{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "gather": { "logged": 1695395317315 }, "id": "RRYSu48huSUW" }, "outputs": [], "source": [ "pip -q install -U langchain huggingface_hub tiktoken PyPDF2 pypdf sentence_transformers together FlagEmbedding faiss-gpu openai text-generation pymupdf" ] }, { "cell_type": "markdown", "metadata": { "id": "gvIjaK53dP5l" }, "source": [ "## RetrievalQA with LLaMA 2-70B on Together API" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "gather": { "logged": 1695395317576 }, "id": "dNA4TsHpu6OM" }, "outputs": [], "source": [ "import os\n", "\n", "os.environ[\"TOGETHER_API_KEY\"] = \"53b21bdab47f250b23da974391f9c0e7fb07ec242aec6c1e17d329c931edfa38\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "J-KFB7J_u_3L", "outputId": "1f31d8b5-5491-47bc-8342-b4f48aa70a9e" }, "outputs": [], "source": [ "pip install asyncpg" ] }, { "cell_type": "markdown", "metadata": { "id": "HqwsGJDhvAQ5" }, "source": [ "# Setting up Together API\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "gather": { "logged": 1695395321792 }, "id": "B3pqftc7nacA" }, "outputs": [], "source": [ "import together\n", "\n", "# set your API key\n", "together.api_key = os.environ[\"TOGETHER_API_KEY\"]\n", "\n", "# list available models and descriptons\n", "# models = together.Models.list()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "gather": { "logged": 1695395322559 }, "id": "mdFedq669R1D", "outputId": "8a5fc3d6-57a6-48af-eb96-058dea32dd70" }, "outputs": [], "source": [ "# together.Models.start(\"togethercomputer/llama-2-70b-chat\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "gather": { "logged": 1695395325479 }, "id": "RgbLVmf-o4j7" }, "outputs": [], "source": [ "import together\n", "\n", "import logging\n", "from typing import Any, Dict, List, Mapping, Optional\n", "\n", "from pydantic import Extra, Field, root_validator\n", "\n", "from langchain.callbacks.manager import CallbackManagerForLLMRun\n", "from langchain.llms.base import LLM\n", "from langchain.llms.utils import enforce_stop_tokens\n", "from langchain.utils import get_from_dict_or_env\n", "\n", "\n", "from gradio_client import Client\n", "\n", "client = Client(\"https://073695670dbd200693.gradio.live/\")\n", "\n", "class TogetherLLM(LLM):\n", " \"\"\"Together large language models.\"\"\"\n", "\n", " model: str = \"togethercomputer/llama-2-70b-chat\"\n", " \"\"\"model endpoint to use\"\"\"\n", "\n", " together_api_key: str = os.environ[\"TOGETHER_API_KEY\"]\n", " \"\"\"Together API key\"\"\"\n", "\n", " temperature: float = 0.7\n", " \"\"\"What sampling temperature to use.\"\"\"\n", "\n", " max_tokens: int = 512\n", " \"\"\"The maximum number of tokens to generate in the completion.\"\"\"\n", "\n", " class Config:\n", " extra = Extra.forbid\n", "\n", " @root_validator()\n", " def validate_environment(cls, values: Dict) -> Dict:\n", " \"\"\"Validate that the API key is set.\"\"\"\n", " api_key = get_from_dict_or_env(\n", " values, \"together_api_key\", \"TOGETHER_API_KEY\"\n", " )\n", " values[\"together_api_key\"] = api_key\n", " return values\n", "\n", " @property\n", " def _llm_type(self) -> str:\n", " \"\"\"Return type of LLM.\"\"\"\n", " return \"together\"\n", "\n", " def _call(\n", " self,\n", " prompt: str,\n", " **kwargs: Any,\n", " ) -> str:\n", " \"\"\"Call to Together endpoint.