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+# RAG with MongoDB Skill
+
+Learn how to build a Retrieval-Augmented Generation (RAG) system using MongoDB Atlas Vector Search, LangChain, and OpenAI! This example demonstrates creating an intelligent question-answering system that can provide accurate responses based on your document content.
+
+## What This Demo Does
+
+📚 **Answer questions from your documents**: Ask questions about your PDF content and get intelligent responses  
+🔍 **Semantic document retrieval**: Finds relevant document chunks using AI-powered similarity search  
+⚡ **Fast vector search**: Uses MongoDB's optimized vector search capabilities for quick retrieval  
+🤖 **AI-powered responses**: Combines retrieved context with OpenAI's GPT-4 for accurate answers  
+
+## What You'll Need
+
+Before getting started, make sure you have:
+
+- ✅ **MongoDB Atlas Cluster** with connection string
+- ✅ **OpenAI API Key** for GPT-4 and metadata generation
+- ✅ **Voyage AI API Key** (free tier available)
+- ✅ **Python 3.8+** installed on your machine
+
+## Step-by-Step Setup
+
+### Step 1: Set Up Your Python Environment
+
+Create an isolated environment for this project:
+
+**Windows:**
+```bash
+python -m venv venv
+venv\Scripts\activate
+```
+
+**macOS/Linux:**
+```bash
+python -m venv venv
+source venv/bin/activate
+```
+
+### Step 2: Install Required Packages
+
+```bash
+pip install -r requirements.txt
+```
+
+### Step 3: Configure Your API Keys
+
+Open the `key_param.py` file and add your credentials:
+
+```python
+LLM_API_KEY="your_openai_api_key_here"
+VOYAGE_API_KEY="your_voyage_api_key_here"
+MONGODB_URI="your_mongodb_connection_string_here"
+```
+
+💡 **Getting your keys:**
+- **MongoDB URI**: Copy from your Atlas cluster's "Connect" button
+- **OpenAI API Key**: Get from [openai.com](https://platform.openai.com)
+- **Voyage API Key**: Sign up at [voyageai.com](https://voyageai.com) for a free API key
+
+## How to Use
+
+### 1. Load Your Data
+
+First, run the data loading script to process your PDF and store embeddings:
+
+```bash
+python load_data.py
+```
+
+⏱️ **Note**: This process may take a couple of minutes as it generates embeddings and metadata for each document chunk.
+
+This will:
+- 📄 **Load and clean** your PDF document
+- ✂️ **Split text** into manageable chunks (500 chars with 150 overlap)  
+- 🏷️ **Generate metadata** using OpenAI (title, keywords, hasCode)
+- 🧠 **Create embeddings** using Voyage AI's voyage-3-large model
+- 💾 **Store everything** in MongoDB Atlas with vector search capabilities
+
+### 2. Create Vector Search Index
+
+After your data is loaded, create a vector search index in your Atlas cluster's **Search & Vector Search** tab (On the left sidebar):
+
+**Database:** `book_mongodb_chunks`  
+**Collection:** `chunked_data`  
+**Index Name:** `vector_index`
+
+**Index Definition:**
+
+```json
+{
+  "fields": [
+    {
+      "numDimensions": 1024,
+      "path": "embedding",
+      "similarity": "dotProduct",
+      "type": "vector"
+    },
+    {
+      "path": "hasCode",
+      "type": "filter"
+    }
+  ]
+}
+```
+
+⚠️ **Important**: Wait for the index to finish building before proceeding. You can check the index status in the Atlas UI - it should show as "Ready" before you can run queries.
+
+### 3. Ask Questions
+
+Run the RAG system to start asking questions:
+
+```bash
+python rag.py
+```
+
+### 4. Customize Your Queries
+
+Edit the query in `rag.py` to ask different questions:
+
+```python
+print(query_data("What is the difference between a collection and database in MongoDB?"))
+# Try other questions like:
+# "How do I create an index in MongoDB?"
+# "What are the benefits of using MongoDB Atlas?"
+# "Explain MongoDB's aggregation pipeline"
+```
+
+### 5. View Results
+
+The system will output intelligent answers based on your document content with:
+- 💭 **Contextual answers** generated from relevant document sections
+- 🎯 **Source-grounded responses** that don't hallucinate beyond your content
+- ⚡ **Fast retrieval** using vector similarity search
+
+## Example Output
+
+```
+Query: "What is the difference between a collection and database in MongoDB?"
+
+Answer: Based on the provided context, a database in MongoDB is a container that holds collections, while a collection is a grouping of MongoDB documents. Think of a database as a filing cabinet and collections as the folders within that cabinet that organize related documents together.
