Summary

MYSH is a compact, POSIX-style Unix command-line shell implemented in C. It implements a practical set of shell features (tokenization, pipelines, redirection, job control and builtins) with a focus on correct process-group and I/O handling for reliable foreground/background execution. The shell is designed for educational and production-style demonstrations of systems programming concepts: parsing, fork/exec, signals, process groups and file descriptor management.

Key features

• Command parsing with tokenization and basic quoting support.
• Pipelines (multi-stage) using the pipe operator |.
• Redirection support: <, >, >>, numeric fd redirection (e.g. 2>file) and fd duplication (e.g. 2>&1).
• Job control: background jobs with &, job table, jobs, bg, fg and SIGCHLD reaping.
• Builtins implemented: cd, pwd, exit, echo, alias, unalias, type, help, export, set, unset, history, and job-control builtins.
• Executor implements fork/exec with process-group management and correct foreground/background semantics.

Design & architecture

MYSH follows a classic shell architecture with clear separation of concerns:

• REPL / read-line to collect logical lines (supports multi-line quotes).
• Tokenizer & parser to produce a small AST (linked list of pipeline stages).
• Executor that sets up pipes, spawns processes, manages process groups (pgid), applies redirections and handles builtin/child execution semantics.
• Job subsystem tracks background jobs, their pids, and states; SIGCHLD is handled and reaped safely in the main loop.

Runtime flow (high level)

1. Read input line from user (logical line handling).
2. Tokenize honoring quoted strings and escapes.
3. Build pipeline AST nodes (each stage records argv and redirections).
4. Execute: allocate pipes, fork per stage, setpgid, dup2 redirs, and exec external binaries or run builtin child routines. Parent waits for foreground jobs or registers background jobs.
5. SIGCHLD handler marks reaping; main loop collects statuses and updates the job table.

Core data model

Key structs used internally (conceptual):

• command_t — argv[], infile/outfile, append flag, background flag, next pointer for pipeline stages.
• job_t — job id, process group id, cmdline (human-readable), pids[], state (RUNNING/STOPPED/DONE), exit code and bookkeeping fields.

Builtins & user behavior

MYSH implements common shell builtins. Builtins which modify shell state (like cd and export) run in the parent when invoked as a single-stage command; other builtins can run in forked children as pipeline stages. Redirections work with builtins by temporarily applying redirections in the parent when necessary.

Quick demo examples

# basics
echo hello

# pipeline
/usr/bin/printf "a\nb\nc\n" | /usr/bin/grep b | /usr/bin/wc -l

# redirection
echo redir_test > /tmp/mysh_test1

# background job and job control
sleep 10 &
jobs
fg %1

Build & run

# build
make

# run interactive
./bin/mysh

# run scripted
printf 'echo hello' | ./bin/mysh

Notes & roadmap

• Current implementation provides a robust baseline for job control and redirection semantics; edge-cases for complex nested quoting remain to be hardened.
• Planned improvements: variable/word expansion, persistent history & line editing, here-documents, and improved terminal control (tcsetpgrp) for more accurate job/foreground handling.

Documentation & validated examples are available in the project technical doc (build, usage examples and code excerpts).

Summary

DocumentBot is an enterprise-grade information retrieval & question-answering system built to let users search, explore and extract answers from large corpora of documents (PDFs, Word, HTML, scanned documents). It combines proven lexical retrieval (BM25/TF-IDF/boolean/fuzzy search) with modern dense retrieval (sentence embeddings + vector search), and optional neural rerankers and answer extraction (RAG-style). Users can choose the retrieval method or let the system pick a hybrid strategy automatically for best precision/recall.

Real-world use cases

• Support knowledge base: helpdesk agents query a product manual corpus and surface exact procedure steps or code snippets within seconds.
• Legal discovery: find relevant clauses across contracts using boolean + fuzzy filters, then surface semantic matches with embeddings.
• Research assistant: extract summaries, citations and passages from a set of research PDFs for literature reviews.
• Enterprise search: combined metadata + semantic search across manuals, SOPs, incident logs and technical docs with interactive suggestions and explainability.

