[Ellipsis] Documentation bots

sunholo/components/README.md

@@ -0,0 +1,20 @@
+# Sunholo Components
+
+This directory contains the core components of the Sunholo project. Each Python file serves a specific purpose in the overall functionality of the project.
+
+## __init__.py
+This file is used to initialize the Sunholo components package. It imports the necessary functions from the other Python files in this directory.
+
+## llm.py
+This file contains the logic for the Language Model.
+
+## prompt.py
+This file contains the logic for generating prompts for the Language Model.
+
+## retriever.py
+This file contains the logic for retrieving information from the Language Model.
+
+## vectorstore.py
+This file contains the logic for storing and retrieving vectors.
+
+For a detailed description of the functions within each Python file, please refer to the respective file. Each function's documentation includes information about what the function does, its parameters, and its return value.

sunholo/components/llm.py

@@ -16,6 +16,9 @@
 
 logging = setup_logging()
 
+"""
+This function picks a language model based on the `vector_name` parameter and returns the chosen language model, its embeddings, and its chat model.
+"""
 def pick_llm(vector_name):
     logging.debug('Picking llm')
 
@@ -47,6 +50,9 @@ def pick_llm(vector_name):
 
     return llm, embeddings, llm_chat
 
+"""
+This function checks if the language model specified by the `vector_name` parameter supports streaming and returns a boolean value.
+"""
 def pick_streaming(vector_name):
 
     llm_str = load_config_key("llm", vector_name, filename = "config/llm_config.yaml")
@@ -57,6 +63,9 @@ def pick_streaming(vector_name):
     return False
 
 
+"""
+This function gets a language model based on the `vector_name` and `model` parameters and the `config_file` configuration file.
+"""
 def get_llm(vector_name, model=None, config_file="config/llm_config.yaml"):
     llm_str = load_config_key("llm", vector_name, filename=config_file)
     model_lookup_filepath = get_module_filepath("lookup/model_lookup.yaml")
@@ -106,6 +115,9 @@ def get_llm(vector_name, model=None, config_file="config/llm_config.yaml"):
     if llm_str is None:
         raise NotImplementedError(f'No llm implemented for {llm_str}')
 
+"""
+This function gets a chat model based on the `vector_name` and `model` parameters and the `config_file` configuration file.
+"""
 def get_llm_chat(vector_name, model=None, config_file="config/llm_config.yaml"):
     llm_str = load_config_key("llm", vector_name, filename=config_file)
     if not model:
@@ -150,13 +162,19 @@ def get_llm_chat(vector_name, model=None, config_file="config/llm_config.yaml"):
     if llm_str is None:
         raise NotImplementedError(f'No llm implemented for {llm_str}')
 
+"""
+This function gets the embeddings for the language model specified by the `vector_name` parameter.
+"""
 def get_embeddings(vector_name):
     llm_str = load_config_key("llm", vector_name, filename="config/llm_config.yaml")
 
     return pick_embedding(llm_str)
 
 
 
+"""
+This function picks the embeddings based on the `llm_str` parameter.
+"""
 def pick_embedding(llm_str: str):
     # get embedding directly from llm_str
     # Configure embeddings based on llm_str
@@ -175,4 +193,4 @@ def pick_embedding(llm_str: str):
         return GoogleGenerativeAIEmbeddings(model="models/embedding-001") #TODO add embedding type
 
     if llm_str is None:
-        raise NotImplementedError(f'No embeddings implemented for {llm_str}')
+        raise NotImplementedError(f'No embeddings implemented for {llm_str}')

sunholo/components/prompt.py

@@ -23,7 +23,7 @@
 
 
 def pick_prompt(vector_name, chat_history=[]):
-    """Pick a custom prompt"""
+    """This function picks a custom prompt based on the `vector_name` and `chat_history` parameters and returns the chosen prompt."""
     logging.debug('Picking prompt')
 
     prompt_str = load_config_key("prompt", vector_name, filename = "config/llm_config.yaml")
@@ -69,13 +69,14 @@ def pick_prompt(vector_name, chat_history=[]):
     else:
         follow_up += ".\n"
 
-    memory_str = "\n## Your Memory (ignore if not relevant to question)\n{context}\n"
+    memory_str = "\n## Your Memory (ignore if not relevant to question)
+{context}\n"
 
     current_conversation = ""
     if chat_summary != "":
         current_conversation =f"## Current Conversation\n{chat_summary}\n"
         current_conversation = current_conversation.replace("{","{{").replace("}","}}") #escape {} characters
-
+    
     buddy_question = ""
     my_q = "## Current Question\n{question}\n"
     if agent_buddy:
@@ -92,28 +93,30 @@ def pick_prompt(vector_name, chat_history=[]):
     return QA_PROMPT
 
 def pick_chat_buddy(vector_name):
+    """This function picks a chat buddy based on the `vector_name` parameter and returns the chosen chat buddy and its description."""
     chat_buddy = load_config_key("chat_buddy", vector_name, filename = "config/llm_config.yaml")
     if chat_buddy is not None:
         logging.info(f"Got chat buddy {chat_buddy} for {vector_name}")
         buddy_description = load_config_key("chat_buddy_description", vector_name)
         return chat_buddy, buddy_description
     return None, None
 
-
 def pick_agent(vector_name):
+    """This function determines if an agent is needed based on the `vector_name` parameter and returns a boolean value."""
     agent_str = load_config_key("agent", vector_name, filename = "config/llm_config.yaml")
     if agent_str == "yes":
         return True
 
     return False
 
 def pick_shared_vectorstore(vector_name, embeddings):
+    """This function picks a shared vector store based on the `vector_name` and `embeddings` parameters and returns the chosen vector store."""
     shared_vectorstore = load_config_key("shared_vectorstore", vector_name, filename = "config/llm_config.yaml")
     vectorstore = pick_vectorstore(shared_vectorstore, embeddings)
     return vectorstore
 
-
 def get_chat_history(inputs, vector_name, last_chars=1000, summary_chars=1500) -> str:
+    """This function gets the chat history based on the `inputs`, `vector_name`, `last_chars`, and `summary_chars` parameters and returns the chat history as a string."""
     from langchain.schema import Document
     from ..summarise import summarise_docs
 
@@ -148,4 +151,4 @@ def get_chat_history(inputs, vector_name, last_chars=1000, summary_chars=1500) -
     summary = text_sum[:summary_chars]
 
     # Concatenate the summary and the last `last_chars` characters of the chat history
-    return summary + "\n### Recent Chat History\n..." + recent_history
+    return summary + "\n### Recent Chat History\n..." + recent_history

sunholo/components/retriever.py

@@ -26,7 +26,9 @@
 
 logging = setup_logging()
 
+
 def load_memories(vector_name):
+    """This function loads the memories for a given `vector_name` and returns the loaded memories."""
     memories = load_config_key("memory", vector_name, filename="config/llm_config.yaml")
     logging.info(f"Found memory settings for {vector_name}: {memories}")
     if len(memories) == 0:
@@ -35,8 +37,9 @@ def load_memories(vector_name):
 
     return memories
 
-def pick_retriever(vector_name, embeddings=None):
 
+def pick_retriever(vector_name, embeddings=None):
+    """This function picks a retriever based on the `vector_name` and `embeddings` parameters and returns the chosen retriever."""
     memories = load_memories(vector_name)
 
     retriever_list = []
@@ -78,4 +81,4 @@ def pick_retriever(vector_name, embeddings=None):
         base_compressor=pipeline, base_retriever=lotr, 
         k=3)
 
-    return retriever
+    return retriever