\"\"\"\n", " # together.api_key = self.together_api_key\n", " # output = together.Complete.create(prompt,\n", " # model=self.model,\n", " # max_tokens=self.max_tokens,\n", " # temperature=self.temperature,\n", " # )\n", " # text = output['output']['choices'][0]['text']\n", " # return text\n", " print(prompt)\n", " result = client.predict(\n", "\t\t\t\tprompt,\t# str in 'Question' Textbox component\n", "\t\t\t\t0.95,\t# int | float (numeric value between 0.05 and 1.0)\n", "\t\t\t\tself.temperature,\t# int | float (numeric value between 0.1 and 1.0)\n", "\t\t\t\t50,\t# int | float (numeric value between 1 and 50)\n", "\t\t\t\t300,\t\n", "\t\t\t\tfn_index=0\n", " )\n", " return result" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "gather": { "logged": 1692868876070 }, "id": "ZlQzln_PRonn", "outputId": "7a2b8ad7-f392-49fe-af3d-e620c4b0bf02" }, "outputs": [], "source": [ "\n", "# !wget -O new_papers_2.zip https://www.dropbox.com/scl/fi/67a80h373n1z38088c9fb/new_papers_2.zip?rlkey=1azfz3w5aazd24ihotwzmol2j&dl=1\n", "# !unzip -q new_papers_2.zip -d new_papers" ] }, { "cell_type": "markdown", "metadata": { "id": "7AnZQpL_IZZZ" }, "source": [ "# LangChain multi-doc retriever with ChromaDB\n", "\n", "***Key Points***\n", "- Multiple Files - PDFs\n", "- ChromaDB\n", "- LLaMA-2 LLM\n", "- BGE Embeddings\n" ] }, { "cell_type": "markdown", "metadata": { "id": "fgfdhZ5uRpFn" }, "source": [ "## Setting up LangChain\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "gather": { "logged": 1692868876936 }, "id": "Y_2-HBI3RpFn" }, "outputs": [], "source": [ "import os" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "gather": { "logged": 1692870096658 }, "id": "XHVE9uFb3Ajj", "outputId": "fea94568-9e6c-459e-8918-8f2b4f297f6e" }, "outputs": [], "source": [ "# from langchain.vectorstores import Chroma\n", "from langchain.vectorstores import FAISS\n", "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", "\n", "from langchain.chains import RetrievalQA\n", "from langchain.document_loaders import TextLoader\n", "from langchain.document_loaders import PyPDFLoader\n", "from langchain.document_loaders import DirectoryLoader\n", "\n", "\n", "# from InstructorEmbedding import INSTRUCTOR\n", "# from langchain.embeddings import HuggingFaceInstructEmbeddings" ] }, { "cell_type": "markdown", "metadata": { "id": "9UcQKUId3X2M" }, "source": [ "## Load multiple and process documents" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "gather": { "logged": 1695396081422 }, "id": "PRSeXXc_3Ypj" }, "outputs": [], "source": [ "# # Load and process the text files\n", "# # loader = TextLoader('single_text_file.txt')\n", "# loader = DirectoryLoader('votum-ml/pdfs/', glob=\"./*.pdf\", loader_cls=PyPDFLoader)\n", "\n", "# documents = loader.load()\n", "\n", "\n", "import asyncio\n", "import asyncpg\n", "\n", "\n", "\n", "\n", "conn = await asyncpg.connect(host=\"legalscraperserver.postgres.database.azure.com\",\n", " database=\"postgres\",\n", " user=\"tejasw\",\n", " password=\"Password1234\",\n", " port=5432,)\n", "row = await conn.fetch(\n", " '''SELECT sections, act_name, text\n", "FROM acts\n", "WHERE sections IS NOT NULL''')\n", "\n", "# AND(act_name LIKE '%The Indian Penal Code, 1860%' OR act_name LIKE '%The Code of Criminal Procedure, 1973%' OR act_name LIKE '%Motor Vehicles Act%')\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "gather": { "logged": 1695396160144 }, "id": "vT6KgAIT_BtB", "outputId": "281aa4cb-27c3-46bf-ad2e-7c43e9dc76e4" }, "outputs": [], "source": [ "import json\n", "import re\n", "\n", "\n", "data = []\n", "\n", "\n", "def remove_between_periods(sentence):\n", " # Define a regular expression pattern to match text between two periods\n", " pattern = r'\\.