Commit Graph

110 Commits

Author SHA1 Message Date
Christophe Bornet
038c287b3a
all: Improve make lint command (#25344)
* Removed `ruff check --select I` as `I` is already selected and checked
in the main `ruff check` command
* Added checks for non-empty `PYTHON_FILES`
* Run `ruff check` only on `PYTHON_FILES`

Co-authored-by: Erick Friis <erick@langchain.dev>
2024-08-23 18:23:52 -07:00
ccurme
b83f1eb0d5
core, partners: implement standard tracing params for LLMs (#25410) 2024-08-16 13:18:09 -04:00
Eugene Yurtsev
e18511bb22
core[minor], anthropic[patch]: Upgrade @root_validator usage to be consistent with pydantic 2 (#25457)
anthropic: Upgrade `@root_validator` usage to be consistent with
pydantic 2
core: support looking up multiple keys from env in from_env factory
2024-08-15 20:09:34 +00:00
Bagatur
eec7bb4f51
anthropic[patch]: Release 0.1.23 (#25394) 2024-08-14 09:03:39 -07:00
ZhangShenao
2c3e3dc6b1
patch[Partners] Unified fix of incorrect variable declarations in all check_imports (#25014)
There are some incorrect declarations of variable `has_failure` in
check_imports. The purpose of this PR is to uniformly fix these errors.
2024-08-03 13:49:41 -04:00
Bagatur
752a71b688
integrations[patch]: release model packages (#24900) 2024-07-31 20:48:20 +00:00
Bagatur
8461934c2b
core[patch], integrations[patch]: convert TypedDict to tool schema support (#24641)
supports following UX

```python
    class SubTool(TypedDict):
        """Subtool docstring"""

        args: Annotated[Dict[str, Any], {}, "this does bar"]

    class Tool(TypedDict):
        """Docstring
        Args:
            arg1: foo
        """

        arg1: str
        arg2: Union[int, str]
        arg3: Optional[List[SubTool]]
        arg4: Annotated[Literal["bar", "baz"], ..., "this does foo"]
        arg5: Annotated[Optional[float], None]
```

- can parse google style docstring
- can use Annotated to specify default value (second arg)
- can use Annotated to specify arg description (third arg)
- can have nested complex types
2024-07-31 18:27:24 +00:00
Bagatur
a6d1fb4275
core[patch]: introduce ToolMessage.status (#24628)
Anthropic models (including via Bedrock and other cloud platforms)
accept a status/is_error attribute on tool messages/results
(specifically in `tool_result` content blocks for Anthropic API). Adding
a ToolMessage.status attribute so that users can set this attribute when
using those models
2024-07-29 14:01:53 -07:00
Bagatur
b3a23ddf93
integration releases (#24725)
Release anthropic, openai, groq, mistralai, robocorp
2024-07-26 12:30:10 -07:00
Erick Friis
3dce2e1d35
all: add release notes to pypi (#24519) 2024-07-22 13:59:13 -07:00
Bagatur
236e957abb
core,groq,openai,mistralai,robocorp,fireworks,anthropic[patch]: Update BaseModel subclass and instance checks to handle both v1 and proper namespaces (#24417)
After this PR chat models will correctly handle pydantic 2 with
bind_tools and with_structured_output.


```python
import pydantic
print(pydantic.__version__)
```
2.8.2

```python
from langchain_openai import ChatOpenAI
from pydantic import BaseModel, Field

class Add(BaseModel):
    x: int
    y: int

model = ChatOpenAI().bind_tools([Add])
print(model.invoke('2 + 5').tool_calls)

model = ChatOpenAI().with_structured_output(Add)
print(type(model.invoke('2 + 5')))
```

```
[{'name': 'Add', 'args': {'x': 2, 'y': 5}, 'id': 'call_PNUFa4pdfNOYXxIMHc6ps2Do', 'type': 'tool_call'}]
<class '__main__.Add'>
```


```python
from langchain_openai import ChatOpenAI
from pydantic.v1 import BaseModel, Field

class Add(BaseModel):
    x: int
    y: int

model = ChatOpenAI().bind_tools([Add])
print(model.invoke('2 + 5').tool_calls)

model = ChatOpenAI().with_structured_output(Add)
print(type(model.invoke('2 + 5')))
```

```python
[{'name': 'Add', 'args': {'x': 2, 'y': 5}, 'id': 'call_hhiHYP441cp14TtrHKx3Upg0', 'type': 'tool_call'}]
<class '__main__.Add'>
```

