…s arg too
Thank you for contributing to LangChain!
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- Where "package" is whichever of langchain, community, core,
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changes.
- Example: "community: add foobar LLM"
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- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
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include
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2. an example notebook showing its use. It lives in
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- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
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Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
Description: For simplicity, migrate the logic of excluding intermediate
nodes in the .get_graph() of langgraph package
(https://github.com/langchain-ai/langgraph/pull/310) at graph creation
time instead of graph rendering time.
Note: #20381 needs to be approved first
---------
Co-authored-by: Angel Igareta <angel.igareta@klarna.com>
Co-authored-by: Nuno Campos <nuno@langchain.dev>
Co-authored-by: Nuno Campos <nuno@boringbits.io>
Description of features on mermaid graph renderer:
- Fixing CDN to use official Mermaid JS CDN:
https://www.jsdelivr.com/package/npm/mermaid?tab=files
- Add device_scale_factor to allow increasing quality of resulting PNG.
Description: This update refines the documentation for
`RunnablePassthrough` by removing an unnecessary import and correcting a
minor syntactical error in the example provided. This change enhances
the clarity and correctness of the documentation, ensuring that users
have a more accurate guide to follow.
Issue: N/A
Dependencies: None
This PR focuses solely on documentation improvements, specifically
targeting the `RunnablePassthrough` class within the `langchain_core`
module. By clarifying the example provided in the docstring, users are
offered a more straightforward and error-free guide to utilizing the
`RunnablePassthrough` class effectively.
As this is a documentation update, it does not include changes that
require new integrations, tests, or modifications to dependencies. It
adheres to the guidelines of minimal package interference and backward
compatibility, ensuring that the overall integrity and functionality of
the LangChain package remain unaffected.
Thank you for considering this documentation refinement for inclusion in
the LangChain project.
Mistral gives us one ID per response, no individual IDs for tool calls.
```python
from langchain.agents import AgentExecutor, create_tool_calling_agent, tool
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_mistralai import ChatMistralAI
prompt = ChatPromptTemplate.from_messages(
[
("system", "You are a helpful assistant"),
("human", "{input}"),
MessagesPlaceholder("agent_scratchpad"),
]
)
model = ChatMistralAI(model="mistral-large-latest", temperature=0)
@tool
def magic_function(input: int) -> int:
"""Applies a magic function to an input."""
return input + 2
tools = [magic_function]
agent = create_tool_calling_agent(model, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
agent_executor.invoke({"input": "what is the value of magic_function(3)?"})
```
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
```python
from langchain.agents import AgentExecutor, create_tool_calling_agent, tool
from langchain_anthropic import ChatAnthropic
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
prompt = ChatPromptTemplate.from_messages(
[
("system", "You are a helpful assistant"),
MessagesPlaceholder("chat_history", optional=True),
("human", "{input}"),
MessagesPlaceholder("agent_scratchpad"),
]
)
model = ChatAnthropic(model="claude-3-opus-20240229")
@tool
def magic_function(input: int) -> int:
"""Applies a magic function to an input."""
return input + 2
tools = [magic_function]
agent = create_tool_calling_agent(model, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
agent_executor.invoke({"input": "what is the value of magic_function(3)?"})
```
```
> Entering new AgentExecutor chain...
Invoking: `magic_function` with `{'input': 3}`
responded: [{'text': '<thinking>\nThe user has asked for the value of magic_function applied to the input 3. Looking at the available tools, magic_function is the relevant one to use here, as it takes an integer input and returns an integer output.\n\nThe magic_function has one required parameter:\n- input (integer)\n\nThe user has directly provided the value 3 for the input parameter. Since the required parameter is present, we can proceed with calling the function.\n</thinking>', 'type': 'text'}, {'id': 'toolu_01HsTheJPA5mcipuFDBbJ1CW', 'input': {'input': 3}, 'name': 'magic_function', 'type': 'tool_use'}]
5
Therefore, the value of magic_function(3) is 5.
> Finished chain.
{'input': 'what is the value of magic_function(3)?',
'output': 'Therefore, the value of magic_function(3) is 5.'}
```
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
core[minor], langchain[patch], openai[minor], anthropic[minor], fireworks[minor], groq[minor], mistralai[minor]
```python
class ToolCall(TypedDict):
name: str
args: Dict[str, Any]
id: Optional[str]
class InvalidToolCall(TypedDict):
name: Optional[str]
args: Optional[str]
id: Optional[str]
error: Optional[str]
class ToolCallChunk(TypedDict):
name: Optional[str]
args: Optional[str]
id: Optional[str]
index: Optional[int]
class AIMessage(BaseMessage):
...
tool_calls: List[ToolCall] = []
invalid_tool_calls: List[InvalidToolCall] = []
...
class AIMessageChunk(AIMessage, BaseMessageChunk):
...
tool_call_chunks: Optional[List[ToolCallChunk]] = None
...
