

Building an AI-Powered Career Activation Platform for African Graduates.pdf pdf continue this chat from our last conversation Building an AI-powered career platform for African graduates requires navigating a "high-stakes" information environment where outdated or inaccurate data can lead to f

pasted

conitinu from this continue the reaseach reference the last chat we jut had

12:01 PM

Synthesized RAG framework architecture and competitive positioning strategy



Picking up right where we left off — we'd identified the gap (no dedicated data quality layer between document extraction and vector store loading), scoped the framework as both Simuka's backbone and a standalone open-source library, and you ranked the build priorities. Now let's go deeper on the technical architecture and competitive positioning.

Let me research a few specific things that'll shape the design decisions.


Synthesized prior context to construct comprehensive research continuation










Now let me put together a comprehensive research document that synthesizes everything and continues from where we left off.


Orchestrated markdown framework to extend prior research discussion

Now let me build the comprehensive research document.


Endeavored to synthesize prior discussion into continued analysis

Creating a comprehensive research document continuing the framework architecture discussion
framework-research.md
Now let me copy it to outputs and present it.


Copy research document to outputs

Script
Done. Let me present the file and give a concise summary.


Presented file
Let me provide a concise summary of what the research found.

Done
Here's what the research confirmed and what it means for your next moves:

The gap is real and validated. I mapped every major tool in the space — Unstructured, Chonkie, RAGAS, Giskard, Evidently, Great Expectations, Anomalo, the full LangChain/LlamaIndex ecosystem. Nobody owns the quality layer between extraction and embedding. Anomalo is the closest — they've built unstructured data quality on AWS that detects truncated text, empty fields, and duplicates before data reaches models 
AWS
 — but it's enterprise SaaS, completely inaccessible to a solo developer. Gartner predicts 30% of GenAI projects will be abandoned in 2025 due to poor data quality 
AWS
, which validates the problem commercially.

The positioning writes itself: "Great Expectations for RAG knowledge bases." Great Expectations is the gold standard for structured data quality — it's a flexible Python framework that lets you write tests reflecting your business logic, validate data where it lives, and get structured results you can plug into CI/CD 
Great Expectations
. But it has zero concept of document quality, chunk coherence, or OCR confidence. Your library fills that exact gap for unstructured data.

Chonkie is your best integration target. It's a lightweight ingestion library with 32+ integrations and a pipeline API that supports fetching, chunking, refining, embedding, and shipping to vector stores 
GitHub
. A validation step plugged into Chonkie's pipeline would reach the most developers with the least friction.

The document covers the full competitive landscape, four-layer architecture, draft API design, 10-week build plan aligned to Simuka's needs, and open-source strategy. The key decisions still open — naming, core data model, configuration format — are the ones we should nail down before writing code.

What do you want to dig into next — naming, or should we start scaffolding the repository structure?

Rag knowledge quality framework research
Document · MD 
will call it gweta, what if it is alos built with mcp, allowing the user to connect to dbs, equiped with a web crwler a user gives it the site they want data from

12:13 PM

Envisioned expanded framework merging validation, ingestion, and database connectivity


Gweta — solid choice. Short, pronounceable, culturally rooted, and almost certainly available on PyPI. And you're now expanding the scope significantly — from a validation library to a full data acquisition + validation framework with MCP connectivity. That changes the architecture in interesting ways. Let me research MCP's current state and the web crawling landscape for RAG.


Validated project name and synthesized comprehensive architecture incorporating MCP








Good — "gweta" appears to be available on PyPI. Now let me synthesize everything into an updated architecture that incorporates MCP and web crawling. This changes Gweta from a validation library into something significantly more powerful.


Synthesized document availability and consolidated key insights



Here's what the research shaped and why your additions of MCP + web crawling fundamentally change what Gweta is:

MCP makes Gweta an agent-native tool, not just a library. The MCP Python SDK implements the full spec, making it easy to build servers that expose resources, prompts and tools using standard transports like stdio, SSE, and Streamable HTTP 
GitHub
. The MCP ecosystem already has dozens of database connectors — DBHub is a zero-dependency MCP server implementing tools for Postgres, MySQL, MariaDB, SQL Server, and SQLite through a single interface 
GitHub
 — but no MCP server exists for quality-validated RAG ingestion. That's Gweta's unique position.