Addresses issues: https://github.com/langchain-ai/langchain/issues/22782

---------

Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
2024-07-22 20:07:39 +00:00
Bagatur
259d4d2029
anthropic[patch]: Release 0.1.20 (#24204) 2024-07-12 13:59:15 -07:00
Bagatur
cb5031f22f
integrations[patch]: require core >=0.2.17 (#24207) 2024-07-12 20:54:01 +00:00
Bagatur
5fd1e67808
core[minor], integrations...[patch]: Support ToolCall as Tool input and ToolMessage as Tool output (#24038)
Changes:
- ToolCall, InvalidToolCall and ToolCallChunk can all accept a "type"
parameter now
- LLM integration packages add "type" to all the above
- Tool supports ToolCall inputs that have "type" specified
- Tool outputs ToolMessage when a ToolCall is passed as input
- Tools can separately specify ToolMessage.content and
ToolMessage.raw_output
- Tools emit events for validation errors (using on_tool_error and
on_tool_end)

Example:
```python
@tool("structured_api", response_format="content_and_raw_output")
def _mock_structured_tool_with_raw_output(
    arg1: int, arg2: bool, arg3: Optional[dict] = None
) -> Tuple[str, dict]:
    """A Structured Tool"""
    return f"{arg1} {arg2}", {"arg1": arg1, "arg2": arg2, "arg3": arg3}


def test_tool_call_input_tool_message_with_raw_output() -> None:
    tool_call: Dict = {
        "name": "structured_api",
        "args": {"arg1": 1, "arg2": True, "arg3": {"img": "base64string..."}},
        "id": "123",
        "type": "tool_call",
    }
    expected = ToolMessage("1 True", raw_output=tool_call["args"], tool_call_id="123")
    tool = _mock_structured_tool_with_raw_output
    actual = tool.invoke(tool_call)
    assert actual == expected

    tool_call.pop("type")
    with pytest.raises(ValidationError):
        tool.invoke(tool_call)

    actual_content = tool.invoke(tool_call["args"])
    assert actual_content == expected.content
```

