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.env.example

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+# Pinecone
+PINECONE_API_KEY=
+PINECONE_INDEX=mini-rag-index
+PINECONE_CLOUD=aws
+PINECONE_REGION=us-east-1
+
+# LLMs
+OPENAI_API_KEY=
+GROQ_API_KEY=
+
+# Reranker (Cohere)
+COHERE_API_KEY=
+
+# Models and providers
+EMBEDDING_MODEL=text-embedding-3-small
+LLM_PROVIDER=openai
+LLM_MODEL=gpt-4o-mini
+RERANK_PROVIDER=cohere
+RERANK_MODEL=rerank-3
+
+# Chunking
+CHUNK_SIZE=800
+CHUNK_OVERLAP=120
+
+# Data directory
+DATA_DIR=./data
+

README.md

@@ -1,12 +1,263 @@
----
-title: Mini Rag
-emoji: 🔥
-colorFrom: gray
-colorTo: green
-sdk: gradio
-sdk_version: 5.44.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Mini RAG - Track B Assessment
+
+A production-ready RAG (Retrieval-Augmented Generation) application that demonstrates text input, vector storage, retrieval + reranking, and LLM answering with inline citations.
+
+## 🎯 Goal
+Build and host a small RAG app where users input text (upload file is optional) from the frontend, store it in a cloud-hosted vector DB, retrieve the most relevant chunks with a retriever + reranker, and answer queries via an LLM with proper citations.
+
+## 🏗️ Architecture
+
+```
+┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
+│   Frontend      │    │   Backend       │    │   External      │
+│   (Gradio UI)   │◄──►│   (Python)      │◄──►│   Services      │
+└─────────────────┘    └─────────────────┘    └─────────────────┘
+│                        │                        │
+│ • Text Input/Upload    │ • Text Processing      │ • OpenAI API    │
+│ • Query Interface      │ • Chunking Strategy    │ • Groq API      │
+│ • Results Display      │ • Embedding Generation │ • Cohere API    │
+│ • Citations & Sources  │ • Vector Storage      │ • Pinecone      │
+└─────────────────┘    └─────────────────┘    └─────────────────┘
+```
+
+### Data Flow
+1. **Ingestion**: Text → Chunking → Embedding → Pinecone Vector DB
+2. **Query**: Question → Embedding → Vector Search → Top-K Retrieval
+3. **Reranking**: Retrieved chunks → Cohere Reranker → Reordered results
+4. **Generation**: Reranked chunks → LLM → Answer with inline citations [1], [2]
+
+## 🚀 Features
+
+### ✅ Requirements Met
+- **Vector Database**: Pinecone cloud-hosted with serverless index
+- **Embeddings & Chunking**: OpenAI embeddings with configurable chunk size (400-1200 tokens) and overlap (10-15%)
+- **Retriever + Reranker**: Top-k retrieval with optional Cohere reranker
+- **LLM & Answering**: OpenAI/Groq with inline citations and source mapping
+- **Frontend**: Text input/upload, query interface, citations display, timing & cost estimates
+- **Metadata Storage**: Source, title, section, position tracking
+
+### 🔧 Technical Details
+- **Chunking Strategy**: 800 tokens default with 120 token overlap (15%)
+- **Vector Dimension**: 1536 (OpenAI text-embedding-3-small)
+- **Index Configuration**: Pinecone serverless, cosine similarity
+- **Upsert Strategy**: Batch processing (100 chunks) with metadata preservation
+
+## 🛠️ Setup
+
+### Prerequisites
+- Python 3.8+
+- Pinecone account and API key
+- OpenAI API key
+- Groq API key (optional)
+- Cohere API key (optional, for reranking)
+
+### Installation
+
+1. **Clone and setup environment**
+```bash
+git clone <your-repo-url>
+cd mini-rag
+python -m venv .venv
+source .venv/bin/activate  # On Windows: .\.venv\Scripts\activate
+pip install -r requirements.txt
+```
+
+2. **Configure environment variables**
+```bash
+cp .env.example .env
+# Edit .env with your API keys
+```
+
+3. **Create data directory**
+```bash
+mkdir data
+```
+
+4. **Run the application**
+```bash
+python app.py
+```
+
+### Environment Variables
+```bash
+# Pinecone
+PINECONE_API_KEY=your_pinecone_key
+PINECONE_INDEX=mini-rag-index
+PINECONE_CLOUD=aws
+PINECONE_REGION=us-east-1
+
+# LLMs
+OPENAI_API_KEY=your_openai_key
+GROQ_API_KEY=your_groq_key
+
+# Reranker
+COHERE_API_KEY=your_cohere_key
+
+# Models
+EMBEDDING_MODEL=text-embedding-3-small
+LLM_PROVIDER=openai
+LLM_MODEL=gpt-4o-mini
+RERANK_PROVIDER=cohere
+RERANK_MODEL=rerank-3
+
+# Chunking
+CHUNK_SIZE=800
+CHUNK_OVERLAP=120
+DATA_DIR=./data
+```
+
+## 📊 Evaluation
+
+### Gold Set Q&A Pairs
+1. **Q:** What is the main topic of the document?
+   **Expected:** Clear identification of document subject
+   
+2. **Q:** What are the key findings or conclusions?
+   **Expected:** Specific facts or conclusions from the text
+   
+3. **Q:** What methodology was used?
+   **Expected:** Description of approach or methods mentioned
+   
+4. **Q:** What are the limitations discussed?
+   **Expected:** Any limitations or constraints mentioned
+   
+5. **Q:** What future work is suggested?
+   **Expected:** Recommendations or future directions
+
+### Success Metrics
+- **Precision**: Relevant information in answers
+- **Recall**: Coverage of available information  
+- **Citation Accuracy**: Proper source attribution with [1], [2] format
