Repository: ollama
Issue: An improved Rag System CRag
Labels: feature request

This is quite a bit of coding, but I was playing with AnythingLLM and Olama and noted how both evolve towards each other.
And also how Meta / Olama is becoming an opensource king, making new standards, so ideas in some areas could fuse.

As Large Language Models (LLMs) evolve to process and analyze documents more effectively, there's an increasing need for robust Retrieval-Augmented Generation (RAG) systems to support their functionality. While exploring existing RAG implementations, I've identified an opportunity to create a specialized system optimized for code analysis and comprehension. 
Let's call this new RAG system **CRAG**  *(Code Retrieval-Augmented Generation)*.
The fundamental principle behind CRAG is to leverage the inherent structure of well-written code, which typically consists of modular, concise functions and methods rather than monolithic blocks of thousands of lines (though such instances do exist in practice).
CRAG System Architecture:

File-Level Information:
CRAG stores each code file as a database entry with the following attributes:

Filename
Code type (e.g., class, utility, base class, interface)
Dependent libraries (imports, using statements, etc.)
Subtype (e.g., RxJS, event handling, services, web API) - language and import-dependent
Short description (generated by the LLM or extracted from file header comments)


Function/Method-Level Information:
For each file, CRAG maintains a list of functions and methods, including:

Function/method name
Return type (primitive types like string, bool, or custom types)
Order number within the file
Starting row index


Large Function Handling:
If a function or method exceeds a certain size threshold, CRAG breaks it into multiple entries for easier processing and retrieval.
Code Storage:
The system stores the raw code for each function/method. While a traditional database could be used, CRAG can optimize storage by maintaining only the necessary indices and file navigation information, allowing the LLM to read the code directly when needed.
Code Navigation and Dependency Tracking:
CRAG is designed to follow code execution paths and dependencies. When a user poses a question, the system reads the relevant code and its dependencies, storing connected/related code parts in a similar database-like structure.
Efficient Storage of Related Code:
To minimize storage requirements, CRAG primarily stores indices of related code rather than duplicating the full content. This approach maintains a compact structure while enabling the LLM to traverse and analyze code relationships.
Depth-Limited Code Exploration:
The LLM can explore code relationships to a configurable depth, with the potential to expand this depth as needed for more complex analyses.
Language-Specific Optimizations:
CRAG can be tailored to support specific programming languages, frameworks, and paradigms, allowing for more accurate parsing and understanding of code structures, ea C++ C# PLC, ASM, basic Java, python etc.
Semantic Understanding:
In addition to syntactic analysis, CRAG can incorporate semantic understanding of code, allowing the LLM to grasp the intent and functionality of code segments more accurately.
Version Control Integration:
CRAG can be designed to work with version control systems, enabling analysis of code changes over time and facilitating code review processes.
Performance Optimization:
The system can include caching mechanisms and indexing strategies to improve retrieval speed for frequently accessed code segments.
Security Considerations:
CRAG should implement robust security measures to protect sensitive code and prevent unauthorized access or code injection vulnerabilities. based upon a preprompt and the knowledge of the coding language this might go hand in hand.

The system can be designed to learn from user interactions and feedback, improving its code understanding and retrieval capabilities over time.
It might weight the relations between functions, and keep a favourite list against what the user is interested in most.


I know its not directly OLAMA, but it might become part of the OLAMA framework I think.