mirror of
https://github.com/hwchase17/langchain.git
synced 2025-06-24 15:43:54 +00:00
fix: core: Include in json output also fields set outside the constructor (#21342)
This commit is contained in:
Nuno Campos
GitHub
parent
ac14f171ac
commit
6f17158606
@ -12,7 +12,7 @@ from typing import (
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from typing_extensions import NotRequired
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from langchain_core.pydantic_v1 import BaseModel, PrivateAttr
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from langchain_core.pydantic_v1 import BaseModel
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class BaseSerialized(TypedDict):
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@ -114,12 +114,6 @@ class Serializable(BaseModel, ABC):
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if (k not in self.__fields__ or try_neq_default(v, k, self))
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]
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_lc_kwargs = PrivateAttr(default_factory=dict)
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def __init__(self, **kwargs: Any) -> None:
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super().__init__(**kwargs)
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self._lc_kwargs = kwargs
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def to_json(self) -> Union[SerializedConstructor, SerializedNotImplemented]:
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if not self.is_lc_serializable():
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return self.to_json_not_implemented()
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@ -128,8 +122,9 @@ class Serializable(BaseModel, ABC):
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# Get latest values for kwargs if there is an attribute with same name
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lc_kwargs = {
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k: getattr(self, k, v)
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for k, v in self._lc_kwargs.items()
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for k, v in self
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if not (self.__exclude_fields__ or {}).get(k, False) # type: ignore
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and _is_field_useful(self, k, v)
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}
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# Merge the lc_secrets and lc_attributes from every class in the MRO
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@ -186,6 +181,23 @@ class Serializable(BaseModel, ABC):
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return to_json_not_implemented(self)
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def _is_field_useful(inst: Serializable, key: str, value: Any) -> bool:
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"""Check if a field is useful as a constructor argument.
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Args:
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inst: The instance.
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key: The key.
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value: The value.
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Returns:
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Whether the field is useful.
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"""
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field = inst.__fields__.get(key)
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if not field:
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return False
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return field.required is True or value or field.get_default() != value
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def _replace_secrets(
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root: Dict[Any, Any], secrets_map: Dict[str, str]
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) -> Dict[Any, Any]:
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@ -1,9 +1,11 @@
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"""Configuration for unit tests."""
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from importlib import util
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from typing import Dict, Sequence
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from uuid import UUID
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import pytest
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from pytest import Config, Function, Parser
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from pytest_mock import MockerFixture
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def pytest_addoption(parser: Parser) -> None:
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@ -85,3 +87,11 @@ def pytest_collection_modifyitems(config: Config, items: Sequence[Function]) ->
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item.add_marker(
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pytest.mark.skip(reason="Skipping not an extended test.")
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)
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@pytest.fixture()
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def deterministic_uuids(mocker: MockerFixture) -> MockerFixture:
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side_effect = (
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UUID(f"00000000-0000-4000-8000-{i:012}", version=4) for i in range(10000)
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)
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return mocker.patch("uuid.uuid4", side_effect=side_effect)
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@ -21,6 +21,7 @@ def test_serdes_message() -> None:
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"type": "constructor",
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"id": ["langchain", "schema", "messages", "AIMessage"],
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"kwargs": {
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"type": "ai",
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"content": [{"text": "blah", "type": "text"}],
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"tool_calls": [{"name": "foo", "args": {"bar": 1}, "id": "baz"}],
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"invalid_tool_calls": [
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@ -46,6 +47,7 @@ def test_serdes_message_chunk() -> None:
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"type": "constructor",
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"id": ["langchain", "schema", "messages", "AIMessageChunk"],
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"kwargs": {
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"type": "AIMessageChunk",
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"content": [{"text": "blah", "type": "text"}],
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"tool_calls": [{"name": "foo", "args": {"bar": 1}, "id": "baz"}],
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"invalid_tool_calls": [
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@ -74,7 +74,6 @@
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]
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}
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},
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"middle": [],
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"last": {
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"lc": 1,
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"type": "constructor",
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@ -109,8 +108,7 @@
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"buz"
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],
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"template": "what did baz say to {buz}",
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"template_format": "f-string",
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"partial_variables": {}
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"template_format": "f-string"
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},
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"name": "PromptTemplate",
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"graph": {
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@ -151,7 +149,6 @@
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]
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}
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},
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"middle": [],
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"last": {
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"lc": 1,
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"type": "not_implemented",
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@ -200,8 +197,7 @@
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}
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]
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}
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},
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"name": null
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}
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},
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"name": "RunnableSequence",
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"graph": {
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@ -284,8 +280,7 @@
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"buz"
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],
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"template": "what did baz say to {buz}",
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"template_format": "f-string",
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"partial_variables": {}
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"template_format": "f-string"
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},
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"name": "PromptTemplate",
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"graph": {
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@ -326,7 +321,6 @@
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]
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}
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},
