Using langchain input types (#11204)

Using langchain input type
pull/11231/head
Eugene Yurtsev 10 months ago committed by GitHub
parent 77c7c9ab97
commit 572968fee3
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GPG Key ID: 4AEE18F83AFDEB23

@ -9,7 +9,6 @@ from urllib.parse import urljoin
import httpx
from langchain.callbacks.tracers.log_stream import RunLogPatch
from langchain.load.dump import dumpd
from langchain.load.load import load, loads
from langchain.schema.runnable import Runnable
from langchain.schema.runnable.config import (
RunnableConfig,
@ -19,6 +18,8 @@ from langchain.schema.runnable.config import (
)
from langchain.schema.runnable.utils import Input, Output
from langserve.serialization import simple_dumpd, simple_loads
def _without_callbacks(config: Optional[RunnableConfig]) -> RunnableConfig:
"""Evict callbacks from the config since those are definitely not supported."""
@ -111,6 +112,7 @@ class RemoteRunnable(Runnable[Input, Output]):
self.url = url
self.sync_client = httpx.Client(base_url=url, timeout=timeout)
self.async_client = httpx.AsyncClient(base_url=url, timeout=timeout)
# Register cleanup handler once RemoteRunnable is garbage collected
weakref.finalize(self, _close_clients, self.sync_client, self.async_client)
@ -121,13 +123,13 @@ class RemoteRunnable(Runnable[Input, Output]):
response = self.sync_client.post(
"/invoke",
json={
"input": dumpd(input),
"input": simple_dumpd(input),
"config": _without_callbacks(config),
"kwargs": kwargs,
},
)
_raise_for_status(response)
return load(response.json())["output"]
return simple_loads(response.text)["output"]
def invoke(
self, input: Input, config: Optional[RunnableConfig] = None, **kwargs: Any
@ -142,13 +144,13 @@ class RemoteRunnable(Runnable[Input, Output]):
response = await self.async_client.post(
"/invoke",
json={
"input": dumpd(input),
"input": simple_dumpd(input),
"config": _without_callbacks(config),
"kwargs": kwargs,
},
)
_raise_for_status(response)
return load(response.json())["output"]
return simple_loads(response.text)["output"]
async def ainvoke(
self, input: Input, config: Optional[RunnableConfig] = None, **kwargs: Any
@ -180,13 +182,13 @@ class RemoteRunnable(Runnable[Input, Output]):
response = self.sync_client.post(
"/batch",
json={
"inputs": dumpd(inputs),
"inputs": simple_dumpd(inputs),
"config": _config,
"kwargs": kwargs,
},
)
_raise_for_status(response)
return load(response.json())["output"]
return simple_loads(response.text)["output"]
def batch(
self,
@ -222,13 +224,13 @@ class RemoteRunnable(Runnable[Input, Output]):
response = await self.async_client.post(
"/batch",
json={
"inputs": dumpd(inputs),
"inputs": simple_dumpd(inputs),
"config": _config,
"kwargs": kwargs,
},
)
_raise_for_status(response)
return load(response.json())["output"]
return simple_loads(response.text)["output"]
async def abatch(
self,
@ -259,11 +261,11 @@ class RemoteRunnable(Runnable[Input, Output]):
run_manager = callback_manager.on_chain_start(
dumpd(self),
dumpd(input),
simple_dumpd(input),
name=config.get("run_name"),
)
data = {
"input": dumpd(input),
"input": simple_dumpd(input),
"config": _without_callbacks(config),
"kwargs": kwargs,
}
@ -283,7 +285,7 @@ class RemoteRunnable(Runnable[Input, Output]):
) as event_source:
for sse in event_source.iter_sse():
if sse.event == "data":
chunk = loads(sse.data)
chunk = simple_loads(sse.data)
yield chunk
if final_output:
@ -313,11 +315,11 @@ class RemoteRunnable(Runnable[Input, Output]):
run_manager = await callback_manager.on_chain_start(
dumpd(self),
dumpd(input),
simple_dumpd(input),
name=config.get("run_name"),
)
data = {
"input": dumpd(input),
"input": simple_dumpd(input),
"config": _without_callbacks(config),
"kwargs": kwargs,
}
@ -334,7 +336,7 @@ class RemoteRunnable(Runnable[Input, Output]):
) as event_source:
async for sse in event_source.aiter_sse():
if sse.event == "data":
chunk = loads(sse.data)
chunk = simple_loads(sse.data)
yield chunk
if final_output:
@ -383,11 +385,11 @@ class RemoteRunnable(Runnable[Input, Output]):
run_manager = await callback_manager.on_chain_start(
dumpd(self),
dumpd(input),
simple_dumpd(input),
name=config.get("run_name"),
)
data = {
"input": dumpd(input),
"input": simple_dumpd(input),
"config": _without_callbacks(config),
"kwargs": kwargs,
"include_names": include_names,
@ -410,7 +412,7 @@ class RemoteRunnable(Runnable[Input, Output]):
) as event_source:
async for sse in event_source.aiter_sse():
if sse.event == "data":
data = loads(sse.data)
data = simple_loads(sse.data)
chunk = RunLogPatch(*data["ops"])
yield chunk

@ -0,0 +1,94 @@
"""Serialization module for Well Known LangChain objects.
