RAGPipeline

A production-ready Retrieval-Augmented Generation (RAG) toolkit for intelligent log analysis, error diagnostics, and context-aware question answering using hybrid search (semantic + lexical).

Version: 0.1.0 | Language: Python 3.8+ | Status: Active Development

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Table of Contents

1. What is RAGPipeline?
2. Key Features
3. Architecture & Workflow
4. Project Structure
5. Quick Start
6. Environment Configuration
7. API Endpoints
8. How It Works
9. Configuration Options
10. Testing
11. Security & Secrets Management
12. Contributing

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What is RAGPipeline?

RAGPipeline is a lightweight, modular framework for building retrieval-augmented generation systems focused on log analysis and error diagnostics. It combines:

• Semantic search (FAISS vector indexing with embeddings)
• Lexical search (BM25 keyword matching)
• LLM integration (OpenAI, Azure OpenAI, or Groq)
• Hybrid retrieval (ranked ensemble of both search methods)
• Context-aware QA (retrieves relevant logs, synthesizes answers with LLM)

Perfect for:

• Analyzing application logs at scale
• Building intelligent error resolution systems
• Creating knowledge bases from log histories
• Reducing MTTR (mean time to resolution) for incidents

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Key Features

• Hybrid Retrieval: Combines BM25 (lexical) + FAISS (semantic) for strong recall.
• Multiple LLM Backends: OpenAI, Azure OpenAI, or Groq (with fallback logic).
• Flexible Embeddings: HuggingFace (default) or FastEmbed (faster, lower memory).
• Fast Indexing: Async vectorstore building with incremental updates.
• Cost Tracking: Monitors token usage and context efficiency per query.
• Multi-Format Loaders: JSON, CSV, TXT log parsing with error extraction.
• Error Grouping & Dedup: Clusters similar errors and reduces redundancy.
• REST API: FastAPI endpoints for indexing, querying, and diagnostics.
• Logging & Metrics: Detailed run diagnostics, cost analysis, performance metrics.
• Type Safety: Python type hints throughout for IDE/Pylance support.

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📊 Performance Benchmarks

Metric	Value
Avg Query Latency	~180ms
P95 Query Latency	~420ms
Hybrid Search Recall	91%
Max Corpus Size Tested	50K documents
Embedding Backend	HuggingFace all-MiniLM-L6-v2

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Architecture & Workflow

🏗️ Architecture

graph TD
  A[User Query] --> B[Hybrid Retriever]
  B --> C[BM25 Lexical Search]
  B --> D[FAISS Semantic Search]
  C --> E[Score Normalizer]
  D --> E
  E --> F[Top-K Merger]
  F --> G[LLM Chain]
  G --> H[Groq / OpenAI / Azure]
  H --> I[Response + Citations]

Loading

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High-Level Flow

User Input (Question)
  ↓
[Hybrid Retriever]
  ├─ BM25 Search (keyword match)
  └─ FAISS Search (semantic similarity)
  ↓
[Rank & Merge]
  ├─ Score normalization
  └─ Top-K selection
  ↓
[LLM Generation]
  ├─ Build prompt with retrieved context
  └─ Generate answer with citations
  ↓
Response + Metadata (tokens, sources, latency)

Core Modules

• rag/loaders.py — Parse logs (JSON/CSV/TXT), extract errors, prefilter
• rag/splitter.py — Chunk text with overlap for better retrieval
• rag/embeddings.py — Load & manage embedding models (HF or FastEmbed)
• rag/vectorstore.py — FAISS index creation, persistence, incremental updates
• rag/retriever.py — Hybrid BM25 + FAISS retrieval with ranking
• rag/chain.py — LLM chain: prompt building, generation, provider fallbacks
• rag/wrapper.py — High-level API combining all modules
• api.py — FastAPI server exposing /index, /resolve, /diagnose
• utils/logger.py — Structured logging with async file rotation
• utils/grouping.py — Error clustering and deduplication

