{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "provenance": [], "machine_shape": "hm", "gpuType": "L4" }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" }, "accelerator": "GPU" }, "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "7FbteuSO3tV5", "outputId": "9d0fad36-250c-4158-cf2c-8fb1e09a53e9" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Collecting langgraph\n", " Downloading langgraph-0.2.14-py3-none-any.whl.metadata (13 kB)\n", "Collecting langsmith\n", " Downloading langsmith-0.1.106-py3-none-any.whl.metadata (13 kB)\n", "Collecting langchain\n", " Downloading langchain-0.2.14-py3-none-any.whl.metadata (7.1 kB)\n", "Collecting langchain_groq\n", " Downloading langchain_groq-0.1.9-py3-none-any.whl.metadata (2.9 kB)\n", "Collecting langchain_community\n", " Downloading 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jsonpointer-3.0.0 langchain-0.2.14 langchain-core-0.2.35 langchain-text-splitters-0.2.2 langchain_community-0.2.12 langchain_groq-0.1.9 langgraph-0.2.14 langgraph-checkpoint-1.0.6 langsmith-0.1.106 marshmallow-3.22.0 mypy-extensions-1.0.0 orjson-3.10.7 tenacity-8.5.0 typing-inspect-0.9.0\n" ] } ], "source": [ "!pip install langgraph langsmith langchain langchain_groq langchain_community" ] }, { "cell_type": "code", "source": [ "from typing import Annotated\n", "from typing_extensions import TypedDict" ], "metadata": { "id": "7nby_Ijb4iDe" }, "execution_count": 2, "outputs": [] }, { "cell_type": "code", "source": [ "!pip install arxiv wikipedia" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "em3GQzI_6K8H", "outputId": "ee1ee594-1699-4ca3-dcbd-5632cb873741" }, "execution_count": 4, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Collecting arxiv\n", " Downloading arxiv-2.1.3-py3-none-any.whl.metadata (6.1 kB)\n", "Collecting wikipedia\n", " Downloading wikipedia-1.4.0.tar.gz (27 kB)\n", " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", "Collecting feedparser~=6.0.10 (from arxiv)\n", " Downloading feedparser-6.0.11-py3-none-any.whl.metadata (2.4 kB)\n", "Requirement already satisfied: requests~=2.32.0 in /usr/local/lib/python3.10/dist-packages (from arxiv) (2.32.3)\n", "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from wikipedia) (4.12.3)\n", "Collecting sgmllib3k (from feedparser~=6.0.10->arxiv)\n", " Downloading sgmllib3k-1.0.0.tar.gz (5.8 kB)\n", " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests~=2.32.0->arxiv) (3.3.2)\n", "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests~=2.32.0->arxiv) (3.7)\n", "Requirement already satisfied: urllib3<3,>=1.21.1 in 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/root/.cache/pip/wheels/5e/b6/c5/93f3dec388ae76edc830cb42901bb0232504dfc0df02fc50de\n", " Building wheel for sgmllib3k (setup.py) ... \u001b[?25l\u001b[?25hdone\n", " Created wheel for sgmllib3k: filename=sgmllib3k-1.0.0-py3-none-any.whl size=6047 sha256=fa8d2a2bc8e9e759c97651247ee5338765bd2ca1f24ee1aa60e5c2954b15c326\n", " Stored in directory: /root/.cache/pip/wheels/f0/69/93/a47e9d621be168e9e33c7ce60524393c0b92ae83cf6c6e89c5\n", "Successfully built wikipedia sgmllib3k\n", "Installing collected packages: sgmllib3k, feedparser, wikipedia, arxiv\n", "Successfully installed arxiv-2.1.3 feedparser-6.0.11 sgmllib3k-1.0.0 wikipedia-1.4.0\n" ] } ] }, { "cell_type": "code", "source": [ "## Working With Tools\n", "\n", "from langchain_community.utilities import ArxivAPIWrapper,WikipediaAPIWrapper\n", "from langchain_community.tools import ArxivQueryRun,WikipediaQueryRun\n", "\n", "## Arxiv And Wikipedia tools\n", "arxiv_wrapper=ArxivAPIWrapper(top_k_results=1,doc_content_chars_max=300)\n", "arxiv_tool=ArxivQueryRun(api_wrapper=arxiv_wrapper)\n", "\n", "api_wrapper=WikipediaAPIWrapper(top_k_results=1,doc_content_chars_max=300)\n", "wiki_tool=WikipediaQueryRun(api_wrapper=api_wrapper)" ], "metadata": { "id": "zXI3qaom5a03" }, "execution_count": 6, "outputs": [] }, { "cell_type": "code", "source": [ "wiki_tool.invoke(\"who is Sharukh Khan\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 