For the web crawler, don't rebuild — wrap Crawl4AI. Crawl4AI turns the web into clean, LLM-ready markdown for RAG, agents, and data pipelines, and it's battle-tested by a 50K+ star community 
GitHub
. It's open-source, Python-native, and free. What it lacks — and what Gweta adds — is the quality layer: authority verification, extraction scoring, chunk validation, differential crawling against existing knowledge bases, and domain rule enforcement.

The positioning is now much stronger than v1. Gweta went from "validation library" to "the complete acquisition → validation → ingestion pipeline for RAG knowledge bases, exposed over MCP." No other tool in the ecosystem does this end-to-end. Crawl4AI crawls but doesn't validate. Chonkie chunks but doesn't validate. RAGAS evaluates outputs but doesn't prevent bad inputs. Great Expectations validates structured data but not documents. Gweta is the middleware connecting all of them with quality awareness.

The v2 architecture doc covers the full MCP server design (tools, resources, prompts), the Crawl4AI integration strategy, database connectivity via SQLAlchemy, the revised package structure, and a 13-week build plan. Five open questions at the end need your answers before we scaffold the repo — want to tackle those now?

Gweta architecture v2
Document · MD 
Rag knowledge quality framework research
Document · MD 
 cralwing we need, 2. support both from day 1 3.wrapmas an optional chuning backend
separete packege 5. support both
12:42 PM

Synthesized five architectural decisions and readied implementation phase


All five decisions locked in. Let me summarize what these mean architecturally, then we can decide if you want to start scaffolding the repo.

Your decisions and their implications:

Crawling is core, not optional — Crawl4AI goes in the main install, not behind gweta[crawl]. This means Playwright is a dependency for everyone, which adds weight but matches your vision of Gweta as a full pipeline, not just a validator. We'll also include a lightweight httpx fetcher for simple static sites where a headless browser is overkill.
Both MCP transports from day one — gweta serve for stdio (Claude Desktop, Cursor) and gweta serve --http for remote agents. This means any LangGraph agent, any IDE, or any custom client can connect. Doubles the addressable market from launch.
Chonkie as optional backend — pip install gweta[chonkie] adds it. Core package ships with a minimal built-in chunker (recursive text splitting — good enough for 80% of cases). Users who want semantic/neural chunking install the Chonkie extra. Keeps the base package lighter.
gweta-zimbabwe as separate package — Smart move. The core gweta stays universal, gweta-zimbabwe contains ZIMRA rules, PBC fee facts, ZNQF mappings, ZIMSTAT thresholds. Other communities could build gweta-kenya, gweta-nigeria, etc. This is also the pattern that makes Gweta feel like a real ecosystem, not a one-country tool.
Both sync and async APIs — Like httpx's dual interface. gweta.crawl() for scripts and notebooks, await gweta.async_crawl() for agents and servers. The internals are async-first (Crawl4AI and MCP both require it), with sync wrappers using asyncio.run().
Ready to scaffold the repository, or do you want to research/discuss anything else first?

lets scaffold the repositoty

12:45 PM

Identified core types file as foundational repository element







Claude's response could not be fully generated




Claude is AI and can make mistakes. Please double-check responses.
Rag knowledge quality framework research · MD
Copy

RAG Knowledge Quality Framework — Research & Architecture Blueprint
Continuing from Previous Session
Context: We identified a genuine gap in the RAG tooling ecosystem — no dedicated library owns the data quality layer between document extraction and vector store loading. The framework will serve dual purposes: (1) the backbone powering Simuka, and (2) a standalone open-source Python library others can adopt. Your priority ranking was: parser-agnostic chunk validation → source authority & freshness tracking → golden dataset testing → domain-specific rule engine.

This document synthesizes the competitive landscape research, architectural decisions, and a concrete build plan.

