JournalPT

Installation

macOS

• Setup python environment:

python3 -m venv env
source env/bin/activate
pip3 install --upgrade pip

pip3 install psycopg2-binary
pip3 install -r requirements.txt

• Install Postgresql

echo "POSTGRES_USERNAME=<your_db_username>" >> .env
echo "POSTGRES_PASSWORD=<your_db_password>" >> .env

alembic upgrade head # Apply db migrations

• Generate OpenAI API Key

echo "OPENAI_API_KEY=<your_api_key_here>" >> .env

• Run the server locally at localhost

python3 app/main.py

API

Use Postman Collection to run the API

• Create user /api/v1/auth/register

curl -XPOST 'http://localhost:8000/api/v1/auth/register' \
--header 'Content-Type: application/json' \
--data-raw '{
    "user_name": "John Doe",
    "user_age": 42,
    "user_password": "abcd1234",
    "user_email": "abcd@xyz.com"
}'

• Create journal entrys /api/v1/journal/create

curl -XPOST 'http://localhost:8000/api/v1/journal/create' \
--header 'Content-Type: application/json' \
--data '{
    "user_id": 0,
    "content": "Today was a very good day",
    "date": "2024-12-08"
}'

curl -XPOST 'http://localhost:8000/api/v1/journal/create' \
--header 'Content-Type: application/json' \
--data '{
    "user_id": 0,
    "content": "Today was a bad day",
    "date": "2024-12-07"
}'

• Summarise with LLM /api/v1/summarise

curl -XPOST 'http://localhost:8000/api/v1/llm/summarise' \
--header 'Content-Type: application/json' \
--data '{
    "user_id": 0,
    "days": [
        "2024-12-08",
        "2024-12-06"
    ]
}'

TODO

v0 - Generate periodic summary from journal entry

Components

1. Chunking 1.1 No chunking 1.2 Linewise 1.3 Rule-based division - smalltalk, specific to an issue e.g. major new event, reference to past event, follow up on a plan

2. Summarization - 2.1 No prompt - as a default - summarize this/frontend button 2.2 System generated prompt - role based summary - 2.3 e.g. with focus on one issue from above, with o/p format as given in i/p - see HowWeFeel app format - emotions wheel, reason, event details 2.3.1 Prompt engg 2.3.2 Instruction base fine tuning

3. Database - 3.1 Metadata - postgres(mongo in V1) 3.2 Vector db - for embeddings - pgvector (qdrant/FAIS/elastic in v1) 3.3 Jobs - Redis/Kafka - async msg queue - to ingest new text embeddings and push out to the db with proper pacing 3.4 Results - Precomputed Store for previous results

4. Text Context for RAG querying - txt/pdf upload as context/prescription plan

5. UI - v1, No UI - v0 5.1 Postman Curl Collection 5.2 Data upload - POST API

v1 - Interactive AI-assisted journalling with user

1. Flow prompting - prompt user for diary entry 1.1 Fixed structure of diary entry 1.2 Current context-based - full chat vs current conv 1.3 Past context-base 1.4 Action taking outside world - book aptmt for the diet plan/gym plan follow up

2. Self-selective routing on which one to pick from above 3 types 2.1 rule-based - listen vs speak - if user wants to discontinue the journaling, prompt them to stick 2.2 some other - 2.3 Temperature/entropy

3. Output - 3.1 concatenated as a chat form 3.2 lossless summarization/ paraphrasing in 1st person 3.3 thematic segmentation - segmenting the whole conv as per separate themes

API Endpoints

1. Authentication endpoints 1.1 Register User 1.2 Login User 1.3 Logout User 1.4 View/Edit Profile 1.5 extra - end-to-end encryption

2. Data - CRUD 2.1 Upload previous - txt/image for ocr 2.2 Get by ID of orig conversation/entry 2.3 Search - by metadata/date 2.4 Delete orig convo and all its generated chunks and summarizations recursively

3. Generation - text-to-vector and back 3.1 extra - Create chunks 3.2 Generate Embeddings 3.3 Query Embeddings

4. Processes - LLM calls - text-to-text 4.1 Segmentation 4.2 Summarization 4.3 extra - rule-based routing based on params like temperature, window size