Updated the RAG architecture to use the new baseline document store

config/architectures.json

@@ -25,7 +25,7 @@
       "description": "An architecture which uses a raw baseline LLM for its core, but augments requests from the user with information which has been retrieved from a knowledge store where the organisational knowledge has previously been stored for this purpose.",
       "steps": [
         {"class":  "InputRequestScreener"},
-        {"class":  "RetrievalAugmentor", "params": {"vector_store": "01_all_products"}},
+        {"class":  "RetrievalAugmentor", "params": {"vector_store": "02_baseline_products"}},
         {"class":  "HFInferenceEndpoint", "params":  {"endpoint_url": "https://yl89ru8gdr1wkbej.eu-west-1.aws.endpoints.huggingface.cloud","model_name": "Unmodified Meta Llama 2 chat", "system_prompt": "You are a helpful domestic appliance advisor. Please answer the following customer question, answering only from the facts provided. Answer based on the background provided, do not make things up, and say if you cannot answer.", "max_new_tokens": 1000}},
         {"class": "ResponseTrimmer", "params": {"regexes":  ["^.{0,20}information provided[0-9A-Za-z,]*? ", "^.{0,20}background[0-9A-Za-z,]*? "]}},
         {"class":  "OutputResponseScreener"}