AWS Claude Chatbot with Embeddings
Summary
Freelancer Client is hiring: AWS Claude Chatbot with Embeddings.
Location: Remote
I have a ten-page source text that I need converted into 384-dimension embeddings and stored in a vector database so I can run fast semantic queries against it. After that, I want Claude Sonnet 3.5 deployed in my AWS account (EC2 or an equivalent managed service is fine) and wired up to those embeddings so that users can chat in plain text and receive interactive language-learning guidance based on the content.
What you'll do:
• Generate 384-dimensional embeddings for the entire document, verify their quality, and load them into a persistent vector store (Pinecone, Amazon Kendra, or Faiss—whichever you prefer and can justify).
• Spin up Claude Sonnet 3.5 in AWS and expose it through a simple web front end or an API endpoint; no voice features are required, text chat only.
• Connect the model to the vector store so that retrieval-augmented generation powers the responses. The chatbot’s role is strictly Interactive Learning with a focus on Language Learning, so prompts, chain-of-thought, or system instructions need to reflect that teaching style.
• Add an “Assess Me” command that returns a short formative assessment of the learner’s last conversation segment—ideally multiple-choice or fill-in-the-blank questions, plus an answer key.
• Provide a quick README explaining how to redeploy the stack and retrain on new material.
• 1. Embeddings file loads correctly and vectors are 384-dimensional.
• 2. Claude Sonnet 3.5 responds through the AWS-hosted interface within two seconds for standard queries.
• 3. Language-learning tone is evident and consistent in replies.
Skills: Java, Python, Amazon Web Services, Node.js, Natural Language Processing, AI Chatbot, AI Model Integration, AI Development, Vector Databases
Budget: $2000–$6000 USD
Source: Freelancer Client via Remote / Online. Apply on the source website.
Original
I have a ten-page source text that I need converted into 384-dimension embeddings and stored in a vector database so I can run fast semantic queries against it. After that, I want Claude Sonnet 3.5 deployed in my AWS account (EC2 or an equivalent managed service is fine) and wired up to those embeddings so that users can chat in plain text and receive interactive language-learning guidance based on the content.
Here is the workflow I have in mind:
• Generate 384-dimensional embeddings for the entire document, verify their quality, and load them into a persistent vector store (Pinecone, Amazon Kendra, or Faiss—whichever you prefer and can justify).
• Spin up Claude Sonnet 3.5 in AWS and expose it through a simple web front end or an API endpoint; no voice features are required, text chat only.
• Connect the model to the vector store so that retrieval-augmented generation powers the responses. The chatbot’s role is strictly Interactive Learning with a focus on Language Learning, so prompts, chain-of-thought, or system instructions need to reflect that teaching style.
• Add an “Assess Me” command that returns a short formative assessment of the learner’s last conversation segment—ideally multiple-choice or fill-in-the-blank questions, plus an answer key.
• Provide a quick README explaining how to redeploy the stack and retrain on new material.
Acceptance criteria
1. Embeddings file loads correctly and vectors are 384-dimensional.
2. Claude Sonnet 3.5 responds through the AWS-hosted interface within two seconds for standard queries.
3. Language-learning tone is evident and consistent in replies.
4. “Assess Me” generates at least three relevant questions tied to the preceding chat context and returns an answer key automatically.
5. All infrastructure scripts (Terraform, CloudFormation, or plain shell) and source code are included in the final hand-off.
Location & Details
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