github/langchain
1
0
Fork 0
mirror of https://github.com/hwchase17/langchain.git synced 2025-06-24 15:43:54 +00:00
Code Issues Packages Projects Releases Wiki Activity

Support multimodal messages (#11320)

Browse Source 
Co-authored-by: Bagatur <baskaryan@gmail.com>
This commit is contained in:
Harrison Chase 2023-11-06 15:14:18 -08:00 committed by GitHub
parent 388f248391
commit c27400efeb
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
21 changed files with 865 additions and 117 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

69
cookbook/openai_v1_cookbook.ipynb
View File

@ -20,6 +20,64 @@
"!pip install \"openai>=1\""
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "c3e067ce-7a43-47a7-bc89-41f1de4cf136",
"metadata": {},
"outputs": [],
"source": [
"from langchain.chat_models import ChatOpenAI\n",
"from langchain.schema.messages import HumanMessage, SystemMessage"
]
},
{
"cell_type": "markdown",
"id": "fa7e7e95-90a1-4f73-98fe-10c4b4e0951b",
"metadata": {},
"source": [
"## [Vision](https://platform.openai.com/docs/guides/vision)\n",
"\n",
"OpenAI released multi-modal models, which can take a sequence of text and images as input."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "1c8c3965-d3c9-4186-b5f3-5e67855ef916",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"AIMessage(content='The image appears to be a diagram illustrating the architecture or components of a software system')"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chat = ChatOpenAI(model=\"gpt-4-vision-preview\")\n",
"chat.invoke(\n",
" [\n",
" HumanMessage(\n",
" content=[\n",
" {\"type\": \"text\", \"text\": \"What is this image showing\"},\n",
" {\n",
" \"type\": \"image_url\",\n",
" \"image_url\": {\n",
" \"url\": \"https://python.langchain.com/assets/images/langchain_stack-da369071b058555da3d491a695651f15.jpg\",\n",
" \"detail\": \"auto\",\n",
" },\n",
" },\n",
" ]\n",
" )\n",
" ]\n",
")"
]
},
{
"cell_type": "markdown",
"id": "71c34763-d1e7-4b9a-a9d7-3e4cc0dfc2c4",
@ -32,17 +90,6 @@
"Only works with certain models. "
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "c3e067ce-7a43-47a7-bc89-41f1de4cf136",
"metadata": {},
"outputs": [],
"source": [
"from langchain.chat_models import ChatOpenAI\n",
"from langchain.schema.messages import HumanMessage, SystemMessage"
]
},
{
"cell_type": "code",
"execution_count": 8,

4
libs/langchain/langchain/agents/openai_functions_multi_agent/base.py
View File

@ -87,7 +87,9 @@ def _parse_ai_message(message: BaseMessage) -> Union[List[AgentAction], AgentFin
final_tools.append(_tool)
return final_tools
return AgentFinish(return_values={"output": message.content}, log=message.content)
return AgentFinish(
return_values={"output": message.content}, log=str(message.content)
)
class OpenAIMultiFunctionsAgent(BaseMultiActionAgent):

2
libs/langchain/langchain/agents/output_parsers/openai_functions.py
View File

@ -72,7 +72,7 @@ class OpenAIFunctionsAgentOutputParser(AgentOutputParser):
)
return AgentFinish(
return_values={"output": message.content}, log=message.content
return_values={"output": message.content}, log=str(message.content)
)
def parse_result(