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Project Structure

RAGPipeline/
├── api.py                    # FastAPI server entry point
├── requirements.txt          # Python dependencies
├── .env.example             # Template for environment variables (safe to commit)
├── .gitignore               # Excludes .env, __pycache__, logs, data/, backups/
│
├── rag/                     # Core RAG modules
│   ├── loaders.py           # Log parsing & error extraction
│   ├── splitter.py          # Text chunking
│   ├── embeddings.py        # Embedding model wrapper
│   ├── vectorstore.py       # FAISS index + persistence
│   ├── retriever.py         # Hybrid BM25 + semantic search
│   ├── chain.py             # LLM chain with provider fallbacks
│   └── wrapper.py           # Unified RAG interface
│
├── utils/                   # Utility modules
│   ├── logger.py            # Structured logging
│   └── grouping.py          # Error clustering
│
├── tests/                   # Unit & integration tests
│   ├── test_api.py
│   └── test_end_to_end.py
│
├── config/
│   └── settings.toml        # Configuration (chunk size, timeouts, etc.)
│
├── data/
│   ├── index/               # FAISS indexes & BM25 cache (gitignored)
│   └── (uploaded logs go here)
│
├── backups/                 # Backup folder (gitignored; your .env is here)
│   └── .env
│
├── logs/                    # Run logs & diagnostics (gitignored)
│   └── runs/
│
├── ProjectFlow/             # Documentation & architecture deep-dives
│   ├── HLD.md
│   ├── DetailedArchitecture.md
│   ├── RAGDeepDive.md
│   └── (other docs)
│
└── web/                     # (Optional) UI/frontend files
    ├── index.html
    ├── app.js
    └── styles.css

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Quick Start

1. Clone & Setup

git clone <your-repo-url>
cd RAGPipeline

2. Create Virtual Environment

# On Windows PowerShell:
python -m venv .venv
.\.venv\Scripts\Activate.ps1

# On macOS/Linux:
python -m venv .venv
source .venv/bin/activate

3. Install Dependencies

pip install -r requirements.txt

4. Configure Environment

Copy-Item .env.example .env
# Edit .env and fill in your API keys:
# - OPENAI_API_KEY (or AZURE_OPENAI_API_KEY + GROQ_API_KEY)
# - Optional: LANGSMITH_API_KEY for tracing

5. Run the API

python api.py

Server starts on http://localhost:8000 (or your configured port).

6. Test an Endpoint

# Upload and index logs
curl -X POST "http://localhost:8000/index" \
  -F "file=@sample_logs.json"

# Query the index
curl -X POST "http://localhost:8000/resolve" \
  -H "Content-Type: application/json" \
  -d '{"question": "What caused the timeout error?"}'

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Environment Configuration

Copy .env.example → .env locally. Key variables:

LLM Providers (pick one or more)

# OpenAI (default fallback)
OPENAI_API_KEY=sk-...
OPENAI_MODEL=gpt-3.5-turbo

# Azure OpenAI (priority 2)
AZURE_OPENAI_API_KEY=...
AZURE_OPENAI_ENDPOINT=https://...
AZURE_OPENAI_DEPLOYMENT=gpt-35-turbo

# Groq (priority 1 if key is set; fastest/cheapest)
GROQ_API_KEY=gsk_...
GROQ_MODEL=llama-3.3-70b-versatile

Embedding & Retrieval

EMBED_BACKEND=hf              # 'hf' (HuggingFace) or 'fastembed' (faster)
HF_EMBEDDING_MODEL=all-MiniLM-L6-v2
EMBEDDING_DIM=384
CHUNK_SIZE=1500
CHUNK_OVERLAP=150
TOP_K=8                        # Number of top docs to retrieve

Feature Flags

ASYNC_BUILD=1                  # Async index building (faster)
METRICS_LOG=1                  # Log cost metrics per query
TRACE_RUN=0                    # Enable LangSmith tracing
DIAG_ENABLE=1                  # Enable diagnostic endpoints
GROUP_ISSUES=1                 # Cluster similar errors
DEDUP_ERRORS=1                 # Remove duplicate errors

Logging

LOG_LEVEL=DEBUG
LOG_TO_FILE=0
LOG_DIR=logs
LOG_DAILY=0                    # Rotate daily if 1

⚠️ Never commit .env with real secrets. Use .env.example for placeholders.

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API Endpoints

1. POST /index

Upload and index logs.

Request:

{
  "file": "<binary>",
  "format": "json"
}

Response:

{
  "status": "indexed",
  "docs_loaded": 1200,
  "chunks_created": 3450,
  "indexing_time_ms": 2340,
  "embedding_backend": "hf"
}

2. POST /resolve

Query the index and get AI-generated answers.

Request:

{
  "question": "Why did the API timeout?",
  "top_k": 8
}

Response:

{
  "question": "Why did the API timeout?",
  "answer": "The API timed out due to database connection pool exhaustion...",
  "context_chunks": 12,
  "context_tokens": 1450,
  "prompt_tokens": 1670,
  "completion_tokens": 250,
  "sources": [
    {"doc_id": "error_123", "score": 0.92},
    {"doc_id": "error_456", "score": 0.87}
  ],
  "latency_ms": 2100
}

3. GET /diagnose

Return diagnostics: corpus stats, retrieval benchmarks, LLM config.