---------

Co-authored-by: Erick Friis <erick@langchain.dev>
2024-07-11 14:54:02 -07:00
Bagatur
a0c2281540
infra: update mypy 1.10, ruff 0.5 (#23721)
```python
"""python scripts/update_mypy_ruff.py"""
import glob
import tomllib
from pathlib import Path

import toml
import subprocess
import re

ROOT_DIR = Path(__file__).parents[1]


def main():
    for path in glob.glob(str(ROOT_DIR / "libs/**/pyproject.toml"), recursive=True):
        print(path)
        with open(path, "rb") as f:
            pyproject = tomllib.load(f)
        try:
            pyproject["tool"]["poetry"]["group"]["typing"]["dependencies"]["mypy"] = (
                "^1.10"
            )
            pyproject["tool"]["poetry"]["group"]["lint"]["dependencies"]["ruff"] = (
                "^0.5"
            )
        except KeyError:
            continue
        with open(path, "w") as f:
            toml.dump(pyproject, f)
        cwd = "/".join(path.split("/")[:-1])
        completed = subprocess.run(
            "poetry lock --no-update; poetry install --with typing; poetry run mypy . --no-color",
            cwd=cwd,
            shell=True,
            capture_output=True,
            text=True,
        )
        logs = completed.stdout.split("\n")

        to_ignore = {}
        for l in logs:
            if re.match("^(.*)\:(\d+)\: error:.*\[(.*)\]", l):
                path, line_no, error_type = re.match(
                    "^(.*)\:(\d+)\: error:.*\[(.*)\]", l
                ).groups()
                if (path, line_no) in to_ignore:
                    to_ignore[(path, line_no)].append(error_type)
                else:
                    to_ignore[(path, line_no)] = [error_type]
        print(len(to_ignore))
        for (error_path, line_no), error_types in to_ignore.items():
            all_errors = ", ".join(error_types)
            full_path = f"{cwd}/{error_path}"
            try:
                with open(full_path, "r") as f:
                    file_lines = f.readlines()
            except FileNotFoundError:
                continue
            file_lines[int(line_no) - 1] = (
                file_lines[int(line_no) - 1][:-1] + f"  # type: ignore[{all_errors}]\n"
            )
            with open(full_path, "w") as f:
                f.write("".join(file_lines))

        subprocess.run(
            "poetry run ruff format .; poetry run ruff --select I --fix .",
            cwd=cwd,
            shell=True,
            capture_output=True,
            text=True,
        )


if __name__ == "__main__":
    main()

```
2024-07-03 10:33:27 -07:00
Bagatur
ebb404527f
anthropic[patch]: Release 0.1.19 (#23783) 2024-07-02 18:17:25 -04:00
Bagatur
ed200bf2c4
anthropic[patch]: expose payload (#23291)
![Screenshot 2024-06-21 at 4 56 02
PM](https://github.com/langchain-ai/langchain/assets/22008038/a2c6224f-3741-4502-9607-1a726a0551c9)
2024-07-02 17:43:47 -04:00
ccurme
7c1cddf1b7
anthropic[patch]: release 0.1.18 (#23778) 2024-07-02 16:46:47 -04:00
ccurme
c9dac59008
anthropic[patch]: fix model name in some integration tests (#23779) 2024-07-02 20:45:52 +00:00
Bagatur
7a6c06cadd
anthropic[patch]: tool output parser fix (#23647) 2024-07-02 16:33:22 -04:00
ccurme
46cbf0e4aa
anthropic[patch]: use core output parsers for structured output (#23776)
Also add to standard tests for structured output.
2024-07-02 16:15:26 -04:00
kiarina
dc396835ed
langchain_anthropic: add stop_reason in ChatAnthropic stream result (#23689)
`ChatAnthropic` can get `stop_reason` from the resulting `AIMessage` in
`invoke` and `ainvoke`, but not in `stream` and `astream`.
This is a different behavior from `ChatOpenAI`.
It is possible to get `stop_reason` from `stream` as well, since it is
needed to determine the next action after the LLM call. This would be
easier to handle in situations where only `stop_reason` is needed.

- Issue: NA
- Dependencies: NA
- Twitter handle: https://x.com/kiarina37
2024-07-02 15:16:20 -04:00
Bagatur
389a568f9a
standard-tests[patch]: add anthropic format integration test (#23717) 2024-07-01 11:06:04 -04:00
Bagatur
b63c7f10bc
anthropic[patch]: Release 0.1.17 (#23650) 2024-06-28 17:07:08 -07:00
Bagatur
fc8fd49328
openai, anthropic, ...: with_structured_output to pass in explicit tool choice (#23645)
...community, mistralai, groq, fireworks