10
libs/langchain/langchain/callbacks/infino_callback.py
View File

@ -1,5 +1,5 @@
import time
from typing import Any, Dict, List, Optional
from typing import Any, Dict, List, Optional, cast
from langchain.callbacks.base import BaseCallbackHandler
from langchain.schema import AgentAction, AgentFinish, LLMResult
@ -232,7 +232,9 @@ class InfinoCallbackHandler(BaseCallbackHandler):
self.chat_openai_model_name = model_name
prompt_tokens = 0
for message_list in messages:
message_string = " ".join(msg.content for msg in message_list)
message_string = " ".join(
cast(str, msg.content) for msg in message_list
)
num_tokens = get_num_tokens(
message_string,
openai_model_name=self.chat_openai_model_name,
@ -249,7 +251,9 @@ class InfinoCallbackHandler(BaseCallbackHandler):
)
# Send the prompt to infino
prompt = " ".join(msg.content for sublist in messages for msg in sublist)
prompt = " ".join(
cast(str, msg.content) for sublist in messages for msg in sublist
)
self._send_to_infino("prompt", prompt, is_ts=False)
# Set the error flag to indicate no error (this will get overridden

10
libs/langchain/langchain/chat_loaders/utils.py
View File

@ -21,6 +21,11 @@ def merge_chat_runs_in_session(
"""
messages: List[BaseMessage] = []
for message in chat_session["messages"]:
if not isinstance(message.content, str):
raise ValueError(
"Chat Loaders only support messages with content type string, "
f"got {message.content}"
)
if not messages:
messages.append(deepcopy(message))
elif (
@ -29,6 +34,11 @@ def merge_chat_runs_in_session(
and messages[-1].additional_kwargs["sender"]
== message.additional_kwargs.get("sender")
):
if not isinstance(messages[-1].content, str):
raise ValueError(
"Chat Loaders only support messages with content type string, "
f"got {messages[-1].content}"
)
messages[-1].content = (
messages[-1].content + delimiter + message.content
).strip()

11
libs/langchain/langchain/chat_models/anthropic.py
View File

@ -1,4 +1,4 @@
from typing import Any, AsyncIterator, Dict, Iterator, List, Optional
from typing import Any, AsyncIterator, Dict, Iterator, List, Optional, cast
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
@ -27,14 +27,15 @@ def _convert_one_message_to_text(
human_prompt: str,
ai_prompt: str,
) -> str:
content = cast(str, message.content)
if isinstance(message, ChatMessage):
message_text = f"\n\n{message.role.capitalize()}: {message.content}"
message_text = f"\n\n{message.role.capitalize()}: {content}"
elif isinstance(message, HumanMessage):
message_text = f"{human_prompt} {message.content}"
message_text = f"{human_prompt} {content}"
elif isinstance(message, AIMessage):
message_text = f"{ai_prompt} {message.content}"
message_text = f"{ai_prompt} {content}"
elif isinstance(message, SystemMessage):
message_text = message.content
message_text = content
else:
raise ValueError(f"Got unknown type {message}")
return message_text

19
libs/langchain/langchain/chat_models/azureml_endpoint.py
View File

@ -1,5 +1,5 @@
import json
from typing import Any, Dict, List, Optional
from typing import Any, Dict, List, Optional, cast
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.chat_models.base import SimpleChatModel
@ -23,26 +23,21 @@ class LlamaContentFormatter(ContentFormatterBase):
@staticmethod
def _convert_message_to_dict(message: BaseMessage) -> Dict:
"""Converts message to a dict according to role"""
content = cast(str, message.content)
if isinstance(message, HumanMessage):
return {
"role": "user",
"content": ContentFormatterBase.escape_special_characters(
message.content
),
"content": ContentFormatterBase.escape_special_characters(content),
}
elif isinstance(message, AIMessage):
return {
"role": "assistant",
"content": ContentFormatterBase.escape_special_characters(
message.content
),
"content": ContentFormatterBase.escape_special_characters(content),
}
elif isinstance(message, SystemMessage):
return {
"role": "system",
"content": ContentFormatterBase.escape_special_characters(
message.content
),
"content": ContentFormatterBase.escape_special_characters(content),
}
elif (
isinstance(message, ChatMessage)
@ -50,9 +45,7 @@ class LlamaContentFormatter(ContentFormatterBase):
):
return {
"role": message.role,
"content": ContentFormatterBase.escape_special_characters(
message.content
),
"content": ContentFormatterBase.escape_special_characters(content),
}
else:
supported = ",".join(