(.*?)\\.'\n", "\n", " # Use re.sub to replace the matched substring with an empty string\n", " modified_sentence = re.sub(pattern, '.', sentence)\n", "\n", " return modified_sentence\n", "\n", "def preprocess_text(text):\n", " \n", " text = text.lower()\n", " \n", " # Remove URLs using regex\n", " text = re.sub(r'http\\S+|www\\S+|https\\S+', '', text)\n", "\n", " # Remove phone numbers (matches formats like +1234567890, 123-456-7890, (123) 456-7890, and more)\n", " text = re.sub(r'\\+?\\d{1,4}[-\\s]?\\(?\\d{1,3}\\)?[-\\s]?\\d{1,4}[-\\s]?\\d{1,4}', '', text)\n", "\n", " # Remove special characters and unwanted sequences (e.g., /xa), except ',' and ':'\n", " text = re.sub(r'[^a-zA-Z0-9\\s,/:\\.]|[\\xa0]', '', text)\n", " \n", " text = text.replace('tweet','')\n", "\n", " # Remove extra whitespace\n", " text = ' '.join(text.split())\n", "\n", " return text\n", "\n", "for act in row:\n", " for section in act['sections']:\n", " print(section)\n", " json_data = json.loads(section)\n", " if 'omitted.' in json_data['section_name'].lower():\n", " continue\n", " json_data['section_name'] = f\"{remove_between_periods(json_data['section_name'])} of {act['act_name'].replace(',','')}\"\n", " d = json_data['section_name'] + ' : ' + json_data['text']\n", " data.append(preprocess_text(d))\n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "gather": { "logged": 1695396204431 }, "jupyter": { "outputs_hidden": false, "source_hidden": false }, "nteract": { "transient": { "deleting": false } } }, "outputs": [], "source": [ "len(data)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "gather": { "logged": 1692868887177 }, "id": "3__nT0D4Fkmg", "outputId": "d77220d1-04d8-41a9-df8d-36c6e61bf1ab" }, "outputs": [], "source": [ "#splitting the text into\n", "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n", "\n", "texts = [text_splitter.split_text(d)[0] for d in data]\n", "\n", "len(texts)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "gather": { "logged": 1692868888971 }, "id": "mi9VSazP0RSN", "outputId": "c15cd59b-b8e5-4ed2-d2b9-7abe5f31806b" }, "outputs": [], "source": [ "texts[0]" ] }, { "cell_type": "markdown", "metadata": { "id": "fhs0C0FYASlM" }, "source": [ "## HF BGE Embeddings" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 401, "referenced_widgets": [ "72336b98bb664e598f8da434dea12bcf", "06cb9e36eb3e440aa62fa3865a0bf7cb", "1e968f495d78483ab22002cf46e7599a", "65a19b80971f4f16ae486e0b33a920d9", "e99f15fea33a4bcfb98bb21e581e77f7", "f54192aed0d8415f85a5a32cb18548ad", "0131eff774774716bb6003568076f496", "6268fe40a4b44006a43f23c48b4fd73b", "448242d61da2461abcede77139f0b740", "8d7cae1057ef41e38b8d3bd8d71127b4", "b23961b8865f44e685351b732acfd160", "8811e27cb7b24fe7a30bbef63c8b034d", "120abc9282ac4eb099153d368c81b2f3", "dd39941179714219a4fbd70898e77c36", "15efa95fc5904f41a22691b44ef8e884", "3a907db6f66941419d5d8619cd133624", "67b402cc9e3a41fc881c62764bf0b952", "3c0e004a5c5e4308a7720c779ca2c3c0", "8c53a2d79f2f4c38b47718c614707250", "65e13b52d29c4d08bafba8bda728bd5f", "11c7410fb7a64903beaacf78aed04247", "35b83316c5fa4bf589af3130e002dabe", "599e033fa98f44819af4c2d7048fbfb4", "8f60547b75a34645ad6f02f610c1ac31", "c4ca4c02ad0e451dae2cce9c2a26ec8d", "f0bc30cc5e1d4f5a869800ac835a6943", "6bfa3a255e8f4b799219b76cd6dfed0b", "db0209e961d64aba9d4c3a8991de2016", "f95edd8ea46a486dadae84f43f316eb6", "43aebf595fc0440c9500f0315f88beaa", "58002026e9ff43ed94d85bae003d2e84", "70ecd38ec9664d5f824589a02e5cfb13", "54e87d4e5d1c4d5fbb16a92270