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"middle": [],
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"last": {
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"lc": 1,
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"type": "not_implemented",
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@ -375,8 +369,7 @@
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}
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]
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}
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},
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"name": null
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}
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},
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"name": "RunnableSequence",
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"graph": {
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@ -445,8 +438,7 @@
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],
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"repr": "<class 'Exception'>"
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}
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],
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"exception_key": null
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]
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},
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"name": "RunnableWithFallbacks",
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"graph": {
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@ -486,8 +478,7 @@
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}
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]
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}
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},
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"name": null
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}
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},
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"name": "RunnableSequence",
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"graph": {
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@ -579,11 +570,7 @@
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"runnable",
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"RunnablePassthrough"
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],
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"kwargs": {
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"func": null,
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"afunc": null,
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"input_type": null
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},
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"kwargs": {},
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"name": "RunnablePassthrough",
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"graph": {
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"nodes": [
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@ -664,7 +651,6 @@
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]
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}
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},
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"middle": [],
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"last": {
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"lc": 1,
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"type": "constructor",
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@ -750,8 +736,7 @@
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}
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]
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}
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},
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"name": null
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}
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},
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"name": "RunnableSequence",
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"graph": {
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@ -933,8 +918,7 @@
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],
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"repr": "<class 'Exception'>"
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}
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],
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"exception_key": null
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]
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},
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"name": "RunnableWithFallbacks",
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"graph": {
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@ -1148,8 +1132,7 @@
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],
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"repr": "<class 'Exception'>"
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}
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],
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"exception_key": null
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]
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},
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"name": "RunnableWithFallbacks",
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"graph": {
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File diff suppressed because one or more lines are too long
@ -48,6 +48,8 @@ from langchain_core.output_parsers import (
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CommaSeparatedListOutputParser,
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StrOutputParser,
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)
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from langchain_core.outputs.chat_generation import ChatGeneration
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from langchain_core.outputs.llm_result import LLMResult
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from langchain_core.prompt_values import ChatPromptValue, StringPromptValue
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from langchain_core.prompts import (
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ChatPromptTemplate,
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@ -111,7 +113,19 @@ class FakeTracer(BaseTracer):
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def _replace_message_id(self, maybe_message: Any) -> Any:
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if isinstance(maybe_message, BaseMessage):
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maybe_message.id = AnyStr()
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maybe_message.id = str(next(self.uuids_generator))
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if isinstance(maybe_message, ChatGeneration):
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maybe_message.message.id = str(next(self.uuids_generator))
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if isinstance(maybe_message, LLMResult):
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for i, gen_list in enumerate(maybe_message.generations):
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for j, gen in enumerate(gen_list):
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maybe_message.generations[i][j] = self._replace_message_id(gen)
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if isinstance(maybe_message, dict):
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for k, v in maybe_message.items():
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maybe_message[k] = self._replace_message_id(v)
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if isinstance(maybe_message, list):
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for i, v in enumerate(maybe_message):
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maybe_message[i] = self._replace_message_id(v)
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return maybe_message
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@ -136,16 +150,8 @@ class FakeTracer(BaseTracer):
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"child_runs": [self._copy_run(child) for child in run.child_runs],
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"trace_id": self._replace_uuid(run.trace_id) if run.trace_id else None,
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"dotted_order": new_dotted_order,
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"inputs": (
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{k: self._replace_message_id(v) for k, v in run.inputs.items()}
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if isinstance(run.inputs, dict)
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else run.inputs
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),
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"outputs": (
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{k: self._replace_message_id(v) for k, v in run.outputs.items()}
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if isinstance(run.outputs, dict)
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else run.outputs
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),
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"inputs": self._replace_message_id(run.inputs),
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"outputs": self._replace_message_id(run.outputs),
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}
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)
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@ -1939,7 +1945,9 @@ async def test_with_listeners_async(mocker: MockerFixture) -> None:
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@freeze_time("2023-01-01")
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def test_prompt_with_chat_model(
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mocker: MockerFixture, snapshot: SnapshotAssertion
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mocker: MockerFixture,
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snapshot: SnapshotAssertion,
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deterministic_uuids: MockerFixture,
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) -> None:
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prompt = (
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SystemMessagePromptTemplate.from_template("You are a nice assistant.")
