glassbox-rag 1.2.0

An open-source, high-transparency modular RAG framework for AI/ML applications

GlassBox RAG

A production-ready, high-transparency modular RAG (Retrieval-Augmented Generation) framework for enterprise AI/ML applications. GlassBox provides full-pipeline observability, an integrated terminal dashboard, extensible plugin architecture, built-in evaluation and experiment tracking, and real-time telemetry -- all from a single pip install.

It also includes advanced pipeline intelligence (GraphRAG, agentic RAG, chain-of-retrieval, HyDE, source attribution), enterprise controls (RBAC, PII redaction, prompt-injection defence, audit logging), an API toolkit (FastAPI + serverless), heterogeneous data connectors, and scale-out primitives (event bus, multi-tenancy, distributed workers, gRPC) -- each shipped as an optional, gracefully-degrading extra.

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Table of Contents

• Features
• Installation
• Quick Start
• CLI Reference
• Configuration
• Terminal Dashboard (TUI)
• Python API Reference
• Plugin Architecture
• Framework Adapters
• Evaluation System
• Experiment Tracking
• Version Management
• Advanced Retrieval Strategies
• GraphRAG and Agentic RAG
• Source Attribution and Provenance
• Enterprise Security
• API Toolkit and Serverless
• Data Syncing and Connectors
• Heterogeneous Data Corpora
• Context Engine and Personalization
• Automated Optimization
• Scale and Distributed Mode
• Solution Blueprints
• Observability
• Deployment
• Architecture
• Testing
• Contributing
• License

---

Features

Core RAG Pipeline

• Modular Encoding Layer -- Swap between OpenAI, Cohere, Google, Ollama, ONNX, and Hugging Face encoders at runtime. Per-query encoder overrides supported.
• Embedding Cache -- In-memory LRU cache with SHA-256 keying, configurable max size (default 2048), and batch hit/miss tracking. Eliminates redundant embedding API calls.
• Adaptive Retrieval -- Semantic, keyword, and hybrid retrieval strategies with configurable weighting. Heuristic-based strategy selection adapts to query characteristics.
• Cross-Encoder Reranking -- Optional reranking pass via cross-encoder, Cohere, or Hugging Face models for improved precision.
• Advanced Chunking -- Recursive, sentence-based, semantic, and fixed-size strategies with overlap control and chunk-size monitoring.
• Document Deduplication -- Content-hash (SHA-256), SimHash (fuzzy), and semantic embedding-based deduplication with configurable thresholds. Runs automatically during batch ingestion.
• Write-Back Protection -- Confidence-gated document updates with configurable modes (read-only, protected, full) and optional human review workflow.

LLM Generation

• Streaming Generation -- Async streaming for real-time token delivery via engine.stream().
• Multiple Backends -- OpenAI, Ollama, Anthropic, Groq, Hugging Face Inference, and any OpenAI-compatible endpoint. Config-driven setup with auto-detection from environment variables (OPENAI_API_KEY, ANTHROPIC_API_KEY, GROQ_API_KEY, HF_TOKEN, OLLAMA_BASE_URL, with GLASSBOX_LLM_BACKEND override).
• Token Counting -- Accurate token usage tracking via tiktoken with per-model cost estimation. Supports custom context window mappings for 20+ models.
• Configurable System Prompts -- Per-request or config-level system prompt overrides.
• Inline Evaluation -- Optional faithfulness, groundedness, and context relevance scoring on every generate() call via the evaluate=True parameter.

Pipeline Intelligence

• GraphRAG -- Knowledge-graph retrieval with multi-hop traversal. NetworkX backend (in-process, pure-Python fallback) for development and Neo4j for production. Query-derived entity extraction seeds the traversal.
• Agentic RAG -- RAGAgent performs iterative, tool-using reasoning: it decides whether context is sufficient, formulates follow-up sub-queries, calls registered tools, and self-corrects a draft answer against retrieved evidence.
• Chain-of-Retrieval (CoRAG) -- Iteratively refines retrieval across multiple LLM-guided steps, fusing and de-duplicating results.
• HyDE -- Hypothetical Document Embeddings: embeds a generated hypothetical answer instead of the raw query for better recall on under-specified questions.
• Adaptive Query Router -- QueryComplexityClassifier routes queries (simple/moderate/complex) to the optimal strategy, including graph and CoRAG.
• Source Attribution -- Sentence-level citation rendering with a reference list; full per-chunk provenance/lineage tracking.

Enterprise and Scale

• Security -- RBAC with per-document ACL filtering, PII detection/redaction (Luhn-validated), prompt-injection defence (query and indirect/stored), tamper-evident audit logging, and data classification -- unified via SecurityGateway.
• API Toolkit -- FastAPI app factory with query/generate/ingest/health endpoints and optional API-key auth, plus framework-agnostic AWS Lambda and GCP Cloud Function handlers.
• Data Syncing -- Incremental sync engine with a persistent delta ledger and connectors (filesystem, webhook, HTTP, Notion, Google Drive, Confluence). Network-backed connectors degrade gracefully and accept seeded documents for offline testing.
• Heterogeneous Corpora -- Text-to-SQL, tabular (DuckDB), object storage (S3), NoSQL (MongoDB), and conversational-log adapters, all emitting the standard document shape.
• Context Engine -- Reciprocal-rank context fusion across sources, real-time API context nodes with TTL caching, and buffered streaming ingestion with backpressure.
• Personalization -- Session memory with follow-up resolution and a per-user context layer (interest boosting, source filtering).
• Automated Optimization -- Parameter auto-tuning (grid + Bayesian-style search) and continuous feedback learning for ranking adjustments.
• Scale-out -- Typed event bus, multi-tenancy with quotas, SQLite WAL crash recovery, versioned index migrations, a distributed task queue (in-memory + Redis Streams), an AOT pipeline compiler, and a gRPC transport.

Terminal Dashboard (TUI)

GlassBox ships with a fully featured terminal dashboard built on Textual. Launch with glassbox-rag tui -- no browser or separate frontend required.

• Overview -- Live metrics (request count, latency, token usage, cost), component health status, and recent activity feed pulled from real trace data.
• Pipeline -- Interactive pipeline visualization with step-by-step execution flow and real-time test execution. Keyboard-driven step reordering.
• Debugger -- Trace listing with filtering, detailed step-by-step timing breakdown, anomaly detection, and trace export.
• Telemetry -- Latency and throughput charts, cost breakdown by operation, and performance percentile tables (p50/p95/p99) sourced from live metrics.
• Config and Plugins -- Interactive configuration editor and plugin documentation with code examples and inline API reference.
• Ingest -- File and directory ingestion interface with progress tracking.
• Evaluation -- Run evaluation suites against golden datasets and view per-query scoring breakdowns.
• Experiments -- Record, list, and compare experiment runs with metrics diff and git commit association.
• Logs -- Live-streaming, level-filterable structured log tab.
• Playground -- Side-by-side A/B query comparison with document-overlap and latency diff.
• Replay -- Step through a recorded pipeline execution with a timeline and per-step input/output.
• Builder -- Visual pipeline builder canvas (b) with a typed-slot stage catalog. Connections are validated by slot type and rejected if they create a cycle; seed the canvas from the live config with one keystroke.
• Docs -- In-app cookbook, starters, and help browser (d) with a searchable, grouped topic sidebar. A Get started group offers copy-paste "first code" snippets (minimal RAG, zero-config, ingest a folder, citations, build a pipeline) above the full Cookbook so guidance is available without leaving the terminal.
• Command Palette -- Fuzzy-searchable action overlay (Ctrl+P) for tab navigation, refresh, export, Start: … commands that open a get-started snippet, and Help: … commands that jump straight to a cookbook recipe.
• Config-driven pipeline view -- The Pipeline tab reflects the actual enabled strategies and hooks (query rewrite, HyDE, GraphRAG, CoRAG, rerank, compression) rather than a fixed diagram.

Screen Snapshots

Text renderings of each tab (the live UI is a full-colour Textual app). Run glassbox-rag tui to interact with them.

