This repository details out on the usage of Ollama and Langchain

Problem Statement

Generate the summary of the function definition, group the similar summaries together

sourcecode.py ==> This is the entry point for the application to run, which imports other code files and perform the necessary operations and displays the final output of the application.

This projects mainly has 3 phases

phase-1

	In the phase-1, identify the list of methods from each class that is present in the directory "/codes"
	
	After completion of phase-1, for each class it will list out list of methods present in the same class
	

	The entire code for the phase-1 is present in the IdentificationOfMethodsFromClass.py
	
	List of Modules used are os,importlib.util,inspect ==> in order to identify the methods present in the class
	
	
phase-2

	In the phase-2, generate the summary for each method from the class.
	
	List of Modules used are langchain
	
	classes used are 
	
		langchain.prompts.prompt import PromptTemplate ==> Using this, created a Prompt that can be sent to the LLM
		langchain.llms.ollama import Ollama ==> Using this, I used the quantized model in my local machine locally.
		
	
	Used the LCEL(Langchain Expression Language), so that I created a chain that with prompt | llm
	
	
	After completion of phase-2, for every method there exists a summary generated using LLM
	
	
	The LLM used is "llama3"
	
	
phase-3

	In the phase-3, group the summaries together based on their functionality
	
	List of Modules used are langchain
	
	classes used are 
	
		langchain.prompts.prompt import PromptTemplate ==> using this, created a prompt that can be sent as input to the LLM
		
		langchain.llms.ollama import Ollama ==> using this, I used the quantized model in my local machine locally
		
		langchain.prompts.few_shot import FewShotPromptTemplate ==> using this, I can create an examples and send to the LLM so that the response generated from LLM will be accurate
		
	Used the LCEL(Langchain Expression Language), so that I created a chain that with prompt | llm
	
	
	After completion of phase-3, there is a group generated which groups the similar functionalities together
	
	
	The LLM used is "llama3"

Output of the project is as below and present in the Output.png