Specialized JSON serialization for well known LangChain objects that
can be expected to be frequently transmitted between chains.
"""
import json
from typing import Any, Union
from langchain.prompts.base import StringPromptValue
from langchain.prompts.chat import ChatPromptValueConcrete
from langchain.schema.document import Document
from langchain.schema.messages import (
AIMessage,
AIMessageChunk,
ChatMessage,
ChatMessageChunk,
FunctionMessage,
FunctionMessageChunk,
HumanMessage,
HumanMessageChunk,
SystemMessage,
SystemMessageChunk,
)
from pydantic import BaseModel, ValidationError
class WellKnownLCObject(BaseModel):
"""A well known LangChain object."""
__root__: Union[
Document,
HumanMessage,
SystemMessage,
ChatMessage,
FunctionMessage,
AIMessage,
HumanMessageChunk,
SystemMessageChunk,
ChatMessageChunk,
FunctionMessageChunk,
AIMessageChunk,
StringPromptValue,
ChatPromptValueConcrete,
]
# Custom JSON Encoder
class _LangChainEncoder(json.JSONEncoder):
"""Custom JSON Encoder that can encode pydantic objects as well."""
def default(self, obj) -> Any:
if isinstance(obj, BaseModel):
return obj.dict()
return super().default(obj)
# Custom JSON Decoder
class _LangChainDecoder(json.JSONDecoder):
"""Custom JSON Decoder that handles well known LangChain objects."""
def __init__(self, *args: Any, **kwargs: Any) -> None:
"""Initialize the LangChainDecoder."""
super().__init__(object_hook=self.decoder, *args, **kwargs)
def decoder(self, value) -> Any:
"""Decode the value."""
if isinstance(value, dict):
try:
obj = WellKnownLCObject.parse_obj(value)
return obj.__root__
except ValidationError:
return {key: self.decoder(v) for key, v in value.items()}
elif isinstance(value, list):
return [self.decoder(item) for item in value]
else:
return value
# PUBLIC API
def simple_dumpd(obj: Any) -> Any:
"""Convert the given object to a JSON serializable object."""
return json.loads(json.dumps(obj, cls=_LangChainEncoder))
def simple_dumps(obj: Any) -> str:
"""Dump the given object as a JSON string."""
return json.dumps(obj, cls=_LangChainEncoder)
def simple_loads(s: str) -> Any:
"""Load the given JSON string."""
return json.loads(s, cls=_LangChainDecoder)

@ -1,31 +1,31 @@
from inspect import isclass
from typing import (
Any,
AsyncIterator,
Dict,
List,
Literal,
Mapping,
Sequence,
Type,
Union,
)
from langchain.load.dump import dumpd, dumps
from langchain.load.load import load
from langchain.schema.runnable import Runnable
from typing_extensions import Annotated
try:
from pydantic.v1 import BaseModel
except ImportError:
from pydantic import BaseModel
from pydantic import BaseModel, create_model
from langserve.serialization import simple_dumpd, simple_dumps
from langserve.validation import (
create_batch_request_model,
create_invoke_request_model,
create_runnable_config_model,
create_stream_log_request_model,
create_stream_request_model,
replace_lc_object_types,
)
try:
@ -66,6 +66,31 @@ class BatchResponse(BaseModel):
"""
def _unpack_input(validated_model: BaseModel) -> Any:
"""Unpack the decoded input from the validated model."""