part of #23644
2024-06-28 16:39:53 -07:00
Bagatur
a7ab93479b
anthropic[patch]: Release 0.1.16 (#23549) 2024-06-26 20:49:13 +00:00
Bagatur
93d0ad97fe
anthropic[patch]: test image input (#23155) 2024-06-19 02:32:15 +00:00
Leonid Ganeline
3dfd055411
anthropic: docstrings (#23145)
Added missed docstrings. Format docstrings to the consistent format
(used in the API Reference)
2024-06-18 22:26:45 -04:00
Bagatur
90559fde70
openai[patch], standard-tests[patch]: don't pass in falsey stop vals (#23153)
adds an image input test to standard-tests as well
2024-06-18 18:13:13 -07:00
Bagatur
d96f67b06f
standard-tests[patch]: Update chat model standard tests (#22378)
- Refactor standard test classes to make them easier to configure
- Update openai to support stop_sequences init param
- Update groq to support stop_sequences init param
- Update fireworks to support max_retries init param
- Update ChatModel.bind_tools to type tool_choice
- Update groq to handle tool_choice="any". **this may be controversial**

---------

Co-authored-by: Chester Curme <chester.curme@gmail.com>
2024-06-17 13:37:41 -07:00
Jacob Lee
181a61982f
anthropic[minor]: Adds streaming tool call support for Anthropic (#22687)
Preserves string content chunks for non tool call requests for
convenience.

One thing - Anthropic events look like this:

```
RawContentBlockStartEvent(content_block=TextBlock(text='', type='text'), index=0, type='content_block_start')
RawContentBlockDeltaEvent(delta=TextDelta(text='<thinking>\nThe', type='text_delta'), index=0, type='content_block_delta')
RawContentBlockDeltaEvent(delta=TextDelta(text=' provide', type='text_delta'), index=0, type='content_block_delta')
...
RawContentBlockStartEvent(content_block=ToolUseBlock(id='toolu_01GJ6x2ddcMG3psDNNe4eDqb', input={}, name='get_weather', type='tool_use'), index=1, type='content_block_start')
RawContentBlockDeltaEvent(delta=InputJsonDelta(partial_json='', type='input_json_delta'), index=1, type='content_block_delta')
```

Note that `delta` has a `type` field. With this implementation, I'm
dropping it because `merge_list` behavior will concatenate strings.

We currently have `index` as a special field when merging lists, would
it be worth adding `type` too?

If so, what do we set as a context block chunk? `text` vs.
`text_delta`/`tool_use` vs `input_json_delta`?

CC @ccurme @efriis @baskaryan
2024-06-14 09:14:43 -07:00
ccurme
73c76b9628
anthropic[patch]: always add tool_result type to ToolMessage content (#22721)
Anthropic tool results can contain image data, which are typically
represented with content blocks having `"type": "image"`. Currently,
these content blocks are passed as-is as human/user messages to
Anthropic, which raises BadRequestError as it expects a tool_result
block to follow a tool_use.

Here we update ChatAnthropic to nest the content blocks inside a
tool_result content block.

Example:
```python
import base64

import httpx
from langchain_anthropic import ChatAnthropic
from langchain_core.messages import AIMessage, HumanMessage, ToolMessage
from langchain_core.pydantic_v1 import BaseModel, Field


# Fetch image
image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg"
image_data = base64.b64encode(httpx.get(image_url).content).decode("utf-8")


class FetchImage(BaseModel):
    should_fetch: bool = Field(..., description="Whether an image is requested.")


llm = ChatAnthropic(model="claude-3-sonnet-20240229").bind_tools([FetchImage])

messages = [
    HumanMessage(content="Could you summon a beautiful image please?"),
    AIMessage(
        content=[
            {
                "type": "tool_use",
                "id": "toolu_01Rn6Qvj5m7955x9m9Pfxbcx",
                "name": "FetchImage",
                "input": {"should_fetch": True},
            },
        ],
        tool_calls=[
            {
                "name": "FetchImage",
                "args": {"should_fetch": True},
                "id": "toolu_01Rn6Qvj5m7955x9m9Pfxbcx",
            },
        ],
    ),
    ToolMessage(
        name="FetchImage",
        content=[
            {
                "type": "image",
                "source": {
                    "type": "base64",
                    "media_type": "image/jpeg",
                    "data": image_data,
                },
            },
        ],
        tool_call_id="toolu_01Rn6Qvj5m7955x9m9Pfxbcx",
    ),
]

llm.invoke(messages)
```

