https://github.com/saba-gul/end-to-end-rag-app-with-groq-and-qdrant
This project demonstrates how to build an end-to-end Retrieval-Augmented Generation (RAG) application using Groq, Qdrant, and LangChain.
https://github.com/saba-gul/end-to-end-rag-app-with-groq-and-qdrant
Last synced: 7 months ago
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This project demonstrates how to build an end-to-end Retrieval-Augmented Generation (RAG) application using Groq, Qdrant, and LangChain.
- Host: GitHub
- URL: https://github.com/saba-gul/end-to-end-rag-app-with-groq-and-qdrant
- Owner: Saba-Gul
- License: mit
- Created: 2024-08-10T22:58:03.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-08-10T23:00:13.000Z (about 1 year ago)
- Last Synced: 2025-01-13T16:28:17.813Z (9 months ago)
- Language: Jupyter Notebook
- Size: 309 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# **End-to-End RAG App with Groq and Qdrant**
This project demonstrates how to build an end-to-end Retrieval-Augmented Generation (RAG) application using Groq, Qdrant, and LangChain. The application processes and retrieves information from a PDF document, and answers questions using both the document and a language model.
## **Installation**
To run the code, you need to install the required Python libraries. You can do this by running:
```bash
!pip install langchain openai tiktoken qdrant-client langchain_openai pypdf langchainhub langchain_community langchain-groq langchain-huggingface
```
## **Usage**
1. **Loading the Document:**
- The PDF document is loaded using `PyPDFLoader`, and the content is split into chunks for efficient processing.
2. **Text Splitting:**
- The document is split into manageable chunks using `RecursiveCharacterTextSplitter`.
3. **Embedding and Indexing:**
- The text chunks are embedded using `HuggingFaceEmbeddings`.
- The embeddings are then stored in a Qdrant vector store for efficient retrieval.
4. **Retrieving Relevant Documents:**
- The Qdrant vector store is used to retrieve documents relevant to a given query.
5. **Generating Answers:**
- The retrieved documents are passed through a prompt template to generate a concise, logical answer using the Groq language model.
## **Key Components**
- **LangChain:** A framework for developing applications with language models.
- **Qdrant:** An open-source vector search engine used for efficient similarity search.
- **Groq:** An open-source language model used to generate responses based on retrieved context.
- **HuggingFace Embeddings:** Used to convert text chunks into embeddings.
- **PyPDFLoader:** Loads and processes PDF documents.
## **Example Queries**
You can use the `rag_chain.invoke()` method to ask questions or summarize the document:
```python
rag_chain.invoke("Who was Marium?")
rag_chain.invoke("Summarize the key points of the document")
rag_chain.invoke("Who was Laila?")
```
## **Customization**
- **Change Model:** You can change the language model by modifying the `groq_llm` instantiation.
- **Adjust Retrieval:** Customize the `search_kwargs` parameter to change the number of documents retrieved.
- **Document Loader:** Replace `PyPDFLoader` with other loaders available in LangChain for different document types.
## **Acknowledgments**
Special thanks to the open-source community for providing the tools and libraries used in this project.