Overview (1) -- live metrics, component health, recent activity

Overview
------------------------------------------------------------
Requests  1,284     p95 latency  412 ms    Tokens  1.9M
Cost      $12.47    Errors       0.3 %     Uptime  4h 12m

Components
  encoder   [ready]    vector_store [qdrant]   database [postgres]
  reranker  [ready]    generator    [openai]   traces   [sqlite]

Recent activity
  12:04:31  generate  "how does reranking work?"   412 ms  ok
  12:04:18  ingest    47 chunks from ./knowledge            ok

Pipeline (2) -- config-driven flow with live step timing

Pipeline
------------------------------------------------------------
Input -> Encode -> Retrieve -> Rerank -> Generate -> Output
                               (active)

Execution timeline          Step details
  encode      62 ms           Retrieve Documents
  retrieve   228 ms           strategy : hybrid (alpha=0.6)
  rerank      49 ms           top_k    : 20 -> 5
  generate   101 ms           store    : qdrant

Alt+Up/Down reorder steps  -  Run Test Query  -  Reset

Debugger (3) -- trace list with step-by-step timing breakdown

Debugger
------------------------------------------------------------
Traces                        Trace 8f3a - generate  (412 ms)
> 8f3a  generate  412 ms  ok    encode_query        62 ms
  7c1b  retrieve  240 ms  ok    adaptive_retrieve  228 ms
  6d90  ingest    1.2 s   ok      vector_search    201 ms
  5a22  generate  980 ms  warn  rerank              49 ms
                                generate           101 ms  (slow)

filter: [ all ]    export trace -> json

Telemetry (4) -- latency/throughput charts and cost breakdown

Telemetry
------------------------------------------------------------
Latency (ms)                    Throughput (req/min)
  500 |         .:ill             60 |      .:ilil
  250 |    .:ill:.                30 |   .:il
    0 +-------------               0 +-----------

Percentiles            Cost by operation
  p50   180 ms           generate  $8.90
  p95   412 ms           encode    $2.10
  p99   705 ms           rerank    $1.47

Config & Plugins (5) -- interactive config editor and plugin reference

Config & Plugins
------------------------------------------------------------
Section          encoding.default_encoder    : openai
> encoding       encoding.cloud.openai.model : text-embedding-3-large
  retrieval      retrieval.rerank_enabled    : true
  generation     retrieval.reranker_type     : cross-encoder
  vector_store

Registered plugins
  vector_store : qdrant  chroma  faiss  pinecone  weaviate
  encoder      : openai  cohere  hf        reranker : cross-encoder  cohere

Ingest (6) -- file/directory ingestion with progress

Ingest
------------------------------------------------------------
Path: [ ./knowledge                       ]  [ Browse ]
Glob: [ **/*.md ]   Batch size: [ 50 ]   [x] deduplicate

Progress
  batch 3/5  [##############--------]  63%
  documents 188   chunks 1,204   duplicates skipped 12
  added 47    errors 0

Evaluation (7) -- run golden datasets, per-query scoring

Evaluation
------------------------------------------------------------
Dataset: golden_v3.json  (42 cases)          [ Run suite ]

Results
  recall@5      0.91  (+0.03)    faithfulness   0.88
  precision@5   0.79  (-0.01)    groundedness   0.90
  ctx_relevance 0.84             regression: none

Per-query
  ok    "reset my password"       recall 1.00
  warn  "cancel subscription"     recall 0.60

Experiments (8) -- record and compare runs with metric diffs

Experiments
------------------------------------------------------------
run         pipeline  dataset   recall@5  faith   commit
> exp-014   v1.2.0    gold_v3     0.91     0.88   a1b2c3d
  exp-013   v1.2.0    gold_v3     0.88     0.86   9f8e7d6
  exp-012   v1.1.0    gold_v2     0.85     0.84   4c3b2a1

Diff  exp-014 vs exp-013
  recall@5 +0.03    faithfulness +0.02    latency -18 ms

Logs (9) -- live-streaming, level-filterable structured logs

Live Logs
------------------------------------------------------------
level: [ INFO ]   filter: [ retriev ]
  12:04:31 INFO   engine     retrieve strategy=hybrid top_k=20
  12:04:31 DEBUG  retriever  vector_search 201 ms -> 20 hits
  12:04:31 INFO   reranker   cross-encoder 20 -> 5
  12:04:32 WARN   generator  response near token budget (92%)

Playground (0) -- side-by-side A/B query comparison

Query Playground
------------------------------------------------------------
Query: [ what is hybrid retrieval? ]            [ Compare ]

  A  top_k=5  alpha=0.5          B  top_k=10  alpha=0.7
  1 (0.82) hybrid combines...    1 (0.88) hybrid combines...
  2 (0.71) dense retrieval...    2 (0.74) BM25 keyword...
  latency 208 ms                 latency 331 ms

overlap: 3/5 docs   -   latency delta +123 ms

Replay (Ctrl+P -> Session Replay) -- step through a recorded trace

Session Replay
------------------------------------------------------------
trace 8f3a   step 3/5    [ |< ] [ < ] [ > ] [ >| ]

Timeline
  o---o---O---.---.   encode  retrieve  [rerank]  generate  output

Step: rerank
  input  : 20 documents
  output : 5 documents  (cross-encoder)
  took   : 49 ms

Builder (b) -- visual pipeline builder with typed-slot validation

Visual Pipeline Builder
------------------------------------------------------------
[ Add stage ]  +Add   [From] [To] ->Connect   Validate   From-Config

Canvas                             Validation
  input_1    (in: -      out: query)    + added retrieve_1
  encode_1   (in: query  out: embed)    -> connected encode_1 to retrieve_1
  retrieve_1 (in: embed  out: docs)     slot mismatch: input -> retrieve
  generate_1 (in: docs   out: answer)     (no matching slot)
  edges: input_1 -[query]-> encode_1    pipeline is valid

Docs (d) -- in-app cookbook, starters, and help browser

Docs, Cookbook & Starters
------------------------------------------------------------
Filter: [                    ]

  -- Get started --          Minimal RAG in 15 lines
    Minimal RAG                engine = GlassBoxEngine(
    Zero-config (from_env)         GlassBoxConfig.from_env())
    Ingest a folder            await engine.initialize()
  -- Cookbook --               await engine.ingest([...])
    Getting started            answer = await engine.generate("...")
    Hybrid & reranking
    Syncing sources          Ctrl+P -> "Start: ..." / "Help: ..."

Evaluation and Experiment Tracking

• Golden Datasets -- Define query-expected document pairs in YAML or JSON format for repeatable evaluation.
• Retrieval Metrics -- recall@k, precision@k, and other configurable retrieval quality metrics.
• Generation Metrics -- LLM-as-judge scoring for faithfulness, groundedness, and context relevance.
• Regression Detection -- Compare evaluation reports with configurable thresholds to catch metric regressions.
• Experiment Tracker -- Record pipeline version, dataset version, git commit, and evaluation results. Compare experiments side-by-side.

Version Management

• Pipeline Versioning -- Auto-incrementing pipeline version snapshots stored in .glassbox/versions/.
• Prompt Versioning -- Track system prompt changes in .glassbox/prompts/.
• Git Integration -- Optional git commit association and branch tracking for experiment reproducibility.

Production Infrastructure

• Rate Limiting -- Redis-backed distributed rate limiting for production use.
• Authentication -- JWT token authentication and API key validation with configurable scoping.
• OpenTelemetry -- Optional trace export to OTLP, Jaeger, Zipkin, or console backends.
• Prometheus Metrics -- Exportable Prometheus metrics for Grafana integration.
• Persistent Trace Backends -- In-memory (default), Redis, or PostgreSQL trace storage with configurable retention.