Trace:
https://smith.langchain.com/public/d27e4fc1-a96d-41e1-9f52-54f5004122db/r
2024-06-13 20:14:23 -07:00
ccurme
f32d57f6f0
anthropic: refactor streaming to use events api; add streaming usage metadata (#22628)
- Refactor streaming to use raw events;
- Add `stream_usage` class attribute and kwarg to stream methods that,
if True, will include separate chunks in the stream containing usage
metadata.

There are two ways to implement streaming with anthropic's python sdk.
They have slight differences in how they surface usage metadata.
1. [Use helper
functions](https://github.com/anthropics/anthropic-sdk-python?tab=readme-ov-file#streaming-helpers).
This is what we are doing now.
```python
count = 1
with client.messages.stream(**params) as stream:
    for text in stream.text_stream:
        snapshot = stream.current_message_snapshot
        print(f"{count}: {snapshot.usage} -- {text}")
        count = count + 1

final_snapshot = stream.get_final_message()
print(f"{count}: {final_snapshot.usage}")
```
```
1: Usage(input_tokens=8, output_tokens=1) -- Hello
2: Usage(input_tokens=8, output_tokens=1) -- !
3: Usage(input_tokens=8, output_tokens=1) --  How
4: Usage(input_tokens=8, output_tokens=1) --  can
5: Usage(input_tokens=8, output_tokens=1) --  I
6: Usage(input_tokens=8, output_tokens=1) --  assist
7: Usage(input_tokens=8, output_tokens=1) --  you
8: Usage(input_tokens=8, output_tokens=1) --  today
9: Usage(input_tokens=8, output_tokens=1) -- ?
10: Usage(input_tokens=8, output_tokens=12)
```
To do this correctly, we need to emit a new chunk at the end of the
stream containing the usage metadata.

2. [Handle raw
events](https://github.com/anthropics/anthropic-sdk-python?tab=readme-ov-file#streaming-responses)
```python
stream = client.messages.create(**params, stream=True)
count = 1
for event in stream:
    print(f"{count}: {event}")
    count = count + 1
```
```
1: RawMessageStartEvent(message=Message(id='msg_01Vdyov2kADZTXqSKkfNJXcS', content=[], model='claude-3-haiku-20240307', role='assistant', stop_reason=None, stop_sequence=None, type='message', usage=Usage(input_tokens=8, output_tokens=1)), type='message_start')
2: RawContentBlockStartEvent(content_block=TextBlock(text='', type='text'), index=0, type='content_block_start')
3: RawContentBlockDeltaEvent(delta=TextDelta(text='Hello', type='text_delta'), index=0, type='content_block_delta')
4: RawContentBlockDeltaEvent(delta=TextDelta(text='!', type='text_delta'), index=0, type='content_block_delta')
5: RawContentBlockDeltaEvent(delta=TextDelta(text=' How', type='text_delta'), index=0, type='content_block_delta')
6: RawContentBlockDeltaEvent(delta=TextDelta(text=' can', type='text_delta'), index=0, type='content_block_delta')
7: RawContentBlockDeltaEvent(delta=TextDelta(text=' I', type='text_delta'), index=0, type='content_block_delta')
8: RawContentBlockDeltaEvent(delta=TextDelta(text=' assist', type='text_delta'), index=0, type='content_block_delta')
9: RawContentBlockDeltaEvent(delta=TextDelta(text=' you', type='text_delta'), index=0, type='content_block_delta')
10: RawContentBlockDeltaEvent(delta=TextDelta(text=' today', type='text_delta'), index=0, type='content_block_delta')
11: RawContentBlockDeltaEvent(delta=TextDelta(text='?', type='text_delta'), index=0, type='content_block_delta')
12: RawContentBlockStopEvent(index=0, type='content_block_stop')
13: RawMessageDeltaEvent(delta=Delta(stop_reason='end_turn', stop_sequence=None), type='message_delta', usage=MessageDeltaUsage(output_tokens=12))
14: RawMessageStopEvent(type='message_stop')
```

Here we implement the second option, in part because it should make
things easier when implementing streaming tool calls in the near future.

