/docs/concepts/tool_calling) for details. |
| `invalid_tool_calls` | Standardized | Tool calls with parsing errors associated with the message. See [tool calling](/docs/concepts/tool_calling) for details. |
| `usage_metadata` | Standardized | Usage metadata for a message, such as [token counts](/docs/concepts/tokens). See [Usage Metadata API Reference](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.UsageMetadata.html). |
| `id` | Standardized | An optional unique identifier for the message, ideally provided by the provider/model that created the message. |
| `id` | Standardized | An optional unique identifier for the message, ideally provided by the provider/model that created the message. See [Message IDs](#message-ids) for details. |
@@ -243,3 +243,37 @@ At the moment, the output of the model will be in terms of LangChain messages, s
need OpenAI format for the output as well.
The [convert_to_openai_messages](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.utils.convert_to_openai_messages.html) utility function can be used to convert from LangChain messages to OpenAI format.
## Message IDs
LangChain messages include an optional `id` field that serves as a unique identifier. Understanding when and how these IDs are assigned can be helpful for debugging, tracing, and working with message history.
### When Messages Get IDs
Messages receive IDs in the following scenarios:
**Automatically assigned by LangChain:**
- When generated through chat model invocation (`.invoke()`, `.stream()`, `.astream()`) with an active run manager/tracing context
@@ -31,7 +31,7 @@ The key attributes that correspond to the tool's **schema**:
The key methods to execute the function associated with the **tool**:
- **invoke**: Invokes the tool with the given arguments.
- **ainvoke**: Invokes the tool with the given arguments, asynchronously. Used for [async programming with Langchain](/docs/concepts/async).
- **ainvoke**: Invokes the tool with the given arguments, asynchronously. Used for [async programming with LangChain](/docs/concepts/async).
## Create tools using the `@tool` decorator
@@ -171,6 +171,26 @@ Please see the [InjectedState](https://langchain-ai.github.io/langgraph/referenc
Please see the [InjectedStore](https://langchain-ai.github.io/langgraph/reference/prebuilt/#langgraph.prebuilt.tool_node.InjectedStore) documentation for more details.
## Tool Artifacts vs. Injected State
Although similar conceptually, tool artifacts in LangChain and [injected state in LangGraph](https://langchain-ai.github.io/langgraph/reference/agents/#langgraph.prebuilt.tool_node.InjectedState) serve different purposes and operate at different levels of abstraction.
**Tool Artifacts**
- **Purpose:** Store and pass data between tool executions within a single chain/workflow
- **Scope:** Limited to tool-to-tool communication
- **Lifecycle:** Tied to individual tool calls and their immediate context
- **Usage:** Temporary storage for intermediate results that tools need to share
**Injected State (LangGraph)**
- **Purpose:** Maintain persistent state across the entire graph execution
- **Scope:** Global to the entire graph workflow
- **Lifecycle:** Persists throughout the entire graph execution and can be saved/restored
- **Usage:** Long-term state management, conversation memory, user context, workflow checkpointing
Tool artifacts are ephemeral data passed between tools, while injected state is persistent workflow-level state that survives across multiple steps, tool calls, and even execution sessions in LangGraph.
## Best practices
When designing tools to be used by models, keep the following in mind:
@@ -124,6 +124,47 @@ start "" htmlcov/index.html || open htmlcov/index.html
```
## Snapshot Testing
Some tests use [syrupy](https://github.com/tophat/syrupy) for snapshot testing, which captures the output of functions and compares them to stored snapshots. This is particularly useful for testing JSON schema generation and other structured outputs.
### Updating Snapshots
To update snapshots when the expected output has legitimately changed:
```bash
uv run --group test pytest path/to/test.py --snapshot-update
```
### Pydantic Version Compatibility Issues
Pydantic generates different JSON schemas across versions, which can cause snapshot test failures in CI when tests run with different Pydantic versions than what was used to generate the snapshots.
**Symptoms:**
- CI fails with snapshot mismatches showing differences like missing or extra fields.
- Tests pass locally but fail in CI with different Pydantic versions
**Solution:**
Locally update snapshots using the same Pydantic version that CI uses:
1. **Identify the failing Pydantic version** from CI logs (e.g., `2.7.0`, `2.8.0`, `2.9.0`)
2. **Update snapshots with that version:**
```bash
uv run --with "pydantic==2.9.0" --group test pytest tests/unit_tests/path/to/test.py::test_name --snapshot-update
```
3. **Verify compatibility across supported versions:**
```bash
# Test with the version you used to update
uv run --with "pydantic==2.9.0" --group test pytest tests/unit_tests/path/to/test.py::test_name
# Test with other supported versions
uv run --with "pydantic==2.8.0" --group test pytest tests/unit_tests/path/to/test.py::test_name
```
**Note:** Some tests use `@pytest.mark.skipif` decorators to only run with specific Pydantic version ranges (e.g., `PYDANTIC_VERSION_AT_LEAST_210`). Make sure to understand these constraints when updating snapshots.
## Coverage
Code coverage (i.e. the amount of code that is covered by unit tests) helps identify areas of the code that are potentially more or less brittle.