1. Competitive Landscape Analysis
What Exists Today (and Where Each Falls Short for Our Use Case)
Document Parsing / Extraction:

Unstructured.io — Dominant open-source parser handling 25+ file types (PDF, DOCX, HTML, etc.). Extracts text, preserves document structure, outputs typed elements (Title, NarrativeText, Table, etc.). Has element-based chunking built in. Gap: No quality scoring of its own output. If OCR produces garbled text, Unstructured partitions it without warning.
pdfplumber — Python library focused on PDF table extraction. Excellent for structured government documents. Gap: No validation layer, no quality metrics.
Chunking:

Chonkie — Lightweight Python chunking library (2025). Supports 9+ chunking strategies (token, sentence, recursive, semantic, code, neural, LLM-guided). Has a pipeline API, 32+ integrations with vector stores and embedding providers. 505KB wheel — extremely minimal. Gap: Pure chunking — no quality validation of chunks before they enter a vector store. Assumes input text is clean.
RAG Orchestration (Saturated):

LlamaIndex, LangChain, Haystack, Pathway, DSPy, RAGFlow — All provide end-to-end RAG pipelines (ingestion → indexing → retrieval → generation). RAGFlow includes deep document understanding and knowledge graphs. Gap: They orchestrate the pipeline but don't validate data quality at the ingestion boundary. They assume what goes in is good.
RAG Evaluation (Post-Generation):

RAGAS — Open-source (400K+ monthly downloads, 20M+ evaluations run). Reference-free evaluation using LLM-as-judge: faithfulness, context relevance, answer quality. Integrates with LangSmith, Arize Phoenix. Gap: Evaluates outputs AFTER generation. Doesn't prevent bad data from entering the knowledge base in the first place.
Giskard — Auto-generates evaluation datasets from knowledge bases. Component-level scoring (retriever vs generator). Gap: Same — post-hoc evaluation, not pre-ingestion validation.
Evidently — Open-source Python library + web UI for experiment tracking and monitoring. Supports RAG metrics, synthetic test generation, production monitoring. Gap: Primarily focused on model monitoring and output quality, not input data quality.
Braintrust, Deepchecks, Maxim AI — Commercial platforms for production RAG evaluation with CI/CD integration and quality gates. Gap: All work at the output layer.
Traditional Data Quality:

Great Expectations — The standard for structured data validation in Python. Declarative "expectations" (rules) that data must meet. Supports Pandas, Spark, SQL. Rich ecosystem with data docs, validation stores, CI/CD integration. Gap: Designed for structured/tabular data. Has no concept of "document quality," "chunk coherence," or "OCR confidence." Cannot validate unstructured text.
Soda Core — YAML-based data quality checks. Similar to Great Expectations but with a different DX. Gap: Same — structured data only.
Anomalo — Commercial platform that recently added unstructured data quality (detects truncated text, empty fields, duplicates before data reaches models). AWS-integrated. Gap: Enterprise SaaS, not an open-source library. Expensive. Not accessible to solo developers or startups.
Vector-Specific ETL:

VectorETL — YAML-configurable pipelines specifically for vector databases. Supports multiple embedding models and vector stores. Gap: Pipeline orchestration, not quality validation.
dlt (data load tool) — Open-source Python library for data pipelines. Lightweight, works well with AI coding assistants. Growing fast. Gap: General ETL, no RAG-specific quality validation.
The Gap — Visualized
DOCUMENT → [Parser] → Raw Text → [Chunker] → Chunks → [Embedder] → Vectors → [Vector DB]
                ↑                      ↑                                           ↑
           Unstructured            Chonkie                                    Chroma/Qdrant
           pdfplumber              LangChain                                  Pinecone
                                   LlamaIndex
                                                                                   ↓
                                                                              [RAG Query]
                                                                                   ↓
                                                                              [Generation]
                                                                                   ↓
                                                                              [Evaluation] ← RAGAS, Giskard
                                                                                               Evidently

❌ NOBODY VALIDATES HERE:
DOCUMENT → [Parser] → Raw Text → ??? → [Chunker] → Chunks → ??? → [Vector DB]
                                  ↑                            ↑
                          Extraction Quality            Chunk Quality
                          OCR confidence                Coherence
                          Encoding corruption           Completeness
                          Table fidelity                Deduplication
                          Boilerplate detection         Metadata validation
                                                        Information density
The gap is real and commercially validated. Anomalo raised significant funding specifically to address unstructured data quality for AI, but their solution is enterprise SaaS on AWS — completely inaccessible to the developer building a RAG app in their terminal. Gartner's prediction that 30% of GenAI projects would be abandoned in 2025 due to poor data quality further validates the problem.