```
Important considerations:
- Parsing logic occurs within different providers;
- ~Changing output type is a breaking change for anyone doing explicit
type checking;~
- ~Langsmith rendering will need to be updated:
https://github.com/langchain-ai/langchainplus/pull/3561~
- ~Langserve will need to be updated~
- Adding chunks:
- ~AIMessage + ToolCallsMessage = ToolCallsMessage if either has
non-null .tool_calls.~
- Tool call chunks are appended, merging when having equal values of
`index`.
- additional_kwargs accumulate the normal way.
- During streaming:
- ~Messages can change types (e.g., from AIMessageChunk to
AIToolCallsMessageChunk)~
- Output parsers parse additional_kwargs (during .invoke they read off
tool calls).
Packages outside of `partners/`:
- https://github.com/langchain-ai/langchain-cohere/pull/7
- https://github.com/langchain-ai/langchain-google/pull/123/files
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
After this PR it will be possible to pass a cache instance directly to a
language model. This is useful to allow different language models to use
different caches if needed.
- **Issue:** close#19276
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
This PR supports using Pydantic v2 objects to generate the schema for
the JSONOutputParser (#19441). This also adds a `json_schema` parameter
to allow users to pass any JSON schema to validate with, not just
pydantic.
core/langchain_core/_api[Patch]: mypy ignore fixes#17048
Related to #17048
Applied mypy fixes to below two files:
libs/core/langchain_core/_api/deprecation.py
libs/core/langchain_core/_api/beta_decorator.py
Summary of Fixes:
**Issue 1**
class _deprecated_property(type(obj)): # type: ignore
error: Unsupported dynamic base class "type" [misc]
Fix:
1. Added an __init__ method to _deprecated_property to initialize the
fget, fset, fdel, and __doc__ attributes.
2. In the __get__, __set__, and __delete__ methods, we now use the
self.fget, self.fset, and self.fdel attributes to call the original
methods after emitting the warning.
3. The finalize function now creates an instance of _deprecated_property
with the fget, fset, fdel, and doc attributes from the original obj
property.
**Issue 2**
def finalize( # type: ignore
wrapper: Callable[..., Any], new_doc: str
) -> T:
error: All conditional function variants must have identical
signatures
Fix: Ensured that both definitions of the finalize function have the
same signature
Twitter Handle -
https://x.com/gupteutkarsha?s=11&t=uwHe4C3PPpGRvoO5Qpm1aA
- **Description:** Improvement for #19599: fixing missing return of
graph.draw_mermaid_png and improve it to make the saving of the rendered
image optional
Co-authored-by: Angel Igareta <angel.igareta@klarna.com>
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: If you're adding a new integration, please
include
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
Removes required usage of `requests` from `langchain-core`, all of which
has been deprecated.
- removes Tracer V1 implementations
- removes old `try_load_from_hub` github-based hub implementations
Removal done in a way where imports will still succeed, and usage will
fail with a `RuntimeError`.
This PR completes work for PR #18798 to expose raw tool output in
on_tool_end.
Affected APIs:
* astream_log
* astream_events
* callbacks sent to langsmith via langsmith-sdk
* Any other code that relies on BaseTracer!
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- This ensures ids are stable across streamed chunks
- Multiple messages in batch call get separate ids
- Also fix ids being dropped when combining message chunks
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: If you're adding a new integration, please
include
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
- **Description:** Add functionality to generate Mermaid syntax and
render flowcharts from graph data. This includes support for custom node
colors and edge curve styles, as well as the ability to export the
generated graphs to PNG images using either the Mermaid.INK API or
Pyppeteer for local rendering.
- **Dependencies:** Optional dependencies are `pyppeteer` if rendering
wants to be done using Pypeteer and Javascript code.
---------
Co-authored-by: Angel Igareta <angel.igareta@klarna.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Description: Fix xml parser to handle strings that only contain the root
tag
Issue: N/A
Dependencies: None
Twitter handle: N/A
A valid xml text can contain only the root level tag. Example: <body>
Some text here
</body>
The example above is a valid xml string. If parsed with the current
implementation the result is {"body": []}. This fix checks if the root
level text contains any non-whitespace character and if that's the case
it returns {root.tag: root.text}. The result is that the above text is
correctly parsed as {"body": "Some text here"}
@ale-delfino
Thank you for contributing to LangChain!
Checklist:
- [x] PR title: Please title your PR "package: description", where
"package" is whichever of langchain, community, core, experimental, etc.
is being modified. Use "docs: ..." for purely docs changes, "templates:
..." for template changes, "infra: ..." for CI changes.
