**Description:** Circular dependencies when parsing references leading
to `RecursionError: maximum recursion depth exceeded` issue. This PR
address the issue by handling previously seen refs as in any typical DFS
to avoid infinite depths.
**Issue:** https://github.com/langchain-ai/langchain/issues/12163
**Twitter handle:** https://twitter.com/theBhulawat
- [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/
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Allows all chat models that implement _stream, but not _astream to still have async streaming to work.
Amongst other things this should resolve issues with streaming community model implementations through langserve since langserve is exclusively async.
Deprecates the old langchain-hub repository. Does *not* deprecate the
new https://smith.langchain.com/hub
@PinkDraconian has correctly raised that in the event someone is loading
unsanitized user input into the `try_load_from_hub` function, they have
the ability to load files from other locations in github than the
hwchase17/langchain-hub repository.
This PR adds some more path checking to that function and deprecates the
functionality in favor of the hub built into LangSmith.
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.
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:** Here is a minimal example to illustrate behavior:
```python
from langchain_core.runnables import RunnableLambda
def my_function(*args, **kwargs):
return 3 + kwargs.get("n", 0)
runnable = RunnableLambda(my_function).bind(n=1)
assert 4 == runnable.invoke({})
assert [4] == list(runnable.stream({}))
assert 4 == await runnable.ainvoke({})
assert [4] == [item async for item in runnable.astream({})]
```
Here, `runnable.invoke({})` and `runnable.stream({})` work fine, but
`runnable.ainvoke({})` raises
```
TypeError: RunnableLambda._ainvoke.<locals>.func() got an unexpected keyword argument 'n'
```
and similarly for `runnable.astream({})`:
```
TypeError: RunnableLambda._atransform.<locals>.func() got an unexpected keyword argument 'n'
```
Here we assume that this behavior is undesired and attempt to fix it.
**Issue:** https://github.com/langchain-ai/langchain/issues/17241,
https://github.com/langchain-ai/langchain/discussions/16446
Sent to LangSmith
Thank you for contributing to LangChain!
Checklist:
- [ ] 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"
- [ ] 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!
- [ ] 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/
- [ ] 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.
The current issue:
Most of the deprecation descriptions are duplicated. For example:
`[Deprecated] Chat Agent.[Deprecated] Chat Agent.` for the [ChatAgent
class](https://api.python.langchain.com/en/latest/langchain_api_reference.html#classes)
description.
NOTE: I've tested it only with new ut! I cannot build API Reference
locally :(
Adds an optional name param to our base message to support passing names
into LLMs.
OpenAI supports having a name on anything except tool message now
(system, ai, user/human).
- **Description :**
Fix: Use shallow copy for schema manipulation in get_format_instructions
Prevents side effects on the original schema object by using a
dictionary comprehension for a safer and more controlled manipulation of
schema key-value pairs, enhancing code reliability.
- **Issue:** #17161
- **Dependencies:** None
- **Twitter handle:** None
This PR replaces the memory stream implementation used by the
LogStreamCallbackHandler.
This implementation resolves an issue in which streamed logs and
streamed events originating from sync code would arrive only after the
entire sync code would finish execution (rather than arriving in real
time as they're generated).
One example is if trying to stream tokens from an llm within a tool. If
the tool was an async tool, but the llm was invoked via stream (sync
variant) rather than astream (async variant), then the tokens would fail
to stream in real time and would all arrived bunched up after the tool
invocation completed.
Pydantic's `dict()` function raises an error here if you pass in a
generator. We have a more robust serialization function in lagnsmith
that we will use instead.
primary problem in pydantic still exists, where `Optional[str]` gets
turned to `string` in the jsonschema `.schema()`
Also fixes the `SchemaSchema` naming issue
---------
Co-authored-by: William Fu-Hinthorn <13333726+hinthornw@users.noreply.github.com>
- **Description:** add a ValidationError handler as a field of
[`BaseTool`](https://github.com/langchain-ai/langchain/blob/master/libs/core/langchain_core/tools.py#L101)
and add unit tests for the code change.
- **Issue:** #12721#13662
- **Dependencies:** None
- **Tag maintainer:**
- **Twitter handle:** @hmdev3
- **NOTE:**
- I'm wondering if the update of document is required.
---------
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
We didn't override the namespace of the ImagePromptTemplate, so it is
listed as being in langchain.schema
This updates the mapping to let the loader deserialize.
Alternatively, we could make a slight breaking change and update the
namespace of the ImagePromptTemplate since we haven't broadly
publicized/documented it yet..
* Add bulk add_messages method to the interface.
* Update documentation for add_ai_message and add_human_message to
denote them as being marked for deprecation. We should stop using them
as they create more incorrect (inefficient) ways of doing things
Adjusted deprecate decorator to make sure decorated async functions are
still recognized as "coroutinefunction" by inspect
Addresses #16402
<!-- Thank you for contributing to LangChain!