Developer Experience

• Query Pipeline Hooks -- Register before/after hooks at nine pipeline stages (PRE/POST_RETRIEVE, PRE/POST_GENERATE, PRE/POST_INGEST, PRE/POST_RERANK, ON_ERROR) with priority ordering and timeout enforcement.
• Plugin System -- Six plugin types (vector_store, database, encoder, reranker, chunker, processor) with a decorator-based registration API and test harness.
• Framework Adapters -- First-class integration with LangChain, LlamaIndex, and Haystack. Safe sync-to-async bridging for use inside running event loops (Jupyter, etc.).
• Fluent Config Builder + DI -- Build configs in code via GlassBoxConfig.builder(), and inject pre-built components with GlassBoxEngine(config, retriever=..., encoder=..., generator=...).
• Document Loaders -- Built-in text, Markdown (heading-split), JSONL, and directory loaders emitting the standard document shape.
• Plugin Registry -- Discoverable plugin/marketplace index with entry-point discovery.
• Solution Blueprints -- Opinionated one-call configurations for common use cases (customer support, legal analysis, internal KB, research assistant).
• Optional, gracefully-degrading extras -- Heavy dependencies (PyYAML, DuckDB, Redis, FastAPI, Neo4j, wasmtime, grpc) are optional; a pure-Python YAML fallback and glassbox_rag.utils.deps feature-matrix keep the core working everywhere.
• CLI -- 12 commands covering project scaffolding, querying, ingestion, evaluation, experiment tracking, index migrations, health checks, data export, documentation browsing, and TUI dashboard.
• Full Type Safety -- Complete PEP 585/604 type annotations validated with mypy. Ships with py.typed marker.

---

Installation

From PyPI

pip install glassbox-rag

With Optional Dependencies

# Common setup: embeddings + generation + token counting + Qdrant + SQLite
pip install "glassbox-rag[auto]"

# Same as auto but with ChromaDB instead of Qdrant
pip install "glassbox-rag[auto-chroma]"

# Full installation with all plugins
pip install "glassbox-rag[all]"

# Specific extras
pip install "glassbox-rag[embeddings]"          # All embedding providers
pip install "glassbox-rag[vector-stores]"       # All vector store backends
pip install "glassbox-rag[databases]"           # All database backends
pip install "glassbox-rag[multimodal]"          # PDF, image, PPTX extraction
pip install "glassbox-rag[telemetry]"           # OpenTelemetry + Prometheus
pip install "glassbox-rag[auth]"               # Redis rate limiting
pip install "glassbox-rag[generation]"         # LLM generation (OpenAI + tiktoken)
pip install "glassbox-rag[tokens]"             # Token counting (tiktoken)
pip install "glassbox-rag[reranking]"          # Cross-encoder reranking
pip install "glassbox-rag[adapters]"           # LangChain, LlamaIndex, Haystack
pip install "glassbox-rag[providers]"          # Anthropic, Groq, Hugging Face inference
pip install "glassbox-rag[graph]"              # GraphRAG (NetworkX + Neo4j)
pip install "glassbox-rag[api]"                # FastAPI serving toolkit
pip install "glassbox-rag[sync]"               # Data sync connectors (httpx)
pip install "glassbox-rag[corpus]"             # Heterogeneous corpora (DuckDB)
pip install "glassbox-rag[distributed]"        # Distributed workers (Redis Streams)
pip install "glassbox-rag[sandbox]"            # WASM plugin sandbox (wasmtime)
pip install "glassbox-rag[grpc]"               # gRPC transport

From Source

git clone https://github.com/averoe/GlassBox.git
cd glassbox-rag
pip install -e ".[dev]"

---

Quick Start

Python API

import asyncio
from glassbox_rag import GlassBoxEngine, GlassBoxConfig, Document

async def main():
    # Initialize -- auto-detects config from environment variables
    config = GlassBoxConfig.from_env()
    engine = GlassBoxEngine(config)
    await engine.initialize()

    # Ingest documents
    documents = [
        {"content": "GlassBox is a modular RAG framework.", "metadata": {"source": "docs"}},
        {"content": "It supports multiple vector stores.", "metadata": {"source": "docs"}},
    ]
    result = await engine.ingest(documents)
    print(f"Ingested {result['chunks_created']} chunks")

    # Retrieve
    results = await engine.retrieve("What is GlassBox?", top_k=5)
    for doc in results.documents:
        print(f"  [{doc.score:.3f}] {doc.content[:80]}")

    # Generate (requires generation backend configured)
    response = await engine.generate("What is GlassBox?", top_k=5)
    print(response.answer)

    # Generate with inline evaluation scoring
    response = await engine.generate("What is GlassBox?", evaluate=True)
    if response.evaluation:
        print(f"Faithfulness: {response.evaluation.faithfulness:.2f}")
        print(f"Groundedness: {response.evaluation.groundedness:.2f}")

    await engine.shutdown()

asyncio.run(main())

Zero-Config Constructor

# No config file needed -- auto-detects from environment variables
engine = GlassBoxEngine.from_env()
await engine.initialize()

Launch the Terminal Dashboard

glassbox-rag tui --config config/default.yaml

# With custom refresh interval
glassbox-rag tui --config config/default.yaml --refresh 10

Cookbook

Task-oriented, layered recipes (first pipeline → configuration → hybrid + reranking → evaluation → syncing → visual builder → custom plugins → production) live in docs/cookbooks/cookbook.md. The same content is browsable inside the TUI under the Docs tab (press d), which also groups a set of copy-paste Get started snippets above the cookbook for getting your first code running. Every snippet and recipe is reachable from the command palette via Ctrl+P → "Start: …" / "Help: …".

---

CLI Reference

GlassBox provides 12 CLI commands accessible via the glassbox-rag entry point.

init -- Scaffold a new project

glassbox-rag init [directory] [options]

Options:
  --encoder {openai,ollama,cohere,google,onnx}   Default encoder (default: openai)
  --vector-store {qdrant,chroma,faiss,pinecone}   Vector store (default: qdrant)
  --database {sqlite,postgresql}                  Database backend (default: sqlite)
  --force                                         Overwrite existing files

Creates config/default.yaml, .env template, .gitignore, and data/ directory.

tui -- Launch the terminal dashboard

glassbox-rag tui [options]

Options:
  --config PATH     Path to configuration file (default: config/default.yaml)
  --refresh INT     Auto-refresh interval in seconds (default: 5)

query -- Run a retrieval query

glassbox-rag query "your query text" [options]

Options:
  --config PATH     Configuration file path
  --top-k INT       Number of results (default: 5)
  --encoder NAME    Encoder override
  --json            Output as JSON

ingest -- Ingest files or directories

glassbox-rag ingest <paths...> [options]

Options:
  --config PATH     Configuration file path
  --encoder NAME    Encoder override
  --glob PATTERNS   Comma-separated file patterns (default: *.txt,*.md,*.csv,*.json,*.rst,*.html)
  --meta KEY=VALUE  Metadata pairs (repeatable)
  --json            Output as JSON

health -- Run a health check

glassbox-rag health [options]

Options:
  --config PATH     Configuration file path
  --json            Output as JSON

Reports status of all components: encoder, retriever, vector store, database, telemetry, reranker, and write-back.

export -- Export metrics, traces, or cache stats

glassbox-rag export [options]

Options:
  --config PATH                 Configuration file path
  --type {metrics,traces,cache,all}  Data type to export (default: all)
  --output PATH                 Output file path (default: stdout)
  --format {json,text}          Output format (default: json)

check -- Validate configuration

glassbox-rag check --config config/default.yaml

docs -- Interactive documentation browser

glassbox-rag docs [topic]
glassbox-rag docs --list

Available topics: config, retrieval, plugins, hooks, api, telemetry

eval -- Evaluation commands

# Run evaluation on a golden dataset
glassbox-rag eval run --dataset path/to/golden.yaml --config config/default.yaml \
  --metrics "recall@5,precision@5,faithfulness" [--json]

# List evaluation reports
glassbox-rag eval list [--limit 20] [--json]

# Compare two reports for regressions
glassbox-rag eval compare <report_a> <report_b> [--threshold 0.05]

experiment -- Experiment tracking

# List recorded experiments
glassbox-rag experiment list [--limit 20] [--json]

# Record a new experiment
glassbox-rag experiment record [--version INT] [--dataset STR] [--trace-id STR] [--json]

# Compare two experiments
glassbox-rag experiment compare <exp_a> <exp_b> [--json]

migrate -- Run index/metadata migrations

glassbox-rag migrate --config config/default.yaml [--target N] [--list] [--json]

Applies registered versioned index migrations (metadata transforms, dual-index re-embedding). Use --list to preview pending migrations.

version -- Show version

glassbox-rag version

---

Configuration

GlassBox uses YAML configuration with ${ENV_VAR} and ${ENV_VAR:default} substitution. A zero-config mode is also available via GlassBoxConfig.from_env().