if hasattr(validated_model, "__root__"):
return validated_model.__root__
else:
return validated_model
_MODEL_REGISTRY = {}
def _resolve_input_type(input_type: Union[Type, BaseModel]) -> BaseModel:
if isclass(input_type) and issubclass(input_type, BaseModel):
input_type_ = input_type
else:
input_type_ = create_model("Input", __root__=(input_type, ...))
hash_ = input_type_.schema_json()
if hash_ not in _MODEL_REGISTRY:
_MODEL_REGISTRY[hash_] = input_type_
return _MODEL_REGISTRY[hash_]
# PUBLIC API
@ -74,7 +99,7 @@ def add_routes(
runnable: Runnable,
*,
path: str = "",
input_type: Type = Any,
input_type: Union[Type, Literal["auto"], BaseModel] = "auto",
config_keys: Sequence[str] = (),
) -> None:
"""Register the routes on the given FastAPI app or APIRouter.
@ -83,9 +108,9 @@ def add_routes(
app: The FastAPI app or APIRouter to which routes should be added.
runnable: The runnable to wrap, must not be stateful.
path: A path to prepend to all routes.
input_type: Optional type to define a schema for the input part of the request.
If not provided, any input that can be de-serialized with LangChain's
serializer will be accepted.
input_type: type to use for input validation.
Default is "auto" which will use the InputType of the runnable.
User is free to provide a custom type annotation.
config_keys: list of config keys that will be accepted, by default
no config keys are accepted.
"""
@ -98,19 +123,22 @@ def add_routes(
"Use `pip install sse_starlette` to install."
)
input_type = replace_lc_object_types(input_type)
if input_type == "auto":
input_type_ = _resolve_input_type(runnable.input_schema)
else:
input_type_ = _resolve_input_type(input_type)
namespace = path or ""
model_namespace = path.strip("/").replace("/", "_")
config = create_runnable_config_model(model_namespace, config_keys)
InvokeRequest = create_invoke_request_model(model_namespace, input_type, config)
BatchRequest = create_batch_request_model(model_namespace, input_type, config)
InvokeRequest = create_invoke_request_model(model_namespace, input_type_, config)
BatchRequest = create_batch_request_model(model_namespace, input_type_, config)
# Stream request is the same as invoke request, but with a different response type
StreamRequest = create_stream_request_model(model_namespace, input_type, config)
StreamRequest = create_stream_request_model(model_namespace, input_type_, config)
StreamLogRequest = create_stream_log_request_model(
model_namespace, input_type, config
model_namespace, input_type_, config
)
@app.post(
@ -124,25 +152,25 @@ def add_routes(
# Request is first validated using InvokeRequest which takes into account
# config_keys as well as input_type.
# After validation, the input is loaded using LangChain's load function.
input = load(request.dict()["input"])
config = _project_dict(request.config, config_keys)
output = await runnable.ainvoke(input, config=config, **request.kwargs)
return InvokeResponse(output=dumpd(output))
output = await runnable.ainvoke(
_unpack_input(request.input), config=config, **request.kwargs
)
return InvokeResponse(output=simple_dumpd(output))
#
@app.post(f"{namespace}/batch", response_model=BatchResponse)
async def batch(request: Annotated[BatchRequest, BatchRequest]) -> BatchResponse:
"""Invoke the runnable with the given inputs and config."""
# Request is first validated using InvokeRequest which takes into account
# config_keys as well as input_type.
# After validation, the input is loaded using LangChain's load function.
inputs = load(request.dict()["inputs"])
if isinstance(request.config, list):
config = [_project_dict(config, config_keys) for config in request.config]
else:
config = _project_dict(request.config, config_keys)
inputs = [_unpack_input(input_) for input_ in request.inputs]
output = await runnable.abatch(inputs, config=config, **request.kwargs)
return BatchResponse(output=dumpd(output))
return BatchResponse(output=simple_dumpd(output))
@app.post(f"{namespace}/stream")
async def stream(
@ -152,17 +180,17 @@ def add_routes(
# Request is first validated using InvokeRequest which takes into account
# config_keys as well as input_type.
# After validation, the input is loaded using LangChain's load function.
input = load(request.dict()["input"])
input_ = _unpack_input(request.input)
config = _project_dict(request.config, config_keys)
async def _stream() -> AsyncIterator[dict]:
"""Stream the output of the runnable."""