ae6c54", "1a4810e47dbf451b8a6d3ab2c816bf64", "a6ff33d70a3c401d8010d3a8f177bf82", "673c6cdc933542fc9f99cffd106ffee4", "3fea5bdf46ad46779579d51cd71a6ea7", "74b14aa171d042fe91c86cd67c150d91", "bc1b2a86311b4f21b284a175b104efe0", "9b805c8d7629453bb83ce2044bea26b0", "1e001fb312664979b6187c5c85ebee7c", "df82a6c3b7c14698b50360b440667698", "60f2eb478ee84fd8b0bcc35abd1c9179", "37421eef29c54855b289cebf13143b09", "05c10e5d60174493af2c49dd0cc55bcc", "9a963eee8e01456a9c6ff820fc9a1394", "444abec95fc340c58a63df8a8e71c1d7", "58ff9c1ea7be4ed4900ef448fe88b6cc", "9f3eb6593c4d4bfeb6d7a8f503f1d03c", "ba685441301d43a3949b99927221c2b0", "83386ff94da24656a1ae65471b55f312", "86eb2d64cbe6467180ab51a9e1b88c97", "d24ee1d8b86747748e5e737ad0b459b6", "2395734dcfc34fc681327354beb3a86a", "0846f8c2f3544519a51f06a903fdb912", "b9935d3733ab4538a9c6c098808bca91", "fd050344a8b7417294272972622b1e50", "5a39b11a60c246d3a18ae934055528cd", "a4066ca4095649049bdb4607dbb14cb5", "3b0fb9694f604e1e8d275e517a61d2f2", "7906c529005c4a159403444bb8e4df95", "f5ab1675368e483787ff2da9050ff2d5", "f235594eb8904996ba571ac9fef56186", "054a1877685942f8a9b0d19f0e40ea09", "1b48c1cdb6914bb19074270dab4e175f", "9a19badd95594721b3f5fef44bd3d709", "ecb2e4310050497ca02326ff23f4e67a", "377dbea85b2447208a72bf4256a974b0", "1d45e345641e4cb0a7eb54aeb455cc07", "7149671d48db4dddaf9ccd8d2dea5d80", "3f9c959a4ae24605abe9f182a4980b42", "a689fc2cfdbd44cb8b8efc9a104530e7", "0ded063225bd4930b8c3a84ac0212bed", "d12e11cb3dc54b3fa80c6f744d4eb816", "1aa15e1bdd8242dcafe1c44898e167cc", "6c7f23dfac85466685361cc5bc6d2414", "b241b6209e034e33a24b8ba7eeba8285", "1c08a7e8bae84703a5272969e507ade2", "f97f4652b3964daabd7540e8c84db808", "0fbe66c557be4183815f350a5fc54cea", "c9243985fc5c4bcfb5e4782f91bf1f08", "da04d057103740a1840ac025db099ba6", "f6ceba2c8e944e07ac37335f6bc27775", "49823e682b3249ae83c79acf807eadca", "3b609ca6ab7e497891ed09246b19fd02", "7860f2ae650f406fbd2dbf5b0b035a7b", "f59159361af34c768bc5158ad49844e1", "85b1e48e37dd439da9ab97ae84e841ee", "aa1c0efe0b5646a89cb23b6642a0dbb4", "9fc0c24783a448c48385f9b3dfde2fbf", "49ad9ddc3dca4d1d96bc12a59764571f", "c5303b4931ea44519318b9bb209f634d", "51c09b1d9214446285e103ee9282e206", "83b683c844a549ae9b0a647dc87cd2bc", "62313c10697f482590fb4eee271540bf", "3cf6dc01e2a0428d840eebe1029bb8ce", "502c36fb431241d2ae3cf12807cd5f15", "94a73b26bf0144abb64f0cf4c64d385d", "44e4249c3f0e4100aa5bb3ca53084060", "c566fb3083984a5e894584249467ae4d", "959d049d809248e49f029b5389b22a90", "b72122ea1c3f4b568972d051e6ffa5a8", "0efdf5a21c674849bf8b2718cbb1cc09", "da5d4e9910114cdfb8d1700124db3c43", "4d22677d3f384744a8297a66f636f051", "5f176465351944c7b2bc28b91594428b", "2a565e6a2c314e649fad873b012815d9", "988ad9b09380430cbf39cd3abdfbd6ff", "1e30c2cf9def45bd8069dc09fbfe844a", "548aba40f7314690815320c348e4223b", "33a420fc6fa44e8db19c06451c624c4e", "b7378b19e6f3469482f141b0af6a8fa7", "c890dead5ddc45e0817338a9349fd74c", "ab8775594daf4028b475709a0f24a888", "306369c675c94a1e9d374f75b5952200", "aba560edd18940c4abe09c0fa7263bb1", "94eb8f0f65c44616a083c381614b6617", "ecd8be14e0cd44679787aeab059516bf", "2a56adb0d9084371b96fc9bb8d869f80", "2090392ba65446bbb53e7c6fb3261b0d", "d1090f09758d4e7a8b3047f6adb15733", "88d16f0c12824d698be908bed4efafc9", "5f9e237c28fa46b3bc82f3cf788a952d", "2114d8772db84490a20b9aa82642f44f", "cb38a38b867846ddae5b51176dd37ef9", "a0f6807767154e328dab28d7547bbfee", "53691fa1d8a348728d859395ff2313fa", "58231fd9a4f74188877e36047e5e17a9", "3c8ecbe8cea243fc8a709fa1100110d2", "81998912dbe2489abbbb84730c5890cc", "264f84496d4144ca97ce6d5aa584dc88", "2c97617f883f41e8961a4235712cc73c" ] }, "gather": { "logged": 