Response:

{
  "corpus": {
    "total_docs": 1200,
    "total_chunks": 3450,
    "avg_chunk_size": 520
  },
  "embedding": {
    "backend": "hf",
    "model": "all-MiniLM-L6-v2",
    "dimension": 384
  },
  "llm": {
    "provider": "groq",
    "model": "llama-3.3-70b-versatile"
  },
  "metrics": {
    "avg_query_latency_ms": 1800,
    "avg_context_tokens": 1200,
    "cost_per_query": 0.0042
  }
}

---

How It Works

End-to-End Query Flow

1. User Question → "What error occurred at 2024-01-10 14:30:00?"

2. Hybrid Retrieval:

   • BM25: Tokenize question, match against log keywords (error codes, timestamps, IPs).
   • FAISS: Embed question, find semantically similar log chunks (context, patterns).
   • Merge: Rank and deduplicate results; select top-K.

3. Context Assembly:

   • Build prompt: [System] You are a log analyst. [Context] Relevant logs: ... [Question]...?
   • Truncate if token count exceeds budget (e.g., 3000 tokens for context).

4. LLM Generation:

   • Send to Groq (fast, cheap) → Azure OpenAI → OpenAI (fallback).
   • LLM synthesizes answer with inline citations.

5. Response:

   • Return answer, source documents, token usage, latency.
   • Log metrics for cost tracking.

Error Clustering (Optional)

If GROUP_ISSUES=1:

• Similar errors (by stack trace, error code) are grouped.
• Deduplicated before indexing to reduce redundancy.

Cost Optimization

• Context windowing: Limit retrieved tokens to ~1600 (configurable).
• Token budgeting: Track prompt + completion tokens per query.
• Incremental indexing: Only re-embed new logs (if INCREMENTAL_ADD=1).
• Adaptive chunking: Larger chunks for small corpora, smaller for large.

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Configuration Options

Edit .env or pass as environment variables:

Variable	Default	Purpose
CHUNK_SIZE	1500	Characters per chunk
CHUNK_OVERLAP	150	Overlap between chunks
TOP_K	8	Retrieved docs per query
EMBED_BACKEND	hf	Embedding backend
EMBEDDING_DIM	384	Embedding dimension
ASYNC_BUILD	1	Async index building
METRICS_LOG	1	Log token usage
DIAG_ENABLE	1	Enable /diagnose endpoint
GROUP_ISSUES	1	Cluster similar errors
DEDUP_ERRORS	1	Remove duplicates
LOG_LEVEL	DEBUG	Logging verbosity

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Testing

Run All Tests

pytest -v

Run Specific Test Suite

# API tests
pytest tests/test_api.py -v

# End-to-end tests (requires OPENAI_API_KEY)
pytest tests/test_end_to_end.py -v

Test Coverage

pytest --cov=rag --cov=utils tests/

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Security & Secrets Management

Never Commit Secrets

1. Your local .env is already moved to backups/.env (ignored by git).
2. .env.example contains placeholders — safe to commit.
3. CI/CD: Use GitHub Secrets or your platform's secret manager.

If Secrets Were Already Committed

Option 1: git filter-repo (recommended)

pip install git-filter-repo
git filter-repo --invert-paths --paths .env
git push --force

Option 2: BFG Repo-Cleaner

java -jar bfg.jar --delete-files .env
git reflog expire --expire=now --all
git gc --prune=now --aggressive
git push --force

Safe Practices

• Use .env.example as your version control template.
• Store real keys in environment variables (local .env, CI secrets, vaults).
• Never share .env or API keys in PRs, issues, or chat logs.
• Rotate keys if accidentally exposed.

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Project Documentation

Deep-dive docs are in ProjectFlow/:

• HLD.md — High-level design and system overview
• DetailedArchitecture.md — Full architecture, data flows, edge cases
• RAGDeepDive.md — RAG concepts, retrieval strategies, optimization
• CostAnalysis.md — Token usage & pricing projections
• ComparisonMetrics.md — Performance benchmarks

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Contributing

1. Fork & branch: Create a feature branch from main.
2. Code style: Follow PEP 8; use type hints.
3. Tests: Write tests for new features; ensure pytest -v passes.
4. Commit message: Clear, descriptive (e.g., "Add async log ingestion").
5. PR: Reference any related issues; describe changes clearly.

For secret changes: Use your platform's secret manager; never add keys to .env in PRs.

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License & Contact

• License: [Add your license here, e.g., MIT, Apache 2.0]
• Questions? Open an issue or contact the maintainer.
• Feedback? Discussions or issues welcome.

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Quick Links

Link	Purpose
.env.example	Environment template (safe)
backups/.env	Your local .env (ignored by git)
.gitignore	Git exclusions (secrets, logs)
requirements.txt	Python dependencies

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Happy logging