diff --git a/docs/use_cases/code/code-analysis-deeplake.ipynb b/docs/use_cases/code/code-analysis-deeplake.ipynb new file mode 100644 index 00000000..d946f277 --- /dev/null +++ b/docs/use_cases/code/code-analysis-deeplake.ipynb @@ -0,0 +1,673 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Use LangChain, GPT and Deep Lake to work with code base\n", + "In this tutorial, we are going to use Langchain + Deep Lake with GPT to analyze the code base of the LangChain itself. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Design" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. Prepare data:\n", + " 1. Upload all python project files using the `langchain.document_loaders.TextLoader`. We will call these files the **documents**.\n", + " 2. Split all documents to chunks using the `langchain.text_splitter.CharacterTextSplitter`.\n", + " 3. Embed chunks and upload them into the DeepLake using `langchain.embeddings.openai.OpenAIEmbeddings` and `langchain.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" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Implementation" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### Integration preparations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We need to set up keys for external services and install necessary python libraries." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "#!python3 -m pip install --upgrade langchain deeplake openai" + ] + }, + { + "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": 5, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + " ········\n" + ] + } + ], + "source": [ + "import os\n", + "from getpass import getpass\n", + "\n", + "os.environ['OPENAI_API_KEY'] = getpass()\n", + "# Please manually enter OpenAI Key" + ] + }, + { + "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 https://app.activeloop.ai" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + " ········\n" + ] + } + ], + "source": [ + "DEEPLAKE_ACCOUNT_NAME = getpass()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + " ········\n" + ] + } + ], + "source": [ + "os.environ['DEEPLAKE_KEY'] = getpass()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "!activeloop login -t $DEEPLAKE_KEY" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Prepare data " + ] + }, + { + "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": 8, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1147\n" + ] + } + ], + "source": [ + "from langchain.document_loaders import TextLoader\n", + "\n", + "root_dir = '../../../..'\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 as e: \n", + " pass\n", + "print(f'{len(docs)}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then, chunk the files" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Created a chunk of size 1620, which is longer than the specified 1000\n", + "Created a chunk of size 1213, which is longer than the specified 1000\n", + "Created a chunk of size 1263, which is longer than the specified 1000\n", + "Created a chunk of size 1448, which is longer than the specified 1000\n", + "Created a chunk of size 1120, which is longer than the specified 1000\n", + "Created a chunk of size 1148, which is longer than the specified 1000\n", + "Created a chunk of size 1826, which is longer than the specified 1000\n", + "Created a chunk of size 1260, which is longer than the specified 1000\n", + "Created a chunk of size 1195, which is longer than the specified 1000\n", + "Created a chunk of size 2147, which is longer than the specified 1000\n", + "Created a chunk 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of size 1376, which is longer than the specified 1000\n", + "Created a chunk of size 1253, which is longer than the specified 1000\n", + "Created a chunk of size 1642, which is longer than the specified 1000\n", + "Created a chunk of size 1419, which is longer than the specified 1000\n", + "Created a chunk of size 1438, which is longer than the specified 1000\n", + "Created a chunk of size 1427, which is longer than the specified 1000\n", + "Created a chunk of size 1684, which is longer than the specified 1000\n", + "Created a chunk of size 1760, which is longer than the specified 1000\n", + "Created a chunk of size 1157, which is longer than the specified 1000\n", + "Created a chunk of size 2504, which is longer than the specified 1000\n", + "Created a chunk of size 1082, which is longer than the specified 1000\n", + "Created a chunk of size 2268, which is longer