2. Framework Identity & Positioning
What It Is
A data quality and validation library specifically for unstructured document ingestion into knowledge bases. The "Great Expectations for RAG knowledge bases."

What It Is NOT
Not another RAG framework (LangChain/LlamaIndex already exist)
Not another document parser (Unstructured already exists)
Not another chunker (Chonkie already exists)
Not another evaluation tool (RAGAS already exists)
Not another vector store (Chroma/Qdrant already exist)
Core Value Proposition
pip install it, point it at your ingestion pipeline, and it tells you — before anything enters your vector database — what's garbage, what's stale, what's duplicated, what's missing metadata, and what's going to cause hallucinations downstream.

Relationship to Simuka
Following the open-core model that has driven the most successful open-source companies:

The framework = open-source core (MIT license). Universal, parser-agnostic, vector-store-agnostic. Any developer building a RAG app can use it.
Simuka = product built ON TOP of the framework. Uses the framework's validation layer internally, plus proprietary components: Zimbabwe-specific domain rules, career pathway logic, Proof-of-Work verification, the LangGraph agent, the frontend.
This mirrors successful patterns: WordPress (open) → WordPress.com/WooCommerce (commercial). Docker (open) → Docker Desktop (commercial). GitLab (open-core) → GitLab Enterprise. Redis, Elastic, MongoDB — all followed this pattern.

The strategic advantage: if the framework gains community adoption, Simuka benefits from community-contributed connectors, validators, and bug fixes. And "built on top of [respected open-source framework]" gives Simuka instant technical credibility.

Naming Considerations
Strong open-source project names tend to follow patterns:

Evocative metaphor — Kafka (data streaming, named for the author), Spark (fast computation), Flask (lightweight container), Pandas (panel data)
Descriptive portmanteau — TensorFlow, NumPy, Scikit-learn, pdfplumber
Memorable/fun — Chonkie (chunking hippo), Celery (task queue), Django (jazz guitarist)
African-rooted (for your context) — connects to Simuka's identity, stands out in a space dominated by Western naming
Name candidates to evaluate (research these for trademark/PyPI availability):

Name	Meaning / Rationale	Style
sifta	"to sift/filter" — sifting bad data from good	Descriptive
chenjedza	Shona for "be careful/validate"	Cultural
gweta	Shona for "to filter/strain"	Cultural
koshesa	Shona for "to clean/purify"	Cultural
ragguard	RAG + guard — direct positioning	Descriptive
vetdata	Vet your data	Descriptive
gatekeeper	Quality gate metaphor	Metaphor
sieve	Filtering metaphor (check availability)	Metaphor
Recommendation: Consider a Shona-rooted name. It's distinctive, memorable, connects to Simuka's cultural identity, and stands out on PyPI. The most successful open-source names are short (1-2 syllables for import statements), pronounceable internationally, and have no PyPI conflicts.

