Topic

LlamaIndex

2 posts about LlamaIndex. Every summary links to the original.

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Heroku
Building Data-Aware AI Applications with Heroku AI and LlamaIndex
Organizations building AI applications over private data must connect large language models with proprietary datasets without managing every infrastructure component. The post presents Heroku Managed Inference and Agents with LlamaIndex as a stack for retrieval-augmented generation (RAG), combining LlamaHub connectors, indexing, retrieval, and managed model services. The implementation provisions Heroku Postgres with pgvector, Claude 4 Sonnet for inference, and Cohere Embed Multilingual embeddings, then uses Python and LlamaIndex to load documents, store 1,024-dimensional vectors, and query them through an OpenAI-compatible embedding endpoint. PGVectorStore supplies the vector store, while Heroku environment variables provide service credentials and connection details. The result is a deployable pattern for querying private documents, with examples spanning financial analysis, insurance claims, customer support, and internal knowledge.

Okta
How I Built an Okta Documentation Chatbot in Python
Okta developer documentation contains information developers need for integrations, but the author observed that it was underused. They built Oktanaut, a Python chatbot in Jupyter Notebook, comparing a LlamaIndex-based prototype trained on Okta documentation with a GPT-3.5-powered version using the OpenAI API. The first favored precision for focused questions, while the second handled a broader range; the final demo used OpenAI API for its larger models and user-friendly experience. For both versions, the author curated questions, answers, and support engineer information, stored training data in Google Drive for Colab, and retained historical interactions so replies could use session context. Panel provided the frontend, and the author chose Jupyter Notebook for simple setup despite noting bugs and glitches.

The best engineering reads, once a week.

Topic

2 posts about LlamaIndex. Every summary links to the original.

Paid picks we use ourselves. Links carry a referral code — a subscription through one earns us a commission at no extra cost to you.

Newest first

Heroku
Building Data-Aware AI Applications with Heroku AI and LlamaIndex
Organizations building AI applications over private data must connect large language models with proprietary datasets without managing every infrastructure component. The post presents Heroku Managed Inference and Agents with LlamaIndex as a stack for retrieval-augmented generation (RAG), combining LlamaHub connectors, indexing, retrieval, and managed model services. The implementation provisions Heroku Postgres with pgvector, Claude 4 Sonnet for inference, and Cohere Embed Multilingual embeddings, then uses Python and LlamaIndex to load documents, store 1,024-dimensional vectors, and query them through an OpenAI-compatible embedding endpoint. PGVectorStore supplies the vector store, while Heroku environment variables provide service credentials and connection details. The result is a deployable pattern for querying private documents, with examples spanning financial analysis, insurance claims, customer support, and internal knowledge.

Okta
How I Built an Okta Documentation Chatbot in Python
Okta developer documentation contains information developers need for integrations, but the author observed that it was underused. They built Oktanaut, a Python chatbot in Jupyter Notebook, comparing a LlamaIndex-based prototype trained on Okta documentation with a GPT-3.5-powered version using the OpenAI API. The first favored precision for focused questions, while the second handled a broader range; the final demo used OpenAI API for its larger models and user-friendly experience. For both versions, the author curated questions, answers, and support engineer information, stored training data in Google Drive for Colab, and retained historical interactions so replies could use session context. Panel provided the frontend, and the author chose Jupyter Notebook for simple setup despite noting bugs and glitches.

The best engineering reads, once a week.