- Example: "community: add foobar LLM"
- [x] PR message: **Delete this entire template message** and replace it
with the following bulleted list
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [x] Pass lint and test: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified to check that you're
passing lint and testing. See contribution guidelines for more
information on how to write/run tests, lint, etc:
https://python.langchain.com/docs/contributing/
- [x] Add tests and docs: If you're adding a new integration, please
include
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @efriis, @eyurtsev, @hwchase17.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
**Description:**
While not technically incorrect, the TypeVar used for the `@beta`
decorator prevented pyright (and thus most vscode users) from correctly
seeing the types of functions/classes decorated with `@beta`.
This is in part due to a small bug in pyright
(https://github.com/microsoft/pyright/issues/7448 ) - however, the
`Type` bound in the typevar `C = TypeVar("C", Type, Callable)` is not
doing anything - classes are `Callables` by default, so by my
understanding binding to `Type` does not actually provide any more
safety - the modified annotation still works correctly for both
functions, properties, and classes.
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** Update to the docstring for class RunnableSerializable,
method configurable_fields
**Issue:** [Add in code documentation to core Runnable methods
#18804](https://github.com/langchain-ai/langchain/issues/18804)
**Dependencies:** None
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
**Description:** Update to the docstring for class RunnableSerializable,
method configurable_alternatives
**Issue:** [Add in code documentation to core Runnable methods
#18804](https://github.com/langchain-ai/langchain/issues/18804)
**Dependencies:** None
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
- .stream() and .astream() call on_llm_new_token, removing the need for
subclasses to do so. Backwards compatible because now we don't pass
run_manager into ._stream and ._astream
- .generate() and .agenerate() now handle `stream: bool` kwarg for
_generate and _agenerate. Subclasses handle this arg by delegating to
._stream(), now one less thing they need to do. Backwards compat because
this is an optional arg that we now never pass to the subclasses
- .generate() and .agenerate() now inspect callback handlers to decide
on a default value for stream:bool if not passed in. This auto enables
streaming when using astream_events and astream_log
- as a result of these three changes any usage of .astream_events and
.astream_log should now yield chat model stream events
- In future PRs we can update all subclasses to reflect these two things
now handled by base class, but in meantime all will continue to work
**Description:**
This PR adds a slightly more helpful message to a Tool Exception
```
# current state
langchain_core.tools.ToolException: Too many arguments to single-input tool
# proposed state
langchain_core.tools.ToolException: Too many arguments to single-input tool. Consider using a StructuredTool instead.
```
**Issue:** Somewhat discussed here 👉#6197
**Dependencies:** None
**Twitter handle:** N/A
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
As mentioned in #18322, the current PydanticOutputParser won't work for
anyone trying to parse to pydantic v2 models. This PR adds a separate
`PydanticV2OutputParser`, as well as a `langchain_core.pydantic_v2`
namespace that will fail on import to any projects using pydantic<2.
Happy to update the docs for output parsers if this is something we're
interesting in adding.
On a separate note, I also updated `check_pydantic.sh` to detect
pydantic imports with leading whitespace and excluded the internal
namespaces. That change can be separated into its own PR if needed.
---------
Co-authored-by: Jan Nissen <jan23@gmail.com>
Added example to the docstring of the "bind" method of Runnable. This
makes it easier to understand the purpose of the method when reviewing
in code editors. E.g. VS Code below.
<img width="833" alt="Screenshot 2024-03-27 at 16 24 18"
src="https://github.com/langchain-ai/langchain/assets/45722942/ad022d4e-7bc0-4f4b-aa7a-838f1816cc52">
---------
Co-authored-by: Chester Curme <chester.curme@gmail.com>
Patch potential XML vulnerability CVE-2024-1455
This patches a potential XML vulnerability in the XMLOutputParser in
langchain-core. The vulnerability in some situations could lead to a
denial of service attack.
At risk are users that:
1) Running older distributions of python that have older version of
libexpat
2) Are using XMLOutputParser with an agent
3) Accept inputs from untrusted sources with this agent (e.g., endpoint
on the web that allows an untrusted user to interact wiith the parser)
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: If you're adding a new integration, please
include
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
DefusedXML is causing parsing errors on previously functional code with
the 0.7.x versions. These do not seem to support newer version of python
well. 0.8.x has only been released as rc, so we're not going to to use
it in the core package
Few-Shot prompt template may use a `SemanticSimilarityExampleSelector`
that in turn uses a `VectorStore` that does I/O operations.
So to work correctly on the event loop, we need:
* async methods for the `VectorStore` (OK)
* async methods for the `SemanticSimilarityExampleSelector` (this PR)
* async methods for `BasePromptTemplate` and `BaseChatPromptTemplate`
(future work)
Previous PR passed _parser attribute which apparently is not meant to be
used by user code and causes non deterministic failures on CI when
testing the transform and a transform methods. Reverting this change
temporarily.