12
libs/langchain/langchain/chat_models/baidu_qianfan_endpoint.py
View File

@ -1,15 +1,7 @@
from __future__ import annotations
import logging
from typing import (
Any,
AsyncIterator,
Dict,
Iterator,
List,
Mapping,
Optional,
)
from typing import Any, AsyncIterator, Dict, Iterator, List, Mapping, Optional, cast
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
@ -211,7 +203,7 @@ class QianfanChatEndpoint(BaseChatModel):
for i in [i for i, m in enumerate(messages) if isinstance(m, SystemMessage)]:
if "system" not in messages_dict:
messages_dict["system"] = ""
messages_dict["system"] += messages[i].content + "\n"
messages_dict["system"] += cast(str, messages[i].content) + "\n"
return {
**messages_dict,

10
libs/langchain/langchain/chat_models/base.py
View File

@ -634,7 +634,10 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
else:
_stop = list(stop)
result = self([HumanMessage(content=text)], stop=_stop, **kwargs)
return result.content
if isinstance(result.content, str):
return result.content
else:
raise ValueError("Cannot use predict when output is not a string.")
def predict_messages(
self,
@ -659,7 +662,10 @@ class BaseChatModel(BaseLanguageModel[BaseMessage], ABC):
result = await self._call_async(
[HumanMessage(content=text)], stop=_stop, **kwargs
)
return result.content
if isinstance(result.content, str):
return result.content
else:
raise ValueError("Cannot use predict when output is not a string.")
async def apredict_messages(
self,

4
libs/langchain/langchain/chat_models/google_palm.py
View File

@ -2,7 +2,7 @@
from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, cast
from tenacity import (
before_sleep_log,
@ -114,7 +114,7 @@ def _messages_to_prompt_dict(
if isinstance(input_message, SystemMessage):
if index != 0:
raise ChatGooglePalmError("System message must be first input message.")
context = input_message.content
context = cast(str, input_message.content)
elif isinstance(input_message, HumanMessage) and input_message.example:
if messages:
raise ChatGooglePalmError(

5
libs/langchain/langchain/chat_models/human.py
View File

@ -58,7 +58,10 @@ def _collect_yaml_input(
if message is None:
return HumanMessage(content="")
if stop:
message.content = enforce_stop_tokens(message.content, stop)
if isinstance(message.content, str):
message.content = enforce_stop_tokens(message.content, stop)
else:
raise ValueError("Cannot use when output is not a string.")
return message
except yaml.YAMLError:
raise ValueError("Invalid YAML string entered.")

7
libs/langchain/langchain/chat_models/minimax.py
View File

@ -1,6 +1,6 @@
"""Wrapper around Minimax chat models."""
import logging
from typing import Any, Dict, List, Optional
from typing import Any, Dict, List, Optional, cast
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
@ -27,10 +27,11 @@ def _parse_chat_history(history: List[BaseMessage]) -> List:
"""Parse a sequence of messages into history."""
chat_history = []
for message in history:
content = cast(str, message.content)
if isinstance(message, HumanMessage):
chat_history.append(_parse_message("USER", message.content))
chat_history.append(_parse_message("USER", content))
if isinstance(message, AIMessage):
chat_history.append(_parse_message("BOT", message.content))
chat_history.append(_parse_message("BOT", content))
return chat_history

6
libs/langchain/langchain/chat_models/openai.py
View File

@ -316,14 +316,16 @@ class ChatOpenAI(BaseChatModel):
@property
def _default_params(self) -> Dict[str, Any]:
"""Get the default parameters for calling OpenAI API."""
return {
params = {
"model": self.model_name,
"max_tokens": self.max_tokens,
"stream": self.streaming,
"n": self.n,
"temperature": self.temperature,
**self.model_kwargs,
}
if "vision" not in self.model_name:
params["max_tokens"] = self.max_tokens
return params
def completion_with_retry(
self, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any

