Visualization of the codebase

.codeboarding/Chatbot_Core_Engine.json

@@ -0,0 +1,125 @@
+{
+  "description": "This graph represents the core functionality of a document processing and question-answering system. The main flow involves ingesting documents, processing them into a searchable format, and then using a language model to answer user queries based on the ingested content. Its purpose is to provide an intelligent interface for users to retrieve information from a collection of documents.",
+  "components": [
+    {
+      "name": "Document Ingestion",
+      "description": "Handles the loading and initial processing of various document types.",
+      "referenced_source_code": [
+        {
+          "qualified_name": "langchain_community.document_loaders.pdf.PyPDFLoader",
+          "reference_file": "document_ingestion.py",
+          "reference_start_line": null,
+          "reference_end_line": null
+        },
+        {
+          "qualified_name": "langchain_community.document_loaders.csv_loader.CSVLoader",
+          "reference_file": "document_ingestion.py",
+          "reference_start_line": null,
+          "reference_end_line": null
+        }
+      ]
+    },
+    {
+      "name": "Text Splitter",
+      "description": "Breaks down large documents into smaller, manageable chunks for efficient processing and embedding.",
+      "referenced_source_code": [
+        {
+          "qualified_name": "langchain.text_splitter.RecursiveCharacterTextSplitter",
+          "reference_file": "text_processing.py",
+          "reference_start_line": null,
+          "reference_end_line": null
+        }
+      ]
+    },
+    {
+      "name": "Vector Store",
+      "description": "Stores and retrieves document embeddings, enabling semantic search.",
+      "referenced_source_code": [
+        {
+          "qualified_name": "langchain_community.vectorstores.chroma.Chroma",
+          "reference_file": "vector_db.py",
+          "reference_start_line": null,
+          "reference_end_line": null
+        }
+      ]
+    },
+    {
+      "name": "Embeddings Model",
+      "description": "Generates numerical representations (embeddings) of text chunks.",
+      "referenced_source_code": [
+        {
+          "qualified_name": "langchain_community.embeddings.ollama.OllamaEmbeddings",
+          "reference_file": "embedding_model.py",
+          "reference_start_line": null,
+          "reference_end_line": null
+        }
+      ]
+    },
+    {
+      "name": "Language Model (LLM)",
+      "description": "Processes user queries and generates answers based on retrieved context.",
+      "referenced_source_code": [
+        {
+          "qualified_name": "langchain_community.llms.ollama.Ollama",
+          "reference_file": "llm_interface.py",
+          "reference_start_line": null,
+          "reference_end_line": null
+        }
+      ]
+    },
+    {
+      "name": "Retrieval Chain",
+      "description": "Orchestrates the retrieval of relevant document chunks and passes them to the LLM for answer generation.",
+      "referenced_source_code": [
+        {
+          "qualified_name": "langchain.chains.retrieval.create_retrieval_chain",
+          "reference_file": "retrieval_chain.py",
+          "reference_start_line": null,
+          "reference_end_line": null
+        }
+      ]
+    },
+    {
+      "name": "Unclassified",
+      "description": "Component for all unclassified files and utility functions (Utility functions/External Libraries/Dependencies)",
+      "referenced_source_code": []
+    }
+  ],
+  "components_relations": [
+    {
+      "relation": "loads documents into",
+      "src_name": "Document Ingestion",
+      "dst_name": "Text Splitter"
+    },
+    {
+      "relation": "splits text for",
+      "src_name": "Text Splitter",
+      "dst_name": "Embeddings Model"
+    },
+    {
+      "relation": "generates embeddings for",
+      "src_name": "Embeddings Model",
+      "dst_name": "Vector Store"
+    },
+    {
+      "relation": "stores embeddings from",
+      "src_name": "Vector Store",
+      "dst_name": "Embeddings Model"
+    },
+    {
+      "relation": "retrieves context for",
+      "src_name": "Vector Store",
+      "dst_name": "Retrieval Chain"
+    },
+    {
+      "relation": "uses",
+      "src_name": "Retrieval Chain",
+      "dst_name": "Language Model (LLM)"
+    },
+    {
+      "relation": "answers queries using",
+      "src_name": "Language Model (LLM)",
+      "dst_name": "Retrieval Chain"
+    }
+  ]
+}

