GPT/LLM Question and Answering

Getting Started

Clone the examples repository.
Run Marqo:
```
docker rm -f marqo
docker pull marqoai/marqo:latest
docker run --name marqo -it --privileged -p 8882:8882 --add-host host.docker.internal:host-gateway marqoai/marqo:latest
```
For mode detailed instructions, check the getting started guide. 3. Obtain an API key for the LLM you want, for example to use OpenAI
```
export OPENAI_API_KEY="..."
```
4. Follow the instructions below or see the original code and article here and here.
Code

ironman.py
import pandas as pd
from utilities import (
    marqo_prompt, 
    extract_text_from_highlights, 
    marqo_template,
    get_extra_data,
    reformat_npcs
)

from langchain.llms import OpenAI 
from langchain.docstore.document import Document
from langchain.chains import LLMChain


#############################################################
#       0. Install Marqo                                          
#############################################################

# run the following docker commands from the terminal to start marqo
# docker rm -f marqo
# docker pull marqoai/marqo:latest
# docker run --name marqo -it --privileged -p 8882:8882 --add-host host.docker.internal:host-gateway marqoai/marqo:latest

#############################################################
#       1. Create some data                                          
#############################################################

NPCs = [{
    "name": "Sara Lee",
    "backstory": "Sara was born in a small village in the mountains. She was always fascinated with nature and as soon as she was old enough, she left her village to study environmental science at a university. She now works as a park ranger.",
    "location": "The mountains",
    "occupation": "Park ranger",
    "family_history": "Sara is an only child and her parents were both farmers. Growing up close to nature instilled in her a deep respect and appreciation for the environment.",
    "work_history": "Sara worked as a research assistant for a university before becoming a park ranger.",
    "favorite_color": "Yellow",
    "hobbies": "Hiking, bird watching, and photography",
    "favorite_food": "Fruits and vegetables",
    "dislikes": "Loud noises",
},
{
    "name": "Jack Smith",
    "backstory": "Jack was born and raised in the city. He has always had a love for cars and as soon as he could he began working on them. He now runs his own successful auto repair shop.",
    "location": "The city",
    "occupation": "Auto mechanic",
    "family_history": "Jack has a younger sister and his father was also a mechanic who ran his own shop.",
    "work_history": "Jack worked as a mechanic at several auto repair shops before opening his own business.",
    "favorite_color": "Blue",
    "hobbies": "Working on cars, fishing, and playing video games",
    "favorite_food": "Steak",
    "dislikes": "Celery",
},
{
    "name": "Evelyn Parker",
    "backstory": "Evelyn grew up in a small town in the countryside. She always had a passion for cooking and eventually moved to the city to attend culinary school. She now works as a chef at a popular restaurant.",
    "location": "The city",
    "occupation": "Chef",
    "family_history": "Evelyn is the youngest of three siblings. Her parents were farmers and instilled in her a love for cooking with fresh ingredients.",
    "work_history": "Evelyn worked as a line cook at several restaurants before attending culinary school and becoming a head chef.",
    "favorite_color": "Green",
    "hobbies": "Cooking, gardening, and reading",
    "favorite_food": "Seafood",
    "dislikes": "Cilantro",
}]

df = pd.DataFrame(reformat_npcs(NPCs))
print(df.head())

# make the data python dicts
documents = df.to_dict(orient='record')

#############################################################
#       2. Setup Marqo                                          
#############################################################

import marqo
from marqo import Client

marqo.set_log_level('WARN')

mq = Client()

index_name = "npc-chat"

try:
    mq.index(index_name).delete()
except:
    pass

index_settings = {
    "index_defaults": {
            "normalize_embeddings": True,
            "text_preprocessing": {
                "split_length": 5,
                "split_overlap": 1,
                "split_method": "sentence"
                                    },
    }
    }

# create the index - if no settings are present then sensible deffaults are used    
mq.create_index(index_name, settings_dict=index_settings)
res = mq.index(index_name).add_documents(documents)

#############################################################
#       3. Regular NPC superhero                                         
#############################################################

# select a character
persona = "Evelyn Parker"

# we pre-opulate them here to complete a conversation but it can easily be made interactive
human_questions = [ "hi, what is your name?",
                    "wow, what are some of your favorite things to do?", 
                    "are you scared of anything?", 
                    "where did you grow up?", 
                    "what do you dislike?"]

history = []
template = marqo_template()
prompt = marqo_prompt(template)

# how many pieces of context to use
n_history = 2

# setup the LLM API call
llm = OpenAI(temperature=0.9)

for question in human_questions:

    history.append(f"\nHUMAN:{question}")
    print(history[-1])

    # search for background related to the question
    results = mq.index(index_name).search(question, filter_string=f"name:({persona})", searchable_attributes=['text'], limit=20) 

    # optionally crop the text to the highlighted region to fit within the context window
    highlights, texts = extract_text_from_highlights(results, token_limit=150)

    # add the truncated/cropped text to the data structure for langchain
    summaries = [Document(page_content=f"Source [{ind}]:"+t) for ind,t in enumerate(texts[:n_history])]

    # get the conversation history
    chain_qa = LLMChain(llm=llm, prompt=prompt)

    llm_results = chain_qa({"summaries": summaries, "conversation": "\n".join(history)}, return_only_outputs=False)

    history.append(llm_results['text'])
    print(history[-1])

#############################################################
#       3. IRONMAN                                   
#############################################################

persona = "Evelyn Parker"

# add some more info
extra_docs = [{"text":text, "name":persona} for text in get_extra_data()]
res = mq.index(index_name).add_documents(extra_docs, non_tensor_fields=['filename'])

# we pre-opulate them here to complete a conversation but it can easily be made interactive
human_questions = [ "hi, what is your name?",
                    "wow, what are some of your favorite things to do?", 
                    "are you scared of anything?", 
                    "where did you grow up?", 
                    "what do you dislike?"]

history = []
template = marqo_template()
prompt = marqo_prompt(template)

# how many pieces of context to use
n_history = 2

for question in human_questions:

    history.append(f"\nHUMAN:{question}")
    print(history[-1])

    # search for background related to the question
    results = mq.index(index_name).search(question, filter_string=f"name:({persona})", searchable_attributes=['text'], limit=20) 

    # optionally crop the text to the highlighted region to fit within the context window
    highlights, texts = extract_text_from_highlights(results, token_limit=150)

    # add the truncated/cropped text to the data structure for langchain
    summaries = [Document(page_content=f"Source [{ind}]:"+t) for ind,t in enumerate(texts[-n_history:])]

    # get the conversation history
    chain_qa = LLMChain(llm=llm, prompt=prompt)

    llm_results = chain_qa({"summaries": summaries, "conversation": "\n".join(history)}, return_only_outputs=False)

    history.append(llm_results['text'])
    print(history[-1])