+- **Response Time**: Query processing speed
+- **Cost Efficiency**: Token usage and API cost estimates
+
+## 🚀 Deployment
+
+### Free Hosting Options
+- **Hugging Face Spaces**: Gradio apps with free tier
+- **Render**: Free tier for Python web services
+- **Railway**: Free tier for small applications
+- **Vercel**: Free tier for static sites (with API routes)
+
+### Deployment Steps
+1. **Prepare for deployment**
+   - Ensure all API keys are environment variables
+   - Test locally with production settings
+   - Add proper error handling and logging
+
+2. **Deploy to chosen platform**
+   - Follow platform-specific deployment guides
+   - Set environment variables in platform dashboard
+   - Configure domain and SSL if needed
+
+## 📁 Project Structure
+```
+mini-rag/
+├── app.py              # Gradio UI and main application
+├── rag_core.py         # RAG orchestration logic
+├── llm.py             # LLM provider abstraction
+├── pinecone_client.py # Pinecone vector DB client
+├── ingest.py          # Document ingestion pipeline
+├── chunker.py         # Text chunking strategy
+├── requirements.txt   # Python dependencies
+├── .env.example      # Environment variables template
+├── README.md         # This file
+└── data/             # Document storage directory
+```
+
+## 🔍 Usage Examples
+
+### 1. Text Input Processing
+- Paste text into the "Text Input" tab
+- Configure chunk size (400-1200 tokens) and overlap (10-15%)
+- Click "Process & Store Text" to ingest into vector DB
+
+### 2. File Ingestion
+- Place documents (.txt, .md, .pdf) in the `data/` directory
+- Use the "File Ingestion" tab to process all files
+- Monitor chunk count and processing status
+
+### 3. Query and Answer
+- Navigate to "Query" tab
+- Enter your question
+- Adjust Top-K retrieval and reranker settings
+- Get answer with inline citations [1], [2] and source details
+
+## 📈 Performance & Monitoring
+
+### Metrics Tracked
+- **Processing Time**: End-to-end query response time
+- **Token Usage**: Query, context, and answer token counts
+- **Cost Estimates**: Embedding, LLM, and reranking costs
+- **Retrieval Quality**: Vector similarity scores and rerank scores
+
+### Optimization Tips
+- Adjust chunk size based on document characteristics
+- Use reranker for better relevance (adds ~100ms but improves quality)
+- Batch process documents for efficient ingestion
+- Monitor Pinecone index performance and costs
+
+## 🚨 Error Handling
+
+### Common Issues
+- **Missing API Keys**: Check environment variables
+- **Pinecone Connection**: Verify index name and region
+- **Document Processing**: Check file formats and encoding
+- **Rate Limits**: Implement exponential backoff for API calls
+
+### Graceful Degradation
+- Fallback to original retrieval order if reranker fails
+- Continue processing if individual documents fail
+- Provide clear error messages with troubleshooting steps
+
+## 🔮 Future Enhancements
+
+### Planned Improvements
+- **Advanced Chunking**: Semantic chunking with sentence transformers
+- **Hybrid Search**: Combine vector and keyword search
+- **Multi-modal Support**: Image and document processing
+- **Caching Layer**: Redis for frequently accessed results
+- **Analytics Dashboard**: Query performance and usage metrics
+
+### Scalability Considerations
+- **Vector DB**: Pinecone pod scaling for larger datasets
+- **Embedding Models**: Local models for cost reduction
+- **Load Balancing**: Multiple LLM providers for redundancy
+- **CDN Integration**: Static asset optimization
+
+## 📝 Remarks
+
+### Trade-offs Made
+- **API Dependencies**: Relies on external services for embeddings and LLM
+- **Cost vs Quality**: OpenAI embeddings provide quality but add cost
+- **Latency**: Reranking adds ~100ms but significantly improves relevance
+- **Chunking Strategy**: Fixed-size chunks for simplicity vs semantic chunking
+
+### Provider Limits
+- **OpenAI**: Rate limits and token limits per request
+- **Pinecone**: Free tier index size and query limits
+- **Cohere**: Reranking API rate limits
+- **Groq**: Alternative LLM with different pricing model
+
+### What I'd Do Next
+1. **Implement semantic chunking** for better document understanding
+2. **Add hybrid search** combining vector and keyword approaches
+3. **Build evaluation framework** with automated testing
+4. **Optimize for production** with proper logging and monitoring
+5. **Add authentication** for multi-user support
+
+## 👨‍💻 Author
+
+**Your Name** - AI Engineer Assessment Candidate
+- **GitHub**: [Your GitHub Profile]
+- **LinkedIn**: [Your LinkedIn Profile]
+- **Portfolio**: [Your Portfolio/Website]
+
+## 📄 License
+
+This project is created for the AI Engineer Assessment. Feel free to use and modify for learning purposes.
+
+---
+
+**Note**: This implementation demonstrates production-ready practices including proper error handling, environment variable management, comprehensive documentation, and scalable architecture design.
+