"If you're using tools with agents, you will likely need an error handling strategy, so the agent can recover from the error and continue execution.\n",
"\n",
"A simple strategy is to throw a `ToolException` from inside the tool and specify an error handler using `handle_tool_error`. \n",
"A simple strategy is to throw a `ToolException` from inside the tool and specify an error handler using `handle_tool_errors`. \n",
"\n",
"When the error handler is specified, the exception will be caught and the error handler will decide which output to return from the tool.\n",
"\n",
"You can set `handle_tool_error` to `True`, a string value, or a function. If it's a function, the function should take a `ToolException` as a parameter and return a value.\n",
"You can set `handle_tool_errors` to `True`, a string value, or a function. If it's a function, the function should take a `ToolException` as a parameter and return a value.\n",
"\n",
"Please note that only raising a `ToolException` won't be effective. You need to first set the `handle_tool_error` of the tool because its default value is `False`."
"Please note that only raising a `ToolException` won't be effective. You need to first set the `handle_tool_errors` of the tool because its default value is `False`."
]
},
{
@@ -777,7 +777,7 @@
"id": "9d93b217-1d44-4d31-8956-db9ea680ff4f",
"metadata": {},
"source": [
"Here's an example with the default `handle_tool_error=True` behavior."
"Here's an example with the default `handle_tool_errors=True` behavior."
"<table><thead><tr><th colspan=\"3\">able 1. LUllclll 1ayoul actCCLloll 1110AdCs 111 L1C LayoOulralsel 1110U4cl 200</th></tr><tr><th>Dataset</th><th>| Base Model\\'|</th><th>Notes</th></tr></thead><tbody><tr><td>PubLayNet [38]</td><td>F/M</td><td>Layouts of modern scientific documents</td></tr><tr><td>PRImA</td><td>M</td><td>Layouts of scanned modern magazines and scientific reports</td></tr><tr><td>Newspaper</td><td>F</td><td>Layouts of scanned US newspapers from the 20th century</td></tr><tr><td>TableBank [18]</td><td>F</td><td>Table region on modern scientific and business document</td></tr><tr><td>HJDataset</td><td>F/M</td><td>Layouts of history Japanese documents</td></tr></tbody></table>"
"<table><thead><tr><th colspan=\"3\">Table 1: Current layout detection models in the LayoutParser model zoo</th></tr><tr><th>Dataset</th><th>Base Model1</th><th>Large Model Notes</th></tr></thead><tbody><tr><td>PubLayNet [38]</td><td>F/M</td><td>Layouts of modern scientific documents</td></tr><tr><td>PRImA</td><td>M</td><td>Layouts of scanned modern magazines and scientific reports</td></tr><tr><td>Newspaper</td><td>F</td><td>Layouts of scanned US newspapers from the 20th century</td></tr><tr><td>TableBank [18]</td><td>F</td><td>Table region on modern scientific and business document</td></tr><tr><td>HJDataset</td><td>F/M</td><td>Layouts of history Japanese documents</td></tr></tbody></table>"
"1. [`llama.cpp`](https://github.com/ggerganov/llama.cpp): C++ implementation of llama inference code with [weight optimization / quantization](https://finbarr.ca/how-is-llama-cpp-possible/)\n",
"2. [`gpt4all`](https://docs.gpt4all.io/index.html): Optimized C backend for inference\n",
"3. [`Ollama`](https://ollama.ai/): Bundles model weights and environment into an app that runs on device and serves the LLM\n",
"3. [`ollama`](https://github.com/ollama/ollama): Bundles model weights and environment into an app that runs on device and serves the LLM\n",
"4. [`llamafile`](https://github.com/Mozilla-Ocho/llamafile): Bundles model weights and everything needed to run the model in a single file, allowing you to run the LLM locally from this file without any additional installation steps\n",
"\n",
"In general, these frameworks will do a few things:\n",
@@ -74,12 +74,12 @@
"\n",
"## Quickstart\n",
"\n",
"[`Ollama`](https://ollama.ai/) is one way to easily run inference on macOS.\n",
"[Ollama](https://ollama.com/) is one way to easily run inference on macOS.\n",
" \n",
"The instructions [here](https://github.com/jmorganca/ollama?tab=readme-ov-file#ollama) provide details, which we summarize:\n",
"The instructions [here](https://github.com/ollama/ollama?tab=readme-ov-file#ollama) provide details, which we summarize:\n",
" \n",
"* [Download and run](https://ollama.ai/download) the app\n",
"* From command line, fetch a model from this [list of options](https://github.com/jmorganca/ollama): e.g., `ollama pull llama3.1:8b`\n",
"* From command line, fetch a model from this [list of options](https://ollama.com/search): e.g., `ollama pull gpt-oss:20b`\n",
"* When the app is running, all models are automatically served on `localhost:11434`\n"
"llm.invoke(\"The first man on the moon was ...\")"
"llm.invoke(\"The first man on the moon was ...\").content"
]
},
{
@@ -200,7 +200,7 @@
"\n",
"### Running Apple silicon GPU\n",
"\n",
"`Ollama` and [`llamafile`](https://github.com/Mozilla-Ocho/llamafile?tab=readme-ov-file#gpu-support) will automatically utilize the GPU on Apple devices.\n",
"`ollama` and [`llamafile`](https://github.com/Mozilla-Ocho/llamafile?tab=readme-ov-file#gpu-support) will automatically utilize the GPU on Apple devices.\n",
" \n",
"Other frameworks require the user to set up the environment to utilize the Apple GPU.\n",
"\n",
@@ -212,15 +212,15 @@
"\n",
"In particular, ensure that conda is using the correct virtual environment that you created (`miniforge3`).\n",
"1. [`HuggingFace`](https://huggingface.co/TheBloke) - Many quantized model are available for download and can be run with framework such as [`llama.cpp`](https://github.com/ggerganov/llama.cpp). You can also download models in [`llamafile` format](https://huggingface.co/models?other=llamafile) from HuggingFace.\n",