17
libs/langchain/langchain/chat_models/pai_eas_endpoint.py
View File

@ -2,7 +2,7 @@ import asyncio
import json
import logging
from functools import partial
from typing import Any, AsyncIterator, Dict, List, Optional
from typing import Any, AsyncIterator, Dict, List, Optional, cast
import requests
@ -133,23 +133,24 @@ class PaiEasChatEndpoint(BaseChatModel):
for message in messages:
"""Converts message to a dict according to role"""
content = cast(str, message.content)
if isinstance(message, HumanMessage):
user_content = user_content + [message.content]
user_content = user_content + [content]
elif isinstance(message, AIMessage):
assistant_content = assistant_content + [message.content]
assistant_content = assistant_content + [content]
elif isinstance(message, SystemMessage):
prompt["system_prompt"] = message.content
prompt["system_prompt"] = content
elif isinstance(message, ChatMessage) and message.role in [
"user",
"assistant",
"system",
]:
if message.role == "system":
prompt["system_prompt"] = message.content
prompt["system_prompt"] = content
elif message.role == "user":
user_content = user_content + [message.content]
user_content = user_content + [content]
elif message.role == "assistant":
assistant_content = assistant_content + [message.content]
assistant_content = assistant_content + [content]
else:
supported = ",".join([role for role in ["user", "assistant", "system"]])
raise ValueError(
@ -294,7 +295,7 @@ class PaiEasChatEndpoint(BaseChatModel):
# yield text, if any
if text:
if run_manager:
await run_manager.on_llm_new_token(content.content)
await run_manager.on_llm_new_token(cast(str, content.content))
yield ChatGenerationChunk(message=content)
# break if stop sequence found

5
libs/langchain/langchain/chat_models/vertexai.py
View File

@ -3,7 +3,7 @@ from __future__ import annotations
import logging
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, Dict, Iterator, List, Optional, Union
from typing import TYPE_CHECKING, Any, Dict, Iterator, List, Optional, Union, cast
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
@ -57,8 +57,9 @@ def _parse_chat_history(history: List[BaseMessage]) -> _ChatHistory:
vertex_messages, context = [], None
for i, message in enumerate(history):
content = cast(str, message.content)
if i == 0 and isinstance(message, SystemMessage):
context = message.content
context = content
elif isinstance(message, AIMessage):
vertex_message = ChatMessage(content=message.content, author="bot")
vertex_messages.append(vertex_message)

9
libs/langchain/langchain/chat_models/yandex.py
View File

@ -1,6 +1,6 @@
"""Wrapper around YandexGPT chat models."""
import logging
from typing import Any, Dict, List, Optional, Tuple
from typing import Any, Dict, List, Optional, Tuple, cast
from langchain.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
@ -34,12 +34,13 @@ def _parse_chat_history(history: List[BaseMessage]) -> Tuple[List[Dict[str, str]
chat_history = []
instruction = ""
for message in history:
content = cast(str, message.content)
if isinstance(message, HumanMessage):
chat_history.append(_parse_message("user", message.content))
chat_history.append(_parse_message("user", content))
if isinstance(message, AIMessage):
chat_history.append(_parse_message("assistant", message.content))
chat_history.append(_parse_message("assistant", content))
if isinstance(message, SystemMessage):
instruction = message.content
instruction = content
return chat_history, instruction