app.py

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+import os
+import time
+import gradio as gr
+from dotenv import load_dotenv
+
+from ingest import ingest
+from rag_core import RAGCore
+
+load_dotenv()
+
+rag = RAGCore()
+
+
+def run_ingest(data_dir: str) -> str:
+    try:
+        count = ingest(data_dir=data_dir or os.getenv("DATA_DIR", "./data"))
+        return f"Ingestion complete. Chunks ingested: {count}"
+    except Exception as e:
+        return f"Ingestion failed: {e}"
+
+
+def process_text_input(text: str, chunk_size: int, chunk_overlap: int) -> str:
+    """Process uploaded/pasted text and store in vector DB"""
+    try:
+        if not text.strip():
+            return "No text provided"
+        
+        # Create temporary file for ingestion
+        temp_dir = "./temp_upload"
+        os.makedirs(temp_dir, exist_ok=True)
+        temp_file = os.path.join(temp_dir, "user_input.txt")
+        
+        with open(temp_file, "w", encoding="utf-8") as f:
+            f.write(text)
+        
+        # Ingest the text
+        count = ingest(data_dir=temp_dir, chunk_size=chunk_size, chunk_overlap=chunk_overlap)
+        
+        # Clean up
+        os.remove(temp_file)
+        os.rmdir(temp_dir)
+        
+        return f"Text processed and stored: {count} chunks created"
+    except Exception as e:
+        return f"Text processing failed: {e}"
+
+
+def answer_query(query: str, top_k: int, use_reranker: bool):
+    try:
+        start_time = time.time()
+        
+        # Retrieve and rerank
+        docs, contexts = rag.retrieve(query, top_k=top_k, rerank=use_reranker)
+        
+        # Generate answer with inline citations
+        answer = rag.generate_with_citations(query, contexts)
+        
+        # Calculate timing and estimates
+        end_time = time.time()
+        processing_time = end_time - start_time
+        
+        # Rough token estimates (very approximate)
+        query_tokens = len(query.split()) * 1.3  # rough tokenization
+        context_tokens = sum(len(c.split()) * 1.3 for c in contexts)
+        answer_tokens = len(answer.split()) * 1.3
+        
+        # Cost estimates (rough, based on typical pricing)
+        embedding_cost = (query_tokens + context_tokens) * 0.0001 / 1000  # $0.0001 per 1K tokens
+        llm_cost = answer_tokens * 0.00003 / 1000  # $0.00003 per 1K tokens for GPT-4o-mini
+        rerank_cost = len(contexts) * 0.0001 if use_reranker else 0  # $0.0001 per document
+        
+        total_cost = embedding_cost + llm_cost + rerank_cost
+        
+        # Format sources with citation numbers
+        sources = []
+        for i, doc in enumerate(docs):
+            source_info = f"[{i+1}] {doc['metadata'].get('source', 'Unknown')}"
+            if 'rerank_score' in doc:
+                source_info += f" (rerank: {doc['rerank_score']:.3f})"
+            else:
+                source_info += f" (score: {doc.get('score', 0):.3f})"
+            sources.append(source_info)
+        
+        sources_text = "\n".join(sources)
+        
+        # Add timing and cost info to answer
+        answer_with_meta = f"{answer}\n\n---\n**Processing Time:** {processing_time:.2f}s\n**Estimated Cost:** ${total_cost:.6f}\n**Tokens:** Query: {query_tokens:.0f}, Context: {context_tokens:.0f}, Answer: {answer_tokens:.0f}"
+        
+        return answer_with_meta, sources_text
+    except Exception as e:
+        return f"Error: {e}", ""
+
+
+def build_ui() -> gr.Blocks:
+    with gr.Blocks(title="Mini RAG - Track B Assessment") as demo:
+        gr.Markdown("""
+        ## Mini RAG - Track B Assessment
+        **Goal:** Build and host a small RAG app with text input, vector storage, retrieval + reranking, and LLM answering with citations.
+        
+        ### Features:
+        - **Text Input/Upload:** Paste text or upload files (.txt, .md, .pdf)
+        - **Vector Storage:** Pinecone cloud-hosted vector database
+        - **Retrieval + Reranking:** Top-k retrieval with optional Cohere reranker
+        - **LLM Answering:** OpenAI/Groq with inline citations [1], [2]
+        - **Metrics:** Request timing and cost estimates
+        """)
+
+        with gr.Tab("Text Input"):
+            gr.Markdown("### Process Text Input")
+            text_input = gr.Textbox(label="Paste your text here", lines=10, placeholder="Enter or paste your document text here...")
+            chunk_size = gr.Slider(400, 1200, value=800, step=100, label="Chunk Size (tokens)")
+            chunk_overlap = gr.Slider(50, 200, value=120, step=10, label="Chunk Overlap (tokens)")
+            process_btn = gr.Button("Process & Store Text")
+            process_out = gr.Textbox(label="Status")
+            process_btn.click(process_text_input, inputs=[text_input, chunk_size, chunk_overlap], outputs=[process_out])
+
+        with gr.Tab("File Ingestion"):
+            gr.Markdown("### Ingest Files from Directory")
+            data_dir = gr.Textbox(label="Data directory", value=os.getenv("DATA_DIR", "./data"))
+            ingest_btn = gr.Button("Run Ingestion")
+            ingest_out = gr.Textbox(label="Status")
+            ingest_btn.click(run_ingest, inputs=[data_dir], outputs=[ingest_out])
+
+        with gr.Tab("Query"):
+            gr.Markdown("### Ask Questions")
+            query = gr.Textbox(label="Question", lines=3, placeholder="Ask a question about your stored documents...")
+            top_k = gr.Slider(1, 20, value=5, step=1, label="Top K retrieval")
+            use_reranker = gr.Checkbox(value=True, label="Use reranker (Cohere)")
+            submit = gr.Button("Ask Question")
+            answer = gr.Markdown(label="Answer with Citations")
+            sources = gr.Markdown(label="Sources")
+            submit.click(answer_query, inputs=[query, top_k, use_reranker], outputs=[answer, sources])
+
+        with gr.Tab("Evaluation"):
+            gr.Markdown("""
+            ### Evaluation Examples (Gold Set)
+            
+            **Sample Q&A pairs for testing:**
+            
+            1. **Q:** What is the main topic of the document?
+               **Expected:** Clear identification of document subject
+               
+            2. **Q:** What are the key findings or conclusions?
+               **Expected:** Specific facts or conclusions from the text
+               
+            3. **Q:** What methodology was used?
+               **Expected:** Description of approach or methods mentioned
+               
+            4. **Q:** What are the limitations discussed?
+               **Expected:** Any limitations or constraints mentioned
+               
+            5. **Q:** What future work is suggested?
+               **Expected:** Recommendations or future directions
+               
+            **Success Metrics:**
+            - **Precision:** Relevant information in answers
+            - **Recall:** Coverage of available information
+            - **Citation Accuracy:** Proper source attribution
+            """)
+
+    return demo
+
+
+if __name__ == "__main__":
+    ui = build_ui()
+    ui.launch()

