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-# 
+# RAG by Ollama
 
-RAG use ollama
+## Overview
 
+This code demo showcases a Retrieval-Augmented Generation (RAG) pipeline using Ollama with langchain components. The RAG approach enhances the generation capabilities of language models by integrating document retrieval, ensuring more accurate and contextually relevant responses. 
 
+## Key Components
 
+```
+pip install langchain langchain-community ollama chromadb
+```
 
+1. **OllamaEmbeddings**: Used to generate embeddings for document chunks and queries.
+2. **RecursiveCharacterTextSplitter**: Splits large documents into smaller, manageable chunks.
+3. **ChromaDB**: A vector database used to store and retrieve embedded document chunks.
+4. **ChatOllama**: A local language model for generating responses.
+5. **LangChain Prompts and Pipelines**: Used to structure the prompts and generate responses.
+
+## Workflow
+
+1. **Load and Split Document**: 
+    - The content of a markdown file is loaded and split into smaller chunks using `RecursiveCharacterTextSplitter`. This ensures that each chunk is within a specified size, making it manageable for embedding and retrieval.
+
+2. **Embed Document Chunks**:
+    - Each chunk is embedded using `OllamaEmbeddings` and stored in a ChromaDB collection. This step transforms textual data into high-dimensional vectors, facilitating efficient retrieval based on semantic similarity.
+
+3. **Initialize Language Model**:
+    - The `ChatOllama` model is initialized with specific parameters to handle the generation of responses.
+
+4. **Define Prompt Template**:
+    - A prompt template is created to structure the input for the language model, ensuring the responses are contextually relevant.
+
+5. **Answer Questions**:
+    - A function `answer_question` is defined to handle queries. It embeds the query, retrieves the most relevant document chunks from ChromaDB, and combines them. The combined text is then fed into the language model pipeline to generate a response.
+
+## Explanation of Key Concepts
+
+### Embedding by Chunks
+
+Embedding by chunks involves breaking down a large document into smaller pieces and generating embeddings for each piece individually. This approach is beneficial for several reasons:
+- **Manageability**: Smaller chunks are easier to process and handle compared to a large document.
+- **Efficient Retrieval**: Embedding smaller chunks allows for more precise retrieval of relevant information based on specific queries.
+- **Scalability**: This method scales well with large documents, ensuring that the embedding and retrieval process remains efficient.
+
+### Retrieval-Augmented Generation (RAG)
+
+RAG combines retrieval and generation to improve the accuracy and relevance of responses:
+- **Retrieval**: Uses embeddings to find the most relevant document chunks from a database.
+- **Generation**: Uses a language model to generate a response based on the retrieved information, ensuring that the response is grounded in actual data.
+
+By embedding document chunks and using a language model to generate responses, this pipeline demonstrates a powerful approach to handling complex queries with accurate and contextually appropriate answers.
+
+## Requirements
+
+To run this code, you'll need the following libraries:
+- `langchain_community`
+- `langchain_core`
+- `chromadb`
+
+Ensure you have these libraries installed and properly configured in your environment.
+
+## Conclusion
+
+This code demo illustrates a practical implementation of a Retrieval-Augmented Generation pipeline, leveraging the capabilities of LangChain components and ChromaDB to deliver accurate and contextually relevant responses to user queries.