cookbook/code-analysis-deeplake.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Use LangChain, GPT and Activeloop's Deep Lake to work with code base\n",
    "In this tutorial, we are going to use Langchain + Activeloop's Deep Lake with GPT to analyze the code base of the LangChain itself. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Design"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Prepare data:\n",
    "   1. Upload all python project files using the `langchain_community.document_loaders.TextLoader`. We will call these files the **documents**.\n",
    "   2. Split all documents to chunks using the `langchain_text_splitters.CharacterTextSplitter`.\n",
    "   3. Embed chunks and upload them into the DeepLake using `langchain.embeddings.openai.OpenAIEmbeddings` and `langchain_community.vectorstores.DeepLake`\n",
    "2. Question-Answering:\n",
    "   1. Build a chain from `langchain.chat_models.ChatOpenAI` and `langchain.chains.ConversationalRetrievalChain`\n",
    "   2. Prepare questions.\n",
    "   3. Get answers running the chain.\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implementation"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Integration preparations"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to set up keys for external services and install necessary python libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#!python3 -m pip install --upgrade langchain langchain-deeplake openai"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up OpenAI embeddings, Deep Lake multi-modal vector store api and authenticate. \n",
    "\n",
    "For full documentation of Deep Lake please follow https://docs.activeloop.ai/ and API reference https://docs.deeplake.ai/en/latest/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from getpass import getpass\n",
    "\n",
    "if \"OPENAI_API_KEY\" not in os.environ:\n",
    "    os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
    "# Please manually enter OpenAI Key"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Authenticate into Deep Lake if you want to create your own dataset and publish it. You can get an API key from the platform at [app.activeloop.ai](https://app.activeloop.ai)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "activeloop_token = getpass(\"Activeloop Token:\")\n",
    "os.environ[\"ACTIVELOOP_TOKEN\"] = activeloop_token"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare data "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load all repository files. Here we assume this notebook is downloaded as the part of the langchain fork and we work with the python files of the `langchain` repo.\n",
    "\n",
    "If you want to use files from different repo, change `root_dir` to the root dir of your repo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CITATION.cff  MIGRATE.md  README.md  libs\t  poetry.toml\n",
      "LICENSE       Makefile\t  docs\t     poetry.lock  pyproject.toml\n"
     ]
    }
   ],
   "source": [
    "!ls \"../../../../../../libs\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2554\n"
     ]
    }
   ],
   "source": [
    "from langchain_community.document_loaders import TextLoader\n",
    "\n",
    "root_dir = \"../../../../../../libs\"\n",
    "\n",
    "docs = []\n",
    "for dirpath, dirnames, filenames in os.walk(root_dir):\n",
    "    for file in filenames:\n",
    "        if file.endswith(\".py\") and \"*venv/\" not in dirpath:\n",
    "            try:\n",
    "                loader = TextLoader(os.path.join(dirpath, file), encoding=\"utf-8\")\n",
    "                docs.extend(loader.load_and_split())\n",
    "            except Exception:\n",
    "                pass\n",
    "print(f\"{len(docs)}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, chunk the files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8244\n"
     ]
    }
   ],
   "source": [
    "from langchain_text_splitters import CharacterTextSplitter\n",
    "\n",
    "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
    "texts = text_splitter.split_documents(docs)\n",
    "print(f\"{len(texts)}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then embed chunks and upload them to the DeepLake.\n",