chunker.py

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+from typing import List
+import re
+
+
+def _split_into_paragraphs(text: str) -> List[str]:
+    """Split text into paragraphs based on double newlines"""
+    blocks = re.split(r"\n\s*\n", text.strip())
+    return [b.strip() for b in blocks if b.strip()]
+
+
+def chunk_text(text: str, chunk_size: int = 800, chunk_overlap: int = 120) -> List[str]:
+    """Split text into chunks with optional overlap"""
+    if not text:
+        return []
+
+    # Simple approach: split by paragraphs first, then by size if needed
+    paragraphs = _split_into_paragraphs(text)
+    chunks = []
+    
+    for para in paragraphs:
+        if len(para) <= chunk_size:
+            # Paragraph fits in one chunk
+            chunks.append(para)
+        else:
+            # Split long paragraph into chunks
+            start = 0
+            while start < len(para):
+                end = min(start + chunk_size, len(para))
+                chunk = para[start:end]
+                if chunk.strip():
+                    chunks.append(chunk.strip())
+                start = end
+    
+    # Add overlap between chunks if requested
+    if chunk_overlap > 0 and len(chunks) > 1:
+        overlapped_chunks = []
+        for i, chunk in enumerate(chunks):
+            if i == 0:
+                overlapped_chunks.append(chunk)
+                continue
+            
+            # Add overlap from previous chunk
+            prev_chunk = chunks[i - 1]
+            overlap_size = min(chunk_overlap, len(prev_chunk))
+            
+            if overlap_size > 0:
+                overlap_text = prev_chunk[-overlap_size:]
+                overlapped_chunk = f"{overlap_text}\n\n{chunk}"
+                overlapped_chunks.append(overlapped_chunk)
+            else:
+                overlapped_chunks.append(chunk)
+        
+        return overlapped_chunks
+    
+    return chunks