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@ -2043,7 +2051,9 @@ def test_prompt_with_chat_model(
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@freeze_time("2023-01-01")
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async def test_prompt_with_chat_model_async(
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mocker: MockerFixture, snapshot: SnapshotAssertion
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mocker: MockerFixture,
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snapshot: SnapshotAssertion,
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deterministic_uuids: MockerFixture,
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) -> None:
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prompt = (
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SystemMessagePromptTemplate.from_template("You are a nice assistant.")
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@ -2770,7 +2780,9 @@ async def test_prompt_with_llm_and_async_lambda(
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@freeze_time("2023-01-01")
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def test_prompt_with_chat_model_and_parser(
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mocker: MockerFixture, snapshot: SnapshotAssertion
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mocker: MockerFixture,
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snapshot: SnapshotAssertion,
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deterministic_uuids: MockerFixture,
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) -> None:
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prompt = (
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SystemMessagePromptTemplate.from_template("You are a nice assistant.")
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@ -2809,7 +2821,9 @@ def test_prompt_with_chat_model_and_parser(
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@freeze_time("2023-01-01")
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def test_combining_sequences(
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mocker: MockerFixture, snapshot: SnapshotAssertion
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mocker: MockerFixture,
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snapshot: SnapshotAssertion,
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deterministic_uuids: MockerFixture,
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) -> None:
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prompt = (
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SystemMessagePromptTemplate.from_template("You are a nice assistant.")
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@ -36,13 +36,6 @@
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"SpecialPerson"
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],
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"kwargs": {
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"another_secret": {
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"lc": 1,
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"type": "secret",
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"id": [
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"ANOTHER_SECRET"
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]
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},
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"secret": {
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"lc": 1,
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"type": "secret",
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@ -50,8 +43,15 @@
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"SECRET"
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]
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},
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"another_visible": "bye",
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"you_can_see_me": "hello"
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"you_can_see_me": "hello",
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"another_secret": {
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"lc": 1,
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"type": "secret",
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"id": [
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"ANOTHER_SECRET"
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]
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},
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"another_visible": "bye"
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}
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}
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'''
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@ -71,6 +71,61 @@
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"LLMChain"
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],