70 }, "id": "BdE09N5-5xCz", "outputId": "23a3c122-7bea-4668-a7bd-6f630df64dd2" }, "execution_count": 7, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "'Page: Shah Rukh Khan\\nSummary: Shah Rukh Khan (pronounced [ˈʃɑːɦɾʊx xɑːn] ; born 2 November 1965), also known by the initialism SRK, is an Indian actor and film producer who works in Hindi cinema. Referred to in the media as the \"Baadshah of Bollywood\" and \"King Khan\", he has appeared in more than 10'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 7 } ] }, { "cell_type": "code", "source": [ "arxiv_tool.invoke(\"Attention is all you need\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 70 }, "id": "thYaNDYy5w_w", "outputId": "24d5bb06-6d9a-4dd0-c5db-ca4ae3e464b6" }, "execution_count": 8, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "\"Published: 2024-07-22\\nTitle: Attention Is All You Need But You Don't Need All Of It For Inference of Large Language Models\\nAuthors: Georgy Tyukin, Gbetondji J-S Dovonon, Jean Kaddour, Pasquale Minervini\\nSummary: The inference demand for LLMs has skyrocketed in recent months, and serving\\nmodels with \"" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 8 } ] }, { "cell_type": "code", "source": [ "tools=[wiki_tool]" ], "metadata": { "id": "ek52WFGI6lTq" }, "execution_count": 10, "outputs": [] }, { "cell_type": "code", "source": [ "## Langgraph Application\n", "from langgraph.graph.message import add_messages\n", "class State(TypedDict):\n", " messages:Annotated[list,add_messages]" ], "metadata": { "id": "eC6q3tTg6slO" }, "execution_count": 11, "outputs": [] }, { "cell_type": "code", "source": [ "from langgraph.graph import StateGraph,START,END" ], "metadata": { "id": "iXiurpCm7AWz" }, "execution_count": 12, "outputs": [] }, { "cell_type": "code", "source": [ "graph_builder= StateGraph(State)\n" ], "metadata": { "id": "X9nAxTc17JNh" }, "execution_count": 45, "outputs": [] }, { "cell_type": "code", "source": [ "from langchain_groq import ChatGroq" ], "metadata": { "id": "Kc-4UeZa7SU_" }, "execution_count": 14, "outputs": [] }, { "cell_type": "code", "source": [ "from google.colab import userdata\n", "groq_api_key=userdata.get(\"groq_api_key\")" ], "metadata": { "id": "s0Gsr9wr7gi9" }, "execution_count": 15, "outputs": [] }, { "cell_type": "code", "source": [ "llm=ChatGroq(groq_api_key=groq_api_key,model_name=\"Gemma2-9b-It\")\n", "llm" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "63tR5KIk7Zyd", "outputId": "a027ab06-88d5-4f7d-f119-8eefdd79a37e" }, "execution_count": 17, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "ChatGroq(client=, async_client=, model_name='Gemma2-9b-It', groq_api_key=SecretStr('**********'))" ] }, "metadata": {}, "execution_count": 17 } ] }, { "cell_type": "code", "source": [ "llm_with_tools=llm.bind_tools(tools=tools)" ], "metadata": { "id": "HS2TyqvY8BcL" }, "execution_count": 39, "outputs": [] }, { "cell_type": "code", "source": [ "def chatbot(state:State):\n", " return {\"messages\":[llm_with_tools.invoke(state[\"messages\"])]}" ], "metadata": { "id": "uhh-mYKn7sPU" }, "execution_count": 40, "outputs": [] }, { "cell_type": "code", "source": [ "from langgraph.prebuilt import ToolNode,tools_condition" ], "metadata": { "id": "81qlR2Kl8uZL" }, "execution_count": 41, "outputs": [] }, { "cell_type": "code", "source": [ "graph_builder.add_node(\"chatbot\",chatbot)\n", "tool_node = ToolNode(tools=tools)\n", "graph_builder.add_node(\"tools\", tool_node)\n", "\n", "graph_builder.add_conditional_edges(\n", " \"chatbot\",\n", " tools_condition,\n", ")\n", "graph_builder.add_edge(\"tools\", \"chatbot\")\n", "graph_builder.add_edge(START,\"chatbot\")\n" ], "metadata": { "id": "3prnJx4M8YLF" }, "execution_count": 46, "outputs": [] }, { "cell_type": "code", "source": [ "graph=graph_builder.compile()" ], "metadata": { "id": "q1ztKyS79-YE" }, "execution_count": 47, "outputs": [] }, { "cell_type": "code", "source": [ "from IPython.display import Image, display\n", "\n", "try:\n", " display(Image(graph.get_graph().draw_mermaid_png()))\n", "except