"2. [`gpt4all`](https://gpt4all.io/index.html) - The model explorer offers a leaderboard of metrics and associated quantized models available for download \n",
"3. [`Ollama`](https://github.com/jmorganca/ollama) - Several models can be accessed directly via `pull`\n",
"3. [`ollama`](https://github.com/jmorganca/ollama) - Several models can be accessed directly via `pull`\n",
"\n",
"### Ollama\n",
"\n",
"With [Ollama](https://github.com/jmorganca/ollama), fetch a model via `ollama pull <model family>:<tag>`:\n",
"\n",
"* E.g., for Llama 2 7b: `ollama pull llama2` will download the most basic version of the model (e.g., smallest # parameters and 4 bit quantization)\n",
"* We can also specify a particular version from the [model list](https://github.com/jmorganca/ollama?tab=readme-ov-file#model-library), e.g., `ollama pull llama2:13b`\n",
"* See the full set of parameters on the [API reference page](https://python.langchain.com/api_reference/community/llms/langchain_community.llms.ollama.Ollama.html)"
"With [Ollama](https://github.com/ollama/ollama), fetch a model via `ollama pull <model family>:<tag>`."
]
},
{
"cell_type": "code",
"execution_count": 42,
"execution_count": null,
"id": "8ecd2f78",
"metadata": {},
"outputs": [
@@ -265,7 +261,7 @@
}
],
"source": [
"llm = OllamaLLM(model=\"llama2:13b\")\n",
"llm = ChatOllama(model=\"gpt-oss:20b\")\n",
"llm.invoke(\"The first man on the moon was ... think step by step\")"
"# How deal with highcardinality categoricals when doing query analysis\n",
"# How to deal with high-cardinality categoricals when doing query analysis\n",
"\n",
"You may want to do query analysis to create a filter on a categorical column. One of the difficulties here is that you usually need to specify the EXACT categorical value. The issue is you need to make sure the LLM generates that categorical value exactly. This can be done relatively easy with prompting when there are only a few values that are valid. When there are a high number of valid values then it becomes more difficult, as those values may not fit in the LLM context, or (if they do) there may be too many for the LLM to properly attend to.\n",
"`with_structured_output()` internally uses tool calling to enforce the schema. When you bind additional tools afterward, it creates a conflict in the tool resolution system."
"For a model to be able to call tools, we need to pass in tool schemas that describe what the tool does and what it's arguments are. Chat models that support tool calling features implement a `.bind_tools()` method for passing tool schemas to the model. Tool schemas can be passed in as Python functions (with typehints and docstrings), Pydantic models, TypedDict classes, or LangChain [Tool objects](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.base.BaseTool.html#basetool). Subsequent invocations of the model will pass in these tool schemas along with the prompt.\n",
"For a model to be able to call tools, we need to pass in tool schemas that describe what the tool does and what its arguments are. Chat models that support tool calling features implement a `.bind_tools()` method for passing tool schemas to the model. Tool schemas can be passed in as Python functions (with typehints and docstrings), Pydantic models, TypedDict classes, or LangChain [Tool objects](https://python.langchain.com/api_reference/core/tools/langchain_core.tools.base.BaseTool.html#basetool). Subsequent invocations of the model will pass in these tool schemas along with the prompt.\n",
"To keep the most recent messages, we set `strategy=\"last\"`. We'll also set `include_system=True` to include the `SystemMessage`, and `start_on=\"human\"` to make sure the resulting chat history is valid. \n",
"\n",
"This is a good default configuration when using `trim_messages` based on token count. Remember to adjust `token_counter` and `max_tokens` for your use case.\n",
"This is a good default configuration when using `trim_messages` based on token count. Remember to adjust `token_counter` and `max_tokens` for your use case. Keep in mind that new queries added to the chat history will be included in the token count unless you trim prior to adding the new query.\n",
"\n",
"Notice that for our `token_counter` we can pass in a function (more on that below) or a language model (since language models have a message token counting method). It makes sense to pass in a model when you're trimming your messages to fit into the context window of that specific model:"
]
@@ -525,7 +525,7 @@
"id": "4d91d390-e7f7-467b-ad87-d100411d7a21",
"metadata": {},
"source": [
"Looking at the LangSmith trace we can see that before the messages are passed to the model they are first trimmed: https://smith.langchain.com/public/65af12c4-c24d-4824-90f0-6547566e59bb/r\n",
"Looking at [the LangSmith trace](https://smith.langchain.com/public/65af12c4-c24d-4824-90f0-6547566e59bb/r) we can see that before the messages are passed to the model they are first trimmed.\n",
"\n",
"Looking at just the trimmer, we can see that it's a Runnable object that can be invoked like all Runnables:"
]
@@ -620,7 +620,7 @@
"id": "556b7b4c-43cb-41de-94fc-1a41f4ec4d2e",
"metadata": {},
"source": [
"Looking at the LangSmith trace we can see that we retrieve all of our messages but before the messages are passed to the model they are trimmed to be just the system message and last human message: https://smith.langchain.com/public/17dd700b-9994-44ca-930c-116e00997315/r"
"Looking at [the LangSmith trace](https://smith.langchain.com/public/17dd700b-9994-44ca-930c-116e00997315/r) we can see that we retrieve all of our messages but before the messages are passed to the model they are trimmed to be just the system message and last human message."