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"kwargs": {
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"prompt": {
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"lc": 1,
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"type": "constructor",
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"id": [
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"langchain",
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"prompts",
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"prompt",
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"PromptTemplate"
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],
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"kwargs": {
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"input_variables": [
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"name"
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],
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"template": "hello {name}!",
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"template_format": "f-string"
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},
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"name": "PromptTemplate",
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"graph": {
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"nodes": [
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{
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"id": 0,
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"type": "schema",
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"data": "PromptInput"
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},
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{
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"id": 1,
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"type": "runnable",
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"data": {
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"id": [
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"langchain",
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"prompts",
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"prompt",
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"PromptTemplate"
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],
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"name": "PromptTemplate"
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}
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},
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{
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"id": 2,
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"type": "schema",
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"data": "PromptTemplateOutput"
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}
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],
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"edges": [
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{
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"source": 0,
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"target": 1
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},
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{
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"source": 1,
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"target": 2
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}
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]
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}
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},
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"llm": {
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"lc": 1,
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"type": "constructor",
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@ -81,15 +136,23 @@
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"OpenAI"
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],
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"kwargs": {
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"model": "davinci",
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"model_name": "davinci",
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"temperature": 0.5,
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"max_tokens": 256,
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"top_p": 1,
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"n": 1,
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"best_of": 1,
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"openai_api_key": {
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"lc": 1,
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"type": "secret",
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"id": [
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"OPENAI_API_KEY"
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]