async for chunk in runnable.astream(
input,
input_,
config=config,
**request.kwargs,
):
yield {"data": dumps(chunk), "event": "data"}
yield {"data": simple_dumps(chunk), "event": "data"}
yield {"event": "end"}
return EventSourceResponse(_stream())
@ -175,13 +203,13 @@ def add_routes(
# Request is first validated using InvokeRequest which takes into account
# config_keys as well as input_type.
# After validation, the input is loaded using LangChain's load function.
input = load(request.dict()["input"])
input_ = _unpack_input(request.input)
config = _project_dict(request.config, config_keys)
async def _stream_log() -> AsyncIterator[dict]:
"""Stream the output of the runnable."""
async for run_log_patch in runnable.astream_log(
input,
input_,
config=config,
include_names=request.include_names,
include_types=request.include_types,
@ -193,7 +221,7 @@ def add_routes(
):
# Temporary adapter
yield {
"data": dumps({"ops": run_log_patch.ops}),
"data": simple_dumps({"ops": run_log_patch.ops}),
"event": "data",
}
yield {"event": "end"}

@ -1,13 +1,11 @@
import typing
from typing import Any, List, Optional, Sequence, Type, Union, get_args, get_origin
from typing import List, Optional, Sequence, Type, Union
from langchain.load.serializable import Serializable
from langchain.schema.runnable import RunnableConfig
try:
from pydantic.v1 import BaseModel, Field, create_model
except ImportError:
from pydantic import BaseModel, Field, create_model, validator
from pydantic import BaseModel, Field, create_model
from typing_extensions import TypedDict
@ -104,97 +102,3 @@ def create_stream_log_request_model(
)
stream_log_request.update_forward_refs()
return stream_log_request
_TYPE_REGISTRY = {}
_SEEN_NAMES = set()
def _create_lc_object_validator(expected_id: Sequence[str]) -> Type[BaseModel]:
"""Create a validator for lc objects.
An LCObject is used to validate LangChain objects in dict representation.
The model is associated with a validator that checks that the id of the LCObject
matches the expected id. This is used to ensure that the LCObject is of the
correct type.
For OpenAPI docs to work, each unique LCObject must have a unique name.
The models are added to the registry to avoid creating duplicate models.
Args:
model_id: The expected id of the LCObject.
Returns:
A pydantic model that can be used to validate LCObjects.
"""
expected_id = tuple(expected_id)
model_id = tuple(["pydantic"]) + expected_id
if model_id in _TYPE_REGISTRY:
return _TYPE_REGISTRY[model_id]
model_name = model_id[-1]
if model_name in _SEEN_NAMES:
# Use fully qualified name
_name = ".".join(model_id)
else:
_name = model_name
if _name in _SEEN_NAMES:
raise AssertionError(f"Duplicate model name: {_name}")
_SEEN_NAMES.add(model_name)
class LCObject(BaseModel):
id: List[str]
lc: Any
type: str
kwargs: Any
@validator("id", allow_reuse=True)
def validate_id_namespace(cls, id: Sequence[str]) -> None:
"""Validate that the LCObject is one of the allowed types."""
if tuple(id) != expected_id:
raise ValueError(f"LCObject id {id} is not allowed: {model_id}")
return id
# Update the name of the model to make it unique.
model = create_model(_name, __base__=LCObject)
_TYPE_REGISTRY[model_id] = model
return model
def replace_lc_object_types(type_annotation: typing.Any) -> typing.Any:
"""Recursively replaces all LangChain objects with a serialized representation.
Args:
type_annotation: The type annotation to replace.
Returns:
The type annotation with all LCObject types replaced.
"""
origin = get_origin(type_annotation)
args = get_args(type_annotation)
if args:
if isinstance(args, (list, tuple)):
new_args = [replace_lc_object_types(arg) for arg in args]
if isinstance(origin, type):
if origin is list:
return typing.List[new_args[0]]
elif origin is tuple:
return typing.Tuple[tuple(new_args)]
else:
raise ValueError(f"Unknown origin type: {origin}")
else:
new_args = [replace_lc_object_types(arg) for arg in args]
return origin[tuple(new_args)]
if isinstance(type_annotation, type):
if issubclass(type_annotation, Serializable):
lc_id = type_annotation.get_lc_namespace() + [type_annotation.__name__]
return _create_lc_object_validator(lc_id)
return type_annotation

@ -419,75 +419,63 @@ files = [
[[package]]
name = "cffi"
version = "1.15.1"
version = "1.16.0"
description = "Foreign Function Interface for Python calling C code."
