Update app.py

app.py

@@ -95,6 +95,79 @@ def vector_search(user_query, collection):
     pipeline = [vector_search_stage, unset_stage, project_stage]
     results = mongo_collection.aggregate(pipeline)
     return list(results)
+
+def mongo_retriever(query):
+    documents = vector_search(query, mongo_collection)
+    return documents
+
+
+template = """
+You are an assistant for generating results based on user questions.
+Use the provided context to generate a result based on the following JSON format:
+{{
+  "name": "Recipe Name",
+  "minutes": 0,
+  "tags": [
+    "tag1",
+    "tag2",
+    "tag3"
+  ],
+  "n_steps": 0,
+  "description": "A GENERAL description of the recipe goes here.",
+  "ingredients": [
+    "ingredient1",
+    "ingredient2",
+    "ingredient3"
+  ],
+  "n_ingredients": 0,
+  "formatted_nutrition": [
+    "Calorie : per serving",
+    "Total Fat : % daily value",
+    "Sugar : % daily value",
+    "Sodium : % daily value",
+    "Protein : % daily value",
+    "Saturated Fat : % daily value",
+    "Total Carbohydrate : % daily value"
+  ],
+  "formatted_steps": [
+    "1. Step 1 of the recipe.",
+    "2. Step 2 of the recipe.",
+    "3. Step 3 of the recipe."
+  ]
+}}
+
+Instructions:
+1. Focus on the user's specific request and avoid irrelevant ingredients or approaches.
+2. Do not return anything other than the JSON.
+3. If the answer is unclear or the context does not fully address the prompt, return []
+4. Base the response on simple, healthy, and accessible ingredients and techniques.
+5. Rewrite the description in third person
+
+Context: {context}
+
+When choosing a recipe from the context, FOLLOW these instructions:
+1. The recipe should be makeable from scratch, using only proper ingredients and not other dishes or pre-made recipes
+
+Question: {question}
+"""
+
+custom_rag_prompt = ChatPromptTemplate.from_template(template)
+
+
+llm = ChatOpenAI(
+    model_name="gpt-3.5-turbo",
+    temperature=0.2)
+
+
+rag_chain = (
+    {"context": mongo_retriever,  "question": RunnablePassthrough()}
+    | custom_rag_prompt
+    | llm
+    | StrOutputParser()
+)
+
+def get_response(query):
+    return rag_chain.invoke(query)
     
 print("HELLO WORLD")
-st.title("qwerty :D")
+st.title("RESSSSULTS")