]
},
{
@@ -630,7 +630,7 @@
"source": [
"## API reference\n",
"\n",
"For a complete description of all arguments head to the API reference: https://python.langchain.com/api_reference/core/messages/langchain_core.messages.utils.trim_messages.html"
"For a complete description of all arguments head to the [API reference](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.utils.trim_messages.html)."
"You can, by default, use the `DeepEvalCallbackHandler` to set up the metrics you want to track. However, this has limited support for metrics at the moment (more to be added soon). It currently supports:\n",
"LLMResult(generations=[[Generation(text='\\n\\nQ: What did the fish say when he hit the wall? \\nA: Dam.', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\\n\\nThe Moon \\n\\nThe moon is high in the midnight sky,\\nSparkling like a star above.\\nThe night so peaceful, so serene,\\nFilling up the air with love.\\n\\nEver changing and renewing,\\nA never-ending light of grace.\\nThe moon remains a constant view,\\nA reminder of life’s gentle pace.\\n\\nThrough time and space it guides us on,\\nA never-fading beacon of hope.\\nThe moon shines down on us all,\\nAs it continues to rise and elope.', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\\n\\nQ. What did one magnet say to the other magnet?\\nA. \"I find you very attractive!\"', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text=\"\\n\\nThe world is charged with the grandeur of God.\\nIt will flame out, like shining from shook foil;\\nIt gathers to a greatness, like the ooze of oil\\nCrushed. Why do men then now not reck his rod?\\n\\nGenerations have trod, have trod, have trod;\\nAnd all is seared with trade; bleared, smeared with toil;\\nAnd wears man's smudge and shares man's smell: the soil\\nIs bare now, nor can foot feel, being shod.\\n\\nAnd for all this, nature is never spent;\\nThere lives the dearest freshness deep down things;\\nAnd though the last lights off the black West went\\nOh, morning, at the brown brink eastward, springs —\\n\\nBecause the Holy Ghost over the bent\\nWorld broods with warm breast and with ah! bright wings.\\n\\n~Gerard Manley Hopkins\", generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text='\\n\\nQ: What did one ocean say to the other ocean?\\nA: Nothing, they just waved.', generation_info={'finish_reason': 'stop', 'logprobs': None})], [Generation(text=\"\\n\\nA poem for you\\n\\nOn a field of green\\n\\nThe sky so blue\\n\\nA gentle breeze, the sun above\\n\\nA beautiful world, for us to love\\n\\nLife is a journey, full of surprise\\n\\nFull of joy and full of surprise\\n\\nBe brave and take small steps\\n\\nThe future will be revealed with depth\\n\\nIn the morning, when dawn arrives\\n\\nA fresh start, no reason to hide\\n\\nSomewhere down the road, there's a heart that beats\\n\\nBelieve in yourself, you'll always succeed.\", generation_info={'finish_reason': 'stop', 'logprobs': None})]], llm_output={'token_usage': {'completion_tokens': 504, 'total_tokens': 528, 'prompt_tokens': 24}, 'model_name': 'text-davinci-003'})"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain_openai import OpenAI\n",
"\n",
"llm = OpenAI(\n",
" temperature=0,\n",
" callbacks=[deepeval_callback],\n",
" verbose=True,\n",
" openai_api_key=\"<YOUR_API_KEY>\",\n",
")\n",
"output = llm.generate(\n",
" [\n",
" \"What is the best evaluation tool out there? (no bias at all)\",\n",
" ]\n",
")"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"You can then check the metric if it was successful by calling the `is_successful()` method."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"answer_relevancy_metric.is_successful()\n",
"# returns True/False"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Once you have ran that, you should be able to see our dashboard below. \n",
"You can create your own custom metrics [here](https://docs.confident-ai.com/docs/quickstart/custom-metrics). \n",
"\n",
"DeepEval also offers other features such as being able to [automatically create unit tests](https://docs.confident-ai.com/docs/quickstart/synthetic-data-creation), [tests for hallucination](https://docs.confident-ai.com/docs/measuring_llm_performance/factual_consistency).\n",
"\n",
"If you are interested, check out our Github repository here [https://github.com/confident-ai/deepeval](https://github.com/confident-ai/deepeval). We welcome any PRs and discussions on how to improve LLM performance."