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}
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},
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"openai_proxy": "",
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"batch_size": 20,
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"max_retries": 2,
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"disallowed_special": "all"
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},
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"name": "OpenAI",
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"graph": {
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@ -130,30 +193,24 @@
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]
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}
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},
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"prompt": {
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"output_key": "text",
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"output_parser": {
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"lc": 1,
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"type": "constructor",
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"id": [
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"langchain",
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"prompts",
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"prompt",
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"PromptTemplate"
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"schema",
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"output_parser",
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"StrOutputParser"
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],
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"kwargs": {
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"input_variables": [
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"name"
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],
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"template": "hello {name}!",
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"template_format": "f-string",
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"partial_variables": {}
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},
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"name": "PromptTemplate",
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"kwargs": {},
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"name": "StrOutputParser",
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"graph": {
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"nodes": [
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{
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"id": 0,
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"type": "schema",
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"data": "PromptInput"
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"data": "StrOutputParserInput"
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},
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{
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"id": 1,
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@ -161,17 +218,17 @@
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"data": {
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"id": [
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"langchain",
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"prompts",
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"prompt",
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"PromptTemplate"
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"schema",
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"output_parser",
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"StrOutputParser"
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],
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"name": "PromptTemplate"
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"name": "StrOutputParser"
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}
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},
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{
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"id": 2,
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"type": "schema",
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"data": "PromptTemplateOutput"
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"data": "StrOutputParserOutput"
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}
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],
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"edges": [
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@ -185,7 +242,8 @@
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}
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]
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}