39
libs/langchain/langchain/schema/messages.py
View File

@ -64,7 +64,7 @@ class BaseMessage(Serializable):
Messages are the inputs and outputs of ChatModels.
"""
content: str
content: Union[str, List[Union[str, Dict]]]
"""The string contents of the message."""
additional_kwargs: dict = Field(default_factory=dict)
@ -87,6 +87,33 @@ class BaseMessage(Serializable):
return prompt + other
def merge_content(
first_content: Union[str, List[Union[str, Dict]]],
second_content: Union[str, List[Union[str, Dict]]],
) -> Union[str, List[Union[str, Dict]]]:
# If first chunk is a string
if isinstance(first_content, str):
# If the second chunk is also a string, then merge them naively
if isinstance(second_content, str):
return first_content + second_content
# If the second chunk is a list, add the first chunk to the start of the list
else:
return_list: List[Union[str, Dict]] = [first_content]
return return_list + second_content
# If both are lists, merge them naively
elif isinstance(second_content, List):
return first_content + second_content
# If the first content is a list, and the second content is a string
else:
# If the last element of the first content is a string
# Add the second content to the last element
if isinstance(first_content[-1], str):
return first_content[:-1] + [first_content[-1] + second_content]
else:
# Otherwise, add the second content as a new element of the list
return first_content + [second_content]
class BaseMessageChunk(BaseMessage):
"""A Message chunk, which can be concatenated with other Message chunks."""
@ -121,13 +148,13 @@ class BaseMessageChunk(BaseMessage):
if isinstance(self, ChatMessageChunk):
return self.__class__(
role=self.role,
content=self.content + other.content,
content=merge_content(self.content, other.content),
additional_kwargs=self._merge_kwargs_dict(
self.additional_kwargs, other.additional_kwargs
),
)
return self.__class__(
content=self.content + other.content,
content=merge_content(self.content, other.content),
additional_kwargs=self._merge_kwargs_dict(
self.additional_kwargs, other.additional_kwargs
),
@ -194,7 +221,7 @@ class AIMessageChunk(AIMessage, BaseMessageChunk):
return self.__class__(
example=self.example,
content=self.content + other.content,
content=merge_content(self.content, other.content),
additional_kwargs=self._merge_kwargs_dict(
self.additional_kwargs, other.additional_kwargs
),
@ -252,7 +279,7 @@ class FunctionMessageChunk(FunctionMessage, BaseMessageChunk):
return self.__class__(
name=self.name,
content=self.content + other.content,
content=merge_content(self.content, other.content),
additional_kwargs=self._merge_kwargs_dict(
self.additional_kwargs, other.additional_kwargs
),
@ -290,7 +317,7 @@ class ChatMessageChunk(ChatMessage, BaseMessageChunk):
return self.__class__(
role=self.role,
content=self.content + other.content,
content=merge_content(self.content, other.content),
additional_kwargs=self._merge_kwargs_dict(
self.additional_kwargs, other.additional_kwargs
),

5
libs/langchain/tests/integration_tests/chat_models/test_fireworks.py
View File

@ -1,5 +1,6 @@
"""Test ChatFireworks wrapper."""
import sys
from typing import cast
import pytest
@ -152,7 +153,7 @@ def test_fireworks_streaming_stop_words(chat: ChatFireworks) -> None:
last_token = ""
for token in chat.stream("I'm Pickle Rick", stop=[","]):
last_token = token.content
last_token = cast(str, token.content)
assert isinstance(token.content, str)
assert last_token[-1] == ","
@ -183,6 +184,6 @@ async def test_fireworks_astream(chat: ChatFireworks) -> None:
async for token in chat.astream(
"Who's the best quarterback in the NFL?", stop=[","]
):
last_token = token.content
last_token = cast(str, token.content)
assert isinstance(token.content, str)
assert last_token[-1] == ","

8
libs/langchain/tests/integration_tests/smith/evaluation/test_runner_utils.py
View File

@ -490,7 +490,13 @@ async def test_arb_func_on_kv_singleio_dataset(
)
def my_func(x: dict) -> str:
return runnable.invoke(x).content
content = runnable.invoke(x).content
if isinstance(content, str):
return content
else:
raise ValueError(
f"Expected message with content type string, got {content}"
)
eval_config = RunEvalConfig(evaluators=[EvaluatorType.QA, EvaluatorType.CRITERIA])
await arun_on_dataset(

665
libs/langchain/tests/unit_tests/schema/runnable/__snapshots__/test_runnable.ambr
View File

