mirror of https://github.com/hwchase17/langchain
openai adapters (#8988)
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com> Co-authored-by: Nuno Campos <nuno@boringbits.io>pull/7456/head^2
Harrison Chase
1 year ago
committed by
GitHub
parent
45f0f9460a
commit
bb6fbf4c71
@ -0,0 +1,323 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "700a516b",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# OpenAI Adapter\n",
|
||||
"\n",
|
||||
"A lot of people get started with OpenAI but want to explore other models. LangChain's integrations with many model providers make this easy to do so. While LangChain has it's own message and model APIs, we've also made it as easy as possible to explore other models by exposing an adapter to adapt LangChain models to the OpenAI api.\n",
|
||||
"\n",
|
||||
"At the moment this only deals with output and does not return other information (token counts, stop reasons, etc)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "6017f26a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import openai\n",
|
||||
"from langchain.adapters import openai as lc_openai"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "b522ceda",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## ChatCompletion.create"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 29,
|
||||
"id": "1d22eb61",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"messages = [{\"role\": \"user\", \"content\": \"hi\"}]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "d550d3ad",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Original OpenAI call"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"id": "e1d27dfa",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"result = openai.ChatCompletion.create(\n",
|
||||
" messages=messages, \n",
|
||||
" model=\"gpt-3.5-turbo\", \n",
|
||||
" temperature=0\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"id": "012d81ae",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'role': 'assistant', 'content': 'Hello! How can I assist you today?'}"
|
||||
]
|
||||
},
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"result[\"choices\"][0]['message'].to_dict_recursive()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "db5b5500",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"LangChain OpenAI wrapper call"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"id": "87c2d515",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"lc_result = lc_openai.ChatCompletion.create(\n",
|
||||
" messages=messages, \n",
|
||||
" model=\"gpt-3.5-turbo\", \n",
|
||||
" temperature=0\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"id": "c67a5ac8",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'role': 'assistant', 'content': 'Hello! How can I assist you today?'}"
|
||||
]
|
||||
},
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"lc_result[\"choices\"][0]['message']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "034ba845",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Swapping out model providers"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"id": "7a2c011c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"lc_result = lc_openai.ChatCompletion.create(\n",
|
||||
" messages=messages, \n",
|
||||
" model=\"claude-2\", \n",
|
||||
" temperature=0, \n",
|
||||
" provider=\"ChatAnthropic\"\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"id": "f7c94827",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"{'role': 'assistant', 'content': ' Hello!'}"
|
||||
]
|
||||
},
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"lc_result[\"choices\"][0]['message']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "cb3f181d",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## ChatCompletion.stream"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "f7b8cd18",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Original OpenAI call"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"id": "fd8cb1ea",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"{'role': 'assistant', 'content': ''}\n",
|
||||
"{'content': 'Hello'}\n",
|
||||
"{'content': '!'}\n",
|
||||
"{'content': ' How'}\n",
|
||||
"{'content': ' can'}\n",
|
||||
"{'content': ' I'}\n",
|
||||
"{'content': ' assist'}\n",
|
||||
"{'content': ' you'}\n",
|
||||
"{'content': ' today'}\n",
|
||||
"{'content': '?'}\n",
|
||||
"{}\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"for c in openai.ChatCompletion.create(\n",
|
||||
" messages = messages,\n",
|
||||
" model=\"gpt-3.5-turbo\", \n",
|
||||
" temperature=0,\n",
|
||||
" stream=True\n",
|
||||
"):\n",
|
||||