optional = false
python-versions = "*"
python-versions = ">=3.8"
files = [
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name = "referencing"
@ -3291,13 +3289,13 @@ telegram = ["requests"]
[[package]]
name = "traitlets"
version = "5.10.0"
version = "5.10.1"
description = "Traitlets Python configuration system"
optional = false
python-versions = ">=3.8"
files = [
{file = "traitlets-5.10.0-py3-none-any.whl", hash = "sha256:417745a96681fbb358e723d5346a547521f36e9bd0d50ba7ab368fff5d67aa54"},
{file = "traitlets-5.10.0.tar.gz", hash = "sha256:f584ea209240466e66e91f3c81aa7d004ba4cf794990b0c775938a1544217cd1"},
{file = "traitlets-5.10.1-py3-none-any.whl", hash = "sha256:07ab9c5bf8a0499fd7b088ba51be899c90ffc936ffc797d7b6907fc516bcd116"},
{file = "traitlets-5.10.1.tar.gz", hash = "sha256:db9c4aa58139c3ba850101913915c042bdba86f7c8a0dda1c6f7f92c5da8e542"},
]
[package.extras]
@ -3346,13 +3344,13 @@ dev = ["flake8", "flake8-annotations", "flake8-bandit", "flake8-bugbear", "flake
[[package]]
name = "urllib3"
version = "2.0.4"
version = "2.0.5"
description = "HTTP library with thread-safe connection pooling, file post, and more."
optional = false
python-versions = ">=3.7"
files = [
{file = "urllib3-2.0.4-py3-none-any.whl", hash = "sha256:de7df1803967d2c2a98e4b11bb7d6bd9210474c46e8a0401514e3a42a75ebde4"},
{file = "urllib3-2.0.4.tar.gz", hash = "sha256:8d22f86aae8ef5e410d4f539fde9ce6b2113a001bb4d189e0aed70642d602b11"},
{file = "urllib3-2.0.5-py3-none-any.whl", hash = "sha256:ef16afa8ba34a1f989db38e1dbbe0c302e4289a47856990d0682e374563ce35e"},
{file = "urllib3-2.0.5.tar.gz", hash = "sha256:13abf37382ea2ce6fb744d4dad67838eec857c9f4f57009891805e0b5e123594"},
]
[package.extras]
@ -3467,13 +3465,13 @@ anyio = ">=3.0.0"
[[package]]
name = "wcwidth"
version = "0.2.6"
version = "0.2.7"
description = "Measures the displayed width of unicode strings in a terminal"
optional = false
python-versions = "*"
files = [
{file = "wcwidth-0.2.6-py2.py3-none-any.whl", hash = "sha256:795b138f6875577cd91bba52baf9e445cd5118fd32723b460e30a0af30ea230e"},
{file = "wcwidth-0.2.6.tar.gz", hash = "sha256:a5220780a404dbe3353789870978e472cfe477761f06ee55077256e509b156d0"},
{file = "wcwidth-0.2.7-py2.py3-none-any.whl", hash = "sha256:fabf3e32999d9b0dab7d19d845149f326f04fe29bac67709ee071dbd92640a36"},
{file = "wcwidth-0.2.7.tar.gz", hash = "sha256:1b6d30a98ddd5ce9bbdb33658191fd2423fc9da203fe3ef1855407dcb7ee4e26"},
]
[[package]]
@ -3801,4 +3799,4 @@ server = ["fastapi", "sse-starlette"]
[metadata]
lock-version = "2.0"
python-versions = ">3.8.1,<4"
content-hash = "e8db4e6e8cd78f7082ac556cd561267c7e3ce1e80d2aa16adb5ff09834ef5d02"
content-hash = "a2e85c00c6ce7a2fd5e0aa4322135b9d8f4a8bc6bc65c1200f913f769fc81d94"

@ -14,7 +14,7 @@ fastapi = {version = ">0.90.1", optional = true}
sse-starlette = {version = "^1.3.0", optional = true}
httpx-sse = {version = "^0.3.1", optional = true}
pydantic = "^1"
langchain = ">=0.0.304"
langchain = { git = "https://github.com/langchain-ai/langchain", subdirectory = "libs/langchain" }
[tool.poetry.group.dev.dependencies]
jupyterlab = "^3.6.1"

@ -0,0 +1,122 @@
import json
from typing import Any
import pytest
from langchain.schema.messages import (
HumanMessage,
SystemMessage,
)
from langserve.serialization import simple_dumps, simple_loads
@pytest.mark.parametrize(
"data, expected_json",
[
# Test with python primitives
(1, 1),
([], []),
({}, {}),
({"a": 1}, {"a": 1}),
(
{"output": [HumanMessage(content="hello")]},
{
"output": [
{
"content": "hello",
"additional_kwargs": {},
