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✅ Strict RAG Mode: OFF

Economic Forecast: ARIMA vs SARIMAX with ensemble sentiment

🎯 Retrieval Ranking Evaluation

Upload a JSON or CSV gold dataset to measure how well the retrieval pipeline finds the right chunks.

JSON format:

[{"query": "...", "gold_doc_id": "file.pdf", "gold_chunk_ids": [12, 13], "gold_page": 3}]

CSV format (columns: query, gold_doc_id, gold_chunk_ids, gold_page). All fields except query are optional — missing ones are safely ignored.

🤖 Hybrid Multilingual RAG + Economic Sentiment + Forecast

ENSSEA — Masters Thesis | Si Tayeb Houari | 2025–2026

🏗️ Architecture Overview

This application is a full end-to-end research pipeline that combines:

1. Hybrid Retrieval-Augmented Generation (RAG)
2. Multilingual Economic Sentiment Analysis
3. Supervised Fine-Tuned Sentiment Model
4. Time-Series Economic Forecasting
5. Retrieval Quality Evaluation

🔍 Tab-by-Tab Explanation

1 · Upload Files

Upload PDF, TXT, CSV, or DOCX files. The system extracts text, splits it into overlapping chunks (≈320 characters with 90-char overlap), embeds every chunk with a multilingual sentence transformer, and stores them in a FAISS flat inner-product index. Metadata (filename, page, year, language) is preserved per chunk.

• Similarity Threshold — chunks scoring below this cosine similarity are filtered before reranking.
• Strict RAG Mode — when ON, the chatbot only answers from the index and never falls back to general knowledge.
• Save / Load Index — serialises the FAISS index + metadata to /tmp so it survives a restart.

2 · Sentiment / Search

Enter a word or phrase. The system:

1. Searches the index for exact keyword hits (regex, case-insensitive).
2. Runs the full hybrid retrieval pipeline to find the most semantically relevant chunks.
3. Computes an ensemble sentiment score using 4 signals:

3 · Smart Chatbot

A grounded conversational agent. For each user message:

1. Intent detection — "explain the PDF" routes to document summarisation mode.
2. RAG retrieve — retrieves top-4 chunks using the pipeline described below.
3. Evidence check — decides whether retrieved evidence is strong, partial, or absent.
4. LLM (Llama-3.3-70B via Groq) — generates the answer with a strict grounding system prompt.
5. Cites source filenames and page numbers inline.

4 · Voice Interface

Record your question via microphone:

• ASR — OpenAI Whisper-Small transcribes the audio (mono float32, any sample rate).
• The transcript is passed to the Smart Chatbot pipeline.
• The text answer is converted to speech with gTTS (Google Text-to-Speech), supporting English and Arabic.
• Common issues (too-short audio, ASR load failure) are caught and shown as readable error messages.

5 · Analytics

Session and corpus statistics:

• Word Cloud — frequency-weighted scatter of the most common tokens.
• Language Distribution — pie chart of Arabic vs English chunks.
• Top Keywords — horizontal bar chart of the 15 most frequent English words (stop-words removed).
• Live session counters (questions asked, RAG hits, fallbacks, build time).

6 · Forecast

Fetches macroeconomic time-series from the World Bank API (GDP growth, CPI inflation, unemployment, exchange rate) and fits two competing models:

Statistical validation:

• ADF test — checks stationarity before Granger testing.
• Granger causality — tests whether sentiment Granger-causes the economic target.
• Diebold-Mariano test — formally tests whether SARIMAX is significantly better than ARIMA.

7 · Auto RAG Eval

Automatically constructs a held-out evaluation set from the indexed chunks and measures:

8 · Retrieval Ranking Eval (new)

Upload a JSON or CSV gold-labelled evaluation set and measure how well the production retrieval pipeline ranks relevant documents:

Relevance matching (in priority order):

1. file == gold_doc_id — exact filename match
2. chunk_id in gold_chunk_ids — exact chunk-level match
3. page == gold_page — page-level match (fallback)

Gold dataset format (JSON):

[
  {
    "query": "What is the GDP growth rate?",
    "gold_doc_id": "report_2023.pdf",
    "gold_chunk_ids": [12, 13],
    "gold_page": 3
  }
]

All fields except query are optional — missing fields are safely skipped.

🛠️ Core Retrieval Pipeline

Every query goes through this exact pipeline (shared by chatbot, voice, and both evaluation tabs):

Query
  │
  ├─ 1. Dense retrieval  (FAISS inner product, top-48 candidates)
  │       └── paraphrase-multilingual-MiniLM-L12-v2
  │
  ├─ 2. Filter  (cosine similarity < threshold → drop)
  │
  ├─ 3. Hybrid scoring
  │       ├── Semantic score  × 0.45
  │       ├── BM25 score      × up to 0.25
  │       ├── Keyword overlap × 0.20
  │       └── Exact match bonus + 0.10
  │
  ├─ 4. Cross-encoder reranker  (ms-marco-MiniLM-L-6-v2)
  │       └── final = hybrid × 0.45 + CE_norm × 0.55
  │
  ├─ 5. Deduplicate  (max 2 chunks per file)
  │
  └─ 6. Return top-N candidates with metadata

🧰 Key Libraries

Developed as part of a Masters thesis in Economics & Data Science at ENSSEA, Algeria.