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}
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},
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"return_final_only": true
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},
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"name": "LLMChain",
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"graph": {
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@ -240,65 +298,6 @@
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"LLMChain"
|
||||
],
|
||||
"kwargs": {
|
||||
"llm": {
|
||||
"lc": 1,
|
||||
"type": "constructor",
|
||||
"id": [
|
||||
"langchain",
|
||||
"chat_models",
|
||||
"openai",
|
||||
"ChatOpenAI"
|
||||
],
|
||||
"kwargs": {
|
||||
"model": "davinci",
|
||||
"temperature": 0.5,
|
||||
"openai_api_key": {
|
||||
"lc": 1,
|
||||
"type": "secret",
|
||||
"id": [
|
||||
"OPENAI_API_KEY"
|
||||
]
|
||||
}
|
||||
},
|
||||
"name": "ChatOpenAI",
|
||||
"graph": {
|
||||
"nodes": [
|
||||
{
|
||||
"id": 0,
|
||||
"type": "schema",
|
||||
"data": "ChatOpenAIInput"
|
||||
},
|
||||
{
|
||||
"id": 1,
|
||||
"type": "runnable",
|
||||
"data": {
|
||||
"id": [
|
||||
"langchain",
|
||||
"chat_models",
|
||||
"openai",
|
||||
"ChatOpenAI"
|
||||
],
|
||||
"name": "ChatOpenAI"
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 2,
|
||||
"type": "schema",
|
||||
"data": "ChatOpenAIOutput"
|
||||
}
|
||||
],
|
||||
"edges": [
|
||||
{
|
||||
"source": 0,
|
||||
"target": 1
|
||||
},
|
||||
{
|
||||
"source": 1,
|
||||
"target": 2
|
||||
}
|
||||
]
|
||||
}
|
||||
},
|
||||
"prompt": {
|
||||
"lc": 1,
|
||||
"type": "constructor",
|
||||
@ -337,8 +336,7 @@
|
||||
"name"
|
||||
],
|
||||
"template": "hello {name}!",
|
||||
"template_format": "f-string",
|
||||
"partial_variables": {}
|
||||
"template_format": "f-string"
|
||||
},
|
||||
"name": "PromptTemplate",
|
||||
"graph": {
|
||||
@ -381,8 +379,7 @@
|
||||
}
|
||||
}
|
||||
}
|
||||
],
|
||||
"partial_variables": {}
|
||||
]
|
||||
},
|
||||
"name": "ChatPromptTemplate",
|
||||
"graph": {
|
||||
@ -422,8 +419,121 @@
|
||||
}
|
||||
]
|
||||
}
|
||||
},
|
||||
"llm": {
|
||||
"lc": 1,
|
||||
"type": "constructor",
|
||||
"id": [
|
||||
"langchain",
|
||||
"chat_models",
|
||||
"openai",
|
||||
"ChatOpenAI"
|
||||
],
|
||||
"kwargs": {
|
||||
"model_name": "davinci",
|
||||
"temperature": 0.5,
|
||||
"openai_api_key": {
|
||||
"lc": 1,
|
||||
"type": "secret",
|
||||
"id": [
|
||||
"OPENAI_API_KEY"
|
||||
]
|
||||
},
|
||||
"openai_proxy": "",
|
||||
"max_retries": 2,
|
||||
"n": 1
|
||||
},
|
||||
"name": "ChatOpenAI",
|
||||
"graph": {
|
||||
"nodes": [
|
||||
{
|
||||
"id": 0,
|
||||
"type": "schema",
|
||||
"data": "ChatOpenAIInput"
|
||||
},
|
||||
{
|
||||
"id": 1,
|
||||
"type": "runnable",
|
||||
"data": {
|
||||
"id": [
|
||||
"langchain",
|
||||
"chat_models",
|
||||
"openai",
|
||||
"ChatOpenAI"
|
||||
],
|
||||
"name": "ChatOpenAI"
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 2,
|
||||
"type": "schema",
|
||||
"data": "ChatOpenAIOutput"
|
||||
}
|
||||
],
|
||||
"edges": [
|
||||
{
|
||||
"source": 0,
|
||||
"target": 1
|
||||
},
|
||||
{
|
||||
"source": 1,
|
||||
"target": 2
|
||||
}
|
||||
]
|
||||
}
|
||||
},
|
||||
"output_key": "text",
|
||||
"output_parser": {
|
||||
"lc": 1,
|
||||
"type": "constructor",
|
||||
"id": [
|
||||
"langchain",
|
||||
"schema",
|
||||
"output_parser",
|
||||
"StrOutputParser"
|
||||
],
|
||||
"kwargs": {},
|
||||
"name": "StrOutputParser",
|
||||
"graph": {
|
||||
"nodes": [
|
||||
{
|
||||
"id": 0,
|
||||
"type": "schema",
|
||||
"data": "StrOutputParserInput"
|
||||
},
|
||||
{
|
||||
"id": 1,
|
||||
"type": "runnable",
|
||||
"data": {
|
||||
"id": [
|
||||
"langchain",
|
||||
"schema",
|
||||
"output_parser",
|
||||
"StrOutputParser"
|
||||
],
|
||||
"name": "StrOutputParser"
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 2,
|
||||
"type": "schema",
|
||||
"data": "StrOutputParserOutput"
|
||||
}
|
||||
],
|
||||
"edges": [
|
||||
{
|
||||
"source": 0,
|
||||
"target": 1
|
||||
},
|
||||
{
|
||||
"source": 1,
|
||||
"target": 2
|
||||
}
|
||||
]
|
||||
}
|
||||
},
|
||||
"return_final_only": true
|
||||
},
|
||||
"name": "LLMChain",
|
||||
"graph": {
|
||||
"nodes": [
|
||||
@ -477,6 +587,61 @@
|
||||
"LLMChain"
|
||||
],
|
||||
"kwargs": {
|
||||
"prompt": {
|
||||
"lc": 1,
|
||||
"type": "constructor",
|
||||
"id": [
|
||||
"langchain",
|
||||
"prompts",
|
||||
"prompt",
|
||||
"PromptTemplate"
|
||||
],
|
||||
"kwargs": {
|
||||
"input_variables": [
|
||||
"name"
|
||||
],
|
||||
"template": "hello {name}!",
|
||||
"template_format": "f-string"
|
||||
},
|
||||
"name": "PromptTemplate",
|
||||
"graph": {
|
||||
"nodes": [
|
||||
{
|
||||
"id": 0,
|
||||
"type": "schema",
|
||||
"data": "PromptInput"
|
||||
},
|
||||
{
|
||||
"id": 1,
|
||||
"type": "runnable",
|
||||
"data": {
|
||||
"id": [
|
||||
"langchain",