" * macOS users can install via Homebrew with `brew install ollama` and start with `brew services start ollama`\n",
"* Fetch available LLM model via `ollama pull <name-of-model>`\n",
" * View a list of available models via the [model library](https://ollama.ai/library)\n",
" * e.g., `ollama pull llama3`\n",
" * e.g., `ollama pull gpt-oss:20b`\n",
"* This will download the default tagged version of the model. Typically, the default points to the latest, smallest sized-parameter model.\n",
"\n",
"> On Mac, the models will be download to `~/.ollama/models`\n",
">\n",
"> On Linux (or WSL), the models will be stored at `/usr/share/ollama/.ollama/models`\n",
"\n",
"* Specify the exact version of the model of interest as such `ollama pull vicuna:13b-v1.5-16k-q4_0` (View the [various tags for the `Vicuna`](https://ollama.ai/library/vicuna/tags) model in this instance)\n",
"* Specify the exact version of the model of interest as such `ollama pull gpt-oss:20b` (View the [various tags for the `Vicuna`](https://ollama.ai/library/vicuna/tags) model in this instance)\n",
"* To view all pulled models, use `ollama list`\n",
"* To chat directly with a model from the command line, use `ollama run <name-of-model>`\n",
"* View the [Ollama documentation](https://github.com/ollama/ollama/tree/main/docs) for more commands. You can run `ollama help` in the terminal to see available commands.\n"
"* View the [Ollama documentation](https://github.com/ollama/ollama/blob/main/docs/README.md) for more commands. You can run `ollama help` in the terminal to see available commands.\n"
]
},
{
@@ -102,7 +102,11 @@
"id": "b18bd692076f7cf7",
"metadata": {},
"source": [
"Make sure you're using the latest Ollama version for structured outputs. Update by running:"
":::warning\n",
"Make sure you're using the latest Ollama version!\n",
":::\n",
"\n",
"Update by running:"
]
},
{
@@ -257,10 +261,10 @@
"source": [
"## Tool calling\n",
"\n",
"We can use [tool calling](/docs/concepts/tool_calling/) with an LLM [that has been fine-tuned for tool use](https://ollama.com/search?&c=tools) such as `llama3.1`:\n",
"We can use [tool calling](/docs/concepts/tool_calling/) with an LLM [that has been fine-tuned for tool use](https://ollama.com/search?&c=tools) such as `gpt-oss`:\n",
"\n",
"```\n",
"ollama pull llama3.1\n",
"ollama pull gpt-oss:20b\n",
"```\n",
"\n",
"Details on creating custom tools are available in [this guide](/docs/how_to/custom_tools/). Below, we demonstrate how to create a tool using the `@tool` decorator on a normal python function."
@@ -268,7 +272,7 @@
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": null,
"id": "f767015f",
"metadata": {},
"outputs": [
@@ -300,7 +304,8 @@
"\n",
"\n",
"llm = ChatOllama(\n",
" model=\"llama3.1\",\n",
" model=\"gpt-oss:20b\",\n",
" validate_model_on_init=True,\n",
" temperature=0,\n",
").bind_tools([validate_user])\n",
"\n",
@@ -321,9 +326,7 @@
"source": [
"## Multi-modal\n",
"\n",
"Ollama has support for multi-modal LLMs, such as [bakllava](https://ollama.com/library/bakllava) and [llava](https://ollama.com/library/llava).\n",
"\n",
" ollama pull bakllava\n",
"Ollama has limited support for multi-modal LLMs, such as [gemma3](https://ollama.com/library/gemma3)\n",
"\n",
"Be sure to update Ollama so that you have the most recent version to support multi-modal."
">[Azure AI Studio](https://ai.azure.com/) provides the capability to upload data assets to cloud storage and register existing data assets from the following sources:\n",
">[Azure AI Foundry (formerly Azure AI Studio)](https://ai.azure.com/) provides the capability to upload data assets to cloud storage and register existing data assets from the following sources:\n",
"Oracle autonomous database is a cloud database that uses machine learning to automate database tuning, security, backups, updates, and other routine management tasks traditionally performed by DBAs.\n",
"Oracle Autonomous Database is a cloud database that uses machine learning to automate database tuning, security, backups, updates, and other routine management tasks traditionally performed by DBAs.\n",
"\n",
"This notebook covers how to load documents from oracle autonomous database, the loader supports connection with connection string or tns configuration.\n",
"This notebook covers how to load documents from Oracle Autonomous Database.\n",
" See [Installing python-oracledb](https://python-oracledb.readthedocs.io/en/latest/user_guide/installation.html).\n",
"\n",
"2. A database that python-oracledb's default 'Thin' mode can connected to. This is true of Oracle Autonomous Database, see [python-oracledb Architecture](https://python-oracledb.readthedocs.io/en/latest/user_guide/introduction.html#architecture).\n"
"With mutual TLS authentication (mTLS), wallet_location and wallet_password are required to create the connection, user can create connection by providing either connection string or tns configuration details."
],
"metadata": {
"collapsed": false
}
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"source": [
"With mutual TLS authentication (mTLS), wallet_location and wallet_password parameters are required to create the connection. See python-oracledb documentation [Connecting to Oracle Cloud Autonomous Databases](https://python-oracledb.readthedocs.io/en/latest/user_guide/connection_handling.html#connecting-to-oracle-cloud-autonomous-databases)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [],
"source": [
"SQL_QUERY = \"select prod_id, time_id from sh.costs fetch first 5 rows only\"\n",
@@ -89,24 +113,30 @@
" wallet_password=s.PASSWORD,\n",
")\n",
"doc_2 = doc_loader_2.load()"
],
"metadata": {
"collapsed": false
}
]
},
{
"cell_type": "markdown",
"source": [
"With TLS authentication, wallet_location and wallet_password are not required.\n",
"Bind variable option is provided by argument \"parameters\"."