"type": "human",
"example": False,
}
]
},
),
# Test with a single message (HumanMessage)
(
HumanMessage(content="Hello"),
{
"additional_kwargs": {},
"content": "Hello",
"example": False,
"type": "human",
},
),
# Test with a list containing mixed elements
(
[HumanMessage(content="Hello"), SystemMessage(content="Hi"), 42, "world"],
[
{
"additional_kwargs": {},
"content": "Hello",
"example": False,
"type": "human",
},
{"additional_kwargs": {}, "content": "Hi", "type": "system"},
42,
"world",
],
),
# # Attention: This test is not correct right now
# # Test with full and chunk messages
# (
# [HumanMessage(content="Hello"), HumanMessageChunk(content="Hi")],
# [
# {
# "additional_kwargs": {},
# "content": "Hello",
# "example": False,
# "type": "human",
# },
# {
# "additional_kwargs": {},
# "content": "Hi",
# "example": False,
# "type": "human",
# },
# ],
# ),
# # Attention: This test is not correct right now
# # Test with full and chunk messages
# (
# [HumanMessageChunk(content="Hello"), HumanMessage(content="Hi")],
# [
# {
# "additional_kwargs": {},
# "content": "Hello",
# "example": False,
# "type": "human",
# },
# {
# "additional_kwargs": {},
# "content": "Hi",
# "example": False,
# "type": "human",
# },
# ],
# ),
# Test with a dictionary containing mixed elements
(
{
"message": HumanMessage(content="Greetings"),
"numbers": [1, 2, 3],
"boom": "Hello, world!",
},
{
"message": {
"additional_kwargs": {},
"content": "Greetings",
"example": False,
"type": "human",
},
"numbers": [1, 2, 3],
"boom": "Hello, world!",
},
),
],
)
def test_serialization(data: Any, expected_json: Any) -> None:
"""Test that the LangChainEncoder encodes the data as expected."""
# Test encoding
assert json.loads(simple_dumps(data)) == expected_json
# Test decoding
assert simple_loads(json.dumps(expected_json)) == data

@ -2,7 +2,7 @@
import asyncio
from asyncio import AbstractEventLoop
from contextlib import asynccontextmanager
from typing import Any, List, Optional
from typing import List, Optional, Union
import httpx
import pytest
@ -34,18 +34,16 @@ def event_loop():
def app(event_loop: AbstractEventLoop) -> FastAPI:
"""A simple server that wraps a Runnable and exposes it as an API."""
async def add_one_or_passthrough(x: Any) -> int:
async def add_one_or_passthrough(
x: Union[int, HumanMessage]
) -> Union[int, HumanMessage]:
"""Add one to int or passthrough."""
if isinstance(x, int):
return x + 1
else:
return x
def raise_on_call(x: int) -> int:
"""Sync function server side should never be called."""
raise AssertionError("Should not be called")
runnable_lambda = RunnableLambda(func=raise_on_call, afunc=add_one_or_passthrough)
runnable_lambda = RunnableLambda(func=add_one_or_passthrough)
app = FastAPI()
try:
add_routes(app, runnable_lambda)
@ -132,7 +130,6 @@ def test_invoke(client: RemoteRunnable) -> None:
"""Test sync invoke."""
assert client.invoke(1) == 2
assert client.invoke(HumanMessage(content="hello")) == HumanMessage(content="hello")
# Test invocation with config
assert client.invoke(1, config={"tags": ["test"]}) == 2
@ -234,6 +231,19 @@ def test_invoke_as_part_of_sequence(client: RemoteRunnable) -> None:
# assert list(runnable.stream([1, 2], config={"tags": ["test"]})) == [3, 4]
def test_pydantic_root():
from pydantic import BaseModel
class Model(BaseModel):
__root__: str
class Q(BaseModel):
input: Model
# s = Model(__root__=[23])
Q(input="hello")
@pytest.mark.asyncio
async def test_invoke_as_part_of_sequence_async(async_client: RemoteRunnable) -> None:
"""Test as part of a sequence.
