Minor template cleaning (#12573)

templates/llama2-functions/llama2_functions/chain.py

@@ -9,32 +9,34 @@ model = Replicate(
 )
 
 # Prompt with output schema specification
-template = """A article will be passed to you. Extract from it all papers that are mentioned by this article. 
-
-Do not extract the name of the article itself. If no papers are mentioned that's fine - you don't need to extract any! Just return an empty list.
-
-Do not make up or guess ANY extra information. Only extract what exactly is in the text.
-
-Respond with json that adheres to the following jsonschema:
-
+template = """You are an AI language model assistant. Your task is to generate 3 different versions of the given user /
+    question to retrieve relevant documents from a vector  database. By generating multiple perspectives on the user / 
+    question, your goal is to help the user overcome some of the limitations  of distance-based similarity search. /
+    Respond with json that adheres to the following jsonschema:
 {{
   "$schema": "http://json-schema.org/draft-07/schema#",
   "type": "object",
   "properties": {{
-    "author": {{
+    "question_1": {{
       "type": "string",
-      "description": "The author of the paper."
+      "description": "First version of the user question."
     }},
-    "title": {{
+    "question_2": {{
       "type": "string",
-      "description": "The title of the paper."
+      "description": "Second version of the user question."
+    }},
+    "question_3": {{
+      "type": "string",
+      "description": "Third version of the user question."
     }}
   }},
-  "required": ["author", "title"],
+  "required": ["question_1","question_2","question_3"],
   "additionalProperties": false
 }}"""  # noqa: E501
 
-prompt = ChatPromptTemplate.from_messages([("system", template), ("human", "{input}")])
+prompt = ChatPromptTemplate.from_messages(
+    [("system", template), ("human", "{question}")]
+)
 
 # Chain
 chain = prompt | model