Embeds text files into vectors, stores them on Pinecone, and enables semantic search using GPT3 and Langchain in a Next.js UI.
dabit3/semantic-search-nextjs-pinecone-langchain-chatgpt is ranked 14,108 with 761 stars.
Same-category comparison
dabit3/semantic-search-nextjs-pinecone-langchain-chatgpt is ranked 14,108 among these 8 peers; anujvyas/Machine-Learning-Projects is ranked 14,097.
Same-category comparison| Name | Rank | Metrics |
|---|
| trekhleb/self-parking-car-evolution | 14,060 | - Language:
- TypeScript
- Stars:
- 766
- Forks:
- 87
- Description:
- 🧬 Training the car to do self-parking using a genetic algorithm.
|
|---|
| BenjiKCF/Neural-Net-with-Financial-Time-Series-Data | 14,061 | - Language:
- Jupyter Notebook
- Stars:
- 765
- Forks:
- 303
- Description:
- This solution presents an accessible, non-trivial example of machine learning (Deep learning) with financial time series using TensorFlow.
|
|---|
| huangjia2019/langchain-in-action | 14,083 | - Language:
- Python
- Stars:
- 763
- Forks:
- 263
- Description:
- Practical LangChain patterns and implementations for real-world LLM applications.
|
|---|
| anujvyas/Machine-Learning-Projects | 14,097 | - Language:
- Jupyter Notebook
- Stars:
- 762
- Forks:
- 141
- Description:
- This repository consists of all my Machine Learning Projects.
|
|---|
| udacity/CVND_Exercises | 14,122 | - Language:
- Jupyter Notebook
- Stars:
- 760
- Forks:
- 669
- Description:
- Exercise notebooks for CVND.
|
|---|
| felipessalvatore/self_driving_pi_car | 14,159 | - Language:
- Python
- Stars:
- 756
- Forks:
- 74
- Description:
- A deep neural network based self-driving car, that combines Lego Mindstorms NXT with the computational power of a Raspberry Pi 3.
|
|---|
| OpenZeppelin/damn-vulnerable-defi | 14,168 | - Language:
- Solidity
- Stars:
- 755
- Forks:
- 1,492
|
|---|
| chris-chris/pysc2-examples | 14,172 | - Language:
- Python
- Stars:
- 755
- Forks:
- 346
- Description:
- StarCraft II - pysc2 Deep Reinforcement Learning Examples.
|
|---|
Key parameters and specifications
Key parameters and specifications| Parameter | Value |
|---|
| Language | TypeScript |
|---|
| Category | Tutorials |
|---|
| Subcategory | AIE roadmap |
|---|
| Keywords | RAG |
|---|
| Stars | 761 |
|---|
| Forks | 106 |
|---|
| Contributors | 2 |
|---|
| Created date | 2023-05-28 |
|---|
| Updated date | 2026-10-06 |
|---|
| Stars in 1 day | 0 |
|---|
| Stars in 7 days | 0 |
|---|
| Rank | 14,108 |
|---|
| Previous rank | 13,979 |
|---|
| Rank change | -129 |
|---|
| 7-day stars rank | 14,296 |
|---|
| Previous 7-day stars rank | 14,311 |
|---|
| 7-day stars rank change | 15 |
|---|
| Total stars rank | 13,913 |
|---|
| Previous total stars rank | 13,890 |
|---|
| Total stars rank change | -23 |
|---|
| Country | USA |
|---|
README summary
js - Full Stack Starter This is a basic starter project for building with the following tools and APIs: Next. js LangchainJS Pineceone Vector Database GPT3 When I started diving into all of this, I felt while I understood some of the individual pieces, it was hard to piece together everything into a cohesive project. I hope this project is useful for anyone looking to build with this stack, and just needing something to start with. What we're building We are
building an app that takes text (text files), embeds them into vectors, stores them into Pinecone, and allows semantic searching of the data. For anyone wondering what Semantic search is, here is an overview (taken directly from ChatGPT4): __Semantic search refers to a search approach that understands the user's intent and the contextual meaning of search queries, instead of merely matching keywords. __ __It uses natural language processing and machine learning to interpret the semantics, or meaning, behind queries. This results
in more accurate and relevant search results. Semantic search can consider user intent, query context, synonym recognition, and natural language understanding. Its applications range from web search engines to personalized recommendation systems. __ Running the app In this section I will walk you through how to deploy and run this app. Prerequisites To run this app, you need the following: 1. Pinecone API Key Up and running To run the app locally, follow these steps: 1. Change into the directory and
install the dependencies using either NPM or Yarn 3. Copy to a new file called and update with your API keys and environment. (Optional) - Add your own custom text or markdown files into the folder. Run the app: Need to know When creating the embeddings and the index, it can take up to 2-4 minutes for the index to fully initialize. There is a settimeout function of 180 seconds in the that waits for the index to be created
. If the initialization takes longer, then it will fail the first time you try to create the embeddings. If this happens, visit the Pinecone console to watch and wait for the status of your index being created to finish, then run the function again. Running a query __The pre-configured app data is about the Lens protocol developer documentation, so it will only understand questions about it unless you replace it with your own data. Here are a couple of
questions you might ask it with the default data__ 1. What is the difference between Lens and traditional social platforms 2. What is the difference between the Lens SDK and the Lens API 3. The base of this project was guided by this Node. js tutorial, with some restructuring and ported over to Next. You can also follow them here on Twitter! Getting your data I recommend checking out GPT Repository Loader which makes it simple to turn any GitHub
repo into a text format, preserving the structure of the files and file contents, making it easy to chop up
Frequently asked questions
How many stars and forks are recorded?