What we built (features)

• Multi-method retrieval — lexical methods (BM25/TF-IDF), boolean + advanced boolean queries, fuzzy matching, and dense retrieval (sentence/paragraph embeddings).
• Vector indexing using high-performance vector DBs (FAISS / Milvus / Pinecone) with configurable ANN indexes (IVF, HNSW, PQ) and dynamic reindexing.
• Hybrid search — combine lexical scores and dense similarity scores with tunable weighting; supports hard filtering by metadata (date, author, doc-type).
• Reranking — optional neural reranker (cross-encoder) to re-order top K lexical/dense hits for higher precision before extraction.
• Answer extraction & RAG — retrieve top passages and generate concise answers using an LLM (optional) or deterministic extractive span selection; support for citation snippets with provenance.
• Query features — autocomplete, spell-correction, query expansion (synonyms/Thesaurus), and interactive suggestions (facets & filters).
• User choice & profiles — user can pick retrieval mode (lexical / dense / hybrid / boolean), or choose presets (fast, precise, exploratory).
• Document ingestion — robust parsing pipeline for PDFs, DOCX, HTML and OCR for scanned docs; chunking & canonicalization with metadata extraction.
• Monitoring & analytics — query latency, top queries, drift detection, relevance feedback (click-through & feedback loop for supervised tuning).

Methods & algorithms implemented

We implemented a wide range of IR techniques so the system can be tuned to the use case:

• Lexical methods: TF-IDF vectorization, BM25 ranking, boolean & advanced Boolean queries, fuzzy matching (Levenshtein).
• Dense methods: sentence/paragraph embeddings (SBERT / sentence-transformers family, and optionally OpenAI embeddings), approximate nearest neighbour (HNSW, IVF, PQ) indexing.
• Neural rerankers: cross-encoder based re-ranking for top-k candidates (higher precision at cost of latency).
• Hybrid scoring: linear combination of lexical and dense scores, with optional learning-to-rank for production tuning.
• Extraction: deterministic span match, fuzzy snippet extraction, and LLM-based synthesis (RAG) with source citation.
• Query expansion & feedback: pseudo-relevance feedback (PRF), synonym expansion, and user-feedback-driven boosting.

Architecture (high level)

The system uses a modular pipeline:

1. Ingest — extract text & metadata from PDFs/DOCX/HTML (pdfminer / tika / pytesseract for OCR), normalize encoding, detect language, split into chunks with overlap.
2. Index — create lexical index (Elasticsearch or Whoosh) and dense index (FAISS / Milvus / Pinecone). Store chunk-level metadata (doc id, page, offset).
3. Query — user query → preprocess (spellcheck, normalize) → run selected retrieval methods in parallel (lexical + dense + boolean) → merge & score → rerank if enabled → extract passages → generate final answer with provenance.
4. Serve — REST API + streaming sockets for interactive suggestions; UI shows snippet highlights and provenance links back to the source document and exact page/offset.

Indexing & chunking strategy

• Chunk documents into 200–800 token windows with 20–30% overlap to preserve context for extraction.
• Store both paragraph & sliding-window chunks to support both precise span retrieval and semantic context.
• Keep metadata fields (title, author, date, tags, file path) for efficient filtering and faceting.

UX — how users interact

Users can:

• Type a natural language question and pick the retrieval mode: Fast (lexical), Semantic (dense), Hybrid, or Boolean (advanced).
• See autocomplete suggestions and related queries derived from top-ranking n-grams + semantic neighbours.
• View search results with highlighted passages, a confidence score, and provenance links to open the original PDF at the right page.
• Provide explicit feedback (thumbs up/down) on answers to improve ranking and collect training data for later tuning.

Example queries & sample output

Q: "How do I reset the device to factory settings?"
-> Top snippet (page 12): "To perform a factory reset, press and hold the reset button for 10 seconds. Confirm on the device prompt..."
Provenance: manual_v2.pdf — page 12 — score: 0.92

Q: "Show warranty clause for defect coverage"
-> Lexical match (BM25) returns clause with exact keywords; Dense match surfaces similar phrasing in related appendix — UI shows both with toggle.

Evaluation & metrics

• Offline: Mean Reciprocal Rank (MRR), nDCG@k, Precision@k computed on annotated Q/A holdout sets.
• Online: click-through rate (CTR) on top result, user satisfaction signals, latency (p99), and reranker cost vs. precision trade-offs.
• A/B test: hybrid weighting compared against purely lexical baseline to quantify lift in MRR and user task completion time.

Build & run (developer notes)

# Install env (example)
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

# Ingest pipeline (example)
python ingest.py --source ./data/manuals --index dense --chunksize 500

# Run API
uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

Production considerations

• Scale vector DB independently (replicas/sharding) and cache top-K lexical results. Use async pipelines for ingestion and reindexing.
• Implement query rate limits, authentication and role-based access control for private document collections.
• Store provenance and audit trail for regulatory requirements (who accessed which doc and when).

Roadmap & next steps

• Enable multilingual embeddings & cross-lingual retrieval.
• Integrate online learning: use user feedback to fine-tune hybrid weighting and reranker models (learning-to-rank).
• Provide exportable citations and summarized answers for downstream workflows (ticket creation, docs update suggestions).

DocumentBot is built as a modular, production-ready IR platform that can be tuned for enterprise search, compliance, legal discovery and support automation.