Check if event stream is closed in memory loop.
Using try/except here to avoid race condition, but this may incur a
small overhead in versions prios to 3.11
Do not prefix function signature
---
* Reason for this is that information is already present with tool
calling models.
* This will save on tokens for those models, and makes it more obvious
what the description is!
* The @tool can get more parameters to allow a user to re-introduce the
the signature if we want
To permit proper coercion of objects like the following:
```python
class MyAsyncCallable:
async def __call__(self, foo):
return await ...
class MyAsyncGenerator:
async def __call__(self, foo):
await ...
yield
```
This PR introduces a v2 implementation of astream events that removes
intermediate abstractions and fixes some issues with v1 implementation.
The v2 implementation significantly reduces relevant code that's
associated with the astream events implementation together with
overhead.
After this PR, the astream events implementation:
- Uses an async callback handler
- No longer relies on BaseTracer
- No longer relies on json patch
As a result of this re-write, a number of issues were discovered with
the existing implementation.
## Changes in V2 vs. V1
### on_chat_model_end `output`
The outputs associated with `on_chat_model_end` changed depending on
whether it was within a chain or not.
As a root level runnable the output was:
```python
"data": {"output": AIMessageChunk(content="hello world!", id='some id')}
```
As part of a chain the output was:
```
"data": {
"output": {
"generations": [
[
{
"generation_info": None,
"message": AIMessageChunk(
content="hello world!", id=AnyStr()
),
"text": "hello world!",
"type": "ChatGenerationChunk",
}
]
],
"llm_output": None,
}
},
```
After this PR, we will always use the simpler representation:
```python
"data": {"output": AIMessageChunk(content="hello world!", id='some id')}
```
**NOTE** Non chat models (i.e., regular LLMs) are still associated with
the more verbose format.
### Remove some `_stream` events
`on_retriever_stream` and `on_tool_stream` events were removed -- these
were not real events, but created as an artifact of implementing on top
of astream_log.
The same information is already available in the `x_on_end` events.
### Propagating Names
Names of runnables have been updated to be more consistent
```python
model = GenericFakeChatModel(messages=infinite_cycle).configurable_fields(
messages=ConfigurableField(
id="messages",
name="Messages",
description="Messages return by the LLM",
)
)
```
Before:
```python
"name": "RunnableConfigurableFields",
```
After:
```python
"name": "GenericFakeChatModel",
```
### on_retriever_end
on_retriever_end will always return `output` which is a list of
documents (rather than a dict containing a key called "documents")
### Retry events
Removed the `on_retry` callback handler. It was incorrectly showing that
the failed function being retried has invoked `on_chain_end`
https://github.com/langchain-ai/langchain/pull/21638/files#diff-e512e3f84daf23029ebcceb11460f1c82056314653673e450a5831147d8cb84dL1394
Add unit tests that show differences between sync / async versions when
streaming.
The inner on_chain_chunk event is missing if mixing sync and async
functionality. Likely due to missing tap_output_iter implementation on
the sync variant of `_transform_stream_with_config`
0.2 is not a breaking release for core (but it is for langchain and
community)
To keep the core+langchain+community packages in sync at 0.2, we will
relax deps throughout the ecosystem to tolerate `langchain-core` 0.2
- it's only node ids that are limited
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.
Issues (nit):
1. `utils.guard_import` prints wrong error message when there is an
import `error.` It prints the whole `module_name` but should be only the
first part as the pip package name. E.i. `langchain_core.utils` -> print
not `langchain-core` but `langchain_core.utils`. Also replace '_' with
'-' in the pip package name.
2. it does not handle the `ModuleNotFoundError` which raised if
`guard_import("wrong_module")`
Fixed issues; added ut-s. Controversial: I've reraised
`ModuleNotFoundError` as `ImportError`, since in case of the error, the
proposed action is the same - we need to install a missed package.
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.