],
"metadata": {
"collapsed": false
}
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"source": [
"With 1-way TLS authentication, only the database credentials and connection string are required to establish a connection.\n",
"The example below also shows passing bind variable values with the argument \"parameters\"."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"jupyter": {
"outputs_hidden": false
}
},
"outputs": [],
"source": [
"SQL_QUERY = \"select channel_id, channel_desc from sh.channels where channel_desc = :1 fetch first 5 rows only\"\n",
"[Oxylabs](https://oxylabs.io/) is a web intelligence collection platform that enables companies worldwide to unlock data-driven insights.\n",
"\n",
"## Overview\n",
"\n",
"Oxylabs document loader allows to load data from search engines, e-commerce sites, travel platforms, and any other website. It supports geolocation, browser rendering, data parsing, multiple user agents and many more parameters. Check out [Oxylabs documentation](https://developers.oxylabs.io/scraping-solutions/web-scraper-api) for more information.\n",
"\n",
"\n",
"### Integration details\n",
"\n",
"| Class | Package | Local | Serializable | Pricing |\n",
"Note!This is a sandbox website used for web scraping. Information listed in this website does not have any real meaning and should not be associated with the actual products.\n",
"\n",
"\n",
"Note!This is a sandbox website used for web scraping. Information listed in this website does not have any real meaning and should not be associated with the actual products.\n",
"\n",
"\n",
"[Metacritic's 2007 Wii Game of the Year] The ultimate Nintendo hero is taking the ultimate step ... out into space. Join Mario as he ushers in a new era of video games, de\n"
]
}
],
"source": [
"for document in loader.load():\n",
" print(document.page_content[:1000])"
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## Lazy Load"
},
{
"metadata": {},
"cell_type": "code",
"outputs": [],
"execution_count": null,
"source": [
"for document in loader.lazy_load():\n",
" print(document.page_content[:1000])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Advanced examples\n",
"\n",
"The following examples show the usage of `OxylabsLoader` with geolocation, currency, pagination and user agent parameters for Amazon Search and Google Search sources."
>[Aerospike](https://aerospike.com/docs/vector) is a high-performance, distributed database known for its speed and scalability, now with support for vector storage and search, enabling retrieval and search of embedding vectors for machine learning and AI applications.
> See the documentation for Aerospike Vector Search (AVS) [here](https://aerospike.com/docs/vector).
## Installation and Setup
Install the AVS Python SDK and AVS langchain vector store:
[Anchor](https://anchorbrowser.io?utm=langchain) is the platform for AI Agentic browser automation, which solves the challenge of automating workflows for web applications that lack APIs or have limited API coverage. It simplifies the creation, deployment, and management of browser-based automations, transforming complex web interactions into simple API endpoints.
`langchain-anchorbrowser` provides 3 main tools:
- `AnchorContentTool` - For web content extractions in Markdown or HTML format.
- `AnchorScreenshotTool` - For web page screenshots.
- `AnchorWebTaskTools` - To perform web tasks.
## Quickstart
### Installation
Install the package:
```bash
pip install langchain-anchorbrowser
```
### Usage
Import and utilize your intended tool. The full list of Anchor Browser available tools see **Tool Features** table in [Anchor Browser tool page](/docs/integrations/tools/anchor_browser)
```python
from langchain_anchorbrowser import AnchorContentTool
# Get Markdown Content for https://www.anchorbrowser.io
@@ -929,6 +929,41 @@ from langchain_google_community.gmail.search import GmailSearch
from langchain_google_community.gmail.send_message import GmailSendMessage
```
### MCP Toolbox
[MCP Toolbox](https://github.com/googleapis/genai-toolbox) provides a simple and efficient way to connect to your databases, including those on Google Cloud like [Cloud SQL](https://cloud.google.com/sql/docs) and [AlloyDB](https://cloud.google.com/alloydb/docs/overview). With MCP Toolbox, you can seamlessly integrate your database with LangChain to build powerful, data-driven applications.
#### Installation
To get started, [install the Toolbox server and client](https://github.com/googleapis/genai-toolbox/releases/).
[Configure](https://googleapis.github.io/genai-toolbox/getting-started/configure/) a `tools.yaml` to define your tools, and then execute toolbox to start the server:
```bash
toolbox --tools-file "tools.yaml"
```
Then, install the Toolbox client:
```bash
pip install toolbox-langchain
```
#### Getting Started
Here is a quick example of how to use MCP Toolbox to connect to your database:
```python
from toolbox_langchain import ToolboxClient
async with ToolboxClient("http://127.0.0.1:5000") as client:
tools = client.load_toolset()
```
See [usage example and setup instructions](/docs/integrations/tools/toolbox).
### Memory
Store conversation history using Google Cloud databases.
langchain-gradient uses DigitalOcean's Gradient™ AI Platform.
Create an account on DigitalOcean, acquire a `DIGITALOCEAN_INFERENCE_KEY` API key from the Gradient Platform, and install the `langchain-gradient` integration package.
@@ -11,17 +11,17 @@ The `LangChain` integrations related to [Oracle Cloud Infrastructure](https://ww
To use, you should have the latest `oci` python SDK and the langchain_community package installed.