Please title your PR "<package>: <description>", where <package> is
whichever of langchain, community, core, experimental, etc. is being
modified.
Replace this entire comment with:
- **Description:** a description of the change,
- **Issue:** the issue # it fixes if applicable,
- **Dependencies:** any dependencies required for this change,
- **Twitter handle:** we announce bigger features on Twitter. If your PR
gets announced, and you'd like a mention, we'll gladly shout you out!
Please make sure your PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` from the root
of the package you've modified to check this locally.
See contribution guidelines for more information on how to write/run
tests, lint, etc: https://python.langchain.com/docs/contributing/
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.
If no one reviews your PR within a few days, please @-mention one of
@baskaryan, @eyurtsev, @hwchase17.
-->
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
… converters
One way to convert anything to an OAI function:
convert_to_openai_function
One way to convert anything to an OAI tool: convert_to_openai_tool
Corresponding bind functions on OAI models: bind_functions, bind_tools
Adjusted `deprecate` decorator to make sure decorated async functions
are still recognized as "coroutinefunction" by `inspect`.
Before change, functions such as `LLMChain.acall` which are decorated as
deprecated are not recognized as coroutine functions. After the change,
they are recognized:
```python
import inspect
from langchain import LLMChain
# Is false before change but true after.
inspect.iscoroutinefunction(LLMChain.acall)
```
Add a version parameter while the method is in beta phase.
The idea is to make it possible to minimize making breaking changes for users while we're iterating on schema.
Once the API is stable we can assign a default version requirement.
This PR adds `astream_events` method to Runnables to make it easier to
stream data from arbitrary chains.
* Streaming only works properly in async right now
* One should use `astream()` with if mixing in imperative code as might
be done with tool implementations
* Astream_log has been modified with minimal additive changes, so no
breaking changes are expected
* Underlying callback code / tracing code should be refactored at some
point to handle things more consistently (OK for now)
- ~~[ ] verify event for on_retry~~ does not work until we implement
streaming for retry
- ~~[ ] Any rrenaming? Should we rename "event" to "hook"?~~
- [ ] Any other feedback from community?
- [x] throw NotImplementedError for `RunnableEach` for now
## Example
See this [Example
Notebook](dbbc7fa0d6/docs/docs/modules/agents/how_to/streaming_events.ipynb)
for an example with streaming in the context of an Agent
## Event Hooks Reference
Here is a reference table that shows some events that might be emitted
by the various Runnable objects.
Definitions for some of the Runnable are included after the table.
| event | name | chunk | input | output |
|----------------------|------------------|---------------------------------|-----------------------------------------------|-------------------------------------------------|
| on_chat_model_start | [model name] | | {"messages": [[SystemMessage,
HumanMessage]]} | |
| on_chat_model_stream | [model name] | AIMessageChunk(content="hello")
| | |
| on_chat_model_end | [model name] | | {"messages": [[SystemMessage,
HumanMessage]]} | {"generations": [...], "llm_output": None, ...} |
| on_llm_start | [model name] | | {'input': 'hello'} | |
| on_llm_stream | [model name] | 'Hello' | | |
| on_llm_end | [model name] | | 'Hello human!' |
| on_chain_start | format_docs | | | |
| on_chain_stream | format_docs | "hello world!, goodbye world!" | | |
| on_chain_end | format_docs | | [Document(...)] | "hello world!,
goodbye world!" |
| on_tool_start | some_tool | | {"x": 1, "y": "2"} | |
| on_tool_stream | some_tool | {"x": 1, "y": "2"} | | |
| on_tool_end | some_tool | | | {"x": 1, "y": "2"} |
| on_retriever_start | [retriever name] | | {"query": "hello"} | |
| on_retriever_chunk | [retriever name] | {documents: [...]} | | |
| on_retriever_end | [retriever name] | | {"query": "hello"} |
{documents: [...]} |
| on_prompt_start | [template_name] | | {"question": "hello"} | |
| on_prompt_end | [template_name] | | {"question": "hello"} |
ChatPromptValue(messages: [SystemMessage, ...]) |
Here are declarations associated with the events shown above:
`format_docs`:
```python
def format_docs(docs: List[Document]) -> str:
'''Format the docs.'''
return ", ".join([doc.page_content for doc in docs])
format_docs = RunnableLambda(format_docs)
```
`some_tool`:
```python
@tool
def some_tool(x: int, y: str) -> dict:
'''Some_tool.'''
return {"x": x, "y": y}
```
`prompt`:
```python
template = ChatPromptTemplate.from_messages(
[("system", "You are Cat Agent 007"), ("human", "{question}")]
).with_config({"run_name": "my_template", "tags": ["my_template"]})
```