@ -466,3 +476,35 @@ async def test_async_client_close() -> None:
del runnable
assert sync_client.is_closed is True
assert async_client.is_closed is True
@pytest.mark.asyncio
async def test_openapi_docs_with_identical_runnables(
event_loop: AbstractEventLoop, mocker: MockerFixture
) -> None:
"""Test client side and server side exceptions."""
async def add_one(x: int) -> int:
"""Add one to simulate a valid function"""
return x + 1
server_runnable = RunnableLambda(func=add_one)
server_runnable2 = RunnableLambda(func=add_one)
app = FastAPI()
add_routes(
app,
server_runnable,
path="/1",
)
# Add another route that uses the same schema (inferred from runnable input schema)
add_routes(
app,
server_runnable2,
path="/2",
config_keys=["tags", "run_name"],
)
async with AsyncClient(app=app, base_url="http://localhost:9999") as async_client:
response = await async_client.get("/openapi.json")
assert response.status_code == 200

@ -1,10 +1,6 @@
import typing
from typing import Optional
import pytest
from langchain.load.dump import dumpd
from langchain.schema import AIMessage, HumanMessage, SystemMessage
from typing_extensions import TypedDict
try:
from pydantic.v1 import BaseModel, ValidationError
@ -15,7 +11,6 @@ from langserve.validation import (
create_batch_request_model,
create_invoke_request_model,
create_runnable_config_model,
replace_lc_object_types,
)
@ -133,104 +128,3 @@ def test_validation(test_case) -> None:
else:
with pytest.raises(ValidationError):
model(**test_case)
def test_replace_lc_object_types() -> None:
"""Replace lc object types in a model."""
updated_type = replace_lc_object_types(typing.List[HumanMessage])
config = create_runnable_config_model("test", [])
invoke_request = create_invoke_request_model("namespace", updated_type, config)
invoke_request(
input=dumpd(
[
HumanMessage(content="Hello, world!"),
HumanMessage(content="Hello, world 2!"),
]
)
)
with pytest.raises(ValidationError):
invoke_request(input=[dumpd(AIMessage(content="Hello, world!"))])
with pytest.raises(ValidationError):
invoke_request(
input=dumpd(
[
AIMessage(content="Hello, world!"),
HumanMessage(content="Hello, world!"),
]
),
)
def test_batch_request_with_lc_serialization() -> None:
"""Test batch request with LC serialization."""
input_type = replace_lc_object_types(typing.List[HumanMessage])
config = create_runnable_config_model("test", [])
batch_request = create_batch_request_model("namespace", input_type, config)
with pytest.raises(ValidationError):
batch_request(inputs=dumpd([[SystemMessage(content="Hello, world!")]]))
with pytest.raises(ValidationError):
batch_request(inputs=dumpd(HumanMessage(content="Hello, world!")))
with pytest.raises(ValidationError):
batch_request(inputs=dumpd([HumanMessage(content="Hello, world!")]))
batch_request(inputs=dumpd([[HumanMessage(content="Hello, world!")]]))
class PlaceHolderTypedDict(TypedDict):
x: int
z: HumanMessage
@pytest.mark.parametrize(
"type_,input,is_valid",
[
(None, None, True),
(str, "hello", True),
(str, 123.0, True),
(float, "qwe", False),
(int, 1, True),
(int, "qwe", False),
(typing.Union[str, int], "hello", True),
(typing.Union[str, int], 3, True),
(typing.List[str], ["a", "b"], True),
(typing.List[str], ["a", None], False),
(typing.List[HumanMessage], [HumanMessage(content="hello, world!")], True),
(typing.List[HumanMessage], [SystemMessage(content="hello, world!")], False),
(
typing.List[typing.Union[HumanMessage, SystemMessage]],
[HumanMessage(content="he"), SystemMessage(content="hello, world!")],
True,
),
(
typing.List[typing.Union[HumanMessage, SystemMessage]],
HumanMessage(content="hello"),
False,
),
(
typing.Union[
typing.List[typing.Union[SystemMessage, HumanMessage, str]], str
],
["hello", "world"],
True,
),
],
)
def test_replace_lc_object_type(
type_: typing.Any, input: typing.Any, is_valid: bool
) -> None:
"""Verify that code runs on different python versions."""
new_type = replace_lc_object_types(type_)
class Model(BaseModel):
input_: new_type
if is_valid:
Model(input_=dumpd(input))
else:
with pytest.raises(ValidationError):
Model(input_=dumpd(input))

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