This would add two new chunks to the stream-- one at the beginning and
one at the end-- with blank content and containing usage metadata. We
add kwargs to the stream methods and a class attribute allowing for this
behavior to be toggled. I enabled it by default. If we merge this we can
add the same kwargs / attribute to OpenAI.

Usage:
```python
from langchain_anthropic import ChatAnthropic

model = ChatAnthropic(
    model="claude-3-haiku-20240307",
    temperature=0
)

full = None
for chunk in model.stream("hi"):
    full = chunk if full is None else full + chunk
    print(chunk)

print(f"\nFull: {full}")
```
```
content='' id='run-8a20843f-25c7-4025-ad72-9add395899e3' usage_metadata={'input_tokens': 8, 'output_tokens': 0, 'total_tokens': 8}
content='Hello' id='run-8a20843f-25c7-4025-ad72-9add395899e3'
content='!' id='run-8a20843f-25c7-4025-ad72-9add395899e3'
content=' How' id='run-8a20843f-25c7-4025-ad72-9add395899e3'
content=' can' id='run-8a20843f-25c7-4025-ad72-9add395899e3'
content=' I' id='run-8a20843f-25c7-4025-ad72-9add395899e3'
content=' assist' id='run-8a20843f-25c7-4025-ad72-9add395899e3'
content=' you' id='run-8a20843f-25c7-4025-ad72-9add395899e3'
content=' today' id='run-8a20843f-25c7-4025-ad72-9add395899e3'
content='?' id='run-8a20843f-25c7-4025-ad72-9add395899e3'
content='' id='run-8a20843f-25c7-4025-ad72-9add395899e3' usage_metadata={'input_tokens': 0, 'output_tokens': 12, 'total_tokens': 12}

Full: content='Hello! How can I assist you today?' id='run-8a20843f-25c7-4025-ad72-9add395899e3' usage_metadata={'input_tokens': 8, 'output_tokens': 12, 'total_tokens': 20}
```
2024-06-07 13:21:46 +00:00
ccurme
c1ef731503
anthropic: update attribute name and alias (#22625)
update name to `stop_sequences` and alias to `stop` (instead of the
other way around), since `stop_sequences` is the name used by anthropic.
2024-06-06 12:29:10 -04:00
ccurme
3999761201
multiple: add stop attribute (#22573) 2024-06-06 12:11:52 -04:00
ccurme
e08879147b
Revert "anthropic: stream token usage" (#22624)
Reverts langchain-ai/langchain#20180
2024-06-06 12:05:08 -04:00
Bagatur
0d495f3f63
anthropic: stream token usage (#20180)
open to other ideas
<img width="1181" alt="Screenshot 2024-04-08 at 5 34 08 PM"
src="https://github.com/langchain-ai/langchain/assets/22008038/03eb11c4-5eb5-43e3-9109-a13f76098fa4">

---------

Co-authored-by: Chester Curme <chester.curme@gmail.com>
2024-06-06 11:51:34 -04:00
Bagatur
cb183a9bf1
docs: update anthropic chat model (#22483)
Related to #22296