Configuration Sections

Section	Description
server	Host, port, workers settings
logging	Log level (DEBUG/INFO/WARNING/ERROR/CRITICAL) and format (json/text)
trace	Trace backend (sqlite default/persistent, memory/redis/postgresql), retention, sample rate
encoding	Encoder setup: default encoder, cloud providers, local providers
vector_store	Vector store backend and connection settings
database	Document storage backend and connection settings
chunking	Strategy (recursive/sentence/semantic/fixed/dynamic), size, overlap
retrieval	Top-k, min score, adaptive strategies, reranking
generation	LLM backend (openai/ollama), model, temperature, system prompt
writeback	Write-back mode (read-only/protected/full), confidence threshold
metrics	Cost and latency tracking toggles
telemetry	OpenTelemetry and Prometheus export settings
security	API key requirements, CORS, rate limiting
auth	JWT authentication settings
evaluation	Evaluation backend, metrics, regression threshold
versioning	Pipeline/prompt versioning directories
git	Git integration (auto-commit, branch association)
query_rewrite	LLM-based query rewriting pre-retrieval
context_compression	LLM-based context compression pre-generation
parent_child	Parent-child document retrieval
multi_query	Multi-query expansion with reciprocal rank fusion
hyde	Hypothetical Document Embeddings pre-retrieval
corag	Chain-of-Retrieval iterative refinement
graph	GraphRAG knowledge-graph retrieval (backend, hops, Neo4j connection)
citations	Source attribution: inline citation markers + reference list on answers
contextual_rag	Prepend a document-level context summary to each chunk before embedding
multi_tenancy	Auto-tag ingest + filter retrieval by the active tenant
indexing_extras	Drift detection, freshness validation, lineage tracking
plugins	Custom plugin loading

Example Configuration

encoding:
  default_encoder: "openai"
  cloud:
    openai:
      api_key: "${OPENAI_API_KEY}"
      model: "text-embedding-3-small"
      embedding_dim: 1536

vector_store:
  type: "qdrant"
  qdrant:
    host: "localhost"
    port: 6333
    collection_name: "glassbox_docs"

database:
  type: "sqlite"
  sqlite:
    path: "./data/glassbox.db"

chunking:
  strategy: "recursive"
  chunk_size: 512
  chunk_overlap: 50

retrieval:
  top_k: 5
  min_score: 0.3
  rerank_enabled: false
  adaptive:
    enabled: true
    strategies:
      - name: "semantic"
      - name: "hybrid"
        weight_semantic: 0.6
        weight_keyword: 0.4

generation:
  backend: "openai"
  model: "gpt-4o-mini"
  temperature: 0.7

trace:
  enabled: true
  backend: "memory"

writeback:
  enabled: true
  mode: "protected"
  protected:
    confidence_threshold: 0.8

metrics:
  enabled: true
  track_tokens: true
  track_latency: true
  track_cost: true

---

Terminal Dashboard (TUI)

The TUI is built with Textual and runs the GlassBox engine in-process -- no separate server required.

Launch with glassbox-rag tui after configuring your project.

A Glimpse

Text renderings of the Overview, Pipeline, and Playground tabs (the live UI is a full-colour Textual app):

Overview
------------------------------------------------------------
Requests  1,284     p95 latency  412 ms    Tokens  1.9M
Cost      $12.47    Errors       0.3 %     Uptime  4h 12m

Components
  encoder   [ready]    vector_store [qdrant]   database [postgres]
  reranker  [off]      generator    [openai]   traces   [sqlite]

Recent activity
  14:02:11  retrieve  "vector db options"    142 ms  ok
  14:02:09  generate  "how does attention?"  890 ms  ok
  14:02:04  ingest    47 chunks                2.1 s  ok

q Quit   r Refresh   1-9,0 Tabs   b Builder   d Docs   Ctrl+P Palette

Pipeline   (actual enabled stages: HyDE + rerank on)
------------------------------------------------------------
Query -> HyDE -> Encode -> Retrieve -> Rerank -> Generate -> Output
 2.1ms   48.7ms   15.3ms    82.4ms     12.0ms    41.2ms     3.1ms

Execution complete   strategy: hybrid   results: 5   total: 204.8 ms

Query Playground     query: "how does attention work?"
------------------------------------------------------------
  A  top_k=3   (58.2 ms)        B  top_k=10  (71.9 ms)
  1 (0.912) Attention maps...   1 (0.912) Attention maps...
  2 (0.874) Scaled dot-prod...  2 (0.874) Scaled dot-prod...
  3 (0.841) Multi-head att...   ... 10 results

overlap: jaccard 0.30   -   latency delta +13.7 ms

A text rendering of every tab is in Screen Snapshots.

Tabs

Tab	Key	Description
Overview	1	System health, live metrics (requests, latency, tokens, cost), component status.
Pipeline	2	Interactive pipeline visualization. Run test queries and observe step-by-step execution with timing. Supports keyboard-driven step reordering (Alt+Up/Down).
Debugger	3	Browse execution traces, filter by status, inspect individual trace steps with duration breakdown, anomaly detection, and trace export.
Telemetry	4	Latency/throughput charts, per-operation cost breakdown, and performance percentile tables (p50/p95/p99).
Config and Plugins	5	Interactive configuration editor and plugin documentation with code examples and inline API reference for all plugin types.
Ingest	6	File and directory ingestion interface with progress tracking and metadata attachment.
Evaluation	7	Run evaluation suites, view per-query scores, and browse historical evaluation reports.
Experiments	8	Record experiments, list historical runs, and compare experiment metrics side-by-side.
Logs	9	Live structured log stream with level filtering and text search.
Playground	0	Side-by-side A/B query comparison with document-overlap and latency diff.
Replay	--	Step through a recorded pipeline execution (open via Command Palette).
Builder	b	Visual pipeline builder canvas with a typed-slot stage catalog. Connections are validated by slot type and rejected on cycles; seed from the live config.
Docs	d	In-app cookbook, get-started starters, and help browser with a searchable, grouped topic sidebar.

Keyboard Shortcuts

Key	Action
q	Quit
r	Refresh all data
1 -- 9, 0	Switch tabs
Ctrl+P	Command palette (fuzzy action search)
Ctrl+E	Export current config
Alt+↑ / Alt+↓	Reorder pipeline steps (Pipeline tab)
? / F1	Keyboard shortcuts help

---

Python API Reference

Core Engine Methods

Method	Description
engine.retrieve(query, top_k=5, encoder=None)	Retrieve relevant documents for a query. Returns RetrievalResult.
engine.ingest(documents, encoder=None)	Ingest documents into the pipeline. Accepts list of dicts with content and metadata keys.
engine.update(document_id, content, metadata=None, confidence_score=1.0)	Update a document with write-back protection. Returns WriteBackResult.
engine.generate(query, encoder=None, top_k=None, system_prompt=None, temperature=None, max_tokens=None, evaluate=False)	Retrieve + generate an answer. Returns GenerateResponse. Set evaluate=True for inline scoring.
engine.stream(query, encoder=None, top_k=None, system_prompt=None, temperature=None, max_tokens=None)	Async streaming retrieve + generate. Yields StreamEvent objects (type: token, metadata, done).
engine.batch_ingest(documents, batch_size=50, encoder=None, on_progress=None, deduplicate=True, max_concurrent_batches=3)	Concurrent batch ingestion with progress callbacks and automatic deduplication.

Observability Methods

Method	Description
engine.list_traces(limit=100)	List execution traces.
engine.get_trace(trace_id)	Get a single trace with full step tree.
engine.get_metrics_summary()	JSON metrics summary (requests, cost, latency, histograms).
engine.get_telemetry_status()	OTel and Prometheus configuration and connection status.
engine.get_cache_stats()	Embedding cache hit/miss/eviction statistics.
engine.get_chunk_report()	Chunk size distribution report.
engine.get_hook_info()	Registered lifecycle hooks and their configuration.
engine.get_token_counter()	Access the engine's token counter for budget allocation.

