InSightful

The AI assistant for tech communities.

Features

• [✔️] Conversation Analysis: InSightful can analyze and provide insights on the topics being discussed in a tech community.
• [✔️] Community Health Analysis: InSightful can analyze the engagement, sentiment, and more of a tech community.
• [✔️] Search Stack Overflow: InSightful can search Stack Overflow for relevant questions and answers.
• [✔️] Browse The Web: InSightful can browse the web for relevant information on community topics.

Overview of workflow

Prerequisites

• Python 3.10.13
• TGI Docker Image
• TEI Docker Image
• ChromaDB Docker Image

Usage

Insightful uses HuggingFace's TGI server for compatible models. Clone this repository and update the environment variables in the .envrc file with your server's address.

For the Retrieval Augmented Generation (RAG) aspect, Insightful uses HuggingFace's TEI server for compatible Embedding models. Update the environment variables in the .envrc file with your server's address.

To store these embeddings, Insightful uses a hosted instance of a ChromaDB server. Update the environment variables in the .envrc file with your vectorstore server's address.

git clone https://github.com/infracloudio/insightful.git
cd insightful

Install the dependencies.

pip install -r requirements.txt

Set the environment variables.

source .envrc

Ensure that all environment variables are correctly set before running the application.

The Docker containers for each service (except Python) must be running before starting the application. Refer to their Docker installation guides for more information.

When running the TGI docker container, replace $model with $TGI_MODEL.

Similarly, replace $model with $TEI_MODEL when running the TEI container.

For the ChromaDB container, set the --port flag to $VECTORDB_PORT:$VECTORDB_PORT.

These flags ensure there are no conflicting ports between the services and allow access to the servers on the same host without issues.

Finally, run the application.

streamlit run app.py