utils/chain_utils.py

from models.model_seeds import seeds
from models.openai.finetuned_models import finetuned_models, get_finetuned_chain
from models.openai.role_models import get_role_chain, get_template_role_models
from models.databricks.scenario_sim_biz import get_databricks_biz_chain
from models.databricks.scenario_sim import get_databricks_chain, get_template_databricks_models

def get_chain(issue, language, source, memory, temperature, texter_name=""):
    if source in ("OA_finetuned"):
        OA_engine = finetuned_models[f"{issue}-{language}"]
        return get_finetuned_chain(OA_engine, memory, temperature)
    elif source in ('OA_rolemodel'):
        seed = seeds.get(issue, "GCT")['prompt']
        template = get_template_role_models(issue, language, texter_name=texter_name, seed=seed)
        return get_role_chain(template, memory, temperature)
    elif source in ('CTL_llama2', 'CTL_llama3'):
        if language == "English":
            language = "en"
        elif language == "Spanish":
            language = "es"
        return get_databricks_biz_chain(source, issue, language, memory, temperature)
    elif source in ('CTL_mistral'):
        if language == "English":
            language = "en"
        elif language == "Spanish":
            language = "es"
        seed = seeds.get(issue, "GCT")['prompt']
        template, texter_name = get_template_databricks_models(issue, language, texter_name=texter_name, seed=seed)
        return get_databricks_chain(source, template, memory, temperature, texter_name)
    
from typing import cast

def custom_chain_predict(llm_chain, input, stop):

    inputs = llm_chain.prep_inputs({"input":input, "stop":stop})
    llm_chain._validate_inputs(inputs)
    outputs = llm_chain._call(inputs)
    llm_chain._validate_outputs(outputs)
    llm_chain.memory.chat_memory.add_user_message(inputs['input'])
    for out in outputs[llm_chain.output_key]:
        llm_chain.memory.chat_memory.add_ai_message(out)
    return outputs[llm_chain.output_key]