Sold by: Genixware
Deployed on AWS
AWS Free Tier
Launch-ready AMI with LangChain and Pinecone SDK pre-installed on Ubuntu 24.04 LTS. Ideal for developers building AI-powered Retrieval-Augmented Generation (RAG) apps.
Overview
This AMI provides a streamlined development environment for building Retrieval-Augmented Generation (RAG) applications using LangChain and Pinecone on Ubuntu 24.04 LTS.
It includes: Pre-installed LangChain SDK (latest stable release) Pinecone SDK and environment setup Python 3.12 with venv and pip Jupyter Notebook configured for rapid prototyping Sample notebooks for LangChain chains, embeddings, and Pinecone vector indexing Streamlit UI demo to visualize RAG flows Basic FastAPI app template for production use
Whether you are an AI researcher or developer building intelligent apps, this AMI saves hours of setup time.
Highlights
- Plug-and-Play RAG Stack: Pre-configured LangChain + Pinecone SDK on Ubuntu 24.04.
- AI App Ready: Start building chatbots, agents, or search tools immediately.
- Includes Jupyter + FastAPI + Streamlit for end-to-end prototyping.
Details
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Delivery method
Delivery option
64-bit (x86) HAQM Machine Image (AMI)
Latest version
Operating system
Ubuntu 22
Deployed on AWS
Features and programs
Financing for AWS Marketplace purchases
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time. Alternatively, you can pay upfront for a contract, which typically covers your anticipated usage for the contract duration. Any usage beyond contract will incur additional usage-based costs.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Cost/hour |
|---|---|
t3.large Recommended | $0.11 |
t3.micro AWS Free Tier | $0.025 |
c5.large | $0.054 |
m5.large | $0.057 |
Vendor refund policy
All purchases of this AMI on AWS Marketplace are considered final and non-refundable once the software has been accessed or deployed.
However, if you experience issues related to: AMI functionality Deployment errors Incomplete installation
you may contact our support team within 7 days of purchase for resolution assistance.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
Delivery details
64-bit (x86) HAQM Machine Image (AMI)
HAQM Machine Image (AMI)
An AMI is a virtual image that provides the information required to launch an instance. HAQM EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.
Version release notes
OS: Ubuntu Server 24.04 LTS (64-bit x86) Python 3.12 with pip and venv LangChain SDK (latest stable version) Pinecone Python SDK pre-installed Pre-configured Virtual Environment at ~/langchain-env Sample Jupyter Notebooks for LangChain chains and Pinecone vector indexing Streamlit Demo App for basic semantic search UI FastAPI Template for production-ready deployment UFW firewall installed but disabled by default Jupyter Notebook set to run on port 8888 Streamlit set to run on port 8501
Additional details
Usage instructions
- Start the instance with 1-click.
- The only access to the LangChain & Pinecone Starter is via SSH. As part of the EC2 instance provisioning process, a keypair is associated with the instance. This is only available during the keypair creation process and cannot be retrieved after the initial creation, so make sure to save it. To access the instance, you will need the following information:
Private key: as created during the EC2 instance provisioning process Username: ubuntu SSH port: 22
Support
Vendor support
For technical support, setup assistance, or custom integration help, please contact our team: Email: support@genixware.org Website:
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by HAQM Web Services.
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