1692868897098 }, "id": "Emj46ATxtV9C", "outputId": "169be147-f31e-4153-cf91-9b1cea6667be" }, "outputs": [], "source": [ "\n", "from langchain.embeddings import HuggingFaceBgeEmbeddings\n", "\n", "model_name = \"BAAI/bge-base-en\"\n", "encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity\n", "\n", "model_norm = HuggingFaceBgeEmbeddings(\n", " model_name=model_name,\n", " model_kwargs={'device': 'cpu'},\n", " encode_kwargs=encode_kwargs\n", ")\n" ] }, { "cell_type": "markdown", "metadata": { "id": "YsYsIy8F4cdm" }, "source": [ "## create the DB\n", "\n", " T4 GPU" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Q_eTIZwf4Dk2", "outputId": "57072a9e-bd8f-4296-b4e1-83c098f868c7" }, "outputs": [], "source": [ "%%time\n", "# Embed and store the texts\n", "# Supplying a persist_directory will store the embeddings on disk\n", "\n", "persist_directory = 'db'\n", "\n", "## Here is the nmew embeddings being used\n", "embedding = model_norm\n", "\n", "vectordb = FAISS.from_texts(texts,\n", " embedding=embedding)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# vectordb.save_local(\"faiss_index\")\n", "\n", "vectordb = FAISS.load_local('faiss_index',embeddings=model_norm)" ] }, { "cell_type": "markdown", "metadata": { "id": "siLXR-XT0JoI" }, "source": [ "## Make a retriever" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "gather": { "logged": 1692870223799 }, "id": "jVWgPJXs1yRq" }, "outputs": [], "source": [ "retriever = vectordb.as_retriever(search_type='similarity',search_kwargs={\"k\": 5})" ] }, { "cell_type": "markdown", "metadata": { "id": "4Ia-4OXa5IeP" }, "source": [ "## Make a chain" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import os\n", "import openai\n", "\n", "from langchain.chat_models import AzureChatOpenAI\n", "from langchain.schema import HumanMessage\n", "\n", "\n", "\n", "model = AzureChatOpenAI(\n", " openai_api_base=\"https://votum.openai.azure.com/\",\n", " openai_api_version= \"2023-07-01-preview\",\n", " # openai_api_version=\"2023-05-15\",\n", " openai_api_key=\"9ce18c180b8d43cb90568fd0ff6daefd\",\n", " openai_api_type=\"azure\",\n", " deployment_name='gpt-4'\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model(\n", " [\n", " HumanMessage(\n", " content=\"What model are you?\"\n", " )\n", " ]\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "gather": { "logged": 1692870227467 }, "id": "dCtX_DK9S-K0" }, "outputs": [], "source": [ "# llm = TogetherLLM(\n", "# model= \"togethercomputer/llama-2-70b-chat\",\n", "# temperature = 0.5,\n", "# max_tokens = 1024\n", "# )\n", "\n", "\n", "from langchain.llms import HuggingFaceTextGenInference\n", "\n", "llm = HuggingFaceTextGenInference(\n", " inference_server_url=\"http://20.83.177.108:8080/\",\n", " max_new_tokens=512,\n", " top_k=10,\n", " top_p=0.95,\n", " typical_p=0.95,\n", " temperature=0.6,\n", " # repetition_penalty=1.1,\n", ")\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "gather": { "logged": 1692870229949 }, "id": "MGx8XblM4shW" }, "outputs": [], "source": [ "\n", "\n", "from langchain.prompts import PromptTemplate\n", "\n", "\n", "prompt_template = \"\"\"You are an expert legal assistant with extensive knowledge about Indian law. Your task is to respond to the given query in a consice and factually correct manner. Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.