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docs: update baidu_qianfan_endpoint.ipynb doc (#15940)
- **Description:** Updated the docs for the chat integration module baidu_qianfan_endpoint.ipynb - **Issue:** #15664 - **Dependencies:**N/A
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Bigtable123
GitHub
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@ -53,9 +53,16 @@
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"- AquilaChat-7B"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Set up"
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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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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -65,83 +72,105 @@
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"from langchain_community.chat_models import QianfanChatEndpoint\n",
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"from langchain_core.language_models.chat_models import HumanMessage\n",
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"\n",
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"os.environ[\"QIANFAN_AK\"] = \"your_ak\"\n",
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"os.environ[\"QIANFAN_SK\"] = \"your_sk\"\n",
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"\n",
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"chat = QianfanChatEndpoint(\n",
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" streaming=True,\n",
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")\n",
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"res = chat([HumanMessage(content=\"write a funny joke\")])"
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"os.environ[\"QIANFAN_AK\"] = \"Your_api_key\"\n",
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"os.environ[\"QIANFAN_SK\"] = \"You_secret_Key\""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Usage"
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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": 6,
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"[INFO] [09-15 20:00:36] logging.py:55 [t:139698882193216]: requesting llm api endpoint: /chat/eb-instant\n",
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"[INFO] [09-15 20:00:37] logging.py:55 [t:139698882193216]: async requesting llm api endpoint: /chat/eb-instant\n"
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"data": {
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"text/plain": [
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"AIMessage(content='您好！请问您需要什么帮助？我将尽力回答您的问题。')"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"chat = QianfanChatEndpoint(streaming=True)\n",
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"messages = [HumanMessage(content=\"Hello\")]\n",
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"chat.invoke(messages)"
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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": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"AIMessage(content='您好！有什么我可以帮助您的吗？')"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"await chat.ainvoke(messages)"
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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": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[AIMessage(content='您好！有什么我可以帮助您的吗？')]"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"chat.batch([messages])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Streaming"
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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": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"chat resp: content='您好，您似乎输入' additional_kwargs={} example=False\n",
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"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\n",
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"chat resp: content='' additional_kwargs={} example=False\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"[INFO] [09-15 20:00:39] logging.py:55 [t:139698882193216]: async requesting llm api endpoint: /chat/eb-instant\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"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'))]\n",
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"astream content='The sea is a vast' additional_kwargs={} example=False\n",
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"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\n",
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"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\n"
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"您好！有什么我可以帮助您的吗？\n"
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]
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}
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],
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"source": [
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"from langchain.schema import HumanMessage\n",
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"from langchain_community.chat_models import QianfanChatEndpoint\n",
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"\n",
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"chatLLM = QianfanChatEndpoint()\n",
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"res = chatLLM.stream([HumanMessage(content=\"hi\")], streaming=True)\n",
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"for r in res:\n",
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" print(\"chat resp:\", r)\n",
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"\n",
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"\n",
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"async def run_aio_generate():\n",
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" resp = await chatLLM.agenerate(\n",
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" messages=[[HumanMessage(content=\"write a 20 words sentence about sea.\")]]\n",
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" )\n",
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" print(resp)\n",
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"\n",
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"\n",
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"await run_aio_generate()\n",
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"\n",
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"\n",
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"async def run_aio_stream():\n",
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" async for res in chatLLM.astream(\n",
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" [HumanMessage(content=\"write a 20 words sentence about sea.\")]\n",
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" ):\n",
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" print(\"astream\", res)\n",
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"\n",
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"\n",
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"await run_aio_stream()"
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"try:\n",
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" for chunk in chat.stream(messages):\n",
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" print(chunk.content, end=\"\", flush=True)\n",
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"except TypeError as e:\n",
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" print(\"\")"
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]
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},
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{
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@ -151,39 +180,36 @@
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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-source model, you could follow these steps:\n",
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"The default model is ERNIE-Bot-turbo, 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:\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 initialization:"
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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 initialization:"
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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": 7,
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"[INFO] [09-15 20:00:50] logging.py:55 [t:139698882193216]: requesting llm api endpoint: /chat/bloomz_7b1\n"
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"data": {
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"text/plain": [
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"AIMessage(content='Hello，可以回答问题了，我会竭尽全力为您解答，请问有什么问题吗？')"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"content='你好！很高兴见到你。' additional_kwargs={} example=False\n"
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]
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"chatBloom = QianfanChatEndpoint(\n",
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"chatBot = QianfanChatEndpoint(\n",
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" streaming=True,\n",
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" model=\"BLOOMZ-7B\",\n",
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" model=\"ERNIE-Bot\",\n",
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")\n",
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"res = chatBloom([HumanMessage(content=\"hi\")])\n",
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"print(res)"
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"\n",
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"messages = [HumanMessage(content=\"Hello\")]\n",
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"chatBot.invoke(messages)"
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]
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},
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{
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@ -202,35 +228,25 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"[INFO] [09-15 20:00:57] logging.py:55 [t:139698882193216]: requesting llm api endpoint: /chat/eb-instant\n"
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"data": {
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"text/plain": [
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"AIMessage(content='您好！有什么我可以帮助您的吗？')"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"content='您好，您似乎输入' additional_kwargs={} example=False\n",
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"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\n",
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"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\n",
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"content='' additional_kwargs={} example=False\n"
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]
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"res = chat.stream(\n",
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" [HumanMessage(content=\"hi\")],\n",
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"chat.invoke(\n",
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" [HumanMessage(content=\"Hello\")],\n",
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" **{\"top_p\": 0.4, \"temperature\": 0.1, \"penalty_score\": 1},\n",
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")\n",
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"\n",
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"for r in res:\n",
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" print(r)"
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")"
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]
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}
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],
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@ -250,7 +266,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.5"
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"version": "3.9.18"
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},
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"vscode": {
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"interpreter": {
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