dabit3/semantic-search-nextjs-pinecone-langchain-chatgpt has 761 stars and 106 forks.
What is the repository rank?
dabit3/semantic-search-nextjs-pinecone-langchain-chatgpt is ranked 14,108.
What language and category are recorded?
dabit3/semantic-search-nextjs-pinecone-langchain-chatgpt has 761 stars, uses TypeScript and belongs to Tutorials.
Which repository is closest in rank?
dabit3/semantic-search-nextjs-pinecone-langchain-chatgpt is compared with anujvyas/Machine-Learning-Projects, ranked 14,097 with 762 stars.
Langchain, Pinecone, and GPT with Next.js - Full Stack Starter
This is a basic starter project for building with the following tools and APIs:
- Next.js
- LangchainJS
- Pineceone Vector Database
- GPT3
When I started diving into all of this, I felt while I understood some of the individual pieces, it was hard to piece together everything into a cohesive project. I hope this project is useful for anyone looking to build with this stack, and just needing something to start with.
What we're building
We are building an app that takes text (text files), embeds them into vectors, stores them into Pinecone, and allows semantic searching of the data.
For anyone wondering what Semantic search is, here is an overview (taken directly from ChatGPT4):
Semantic search refers to a search approach that understands the user's intent and the contextual meaning of search queries, instead of merely matching keywords.
It uses natural language processing and machine learning to interpret the semantics, or meaning, behind queries. This results in more accurate and relevant search results. Semantic search can consider user intent, query context, synonym recognition, and natural language understanding. Its applications range from web search engines to personalized recommendation systems.
Running the app
In this section I will walk you through how to deploy and run this app.
Prerequisites
To run this app, you need the following:
- An OpenAI API key
- Pinecone API Key
Up and running
To run the app locally, follow these steps:
- Clone this repo
git clone git@github.com:dabit3/semantic-search-nextjs-pinecone-langchain-chatgpt.git
-
Change into the directory and install the dependencies using either NPM or Yarn
-
Copy .example.env.local to a new file called .env.local and update with your API keys and environment.
Be sure your environment is an actual environment given to you by Pinecone, like us-west4-gcp-free
-
(Optional) - Add your own custom text or markdown files into the /documents folder.
-
Run the app:
npm run dev
Need to know
When creating the embeddings and the index, it can take up to 2-4 minutes for the index to fully initialize. There is a settimeout function of 180 seconds in the utils that waits for the index to be created.
If the initialization takes longer, then it will fail the first time you try to create the embeddings. If this happens, visit the Pinecone console to watch and wait for the status of your index being created to finish, then run the function again.
Running a query
The pre-configured app data is about the Lens protocol developer documentation, so it will only understand questions about it unless you replace it with your own data. Here are a couple of questions you might ask it with the default data
- What is the difference between Lens and traditional social platforms
- What is the difference between the Lens SDK and the Lens API
- How to query Lens data in bulk?
The base of this project was guided by this Node.js tutorial, with some restructuring and ported over to Next.js. You can also follow them here on Twitter!
Getting your data
I recommend checking out GPT Repository Loader which makes it simple to turn any GitHub repo into a text format, preserving the structure of the files and file contents, making it easy to chop up and save into pinecone using my codebase.