\n", "\n", "{context}\n", "\n", "Question: {question}\n", "Response:\"\"\"\n", "\n", "\n", "PROMPT = PromptTemplate(\n", " template=prompt_template, input_variables=[\"context\", \"question\"]\n", ")\n", "\n", "qa_chain = RetrievalQA.from_chain_type(llm=llm,\n", " chain_type_kwargs={\"prompt\": PROMPT},\n", " retriever=retriever,\n", " return_source_documents=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "gather": { "logged": 1692873421219 }, "id": "wKfX4vX-5RFT", "outputId": "2a22808f-7c7e-4ccc-fcfd-026b0065c201" }, "outputs": [], "source": [ "import textwrap\n", "\n", "def wrap_text_preserve_newlines(text, width=110):\n", " # Split the input text into lines based on newline characters\n", " lines = text.split('\\n')\n", "\n", " # Wrap each line individually\n", " wrapped_lines = [textwrap.fill(line, width=width) for line in lines]\n", "\n", " # Join the wrapped lines back together using newline characters\n", " wrapped_text = '\\n'.join(wrapped_lines)\n", "\n", " return wrapped_text\n", "\n", "def process_llm_response(llm_response):\n", " # print(wrap_text_preserve_newlines(llm_response['result']))\n", " print(llm_response['result'])\n", " # print('\\n\\nSources:')\n", " # for source in llm_response[\"source_documents\"]:\n", " # print(source.metadata['source'])\n", "\n", "\n", "#, in case you can't find any relevant statutes respond with 'i don't know' rather than providing incorrect answer.\n", "query = \"\"\"{user_text}\"\"\"\n", "\n", " \n", "llm_response = qa_chain(query.format(user_text='How much alcohol can i legally consume before driving'))\n", "print(llm_response)\n", "# process_llm_response(llm_response)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pip install langchain openai" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from langchain.llms import OpenAI\n", "from langchain.agents import initialize_agent, Tool\n", "from langchain.agents import AgentType\n", "from langchain.agents.react.base import DocstoreExplorer\n", "\n", "\n", "docstore = DocstoreExplorer(vectordb)\n", "tools = [\n", " Tool(\n", " name=\"Search\",\n", " func=docstore.search,\n", " description=\"useful for when you need to ask with search\",\n", " ),\n", " Tool(\n", " name=\"Lookup\",\n", " func=docstore.lookup,\n", " description=\"useful for when you need to ask with lookup\",\n", " ),\n", "]\n", "\n", "import langchain\n", "langchain.verbose= True\n", "llm = OpenAI(temperature=0)\n", "\n", "react = initialize_agent(tools, model, agent=AgentType.REACT_DOCSTORE, verbose=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from langchain.agents import create_pandas_dataframe_agent\n", "from langchain.chat_models import ChatOpenAI\n", "from langchain.agents.agent_types import AgentType\n", "from langchain.tools import tool\n", "import pandas as pd\n", "\n", "html = \"\"\"\n", "
Financial YearCost Inflation Index (CII)
2001-02 (Base year)100
2002-03105
2003-04109
2004-05113
2005-06117
2006-07122
2007-08129
2008-09137
2009-10148
2010-11167
2011-12184
2012-13200
2013-14220
2014-15240
2015-16254
2016-17264
2017-18272
2018-19280
2019-20289
2020-21301
2021-22317
2022-23331
2023-24348