@ -1685,8 +1685,25 @@
'type': 'object',
}),
'content': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'items': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'type': 'object',
}),
]),
}),
'type': 'array',
}),
]),
'title': 'Content',
'type': 'string',
}),
'example': dict({
'default': False,
@ -1716,8 +1733,25 @@
'type': 'object',
}),
'content': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'items': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'type': 'object',
}),
]),
}),
'type': 'array',
}),
]),
'title': 'Content',
'type': 'string',
}),
'role': dict({
'title': 'Role',
@ -1791,8 +1825,25 @@
'type': 'object',
}),
'content': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'items': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'type': 'object',
}),
]),
}),
'type': 'array',
}),
]),
'title': 'Content',
'type': 'string',
}),
'name': dict({
'title': 'Name',
@ -1822,8 +1873,25 @@
'type': 'object',
}),
'content': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'items': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'type': 'object',
}),
]),
}),
'type': 'array',
}),
]),
'title': 'Content',
'type': 'string',
}),
'example': dict({
'default': False,
@ -1878,8 +1946,25 @@
'type': 'object',
}),
'content': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'items': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'type': 'object',
}),
]),
}),
'type': 'array',
}),
]),
'title': 'Content',
'type': 'string',
}),
'type': dict({
'default': 'system',
@ -1944,8 +2029,25 @@
'type': 'object',
}),
'content': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'items': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'type': 'object',
}),
]),
}),
'type': 'array',
}),
]),
'title': 'Content',
'type': 'string',
}),
'example': dict({
'default': False,
@ -1975,8 +2077,25 @@
'type': 'object',
}),
'content': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'items': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'type': 'object',
}),
]),
}),
'type': 'array',
}),
]),
'title': 'Content',
'type': 'string',
}),
'role': dict({
'title': 'Role',
@ -2050,8 +2169,25 @@
'type': 'object',
}),
'content': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'items': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'type': 'object',
}),
]),
}),
'type': 'array',
}),
]),
'title': 'Content',
'type': 'string',
}),
'name': dict({
'title': 'Name',
@ -2081,8 +2217,25 @@
'type': 'object',
}),
'content': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'items': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'type': 'object',
}),
]),
}),
'type': 'array',
}),
]),
'title': 'Content',
'type': 'string',
}),
'example': dict({
'default': False,
@ -2137,8 +2290,25 @@
'type': 'object',
}),
'content': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'items': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'type': 'object',
}),
]),
}),
'type': 'array',
}),
]),
'title': 'Content',
'type': 'string',
}),
'type': dict({
'default': 'system',
@ -2187,8 +2357,25 @@
'type': 'object',
}),
'content': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'items': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'type': 'object',
}),
]),
}),
'type': 'array',
}),
]),
'title': 'Content',
'type': 'string',
}),
'example': dict({
'default': False,
@ -2218,8 +2405,25 @@
'type': 'object',
}),
'content': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'items': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'type': 'object',
}),
]),
}),
'type': 'array',
}),
]),
'title': 'Content',
'type': 'string',
}),
'role': dict({
'title': 'Role',
@ -2249,8 +2453,25 @@
'type': 'object',
}),
'content': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'items': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'type': 'object',
}),
]),
}),
'type': 'array',
}),
]),
'title': 'Content',
'type': 'string',
}),
'name': dict({
'title': 'Name',
@ -2280,8 +2501,25 @@
'type': 'object',
}),
'content': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'items': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'type': 'object',
}),
]),
}),
'type': 'array',
}),
]),
'title': 'Content',
'type': 'string',
}),
'example': dict({
'default': False,
@ -2314,8 +2552,25 @@
'type': 'object',
}),
'content': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'items': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'type': 'object',
}),
]),
}),
'type': 'array',
}),
]),
'title': 'Content',
'type': 'string',
}),
'type': dict({
'default': 'system',
@ -2355,8 +2610,25 @@
'type': 'object',
}),
'content': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'items': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'type': 'object',
}),
]),
}),
'type': 'array',
}),
]),
'title': 'Content',
'type': 'string',
}),
'example': dict({
'default': False,
@ -2386,8 +2658,25 @@
'type': 'object',
}),
'content': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'items': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'type': 'object',
}),
]),
}),
'type': 'array',
}),
]),
'title': 'Content',
'type': 'string',
}),
'role': dict({
'title': 'Role',
@ -2461,8 +2750,25 @@
'type': 'object',
}),
'content': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'items': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'type': 'object',