Constructors

Constructor	Description
GlassBoxEngine(config)	Standard constructor from a GlassBoxConfig object.
GlassBoxEngine.from_env()	Zero-config constructor. Auto-detects API keys and backends from environment variables.
GlassBoxConfig.from_env()	Build configuration purely from environment variables (OPENAI_API_KEY, OLLAMA_BASE_URL, GLASSBOX_*, etc.).

Response Types

GenerateResponse -- returned by engine.generate():

Field	Type	Description
answer	str	Generated answer text
sources	list[dict]	Source documents used for generation
retrieval	dict	Retrieval metadata (strategy, timing, count)
generation	dict	Generation metadata (model, tokens, timing)
trace_id	str	Trace ID for debugging
pipeline_version	int	Current pipeline version number
evaluation	InlineEvaluation or None	Faithfulness, groundedness, context_relevance scores (only when evaluate=True)

Supports dict-like access for backward compatibility: response["answer"] works alongside response.answer.

---

Plugin Architecture

GlassBox supports six plugin types. The plugin registry is thread-safe and supports both built-in and third-party plugins.

Supported Plugin Types

Type	Description	Built-in Implementations
vector_store	Vector similarity search	Qdrant, Chroma, FAISS, Pinecone, Weaviate, Supabase
database	Document storage	PostgreSQL, SQLite, MongoDB, MySQL, Supabase
encoder	Text embedding providers	OpenAI, Cohere, Google, Ollama, ONNX, Hugging Face
reranker	Post-retrieval reranking	Cross-encoder, Cohere, Hugging Face
chunker	Text chunking strategies	Recursive, Sentence, Semantic, Fixed
processor	Content processors	PDF, Image (OCR), PPTX

Writing a Vector Store Plugin

from glassbox_rag.plugins.base import VectorStorePlugin

class CustomVectorStore(VectorStorePlugin):
    async def initialize(self) -> bool:
        # Connect to your backend
        return True

    async def shutdown(self) -> None:
        # Clean up connections
        pass

    async def health_check(self) -> bool:
        return True

    async def add_vectors(self, vectors, ids=None, contents=None, metadata=None):
        # Store vectors and return list of IDs
        return ["id-1", "id-2"]

    async def search(self, query_vector, top_k=5):
        # Return list of (id, score, content, metadata) tuples
        return [("id-1", 0.95, "document text", {"source": "example"})]

    async def get_vector(self, vector_id):
        return {"id": vector_id, "content": "...", "metadata": {}}

    async def delete_vector(self, vector_id):
        return True

    async def count(self):
        return 42

Writing a Database Plugin

from glassbox_rag.plugins.base import DatabasePlugin

class CustomDatabase(DatabasePlugin):
    async def initialize(self) -> bool:
        return True

    async def shutdown(self) -> None:
        pass

    async def health_check(self) -> bool:
        return True

    async def connect(self) -> bool:
        return True

    async def insert(self, table, data):
        return "record-id"

    async def update(self, table, record_id, data):
        return True

    async def delete(self, table, record_id):
        return True

    async def query(self, table, filters=None):
        return [{"id": "1", "content": "..."}]

    async def search_text(self, terms, top_k=10):
        return [{"id": "1", "content": "...", "score": 0.9}]

    async def ensure_tables(self) -> None:
        pass

Registering Custom Plugins

Decorator-based registration:

from glassbox_rag.plugins.sdk import register_plugin

@register_plugin("vector_store", "my_store")
class MyStore(VectorStorePlugin):
    ...

Programmatic registration:

from glassbox_rag.plugins.sdk import plugin_registry

plugin_registry.register("vector_store", "my_store", MyStore)

Configuration-based loading:

plugins:
  custom:
    - type: vector_store
      name: my_store
      module: mypackage.my_vector_store
      class: MyVectorStore

Plugin Test Harness

from glassbox_rag.plugins.sdk import PluginTestHarness

results = await PluginTestHarness.test_vector_store(my_store_instance)
print(results["passed"])   # ["initialize", "health_check", "add_vectors", ...]
print(results["failed"])   # []

Query Pipeline Hooks

from glassbox_rag import GlassBoxEngine, HookPoint

engine = GlassBoxEngine(config)
await engine.initialize()

# Decorator-based registration
@engine.hooks.on(HookPoint.POST_RETRIEVE, priority=10)
async def log_results(context):
    print(f"Retrieved {len(context['documents'])} documents")
    return context

# Direct registration
async def filter_low_scores(context):
    context["documents"] = [d for d in context["documents"] if d.score > 0.5]
    return context

engine.hooks.register(HookPoint.POST_RETRIEVE, filter_low_scores, priority=20)

Available Hook Points:

Hook Point	Trigger
HookPoint.PRE_RETRIEVE	Before query encoding and retrieval
HookPoint.POST_RETRIEVE	After retrieval, before returning results
HookPoint.PRE_GENERATE	Before LLM generation (RAG context ready)
HookPoint.POST_GENERATE	After LLM response is generated
HookPoint.PRE_INGEST	Before document ingestion
HookPoint.POST_INGEST	After document ingestion (drift detector runs here)
HookPoint.PRE_RERANK	Before reranking pass
HookPoint.POST_RERANK	After reranking pass
HookPoint.ON_ERROR	When any pipeline operation fails (errors swallowed)

Hooks execute in priority order (lower values run first). Each hook has a configurable timeout (default: 30 seconds).

---

Framework Adapters

GlassBox integrates with popular LLM frameworks. All adapters include safe sync-to-async bridging for use inside running event loops (Jupyter, Colab, etc.).

LangChain

from glassbox_rag.adapters.langchain import GlassBoxRetriever, GlassBoxEmbeddings

# As a retriever (LCEL-compatible)
retriever = GlassBoxRetriever(engine=engine, top_k=5, encoder="openai")
docs = await retriever.ainvoke("your query")        # async (primary)
docs = retriever.invoke("your query")               # sync (auto-bridged)

# As an embeddings provider
embeddings = GlassBoxEmbeddings(engine=engine, encoder="openai")
vectors = await embeddings.aembed_documents(["text1", "text2"])
query_vec = await embeddings.aembed_query("your query")

Registers as a virtual subclass of langchain_core.retrievers.BaseRetriever and langchain_core.embeddings.Embeddings when langchain-core is installed.

LlamaIndex

from glassbox_rag.adapters.llamaindex import GlassBoxQueryEngine, GlassBoxLlamaRetriever

# As a query engine (retrieve + generate)
query_engine = GlassBoxQueryEngine(engine=engine, top_k=5)
response = await query_engine.aquery("your query")  # returns llama_index Response

# As a retriever only
retriever = GlassBoxLlamaRetriever(engine=engine, top_k=5)
nodes = await retriever.aretrieve("your query")     # returns NodeWithScore list

Haystack

from glassbox_rag.adapters.haystack import GlassBoxHaystackRetriever

# As a Haystack component (decorated with @component when haystack-ai is installed)
retriever = GlassBoxHaystackRetriever(engine=engine, top_k=5)
result = await retriever.run(query="your query")
haystack_docs = result["documents"]

Install adapters with:

pip install "glassbox-rag[adapters-langchain]"    # langchain-core
pip install "glassbox-rag[adapters-llamaindex]"    # llama-index-core
pip install "glassbox-rag[adapters-haystack]"      # haystack-ai
pip install "glassbox-rag[adapters]"               # all three

---

Evaluation System

GlassBox includes a built-in evaluation framework for measuring retrieval and generation quality.