This mitigates a security concern for users still using older versions of libexpat that causes an attacker to compromise the availability of the system if an attacker manages to surface malicious payload to this XMLParser.
For prompt templates with only 1 variable (common in e.g.,
MessageGraph), it's convenient to wrap the incoming object in the
variable before formatting.
The downside of this, of course, would be that some number of
invocations will successfully format when the user may have intended to
format it properly before
Classes and functions defined in __init__.py are not parsed into the API
Reference.
For example:
- libs/core/langchain_core/messages/__init__.py : AnyMessage,
MessageLikeRepresentation, get_buffer_string(), messages_from_dict(),
...
Opinionated: __init__.py is not a typical place to define artifacts.
Moved artifacts from __init__ into utils.py.
Added `MessageLikeRepresentation` to __all__ since it is used outside of
`messages`, for example, in
`libs/core/langchain_core/language_models/base.py`
Added `_message_from_dict` to __all__ since it is used outside of
`messages`(???) I would add `message_from_dict` (without underscore) as
an alias. Please, advise.
Covered by tests in
`libs/core/tests/unit_tests/language_models/chat_models/test_base.py`,
`libs/core/tests/unit_tests/language_models/llms/test_base.py` and
`libs/core/tests/unit_tests/runnables/test_runnable_events.py`
**Description:**
Currently, `CacheBackedEmbeddings` computes vectors for *all* uncached
documents before updating the store. This pull request updates the
embedding computation loop to compute embeddings in batches, updating
the store after each batch.
I noticed this when I tried `CacheBackedEmbeddings` on our 30k document
set and the cache directory hadn't appeared on disk after 30 minutes.
The motivation is to minimize compute/data loss when problems occur:
* If there is a transient embedding failure (e.g. a network outage at
the embedding endpoint triggers an exception), at least the completed
vectors are written to the store instead of being discarded.
* If there is an issue with the store (e.g. no write permissions), the
condition is detected early without computing (and discarding!) all the
vectors.
**Issue:**
Implements enhancement #18026.
**Testing:**
I was unable to run unit tests; details in [this
post](https://github.com/langchain-ai/langchain/discussions/15019#discussioncomment-8576684).
---------
Signed-off-by: chrispy <chrispy@synopsys.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Classes and functions defined in __init__.py are not parsed into the API
Reference.
For example: libs/core/langchain_core/globals/__init__.py :
`set_verbose` `get_llm_cache`, `set_llm_cache`, ...
And the whole `langchain_core.globals` namespace is not visible in the
API Reference. The refactoring is just file renaming.
- **Description:** Enhanced the `BaseChatModel` to support an
`Optional[Union[bool, BaseCache]]` type for the `cache` attribute,
allowing for both boolean flags and custom cache implementations.
Implemented logic within chat model methods to utilize the provided
custom cache implementation effectively. This change aims to provide
more flexibility in caching strategies for chat models.
- **Issue:** Implements enhancement request #17242.
- **Dependencies:** No additional dependencies required for this change.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Issue : For functions which have an argument with the name 'title', the
convert_pydantic_to_openai_function generates an incorrect output and
omits the argument all together. This is because the _rm_titles function
removes all instances of the the key 'title' from the output.
Description : Updates the _rm_titles function to check the presence of
the 'type' key as well before removing the 'title' key. As the title key
that we wish to omit always has a type key along with it.
Potential gap if there is a function defined which has both title and
key as argument names, in which case this would fail. Maybe we could set
a filter on the function argument names and reject those with keyword
argument names.
No dependencies. Passed all tests.
- [x] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core,
experimental, etc. is being modified. Use "docs: ..." for purely docs
changes, "templates: ..." for template changes, "infra: ..." for CI
changes.
- Example: "community: add foobar LLM"
- [x] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [x] **Add tests and docs**: If you're adding a new integration, please
include
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.
- [x] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
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If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
The root run id (~trace id's) is useful for assigning feedback, but the
current recommended approach is to use callbacks to retrieve it, which
has some drawbacks:
1. Doesn't work for streaming until after the first event
2. Doesn't let you call other endpoints with the same trace ID in
parallel (since you have to wait until the call is completed/started to
use
This PR lets you provide = "run_id" in the runnable config.
Couple considerations:
1. For batch calls, we split the trace up into separate trees (to permit
better rendering). We keep the provided run ID for the first one and
generate a unique one for other elements of the batch.
2. For nested calls, the provided ID is ONLY used on the top root/trace.
### Example Usage
```
chain.invoke("foo", {"run_id": uuid.uuid4()})
```
