{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Uncommend and run if dependencies are not installed" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "8vjlmdIJddRp", "outputId": "0f6a5b73-2ca2-447f-a292-b19b7d8329ea" }, "outputs": [], "source": [ "# !pip install pypdf\n", "# !pip install dotenv\n", "# !pip install -q streamlit\n", "# %pip install -qU langchain-groq\n", "# !pip install -U langchain-community" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import os\n", "import sys\n", "import openai\n", "import requests\n", "from langchain_groq import ChatGroq\n", "from langchain.chains import RetrievalQA\n", "from langchain.vectorstores import Chroma\n", "from langchain.prompts import PromptTemplate\n", "from langchain.embeddings import HuggingFaceEmbeddings\n", "from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "loHEfU3DMTIh" }, "outputs": [], "source": [ "sys.path.append('../..')\n", "\n", "from dotenv import load_dotenv, find_dotenv\n", "_ = load_dotenv(find_dotenv()) # read local .env file\n", "openai.api_key = \"YOUR OPENAI_API_KEY\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "os.environ[\"GROQ_API_KEY\"] = \"YOUR GROQ_API_KEY\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "id": "9fIcVP9VkY0L" }, "outputs": [], "source": [ "from langchain.document_loaders import PyPDFLoader" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "id": "yXJbveaAeIDb" }, "outputs": [], "source": [ "def download_arxiv(arxiv_id, save_path):\n", " url = f\"https://arxiv.org/pdf/{arxiv_id}.pdf\"\n", " response = requests.get(url)\n", " with open(save_path, \"wb\") as f:\n", " f.write(response.content)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "id": "46uB0lQahdww" }, "outputs": [], "source": [ "download_arxiv(2211.10381, \"2211.10381.pdf\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "id": "SNRbQVe5hmFv" }, "outputs": [], "source": [ "loader = PyPDFLoader(\"2211.10381.pdf\")\n", "pages = loader.load()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "id": "IP_fVGzWiLvA" }, "outputs": [], "source": [ "combinae_pages = \"\\n\".join([p.page_content for p in pages])" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "id": "dYyGXFacaw-X" }, "outputs": [], "source": [ "text_splitter = RecursiveCharacterTextSplitter(\n", " chunk_size=1000,\n", " chunk_overlap=200,\n", " length_function=len,\n", " separators = [\"\\n\\n\", \"\\n\", \"(?<=\\.)\", \" \", \"\"]\n", ")" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "id": "yfXRsTM1bLjM" }, "outputs": [], "source": [ "docs = text_splitter.split_documents(pages)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 126 }, "id": "UaZ_UTNan3cZ", "outputId": "0c589076-32ea-45d3-d9eb-1073345579fe" }, "outputs": [ { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'Environmental Data Science (2023), xx: 1–22\\ndoi:10.1017/xxxx\\nRESEARCH ARTICLE\\nEnvironmental Sensor Placement with Convolutional Gaus-\\nsian Neural Processes\\nTom R. Andersson 1\\n *, Wessel P. Bruinsma 2, Stratis Markou 3, James Requeima 4, Alejandro Coca-\\nCastro5, Anna Vaughan 3, Anna-Louise Ellis 6, Matthew A. Lazzara 7,8, Dani Jones †1, J. Scott\\nHosking†1,5 and Richard E. Turner†2,3\\n1British Antarctic Survey, NERC, UKRI\\n2Microsoft Research AI4Science\\n3University of Cambridge\\n4Vector Institute\\n5The Alan Turing Institute\\n6Met Office\\n7University of Wisconsin-Madison\\n8Madison Area Technical