langchain_expressions.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dotenv import load_dotenv\n",
    "load_dotenv()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.prompts import ChatPromptTemplate\n",
    "from langchain.chat_models import ChatOpenAI\n",
    "from langchain.chains import LLMChain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "llm = ChatOpenAI()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "prompt = ChatPromptTemplate.from_template(\"tell me a joke about {input}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "chain = LLMChain(llm=llm, prompt=prompt)\n",
    "chain.predict(input=\"A pig\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First pipe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "chain = prompt | llm\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "chain.invoke({\"input\": \"A pig\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.schema.output_parser import StrOutputParser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "chain = prompt | llm | StrOutputParser()\n",
    "chain.invoke({\"input\": \"A pig\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "prompt = ChatPromptTemplate.from_template(\n",
    "    \"tell me 5 jokes about {input}\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "chain = prompt | llm.bind(stop=[\"\\n\"]) | StrOutputParser()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "chain.invoke({\"input\": \"pigs\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### OPENAI Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "functions = [\n",
    "    {\n",
    "      \"name\": \"joke\",\n",
    "      \"description\": \"A joke\",\n",
    "      \"parameters\": {\n",
    "        \"type\": \"object\",\n",
    "        \"properties\": {\n",
    "          \"setup\": {\n",
    "            \"type\": \"string\",\n",
    "            \"description\": \"The setup for the joke\"\n",
    "          },\n",
    "          \"punchline\": {\n",
    "            \"type\": \"string\",\n",
    "            \"description\": \"The punchline for the joke\"\n",
    "          }\n",
    "        },\n",
    "        \"required\": [\"setup\", \"punchline\"]\n",
    "      }\n",
    "    }\n",
    "  ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.output_parsers.openai_functions import JsonOutputFunctionsParser\n",
    "chain = (\n",
    "    prompt \n",
    "    | llm.bind(function_call={\"name\": \"joke\"}, functions= functions) \n",
    "    | JsonOutputFunctionsParser()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "chain.invoke(input={\"input\": \"bears\"})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Working with vectorstores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.vectorstores import Chroma\n",
    "from langchain.embeddings import OpenAIEmbeddings\n",
    "from langchain.schema.runnable import RunnablePassthrough\n",
    "\n",
    "\n",
    "vectorstore = Chroma.from_texts([\"Cats are typically 9.1 kg in weight.\", \n",
    "                                 \"Cats have retractable claws.\", \n",
    "                                 \"A group of cats is called a clowder.\", \n",
    "                                 \"Cats can rotate their ears 180 degrees.\", \n",
    "                                 \"The world's oldest cat lived to be 38 years old.\"], \n",
    "                                embedding=OpenAIEmbeddings())\n",
    "retriever = vectorstore.as_retriever()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "template = \"\"\"Answer the question based only on the following context:\n",
    "{context}\n",
    "\n",
    "Question: {question}\n",
    "\"\"\"\n",
    "prompt = ChatPromptTemplate.from_template(template)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "chain = (\n",
    "    {\"context\": retriever, \"question\": RunnablePassthrough()} \n",
    "    | prompt \n",
    "    | llm \n",
    "    | StrOutputParser()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "chain.invoke(\"how old is the oldest cat?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from operator import itemgetter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "question = {\"bla\": \"test\", \"x\": \"wuff\"}\n",
    "itemgetter(\"bla\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "get_bla = itemgetter(\"bla\")\n",
    "get_bla(question)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.schema.runnable import RunnablePassthrough\n",
    "\n",
    "template = \"\"\"Answer the question based only on the following context:\n",
    "{context}\n",
    "\n",
    "Question: {question}\n",
    "\n",
    "Answer in the following language: {language}\n",
    "\"\"\"\n",
    "prompt = ChatPromptTemplate.from_template(template)\n",
    "\n",
    "# chain = {\n",
    "#     \"context\": itemgetter(\"question\") | retriever,\n",
    "#     \"question\": itemgetter(\"question\"),\n",
    "#     \"language\": itemgetter(\"language\")\n",
    "# } | prompt | model | StrOutputParser()\n",
    "\n",
    "chain = {\n",
    "    \"context\": (lambda x: x[\"question\"]) | retriever, \n",
    "    \"question\": (lambda x: x[\"question\"]), \n",
    "    \"language\": (lambda x: x[\"language\"])\n",
    "} | prompt | llm | StrOutputParser()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "chain.invoke({\"question\": \"how old is the oldest cat?\", \"language\": \"german\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install duckduckgo-search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.tools import DuckDuckGoSearchRun\n",
    "search = DuckDuckGoSearchRun()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "template = \"\"\"turn the following user input into a search query for a search engine:\n",
    "\n",
    "{input}\"\"\"\n",
    "\n",
    "prompt = ChatPromptTemplate.from_template(template)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "chain = prompt | llm | StrOutputParser() | search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "chain.invoke({\"input\": \"whats the name of the oldest cat?\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Arbitrary functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.schema.runnable import RunnableLambda\n",
    "\n",
    "def length_function(text):\n",
    "    return len(text)\n",
    "\n",
    "def _multiple_length_function(text1, text2):\n",
    "    return len(text1) * len(text2)\n",
    "\n",
    "def multiple_length_function(_dict):\n",
    "    return _multiple_length_function(_dict[\"text1\"], _dict[\"text2\"])\n",
    "\n",
    "prompt = ChatPromptTemplate.from_template(\"what is {a} + {b}\")\n",
    "\n",
    "chain = {\n",
    "    \"a\": itemgetter(\"foo\") | RunnableLambda(length_function),\n",
    "    \"b\": {\"text1\": itemgetter(\"foo\"), \"text2\": itemgetter(\"bar\")} | RunnableLambda(multiple_length_function)\n",
    "} | prompt | llm | StrOutputParser()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "chain.invoke({\"foo\": \"bar\", \"bar\": \"gah\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ChatOpenAI()\n",
    "prompt = ChatPromptTemplate.from_template(\"tell me a joke about {topic}\")\n",
    "chain = prompt | model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for s in chain.stream({\"topic\": \"bears\"}):\n",
    "    print(s.content, end=\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "chain.invoke({\"topic\": \"bears\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "chain.batch([{\"topic\": \"bears\"}, {\"topic\": \"cats\"}])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "async for s in chain.astream({\"topic\": \"bears\"}):\n",
    "    print(s.content, end=\"\")\n",
    "await chain.ainvoke({\"topic\": \"bears\"})\n",
    "await chain.abatch([{\"topic\": \"bears\"}])"
   ]
  }
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