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Hamza 2026-05-21 11:07:05 +02:00
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@ -18,7 +18,6 @@
- [Step 4: Faiss Vector Store](#step-4-faiss-vector-store) - [Step 4: Faiss Vector Store](#step-4-faiss-vector-store)
- [Step 5: Ollama Chat Model](#step-5-ollama-chat-model) - [Step 5: Ollama Chat Model](#step-5-ollama-chat-model)
- [Step 6: Buffer Memory](#step-6-buffer-memory) - [Step 6: Buffer Memory](#step-6-buffer-memory)
- [Step 7: Conversational Retrieval QA Chain](#step-7-conversational-retrieval-qa-chain)
7. [Upsert Documents](#upsert-documents) 7. [Upsert Documents](#upsert-documents)
8. [Using Cloud LLM APIs (Optional)](#using-cloud-llm-apis-optional) 8. [Using Cloud LLM APIs (Optional)](#using-cloud-llm-apis-optional)
9. [Deploy Chatbot to Users](#deploy-chatbot-to-users) 9. [Deploy Chatbot to Users](#deploy-chatbot-to-users)
@ -279,21 +278,6 @@ Connect the **BufferMemory** output to the **Memory** input of the QA Chain.
--- ---
### Step 7: Conversational Retrieval QA Chain
Search for **"Conversational Retrieval QA Chain"** and drag it onto the canvas.
Connect:
- **ChatOllama** → **Chat Model**
- **Faiss Retriever** → **Vector Store Retriever**
- **BufferMemory** → **Memory**
Leave **Input Moderation** and **Return Source Documents** as optional based on your needs.
**Save the flow** using the save button (top right).
---
## Upsert Documents ## Upsert Documents
Before the chatbot can answer questions, you must index your documents into the vector store. Before the chatbot can answer questions, you must index your documents into the vector store.