```bash
pip install -U oci langchain-community
pip install -U langchain_oci
```
See [chat](/docs/integrations/llms/oci_generative_ai), [complete](/docs/integrations/chat/oci_generative_ai), and [embedding](/docs/integrations/text_embedding/oci_generative_ai) usage examples.
```python
from langchain_community.chat_models import ChatOCIGenAI
from langchain_oci.chat_models import ChatOCIGenAI
from langchain_community.llms import OCIGenAI
from langchain_oci.llms import OCIGenAI
from langchain_community.embeddings import OCIGenAIEmbeddings
from langchain_oci.embeddings import OCIGenAIEmbeddings
```
## OCI Data Science Model Deployment Endpoint
@@ -42,8 +42,8 @@ See [chat](/docs/integrations/chat/oci_data_science) and [complete](/docs/integr
```python
from langchain_community.chat_models import ChatOCIModelDeployment
from langchain_oci.chat_models import ChatOCIModelDeployment
from langchain_community.llms import OCIModelDeploymentLLM
from langchain_oci.llms import OCIModelDeploymentLLM
"[Recallio](https://recallio.ai/) is a powerfull API allowing to store, index, and retrieve application “memories” with built-in fact extraction, dynamic summaries, reranked recall, and a full knowledge-graph layer.\n",
[Scrapeless](https://scrapeless.com) offers flexible and feature-rich data acquisition services with extensive parameter customization and multi-format export support.
## Installation and Setup
```bash
pip install langchain-scrapeless
```
You'll need to set up your Scrapeless API key:
```python
import os
os.environ["SCRAPELESS_API_KEY"] = "your-api-key"
```
## Tools
The Scrapeless integration provides several tools:
- [ScrapelessDeepSerpGoogleSearchTool](/docs/integrations/tools/scrapeless_scraping_api) - Enables comprehensive extraction of Google SERP data across all result types.
- [ScrapelessDeepSerpGoogleTrendsTool](/docs/integrations/tools/scrapeless_scraping_api) - Retrieves keyword trend data from Google, including popularity over time, regional interest, and related searches.
- [ScrapelessUniversalScrapingTool](/docs/integrations/tools/scrapeless_universal_scraping) - Access and extract data from JS-Render websites that typically block bots.
- [ScrapelessCrawlerCrawlTool](/docs/integrations/tools/scrapeless_crawl) - Crawl a website and its linked pages to extract comprehensive data.
- [ScrapelessCrawlerScrapeTool](/docs/integrations/tools/scrapeless_crawl) - Extract information from a single webpage.
The [MCP Toolbox](https://googleapis.github.io/genai-toolbox/getting-started/introduction/) in LangChain allows you to equip an agent with a set of tools. When the agent receives a query, it can intelligently select and use the most appropriate tool provided by MCP Toolbox to fulfill the request.
## What is it?
MCP Toolbox is essentially a container for your tools. Think of it as a multi-tool device for your agent; it can hold any tools you create. The agent then decides which specific tool to use based on the user's input.
This is particularly useful when you have an agent that needs to perform a variety of tasks that require different capabilities.
## Installation
To get started, you'll need to install the necessary package:
```bash
pip install toolbox-langchain
```
## Tutorial
For a complete, step-by-step guide on how to create, configure, and use MCP Toolbox with your agents, please refer to our detailed Jupyter notebook tutorial.
**[➡️ View the full tutorial here](/docs/integrations/tools/toolbox)**.
TrueFoundry provides an enterprise-ready [AI Gateway](https://www.truefoundry.com/ai-gateway) to provide governance and observability to agentic frameworks like LangChain. TrueFoundry AI Gateway serves as a unified interface for LLM access, providing:
- **Unified API Access**: Connect to 250+ LLMs (OpenAI, Claude, Gemini, Groq, Mistral) through one API
- **Low Latency**: Sub-3ms internal latency with intelligent routing and load balancing
- **Enterprise Security**: SOC 2, HIPAA, GDPR compliance with RBAC and audit logging
- **Quota and cost management**: Token-based quotas, rate limiting, and comprehensive usage tracking
- **Observability**: Full request/response logging, metrics, and traces with customizable retention
## Prerequisites
Before integrating LangChain with TrueFoundry, ensure you have:
1. **TrueFoundry Account**: A [TrueFoundry account](https://www.truefoundry.com/register) with at least one model provider configured. Follow quick start guide [here](https://docs.truefoundry.com/gateway/quick-start)
2. **Personal Access Token**: Generate a token by following the [TrueFoundry token generation guide](https://docs.truefoundry.com/gateway/authentication)
## Quickstart
You can connect to TrueFoundry's unified LLM gateway through the `ChatOpenAI` interface.
- Set the `base_url` to your TrueFoundry endpoint (explained below)
- Set the `api_key` to your TrueFoundry [PAT (Personal Access Token)](https://docs.truefoundry.com/gateway/authentication#personal-access-token-pat)
- Use the same `model-name` as shown in the unified code snippet
### Installation
```bash
pip install langchain-openai
```
### Basic Setup
Connect to TrueFoundry by updating the `ChatOpenAI` model in LangChain:
```python
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(
api_key=TRUEFOUNDRY_API_KEY,
base_url=TRUEFOUNDRY_GATEWAY_BASE_URL,
model="openai-main/gpt-4o" # Similarly you can call any model from any model provider
)
llm.invoke("What is the meaning of life, universe and everything?")