3. Technical Architecture
Design Principles
Parser-agnostic — Validates output from Unstructured, pdfplumber, PyPDF, custom parsers. Doesn't care how text was extracted.
Chunker-agnostic — Works with Chonkie chunks, LangChain documents, LlamaIndex nodes, or raw text segments.
Vector-store-agnostic — Validates before loading. Doesn't need to know the destination (Chroma, Qdrant, Pinecone, Weaviate).
Framework-agnostic — Plugs into LangChain pipelines, LlamaIndex ingestion, standalone scripts, or Simuka's custom pipeline.
Lightweight core — No heavy ML dependencies for baseline validation. Pure heuristics by default. Optional LLM-based validation for users who want deeper analysis.
Declarative rules — Inspired by Great Expectations. Users define "expectations" for their knowledge base, and the framework validates against them.
Layer Architecture
┌─────────────────────────────────────────────────────────────────────┐
│                        USER APPLICATION                             │
│  (Simuka, or any RAG app)                                          │
├─────────────────────────────────────────────────────────────────────┤
│                                                                     │
│  Layer 4: DOMAIN RULE ENGINE                                        │
│  ┌─────────────────────────────────────────────────────────────┐   │
│  │ User-defined validation rules per domain                     │   │
│  │ • "Startup costs must be < $500 for Zimbabwe context"        │   │
│  │ • "Legal citations must match statute format XX/YYYY"        │   │
│  │ • "Medical dosages must cross-reference BNF"                 │   │
│  │ → Configurable YAML/Python rule definitions                  │   │
│  │ → Domain-specific fact registries                            │   │
│  └─────────────────────────────────────────────────────────────┘   │
│                                                                     │
│  Layer 3: KNOWLEDGE BASE HEALTH                                     │
│  ┌─────────────────────────────────────────────────────────────┐   │
│  │ Ongoing quality monitoring for existing knowledge bases       │   │
│  │ • Golden dataset testing (Q&A benchmark pairs)               │   │
│  │ • Staleness detection (source freshness tracking)            │   │
│  │ • Source authority scoring & weighting                        │   │
│  │ • Coverage gap analysis                                      │   │
│  │ • Drift detection over time                                  │   │
│  │ → Scheduled health checks, CI/CD integration                 │   │
│  └─────────────────────────────────────────────────────────────┘   │
│                                                                     │
│  Layer 2: CHUNK QUALITY VALIDATION                                  │
│  ┌─────────────────────────────────────────────────────────────┐   │
│  │ Validates chunks BEFORE they enter any vector store           │   │
│  │ • Coherence scoring (does this chunk make sense alone?)       │   │
│  │ • Information density (signal vs noise ratio)                 │   │
│  │ • Metadata completeness (required fields present?)            │   │
│  │ • Duplicate / near-duplicate detection                        │   │
│  │ • Relevance classification (on-topic vs off-topic)            │   │
│  │ • Boundary quality (did chunking split mid-sentence?)         │   │
│  │ → Works with ANY chunker output                               │   │
│  └─────────────────────────────────────────────────────────────┘   │
│                                                                     │
│  Layer 1: EXTRACTION QUALITY SCORING                                │
│  ┌─────────────────────────────────────────────────────────────┐   │
│  │ Validates parser output BEFORE chunking                       │   │
│  │ • OCR confidence estimation                                   │   │
│  │ • Encoding corruption detection                               │   │
│  │ • Table extraction fidelity scoring                           │   │
│  │ • Boilerplate / noise filtering                               │   │
│  │ • Language detection & consistency                            │   │
│  │ • Structural element preservation check                       │   │
│  │ → Works with ANY parser output                                │   │
│  └─────────────────────────────────────────────────────────────┘   │
│                                                                     │
├─────────────────────────────────────────────────────────────────────┤
│  INTEGRATIONS LAYER                                                 │
│  Adapters for: Unstructured | pdfplumber | LangChain | LlamaIndex  │
│  | Chonkie | Chroma | Qdrant | Pinecone | Weaviate | custom        │
└─────────────────────────────────────────────────────────────────────┘
API Design (Draft)
The API should feel familiar to Great Expectations users but adapted for unstructured data:

python
# === LAYER 1: Extraction Quality ===
from framework import ExtractionValidator

validator = ExtractionValidator()

# Validate parser output before chunking
result = validator.validate(
    text="Extracted text from PDF...",
    source_metadata={
        "source": "zimra_rev1_form.pdf",
        "parser": "pdfplumber",
        "pages": 12
    }
)

print(result.quality_score)      # 0.0 - 1.0
print(result.ocr_confidence)     # estimated OCR quality
print(result.issues)             # list of detected problems
# [Issue(type="low_density", message="Page 7 has <10 words, likely scan failure")]
print(result.is_acceptable)      # True/False based on configured threshold


# === LAYER 2: Chunk Quality ===
from framework import ChunkValidator

validator = ChunkValidator(
    required_metadata=["source", "authority_level", "last_verified"],
    min_information_density=0.3,
    max_duplicate_similarity=0.92,
)