" print(c[\"choices\"][0]['delta'].to_dict_recursive())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "0b2a076b",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"LangChain OpenAI wrapper call"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 30,
|
||||
"id": "9521218c",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"{'role': 'assistant', 'content': ''}\n",
|
||||
"{'content': 'Hello'}\n",
|
||||
"{'content': '!'}\n",
|
||||
"{'content': ' How'}\n",
|
||||
"{'content': ' can'}\n",
|
||||
"{'content': ' I'}\n",
|
||||
"{'content': ' assist'}\n",
|
||||
"{'content': ' you'}\n",
|
||||
"{'content': ' today'}\n",
|
||||
"{'content': '?'}\n",
|
||||
"{}\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"for c in lc_openai.ChatCompletion.create(\n",
|
||||
" messages = messages,\n",
|
||||
" model=\"gpt-3.5-turbo\", \n",
|
||||
" temperature=0,\n",
|
||||
" stream=True\n",
|
||||
"):\n",
|
||||
" print(c[\"choices\"][0]['delta'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "0fc39750",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Swapping out model providers"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 31,
|
||||
"id": "68f0214e",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"{'role': 'assistant', 'content': ' Hello'}\n",
|
||||
"{'content': '!'}\n",
|
||||
"{}\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"for c in lc_openai.ChatCompletion.create(\n",
|
||||
" messages = messages,\n",
|
||||
" model=\"claude-2\", \n",
|
||||
" temperature=0,\n",
|
||||
" stream=True,\n",
|
||||
" provider=\"ChatAnthropic\",\n",
|
||||
"):\n",
|
||||
" print(c[\"choices\"][0]['delta'])"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.1"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@ -0,0 +1,208 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import importlib
|
||||
from typing import (
|
||||
Any,
|
||||
AsyncIterator,
|
||||
Dict,
|
||||
Iterable,
|
||||
List,
|
||||
Mapping,
|
||||
Sequence,
|
||||
Union,
|
||||
overload,
|
||||
)
|
||||
|
||||
from typing_extensions import Literal
|
||||
|
||||
from langchain.schema.messages import (
|
||||
AIMessage,
|
||||
AIMessageChunk,
|
||||
BaseMessage,
|
||||
BaseMessageChunk,
|
||||
ChatMessage,
|
||||
FunctionMessage,
|
||||
HumanMessage,
|
||||
SystemMessage,
|
||||
)
|
||||
|
||||
|
||||
async def aenumerate(
|
||||
iterable: AsyncIterator[Any], start: int = 0
|
||||
) -> AsyncIterator[tuple[int, Any]]:
|
||||
"""Async version of enumerate."""
|
||||
i = start
|
||||
async for x in iterable:
|
||||
yield i, x
|
||||
i += 1
|
||||
|
||||
|
||||
def convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
|
||||
role = _dict["role"]
|
||||
if role == "user":
|
||||
return HumanMessage(content=_dict["content"])
|
||||
elif role == "assistant":
|
||||
# Fix for azure
|
||||
# Also OpenAI returns None for tool invocations
|
||||
content = _dict.get("content", "") or ""
|
||||
if _dict.get("function_call"):
|
||||
additional_kwargs = {"function_call": dict(_dict["function_call"])}
|
||||
else:
|
||||
additional_kwargs = {}
|
||||
return AIMessage(content=content, additional_kwargs=additional_kwargs)
|
||||
elif role == "system":
|
||||
return SystemMessage(content=_dict["content"])
|
||||
elif role == "function":
|
||||
return FunctionMessage(content=_dict["content"], name=_dict["name"])
|
||||
else:
|
||||
return ChatMessage(content=_dict["content"], role=role)
|
||||
|
||||
|
||||
def convert_message_to_dict(message: BaseMessage) -> dict:
|
||||
message_dict: Dict[str, Any]
|
||||
if isinstance(message, ChatMessage):
|
||||
message_dict = {"role": message.role, "content": message.content}
|
||||
elif isinstance(message, HumanMessage):
|
||||
message_dict = {"role": "user", "content": message.content}
|
||||
elif isinstance(message, AIMessage):
|
||||
message_dict = {"role": "assistant", "content": message.content}
|
||||
if "function_call" in message.additional_kwargs:
|
||||
message_dict["function_call"] = message.additional_kwargs["function_call"]
|
||||
# If function call only, content is None not empty string
|
||||
if message_dict["content"] == "":
|
||||
message_dict["content"] = None
|
||||
elif isinstance(message, SystemMessage):
|
||||
message_dict = {"role": "system", "content": message.content}
|
||||
elif isinstance(message, FunctionMessage):
|
||||
message_dict = {
|
||||
"role": "function",
|
||||
"content": message.content,
|
||||
"name": message.name,
|
||||
}
|
||||
else:
|
||||
raise TypeError(f"Got unknown type {message}")
|
||||
if "name" in message.additional_kwargs:
|
||||
message_dict["name"] = message.additional_kwargs["name"]
|
||||
return message_dict
|
||||
|
||||
|
||||
def convert_openai_messages(messages: Sequence[Dict[str, Any]]) -> List[BaseMessage]:
|
||||
"""Convert dictionaries representing OpenAI messages to LangChain format.
