renamed: todo.md -> Project TODO List.md

todo.md → Project TODO List.md

@@ -1,11 +1,15 @@
+### Todo list 
+
+---
+
 [GitHub]
 
 - Clean all of my Jupyter notebook Gists to create an agent
   - With production-grade code stored locally, it may be useful as a codebase for LLM agents.
   - By supplying enough production-grade code that can be repurposed for our intended use case, we can steer our Generator towards desirable outcomes.
   - [Objective] : "Build a knowledgebase for leverage in programming production-grade code, specifically related to the creation of more machine learning code, much like it."
-  - [Thoughts] : "By creating a codebase of repurposable Python modules, 
-    we can use LangChain to query the top_k results for functions to serve 
+  - [Thoughts] : "By creating a codebase of repurposable Python modules,
+    we can use LangChain to query the top_k results for functions to serve
     contextual needs."
 
 [LangChain]
@@ -15,14 +19,13 @@
   - Fix by removing argparsing and implement default settings, with a configuration file
   - Config file settings:
     - Embedding Engine: [OpenAI, HuggingFace, etc.]
-    - 
   - Lacks .env var loading(API keys, model names[OpenAI, HuggingFace])
   - Ambiguity regarding (EmbeddingManager and DocumentRetriever)
     - Needs comments and to load via .env file
       - Differentiate EmbeddingManager and DocumentRetriever by explaining how they're implemented into the pipeline stream created by the module.
       - One generates embeddings
-      - `DocumentRetriever` queries them locally 
-        (HF model is cached after first download. Therefore, all runs after the first, 
+      - `DocumentRetriever` queries them locally
+        (HF model is cached after first download. Therefore, all runs after the first,
         are entirely local since we're using ChromaDB)
 
 [OpenAI]