# Validate a batch of chunks before loading
results = validator.validate_batch(chunks=[
    {"text": "PBC registration costs $100...", "metadata": {...}},
    {"text": "the the the the", "metadata": {...}},  # garbage
    {"text": "PBC registration costs $100...", "metadata": {...}},  # duplicate
])

print(results.summary())
# Validated 3 chunks: 1 passed, 1 failed (low_density), 1 flagged (near_duplicate)
# Quality score: 0.33

# Filter to only good chunks
good_chunks = results.accepted()


# === LAYER 3: Knowledge Base Health ===
from framework import KnowledgeBaseHealthCheck

health = KnowledgeBaseHealthCheck(
    vector_store=chroma_collection,  # adapter for any vector store
    golden_dataset="tests/golden_qa.json",
)

report = health.run()
print(report.retrieval_accuracy)  # % of golden questions with correct chunks in top-k
print(report.stale_sources)       # documents past their freshness threshold
print(report.coverage_gaps)       # topics in golden dataset with no matching chunks


# === LAYER 4: Domain Rules ===
from framework import DomainRuleEngine

rules = DomainRuleEngine.from_yaml("rules/zimbabwe_business.yaml")
# Example YAML:
# rules:
#   - name: startup_cost_ceiling
#     field: startup_cost
#     condition: "value <= 500"
#     message: "Startup costs must be under $500 for Zimbabwe informal sector"
#   - name: pbc_fee_accuracy
#     known_facts:
#       pbc_registration_cost: 100
#     tolerance: 0

result = rules.validate_response(
    response="PBC registration costs $200",
    source_chunks=[...],
)
# DomainValidationResult(valid=False, violations=[
#   Violation(rule="pbc_fee_accuracy", message="Response says $200, known fact is $100")
# ])
Adapter Pattern for Framework Agnosticism
python
# Built-in adapters for popular tools
from framework.adapters import (
    UnstructuredAdapter,    # wraps Unstructured partition output
    PdfplumberAdapter,      # wraps pdfplumber extraction
    LangChainAdapter,       # wraps LangChain Document objects
    LlamaIndexAdapter,      # wraps LlamaIndex Node objects
    ChonkieAdapter,         # wraps Chonkie Chunk objects
    ChromaAdapter,          # wraps Chroma collection for health checks
    QdrantAdapter,          # wraps Qdrant collection
)

# Example: validate LangChain documents
from langchain.text_splitter import RecursiveCharacterTextSplitter

splitter = RecursiveCharacterTextSplitter(chunk_size=500)
docs = splitter.split_documents(raw_docs)

# Wrap in adapter, then validate
adapted = LangChainAdapter(docs)
results = chunk_validator.validate_batch(adapted)

# Only load good chunks
good_docs = results.accepted(unwrap=True)  # returns original LangChain Documents
vectorstore.add_documents(good_docs)
4. Build Strategy
What to Build First (MVP for Simuka + Library Launch)
Given your priority ranking and the fact that Simuka needs a working foundation NOW:

Phase 1 — Core (Weeks 1-3): Chunk Validation + Extraction Scoring

ExtractionValidator with heuristic-only checks (no ML dependencies)
Text length scoring, encoding detection, gibberish detection
Table extraction fidelity (compare expected vs actual structure)
Empty/near-empty page detection
ChunkValidator with heuristic checks
Information density (word diversity, stop-word ratio)
Near-duplicate detection (simhash or minhash)
Metadata completeness enforcement
Boundary quality (sentence completion check)
Adapters for pdfplumber and Chroma (what Simuka uses)
CLI tool: framework validate ./chunks/ --report
Simuka integration: Wire into the existing zw_knowledge_pipeline.py between extraction and Chroma loading
Phase 2 — Authority & Freshness (Weeks 4-5)

Source authority registry (YAML-configurable tiers)
Freshness tracking with configurable staleness thresholds per source type
Automatic staleness warnings in validation results
Simuka integration: Replace the hardcoded authority levels in the pipeline with the framework's registry
Phase 3 — Golden Dataset Testing (Weeks 6-7)

Golden dataset format specification (JSON with Q&A pairs + expected source chunks)
Automated test runner against any vector store
Retrieval accuracy scoring (hit rate, MRR, precision@k)
CI/CD-friendly output (JUnit XML, JSON reports)
Simuka integration: Create the 50-100 golden Q&A pairs for Zimbabwe Business pathway
Phase 4 — Domain Rule Engine (Weeks 8-10)

