QianfanChatEndpoint
Baidu AI Cloud Qianfan Platform is a one-stop large model development and service operation platform for enterprise developers. Qianfan not only provides including the model of Wenxin Yiyan (ERNIE-Bot) and the third-party open-source models, but also provides various AI development tools and the whole set of development environment, which facilitates customers to use and develop large model applications easily.
Basically, those model are split into the following type:
- Embedding
- Chat
- Completion
In this notebook, we will introduce how to use langchain with
Qianfan mainly
in Chat
corresponding to the package langchain/chat_models
in
langchain:
API Initialization
To use the LLM services based on Baidu Qianfan, you have to initialize these parameters:
You could either choose to init the AK,SK in environment variables or init params:
export QIANFAN_AK=XXX
export QIANFAN_SK=XXX
Current supported models:
- ERNIE-Bot-turbo (default models)
- ERNIE-Bot
- BLOOMZ-7B
- Llama-2-7b-chat
- Llama-2-13b-chat
- Llama-2-70b-chat
- Qianfan-BLOOMZ-7B-compressed
- Qianfan-Chinese-Llama-2-7B
- ChatGLM2-6B-32K
- AquilaChat-7B
"""For basic init and call"""
import os
from langchain.chat_models import QianfanChatEndpoint
from langchain_core.language_models.chat_models import HumanMessage
os.environ["QIANFAN_AK"] = "your_ak"
os.environ["QIANFAN_SK"] = "your_sk"
chat = QianfanChatEndpoint(
streaming=True,
)
res = chat([HumanMessage(content="write a funny joke")])
[INFO] [09-15 20:00:29] logging.py:55 [t:139698882193216]: requesting llm api endpoint: /chat/eb-instant
from langchain.chat_models import QianfanChatEndpoint
from langchain.schema import HumanMessage
chatLLM = QianfanChatEndpoint(
streaming=True,
)
res = chatLLM.stream([HumanMessage(content="hi")], streaming=True)
for r in res:
print("chat resp:", r)
async def run_aio_generate():
resp = await chatLLM.agenerate(
messages=[[HumanMessage(content="write a 20 words sentence about sea.")]]
)
print(resp)
await run_aio_generate()
async def run_aio_stream():
async for res in chatLLM.astream(
[HumanMessage(content="write a 20 words sentence about sea.")]
):
print("astream", res)
await run_aio_stream()
[INFO] [09-15 20:00:36] logging.py:55 [t:139698882193216]: requesting llm api endpoint: /chat/eb-instant
[INFO] [09-15 20:00:37] logging.py:55 [t:139698882193216]: async requesting llm api endpoint: /chat/eb-instant
[INFO] [09-15 20:00:39] logging.py:55 [t:139698882193216]: async requesting llm api endpoint: /chat/eb-instant
chat resp: content='您好,您似乎输入' additional_kwargs={} example=False
chat resp: content='了一个话题标签,请问需要我帮您找到什么资料或者帮助您解答什么问题吗?' additional_kwargs={} example=False
chat resp: content='' additional_kwargs={} example=False
generations=[[ChatGeneration(text="The sea is a vast expanse of water that covers much of the Earth's surface. It is a source of travel, trade, and entertainment, and is also a place of scientific exploration and marine conservation. The sea is an important part of our world, and we should cherish and protect it.", generation_info={'finish_reason': 'finished'}, message=AIMessage(content="The sea is a vast expanse of water that covers much of the Earth's surface. It is a source of travel, trade, and entertainment, and is also a place of scientific exploration and marine conservation. The sea is an important part of our world, and we should cherish and protect it.", additional_kwargs={}, example=False))]] llm_output={} run=[RunInfo(run_id=UUID('d48160a6-5960-4c1d-8a0e-90e6b51a209b'))]
astream content='The sea is a vast' additional_kwargs={} example=False
astream content=' expanse of water, a place of mystery and adventure. It is the source of many cultures and civilizations, and a center of trade and exploration. The sea is also a source of life and beauty, with its unique marine life and diverse' additional_kwargs={} example=False
astream content=' coral reefs. Whether you are swimming, diving, or just watching the sea, it is a place that captivates the imagination and transforms the spirit.' additional_kwargs={} example=False
Use different models in Qianfan
In the case you want to deploy your own model based on Ernie Bot or third-party open-source model, you could follow these steps:
- (Optional, if the model are included in the default models, skip it)Deploy your model in Qianfan Console, get your own customized deploy endpoint.
- Set up the field called
endpoint
in the initialization:
- Set up the field called
chatBloom = QianfanChatEndpoint(
streaming=True,
model="BLOOMZ-7B",
)
res = chatBloom([HumanMessage(content="hi")])
print(res)
[INFO] [09-15 20:00:50] logging.py:55 [t:139698882193216]: requesting llm api endpoint: /chat/bloomz_7b1
content='你好!很高兴见到你。' additional_kwargs={} example=False
Model Params:
For now, only ERNIE-Bot
and ERNIE-Bot-turbo
support model params
below, we might support more models in the future.
- temperature
- top_p
- penalty_score
res = chat.stream(
[HumanMessage(content="hi")],
**{"top_p": 0.4, "temperature": 0.1, "penalty_score": 1},
)
for r in res:
print(r)
[INFO] [09-15 20:00:57] logging.py:55 [t:139698882193216]: requesting llm api endpoint: /chat/eb-instant
content='您好,您似乎输入' additional_kwargs={} example=False
content='了一个文本字符串,但并没有给出具体的问题或场景。' additional_kwargs={} example=False
content='如果您能提供更多信息,我可以更好地回答您的问题。' additional_kwargs={} example=False
content='' additional_kwargs={} example=False