And update anthropic to accept base_url
2024-06-04 12:42:06 -07:00
Bagatur
678a19a5f7
infra: bump anthropic mypy 1 (#22373) 2024-06-03 08:21:55 -07:00
Bagatur
a8098f5ddb
anthropic[patch]: Release 0.1.15, fix sdk tools break (#22369) 2024-05-31 12:10:22 -07:00
Erick Friis
42ffcb2ff1
anthropic: release 0.1.14rc2, test release note gen (#22147) 2024-05-24 12:40:10 -07:00
Bagatur
baa3c975cb
anthropic[patch]: allow tool call mutation (#22130)
If tool_use blocks and tool_calls with overlapping IDs are present,
prefer the values of the tool_calls. Allows for mutating AIMessages just
via tool_calls.
2024-05-24 08:18:14 -07:00
Eugene Yurtsev
2d693c484e
docs: fix some spelling mistakes caught by newest version of code spell (#22090)
Going to merge this even though it doesn't pass all tests, and open a
separate PR for the remaining spelling mistakes.
2024-05-23 16:59:11 -04:00
ccurme
152c8cac33
anthropic, openai: cut pre-releases (#22083) 2024-05-23 15:02:23 -04:00
ccurme
fbfed65fb1
core, partners: add token usage attribute to AIMessage (#21944)
```python
class UsageMetadata(TypedDict):
    """Usage metadata for a message, such as token counts.

    Attributes:
        input_tokens: (int) count of input (or prompt) tokens
        output_tokens: (int) count of output (or completion) tokens
        total_tokens: (int) total token count
    """

    input_tokens: int
    output_tokens: int
    total_tokens: int
```
```python
class AIMessage(BaseMessage):
    ...
    usage_metadata: Optional[UsageMetadata] = None
    """If provided, token usage information associated with the message."""
    ...
```
2024-05-23 14:21:58 -04:00
Bagatur
50186da0a1
infra: rm unused # noqa violations (#22049)
Updating #21137
2024-05-22 15:21:08 -07:00
ccurme
4be5537837
Revert "anthropic: set default model" (#21987)
Reverts langchain-ai/langchain#21986
2024-05-21 17:28:32 +00:00
ccurme
35439cf3bd
anthropic: set default model (#21986)
Various docs reference `ChatAnthropic()`, but this currently raises
ValidationError.
2024-05-21 17:24:31 +00:00
ccurme
4470d3b4a0
partners: bump core in packages implementing ls_params (#21868)
These packages all import `LangSmithParams` which was released in
langchain-core==0.2.0.

N.B. we will need to release `openai` and then bump `langchain-openai`
in `together` and `upstage`.
2024-05-20 11:51:43 -07:00
ccurme
181dfef118
core, standard tests, partner packages: add test for model params (#21677)
1. Adds `.get_ls_params` to BaseChatModel which returns
```python
class LangSmithParams(TypedDict, total=False):
    ls_provider: str
    ls_model_name: str
    ls_model_type: Literal["chat"]
    ls_temperature: Optional[float]
    ls_max_tokens: Optional[int]
    ls_stop: Optional[List[str]]
```
by default it will only return
```python
{ls_model_type="chat", ls_stop=stop}
```

2. Add these params to inheritable metadata in
`CallbackManager.configure`

3. Implement `.get_ls_params` and populate all params for Anthropic +
all subclasses of BaseChatOpenAI

Sample trace:
https://smith.langchain.com/public/d2962673-4c83-47c7-b51e-61d07aaffb1b/r

**OpenAI**:
<img width="984" alt="Screenshot 2024-05-17 at 10 03 35 AM"
src="https://github.com/langchain-ai/langchain/assets/26529506/2ef41f74-a9df-4e0e-905d-da74fa82a910">

**Anthropic**:
<img width="978" alt="Screenshot 2024-05-17 at 10 06 07 AM"
src="https://github.com/langchain-ai/langchain/assets/26529506/39701c9f-7da5-4f1a-ab14-84e9169d63e7">

**Mistral** (and all others for which params are not yet populated):
<img width="977" alt="Screenshot 2024-05-17 at 10 08 43 AM"
src="https://github.com/langchain-ai/langchain/assets/26529506/37d7d894-fec2-4300-986f-49a5f0191b03">
2024-05-17 13:51:26 -04:00