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add bind tools & document loader 
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‎langchain/6.runnable_bind_tools.ipynb‎

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{
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"cells": [
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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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"## Rannable 对象绑定函数"
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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": 19,
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"metadata": {},
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"outputs": [],
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"source": [
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"from pprint import pprint\n",
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"from pydantic import BaseModel, Field\n",
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"from langchain_core.prompts import PromptTemplate\n",
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"from chat_model_client import get_model\n",
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"from langchain_core.output_parsers import PydanticOutputParser\n",
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"from langchain_core.runnables import (\n",
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" RunnablePassthrough\n",
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")"
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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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"1. Tools声明"
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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": 20,
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"metadata": {},
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"outputs": [],
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"source": [
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"tools = [\n",
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" {\n",
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" \"type\": \"function\",\n",
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" \"function\": {\n",
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" \"name\": \"solver\",\n",
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" \"description\": \"表述并解一个方程\",\n",
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" \"parameters\": {\n",
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" \"type\": \"object\",\n",
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" \"properties\": {\n",
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" \"equation\": {\n",
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" \"type\": \"string\",\n",
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" \"description\": \"待解的数学表达式\"\n",
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" },\n",
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" \"solution\": {\n",
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" \"type\": \"string\",\n",
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" \"description\": \"方程的解\"\n",
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" }\n",
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" },\n",
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" \"required\": [\"equation\", \"solution\"]\n",
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" }\n",
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" }\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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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"2. 声明返回结构"
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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": 21,
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"metadata": {},
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"outputs": [],
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"source": [
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"class Solver(BaseModel):\n",
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" equation: str = Field(\n",
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" description=\"数学表达式\"\n",
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" ),\n",
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" solution: str = Field(\n",
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" description=\"方程的解\"\n",
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" )"
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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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"2. 绑定tools"
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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": 22,
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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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"/Users/shaon/anaconda3/envs/wrenv3.10/lib/python3.10/site-packages/pydantic/json_schema.py:2191: PydanticJsonSchemaWarning: Default value (FieldInfo(annotation=NoneType, required=True, description='数学表达式'),) is not JSON serializable; excluding default from JSON schema [non-serializable-default]\n",
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" warnings.warn(message, PydanticJsonSchemaWarning)\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"RunnableBinding(bound=ChatOpenAI(client=<openai.resources.chat.completions.Completions object at 0x136366d40>, async_client=<openai.resources.chat.completions.AsyncCompletions object at 0x136384730>, root_client=<openai.OpenAI object at 0x136367250>, root_async_client=<openai.AsyncOpenAI object at 0x136366dd0>, model_name='deepseek-chat', temperature=0.0, model_kwargs={}, openai_api_key=SecretStr('**********'), openai_api_base='https://api.deepseek.com', max_tokens=2000), kwargs={'tools': [{'type': 'function', 'function': {'name': 'solver', 'description': '表述并解一个方程', 'parameters': {'type': 'object', 'properties': {'equation': {'type': 'string', 'description': '待解的数学表达式'}, 'solution': {'type': 'string', 'description': '方程的解'}}, 'required': ['equation', 'solution']}}}]}, config={}, config_factories=[])"
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]
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},
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"execution_count": 22,
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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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"query = \"x的3次方加上11等于75\"\n",
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"parser = PydanticOutputParser(pydantic_object=Solver)\n",
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"prompt = PromptTemplate(\n",
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" template=\"你是一个乐于助人的助手。写出下面的方程，然后求解。\\n{format_instructions}\\n{query}\\n如果输出的是代码块，请不要包括首尾的```符号\",\n",
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" input_variables=[\"query\"],\n",
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" partial_variables={\"format_instructions\": parser.get_format_instructions()},\n",
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")\n",
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"\n",
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"# prompt = ChatPromptTemplate([\n",
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"# (\"system\", \"你是一个乐于助人的助手。写出下面的方程，然后求解\"),\n",
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"# (\"human\", \"{input}\")\n",
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"# ])\n",
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"\n",
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"llm = get_model('deepseek')\n",
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"llm.bind_tools(tools=tools)"
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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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"3. 构建并执行runnable"
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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": 23,
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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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"Solver(equation='x^3 + 11 = 75', solution='x = 4')\n"
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]
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}
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],
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"source": [
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"\n",
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"chain = {\"query\": RunnablePassthrough()} | prompt | llm | parser\n",
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"output = chain.invoke({\"query\": query})\n",
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"\n",
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"pprint(output)"
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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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"4. 链路检查"
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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": 24,
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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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"+----------------------+ \n",
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"| Parallel<query>Input | \n",
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"+----------------------+ \n",
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" * \n",
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" * \n",
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" * \n",
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" +-------------+ \n",
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" | Passthrough | \n",
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" +-------------+ \n",
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" * \n",
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" * \n",
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" * \n",
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" +----------------+ \n",
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" | PromptTemplate | \n",
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" +----------------+ \n",
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" * \n",
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" * \n",
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" * \n",
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" +------------+ \n",
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" | ChatOpenAI | \n",
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" +------------+ \n",
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" * \n",
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" * \n",
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" * \n",
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"+----------------------+ \n",
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"| PydanticOutputParser | \n",
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"+----------------------+ \n",
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" * \n",
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" * \n",
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" * \n",
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" +--------+ \n",
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" | Solver | \n",
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" +--------+ \n",
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"None\n"
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]
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}
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],
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"source": [
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"pprint(chain.get_graph().print_ascii())"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "wrenv3.10",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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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": "3.10.16"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎langchain/7.document_loader_from_arxiv.ipynb‎

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‎langchain/chat_model_client.py‎

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base_url=env_config['OLLAMA_BASE_URL'],
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temperature=0, # 设置成0最稳定；structured generation中稳定最重要
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max_tokens=2000,
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frequency_penalty=0,
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presence_penalty=0,
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top_p=1.0
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model_kwargs={
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'frequency_penalty':0,
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'presence_penalty':0,
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'top_p':1.0,
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}
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# frequency_penalty=0,
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# presence_penalty=0,
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# top_p=1.0
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)
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return llm
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‎requirement.txt‎

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langchain-ollama==0.2.2
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langchain-openai=0.3.1
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## tools
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arxiv==2.1.3
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pymupdf==1.25.2
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## parser
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defusedxml==0.7.1
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markdownify==0.14.1

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