Golden Datasets

Define expected results in YAML:

queries:
  - query: "What is GlassBox?"
    expected_doc_ids: ["doc-1", "doc-3"]
    expected_answer_keywords: ["modular", "RAG", "framework"]
  - query: "How does chunking work?"
    expected_doc_ids: ["doc-5"]

Running Evaluations

from glassbox_rag.evaluation import EvaluationRunner, GoldenDataset

dataset = GoldenDataset.from_yaml(".glassbox/datasets/golden.yaml")
runner = EvaluationRunner(pipeline=engine, eval_config=config.evaluation)
report = await runner.run(dataset, metrics=["recall@5", "precision@5", "faithfulness"])
print(report.summary())

Regression Testing

regression = report_a.compare(report_b, threshold=0.05)
print(regression.summary())

CLI Evaluation

glassbox-rag eval run --dataset golden.yaml --metrics "recall@5,faithfulness"
glassbox-rag eval compare report_a.json report_b.json --threshold 0.05

---

Experiment Tracking

Record and compare pipeline experiments with full provenance tracking.

from glassbox_rag import ExperimentTracker

tracker = ExperimentTracker.from_workspace()

# Record an experiment
record = tracker.record(config_version=1, dataset_version="v2", trace_id="abc123")

# List experiments
experiments = tracker.list(limit=20)

# Compare two experiments
report = tracker.compare("exp-a-id", "exp-b-id")
print(report.summary)

Experiments are stored in .glassbox/experiments/ and include:

• Pipeline version number
• Git commit hash (auto-detected)
• Dataset version identifier
• Evaluation metric results
• Timestamp

---

Version Management

from glassbox_rag import VersionManager

vm = VersionManager()
vm.snapshot(config)                    # Save current pipeline configuration
current_version = vm.current_version   # Get current version number

Versioning state is stored in .glassbox/versions/ with auto-incrementing version numbers. The pipeline version is automatically attached to every GenerateResponse.

---

Advanced Retrieval Strategies

GlassBox supports configurable retrieval strategies that can be composed as pipeline hooks.

Query Rewriting

LLM-based query rewriting for improved retrieval quality:

query_rewrite:
  enabled: true
  prompt_template: "Rewrite the following user query into a clearer form.\n\nQuery: {query}"

Context Compression

Post-retrieval context compression to reduce noise before generation:

context_compression:
  enabled: true

Parent-Child Retrieval

Retrieve child chunks but return parent documents for broader context:

parent_child:
  enabled: true

Multi-Query Expansion

Expand a single query into multiple sub-queries with reciprocal rank fusion:

multi_query:
  enabled: true
  num_queries: 3
  fusion_method: "rrf"

HyDE (Hypothetical Document Embeddings)

Embed a generated hypothetical answer instead of the raw query:

hyde:
  enabled: true
  keep_original: true   # append the original query to the hypothetical passage

Chain-of-Retrieval (CoRAG)

Iteratively refine retrieval over multiple LLM-guided steps (requires a generation backend):

corag:
  enabled: true
  max_steps: 3
  docs_per_step: 5

When enabled, engine.retrieve() runs the chain and returns fused, de-duplicated documents with strategy="corag".

GraphRAG

Multi-hop knowledge-graph retrieval. When enabled, a graph strategy is registered and the adaptive router sends relationship / multi-entity queries to it:

graph:
  enabled: true
  backend: "networkx"     # networkx (in-process) or neo4j
  max_hops: 2
  max_seeds: 5
  # Neo4j connection (backend: neo4j)
  uri: "bolt://localhost:7687"
  user: "neo4j"
  password: "${NEO4J_PASSWORD}"
  database: "neo4j"

# Programmatic graph construction
from glassbox_rag.plugins.graph_stores import create_graph_store, Entity, Relationship

store = create_graph_store("networkx")
await store.initialize()
await store.upsert_entity(Entity(id="1", name="Transformers", type="architecture"))
await store.upsert_entity(Entity(id="2", name="Attention", type="mechanism"))
await store.upsert_relationship(Relationship("1", "2", type="uses"))

Contextual RAG

Prepend a document-level context summary to each chunk before embedding (Anthropic-style Contextual Retrieval), improving recall for ambiguous chunks:

contextual_rag:
  enabled: true
  max_context_chars: 200

TreeRAG (Hierarchical Summaries)

Build a RAPTOR-style summary tree and ingest all levels (collapsed-tree retrieval):

from glassbox_rag.core.strategies import TreeRAGBuilder

tree = await TreeRAGBuilder(generator=engine._generator, branching=4).build(chunk_texts)
await engine.ingest(tree.to_documents())   # leaves + multi-level summaries

Dynamic Chunking

Content-adaptive chunk sizing (dense/structured text → smaller chunks, prose → larger):

chunking:
  strategy: "dynamic"
  chunk_size: 512      # base target; adapts per document

Automated Optimization

Tune retrieval parameters against a golden set (closes the optimize↔evaluate loop):

from glassbox_rag.benchmarks import BenchmarkCase

cases = [BenchmarkCase(query="...", relevant_ids={"doc-1"})]
result = await engine.auto_tune(cases)   # searches top_k/min_score, applies best
print(result["best_params"])

Multi-Tenancy

Isolate tenants at ingest + query time:

multi_tenancy:
  enabled: true

from glassbox_rag.core.tenancy import TenantContext, tenant_scope

engine.tenant_manager.register(TenantContext("acme"))
with tenant_scope(engine.tenant_manager, "acme"):
    await engine.ingest(docs)              # chunks tagged _tenant=acme
    result = await engine.retrieve("...")  # results filtered to acme

Indexing Extras

indexing_extras:
  drift_detection_enabled: true    # Detect embedding drift across ingestion batches
  drift_threshold: 0.15            # Cosine distance threshold for drift alerts
  freshness_validation: true       # Warn if encoder config changed since last ingestion
  lineage_tracking: true           # Track chunk-to-document lineage metadata

---

GraphRAG and Agentic RAG

Agentic RAG

RAGAgent performs multi-hop, tool-using reasoning with self-correction:

from glassbox_rag.core.agent import RAGAgent, Tool

async def retrieve_fn(query: str):
    result = await engine.retrieve(query, top_k=5)
    return result.documents

async def calculator(expression: str = ""):
    return eval(expression, {"__builtins__": {}})  # illustrative only

agent = RAGAgent(retrieve_fn, generator=engine._generator, max_iterations=3)
agent.register_tool(Tool("calc", "evaluate arithmetic", calculator))

result = await agent.run("How do Transformers relate to attention, and why?")
print(result.answer)
for step in result.steps:      # full, inspectable reasoning trace
    print(step.kind, step.detail)

Adaptive Query Router

from glassbox_rag.core.query_router import QueryComplexityClassifier

router = QueryComplexityClassifier(available_strategies=["semantic", "hybrid", "graph", "corag"])
decision = router.classify("Compare the relationship between BERT and GPT")
print(decision.complexity, decision.strategy)   # complex -> graph/corag

---

Source Attribution and Provenance

Enable citations in config and engine.generate() automatically attaches inline markers + a reference list and returns a structured citations list:

citations:
  enabled: true
  style: "numeric"    # numeric | bracket | footnote
  inline: true        # rewrite the answer with [n] markers + a Sources block
  min_overlap: 0.15

response = await engine.generate("What is GlassBox?")
print(response.answer)        # "... modular RAG framework [1]. ... Sources: [1] ..."
print(response.citations)     # [{"index": 1, "document_id": "...", "title": "...", ...}]

You can also use the renderer and provenance tracker directly:

from glassbox_rag.core.citations import CitationRenderer, CitationStyle
from glassbox_rag.core.provenance import ProvenanceTracker

# Attach inline citations to an answer
renderer = CitationRenderer(style=CitationStyle.NUMERIC)
attributed = renderer.attribute(answer_text, retrieved_documents)
print(attributed.with_references())

# Track full chunk lineage: source -> document -> chunk -> embedding
tracker = ProvenanceTracker()
tracker.track(chunk_id="c1", content="...", source_id="doc.pdf",
              document_id="d1", encoder="openai", embedding_dim=1536)
print(tracker.lineage("c1"))

---

Enterprise Security

The glassbox_rag.security package provides opt-in, composable controls:

from glassbox_rag.security import (
    SecurityGateway, RBACManager, Principal, Permission,
    PIIDetector, PromptInjectionDetector, AuditLog,
)

rbac = RBACManager()
rbac.add_principal(Principal(id="alice", roles={"viewer"}))

gateway = SecurityGateway(rbac=rbac, audit=AuditLog(".glassbox/audit.log"))

result = gateway.guard_query(user_query, retrieved_documents, principal_id="alice")
if result.allowed:
    context = result.documents          # ACL-filtered + PII-redacted + injection-sanitised
else:
    print("Blocked:", result.reason)    # e.g. prompt_injection_blocked

• RBAC -- roles/permissions with per-document ACL filtering.
• PII -- PIIDetector.redact(text, reversible=True) returns a token map for later restoration.
• Prompt injection -- scans queries and retrieved documents (indirect injection).
• Audit -- append-only, hash-chained trail (AuditLog.verify_chain()).

