In our previous Langchains series, we've delved from the fundamentals to intricate NLP and Mathematics. Today, we'll zero in on pivotal use cases: Offline Document Analysis for Q&A from local systems, decoding GitHub repository code, and gleaning insights from CSV data. Let's embark on this journey!
We have come quite far in Langchains. From basics to NLP use cases to Mathematics and whatnot. If you have missed previous parts of our Langchain tutorials, we have got you covered:
In this particular post, we will be discussing the most useful use cases i.e.:
📃 Offline Document Analysis: Question Answering from an offline document present in the local system
💻 Github repo code analysis: Understanding the codes in a GitHub repo
📟 CSV analysis: Analyzing tabular data for insights and other important queries
So let’s get started
We will start with pip installing a few packages and importing required libraries.
Next, we will load a sample text file ‘abc.txt’ about how to become an NLP Engineer.
We will add an additional step that will 1) Segment the entire document and 2)Generate and save embeddings for segments for vector search when we ask some questions to the Chain object.
As a last step, we will create a RetrievalQA chain object.
Time for some Q&A regarding the document
The output is attached below
Next in the line is analyzing a github repo’s code.
Pip install the requirements first
Import all packages and libraries required
Next, we will clone a streamlit-based git repo at location ‘/content/portfolio/’’
From this git repo, we will load all the Python files (.py files) into the memory.
We will be doing some preprocessing over these python files.
Similar to what we did in the previous tutorial on offline documents, we will be vectorizing the code base using Chroma.
Eventually, we will create the chain object, enable memory, and create a ConversationalRetrievalChain.
Time for a Q&A
Moving on to the last segment.
This segment can be a life savior if you’re a Data Analyst or Business Analysts or even a Data scientist. Let’s get started.
As usual, pip install packages and import libraries.
Next, we will call the create_csv_agent() and pass LLM object alongside the locally available CSV file path i.e. titanic in this case.
We will run a few queries now about the Titanic dataset from Kaggle.
In conclusion, this tutorial on Langchain has provided us with a powerful set of tools and techniques for offline document analysis, GitHub repository analysis, and CSV analysis. We've explored the capabilities of Langchain, a versatile language model-based platform that offers an array of functionalities to streamline data processing and extraction.
Through this tutorial, we've learned how to harness the potential of Langchain to analyze offline documents, making sense of unstructured text data with ease. We've seen how it can be used to extract valuable insights from GitHub repositories, aiding developers and data scientists in understanding codebases and project histories. Additionally, we've delved into the world of CSV analysis, where Langchain's natural language processing capabilities can be applied to gain meaningful insights from structured data.
This tutorial has not only equipped us with practical skills but also showcased the immense potential of Langchain in various data analysis tasks. With the knowledge and tools acquired here, you can now embark on your own projects, leveraging Langchain to streamline your document analysis, GitHub repository exploration, and CSV data extraction needs.
As you continue to explore and utilize Langchain in your work, remember that its versatility and capabilities are boundless. The world of data analysis and natural language processing is continually evolving, and Langchain is a valuable asset that can help you stay at the forefront of these advancements. So, whether you're a data enthusiast, a developer, or a researcher, Langchain can be your go-to tool for extracting valuable insights from a wide range of data sources. Happy analyzing!
Disclaimer: The views and opinions expressed in this blog post are solely those of the authors and do not reflect the official policy or position of any of the mentioned tools. This blog post is not a form of advertising and no remuneration was received for the creation and publication of this post. The intention is to share our findings and experiences using these tools and is intended purely for informational purposes.