}),
]),
}),
'type': 'array',
}),
]),
'title': 'Content',
'type': 'string',
}),
'name': dict({
'title': 'Name',
@ -2492,8 +2798,25 @@
'type': 'object',
}),
'content': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'items': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'type': 'object',
}),
]),
}),
'type': 'array',
}),
]),
'title': 'Content',
'type': 'string',
}),
'example': dict({
'default': False,
@ -2548,8 +2871,25 @@
'type': 'object',
}),
'content': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'items': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'type': 'object',
}),
]),
}),
'type': 'array',
}),
]),
'title': 'Content',
'type': 'string',
}),
'type': dict({
'default': 'system',
@ -2589,8 +2929,25 @@
'type': 'object',
}),
'content': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'items': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'type': 'object',
}),
]),
}),
'type': 'array',
}),
]),
'title': 'Content',
'type': 'string',
}),
'example': dict({
'default': False,
@ -2620,8 +2977,25 @@
'type': 'object',
}),
'content': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'items': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'type': 'object',
}),
]),
}),
'type': 'array',
}),
]),
'title': 'Content',
'type': 'string',
}),
'role': dict({
'title': 'Role',
@ -2695,8 +3069,25 @@
'type': 'object',
}),
'content': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'items': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'type': 'object',
}),
]),
}),
'type': 'array',
}),
]),
'title': 'Content',
'type': 'string',
}),
'name': dict({
'title': 'Name',
@ -2726,8 +3117,25 @@
'type': 'object',
}),
'content': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'items': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'type': 'object',
}),
]),
}),
'type': 'array',
}),
]),
'title': 'Content',
'type': 'string',
}),
'example': dict({
'default': False,
@ -2782,8 +3190,25 @@
'type': 'object',
}),
'content': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'items': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'type': 'object',
}),
]),
}),
'type': 'array',
}),
]),
'title': 'Content',
'type': 'string',
}),
'type': dict({
'default': 'system',
@ -2815,8 +3240,25 @@
'type': 'object',
}),
'content': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'items': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'type': 'object',
}),
]),
}),
'type': 'array',
}),
]),
'title': 'Content',
'type': 'string',
}),
'example': dict({
'default': False,
@ -2846,8 +3288,25 @@
'type': 'object',
}),
'content': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'items': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'type': 'object',
}),
]),
}),
'type': 'array',
}),
]),
'title': 'Content',
'type': 'string',
}),
'role': dict({
'title': 'Role',
@ -2921,8 +3380,25 @@
'type': 'object',
}),
'content': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'items': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'type': 'object',
}),
]),
}),
'type': 'array',
}),
]),
'title': 'Content',
'type': 'string',
}),
'name': dict({
'title': 'Name',
@ -2952,8 +3428,25 @@
'type': 'object',
}),
'content': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'items': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'type': 'object',
}),
]),
}),
'type': 'array',
}),
]),
'title': 'Content',
'type': 'string',
}),
'example': dict({
'default': False,
@ -3019,8 +3512,25 @@
'type': 'object',
}),
'content': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'items': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'type': 'object',
}),
]),
}),
'type': 'array',
}),
]),
'title': 'Content',
'type': 'string',
}),
'type': dict({
'default': 'system',
@ -3076,8 +3586,25 @@
'type': 'object',
}),
'content': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'items': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'type': 'object',
}),
]),
}),
'type': 'array',
}),
]),
'title': 'Content',
'type': 'string',
}),
'example': dict({
'default': False,
@ -3107,8 +3634,25 @@
'type': 'object',
}),
'content': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'items': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'type': 'object',
}),
]),
}),
'type': 'array',
}),
]),
'title': 'Content',
'type': 'string',
}),
'role': dict({
'title': 'Role',
@ -3138,8 +3682,25 @@
'type': 'object',
}),
'content': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'items': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'type': 'object',
}),
]),
}),
'type': 'array',
}),
]),
'title': 'Content',
'type': 'string',
}),
'name': dict({
'title': 'Name',
@ -3169,8 +3730,25 @@
'type': 'object',
}),
'content': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'items': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'type': 'object',
}),
]),
}),
'type': 'array',
}),
]),
'title': 'Content',
'type': 'string',
}),
'example': dict({
'default': False,
@ -3203,8 +3781,25 @@
'type': 'object',
}),
'content': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'items': dict({
'anyOf': list([
dict({
'type': 'string',
}),
dict({
'type': 'object',
}),
]),
}),
'type': 'array',
}),
]),
'title': 'Content',
'type': 'string',
}),
'type': dict({
'default': 'system',

