Skip to content
Leon van Zyl
0:14:44
3 119
153
52
Last update : 16/10/2024

🧠 Mastering RAG with Flowise Document Stores 🚀

Tired of messy RAG workflows? 😩 Flowise Document Stores are here to streamline your AI development! This breakdown reveals how to build, maintain, and optimize your RAG chatbots effortlessly.

💡 Why Document Stores Matter

Imagine this: you’re building a chatbot that answers questions about your company. You have tons of documents, and it’s a nightmare to manage them within your chatbot’s code. Document Stores solve this by:

  • Separation of Concerns: They decouple your AI logic from your knowledge base, making your application cleaner and easier to maintain.
  • Flexibility: Add or remove data sources on the fly without touching your core chatbot logic.
  • Observability: Easily monitor and fine-tune your data pipeline.

🏗️ Building Your First Document Store

  1. Create a Chat Flow: Start by creating a new chat flow in Flowise.
  2. Add a Conversational Retrieval QA Chain: This chain will handle the question-answering process.
  3. Choose Your Chat Model: Select a model like LLaMa 2 or GPT-3.
  4. Create a Document Store: Go to “Document Stores” and click “Add New.”
  5. Add Document Loaders: Choose from various loaders like web scrapers or file uploads to populate your store.
  6. Preview and Process: Review the extracted chunks and process them to store them temporarily.

🔗 Connecting to a Vector Store

  1. Configure Upsert Settings: Go to “Upsert Config” in your Document Store.
  2. Choose Embeddings: Select an embeddings model like OpenAI or Nomic Embed.
  3. Select Vector Store: Pick your preferred vector database, such as Pinecone.
  4. Configure Credentials: Enter your API keys and index names.
  5. Add a Record Manager (Optional): Prevent duplicates and stale data using a database like Postgres.
  6. Upsert Your Data: Click “Upsert” to load your chunks into the vector store.

🧪 Testing and Refinement

  1. Test Retrieval: Use the built-in “Test Retrieval” feature to query your vector store and see how well it retrieves relevant information.
  2. Fine-tune Parameters: Adjust settings like the number of documents returned, metadata filters, and search type to optimize retrieval accuracy.
  3. Iterate and Improve: Continuously test and refine your Document Store configuration to achieve the best possible results for your RAG application.

🧰 Resource Toolbox

🚀 Take Your RAG Skills to the Next Level

Flowise Document Stores provide a powerful and intuitive way to manage your RAG knowledge base. By following these steps, you can build more robust, maintainable, and efficient AI applications. Happy building! 🎉

Other videos of

Play Video
Leon van Zyl
0:35:01
2 908
292
56
Last update : 07/11/2024
Play Video
Leon van Zyl
0:14:18
1 556
138
25
Last update : 07/11/2024
Play Video
Leon van Zyl
0:08:19
3 660
225
59
Last update : 30/10/2024
Play Video
Leon van Zyl
0:17:40
2 383
66
18
Last update : 30/10/2024
Play Video
Leon van Zyl
0:21:32
3 563
170
29
Last update : 30/10/2024
Play Video
Leon van Zyl
0:04:36
356
28
4
Last update : 21/10/2024
Play Video
Leon van Zyl
0:32:12
524
58
19
Last update : 17/10/2024
Play Video
Leon van Zyl
0:08:49
1 738
74
19
Last update : 16/10/2024
Play Video
Leon van Zyl
0:11:47
11 988
611
59
Last update : 02/10/2024