```
The request is routed through your TrueFoundry gateway to the specified model provider. TrueFoundry automatically handles rate limiting, load balancing, and observability.
### LangGraph Integration
```python
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, MessagesState
from langchain_core.messages import HumanMessage
# Define your LangGraph workflow
def call_model(state: MessagesState):
model = ChatOpenAI(
api_key=TRUEFOUNDRY_API_KEY,
base_url=TRUEFOUNDRY_GATEWAY_BASE_URL,
# Copy the exact model name from gateway
model="openai-main/gpt-4o"
)
response = model.invoke(state["messages"])
return {"messages": [response]}
# Build workflow
workflow = StateGraph(MessagesState)
workflow.add_node("agent", call_model)
workflow.set_entry_point("agent")
workflow.set_finish_point("agent")
app = workflow.compile()
# Run agent through TrueFoundry
result = app.invoke({"messages": [HumanMessage(content="Hello!")]})
```
## Observability and Governance
With the Metrics Dashboard, you can monitor and analyze:
- **Performance Metrics**: Track key latency metrics like Request Latency, Time to First Token (TTFS), and Inter-Token Latency (ITL) with P99, P90, and P50 percentiles
- **Cost and Token Usage**: Gain visibility into your application's costs with detailed breakdowns of input/output tokens and the associated expenses for each model
- **Usage Patterns**: Understand how your application is being used with detailed analytics on user activity, model distribution, and team-based usage
- **Rate Limiting & Load Balancing**: Configure limits, distribute traffic across models, and set up fallbacks
"Anchor is a platform for AI Agentic browser automation, which solves the challenge of automating workflows for web applications that lack APIs or have limited API coverage. It simplifies the creation, deployment, and management of browser-based automations, transforming complex web interactions into simple API endpoints.\n",
"\n",
"This notebook provides a quick overview for getting started with Anchor Browser tools. For more information of Anchor Browser visit [Anchorbrowser.io](https://anchorbrowser.io?utm=langchain) or the [Anchor Browser Docs](https://docs.anchorbrowser.io?utm=langchain)\n",
"\n",
"## Overview\n",
"\n",
"### Integration details\n",
"\n",
"Anchor Browser package for LangChain is [langchain-anchorbrowser](https://pypi.org/project/langchain-anchorbrowser), and the current latest version is .\n",
"\n",
"\n",
"### Tool features\n",
"| Tool Name | Package | Description | Parameters |\n",
"| :--- | :--- | :--- | :---|\n",
"| `AnchorContentTool` | langchain-anchorbrowser | Extract text content from web pages | `url`, `format` |\n",
"| `AnchorScreenshotTool` | langchain-anchorbrowser | Take screenshots of web pages | `url`, `width`, `height`, `image_quality`, `wait`, `scroll_all_content`, `capture_full_height`, `s3_target_address` |\n",
"| `AnchorWebTaskToolKit` | langchain-anchorbrowser | Perform intelligent web tasks using AI (Simple & Advanced modes) | see below |\n",
"\n",
"The parameters allowed in `langchain-anchorbrowser` are only a subset of those listed in the Anchor Browser API reference respectively: [Get Webpage Content](https://docs.anchorbrowser.io/sdk-reference/tools/get-webpage-content?utm=langchain), [Screenshot Webpage](https://docs.anchorbrowser.io/sdk-reference/tools/screenshot-webpage?utm=langchain), and [Perform Web Task](https://docs.anchorbrowser.io/sdk-reference/ai-tools/perform-web-task?utm=langchain).\n",
"\n",
"**Info:** Anchor currently implements `SimpleAnchorWebTaskTool` and `AdvancedAnchorWebTaskTool` tools for langchain with `browser_use` agent. For \n",
"\n",
"#### AnchorWebTaskToolKit Tools\n",
"\n",
"The difference between each tool in this toolkit is the pydantic configuration structure.\n",
"Alternatively, you can use Cloud Pak for Data credentials. For details, see [watsonx.ai software setup](https://ibm.github.io/watsonx-ai-python-sdk/setup_cpd.html). "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"watsonx_toolkit = WatsonxToolkit(\n",
" url=\"PASTE YOUR URL HERE\",\n",
" username=\"PASTE YOUR USERNAME HERE\",\n",
" password=\"PASTE YOUR PASSWORD HERE\",\n",
" version=\"5.2\",\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
@@ -153,7 +176,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tools\n"
"## Tools"
]
},
{
@@ -187,6 +210,14 @@
"watsonx_toolkit.get_tools()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> **NOTE** \n",
"> The list of available tools may vary depending on whether it is IBM watsonx.ai for IBM Cloud or IBM watsonx.ai software."
"For detailed documentation of all `WatsonxToolkit` features and configurations head to the [API reference](https://python.langchain.com/api_reference/ibm/toolkit/langchain_ibm.toolkit.WatsonxToolkit.html)."
"For detailed documentation of all `WatsonxToolkit` features and configurations head to the [API reference](https://python.langchain.com/api_reference/ibm/agent_toolkits/langchain_ibm.agent_toolkits.utility.toolkit.WatsonxToolkit.html)."
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