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Use LangChain, GPT and Deep Lake to work with code base
 Contents 
Design
Implementation
Integration preparations
Prepare data
Question Answering
Use LangChain, GPT and Deep Lake to work with code base#
In this tutorial, we are going to use Langchain + Deep Lake with GPT to analyze the code base of the LangChain itself.
Design#
Prepare data:
Upload all python project files using the langchain.document_loaders.TextLoader. We will call these files the documents.
Split all documents to chunks using the langchain.text_splitter.CharacterTextSplitter.
Embed chunks and upload them into the DeepLake using langchain.embeddings.openai.OpenAIEmbeddings and langchain.vectorstores.DeepLake
Question-Answering:
Build a chain from langchain.chat_models.ChatOpenAI and langchain.chains.ConversationalRetrievalChain
Prepare questions.
Get answers running the chain.
Implementation#
Integration preparations#
We need to set up keys for external services and install necessary python libraries.
#!python3 -m pip install --upgrade langchain deeplake openai
Set up OpenAI embeddings, Deep Lake multi-modal vector store api and authenticate.
For full documentation of Deep Lake please follow https://docs.activeloop.ai/ and API reference https://docs.deeplake.ai/en/latest/
import os
from getpass import getpass
os.environ['OPENAI_API_KEY'] = getpass()
# Please manually enter OpenAI Key
 ········
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
os.environ['ACTIVELOOP_TOKEN'] = getpass.getpass('Activeloop Token:')
 ········
Prepare data#
rtdocs_stable/api.python.langchain.com/en/stable/use_cases/code/code-analysis-deeplake.html