    "\n",
    "This can take several minutes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OpenAIEmbeddings(client=<class 'openai.api_resources.embedding.Embedding'>, model='text-embedding-ada-002', deployment='text-embedding-ada-002', openai_api_version='', openai_api_base='', openai_api_type='', openai_proxy='', embedding_ctx_length=8191, openai_api_key='', openai_organization='', allowed_special=set(), disallowed_special='all', chunk_size=1000, max_retries=6, request_timeout=None, headers=None, tiktoken_model_name=None, show_progress_bar=False, model_kwargs={})"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from langchain_openai import OpenAIEmbeddings\n",
    "\n",
    "embeddings = OpenAIEmbeddings()\n",
    "embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain_deeplake.vectorstores import DeeplakeVectorStore\n",
    "\n",
    "username = \"<USERNAME_OR_ORG>\"\n",
    "\n",
    "\n",
    "db = DeeplakeVectorStore.from_documents(\n",
    "    documents=texts,\n",
    "    embedding=embeddings,\n",
    "    dataset_path=f\"hub://{username}/langchain-code\",\n",
    "    overwrite=True,\n",
    ")\n",
    "db"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question Answering\n",
    "First load the dataset, construct the retriever, then construct the Conversational Chain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "db = DeeplakeVectorStore(\n",
    "    dataset_path=f\"hub://{username}/langchain-code\",\n",
    "    read_only=True,\n",
    "    embedding_function=embeddings,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "retriever = db.as_retriever()\n",
    "retriever.search_kwargs[\"distance_metric\"] = \"cos\"\n",
    "retriever.search_kwargs[\"fetch_k\"] = 20\n",
    "retriever.search_kwargs[\"maximal_marginal_relevance\"] = True\n",
    "retriever.search_kwargs[\"k\"] = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain.chains import ConversationalRetrievalChain\n",
    "from langchain_openai import ChatOpenAI\n",
    "\n",
    "model = ChatOpenAI(model=\"gpt-3.5-turbo-0613\")  # 'ada' 'gpt-3.5-turbo-0613' 'gpt-4',\n",
    "qa = RetrievalQA.from_llm(model, retriever=retriever)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-> **Question**: What is the class hierarchy? \n",
      "\n",
      "**Answer**: The class hierarchy for Memory is as follows:\n",
      "\n",
      "    BaseMemory --> BaseChatMemory --> <name>Memory  # Examples: ZepMemory, MotorheadMemory\n",
      "\n",
      "The class hierarchy for ChatMessageHistory is as follows:\n",
      "\n",
      "    BaseChatMessageHistory --> <name>ChatMessageHistory  # Example: ZepChatMessageHistory\n",
      "\n",
      "The class hierarchy for Prompt is as follows:\n",
      "\n",
      "    BasePromptTemplate --> PipelinePromptTemplate\n",
      "                           StringPromptTemplate --> PromptTemplate\n",
      "                                                    FewShotPromptTemplate\n",
      "                                                    FewShotPromptWithTemplates\n",
      "                           BaseChatPromptTemplate --> AutoGPTPrompt\n",
      "                                                      ChatPromptTemplate --> AgentScratchPadChatPromptTemplate\n",
      " \n",
      "\n",
      "-> **Question**: What classes are derived from the Chain class? \n",
      "\n",
      "**Answer**: The classes derived from the Chain class are:\n",
      "\n",
      "- APIChain\n",
      "- OpenAPIEndpointChain\n",
      "- AnalyzeDocumentChain\n",
      "- MapReduceDocumentsChain\n",
      "- MapRerankDocumentsChain\n",
      "- ReduceDocumentsChain\n",
      "- RefineDocumentsChain\n",
      "- StuffDocumentsChain\n",
      "- ConstitutionalChain\n",
      "- ConversationChain\n",
      "- ChatVectorDBChain\n",
      "- ConversationalRetrievalChain\n",
      "- FalkorDBQAChain\n",
      "- FlareChain\n",
      "- ArangoGraphQAChain\n",
      "- GraphQAChain\n",
      "- GraphCypherQAChain\n",
      "- HugeGraphQAChain\n",
      "- KuzuQAChain\n",
      "- NebulaGraphQAChain\n",
      "- NeptuneOpenCypherQAChain\n",