ingest.py

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+import os
+import uuid
+from typing import List, Dict, Any
+from dotenv import load_dotenv
+
+from chunker import chunk_text
+from llm import LLMProvider
+from pinecone_client import PineconeClient
+
+try:
+    from pypdf import PdfReader
+except Exception:  # pragma: no cover
+    PdfReader = None
+
+load_dotenv()
+
+
+def read_txt(path: str) -> str:
+    with open(path, "r", encoding="utf-8", errors="ignore") as f:
+        return f.read()
+
+
+def read_pdf(path: str) -> str:
+    if PdfReader is None:
+        raise RuntimeError("pypdf is not installed. Please install pypdf to read PDFs.")
+    reader = PdfReader(path)
+    texts: List[str] = []
+    for page in reader.pages:
+        texts.append(page.extract_text() or "")
+    return "\n".join(texts)
+
+
+def load_documents(data_dir: str) -> List[Dict[str, Any]]:
+    docs: List[Dict[str, Any]] = []
+    for root, _, files in os.walk(data_dir):
+        for name in files:
+            path = os.path.join(root, name)
+            ext = os.path.splitext(name)[1].lower()
+            try:
+                if ext in [".txt", ".md", ".log"]:
+                    text = read_txt(path)
+                elif ext in [".pdf"]:
+                    text = read_pdf(path)
+                else:
+                    continue
+                if text and text.strip():
+                    docs.append({"path": path, "text": text})
+            except Exception as e:  # skip problematic files
+                print(f"[warn] Failed to read {path}: {e}")
+    return docs
+
+
+def ingest(data_dir: str = None, chunk_size: int = None, chunk_overlap: int = None) -> int:
+    data_dir = data_dir or os.getenv("DATA_DIR", "./data")
+    chunk_size = int(chunk_size or os.getenv("CHUNK_SIZE", 800))
+    chunk_overlap = int(chunk_overlap or os.getenv("CHUNK_OVERLAP", 120))
+
+    os.makedirs(data_dir, exist_ok=True)
+
+    docs = load_documents(data_dir)
+    if not docs:
+        print(f"No documents found in {data_dir}")
+        return 0
+
+    llm = LLMProvider()
+    pc = PineconeClient()
+
+    # Ensure index exists based on embedding dimension
+    test_vec = llm.embed_texts(["dimension probe"])[0]
+    pc.ensure_index(dimension=len(test_vec))
+
+    total_chunks = 0
+    batch: List[Dict[str, Any]] = []
+
+    for doc in docs:
+        path = doc["path"]
+        chunks = chunk_text(doc["text"], chunk_size=chunk_size, chunk_overlap=chunk_overlap)
+        embeddings = llm.embed_texts(chunks)
+        for i, (text, vec) in enumerate(zip(chunks, embeddings)):
+            total_chunks += 1
+            item = {
+                "id": str(uuid.uuid4()),
+                "values": vec,
+                "metadata": {
+                    "text": text,
+                    "source": path,
+                    "chunk": i,
+                },
+            }
+            batch.append(item)
+            if len(batch) >= 100:
+                pc.upsert_embeddings(batch)
+                batch = []
+    if batch:
+        pc.upsert_embeddings(batch)
+
+    print(f"Ingested {total_chunks} chunks from {len(docs)} documents.")
+    return total_chunks
+
+
+if __name__ == "__main__":
+    ingest()
+

llm.py

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+import os
+from typing import List, Dict, Any, Optional
+from dotenv import load_dotenv
+
+# OpenAI SDK v1
+from openai import OpenAI
+
+# Groq
+from groq import Groq
+
+# Cohere
+import cohere
+
+load_dotenv()
+
+
+class LLMProvider:
+    def __init__(self) -> None:
+        self.provider = os.getenv("LLM_PROVIDER", "openai").lower()
+        self.llm_model = os.getenv("LLM_MODEL", "gpt-4o-mini")
+        self.embedding_model = os.getenv("EMBEDDING_MODEL", "text-embedding-3-small")
+        self.rerank_provider = os.getenv("RERANK_PROVIDER", "cohere").lower()
+        self.rerank_model = os.getenv("RERANK_MODEL", "rerank-3")
+
+        self._openai_client: Optional[OpenAI] = None
+        self._groq_client: Optional[Groq] = None
+        self._cohere_client: Optional[cohere.Client] = None
+
+        # Initialize clients with explicit parameters
+        openai_key = os.getenv("OPENAI_API_KEY")
+        if openai_key:
+            try:
+                self._openai_client = OpenAI(api_key=openai_key)
+            except Exception as e:
+                print(f"Warning: Failed to initialize OpenAI client: {e}")
+                self._openai_client = None
+        
+        groq_key = os.getenv("GROQ_API_KEY")
+        if groq_key:
+            try:
+                self._groq_client = Groq(api_key=groq_key)
+            except Exception as e:
+                print(f"Warning: Failed to initialize Groq client: {e}")
+                self._groq_client = None
+        
+        cohere_key = os.getenv("COHERE_API_KEY")
+        if cohere_key:
+            try:
+                self._cohere_client = cohere.Client(api_key=cohere_key)
+            except Exception as e:
+                print(f"Warning: Failed to initialize Cohere client: {e}")
+                self._cohere_client = None
+
+    # Embeddings (via OpenAI by default)
+    def embed_texts(self, texts: List[str]) -> List[List[float]]:
+        if not self._openai_client:
+            raise ValueError("Embeddings require OPENAI_API_KEY set in environment")
+        resp = self._openai_client.embeddings.create(model=self.embedding_model, input=texts)
+        return [d.embedding for d in resp.data]
+
+    # Chat completion via selected provider
+    def chat(self, messages: List[Dict[str, str]], temperature: float = 0.2, max_tokens: int = 512) -> str:
+        if self.provider == "openai":
+            if not self._openai_client:
+                raise ValueError("OPENAI_API_KEY is missing")
+            resp = self._openai_client.chat.completions.create(
+                model=self.llm_model,
+                messages=messages,
+                temperature=temperature,
+                max_tokens=max_tokens,
+            )
+            return resp.choices[0].message.content or ""
+        elif self.provider == "groq":
+            if not self._groq_client:
+                raise ValueError("GROQ_API_KEY is missing")
+            resp = self._groq_client.chat.completions.create(
+                model=self.llm_model,
+                messages=messages,
+                temperature=temperature,
+                max_tokens=max_tokens,
+            )
+            return resp.choices[0].message.content or ""
+        else:
+            raise ValueError(f"Unsupported LLM_PROVIDER: {self.provider}")
+
+    def rerank(self, query: str, documents: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+        # documents: list of {text: str, metadata: dict, score: float}
+        if self.rerank_provider == "cohere" and self._cohere_client:
+            inputs = [d["text"] for d in documents]
+            result = self._cohere_client.rerank(
+                model=self.rerank_model,
+                query=query,
+                documents=inputs,
+                top_n=len(inputs),
+            )
+            # result is ordered by relevance
+            ranked: List[Dict[str, Any]] = []
+            for item in result:
+                idx = item.index
+                doc = documents[idx]
+                ranked.append({**doc, "rerank_score": float(item.relevance_score)})
+            return ranked
+        # Fallback: return original order
+        return documents

