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{
"cells": [
{
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"attachments": {},
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"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. "
]
},
{
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"attachments": {},
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"cell_type": "markdown",
"metadata": {},
"source": [
"## Design"
]
},
{
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"attachments": {},
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"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"
]
},
{
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"attachments": {},
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"cell_type": "markdown",
"metadata": {},
"source": [
"## Implementation"
]
},
{
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"attachments": {},
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"cell_type": "markdown",
"metadata": {
"tags": []
},
"source": [
"### Integration preparations"
]
},
{
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"attachments": {},
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"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"
]
},
{
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"attachments": {},
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"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": [
{
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"name": "stdout",
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"output_type": "stream",
"text": [
" ········\n"
]
}
],
"source": [
"import os\n",
"from getpass import getpass\n",
"\n",
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"os.environ[\"OPENAI_API_KEY\"] = getpass()\n",
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"# Please manually enter OpenAI Key"
]
},
{
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"attachments": {},
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"cell_type": "markdown",
"metadata": {},
"source": [
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"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)"
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]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"tags": []
},
"outputs": [
{
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"name": "stdout",
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"output_type": "stream",
"text": [
" ········\n"
]
}
],
"source": [
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"os.environ[\"ACTIVELOOP_TOKEN\"] = getpass.getpass(\"Activeloop Token:\")"
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]
},
{
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"attachments": {},
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"cell_type": "markdown",
"metadata": {},
"source": [
"### Prepare data "
]
},
{
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"attachments": {},
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"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",
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"root_dir = \"../../../..\"\n",
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"\n",
"docs = []\n",
"for dirpath, dirnames, filenames in os.walk(root_dir):\n",
" for file in filenames:\n",
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" if file.endswith(\".py\") and \"/.venv/\" not in dirpath:\n",
" try:\n",
" loader = TextLoader(os.path.join(dirpath, file), encoding=\"utf-8\")\n",
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" docs.extend(loader.load_and_split())\n",
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" except Exception as e:\n",
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" pass\n",
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"print(f\"{len(docs)}\")"
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]
},
{
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"attachments": {},
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"cell_type": "markdown",
"metadata": {},
"source": [
"Then, chunk the files"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
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]
},
{
"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)}\")"
]
},
{
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"attachments": {},
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"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=<class 'openai.api_resources.embedding.Embedding'>, 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",
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"db = DeepLake.from_documents(\n",
" texts, embeddings, dataset_path=f\"hub://{DEEPLAKE_ACCOUNT_NAME}/langchain-code\"\n",
")\n",
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"db"
]
},
{
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"attachments": {},
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"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": [
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"db = DeepLake(\n",
" dataset_path=f\"hub://{DEEPLAKE_ACCOUNT_NAME}/langchain-code\",\n",
" read_only=True,\n",
" embedding_function=embeddings,\n",
")"
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]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"retriever = db.as_retriever()\n",
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"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"
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]
},
{
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"attachments": {},
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"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",
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" if \"something\" in x[\"text\"].data()[\"value\"]:\n",
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" return False\n",
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"\n",
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" # filter based on path e.g. extension\n",
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" metadata = x[\"metadata\"].data()[\"value\"]\n",
" return \"only_this\" in metadata[\"source\"] or \"also_that\" in metadata[\"source\"]\n",
"\n",
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"\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",
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"model = ChatOpenAI(model_name=\"gpt-3.5-turbo\") # 'ada' 'gpt-3.5-turbo' 'gpt-4',\n",
"qa = ConversationalRetrievalChain.from_llm(model, retriever=retriever)"
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]
},
{
"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",
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"]\n",
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"chat_history = []\n",
"\n",
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"for question in questions:\n",
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" result = qa({\"question\": question, \"chat_history\": chat_history})\n",
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" chat_history.append((question, result[\"answer\"]))\n",
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" print(f\"-> **Question**: {question} \\n\")\n",
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" print(f\"**Answer**: {result['answer']} \\n\")"
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]
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"-> **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"
]
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