---

API Toolkit and Serverless

# FastAPI app (requires glassbox-rag[api])
from glassbox_rag.api_toolkit import create_api

app = create_api(engine, require_api_key="my-secret")
# uvicorn module:app  →  POST /query, POST /generate, POST /ingest, GET /health

# AWS Lambda handler (no web framework required)
from glassbox_rag.api_toolkit import lambda_handler_factory

def build_engine():
    return GlassBoxEngine.from_env()

handler = lambda_handler_factory(build_engine)   # (event, context) -> response

A GCP cloud_function_factory is also provided.

---

Data Syncing and Connectors

from glassbox_rag.sync import SyncEngine, FileSystemConnector, SyncLedger

sync = SyncEngine(
    FileSystemConnector("./knowledge", glob="**/*.md"),
    engine,
    ledger=SyncLedger(".glassbox/sync.json"),
)
delta = await sync.sync_once()      # ingests added/changed, deletes removed
print(delta.summary())              # {"added": .., "changed": .., "deleted": .., "unchanged": ..}

# Or run continuously
# await sync.run_polling(interval_seconds=300)

# Network-backed sources (pip install glassbox-rag[sync]):
from glassbox_rag.sync import GoogleDriveConnector, ConfluenceConnector

drive = GoogleDriveConnector(folder_id="abc123")   # GOOGLE_APPLICATION_CREDENTIALS
wiki = ConfluenceConnector(space_key="ENG")        # CONFLUENCE_URL / _USER / _TOKEN

Connectors: FileSystemConnector, WebhookConnector, HTTPConnector, NotionConnector, GoogleDriveConnector, ConfluenceConnector. The network-backed connectors read credentials from the environment, request read-only scopes, and degrade gracefully (log + return []) when a dependency or credential is missing. Each accepts seed_documents=[...] for offline/unit testing.

---

Heterogeneous Data Corpora

from glassbox_rag.corpus import TabularCorpus, TextToSQL, TableSchema, ConversationalCorpus

# Tabular (DuckDB, with a stdlib csv fallback)
tc = TabularCorpus()
tc.load("data/products.csv")
docs = tc.rows_to_documents(template="{name}: {description} (${price})")

# Text-to-SQL over a declared schema
t2s = TextToSQL([TableSchema("users", {"id": "int", "name": "text"})],
                generator=engine._generator, read_only=True)
sql = await t2s.to_sql("how many users are there?")

Adapters:

• Text-to-SQL — dialect-aware (SQLite/PostgreSQL/MySQL) with schema validation and read-only guards.
• Tabular — CSV / Parquet / Excel (.xlsx) via DuckDB (with a stdlib CSV fallback).
• Object storage — S3, Google Cloud Storage, and Azure Blob (sync-ledger friendly).
• NoSQL — MongoDB, DynamoDB, and Elasticsearch (BM25 + dense-vector hybrid). Connectors accept principal_roles for RBAC data-layer filtering.
• Conversational — ConversationDocument with temporal (before/after) filters and adjacent-window expansion.

---

Context Engine and Personalization

from glassbox_rag.context import ContextFusion, ContextSource
from glassbox_rag.personalization import SessionMemory, Turn, UserContextLayer, UserProfile

# Fuse results from multiple retrievers (RRF)
fused = ContextFusion().fuse([
    ContextSource("vector", vector_docs, weight=1.0),
    ContextSource("graph", graph_docs, weight=0.8),
], top_k=10)

# Session memory with follow-up resolution
mem = SessionMemory(max_turns=10)
mem.add("session-1", Turn("Tell me about Transformers", answer))
standalone = mem.resolve_followup("session-1", "how does it scale?")

# Per-user personalization (interest boosting + source filtering)
users = UserContextLayer()
users.upsert(UserProfile(user_id="u1", interests=["python"], allowed_sources={"wiki"}))
ranked = users.personalize("u1", documents)

---

Automated Optimization

from glassbox_rag.optimization import AutoTuner, ParamSpace, FeedbackLearner, FeedbackEvent

# Search retrieval parameters against an async objective (e.g. eval recall)
space = ParamSpace().add("top_k", [3, 5, 10]).add("alpha", [0.3, 0.5, 0.7])
async def objective(params):
    return await evaluate_recall(params)
result = await AutoTuner().optimize(space, objective, n_trials=20)
print(result.best_params, result.best_score)

# Continuous feedback learning re-ranks by learned quality priors
learner = FeedbackLearner()
learner.record(FeedbackEvent(query="python tutorial", document_id="doc1", signal=1.0))
reranked = learner.rerank("python tutorial", documents)

---

Scale and Distributed Mode

from glassbox_rag.distributed import create_task_queue, Worker, Task
from glassbox_rag.core.tenancy import TenantManager, TenantContext, TenantQuota
from glassbox_rag.core.events import EventBus, QueryReceived, ChunksRetrieved

# Distributed task queue (in-memory default; Redis Streams via [distributed])
queue = create_task_queue("redis", url="redis://localhost:6379")
worker = Worker(queue)

@worker.handler("ingest")
async def _ingest(task: Task):
    await engine.batch_ingest(task.payload["documents"])

# Multi-tenancy with quotas + namespace isolation
tenants = TenantManager()
tenants.register(TenantContext("acme", quota=TenantQuota(max_documents=100_000)))

# Typed event bus — every engine exposes one as an observability channel.
# The engine publishes retrieval.chunks and generation.answer events; subscribe
# without touching internals. (Hooks are never double-fired: the bus is not
# auto-bridged — call engine.event_bus.bridge_hook_manager(engine.hooks) to opt in.)
engine.event_bus.subscribe(ChunksRetrieved.name, lambda e: print("retrieved:", e.payload))

The adaptive retriever can also delegate strategy selection to the shared QueryComplexityClassifier instead of its built-in heuristic:

retrieval:
  adaptive:
    enabled: true
    use_router: true      # route via QueryComplexityClassifier (one routing brain)

Also included: SQLite WAL crash recovery (glassbox_rag.core.wal), versioned index migrations (glassbox_rag.versioning.migrations), an AOT pipeline compiler (glassbox_rag.pipelines.compiler), and a gRPC transport (glassbox_rag.transport).

---

Solution Blueprints

Opinionated, one-call configurations for common use cases:

from glassbox_rag.blueprints import get_blueprint, list_blueprints

blueprint = get_blueprint("customer_support")   # or legal_analysis, internal_kb, research_assistant
config = blueprint.to_config()
engine = GlassBoxEngine(config)

---

Observability

OpenTelemetry

Enable OTLP export in configuration:

telemetry:
  otel_enabled: true
  otel_exporter: "otlp"              # otlp, jaeger, zipkin, console
  otel_endpoint: "http://localhost:4317"
  service_name: "glassbox-rag"

Prometheus Metrics

Metrics can be exported in Prometheus text format via the engine's telemetry module:

content = engine.telemetry.get_prometheus_metrics()

Tracked metrics include: total requests, tokens used, estimated cost, per-operation latency percentiles (p50/p95/p99), embedding cache hit rate, active request count, vector and chunk counts.

Trace Storage

Traces can be stored in memory (default), Redis, or PostgreSQL:

trace:
  enabled: true
  backend: "redis"            # or "postgresql" or "memory"
  retention_days: 30
  sample_rate: 1.0            # 0.0 to 1.0

---

Deployment

GlassBox is a CLI / TUI tool and library (there is no built-in HTTP server — expose one yourself with the optional API toolkit). The provided Docker assets package the CLI and stand up its backing services.

Docker

docker build -t glassbox-rag .