|
||||
"prompts",
|
||||
"prompt",
|
||||
"PromptTemplate"
|
||||
],
|
||||
"name": "PromptTemplate"
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 2,
|
||||
"type": "schema",
|
||||
"data": "PromptTemplateOutput"
|
||||
}
|
||||
],
|
||||
"edges": [
|
||||
{
|
||||
"source": 0,
|
||||
"target": 1
|
||||
},
|
||||
{
|
||||
"source": 1,
|
||||
"target": 2
|
||||
}
|
||||
]
|
||||
}
|
||||
},
|
||||
"llm": {
|
||||
"lc": 1,
|
||||
"type": "constructor",
|
||||
@ -487,15 +652,23 @@
|
||||
"OpenAI"
|
||||
],
|
||||
"kwargs": {
|
||||
"model": "davinci",
|
||||
"model_name": "davinci",
|
||||
"temperature": 0.5,
|
||||
"max_tokens": 256,
|
||||
"top_p": 1,
|
||||
"n": 1,
|
||||
"best_of": 1,
|
||||
"openai_api_key": {
|
||||
"lc": 1,
|
||||
"type": "secret",
|
||||
"id": [
|
||||
"OPENAI_API_KEY"
|
||||
]
|
||||
}
|
||||
},
|
||||
"openai_proxy": "",
|
||||
"batch_size": 20,
|
||||
"max_retries": 2,
|
||||
"disallowed_special": "all"
|
||||
},
|
||||
"name": "OpenAI",
|
||||
"graph": {
|
||||
@ -536,30 +709,24 @@
|
||||
]
|
||||
}
|
||||
},
|
||||
"prompt": {
|
||||
"output_key": "text",
|
||||
"output_parser": {
|
||||
"lc": 1,
|
||||
"type": "constructor",
|
||||
"id": [
|
||||
"langchain",
|
||||
"prompts",
|
||||
"prompt",
|
||||
"PromptTemplate"
|
||||
"schema",
|
||||
"output_parser",
|
||||
"StrOutputParser"
|
||||
],
|
||||
"kwargs": {
|
||||
"input_variables": [
|
||||
"name"
|
||||
],
|
||||
"template": "hello {name}!",
|
||||
"template_format": "f-string",
|
||||
"partial_variables": {}
|
||||
},
|
||||
"name": "PromptTemplate",
|
||||
"kwargs": {},
|
||||
"name": "StrOutputParser",
|
||||
"graph": {
|
||||
"nodes": [
|
||||
{
|
||||
"id": 0,
|
||||
"type": "schema",
|
||||
"data": "PromptInput"
|
||||
"data": "StrOutputParserInput"
|
||||
},
|
||||
{
|
||||
"id": 1,
|
||||
@ -567,17 +734,17 @@
|
||||
"data": {
|
||||
"id": [
|
||||
"langchain",
|
||||
"prompts",
|
||||
"prompt",
|
||||
"PromptTemplate"
|
||||
"schema",
|
||||
"output_parser",
|
||||
"StrOutputParser"
|
||||
],
|
||||
"name": "PromptTemplate"
|
||||
"name": "StrOutputParser"
|
||||
}
|
||||
},
|
||||
{
|
||||
"id": 2,
|
||||
"type": "schema",
|
||||
"data": "PromptTemplateOutput"
|
||||
"data": "StrOutputParserOutput"
|
||||
}
|
||||
],
|
||||
"edges": [
|
||||
@ -591,7 +758,8 @@
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
},
|
||||
"return_final_only": true
|
||||
},
|
||||
"name": "LLMChain",
|
||||
"graph": {
|
||||
@ -646,15 +814,23 @@
|
||||
"OpenAI"
|
||||
],
|
||||
"kwargs": {
|
||||
"model": "davinci",
|
||||
"model_name": "davinci",
|
||||
"temperature": 0.7,
|
||||
"max_tokens": 256,
|
||||
"top_p": 1,
|
||||
"n": 1,
|
||||
"best_of": 1,
|
||||
"openai_api_key": {
|
||||
"lc": 1,
|
||||
"type": "secret",
|
||||
"id": [
|
||||
"OPENAI_API_KEY"
|
||||
]
|
||||
}
|
||||
},
|
||||
"openai_proxy": "",
|
||||
"batch_size": 20,
|
||||
"max_retries": 2,
|
||||
"disallowed_special": "all"
|
||||
},
|
||||
"name": "OpenAI",
|
||||
"graph": {
|
||||
|
||||
@ -242,11 +242,6 @@ def test_aliases_hidden() -> None:
|
||||
"type": "secret",
|
||||
"id": ["MY_FAVORITE_SECRET"],
|
||||
},
|
||||
"my_favorite_secret_alias": {
|
||||
"lc": 1,
|
||||
"type": "secret",
|
||||
"id": ["MY_FAVORITE_SECRET"],
|
||||
},
|
||||
"my_other_secret": {"lc": 1, "type": "secret", "id": ["MY_OTHER_SECRET"]},
|
||||
},
|
||||
}
|
||||
|
||||
@ -17,7 +17,9 @@ class NotSerializable:
|
||||
def test_loads_openai_llm() -> None:
|
||||
from langchain_openai import OpenAI
|
||||
|
||||
llm = CommunityOpenAI(model="davinci", temperature=0.5, openai_api_key="hello") # type: ignore[call-arg]
|
||||
llm = CommunityOpenAI(
|
||||
model="davinci", temperature=0.5, openai_api_key="hello", top_p=0.8
|
||||
) # type: ignore[call-arg]
|
||||
llm_string = dumps(llm)
|
||||
llm2 = loads(llm_string, secrets_map={"OPENAI_API_KEY": "hello"})
|
||||
|
||||
@ -31,7 +33,9 @@ def test_loads_openai_llm() -> None:
|
||||
def test_loads_llmchain() -> None:
|
||||
from langchain_openai import OpenAI
|
||||
|
||||
llm = CommunityOpenAI(model="davinci", temperature=0.5, openai_api_key="hello") # type: ignore[call-arg]
|
||||
llm = CommunityOpenAI(
|
||||
model="davinci", temperature=0.5, openai_api_key="hello", top_p=0.8
|
||||
) # type: ignore[call-arg]
|
||||
prompt = PromptTemplate.from_template("hello {name}!")
|
||||
chain = LLMChain(llm=llm, prompt=prompt)
|
||||
chain_string = dumps(chain)
|
||||
@ -54,7 +58,7 @@ def test_loads_llmchain_env() -> None:
|
||||
if not has_env:
|
||||
os.environ["OPENAI_API_KEY"] = "env_variable"
|
||||
|
||||
llm = OpenAI(model="davinci", temperature=0.5) # type: ignore[call-arg]
|
||||
llm = OpenAI(model="davinci", temperature=0.5, top_p=0.8) # type: ignore[call-arg]
|
||||
prompt = PromptTemplate.from_template("hello {name}!")
|
||||
chain = LLMChain(llm=llm, prompt=prompt)
|
||||
chain_string = dumps(chain)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user