pinecone_client.py

@@ -0,0 +1,53 @@
+import os
+from typing import List, Dict, Any, Optional
+from dotenv import load_dotenv
+
+# pinecone-client v5
+from pinecone import Pinecone, ServerlessSpec
+
+
+load_dotenv()
+
+
+class PineconeClient:
+    def __init__(
+        self,
+        api_key: Optional[str] = None,
+        index_name: Optional[str] = None,
+        cloud: str = "aws",
+        region: str = "us-east-1",
+    ) -> None:
+        self.api_key = api_key or os.getenv("PINECONE_API_KEY")
+        self.index_name = index_name or os.getenv("PINECONE_INDEX", "mini-rag-index")
+        self.cloud = os.getenv("PINECONE_CLOUD", cloud)
+        self.region = os.getenv("PINECONE_REGION", region)
+        if not self.api_key:
+            raise ValueError("PINECONE_API_KEY is required")
+        self.pc = Pinecone(api_key=self.api_key)
+        self._index = None
+
+    def ensure_index(self, dimension: int, metric: str = "cosine") -> None:
+        indexes = {idx["name"] for idx in self.pc.list_indexes()}
+        if self.index_name not in indexes:
+            self.pc.create_index(
+                name=self.index_name,
+                dimension=dimension,
+                metric=metric,
+                spec=ServerlessSpec(cloud=self.cloud, region=self.region),
+            )
+        # Connect to index
+        self._index = self.pc.Index(self.index_name)
+
+    @property
+    def index(self):
+        if self._index is None:
+            self._index = self.pc.Index(self.index_name)
+        return self._index
+
+    def upsert_embeddings(self, items: List[Dict[str, Any]]) -> None:
+        # items: {id: str, values: List[float], metadata: dict}
+        self.index.upsert(vectors=items)
+
+    def query(self, vector: List[float], top_k: int = 5, filter: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
+        return self.index.query(vector=vector, top_k=top_k, include_metadata=True, filter=filter)
+

rag_core.py

@@ -0,0 +1,76 @@
+import os
+from typing import List, Dict, Any, Tuple
+from dotenv import load_dotenv
+
+from llm import LLMProvider
+from pinecone_client import PineconeClient
+
+load_dotenv()
+
+
+def _build_prompt(query: str, contexts: List[str]) -> List[Dict[str, str]]:
+    system = (
+        "You are a helpful assistant. Answer the user's question using the provided context. "
+        "If the answer isn't in the context, say you don't know. Be concise."
+    )
+    context_block = "\n\n".join([f"[Source {i+1}]\n{c}" for i, c in enumerate(contexts)])
+    user = f"Question: {query}\n\nContext:\n{context_block}"
+    return [
+        {"role": "system", "content": system},
+        {"role": "user", "content": user},
+    ]
+
+
+def _build_citation_prompt(query: str, contexts: List[str]) -> List[Dict[str, str]]:
+    system = (
+        "You are a helpful assistant. Answer the user's question using the provided context. "
+        "IMPORTANT: Use inline citations [1], [2], [3] etc. to reference specific sources. "
+        "Each citation number should correspond to the source number from the context. "
+        "If the answer isn't in the context, say you don't know. Be concise and accurate."
+    )
+    context_block = "\n\n".join([f"[Source {i+1}]\n{c}" for i, c in enumerate(contexts)])
+    user = f"Question: {query}\n\nContext:\n{context_block}\n\nAnswer with inline citations [1], [2], etc.:"
+    return [
+        {"role": "system", "content": system},
+        {"role": "user", "content": user},
+    ]
+
+
+class RAGCore:
+    def __init__(self) -> None:
+        self.llm = LLMProvider()
+        self.pc = PineconeClient()
+
+    def ensure_index(self, embedding_dim: int) -> None:
+        self.pc.ensure_index(dimension=embedding_dim)
+
+    def retrieve(self, query: str, top_k: int = 5, rerank: bool = True) -> Tuple[List[Dict[str, Any]], List[str]]:
+        q_vec = self.llm.embed_texts([query])[0]
+        results = self.pc.query(vector=q_vec, top_k=top_k)
+        matches = results.get("matches", [])
+        docs: List[Dict[str, Any]] = []
+        for m in matches:
+            md = m.get("metadata", {}) or {}
+            text = md.get("text", "")
+            docs.append({
+                "id": m.get("id"),
+                "text": text,
+                "score": float(m.get("score", 0.0)),
+                "metadata": md,
+            })
+        if rerank:
+            docs = self.llm.rerank(query, docs)
+        contexts = [d["text"] for d in docs]
+        return docs, contexts
+
+    def generate(self, query: str, contexts: List[str]) -> str:
+        messages = _build_prompt(query, contexts)
+        return self.llm.chat(messages)
+
+    def generate_with_citations(self, query: str, contexts: List[str]) -> str:
+        """Generate answer with inline citations [1], [2], etc."""
+        if not contexts:
+            return "No relevant context found to answer this question."
+        
+        messages = _build_citation_prompt(query, contexts)
+        return self.llm.chat(messages)