# Any CLI command (the TUI needs an interactive terminal, so add -it):
docker run --rm -it -v "$(pwd)/config:/app/config:ro" \
  -e OPENAI_API_KEY=your_key glassbox-rag tui

Docker Compose (local stack)

docker-compose.yml brings up the backing services (Qdrant, PostgreSQL, Redis, and optional Ollama) and a glassbox container wired to them via from_env():

docker compose up -d qdrant postgres redis      # start backends
docker compose run --rm glassbox tui            # launch the dashboard
docker compose run --rm glassbox ingest ./data  # or any CLI command
docker compose --profile ollama up -d ollama    # optional local models

Integration testing

docker-compose.test.yml runs the integration suite against real Qdrant / PostgreSQL / Redis / Chroma backends:

docker compose -f docker-compose.test.yml up --build --abort-on-container-exit

---

Architecture

  CLI / Python API
       |
       v
  GlassBox Engine
       |
       +-- Encoding Layer -----> [OpenAI | Cohere | Ollama | ONNX | Google | HF]
       +-- Chunker ------------> [Recursive | Sentence | Semantic | Fixed]
       +-- Deduplicator -------> [Content Hash | SimHash | Semantic Embedding]
       +-- Retriever ----------> [Semantic | Keyword | Hybrid | Adaptive]
       +-- Reranker -----------> [Cross-Encoder | Cohere | HuggingFace]
       +-- Generator ----------> [OpenAI | Ollama] (with streaming)
       +-- Vector Store -------> [Qdrant | Chroma | FAISS | Pinecone | Weaviate | Supabase]
       +-- Database -----------> [PostgreSQL | SQLite | MongoDB | MySQL | Supabase]
       +-- Write-Back Manager -> [Protected | Full | Read-Only]
       +-- Hook Manager -------> [Pre/Post Retrieve | Pre/Post Generate | ...]
       |
       +-- Trace Tracker ------> [Memory | Redis | PostgreSQL]
       +-- Metrics Tracker ----> [Prometheus | JSON]
       +-- Telemetry ----------> [OpenTelemetry | Console]
       |
       +-- Evaluation ----------> [Retrieval Metrics | Generation Metrics | Regression]
       +-- Experiments ---------> [Record | Compare | Git Association]
       +-- Versioning ----------> [Pipeline Snapshots | Prompt Versioning]
       |
       +-- Strategies ----------> [Query Rewrite | Multi-Query | Parent-Child | Context Compression | HyDE | CoRAG]
       +-- GraphRAG ------------> [NetworkX | Neo4j] + Graph Retriever (multi-hop)
       +-- Agentic RAG ---------> [Tool Calling | Multi-Hop | Self-Correction]
       +-- Security ------------> [RBAC | PII | Prompt-Injection | Audit | Classification]
       +-- Indexing Extras -----> [Drift Detection | Freshness Validation | Lineage Tracking]
       |
       v
  Terminal Dashboard (TUI)
       |
       +-- Overview (live metrics + health)
       +-- Pipeline (interactive visualization)
       +-- Debugger (trace inspection)
       +-- Telemetry (charts + tables)
       +-- Config & Plugins (editor + docs)
       +-- Ingest (file ingestion)
       +-- Evaluation (golden dataset scoring)
       +-- Experiments (experiment management)

Project Structure

glassbox-rag/
  src/glassbox_rag/
    __init__.py              # Public API and version
    __main__.py              # python -m glassbox_rag support
    config.py                # Pydantic v2 configuration models
    cli.py                   # CLI entry point (12 commands)
    core/
      engine.py              # Main orchestrator
      retriever.py           # Adaptive retrieval strategies
      chunker.py             # Text chunking with size monitoring
      encoder.py             # Modular encoding layer with cache
      generator.py           # LLM generation with streaming
      hooks.py               # Pipeline hook system with timeouts
      dedup.py               # Document deduplication (exact, fuzzy, semantic)
      tokens.py              # Token counting with context window budgeting
      writeback.py           # Write-back protection
      metrics.py             # Cost and latency tracking
      reranker.py            # Cross-encoder reranking
      indexing_extras.py     # Lineage, drift detection, freshness validation
      agent.py               # Agentic RAG (tool calling, self-correction)
      graph_retriever.py     # GraphRAG multi-hop retrieval
      citations.py           # Source attribution / citation rendering
      provenance.py          # Chunk lineage tracking
      query_router.py        # Adaptive query complexity router
      events.py              # Typed event bus
      tenancy.py             # Multi-tenancy + quotas
      wal.py                 # SQLite WAL crash recovery
      strategies/
        query_rewrite.py     # LLM-based query rewriting hook
        multi_query.py       # Multi-query expansion with RRF
        parent_child.py      # Parent-child document retrieval
        context_compression.py  # LLM-based context compression
        hyde.py              # Hypothetical Document Embeddings hook
        corag.py             # Chain-of-Retrieval strategy
    trace/
      tracker.py             # Concurrency-safe trace system
      visualizer.py          # ASCII trace rendering
      backends.py            # Persistent trace storage (Redis, PostgreSQL)
    evaluation/
      runner.py              # Evaluation runner and reporting
      datasets.py            # Golden dataset loader
      retrieval.py           # Retrieval quality metrics
      generation.py          # LLM-as-judge generation metrics
    experiments/
      experiment_tracker.py  # Experiment recording and comparison
    versioning/
      version_manager.py     # Pipeline and prompt versioning
    git/
      manager.py             # Git integration for experiment provenance
    pipelines/
      manager.py             # Multi-pipeline workspace management
      dag.py                 # DAG-based pipeline execution
      context.py             # Typed pipeline context
      introspect.py          # Config-driven pipeline description
      compiler.py            # AOT pipeline compiler
    tui/
      app.py                 # Main TUI application
      client.py              # In-process engine client
      theme.py               # TUI styling
      commands.py            # Command palette provider (Ctrl+P)
      screens/               # Overview, Pipeline, Debugger, Telemetry, Config,
                             # Ingest, Evaluation, Experiments, Logs, Playground, Replay
      widgets/               # Reusable TUI widgets (metric cards,
                             # trace tree, pipeline view, etc.)
    plugins/
      base.py                # Abstract plugin interfaces
      sdk.py                 # Plugin registry and developer SDK
      registry.py            # Plugin marketplace index + entry-point discovery
      vector_stores/         # Qdrant, Chroma, FAISS, Pinecone, Weaviate
      databases/             # PostgreSQL, SQLite, MongoDB, MySQL
      graph_stores/          # NetworkX + Neo4j (GraphRAG)
      supabase.py            # Supabase (vector store + database)
    security/                # RBAC, PII, prompt-injection, audit, gateway
    api_toolkit/             # FastAPI app + serverless handlers
    sync/                    # Sync engine, connectors, delta ledger
    corpus/                  # Text-to-SQL, tabular, object storage, NoSQL, conversational
    context/                 # Context fusion, real-time nodes, streaming ingestion
    personalization/         # Session memory, user context layer
    optimization/            # Auto-tuning, feedback learning
    distributed/             # Task queue (in-memory + Redis Streams), workers
    accel/                   # Accelerated kernels (cosine, BM25, SimHash, LRU)
    sandbox/                 # WASM plugin sandbox
    transport/               # gRPC service + .proto
    blueprints/              # Solution blueprints
    benchmarks/              # Reproducible benchmark harness
    loaders.py               # Document loaders (text, markdown, jsonl, dir)
    adapters/
      langchain.py           # LangChain retriever and embeddings
      llamaindex.py          # LlamaIndex query engine and retriever
      haystack.py            # Haystack component retriever
      _utils.py              # Sync-to-async bridging utilities
    utils/
      auth.py                # JWT authentication + Redis rate limiting
      multimodal.py          # PDF, image, PPTX extraction
      telemetry.py           # OTel + Prometheus integration
      logging.py             # Structured logging
      helpers.py             # Shared utility functions
  config/
    default.yaml             # Default configuration
  examples/                  # Example scripts
  tests/                     # Unit and integration test suites

---

Testing

# Run all tests
pytest tests/ -v

# Run with coverage
pytest tests/ --cov=glassbox_rag --cov-report=html

# Run specific categories
pytest tests/unit/ -v
pytest tests/integration/ -v

# Run integration tests that require external services
pytest tests/integration/ -v -m integration_real

---

Contributing

Contributions are welcome. Please:

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Ensure all tests pass and type checks succeed
5. Submit a pull request

See CONTRIBUTING.md for detailed guidelines.

---

License

Licensed under the Apache License 2.0. See the LICENSE file for details.

---

Links

• Repository: github.com/averoe/GlassBox
• Issues: github.com/averoe/GlassBox/issues
• Changelog: CHANGELOG.md