College\\n†\\nJoint senior authors\\n*Corresponding author. Email: tomand@bas.ac.uk\\nReceived: 1 February 2023; Revised: 29 March 2023; Accepted: 05 May 2023\\nKeywords: sensor placement, neural processes, active learning, meta-learning\\nAbstract\\nEnvironmental sensors are crucial for monitoring weather conditions and the impacts of climate change. However,'" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "docs[0].page_content" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "id": "dNljVrFPt70m" }, "outputs": [], "source": [ "from sentence_transformers import SentenceTransformer" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 369, "referenced_widgets": [ "fbdc7b78c648497d9744dcfad695c81f", "006b62641bcf4c4cbf93a31b6588d978", "d91229ccb4c442a5a1c2a0a102146416", "96a94f9f40bd4ef0a7384abec2193793", "870b5a1747594eee9c33b7618c2f9bd9", "0c67dffa16094f27b5e348ea4f09ab0a", "5329702ba0d54551a6092a3a80b4a6d1", "349f74cc9d994fe8bf75ce9fcc409cc3", "7c356bcc7d79405a8773ec659fe80e3f", "6a45b3b3c5604b91a08b69865d8d3905", "075c067cdf0240b489ac5a1a21620f93", "a85253fb837b491bb4d038bbaa037d07", "48ecdb7831e0439b890137ecfa2afaf8", "889523fabc9a48a2b880c3c81547151d", "5da9634fcfdf4a2e999c650fb82f0d62", 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"a758d6e1857845b688b74d66ce9ecd2e", "b74f76068882466bb591a9820f6c4bc9", "f6c66698f8b24d2e8b38b158c5f35562", "a07a6c681584420caa430344e4bf7f52", "7c8cbe27a96f4891b4afa28bf736c1c7", "95397c1e8007478899c42369c6409991", "e8398b484dd547c2aa0bbfb96f0de999", "d5703f06d0ce4db9afb336bd5ec96c74", "d2cc2ebc20fe4ac7b1fc2baf90d28066", "40d9bdb433df4f89b7cdf66bcd118c4d", "b26d27697da942a29df036c8c794632e", "f9b0702df5114006b7d023bc59a95566", "5c251ba920ed427dad13283a65cc5a4c", "72b9e75e95d44b08ace2444d005e5b30", "d4caac8ca5fb4f2c8b7ad4c67b58bfea", "793d0d7da5334d8b98f722aac8df55fd", "2005d5a27efb465c966e3a6826e54919", "7ed37041f7514db99374101a85fa2447", "53cdf9a61ce64a78a1f5d83011170b8b", "337be473275e494d8c90cbe362911c31", "8282066f376748a5b2ce043b1cdca769", "71e228ea7a674ed69ef52e1acb54a75c", "7c934c84fc4d46028dabcf3041cfffb6", "4c5c018e87d74a138b4f49b3c3d277c9", "e832d7fc18e0488f8cc8147834f90e6d", "b40c29481e984c9db882c24ae3f98b45", "6d980a3a097a4cb4898c69c352f8fadf", 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"6e56cc8404dd44f1ad503e760917f71f", "6e07d988c4ad4176a91eea376ee888cd", "972892fdd1544ceb9686ad7abc4b8077", "8f1bf2c0658c4d5788cf3ebfac2f1dd7", "a7568675418b48638236f0c5efc02f90", "f1844810f7974753849f68187ccffe9d", "8f36633f527c407a9733238df0df6166", "c4e43eb822b74788a889e0b382a31dab", "8a1220fcb5b34c8dafbe106c87404632", "50436421abc840a3bd9fa32e02c7c60e", "793b93347a8c4e099171e272ab286d2d", "abf72e77691b48e99374343cc7881bc6", "e3a0e79dba3041b3bd334b155fab789a", "ea32f293e7624e749e48fdba558962c4", "42de21acf5d74170934c11a10312aee0", "4b713f30a62f421a88451afe266333c5", "4d497d18f80843dba69ea62563a464f6", "e5d5131e46d4483b807c8243323cf445", "2beb3cbdc3ad4d419c40a054cfffb36b", "e361e8ec1988435cb441cd9bf9cc1443", "701f5245d3544476aca0112fb60584dc", "177164f68e664004b394354b75360a43", "be5e7d82477a463caf6e3fd63e791b81", "c27dcb8135f141a7b06ed83dd5738247", "59eb19e2b56540508cedbab6b99afcd1" ] }, "id": "_pEwUnonypl2", "outputId": "fcfa57c6-07b9-4b0d-94c5-74b90df68576" }, "outputs": [], "source": [ "st_model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "id": "mvimkiQW44gu" }, "outputs": [], "source": [ "vectors = st_model.encode([i.page_content for i in docs], convert_to_numpy=True)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "QwmgwmcX5BCp", "outputId": "ced8e9a9-351b-4780-ba62-8faeb5987579" }, "outputs": [ { "data": { "text/plain": [ "(139, 384)" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vectors.