requirements.txt

@@ -0,0 +1,19 @@
+# Core
+python-dotenv==1.0.1
+numpy==1.26.4
+
+# Vector DB
+pinecone-client==5.0.0
+
+# LLMs
+openai==1.40.3
+groq==0.9.0
+
+# Reranker (API-based)
+cohere==5.6.2
+
+# UI
+gradio==4.44.0
+
+# Ingestion helpers
+pypdf==4.2.0

sample_document.txt

@@ -0,0 +1,78 @@
+Artificial Intelligence and Machine Learning: A Comprehensive Overview
+
+Introduction
+Artificial Intelligence (AI) and Machine Learning (ML) represent the cutting edge of computational technology, enabling machines to perform tasks that traditionally required human intelligence. This document provides a comprehensive overview of these technologies, their applications, and their implications for the future.
+
+Main Topic and Scope
+The primary focus of this document is to explore the fundamental concepts, methodologies, and practical applications of AI and ML systems. We examine both theoretical foundations and real-world implementations, providing readers with a balanced understanding of the field's current state and future potential.
+
+Key Findings and Conclusions
+1. AI and ML technologies have demonstrated remarkable progress in recent years, particularly in areas such as natural language processing, computer vision, and autonomous systems.
+
+2. The integration of AI into various industries has led to significant improvements in efficiency, accuracy, and decision-making capabilities.
+
+3. Machine learning models, particularly deep learning architectures, have achieved breakthrough performance in numerous benchmark tasks.
+
+4. The democratization of AI tools and frameworks has lowered barriers to entry, enabling more organizations to leverage these technologies.
+
+5. Ethical considerations and responsible AI development have become increasingly important as these technologies become more pervasive.
+
+Methodology and Approach
+Our analysis employs a multi-faceted methodology that combines:
+- Literature review of peer-reviewed research papers and technical publications
+- Case study analysis of successful AI implementations across different sectors
+- Expert interviews with leading researchers and practitioners in the field
+- Comparative analysis of different AI/ML approaches and their effectiveness
+- Statistical analysis of performance metrics and success rates
+
+The research methodology emphasizes both quantitative and qualitative assessment, ensuring comprehensive coverage of the subject matter while maintaining scientific rigor.
+
+Technical Implementation Details
+The technical foundation of modern AI systems relies on several key components:
+- Neural networks and deep learning architectures
+- Large language models and transformer-based approaches
+- Computer vision algorithms and image processing techniques
+- Reinforcement learning frameworks and optimization algorithms
+- Natural language processing pipelines and semantic understanding systems
+
+These components work together to create sophisticated AI systems capable of understanding, learning, and adapting to complex environments.
+
+Limitations and Constraints
+Despite significant advances, current AI and ML systems face several important limitations:
+
+1. Data Dependency: Most ML models require large amounts of high-quality training data, which may not always be available or accessible.
+
+2. Computational Requirements: Advanced AI models often require substantial computational resources, limiting their deployment in resource-constrained environments.
+
+3. Interpretability: Many modern ML models operate as "black boxes," making it difficult to understand how they arrive at their decisions.
+
+4. Bias and Fairness: AI systems can inherit and amplify biases present in their training data, leading to unfair or discriminatory outcomes.
+
+5. Generalization: Models trained on specific datasets may struggle to generalize to new, unseen scenarios or domains.
+
+6. Security Vulnerabilities: AI systems can be vulnerable to adversarial attacks and manipulation, raising concerns about their reliability in critical applications.
+
+Future Work and Recommendations
+Based on our analysis, we recommend several areas for future research and development:
+
+1. Enhanced Interpretability: Develop new methods and tools for making AI systems more transparent and understandable to users and stakeholders.
+
+2. Robustness and Reliability: Improve the robustness of AI systems against adversarial attacks and unexpected inputs.
+
+3. Efficient Learning: Develop more efficient learning algorithms that require less data and computational resources.
+
+4. Ethical AI Development: Establish comprehensive frameworks and guidelines for responsible AI development and deployment.
+
+5. Cross-Domain Applications: Explore the application of AI techniques across different domains and industries.
+
+6. Human-AI Collaboration: Develop systems that enhance human capabilities rather than replace them entirely.
+
+7. Continuous Learning: Implement systems that can learn and adapt continuously from new data and experiences.
+
+8. Standardization: Establish industry standards and best practices for AI system development and evaluation.
+
+Conclusion
+Artificial Intelligence and Machine Learning represent transformative technologies with the potential to revolutionize numerous aspects of society and industry. While significant progress has been made, important challenges remain in areas such as interpretability, fairness, and robustness. The successful development and deployment of AI systems will require continued research, responsible development practices, and thoughtful consideration of ethical implications.
+
+The future of AI and ML is bright, but it requires careful stewardship to ensure these technologies benefit humanity while minimizing potential risks and negative consequences. By addressing current limitations and focusing on responsible development, we can unlock the full potential of these remarkable technologies.
+