+```
+
+## How It Works
+
+1. **Document Processing**: Your PDF gets chunked into smaller pieces with metadata extraction
+2. **Vector Embedding**: Each chunk gets converted to a high-dimensional vector using Voyage AI
+3. **Semantic Search**: When you ask a question, it finds the most relevant chunks using vector similarity
+4. **Context Assembly**: Top matching chunks get combined into context for the AI
+5. **Answer Generation**: OpenAI GPT-4 generates answers based only on the retrieved context
+
+## Troubleshooting
+
+**🚫 "No vector index found"**: Make sure your Atlas vector search index is created and active  
+**🔑 "Authentication failed"**: Verify your API keys in `key_param.py`  
+**📦 "Module not found"**: Ensure you activated your virtual environment  
+**📄 "File not found"**: Check that your PDF is in the `sample_files` directory
+
+## Learn More
+
+- 📚 [MongoDB Atlas Vector Search Documentation](https://docs.atlas.mongodb.com/atlas-vector-search/)
+- 🎓 [Earn the Vector Search Fundamentals Badge](https://learn.mongodb.com/courses/vector-search-fundamentals)
+- 🎓 [Earn the RAG with MongoDB Badge](https://learn.mongodb.com/courses/rag-with-mongodb)
+- 🤖 [Voyage AI](https://voyageai.com/)
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diff --git a/RAG-with-MongoDB-Skill/key_param.py b/RAG-with-MongoDB-Skill/key_param.py
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+LLM_API_KEY="your_openai_api_key_here"
+VOYAGE_API_KEY="your_voyage_api_key_here"
+MONGODB_URI="your_mongodb_connection_string_here"
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diff --git a/RAG-with-MongoDB-Skill/load_data.py b/RAG-with-MongoDB-Skill/load_data.py
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+from pymongo import MongoClient
+from langchain_openai import ChatOpenAI
+from langchain_voyageai import VoyageAIEmbeddings
+from langchain_community.vectorstores import MongoDBAtlasVectorSearch
+from langchain_community.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.document_transformers.openai_functions import (
+    create_metadata_tagger,
+)
+
+import key_param
+
+# Set the MongoDB URI, DB, Collection Names
+
+client = MongoClient(key_param.MONGODB_URI)
+dbName = "book_mongodb_chunks"
+collectionName = "chunked_data"
+collection = client[dbName][collectionName]
+
+loader = PyPDFLoader(".\sample_files\mongodb.pdf")
+pages = loader.load()
+cleaned_pages = []
+
+for page in pages:
+    if len(page.page_content.split(" ")) > 20:
+        cleaned_pages.append(page)
+
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=150)
+
+schema = {
+    "properties": {
+        "title": {"type": "string"},
+        "keywords": {"type": "array", "items": {"type": "string"}},
+        "hasCode": {"type": "boolean"},
+    },
+    "required": ["title", "keywords", "hasCode"],
+}
+
+llm = ChatOpenAI(
+    openai_api_key=key_param.LLM_API_KEY, temperature=0, model="gpt-4"
+)
+
+document_transformer = create_metadata_tagger(metadata_schema=schema, llm=llm)
+
+docs = document_transformer.transform_documents(cleaned_pages)
+
+split_docs = text_splitter.split_documents(docs)
+
+embeddings = VoyageAIEmbeddings(voyage_api_key=key_param.VOYAGE_API_KEY, model="voyage-3-large")
+
+
+vectorStore = MongoDBAtlasVectorSearch.from_documents(
+    split_docs, embeddings, collection=collection
+)
diff --git a/RAG-with-MongoDB-Skill/rag.py b/RAG-with-MongoDB-Skill/rag.py
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+from langchain_mongodb import MongoDBAtlasVectorSearch
+from langchain_openai import ChatOpenAI
+from langchain.prompts import PromptTemplate
+from langchain_core.runnables import RunnablePassthrough
+from langchain_core.output_parsers import StrOutputParser
+import key_param
+from langchain_voyageai import VoyageAIEmbeddings
+
+dbName = "book_mongodb_chunks"
+collectionName = "chunked_data"
+index = "vector_index"
+
+vectorStore = MongoDBAtlasVectorSearch.from_connection_string(
+    key_param.MONGODB_URI,
+    dbName + "." + collectionName,
+    VoyageAIEmbeddings(voyage_api_key=key_param.VOYAGE_API_KEY, model="voyage-3-large"),
+    index_name=index,
+)
+
+def query_data(query):
+    retriever = vectorStore.as_retriever(
+        search_type="similarity",
+        search_kwargs={
+            "k": 3
+        },
+    )
+
+    template = """
+    Use the following pieces of context to answer the question at the end.
+    If you don't know the answer, just say that you don't know, don't try to make up an answer.
+    Do not answer the question if there is no given context.
+    Do not answer the question if it is not related to the context.
+    Do not give recommendations to anything other than MongoDB.
+    Context:
+    {context}
+    Question: {question}
+    """
+
+    custom_rag_prompt = PromptTemplate.from_template(template)
+
+    retrieve = {
+        "context": retriever | (lambda docs: "\n\n".join([d.page_content for d in docs])), 
+        "question": RunnablePassthrough()
+        }
+
+    llm = ChatOpenAI(openai_api_key=key_param.LLM_API_KEY, temperature=0, model="gpt-4")
+
+    response_parser = StrOutputParser()
+
+    rag_chain = (
+        retrieve
+        | custom_rag_prompt
+        | llm
+        | response_parser
+    )
+
+    answer = rag_chain.invoke(query)
+    
+
+    return answer
+
+print(query_data("What is the difference between a collection and database in MongoDB?"))
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diff --git a/RAG-with-MongoDB-Skill/requirements.txt b/RAG-with-MongoDB-Skill/requirements.txt
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+langchain==0.3.27
+langchain_community==0.3.30
+langchain_core==0.3.78
+langchain_mongodb==0.7.0
+pymongo==4.15.2
+langchain-voyageai==0.1.3
+langchain_openai==0.3.35
+pypdf==6.1.1
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diff --git a/RAG-with-MongoDB-Skill/sample_files/mongodb.pdf b/RAG-with-MongoDB-Skill/sample_files/mongodb.pdf
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