biblia 0.9.4

Biblical study assistant with AI-powered analysis and research

Biblia

A terminal-based biblical research assistant built on an agentic pipeline architecture. Each study session uses Google Gemini 2.5 Flash as an editorial curator — not a search engine — informed by web context, authoritative ESV scripture, the liturgical calendar, and a persistent personal memory graph that grows with every session.

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Research Background

Why an agent, not a chatbot?

A chatbot answers a single prompt in isolation. An agent maintains state, calls external tools, and feeds the outputs of each tool into the inputs of the next. Biblia is structured as a tool-dispatch pipeline: every Gemini call in the teaching sequence receives a growing context object — the previous call's output becomes part of the next call's prompt.

By the time Gemini writes a prayer, it holds:

• The original question
• Web search snippets from Serper (what commentators are saying)
• The theological analysis it just produced
• Actual ESV verse texts fetched from the API for each inferred reference
• The application points it generated in the preceding step
• A formatted block from the MemoryGraph: past topics, their key verses, how they relate

This is the distinction between a language model as a generator and a language model as a curator and synthesizer. The model is not answering in a vacuum — it is editing, weighting, and composing across heterogeneous inputs.

Personalization through the memory graph

Biblical study is not a sequence of independent queries. Doctrine builds on doctrine; a teaching on grace should know you have already studied the law. The MemoryGraph (~/.biblia/memory.json) records every topic taught and every verse read as a concept node. Nodes link to co-occurring concepts studied in the same session. On every subsequent query, the graph scores stored concepts by word-overlap with the new query and injects the most relevant past teachings as context.

This means:

• The application section of a teaching on "sanctification" cites your prior study of "justification"
• The verse selection for Friday avoids passages you read on Monday
• A reflection after three sessions on Pauline theology is grounded in the cumulative arc, not just the last command

Verse selection as editorial curation

The verse command does not draw randomly from a fixed list. Gemini receives the liturgical day context (season, day of week, their thematic character) alongside the memory graph and recently read references, then selects one passage and provides a one-sentence rationale. The ESV API fetches the authoritative text. The devotional's first paragraph makes the editorial reasoning explicit — why this verse, today, for this student.

The liturgical calendar is computed from first principles using the Anonymous Gregorian Easter algorithm, covering Advent, Christmas, Epiphany, Lent, Holy Week, Eastertide, Pentecost, and Ordinary Time.

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Architecture

Pipeline overview

flowchart TD
    User([User]) --> REPL[REPL\nmain.py]

    REPL -->|teach| TP[Teach Pipeline]
    REPL -->|verse| VP[Verse Pipeline]
    REPL -->|reflect| RP[Reflect Pipeline]
    REPL -->|history| HP[History View]

    subgraph TP [Teach Pipeline]
        direction TB
        T1[Serper: web search] --> T2[Gemini: theological insights\nquery + snippets + memory]
        T2 --> T3[Gemini: infer references\nfrom insights]
        T3 --> T4[ESV API: fetch verse texts]
        T4 --> T5[Gemini: application\ninsights + scripture + memory]
        T5 --> T6[Gemini: prayer\neverything accumulated]
        T6 --> T7[MemoryGraph.add_teaching]
    end

    subgraph VP [Verse Pipeline]
        direction TB
        V1[Liturgical Calendar\nseason + day theme] --> V2[MemoryGraph: recent context\n+ recently read refs]
        V2 --> V3[Gemini: select ONE reference\n+ rationale]
        V3 --> V4[ESV API: fetch text]
        V4 --> V5[Gemini: devotional\nwith personalised rationale]
        V5 --> V6[MemoryGraph.add_verse]
    end

    subgraph RP [Reflect Pipeline]
        direction TB
        R1[StudySession: session history] --> R2[MemoryGraph: related context]
        R2 --> R3[Gemini: reflection\nINSIGHTS / APPLICATION / PRAYER]
    end

    subgraph HP [History View]
        direction TB
        H1[StudySession: current session\ntimeline]
        H2[MemoryGraph: past teachings\n+ past verses]
    end

    MG[(MemoryGraph\n~/.biblia/memory.json)] --> T2
    MG --> T5
    MG --> T6
    MG --> V2
    MG --> R2
    T7 --> MG
    V6 --> MG

Data flow through a teach session

sequenceDiagram
    actor User
    participant Agent as BibleAgent
    participant Serper
    participant Gemini
    participant ESV as ESV API
    participant MG as MemoryGraph

    User->>Agent: teach → "love and passion"
    Agent->>MG: get_context("love and passion")
    MG-->>Agent: prior study context

    Agent->>Serper: search("love and passion")
    Serper-->>Agent: web snippets

    Agent->>Gemini: insights prompt\n[query + snippets + memory]
    Gemini-->>Agent: theological analysis

    Agent->>Gemini: refs prompt [insights]
    Gemini-->>Agent: "1 Cor 13:4, John 3:16, ..."