test_system.py

@@ -0,0 +1,185 @@
+#!/usr/bin/env python3
+"""
+Test script for Mini RAG system
+Run this to verify all components work before deployment
+"""
+
+import os
+import sys
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+
+def test_imports():
+    """Test that all required modules can be imported"""
+    print("Testing imports...")
+    try:
+        from chunker import chunk_text
+        from llm import LLMProvider
+        from pinecone_client import PineconeClient
+        from rag_core import RAGCore
+        from ingest import load_documents
+        print("✅ All imports successful")
+        return True
+    except ImportError as e:
+        print(f"❌ Import failed: {e}")
+        return False
+
+def test_chunking():
+    """Test text chunking functionality"""
+    print("\nTesting chunking...")
+    try:
+        from chunker import chunk_text
+        
+        test_text = "This is a test document. " * 50  # Create long text
+        chunks = chunk_text(test_text, chunk_size=100, chunk_overlap=20)
+        
+        if len(chunks) > 1:
+            print(f"✅ Chunking works: {len(chunks)} chunks created")
+            return True
+        else:
+            print("❌ Chunking failed: expected multiple chunks")
+            return False
+    except Exception as e:
+        print(f"❌ Chunking test failed: {e}")
+        return False
+
+def test_environment():
+    """Test environment variable configuration"""
+    print("\nTesting environment variables...")
+    
+    required_vars = ['PINECONE_API_KEY', 'OPENAI_API_KEY']
+    optional_vars = ['GROQ_API_KEY', 'COHERE_API_KEY']
+    
+    missing_required = []
+    for var in required_vars:
+        if not os.getenv(var):
+            missing_required.append(var)
+    
+    if missing_required:
+        print(f"❌ Missing required environment variables: {missing_required}")
+        print("Please set these in your .env file")
+        return False
+    
+    print("✅ Required environment variables set")
+    
+    # Check optional variables
+    for var in optional_vars:
+        if os.getenv(var):
+            print(f"✅ {var} is set")
+        else:
+            print(f"⚠️  {var} not set (optional)")
+    
+    return True
+
+def test_document_loading():
+    """Test document loading functionality"""
+    print("\nTesting document loading...")
+    try:
+        from ingest import load_documents
+        
+        # Check if data directory exists
+        data_dir = "./data"
+        if not os.path.exists(data_dir):
+            print(f"⚠️  Data directory {data_dir} not found")
+            return False
+        
+        docs = load_documents(data_dir)
+        if docs:
+            print(f"✅ Document loading works: {len(docs)} documents found")
+            for doc in docs:
+                print(f"   - {doc['path']} ({len(doc['text'])} characters)")
+            return True
+        else:
+            print("⚠️  No documents found in data directory")
+            return False
+            
+    except Exception as e:
+        print(f"❌ Document loading test failed: {e}")
+        return False
+
+def test_llm_provider():
+    """Test LLM provider initialization"""
+    print("\nTesting LLM provider...")
+    try:
+        from llm import LLMProvider
+        
+        llm = LLMProvider()
+        print(f"✅ LLM provider initialized: {llm.provider}")
+        print(f"   - Embedding model: {llm.embedding_model}")
+        print(f"   - LLM model: {llm.llm_model}")
+        print(f"   - Reranker: {llm.rerank_provider}")
+        
+        return True
+    except Exception as e:
+        print(f"❌ LLM provider test failed: {e}")
+        return False
+
+def test_pinecone_client():
+    """Test Pinecone client initialization"""
+    print("\nTesting Pinecone client...")
+    try:
+        from pinecone_client import PineconeClient
+        
+        pc = PineconeClient()
+        print(f"✅ Pinecone client initialized")
+        print(f"   - Index: {pc.index_name}")
+        print(f"   - Cloud: {pc.cloud}")
+        print(f"   - Region: {pc.region}")
+        
+        return True
+    except Exception as e:
+        print(f"❌ Pinecone client test failed: {e}")
+        return False
+
+def test_rag_core():
+    """Test RAG core initialization"""
+    print("\nTesting RAG core...")
+    try:
+        from rag_core import RAGCore
+        
+        rag = RAGCore()
+        print("✅ RAG core initialized")
+        
+        return True
+    except Exception as e:
+        print(f"❌ RAG core test failed: {e}")
+        return False
+
+def main():
+    """Run all tests"""
+    print("🧪 Mini RAG System Test Suite")
+    print("=" * 40)
+    
+    tests = [
+        test_imports,
+        test_environment,
+        test_chunking,
+        test_document_loading,
+        test_llm_provider,
+        test_pinecone_client,
+        test_rag_core,
+    ]
+    
+    passed = 0
+    total = len(tests)
+    
+    for test in tests:
+        if test():
+            passed += 1
+    
+    print("\n" + "=" * 40)
+    print(f"Test Results: {passed}/{total} tests passed")
+    
+    if passed == total:
+        print("🎉 All tests passed! System is ready for deployment.")
+        return True
+    else:
+        print("⚠️  Some tests failed. Please fix issues before deployment.")
+        return False
+
+if __name__ == "__main__":
+    success = main()
+    sys.exit(0 if success else 1)
+