65
libs/langchain/tests/unit_tests/schema/runnable/test_runnable.py
View File

@ -295,7 +295,18 @@ def test_schemas(snapshot: SnapshotAssertion) -> None:
"description": "A Message from an AI.",
"type": "object",
"properties": {
"content": {"title": "Content", "type": "string"},
"content": {
"title": "Content",
"anyOf": [
{"type": "string"},
{
"type": "array",
"items": {
"anyOf": [{"type": "string"}, {"type": "object"}]
},
},
],
},
"additional_kwargs": {
"title": "Additional Kwargs",
"type": "object",
@ -319,7 +330,18 @@ def test_schemas(snapshot: SnapshotAssertion) -> None:
"description": "A Message from a human.",
"type": "object",
"properties": {
"content": {"title": "Content", "type": "string"},
"content": {
"title": "Content",
"anyOf": [
{"type": "string"},
{
"type": "array",
"items": {
"anyOf": [{"type": "string"}, {"type": "object"}]
},
},
],
},
"additional_kwargs": {
"title": "Additional Kwargs",
"type": "object",
@ -343,7 +365,18 @@ def test_schemas(snapshot: SnapshotAssertion) -> None:
"description": "A Message that can be assigned an arbitrary speaker (i.e. role).", # noqa: E501
"type": "object",
"properties": {
"content": {"title": "Content", "type": "string"},
"content": {
"title": "Content",
"anyOf": [
{"type": "string"},
{
"type": "array",
"items": {
"anyOf": [{"type": "string"}, {"type": "object"}]
},
},
],
},
"additional_kwargs": {
"title": "Additional Kwargs",
"type": "object",
@ -363,7 +396,18 @@ def test_schemas(snapshot: SnapshotAssertion) -> None:
"description": "A Message for priming AI behavior, usually passed in as the first of a sequence\nof input messages.", # noqa: E501
"type": "object",
"properties": {
"content": {"title": "Content", "type": "string"},
"content": {
"title": "Content",
"anyOf": [
{"type": "string"},
{
"type": "array",
"items": {
"anyOf": [{"type": "string"}, {"type": "object"}]
},
},
],
},
"additional_kwargs": {
"title": "Additional Kwargs",
"type": "object",
@ -382,7 +426,18 @@ def test_schemas(snapshot: SnapshotAssertion) -> None:
"description": "A Message for passing the result of executing a function back to a model.", # noqa: E501
"type": "object",
"properties": {
"content": {"title": "Content", "type": "string"},
"content": {
"title": "Content",
"anyOf": [
{"type": "string"},
{
"type": "array",
"items": {
"anyOf": [{"type": "string"}, {"type": "object"}]
},
},
],
},
"additional_kwargs": {
"title": "Additional Kwargs",
"type": "object",