A minimum Python package built on top of the LangChain framework to interact with LLM.

Some of the core features of this package includes: easy-to-use API, support fewshot prompting strategy, structual output(json) and MLflow integration to better inspect the output and one can also easily add the logging metrics/artifacts. For more infomation, check out the examples in the examples folder

1. prompt template

Users can play around with the prompt and save it as the json file through LangChain PromptTemplate API.

from langchain.prompts import PromptTemplate

system_template = """\
You are an experienced expert in translating English addresses to Traditional Chinese.
Your task is to translate the English address to Traditional Chinese using Json format.
"Notice: Do not include the country and postal code in your response".\
"""
system_prompt_template = PromptTemplate.from_template(system_template)
system_prompt_template.save('system_prompt.json')

The human prompt's input variables must have instructions and output_instructions plus one variable as the rquest variable. In the following case, owner_address is the request variable

human_template = """\
{instructions}
Translate the following address in Traditional Chinese:
{owner_address}
Output Instructions:
{output_instructions}
Besides, don't forget to escape a single quote in your response json string.
"""

2. output data class

Users can specify the responses' keys from LLM through LangChain pydantic module. In the following case, LLM will respone a json which contains translated_address key.

from langchain_core.pydantic_v1 import BaseModel, Field

class LLMResponse(BaseModel):
    translated_address: str = Field(description="the translated address in Traditional Chinese")

3. create the prompt object

After Combining the system and human promt with pydantic data class, users are ready to create the prompt

# notice that users should also specify the input variables in the system prompt as keyword arguments in this class
prompt = Prompt(LLMResponse, 'system_promp.json', 'human_prompt')

1. create the LLM class and specify the experiment_name and run_name for MLflow logging service

from llm_research import OpenAILLM
model = OpenAILLM(model="gpt-3.5-turbo-1106", temperature=0., timeout=120, verbose=True)
model.init_request(experiment_name='10-test', run_name='chatgpt3.5')

2. start requesting

The input data should be formatted in json line files and each json instance should contain keys for request variable. For example, if users has owner_address as the request variable in the human prompt, there must be a key owner_address in each json instance. Notice that instructions and output_instructions shouldn't be excluded. For fewshot examples, it should also be formatted as json lines and besides the request variable of human prompt should be one of the keys in each json instances, all fields of the pydantic data class should also be included. Based on the previous example, ther json must have two keys-owner_address and translated_address.

model.request_batch(
    prompt,
    'data.jsonl',
    'fewshot_examples.jsonl'
)

3. end the requests Dont' forget to end the request procedure to close the MLflow logging service.

model.end_request()