than the specified 1000\n", + "Created a chunk of size 1784, which is longer than the specified 1000\n", + "Created a chunk of size 1311, which is longer than the specified 1000\n", + "Created a chunk of size 2972, which is longer than the specified 1000\n", + "Created a chunk of size 1144, which is longer than the specified 1000\n", + "Created a chunk of size 1825, which is longer than the specified 1000\n", + "Created a chunk of size 1508, which is longer than the specified 1000\n", + "Created a chunk of size 2901, which is longer than the specified 1000\n", + "Created a chunk of size 1715, which is longer than the specified 1000\n", + "Created a chunk of size 1062, which is longer than the specified 1000\n", + "Created a chunk of size 1206, which is longer than the specified 1000\n", + "Created a chunk of size 1102, which is longer than the specified 1000\n", + "Created a chunk of size 1184, which is longer than the specified 1000\n", + "Created a chunk of size 1002, which is longer than the specified 1000\n", + "Created a chunk of size 1065, which is longer than the specified 1000\n", + "Created a chunk of size 1871, which is longer than the specified 1000\n", + "Created a chunk of size 1754, which is longer than the specified 1000\n", + "Created a chunk of size 2413, which is longer than the specified 1000\n", + "Created a chunk of size 1771, which is longer than the specified 1000\n", + "Created a chunk of size 2054, which is longer than the specified 1000\n", + "Created a chunk of size 2000, which is longer than the specified 1000\n", + "Created a chunk of size 2061, which is longer than the specified 1000\n", + "Created a chunk of size 1066, which is longer than the specified 1000\n", + "Created a chunk of size 1419, which is longer than the specified 1000\n", + "Created a chunk of size 1368, which is longer than the specified 1000\n", + "Created a chunk of size 1008, which is longer than the specified 1000\n", + "Created a chunk of size 1227, which is longer than the specified 1000\n", + "Created a chunk of size 1745, which is longer than the specified 1000\n", + "Created a chunk of size 2296, which is longer than the specified 1000\n", + "Created a chunk of size 1083, which is longer than the specified 1000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3477\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)\n", + "print(f\"{len(texts)}\")" + ] + }, + { + "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": 14, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "OpenAIEmbeddings(client=, model='text-embedding-ada-002', document_model_name='text-embedding-ada-002', query_model_name='text-embedding-ada-002', embedding_ctx_length=8191, openai_api_key=None, openai_organization=None, allowed_special=set(), disallowed_special='all', chunk_size=1000, max_retries=6)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from langchain.embeddings.openai import OpenAIEmbeddings\n", + "\n", + "embeddings = OpenAIEmbeddings()\n", + "embeddings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from langchain.vectorstores import DeepLake\n", + "\n", + "db = DeepLake.from_documents(texts, embeddings, dataset_path=f\"hub://{DEEPLAKE_ACCOUNT_NAME}/langchain-code\")\n", + "db" + ] + }, + { + "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": 16, + "metadata": { + "tags": [] + }, + "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/user_name/langchain-code\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "hub://user_name/langchain-code loaded successfully.\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Deep Lake Dataset in hub://user_name/langchain-code already exists, loading from the storage\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataset(path='hub://user_name/langchain-code', read_only=True, tensors=['embedding', 'ids', 'metadata', 'text'])\n", + "\n", + " tensor htype shape dtype compression\n", + " ------- ------- ------- ------- ------- \n", + " embedding generic (3477, 1536) float32 None \n", + " ids text (3477, 1) str None \n", + " metadata json (3477, 1) str None \n", + " text text (3477, 1) str None \n" + ] + } + ], + "source": [ + "db = DeepLake(dataset_path=f\"hub://{DEEPLAKE_ACCOUNT_NAME}/langchain-code\", read_only=True, embedding_function=embeddings)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "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": "markdown", + "metadata": {}, + "source": [ + "You can also specify user