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Create embeddings, store in vector DB, and query to find the most relevant documents" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "YKG7l8ZX528O", "outputId": "820573a8-ab55-4098-eea3-08f0aa4dbaa8" }, "outputs": [], "source": [ "from langchain.vectorstores import Chroma\n", "from langchain.embeddings import HuggingFaceEmbeddings\n", "from langchain.schema import Document\n", "\n", "embeddings = HuggingFaceEmbeddings(model_name=\"all-MiniLM-L6-v2\")\n", "\n", "vectordb = Chroma.from_documents(\n", " documents=docs,\n", " embedding=embeddings,\n", " persist_directory=\"./chroma_db\"\n", ")\n", "\n", "vectordb.persist()\n", "\n", "query = \"What is the JointMI acquisition function?\"\n", "results = vectordb.similarity_search_with_score(query, k=3)\n", "\n", "for doc, score in results:\n", " print(f\"Score: {score:.3f}\\n{doc.page_content}\\n---\\n\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Check how LLM works" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "vj3P9BB3tacV" }, "outputs": [], "source": [ "from langchain_groq import ChatGroq\n", "\n", "llm = ChatGroq(\n", " model=\"llama-3.1-8b-instant\",\n", " temperature=0,\n", " max_tokens=None,\n", " timeout=None,\n", " max_retries=2,\n", ")\n", "\n", "messages = [\n", " (\n", " \"system\",\n", " \"You are a helpful assistant that gives the answer to the question given the context\",\n", " ),\n", " (\"human\", \"I love programming.\"),\n", "]\n", "ai_msg = llm.invoke(messages)\n", "ai_msg" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Create chatbot" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "yg96XIZ55J-g", "outputId": "427bbc94-2399-492d-d21f-9f553d92e643" }, "outputs": [], "source": [ "retriever = vectordb.as_retriever(search_kwargs={\"k\": 3})\n", "\n", "# Step 5: Instantiate ChatGroq LLM\n", "llm = ChatGroq(\n", " model=\"llama-3.1-8b-instant\",\n", " temperature=0.2,\n", " max_tokens=512,\n", ")\n", "\n", "# Step 6: Optional custom prompt (can use defaults too)\n", "prompt_template = PromptTemplate.from_template(\"\"\"\n", "You are a research assistant. Use the context below to answer the question.\n", "If the answer is not in the context, say \"I don't know.\"\n", "\n", "Context:\n", "{context}\n", "\n", "Question:\n", "{question}\n", "\"\"\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "qa_chain = RetrievalQA.from_chain_type(\n", " llm=llm,\n", " retriever=retriever,\n", " chain_type=\"stuff\", # or use \"map_reduce\" if context is long\n", " return_source_documents=True,\n", " chain_type_kwargs={\"prompt\": prompt_template}\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "query = \"What is the JointMI acquisition function?\"\n", "result = qa_chain(query)\n", "\n", "print(\"Answer:\", result[\"result\"])\n", "print(\"\\n Sources:\\n\")\n", "for doc in result[\"source_documents\"]:\n", " print(doc.page_content[:300], \"\\n---\\n\")" ] } ], "metadata": { "colab": { "provenance": [] }, "kernelspec": { "display_name": 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