      "- GraphSparqlQAChain\n",
      "- HypotheticalDocumentEmbedder\n",
      "- LLMChain\n",
      "- LLMBashChain\n",
      "- LLMCheckerChain\n",
      "- LLMMathChain\n",
      "- LLMRequestsChain\n",
      "- LLMSummarizationCheckerChain\n",
      "- MapReduceChain\n",
      "- OpenAIModerationChain\n",
      "- NatBotChain\n",
      "- QAGenerationChain\n",
      "- QAWithSourcesChain\n",
      "- RetrievalQAWithSourcesChain\n",
      "- VectorDBQAWithSourcesChain\n",
      "- RetrievalQA\n",
      "- VectorDBQA\n",
      "- LLMRouterChain\n",
      "- MultiPromptChain\n",
      "- MultiRetrievalQAChain\n",
      "- MultiRouteChain\n",
      "- RouterChain\n",
      "- SequentialChain\n",
      "- SimpleSequentialChain\n",
      "- TransformChain\n",
      "- TaskPlaningChain\n",
      "- QueryChain\n",
      "- CPALChain\n",
      " \n",
      "\n",
      "-> **Question**: What kind of retrievers does LangChain have? \n",
      "\n",
      "**Answer**: The LangChain class includes various types of retrievers such as:\n",
      "\n",
      "- ArxivRetriever\n",
      "- AzureAISearchRetriever\n",
      "- BM25Retriever\n",
      "- ChaindeskRetriever\n",
      "- ChatGPTPluginRetriever\n",
      "- ContextualCompressionRetriever\n",
      "- DocArrayRetriever\n",
      "- ElasticSearchBM25Retriever\n",
      "- EnsembleRetriever\n",
      "- GoogleVertexAISearchRetriever\n",
      "- AmazonKendraRetriever\n",
      "- KNNRetriever\n",
      "- LlamaIndexGraphRetriever and LlamaIndexRetriever\n",
      "- MergerRetriever\n",
      "- MetalRetriever\n",
      "- MilvusRetriever\n",
      "- MultiQueryRetriever\n",
      "- ParentDocumentRetriever\n",
      "- PineconeHybridSearchRetriever\n",
      "- PubMedRetriever\n",
      "- RePhraseQueryRetriever\n",
      "- RemoteLangChainRetriever\n",
      "- SelfQueryRetriever\n",
      "- SVMRetriever\n",
      "- TFIDFRetriever\n",
      "- TimeWeightedVectorStoreRetriever\n",
      "- VespaRetriever\n",
      "- WeaviateHybridSearchRetriever\n",
      "- WebResearchRetriever\n",
      "- WikipediaRetriever\n",
      "- ZepRetriever\n",
      "- ZillizRetriever \n",
      "\n"
     ]
    }
   ],
   "source": [
    "questions = [\n",
    "    \"What is the class hierarchy?\",\n",
    "    \"What classes are derived from the Chain class?\",\n",
    "    \"What kind of retrievers does LangChain have?\",\n",
    "]\n",
    "chat_history = []\n",
    "qa_dict = {}\n",
    "\n",
    "for question in questions:\n",
    "    result = qa({\"question\": question, \"chat_history\": chat_history})\n",
    "    chat_history.append((question, result[\"answer\"]))\n",
    "    qa_dict[question] = result[\"answer\"]\n",
    "    print(f\"-> **Question**: {question} \\n\")\n",
    "    print(f\"**Answer**: {result['answer']} \\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'question': 'LangChain possesses a variety of retrievers including:\\n\\n1. ArxivRetriever\\n2. AzureAISearchRetriever\\n3. BM25Retriever\\n4. ChaindeskRetriever\\n5. ChatGPTPluginRetriever\\n6. ContextualCompressionRetriever\\n7. DocArrayRetriever\\n8. ElasticSearchBM25Retriever\\n9. EnsembleRetriever\\n10. GoogleVertexAISearchRetriever\\n11. AmazonKendraRetriever\\n12. KNNRetriever\\n13. LlamaIndexGraphRetriever\\n14. LlamaIndexRetriever\\n15. MergerRetriever\\n16. MetalRetriever\\n17. MilvusRetriever\\n18. MultiQueryRetriever\\n19. ParentDocumentRetriever\\n20. PineconeHybridSearchRetriever\\n21. PubMedRetriever\\n22. RePhraseQueryRetriever\\n23. RemoteLangChainRetriever\\n24. SelfQueryRetriever\\n25. SVMRetriever\\n26. TFIDFRetriever\\n27. TimeWeightedVectorStoreRetriever\\n28. VespaRetriever\\n29. WeaviateHybridSearchRetriever\\n30. WebResearchRetriever\\n31. WikipediaRetriever\\n32. ZepRetriever\\n33. ZillizRetriever\\n\\nIt also includes self query translators like:\\n\\n1. ChromaTranslator\\n2. DeepLakeTranslator\\n3. MyScaleTranslator\\n4. PineconeTranslator\\n5. QdrantTranslator\\n6. WeaviateTranslator\\n\\nAnd remote retrievers like:\\n\\n1. RemoteLangChainRetriever'}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qa_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The class hierarchy for Memory is as follows:\n",