defined functions using [Deep Lake filters](https://docs.deeplake.ai/en/latest/deeplake.core.dataset.html#deeplake.core.dataset.Dataset.filter)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "def filter(x):\n", + " # filter based on source code\n", + " if 'something' 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 'only_this' in metadata['source'] or 'also_that' in metadata['source']\n", + "\n", + "### turn on below for custom filtering\n", + "# retriever.search_kwargs['filter'] = filter" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from langchain.chat_models import ChatOpenAI\n", + "from langchain.chains import ConversationalRetrievalChain\n", + "\n", + "model = ChatOpenAI(model='gpt-3.5-turbo') # 'ada' 'gpt-3.5-turbo' 'gpt-4',\n", + "qa = ConversationalRetrievalChain.from_llm(model,retriever=retriever)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "questions = [\n", + " \"What is the class hierarchy?\",\n", + " # \"What classes are derived from the Chain class?\",\n", + " # \"What classes and functions in the ./langchain/utilities/ forlder are not covered by unit tests?\",\n", + " # \"What one improvement do you propose in code in relation to the class herarchy for the Chain class?\",\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\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "-> **Question**: What is the class hierarchy? \n", + "\n", + "**Answer**: There are several class hierarchies in the provided code, so I'll list a few:\n", + "\n", + "1. `BaseModel` -> `ConstitutionalPrinciple`: `ConstitutionalPrinciple` is a subclass of `BaseModel`.\n", + "2. `BasePromptTemplate` -> `StringPromptTemplate`, `AIMessagePromptTemplate`, `BaseChatPromptTemplate`, `ChatMessagePromptTemplate`, `ChatPromptTemplate`, `HumanMessagePromptTemplate`, `MessagesPlaceholder`, `SystemMessagePromptTemplate`, `FewShotPromptTemplate`, `FewShotPromptWithTemplates`, `Prompt`, `PromptTemplate`: All of these classes are subclasses of `BasePromptTemplate`.\n", + "3. `APIChain`, `Chain`, `MapReduceDocumentsChain`, `MapRerankDocumentsChain`, `RefineDocumentsChain`, `StuffDocumentsChain`, `HypotheticalDocumentEmbedder`, `LLMChain`, `LLMBashChain`, `LLMCheckerChain`, `LLMMathChain`, `LLMRequestsChain`, `PALChain`, `QAWithSourcesChain`, `VectorDBQAWithSourcesChain`, `VectorDBQA`, `SQLDatabaseChain`: All of these classes are subclasses of `Chain`.\n", + "4. `BaseLoader`: `BaseLoader` is a subclass of `ABC`.\n", + "5. `BaseTracer` -> `ChainRun`, `LLMRun`, `SharedTracer`, `ToolRun`, `Tracer`, `TracerException`, `TracerSession`: All of these classes are subclasses of `BaseTracer`.\n", + "6. `OpenAIEmbeddings`, `HuggingFaceEmbeddings`, `CohereEmbeddings`, `JinaEmbeddings`, `LlamaCppEmbeddings`, `HuggingFaceHubEmbeddings`, `TensorflowHubEmbeddings`, `SagemakerEndpointEmbeddings`, `HuggingFaceInstructEmbeddings`, `SelfHostedEmbeddings`, `SelfHostedHuggingFaceEmbeddings`, `SelfHostedHuggingFaceInstructEmbeddings`, `FakeEmbeddings`, `AlephAlphaAsymmetricSemanticEmbedding`, `AlephAlphaSymmetricSemanticEmbedding`: All of these classes are subclasses of `BaseLLM`. \n", + "\n", + "\n", + "-> **Question**: What classes are derived from the Chain class? \n", + "\n", + "**Answer**: There are multiple classes that are derived from the Chain class. Some of them are:\n", + "- APIChain\n", + "- AnalyzeDocumentChain\n", + "- ChatVectorDBChain\n", + "- CombineDocumentsChain\n", + "- ConstitutionalChain\n", + "- ConversationChain\n", + "- GraphQAChain\n", + "- HypotheticalDocumentEmbedder\n", + "- LLMChain\n", + "- LLMCheckerChain\n", + "- LLMRequestsChain\n", + "- LLMSummarizationCheckerChain\n", + "- MapReduceChain\n", + "- OpenAPIEndpointChain\n", + "- PALChain\n", + "- QAWithSourcesChain\n", + "- RetrievalQA\n", + "- RetrievalQAWithSourcesChain\n", + "- SequentialChain\n", + "- SQLDatabaseChain\n", + "- TransformChain\n", + "- VectorDBQA\n", + "- VectorDBQAWithSourcesChain\n", + "\n", + "There might be more classes that are derived from the Chain class as it is possible to create custom classes that extend the Chain class.\n", + "\n", + "\n", + "-> **Question**: What classes and functions in the ./langchain/utilities/ forlder are not covered by unit tests? \n", + "\n", + "**Answer**: All classes and functions in the `./langchain/utilities/` folder seem to have unit tests written for them. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.10.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}