    Agent->>ESV: fetch_many(references)
    ESV-->>Agent: actual verse texts

    Agent->>Gemini: application prompt\n[insights + verse texts + memory]
    Gemini-->>Agent: practical application

    Agent->>Gemini: prayer prompt\n[query + insights + scripture + application + memory]
    Gemini-->>Agent: composed prayer

    Agent->>MG: add_teaching(query, insights, refs)
    Agent->>User: display full teaching

Key services

Service	Role
GeminiLLM	All generation — analysis, selection, composition. 429 quota errors handled with retry-after backoff.
ESVService	Authoritative verse text. All displayed scripture is fetched here, never hallucinated.
SerperService	Web search enrichment for teaching context. Result cached per query in-session.
MemoryGraph	Concept graph persisted to ~/.biblia/memory.json. Nodes = topics + verse refs. Edges = co-occurrence within sessions.
LiturgicalCalendar	Pure date arithmetic — Easter algorithm, Advent start, all major seasons. No external calls.

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Installation

pip install biblia

Or from source:

git clone https://github.com/ashioyajotham/bible.git
cd bible
pip install -e .

No GPU, no PyTorch, no HuggingFace stack. Install footprint ~50 MB.

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Configuration

Run bible --setup on first launch, or set keys directly:

bible --setup

Three keys are required:

Key	Source	Purpose
GEMINI_API_KEY	Google AI Studio	All AI generation and curation
SERPER_API_KEY	serper.dev	Web search for teaching enrichment
ESV_API_KEY	api.esv.org	Authoritative verse text

Keys are stored in the system keyring (Windows Credential Manager / macOS Keychain / libsecret on Linux), with ~/.biblia/.env as fallback. They load automatically on startup — no manual export required.

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Usage

bible [--theme dark|scholar|minimal] [--verbose] [--setup] [--health]

CLI flags

Flag	Description
--theme dark|scholar|minimal	UI colour theme (default: dark)
--verbose	Enable debug-level logging — shows full prompt/response traces, suppressed by default
--setup	Interactive API key setup; stores keys in system keyring and exits
--health	Run a one-shot service health check (key status, Gemini, Serper, ESV, memory graph) and exit

REPL commands

Command	Alias	Description
teach	t	Full theological analysis: web search → synthesis → scripture → application → prayer
verse	v	Liturgically-aware daily passage selected by Gemini; devotional with rationale
reflect	r	Synthesise the session into insights, application, and prayer
history	hi	Browse teachings, verses, and reflections — current session and cross-session memory
logs	log	Agent health check: key status, service reachability, recent log entries
export	e	Export the session — Markdown or PDF; prompts for format and filename
config	cfg	View, set, or delete API keys without leaving the app
help	h	Command reference
exit	q	Quit

Themes

Theme	Character
dark	Cyan accents on dark background — default
scholar	Amber tones — warm, document-like
minimal	White borders only — maximum readability

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Project structure

biblia/
├── agent/
│   ├── bible_agent.py          — command dispatch + full pipeline logic
│   ├── base_agent.py           — holds ModelManager, MemoryGraph, formatter, session
│   ├── search_agent.py         — thin Serper wrapper
│   └── components/
│       ├── memory.py           — MemoryGraph: concept nodes, edges, persistence
│       └── session.py          — StudySession: in-session teachings/verses/reflections
├── services/
│   ├── esv_service.py          — ESV Bible API: fetch() and fetch_many()
│   ├── serper_service.py       — Serper search API
│   ├── model_manager.py        — singleton GeminiLLM factory
│   └── llm/
│       ├── gemini_llm.py       — Gemini 2.5 Flash wrapper with 429 retry backoff
│       └── model_types.py
├── utils/
│   ├── health.py               — service health checks: keys, Gemini, Serper, ESV, memory
│   ├── liturgical.py           — Western Christian calendar: Easter algorithm, all seasons
│   └── formatters/
│       ├── console_formatter.py — Rich TUI renderer; theme-aware
│       └── markdown_formatter.py
├── config/
│   ├── keys.py                 — resolution chain: env → keyring → ~/.biblia/.env
│   ├── settings.py             — metaclass Config for live os.getenv() on every access
│   └── setup.py                — interactive key setup, config REPL command
├── models/
│   ├── verse.py
│   └── verse_categories.py
└── main.py                     — CLI entry point, REPL loop, --health / --setup flags
tests/
├── test_bible_agent.py         — unit tests (command dispatch, session management)
├── test_integration.py         — full pipeline tests (mocked APIs)
├── test_memory.py              — MemoryGraph: add, link, context, persistence
├── test_liturgical.py          — Easter dates, Advent start, season detection
└── conftest.py

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Running tests

pytest

All external APIs are mocked. No keys required to run the suite.

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Disclaimer

Biblia is a research and study tool. Output should be cross-referenced with authoritative theological sources, commentaries, and pastoral guidance. The ESV text is fetched directly from the API; all other content is AI-generated and should be treated accordingly.

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License

MIT — see LICENSE.