Client of Baidu Intelligent Cloud's Qianfan LLM Platform¶

Baidu Intelligent Cloud's Qianfan LLM Platform offers API services for all Baidu LLMs, such as ERNIE-3.5-8K and ERNIE-4.0-8K. It also provides a small number of open-source LLMs like Llama-2-70b-chat.

Before using the chat client, you need to activate the LLM service on the Qianfan LLM Platform console's online service page. Then, Generate an Access Key and a Secret Key in the Security Authentication page of the console.

Installation¶

Install the necessary package:

In [ ]:

%pip install llama-index-llms-qianfan

%pip install llama-index-llms-qianfan

Initialization¶

In [ ]:

from llama_index.llms.qianfan import Qianfan
import asyncio

access_key = "XXX"
secret_key = "XXX"
model_name = "ERNIE-Speed-8K"
endpoint_url = "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie_speed"
context_window = 8192
llm = Qianfan(access_key, secret_key, model_name, endpoint_url, context_window)

from llama_index.llms.qianfan import Qianfan import asyncio access_key = "XXX" secret_key = "XXX" model_name = "ERNIE-Speed-8K" endpoint_url = "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/ernie_speed" context_window = 8192 llm = Qianfan(access_key, secret_key, model_name, endpoint_url, context_window)

Synchronous Chat¶

Generate a chat response synchronously using the chat method:

In [ ]:

from llama_index.core.base.llms.types import ChatMessage

messages = [
    ChatMessage(role="user", content="Tell me a joke."),
]
chat_response = llm.chat(messages)
print(chat_response.message.content)

from llama_index.core.base.llms.types import ChatMessage messages = [ ChatMessage(role="user", content="Tell me a joke."), ] chat_response = llm.chat(messages) print(chat_response.message.content)

Synchronous Stream Chat¶

Generate a streaming chat response synchronously using the stream_chat method:

In [ ]:

messages = [
    ChatMessage(role="system", content="You are a helpful assistant."),
    ChatMessage(role="user", content="Tell me a story."),
]
content = ""
for chat_response in llm.stream_chat(messages):
    content += chat_response.delta
    print(chat_response.delta, end="")

messages = [ ChatMessage(role="system", content="You are a helpful assistant."), ChatMessage(role="user", content="Tell me a story."), ] content = "" for chat_response in llm.stream_chat(messages): content += chat_response.delta print(chat_response.delta, end="")

Asynchronous Chat¶

Generate a chat response asynchronously using the achat method:

In [ ]:

async def async_chat():
    messages = [
        ChatMessage(role="user", content="Tell me an async joke."),
    ]
    chat_response = await llm.achat(messages)
    print(chat_response.message.content)


asyncio.run(async_chat())

async def async_chat(): messages = [ ChatMessage(role="user", content="Tell me an async joke."), ] chat_response = await llm.achat(messages) print(chat_response.message.content) asyncio.run(async_chat())

Asynchronous Stream Chat¶

Generate a streaming chat response asynchronously using the astream_chat method:

In [ ]:

async def async_stream_chat():
    messages = [
        ChatMessage(role="system", content="You are a helpful assistant."),
        ChatMessage(role="user", content="Tell me an async story."),
    ]
    content = ""
    response = await llm.astream_chat(messages)
    async for chat_response in response:
        content += chat_response.delta
        print(chat_response.delta, end="")


asyncio.run(async_stream_chat())

async def async_stream_chat(): messages = [ ChatMessage(role="system", content="You are a helpful assistant."), ChatMessage(role="user", content="Tell me an async story."), ] content = "" response = await llm.astream_chat(messages) async for chat_response in response: content += chat_response.delta print(chat_response.delta, end="") asyncio.run(async_stream_chat())