Chatbot de preguntas y respuestas sobre reglas de cricket IPL usando RAG y Google Gemini API

• •Pueden requerirse credenciales de autenticación para la API de destino
• •Clave de API de Google Gemini

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        "content": "## Step 2\n## 2.1 Chat Trigger to initiate n8n native chat interface\n## 2.2 Simple Memory keeps the last 20 chat turns for context. This value can be edited within the node\n## 2.3 Simple Vector Store (retrieve-as-tool mode) receives the user’s query embedding, \n## finds the top-10 most relevant chunks stored in step 1, and supplies them as tool output. This will drive RAG\n**The name of vector store should match from Step 1, the embedding rule should match step 1\n## 2.4 Google Gemini Chat Model is the language model that is used as the llm model\n## 2.5 AI Agent orchestrates everything:\n** Uses the system prompt (“You are a cricket expert… If info is missing, say ‘Sorry I don’t know’”). to prompt the model\n** Has access to the memory (2.2) and the RAG tool (2.3).\n** Generates the final response with Google Gemini, strictly limited to the retrieved IPL cricket rules data.\n\n\n\n\n\n\n## Note: Google gemini API key credential needed\n##Using simple memory store nodes provided by n8n is the best way to get started to test out the workflow before you switch to more enterprise grade vector store nodes"
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        "content": "## Step 1\n## Load the reference material (run once via the Manual Trigger)\n## 1.1 Manual Trigger → HTTP Request downloads the IPL “Match Playing Conditions” PDF. \n## 1.2 Default Data Loader extracts text from the PDF.\n   **Type of data is binary\n## 1.3 Recursive Character Text Splitter breaks the text into overlapping chunks.\n   **This step ensures that the data chunks that are created in vector store have some overlap and hence less chance of hallucination\n   **Chunk size and chunk overlap are 2 variables to manage this \n## 1.4 Embeddings Google Gemini (1) converts each chunk to a vector.\n   **Connect the model with google gemini model. You will need your own api key for this\n   **Make note of the embedding model also since the same embedding model has to be selected in Step 2\n## 1.5 Simple Vector Store 1 inserts those vectors into an in-memory store under key\n   **Make note of the vector store name since it is same vector store you will have to use in Step 2\n\n\n## Note: Google gemini API key credential needed\n##Using Vector store nodes provided by n8n is the best way to get started to test out the workflow before you switch to more enterprise grade vector store nodes"
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        "content": "## This workflow has 2 Broad Steps\n## Step 1 - Vector store creation with set of ipl rules using Google Gemini Embedding. This will we used to drive RAG for model grouding    \n## Step 2 - Connecting the vector store with google gemini API model and enabling a chat interface to drive the chat bot\n"
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        "content": "## Step 1.1"
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