\n", "\"\"\"\n", "\n", "df = pd.read_html(html)[0]\n", "\n", "\n", "@tool\n", "def read_cii(query: str) -> str:\n", " \"\"\"Search for latest indexation value for the given query year.\"\"\"\n", " df_agent = create_pandas_dataframe_agent(\n", " model, df, verbose=True)\n", " res = df_agent.run(f'what was the indexation in the year {query}')\n", " return res\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from langchain.llms import OpenAI\n", "from langchain.agents import initialize_agent, Tool\n", "from langchain.agents import AgentType\n", "from langchain.agents import load_tools\n", "import os\n", "from langchain.chat_models import AzureChatOpenAI\n", "from langchain.schema import HumanMessage\n", "import langchain\n", "\n", "\n", "langchain.verbose = True\n", "\n", "model = AzureChatOpenAI(\n", " openai_api_base=\"https://votum.openai.azure.com/\",\n", " openai_api_version=\"2023-07-01-preview\",\n", " # openai_api_version=\"2023-05-15\",\n", " openai_api_key=\"9ce18c180b8d43cb90568fd0ff6daefd\",\n", " openai_api_type=\"azure\",\n", " deployment_name='gpt-4'\n", ")\n", "\n", "\n", "os.environ[\"SERPAPI_API_KEY\"] = '94de7df75e512ca1fe42b3f51a034a0f0e4683e0f880f9cf7dee1a0eb36a069e'\n", "\n", "\n", "tools = load_tools([\"serpapi\", \"llm-math\"], llm=model)\n", "\n", "# ZERO_SHOT_REACT_DESCRIPTION ,CHAT_ZERO_SHOT_REACT_DESCRIPTION\n", "agent_executor = initialize_agent(\n", " [*tools], model, agent=AgentType.REACT_DOCSTORE, verbose=True)\n", "\n", "\n", "agent_executor.invoke(\n", " {\"input\": \"You are an expert Chartered Accountant from India. Answer the following.\\nMr X bought equity shares on 15th Dec, 2016 for Rs. 10,000. FMV of the shares was Rs. 12,000 as on 31st Jan, 18. He sold the shares on 10th May, 2018 for Rs. 15,000. What will be the long-term capital gain or loss?\"})\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "agent_executor = initialize_agent(tools, llm, agent=AgentType.REACT_DOCSTORE, verbose=True)\n", "agent_executor.invoke({\"input\": \"What is the punishment for drinking and driving?\"})" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "W4aLRBjEBOW9", "outputId": "64ddbae5-5b2b-4e01-8cc9-4699e431f0ac" }, "outputs": [], "source": [ "together.Models.stop(\"togethercomputer/llama-2-70b-chat\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "65Bmvfjk9MOf" }, "outputs": [], "source": [ "import re\n", "\n", "import numpy as np\n", "\n", "from langchain.schema import BaseRetriever\n", "from langchain.callbacks.manager import (\n", " AsyncCallbackManagerForRetrieverRun,\n", " CallbackManagerForRetrieverRun,\n", ")\n", "from langchain.utilities import GoogleSerperAPIWrapper\n", "from langchain.schema import Document\n", "from typing import Any, List\n", "\n", "\n", "\n", "\n", "\n", "class SerperSearchRetriever(BaseRetriever):\n", " search: GoogleSerperAPIWrapper = None\n", "\n", " def _get_relevant_documents(\n", " self, query: str, *, run_manager: CallbackManagerForRetrieverRun, **kwargs: Any\n", " ) -> List[Document]:\n", " return [Document(page_content=self.search.run(query))]\n", "\n", " async def _aget_relevant_documents(\n", " self,\n", " query: str,\n", " *,\n", " run_manager: AsyncCallbackManagerForRetrieverRun,\n", " **kwargs: Any,\n", " ) -> List[Document]:\n", " raise NotImplementedError()\n", "\n", "\n", "retriever = SerperSearchRetriever(search=GoogleSerperAPIWrapper())\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# We set this so we can see what exactly is going on\n", "from langchain.chains import FlareChain\n", "import langchain\n", "\n", "langchain.verbose = True\n", "\n", "os.environ['OPENAI_API_KEY'] = 'wd'\n", "\n", "\n", "flare = FlareChain.from_llm(\n", " llm=model,\n", " retriever=retriever,\n", " max_generation_len=164,\n", " min_prob=0.3,\n", ")\n", "\n", "\n", "query = \"explain in great detail the difference between the langchain framework and baby