|
||||
|
||||
Args:
|
||||
messages: List of dictionaries representing OpenAI messages
|
||||
|
||||
Returns:
|
||||
List of LangChain BaseMessage objects.
|
||||
"""
|
||||
return [convert_dict_to_message(m) for m in messages]
|
||||
|
||||
|
||||
def _convert_message_chunk_to_delta(chunk: BaseMessageChunk, i: int) -> Dict[str, Any]:
|
||||
_dict: Dict[str, Any] = {}
|
||||
if isinstance(chunk, AIMessageChunk):
|
||||
if i == 0:
|
||||
# Only shows up in the first chunk
|
||||
_dict["role"] = "assistant"
|
||||
if "function_call" in chunk.additional_kwargs:
|
||||
_dict["function_call"] = chunk.additional_kwargs["function_call"]
|
||||
# If the first chunk is a function call, the content is not empty string,
|
||||
# not missing, but None.
|
||||
if i == 0:
|
||||
_dict["content"] = None
|
||||
else:
|
||||
_dict["content"] = chunk.content
|
||||
else:
|
||||
raise ValueError(f"Got unexpected streaming chunk type: {type(chunk)}")
|
||||
# This only happens at the end of streams, and OpenAI returns as empty dict
|
||||
if _dict == {"content": ""}:
|
||||
_dict = {}
|
||||
return {"choices": [{"delta": _dict}]}
|
||||
|
||||
|
||||
class ChatCompletion:
|
||||
@overload
|
||||
@staticmethod
|
||||
def create(
|
||||
messages: Sequence[Dict[str, Any]],
|
||||
*,
|
||||
provider: str = "ChatOpenAI",
|
||||
stream: Literal[False] = False,
|
||||
**kwargs: Any,
|
||||
) -> dict:
|
||||
...
|
||||
|
||||
@overload
|
||||
@staticmethod
|
||||
def create(
|
||||
messages: Sequence[Dict[str, Any]],
|
||||
*,
|
||||
provider: str = "ChatOpenAI",
|
||||
stream: Literal[True],
|
||||
**kwargs: Any,
|
||||
) -> Iterable:
|
||||
...
|
||||
|
||||
@staticmethod
|
||||
def create(
|
||||
messages: Sequence[Dict[str, Any]],
|
||||
*,
|
||||
provider: str = "ChatOpenAI",
|
||||
stream: bool = False,
|
||||
**kwargs: Any,
|
||||
) -> Union[dict, Iterable]:
|
||||
models = importlib.import_module("langchain.chat_models")
|
||||
model_cls = getattr(models, provider)
|
||||
model_config = model_cls(**kwargs)
|
||||
converted_messages = convert_openai_messages(messages)
|
||||
if not stream:
|
||||
result = model_config.invoke(converted_messages)
|
||||
return {"choices": [{"message": convert_message_to_dict(result)}]}
|
||||
else:
|
||||
return (
|
||||
_convert_message_chunk_to_delta(c, i)
|
||||
for i, c in enumerate(model_config.stream(converted_messages))
|
||||
)
|
||||
|
||||
@overload
|
||||
@staticmethod
|
||||
async def acreate(
|
||||
messages: Sequence[Dict[str, Any]],
|
||||
*,
|
||||
provider: str = "ChatOpenAI",
|
||||
stream: Literal[False] = False,
|
||||
**kwargs: Any,
|
||||
) -> dict:
|
||||
...
|
||||
|
||||
@overload
|
||||
@staticmethod
|
||||
async def acreate(
|
||||
messages: Sequence[Dict[str, Any]],
|
||||
*,
|
||||
provider: str = "ChatOpenAI",
|
||||
stream: Literal[True],
|
||||
**kwargs: Any,
|
||||
) -> AsyncIterator:
|
||||
...
