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import os
from langchain.llms.openai import OpenAI
from langchain.llms.bedrock import Bedrock
from langchain_google_genai import GoogleGenerativeAI
from langchain.prompts import PromptTemplate
from langchain.chains import SimpleSequentialChain
from langchain.chains import LLMChain
# Pixegami
PROMPT_TEMPLATE_TEXT = """
Generate a table in CSV format from the following bank statement data.
Add a column called "Category" and populate it with one of the following values:
[Groceries, Transport, Entertainment, Shopping, Utilities, Eating Out, Unknown]
{statement}
"""
CODING_TEMPLATE_TEXT = """
First, hard-code this data as a Python variable called 'items', with the category name and value.
Then write a Python script to sum this data by 'Category' and print the results.
{categorized_transactions}
"""
# OpenAI (GPT-3.5) LLM
llm_open_ai = OpenAI(max_tokens=1024)
# AWS Bedrock LLM
BEDROCK_CLAUDE_MODEL = "anthropic.claude-v2"
BEDROCK_LLAMA_MODEL = "meta.llama2-70b-chat-v1"
llm_bedrock = Bedrock(
credentials_profile_name="default",
model_id=BEDROCK_CLAUDE_MODEL,
model_kwargs={"max_tokens_to_sample": 1024},
)
# Google Gemini LLM
llm_gemini = GoogleGenerativeAI(
model="gemini-pro",
max_output_tokens=1024,
google_api_key=os.environ["GOOGLE_AI_API_KEY"],
)
llm = llm_gemini # Or llm_bedrock or llm_open_ai.
# Create the individual prompt templates.
categorization_template = PromptTemplate.from_template(PROMPT_TEMPLATE_TEXT)
coding_template = PromptTemplate.from_template(CODING_TEMPLATE_TEXT)
# Create the chains.
categorization_chain = LLMChain(llm=llm, prompt=categorization_template)
coding_chain = LLMChain(llm=llm, prompt=coding_template)
# Join them into a sequential chain.
overall_chain = SimpleSequentialChain(
chains=[categorization_chain, coding_chain], verbose=True
)
# Load the bank statement data.
with open("bank_statement.csv", "r") as f:
bank_statement_data = f.read()
# Run the chain using the bank statement data as input.
overall_chain.run(bank_statement_data) |