autogpt-codebase.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import getpass\n",
    "\n",
    "from langchain.embeddings.openai import OpenAIEmbeddings\n",
    "from langchain.vectorstores import DeepLake\n",
    "\n",
    "os.environ['OPENAI_API_KEY'] = \"API KEY HERE\"\n",
    "os.environ['ACTIVELOOP_TOKEN'] = \"API KEY HERE\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "embeddings = OpenAIEmbeddings(disallowed_special=())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cloning into 'Auto-GPT'...\n",
      "remote: Enumerating objects: 10997, done.\u001b[K\n",
      "remote: Counting objects: 100% (258/258), done.\u001b[K\n",
      "remote: Compressing objects: 100% (202/202), done.\u001b[K\n",
      "remote: Total 10997 (delta 102), reused 169 (delta 54), pack-reused 10739\u001b[K\n",
      "Receiving objects: 100% (10997/10997), 6.61 MiB | 12.89 MiB/s, done.\n",
      "Resolving deltas: 100% (7342/7342), done.\n"
     ]
    }
   ],
   "source": [
    "!git clone https://github.com/Significant-Gravitas/Auto-GPT # replace any repository of your choice "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from langchain.document_loaders import TextLoader\n",
    "\n",
    "root_dir = './Auto-GPT'\n",
    "docs = []\n",
    "for dirpath, dirnames, filenames in os.walk(root_dir):\n",
    "    for file in filenames:\n",
    "        try: \n",
    "            loader = TextLoader(os.path.join(dirpath, file), encoding='utf-8')\n",
    "            docs.extend(loader.load_and_split())\n",
    "        except Exception as e: \n",
    "            pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Created a chunk of size 2329, which is longer than the specified 1000\n",
      "Created a chunk of size 1827, which is longer than the specified 1000\n",
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      "Created a chunk of size 1540, which is longer than the specified 1000\n"
     ]
    }
   ],
   "source": [
    "from langchain.text_splitter import CharacterTextSplitter\n",
    "\n",
    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
    "texts = text_splitter.split_documents(docs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your Deep Lake dataset has been successfully created!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "-"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This dataset can be visualized in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/rihp/auto-gpt\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " \r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hub://rihp/auto-gpt loaded successfully.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Evaluating ingest: 100%|██████████| 1/1 [00:31<00:00\n",
      "|"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset(path='hub://rihp/auto-gpt', tensors=['embedding', 'ids', 'metadata', 'text'])\n",
      "\n",
      "  tensor     htype      shape      dtype  compression\n",
      "  -------   -------    -------    -------  ------- \n",
      " embedding  generic  (920, 1536)  float32   None   \n",
      "    ids      text     (920, 1)      str     None   \n",
      " metadata    json     (920, 1)      str     None   \n",
      "   text      text     (920, 1)      str     None   \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " \r"
     ]
    },
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       " '9cb71592-f1dc-11ed-804a-acde48001122']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "username = \"rihp\" # replace with your username from app.activeloop.ai\n",
    "db = DeepLake(dataset_path=f\"hub://{username}/auto-gpt\", embedding_function=embeddings, public=True) #dataset would be publicly available\n",
    "db.add_documents(texts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This dataset can be visualized in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/rihp/auto-gpt\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hub://rihp/auto-gpt loaded successfully.\n",
      "\n",
      "Deep Lake Dataset in hub://rihp/auto-gpt already exists, loading from the storage\n",
      "Dataset(path='hub://rihp/auto-gpt', read_only=True, tensors=['embedding', 'ids', 'metadata', 'text'])\n",
      "\n",
      "  tensor     htype      shape      dtype  compression\n",
      "  -------   -------    -------    -------  ------- \n",
      " embedding  generic  (920, 1536)  float32   None   \n",
      "    ids      text     (920, 1)      str     None   \n",
      " metadata    json     (920, 1)      str     None   \n",
      "   text      text     (920, 1)      str     None   \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " \r"
     ]
    }
   ],
   "source": [
    "db = DeepLake(dataset_path=\"hub://rihp/auto-gpt\", read_only=True, embedding_function=embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "retriever = db.as_retriever()\n",
    "retriever.search_kwargs['distance_metric'] = 'cos'\n",
    "retriever.search_kwargs['fetch_k'] = 100\n",
    "retriever.search_kwargs['maximal_marginal_relevance'] = True\n",
    "retriever.search_kwargs['k'] = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter(x):\n",
    "    # filter based on source code\n",
    "    if 'com.google' in x['text'].data()['value']:\n",
    "        return False\n",
    "    \n",
    "    # filter based on path e.g. extension\n",
    "    metadata =  x['metadata'].data()['value']\n",
    "    return 'scala' in metadata['source'] or 'py' in metadata['source']\n",
    "\n",
    "### turn on below for custom filtering\n",
    "# retriever.search_kwargs['filter'] = filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.chat_models import ChatOpenAI\n",
    "from langchain.chains import ConversationalRetrievalChain\n",
    "\n",
    "model = ChatOpenAI(model_name='gpt-3.5-turbo') # switch to 'gpt-4'\n",
    "qa = ConversationalRetrievalChain.from_llm(model, retriever=retriever)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> **Question**: What are the agent modules doing and how can they be improved? \n",
      "\n",
      "**Answer**: I'm sorry, I don't have enough information to answer your question. Can you please provide more context or specify which agent modules you are referring to? \n",
      "\n",
      "-> **Question**: How can we improve the tests? \n",
      "\n",
      "**Answer**: Certainly! The context provided was related to testing for the Auto-GPT system. Specifically, it mentions a test called \"goal_oriented\" which is a part of the integration testing suite. The suggestion given is to browse the repository for issues that can be fixed and to run the aforementioned command to determine if the failure is due to a change in the prompt generation or if the Auto-GPT system itself is broken. Does this help? \n",
      "\n",
      "-> **Question**: How can we make more difficult challenges for the bot? \n",
      "\n",
      "**Answer**: To make more difficult challenges for the bot, you could consider:\n",
      "\n",
      "1. Increasing the complexity of the tasks: Make the tasks more complex by adding more steps, more variables, or more layers of instructions.\n",
      "\n",
      "2. Introducing noise and ambiguity: Add noise to the input data or instructions to test the bot's ability to filter out irrelevant information. You could also add ambiguous instructions or information that requires the bot to interpret and make decisions.\n",
      "\n",
      "3. Testing the bot's ability to generalize: Test the bot's ability to generalize by introducing new or unfamiliar scenarios that it has not encountered before.\n",
      "\n",
      "4. Incorporating real-world scenarios: Create challenges that simulate real-world scenarios and test the bot's ability to handle them. For example, you could create a challenge that involves booking a flight or making a hotel reservation.\n",
      "\n",
      "5. Combining different types of challenges: Combine memory challenges with information retrieval challenges or other types of challenges to create more complex and integrated tasks. This will test the bot's ability to handle multiple types of information and tasks at once. \n",
      "\n"
     ]
    }
   ],
   "source": [
    "questions = [\n",
    "    \"What are the agent modules doing and how can they be improved?\",\n",
    "    \"How can we improve the tests?\",\n",
    "    \"How can we make more difficult challenges for the bot?\" ,  \n",
    "] \n",
    "chat_history = []\n",
    "\n",
    "for question in questions:  \n",
    "    result = qa({\"question\": question, \"chat_history\": chat_history})\n",
    "    chat_history.append((question, result['answer']))\n",
    "    print(f\"-> **Question**: {question} \\n\")\n",
    "    print(f\"**Answer**: {result['answer']} \\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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