|
||||
|
||||
@staticmethod
|
||||
async def acreate(
|
||||
messages: Sequence[Dict[str, Any]],
|
||||
*,
|
||||
provider: str = "ChatOpenAI",
|
||||
stream: bool = False,
|
||||
**kwargs: Any,
|
||||
) -> Union[dict, AsyncIterator]:
|
||||
models = importlib.import_module("langchain.chat_models")
|
||||
model_cls = getattr(models, provider)
|
||||
model_config = model_cls(**kwargs)
|
||||
converted_messages = convert_openai_messages(messages)
|
||||
if not stream:
|
||||
result = await model_config.ainvoke(converted_messages)
|
||||
return {"choices": [{"message": convert_message_to_dict(result)}]}
|
||||
else:
|
||||
return (
|
||||
_convert_message_chunk_to_delta(c, i)
|
||||
async for i, c in aenumerate(model_config.astream(converted_messages))
|
||||
)
|
||||
@ -0,0 +1,107 @@
|
||||
from typing import Any
|
||||
|
||||
import openai
|
||||
import pytest
|
||||
|
||||
from langchain.adapters import openai as lcopenai
|
||||
|
||||
|
||||
def _test_no_stream(**kwargs: Any) -> None:
|
||||
result = openai.ChatCompletion.create(**kwargs)
|
||||
lc_result = lcopenai.ChatCompletion.create(**kwargs)
|
||||
if isinstance(lc_result, dict):
|
||||
if isinstance(result, dict):
|
||||
result_dict = result["choices"][0]["message"].to_dict_recursive()
|
||||
lc_result_dict = lc_result["choices"][0]["message"]
|
||||
assert result_dict == lc_result_dict
|
||||
return
|
||||
|
||||
|
||||
def _test_stream(**kwargs: Any) -> None:
|
||||
result = []
|
||||
for c in openai.ChatCompletion.create(**kwargs):
|
||||
result.append(c["choices"][0]["delta"].to_dict_recursive())
|
||||
|
||||
lc_result = []
|
||||
for c in lcopenai.ChatCompletion.create(**kwargs):
|
||||
lc_result.append(c["choices"][0]["delta"])
|
||||
assert result == lc_result
|
||||
|
||||
|
||||
async def _test_async(**kwargs: Any) -> None:
|
||||
result = await openai.ChatCompletion.acreate(**kwargs)
|
||||
lc_result = await lcopenai.ChatCompletion.acreate(**kwargs)
|
||||
if isinstance(lc_result, dict):
|
||||
if isinstance(result, dict):
|
||||
result_dict = result["choices"][0]["message"].to_dict_recursive()
|
||||
lc_result_dict = lc_result["choices"][0]["message"]
|
||||
assert result_dict == lc_result_dict
|
||||
return
|
||||
|
||||
|
||||
async def _test_astream(**kwargs: Any) -> None:
|
||||
result = []
|
||||
async for c in await openai.ChatCompletion.acreate(**kwargs):
|
||||
result.append(c["choices"][0]["delta"].to_dict_recursive())
|
||||
|
||||
lc_result = []
|
||||
async for c in await lcopenai.ChatCompletion.acreate(**kwargs):
|
||||
lc_result.append(c["choices"][0]["delta"])
|
||||
assert result == lc_result
|
||||
|
||||
|
||||
FUNCTIONS = [
|
||||
{
|
||||
"name": "get_current_weather",
|
||||
"description": "Get the current weather in a given location",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"location": {
|
||||
"type": "string",
|
||||
"description": "The city and state, e.g. San Francisco, CA",
|
||||
},
|
||||
"unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
|
||||
},
|
||||
"required": ["location"],
|
||||
},
|
||||
}
|
||||
]
|
||||
|
||||
|
||||
async def _test_module(**kwargs: Any) -> None:
|
||||
_test_no_stream(**kwargs)
|
||||
await _test_async(**kwargs)
|
||||
_test_stream(stream=True, **kwargs)
|
||||
await _test_astream(stream=True, **kwargs)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_normal_call() -> None:
|
||||
await _test_module(
|
||||
messages=[{"role": "user", "content": "hi"}],
|
||||
model="gpt-3.5-turbo",
|
||||
temperature=0,
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_function_calling() -> None:
|
||||
await _test_module(
|
||||
messages=[{"role": "user", "content": "whats the weather in boston"}],
|
||||
model="gpt-3.5-turbo",
|
||||
functions=FUNCTIONS,
|
||||
temperature=0,
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_answer_with_function_calling() -> None:
|
||||
await _test_module(
|
||||
messages=[
|
||||
{"role": "user", "content": "say hi, then whats the weather in boston"}
|
||||
],
|
||||
model="gpt-3.5-turbo",
|
||||
functions=FUNCTIONS,
|
||||
temperature=0,
|
||||
)
|
||||
Loading…
Reference in New Issue