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https://github.com/hwchase17/langchain.git
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Revert "Add baichuan model" (#11901)
cc @cloudscool, apologies your PR wasn't actually passing CI
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Bagatur
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@@ -21,7 +21,6 @@ from langchain.chat_models.anthropic import ChatAnthropic
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from langchain.chat_models.anyscale import ChatAnyscale
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from langchain.chat_models.azure_openai import AzureChatOpenAI
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from langchain.chat_models.baidu_qianfan_endpoint import QianfanChatEndpoint
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from langchain.chat_models.baichuan_baichuaninc_endpoint import BaichuanChatEndpoint
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from langchain.chat_models.bedrock import BedrockChat
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from langchain.chat_models.cohere import ChatCohere
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from langchain.chat_models.ernie import ErnieBotChat
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@@ -64,5 +63,4 @@ __all__ = [
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"ChatKonko",
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"QianfanChatEndpoint",
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"ChatFireworks",
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"BaichuanChatEndpoint",
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]
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@@ -1,161 +0,0 @@
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"""Baichuan chat wrapper."""
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from __future__ import annotations
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import requests
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import json
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import time
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import hashlib
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import logging
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from typing import (
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Any,
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AsyncIterator,
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Dict,
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Iterator,
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List,
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Mapping,
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Optional,
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)
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from langchain.callbacks.manager import (
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AsyncCallbackManagerForLLMRun,
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CallbackManagerForLLMRun,
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)
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from langchain.chat_models.base import BaseChatModel
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from langchain.pydantic_v1 import Field, root_validator
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from langchain.schema import ChatGeneration, ChatResult
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from langchain.schema.messages import (
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AIMessage,
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AIMessageChunk,
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BaseMessage,
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BaseMessageChunk,
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ChatMessage,
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FunctionMessage,
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HumanMessage,
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SystemMessage,
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)
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from langchain.schema.output import ChatGenerationChunk
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from langchain.utils import get_from_dict_or_env
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logger = logging.getLogger(__name__)
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def _convert_resp_to_message_chunk(resp: Mapping[str, Any]) -> BaseMessageChunk:
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return AIMessageChunk(
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content=resp["result"],
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role="assistant",
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)
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def convert_message_to_dict(message: BaseMessage) -> dict:
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"""Convert a message to a dictionary that can be passed to the API."""
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message_dict: Dict[str, Any]
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if isinstance(message, ChatMessage):
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message_dict = {"role": message.role, "content": message.content}
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elif isinstance(message, HumanMessage):
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message_dict = {"role": "user", "content": message.content}
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elif isinstance(message, AIMessage):
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message_dict = {"role": "assistant", "content": message.content}
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if "function_call" in message.additional_kwargs:
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message_dict["functions"] = message.additional_kwargs["function_call"]
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# If function call only, content is None not empty string
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if message_dict["content"] == "":
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message_dict["content"] = None
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elif isinstance(message, FunctionMessage):
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message_dict = {
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"role": "function",
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"content": message.content,
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"name": message.name,
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}
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else:
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raise TypeError(f"Got unknown type {message}")
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return message_dict
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def calculate_md5(input_string):
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md5 = hashlib.md5()
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md5.update(input_string.encode('utf-8'))
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encrypted = md5.hexdigest()
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return encrypted
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class BaichuanChatEndpoint():
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"""
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Currently only enterprise registration is supported for use
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To use, you should have the environment variable ``Baichuan_AK`` and ``Baichuan_SK`` set with your
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api_key and secret_key.
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ak, sk are required parameters
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which you could get from https: // api.baichuan-ai.com
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Example:
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.. code-block: : python
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from langchain.chat_models import BaichuanChatEndpoint
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baichuan_chat = BaichuanChatEndpoint("your_ak", "your_sk","Baichuan2-13B")
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result=baichuan_chat.predict(message)
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print(result.text")
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Because Baichuan was no pip package made,So we will temporarily use this method and iterate and upgrade in the future
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Args: They cannot be empty
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baichuan_ak (str): api_key
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baichuan_sk (str): secret_key
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model (str): Default Baichuan2-7B，Baichuan2-13B，Baichuan2-53B which is commercial.
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streaming (bool): Default False
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Returns:
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Execute predict return response.
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"""
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baichuan_ak: Optional[str] = None
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baichuan_sk: Optional[str] = None
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request_timeout: Optional[int] = 60
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"""request timeout for chat http requests"""
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top_p: Optional[float] = 0.8
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temperature: Optional[float] = 0.95
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endpoint: Optional[str] = None
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"""Endpoint of the Qianfan LLM, required if custom model used."""
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def __init__(self, baichuan_ak, baichuan_sk, model="Baichuan2-7B", streaming=False):
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self.baichuan_ak = baichuan_ak
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self.baichuan_sk = baichuan_sk
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self.model = "Baichuan2-7B" if model is None else model
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self.streaming = False if streaming is not None and streaming is False else True
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def predict(self, messages: List[BaseMessage]) -> Response:
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if self.streaming is not None and self.streaming is False:
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url = "https://api.baichuan-ai.com/v1/chat"
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elif self.streaming is not None and self.streaming is True:
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url = "https://api.baichuan-ai.com/v1/stream/chat"
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data = {
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"model": self.model,
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"messages": [
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{
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"role": "user",
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"content": messages
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}
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]
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}
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json_data = json.dumps(data)
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time_stamp = int(time.time())
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signature = calculate_md5(self.baichuan_sk + json_data + str(time_stamp))
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headers = {
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"Content-Type": "application/json",
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"Authorization": "Bearer " + self.baichuan_ak,
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"X-BC-Request-Id": "your requestId",
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"X-BC-Timestamp": str(time_stamp),
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"X-BC-Signature": signature,
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"X-BC-Sign-Algo": "MD5",
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}
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response = requests.post(url, data=json_data, headers=headers)
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return response
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