YAML-based rule definitions
Known facts registry with tolerance settings
Numerical cross-referencing against source chunks
Response validation against domain constraints
Simuka integration: Migrate the Verification Agent's known facts into the framework's rule engine
What to Defer (Post-MVP)
LLM-based validation checks (optional add-on, not core)
Web UI dashboard for quality metrics
Additional adapters (LlamaIndex, Qdrant, Pinecone, Weaviate)
Cloud-hosted version
Multi-language support beyond English
5. Open-Source Strategy
License
MIT. Maximizes adoption. The framework's value comes from network effects — more users means more adapters, more validators, more community-reported edge cases. Restricting the license would kill adoption before it starts.

Launch Strategy
Solve your own problem first. Use the framework in Simuka for 4-6 weeks. Find the sharp edges. Fix them.
Write the documentation before the launch post. Great Expectations succeeded partly because their docs were excellent from day one. Write clear getting-started guides with real examples.
Launch on PyPI with a compelling README. The README should show the problem (bad data in, bad answers out) and the solution (three lines of code to validate) in under 30 seconds of reading.
Post to Hacker News / r/MachineLearning / r/LangChain. Frame it as "Great Expectations for RAG" — developers immediately understand the positioning.
Build in public. Tweet/post progress. The African developer angle is genuinely novel in the RAG tooling space and will attract attention.
Monetization Path (Long-term, Not Now)
Open-source core: Free forever. Validation, scoring, golden datasets, rule engine.
Cloud platform (future): Hosted dashboards, team collaboration, automated monitoring, Slack alerts. This is the Great Expectations → GX Cloud model.
Simuka: The first and most visible product built on the framework.
6. Key Technical Decisions Still Open
These need answers before writing code:

Core data model: What does a "Chunk" look like internally? Should we define our own Chunk dataclass, or work entirely through adapters? (Recommendation: Own dataclass with adapters to/from external formats — cleaner API, easier testing.)
Configuration format: YAML (like Soda Core, VectorETL) or Python (like Great Expectations, RAGAS)? (Recommendation: Both. YAML for rules/config, Python for programmatic use. YAML is more accessible to non-developers who might configure domain rules.)
Scoring granularity: Single quality score (0-1) or multi-dimensional (extraction_quality, chunk_coherence, metadata_completeness, etc.)? (Recommendation: Multi-dimensional with an aggregate. Users need to know WHERE the problem is, not just that there is one.)
Duplicate detection algorithm: Simhash (fast, memory-efficient) or MinHash (more accurate for near-duplicates)? (Recommendation: MinHash by default with simhash as lightweight option. MinHash is better for the text similarity use case.)
How do adapters handle streaming? If someone is processing 10,000 documents, do we validate one-by-one or batch? (Recommendation: Both. validate() for single, validate_batch() for batches with configurable parallelism.)
7. Research Gaps Remaining
Before building, we should investigate:

Chonkie's internal data model — Can we hook into Chonkie's pipeline API to add a validation step? This would be the highest-leverage integration.
Unstructured's element metadata — What quality signals does Unstructured already expose that we can leverage rather than recompute?
Great Expectations' extension model — Can we build this AS a Great Expectations plugin rather than a standalone library? (Probably not — GX is too tightly coupled to tabular data — but worth verifying.)
Existing gibberish/OCR quality detection libraries — What Python libraries exist for detecting garbled text? langdetect, gcld3, custom heuristics?
MinHash implementation options — datasketch library vs custom implementation for near-duplicate detection.
8. Next Steps
Name decision — Pick a name, verify PyPI/GitHub availability
Scaffold the repository — pyproject.toml, src layout, tests, CI
Build Layer 1 + Layer 2 MVP — Extraction scoring + chunk validation with heuristics only
Integrate into Simuka pipeline — Replace ad-hoc validation with framework calls
Write golden dataset — 50 Q&A pairs for Zimbabwe Business pathway
Internal dogfooding — Run Simuka on validated data for 2-4 weeks
Public launch — PyPI, GitHub, documentation site
Document generated: February 2026 Status: Research phase — pre-implementation

