Added LangChain section to README

Updated TODO list

README.md

@@ -25,6 +25,48 @@ Please also see the [OUT-prompt-cheatsheet](GPT-Prompt-Examples/OUT-prompt-cheat
 
 ---
 
+#### 3. **[LangChain: Pluggable Components](./LangChain/)**
+
+This section describes the pre-built Python modules in the `LangChain` directory that can be side-loaded for various functionalities.
+
+`langchain_conv_agent.py`
+
+This module offers a set of functionalities for conversational agents in LangChain. Specifically, it provides:
+
+- Argument parsing for configuring the agent
+- Document loading via `PyPDFDirectoryLoader`
+- Text splitting using `RecursiveCharacterTextSplitter`
+- Various embeddings options like `OpenAIEmbeddings`, `CacheBackedEmbeddings`, and `HuggingFaceEmbeddings`
+
+**Usage:**
+To use this module, simply import the functionalities you need and configure them accordingly.
+
+```python
+from langchain_conv_agent import YOUR_FUNCTION
+```
+
+---
+
+`query_local_docs.py`
+
+This module focuses on querying local documents and employs the following features:
+
+- Environment variable loading via `dotenv`
+- Document loading via `PyPDFLoader`
+- Text splitting through `RecursiveCharacterTextSplitter`
+- Vector storage options like `Chroma`
+- Embedding options via `OpenAIEmbeddings`
+
+**Usage:**
+Similar to `langchain_conv_agent.py`, you can import the functionalities you require.
+
+```python
+from query_local_docs import YOUR_FUNCTION
+```
+
+These modules are designed to be extensible and can be easily integrated into your LangChain projects.
+
+
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todo.txt

@@ -10,3 +10,21 @@
   - [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_conv_agent.py
+  - Lacks single execution runnability
+  - 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, \
+      are entirely local since we're using ChromaDB)