      "\n",
      "    BaseMemory --> BaseChatMemory --> <name>Memory  # Examples: ZepMemory, MotorheadMemory\n",
      "\n",
      "The class hierarchy for ChatMessageHistory is as follows:\n",
      "\n",
      "    BaseChatMessageHistory --> <name>ChatMessageHistory  # Example: ZepChatMessageHistory\n",
      "\n",
      "The class hierarchy for Prompt is as follows:\n",
      "\n",
      "    BasePromptTemplate --> PipelinePromptTemplate\n",
      "                           StringPromptTemplate --> PromptTemplate\n",
      "                                                    FewShotPromptTemplate\n",
      "                                                    FewShotPromptWithTemplates\n",
      "                           BaseChatPromptTemplate --> AutoGPTPrompt\n",
      "                                                      ChatPromptTemplate --> AgentScratchPadChatPromptTemplate\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(qa_dict[\"What is the class hierarchy?\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The classes derived from the Chain class are:\n",
      "\n",
      "- APIChain\n",
      "- OpenAPIEndpointChain\n",
      "- AnalyzeDocumentChain\n",
      "- MapReduceDocumentsChain\n",
      "- MapRerankDocumentsChain\n",
      "- ReduceDocumentsChain\n",
      "- RefineDocumentsChain\n",
      "- StuffDocumentsChain\n",
      "- ConstitutionalChain\n",
      "- ConversationChain\n",
      "- ChatVectorDBChain\n",
      "- ConversationalRetrievalChain\n",
      "- FlareChain\n",
      "- ArangoGraphQAChain\n",
      "- GraphQAChain\n",
      "- GraphCypherQAChain\n",
      "- HugeGraphQAChain\n",
      "- KuzuQAChain\n",
      "- NebulaGraphQAChain\n",
      "- NeptuneOpenCypherQAChain\n",
      "- GraphSparqlQAChain\n",
      "- HypotheticalDocumentEmbedder\n",
      "- LLMChain\n",
      "- LLMBashChain\n",
      "- LLMCheckerChain\n",
      "- LLMMathChain\n",
      "- LLMRequestsChain\n",
      "- LLMSummarizationCheckerChain\n",
      "- MapReduceChain\n",
      "- OpenAIModerationChain\n",
      "- NatBotChain\n",
      "- QAGenerationChain\n",
      "- QAWithSourcesChain\n",
      "- RetrievalQAWithSourcesChain\n",
      "- VectorDBQAWithSourcesChain\n",
      "- RetrievalQA\n",
      "- VectorDBQA\n",
      "- LLMRouterChain\n",
      "- MultiPromptChain\n",
      "- MultiRetrievalQAChain\n",
      "- MultiRouteChain\n",
      "- RouterChain\n",
      "- SequentialChain\n",
      "- SimpleSequentialChain\n",
      "- TransformChain\n",
      "- TaskPlaningChain\n",
      "- QueryChain\n",
      "- CPALChain\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(qa_dict[\"What classes are derived from the Chain class?\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The LangChain class includes various types of retrievers such as:\n",
      "\n",
      "- ArxivRetriever\n",
      "- AzureAISearchRetriever\n",
      "- BM25Retriever\n",
      "- ChaindeskRetriever\n",
      "- ChatGPTPluginRetriever\n",
      "- ContextualCompressionRetriever\n",
      "- DocArrayRetriever\n",
      "- ElasticSearchBM25Retriever\n",
      "- EnsembleRetriever\n",
      "- GoogleVertexAISearchRetriever\n",
      "- AmazonKendraRetriever\n",
      "- KNNRetriever\n",
      "- LlamaIndexGraphRetriever and LlamaIndexRetriever\n",
      "- MergerRetriever\n",
      "- MetalRetriever\n",
      "- MilvusRetriever\n",
      "- MultiQueryRetriever\n",
      "- ParentDocumentRetriever\n",
      "- PineconeHybridSearchRetriever\n",
      "- PubMedRetriever\n",
      "- RePhraseQueryRetriever\n",
      "- RemoteLangChainRetriever\n",
      "- SelfQueryRetriever\n",
      "- SVMRetriever\n",
      "- TFIDFRetriever\n",
      "- TimeWeightedVectorStoreRetriever\n",
      "- VespaRetriever\n",
      "- WeaviateHybridSearchRetriever\n",
      "- WebResearchRetriever\n",
      "- WikipediaRetriever\n",
      "- ZepRetriever\n",
      "- ZillizRetriever\n"
     ]
    }
   ],
   "source": [
    "print(qa_dict[\"What kind of retrievers does LangChain have?\"])"
   ]
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