Exception:\n", " # This requires some extra dependencies and is optional\n", " pass" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 236 }, "id": "bg6et4vC-B_Y", "outputId": "0a887337-8b74-4cb9-adfd-3ab7abc47900" }, "execution_count": 48, "outputs": [ { "output_type": "display_data", "data": { "image/jpeg": 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\n", "text/plain": [ "" ] }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "user_input=\"Hi there!, My name is John\"\n", "\n", "events=graph.stream(\n", " {\"messages\": [(\"user\", user_input)]},stream_mode=\"values\"\n", ")\n", "\n", "for event in events:\n", " event[\"messages\"][-1].pretty_print()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "_1M_0ihg-OK3", "outputId": "1cd34556-05a4-4bec-d20e-945fe3c46a44" }, "execution_count": 36, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "================================\u001b[1m Human Message \u001b[0m=================================\n", "\n", "Hi there!, My name is John\n", "==================================\u001b[1m Ai Message \u001b[0m==================================\n", "Tool Calls:\n", " wikipedia (call_zd9v)\n", " Call ID: call_zd9v\n", " Args:\n", " query: John\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/wikipedia/wikipedia.py:389: GuessedAtParserWarning: No parser was explicitly specified, so I'm using the best available HTML parser for this system (\"lxml\"). This usually isn't a problem, but if you run this code on another system, or in a different virtual environment, it may use a different parser and behave differently.\n", "\n", "The code that caused this warning is on line 389 of the file /usr/local/lib/python3.10/dist-packages/wikipedia/wikipedia.py. To get rid of this warning, pass the additional argument 'features=\"lxml\"' to the BeautifulSoup constructor.\n", "\n", " lis = BeautifulSoup(html).find_all('li')\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "=================================\u001b[1m Tool Message \u001b[0m=================================\n", "Name: wikipedia\n", "\n", "No good Wikipedia Search Result was found\n", "==================================\u001b[1m Ai Message \u001b[0m==================================\n", "\n", "Hello John, it's nice to meet you!\n" ] } ] }, { "cell_type": "code", "source": [ "user_input = \"what is RLHF.\"\n", "\n", "# The config is the **second positional argument** to stream() or invoke()!\n", "events = graph.stream(\n", " {\"messages\": [(\"user\", user_input)]},stream_mode=\"values\"\n", ")\n", "for event in events:\n", " event[\"messages\"][-1].pretty_print()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "HnWrp7Xe-xey", "outputId": "abcb7807-11bd-4a09-dafc-bb32dfff3c9e" }, "execution_count": 37, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "================================\u001b[1m Human Message \u001b[0m=================================\n", "\n", "what is RLHF.\n", "==================================\u001b[1m Ai Message \u001b[0m==================================\n", "Tool Calls:\n", " wikipedia (call_p19b)\n", " Call ID: call_p19b\n", " Args:\n", " query: RLHF\n", "=================================\u001b[1m Tool Message \u001b[0m=================================\n", "Name: wikipedia\n", "\n", "Page: Reinforcement learning from human feedback\n", "Summary: In machine learning, reinforcement learning from human feedback (RLHF) is a technique to align an agent with human preferences. It involves training a reward model to represent preferences previously gathered from a sample of humans, which ca\n", "==================================\u001b[1m Ai Message \u001b[0m==================================\n", "\n", "Reinforcement learning from human feedback (RLHF) is a technique to align an agent with human preferences. It involves training a reward model to represent preferences previously gathered from a sample of humans, which can then be used to train a reinforcement learning agent.\n" ] } ] } ] }