agi\"\n", "flare.run(query)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "os.environ[\"SERPER_API_KEY\"] = '94de7df75e512ca1fe42b3f51a034a0f0e4683e0f880f9cf7dee1a0eb36a069e'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from langchain.chat_models import ChatOpenAI\n", "import openai\n", "from langchain.adapters import openai as lc_openai\n", "import os\n", "\n", "from langchain.agents import initialize_agent, Tool\n", "from langchain.agents import AgentType\n", "from langchain.agents import load_tools\n", "\n", "openai.api_base = \"http://20.83.177.108:8080/v1\"\n", "openai.api_key = \"none\"\n", "\n", "\n", "\n", "# create a request activating streaming response\n", "# for chunk in openai.ChatCompletion.create(\n", "# model=\"Qwen\",\n", "# messages=[\n", "# {\"role\": \"user\", \"content\": \"Hi\"}\n", "# ],\n", "# stream=True\n", "# # Specifying stop words in streaming output format is not yet supported and is under development.\n", "# ):\n", "# if hasattr(chunk.choices[0].delta, \"content\"):\n", "# print(chunk.choices[0].delta.content, end=\"\", flush=True)\n", "\n", "\n", "# response = openai.ChatCompletion.create(\n", " # openai_api_base='http://20.83.177.108:8080/v1',\n", " # openai_api_key='none',\n", "# model=\"Qwen\",\n", "# messages=[\n", "# {\"role\": \"user\", \"content\": \"Hi\"}\n", "# ],\n", "# stream=False,\n", "# # You can add custom stop words here, e.g., stop=[\"Observation:\"] for ReAct prompting.\n", "# stop=[]\n", "# )\n", "# print(response.choices[0].message.content)\n", "\n", "\n", "os.environ[\"SERPAPI_API_KEY\"] = '94de7df75e512ca1fe42b3f51a034a0f0e4683e0f880f9cf7dee1a0eb36a069e'\n", "llm = ChatOpenAI(openai_api_base='http://20.83.177.108:8080/v1',\n", " openai_api_key='none',)\n", "\n", "tools = load_tools([\"serpapi\", \"llm-math\"], llm=llm)\n", "\n", "agent_executor = initialize_agent(\n", " [*tools], llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)\n", "\n", "\n", "agent_executor.invoke(\n", " {\"input\": \"?\"})\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from langchain.chat_models import ChatOpenAI\n", "from langchain_experimental.plan_and_execute import PlanAndExecute, load_agent_executor, load_chat_planner\n", "from langchain.llms import OpenAI\n", "from langchain.utilities import SerpAPIWrapper\n", "from langchain.agents.tools import Tool\n", "from langchain.chains import LLMMathChain\n", "from langchain.agents import load_tools\n", "\n", "import os\n", "\n", "os.environ[\"SERPAPI_API_KEY\"] = '94de7df75e512ca1fe42b3f51a034a0f0e4683e0f880f9cf7dee1a0eb36a069e'\n", "tools = load_tools([\"serpapi\", \"llm-math\"], llm=model)\n", "planner = load_chat_planner(model)\n", "executor = load_agent_executor(model, tools, verbose=True)\n", "plan_agent = PlanAndExecute(planner=planner, executor=executor, verbose=True)\n", "\n", "\n", "plan_agent.run(\n", " 'Plan a 5 day trip for me to Paris, My budget is $2000. I would prefer 4 star hotel and minimal traveling. I live in Delhi, India,')\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "T4", "machine_shape": "hm", "provenance": [] }, "kernel_info": { "name": "python3" }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.2" }, "microsoft": { "host": { "AzureML": { "notebookHasBeenCompleted": true } }, "ms_spell_check": { "ms_spell_check_language": "en" } }, "nteract": { "version": "nteract-front-end@1.0.0" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "0131eff774774716bb6003568076f496": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "DescriptionStyleModel", 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