- support two-tuples of any sequence type (eg. json.loads never produces
tuples)
- support type alias for role key
- if id is passed in in dict form use it
- if tool_calls passed in in dict form use them
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Removed redundant self/cls from required args of class functions in
_get_python_function_required_args:
```python
class MemberTool:
def search_member(
self,
keyword: str,
*args,
**kwargs,
):
"""Search on members with any keyword like first_name, last_name, email
Args:
keyword: Any keyword of member
"""
headers = dict(authorization=kwargs['token'])
members = []
try:
members = request_(
method='SEARCH',
url=f'{service_url}/apiv1/members',
headers=headers,
json=dict(query=keyword),
)
except Exception as e:
logger.info(e.__doc__)
return members
convert_to_openai_tool(MemberTool.search_member)
```
expected result:
```
{'type': 'function', 'function': {'name': 'search_member', 'description': 'Search on members with any keyword like first_name, last_name, username, email', 'parameters': {'type': 'object', 'properties': {'keyword': {'type': 'string', 'description': 'Any keyword of member'}}, 'required': ['keyword']}}}
```
#20685
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
I can't seem to reproduce, but i got this:
```
SystemError: AST constructor recursion depth mismatch (before=102, after=37)
```
And the operation isn't critical for the actual forward pass so seems
preferable to expand our caught exceptions
**Description**: This update enhances the `extract_sub_links` function
within the `langchain_core/utils/html.py` module to include query
parameters in the extracted URLs.
**Issue**: N/A
**Dependencies**: No additional dependencies required for this change.
**Twitter handle**: N/A
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
- **Description:** Changes
`lanchain_core.output_parsers.CommaSeparatedListOutputParser` to handle
`,` as a delimiter alongside the previous implementation which used `, `
as delimiter.
- **Issue:** Started noticing that some results returned by LLMs were
not getting parsed correctly when the output contained `,` instead of `,
`.
- **Dependencies:** No
- **Twitter handle:** not active on twitter.
<!---
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, hwchase17.
-->
This PR moves the interface and the logic to core.
The following changes to namespaces:
`indexes` -> `indexing`
`indexes._api` -> `indexing.api`
Testing code is intentionally duplicated for now since it's testing
different
implementations of the record manager (in-memory vs. SQL).
Common logic will need to be pulled out into the test client.
A follow up PR will move the SQL based implementation outside of
LangChain.
Causes an issue for this code
```python
from langchain.chat_models.openai import ChatOpenAI
from langchain.output_parsers.openai_tools import JsonOutputToolsParser
from langchain.schema import SystemMessage
prompt = SystemMessage(content="You are a nice assistant.") + "{question}"
llm = ChatOpenAI(
model_kwargs={
"tools": [
{
"type": "function",
"function": {
"name": "web_search",
"description": "Searches the web for the answer to the question.",
"parameters": {
"type": "object",
"properties": {
"query": {
"type": "string",
"description": "The question to search for.",
},
},
},
},
}
],
},
streaming=True,
)
parser = JsonOutputToolsParser(first_tool_only=True)
llm_chain = prompt | llm | parser | (lambda x: x)
for chunk in llm_chain.stream({"question": "tell me more about turtles"}):
print(chunk)
# message = llm_chain.invoke({"question": "tell me more about turtles"})
# print(message)
```
Instead by definition, we'll assume that RunnableLambdas consume the
entire stream and that if the stream isn't addable then it's the last
message of the stream that's in the usable format.
---
If users want to use addable dicts, they can wrap the dict in an
AddableDict class.
---
Likely, need to follow up with the same change for other places in the
code that do the upgrade
This PR moves the implementations for chat history to core. So it's
easier to determine which dependencies need to be broken / add
deprecation warnings
**Description:** Move `FileCallbackHandler` from community to core
**Issue:** #20493
**Dependencies:** None
(imo) `FileCallbackHandler` is a built-in LangChain callback handler
like `StdOutCallbackHandler` and should properly be in in core.
- would happen when user's code tries to access attritbute that doesnt
exist, we prefer to let this crash in the user's code, rather than here
- also catch more cases where a runnable is invoked/streamed inside a
lambda. before we weren't seeing these as deps
- Add conditional: bool property to json representation of the graphs
- Add option to generate mermaid graph stripped of styles (useful as a
text representation of graph)
…s arg too
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: 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>
