chatbot/engineer_prompt.py

from langchain.chat_models import PromptLayerChatOpenAI
from langchain.schema import HumanMessage, AIMessage, SystemMessage
from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory
from langchain.prompts.chat import (
    ChatPromptTemplate,
    SystemMessagePromptTemplate,
    HumanMessagePromptTemplate,
    AIMessagePromptTemplate,
    MessagesPlaceholder,
)

# TODO Multiple chains sequenced?

ingredients_prompt = ChatPromptTemplate.from_messages(
    [
        SystemMessagePromptTemplate.from_template(
            """
The following is a conversation between a human and a friendly AI chef. 
The AI is compassionate to animals and only recommends vegan recipes based on the ingredients, allergies, and other preferences the human has.

Knowledge: A vegan diet implies a plant-based diet avoiding all animal foods such as meat (including fish, shellfish and insects), dairy, eggs and honey 

Let's think step by step.
If the human messages are unrelated to vegan recipes, remind them of your purpose to recommend vegan recipes. 
    """.strip()
        ),
        AIMessagePromptTemplate.from_template(
            "What ingredients do you wish to cook with?"
        ),
        HumanMessagePromptTemplate.from_template("Ingredients: {ingredients}"),
        AIMessagePromptTemplate.from_template(
            "Do you have any allergies I should be aware of?"
        ),
        HumanMessagePromptTemplate.from_template("Allergies: {allergies}"),
        AIMessagePromptTemplate.from_template(
            "Do you have any preferences I should consider for the recipe such preparation time, difficulty, or cuisine region?"
        ),
        HumanMessagePromptTemplate.from_template(
            """
Give me a vegan recipe that includes at least a few of the ingredients provided (if any).
Respect the human's allergies (if any).
Follow these other preferences as closely as possible if they are inline with your purpose of recommending vegan recipes:

###
{recipe_freeform_input}
###

Output format:

**Vegan recipe name**
Preparation time (humanized)

Ingredients (List of ingredients with quantities):
- <quantity and unit> <ingredient>

Steps (detailed):
1.
2.
3.
...
""".strip()
        ),
    ]
)

# MessagesPlaceholder(variable_name="history"),
# HumanMessagePromptTemplate.from_template("{input}"),
chat = PromptLayerChatOpenAI(temperature=1, pl_tags=["langchain"], return_pl_id=True)
memory = ConversationBufferMemory(return_messages=True)

chat_msgs = ingredients_prompt.format_prompt(
    ingredients="tofu, pickles, olives, tomatoes, lettuce, bell peppers, carrots, bread",
    allergies="",
    recipe_freeform_input="The preparation time should be less than 30 minutes. I really love Thai food!",
)
chat_msgs = chat_msgs.to_messages()
results = chat.generate([chat_msgs])
chat_msgs.extend(
    [
        results.generations[0][0].message,
        MessagesPlaceholder(variable_name="history"),
        HumanMessagePromptTemplate.from_template("{input}"),
    ]
)
open_prompt = ChatPromptTemplate.from_messages(chat_msgs)
conversation = ConversationChain(
    llm=chat, verbose=True, memory=memory, prompt=open_prompt
)

result = conversation.predict(input="Recommend a different recipe please.")
print(result)

#! PL score example
# chat_results = chat.generate([[HumanMessage(content=prompt)]])

# for res in chat_results.generations:
#     pl_request_id = res[0].generation_info["pl_request_id"]
#     print(res[0].text)
#     score = int(input("Enter a score from 0 to 100 for how the prompt performed: "))
#     promptlayer.track.score(request_id=pl_request_id, score=score)