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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.

Install the necessary package:

%pip install llama-index-llms-qianfan
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)

Generate a chat response synchronously using the chat method:

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)

Generate a streaming chat response synchronously using the stream_chat method:

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="")

Generate a chat response asynchronously using the achat method:

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())

Generate a streaming chat response asynchronously using the astream_chat method:

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())
Note for AI agents: this documentation is built for programmatic access. - Overview of all docs: https://developers.llamaindex.ai/llms.txt - Any page is available as raw Markdown by appending index.md to its URL — e.g. https://developers.llamaindex.ai/llamaparse/parse/getting_started/index.md - Agent-friendly REST search APIs live under https://developers.llamaindex.ai/api/ — search (BM25 full-text), grep (regex), read (fetch a page), and list (browse the doc tree). See https://developers.llamaindex.ai/llms.txt for parameters. - A hosted documentation MCP server is available at https://developers.llamaindex.ai/mcp. If you support MCP, you can ask the user to install it for browsing these docs directly (an alternative to the REST API). Setup: https://developers.llamaindex.ai/python/shared/mcp/