mirror of
https://github.com/hwchase17/langchain
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- Description： * Baidu AI Cloud's [Qianfan Platform](https://cloud.baidu.com/doc/WENXINWORKSHOP/index.html) is an all-in-one platform for large model development and service deployment, catering to enterprise developers in China. Qianfan Platform offers a wide range of resources, including the Wenxin Yiyan model (ERNIE-Bot) and various third-party open-source models. - Issue: none - Dependencies: * qianfan - Tag maintainer: @baskaryan - Twitter handle: --------- Co-authored-by: Bagatur <baskaryan@gmail.com>
125 lines
3.8 KiB
Plaintext
125 lines
3.8 KiB
Plaintext
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"source": [
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"# Baidu Qianfan\n",
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"\n",
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"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.\n",
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"\n",
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"Basically, those model are split into the following type:\n",
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"\n",
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"- Embedding\n",
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"- Chat\n",
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"- Completion\n",
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"\n",
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"In this notebook, we will introduce how to use langchain with [Qianfan](https://cloud.baidu.com/doc/WENXINWORKSHOP/index.html) mainly in `Embedding` corresponding\n",
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" to the package `langchain/embeddings` in langchain:\n",
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"\n",
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"\n",
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"\n",
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"## API Initialization\n",
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"\n",
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"To use the LLM services based on Baidu Qianfan, you have to initialize these parameters:\n",
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"\n",
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"You could either choose to init the AK,SK in enviroment variables or init params:\n",
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"\n",
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"```base\n",
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"export QIANFAN_AK=XXX\n",
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"export QIANFAN_SK=XXX\n",
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"```\n"
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]
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"\"\"\"For basic init and call\"\"\"\n",
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"from langchain.embeddings.baidu_qianfan_endpoint import QianfanEmbeddingsEndpoint \n",
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"\n",
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"import os\n",
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"os.environ[\"QIANFAN_AK\"] = \"xx\"\n",
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"os.environ[\"QIANFAN_SK\"] = \"xx\"\n",
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"\n",
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"embed = QianfanEmbeddingsEndpoint(qianfan_ak='xxx', \n",
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" qianfan_sk='xxx')\n",
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"res = embed.embed_documents([\"hi\", \"world\"])\n",
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"\n",
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"import asyncio\n",
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"\n",
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"async def aioEmbed():\n",
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" res = await embed.aembed_query(\"qianfan\")\n",
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" print(res)\n",
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"await aioEmbed()\n",
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"\n",
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"import asyncio\n",
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"async def aioEmbedDocs():\n",
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" res = await embed.aembed_documents([\"hi\", \"world\"])\n",
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" for r in res:\n",
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" print(\"\", r[:8])\n",
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"await aioEmbedDocs()\n",
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"\n",
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"\n"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Use different models in Qianfan\n",
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"\n",
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"In the case you want to deploy your own model based on Ernie Bot or third-party open sources model, you could follow these steps:\n",
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"\n",
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"- 1. （Optional, if the model are included in the default models, skip it）Deploy your model in Qianfan Console, get your own customized deploy endpoint.\n",
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"- 2. Set up the field called `endpoint` in the initlization:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"embed = QianfanEmbeddingsEndpoint(qianfan_ak='xxx', \n",
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" qianfan_sk='xxx',\n",
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" model=\"bge_large_zh\",\n",
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" endpoint=\"bge_large_zh\")\n",
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"\n",
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"res = embed.embed_documents([\"hi\", \"world\"])"
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]
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}
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],
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