.codeboarding/Chatbot_Core_Engine.rst

@@ -0,0 +1,96 @@
+Chatbot Core Engine
+===================
+
+.. mermaid::
+
+   graph LR
+      Document_Ingestion["Document Ingestion"]
+      Text_Splitter["Text Splitter"]
+      Vector_Store["Vector Store"]
+      Embeddings_Model["Embeddings Model"]
+      Language_Model_LLM_["Language Model (LLM)"]
+      Retrieval_Chain["Retrieval Chain"]
+      Unclassified["Unclassified"]
+      Document_Ingestion -- "loads documents into" --> Text_Splitter
+      Text_Splitter -- "splits text for" --> Embeddings_Model
+      Embeddings_Model -- "generates embeddings for" --> Vector_Store
+      Vector_Store -- "stores embeddings from" --> Embeddings_Model
+      Vector_Store -- "retrieves context for" --> Retrieval_Chain
+      Retrieval_Chain -- "uses" --> Language_Model_LLM_
+      Language_Model_LLM_ -- "answers queries using" --> Retrieval_Chain
+
+| |codeboarding-badge| |demo-badge| |contact-badge|
+
+.. |codeboarding-badge| image:: https://img.shields.io/badge/Generated%20by-CodeBoarding-9cf?style=flat-square
+   :target: https://github.com/CodeBoarding/CodeBoarding
+.. |demo-badge| image:: https://img.shields.io/badge/Try%20our-Demo-blue?style=flat-square
+   :target: https://www.codeboarding.org/demo
+.. |contact-badge| image:: https://img.shields.io/badge/Contact%20us%20-%20contact@codeboarding.org-lightgrey?style=flat-square
+   :target: mailto:contact@codeboarding.org
+
+Details
+-------
+
+This graph represents the core functionality of a document processing and question-answering system. The main flow involves ingesting documents, processing them into a searchable format, and then using a language model to answer user queries based on the ingested content. Its purpose is to provide an intelligent interface for users to retrieve information from a collection of documents.
+
+Document Ingestion
+^^^^^^^^^^^^^^^^^^
+
+Handles the loading and initial processing of various document types.
+
+**Related Classes/Methods**:
+
+* langchain_community.document_loaders.pdf.PyPDFLoader
+* langchain_community.document_loaders.csv_loader.CSVLoader
+
+Text Splitter
+^^^^^^^^^^^^^
+
+Breaks down large documents into smaller, manageable chunks for efficient processing and embedding.
+
+**Related Classes/Methods**:
+
+* langchain.text_splitter.RecursiveCharacterTextSplitter
+
+Vector Store
+^^^^^^^^^^^^
+
+Stores and retrieves document embeddings, enabling semantic search.
+
+**Related Classes/Methods**:
+
+* langchain_community.vectorstores.chroma.Chroma
+
+Embeddings Model
+^^^^^^^^^^^^^^^^
+
+Generates numerical representations (embeddings) of text chunks.
+
+**Related Classes/Methods**:
+
+* langchain_community.embeddings.ollama.OllamaEmbeddings
+
+Language Model (LLM)
+^^^^^^^^^^^^^^^^^^^^
+
+Processes user queries and generates answers based on retrieved context.
+
+**Related Classes/Methods**:
+
+* langchain_community.llms.ollama.Ollama
+
+Retrieval Chain
+^^^^^^^^^^^^^^^
+
+Orchestrates the retrieval of relevant document chunks and passes them to the LLM for answer generation.
+
+**Related Classes/Methods**:
+
+* langchain.chains.retrieval.create_retrieval_chain
+
+Unclassified
+^^^^^^^^^^^^
+
+Component for all unclassified files and utility functions (Utility functions/External Libraries/Dependencies)
+
+**Related Classes/Methods**: *None*

.codeboarding/Data_Storage_Training.json

@@ -0,0 +1,63 @@
+{
+  "description": "The ChatterBot system is structured around three core components: the `Storage Adapters`, `Training Module`, and `Corpus Data Loader`. The `Corpus Data Loader` is responsible for providing raw conversational data, which is then consumed by the `Training Module`. The `Training Module` processes this data to learn and update the chatbot's knowledge base, persisting and retrieving conversational statements through the `Storage Adapters`. This design ensures a clear separation of concerns, allowing for flexible data storage and diverse training methodologies.",
+  "components": [
+    {
+      "name": "Storage Adapters",
+      "description": "Provides an abstract interface for all data persistence and retrieval operations within ChatterBot. It allows for interchangeable storage backends (e.g., SQL, NoSQL, in-memory) without affecting the core chatbot logic, managing the storage of statements, responses, and other conversational data.",
+      "referenced_source_code": [
+        {
+          "qualified_name": "chatterbot.storage.StorageAdapter",
+          "reference_file": "chatterbot/storage/storage_adapter.py",
+          "reference_start_line": null,
+          "reference_end_line": null
+        }
+      ]
+    },
+    {
+      "name": "Training Module",
+      "description": "Responsible for the entire lifecycle of training the chatbot. It takes raw conversational data (corpus) and processes it to populate and update the chatbot's knowledge base, making it capable of generating responses.",
+      "referenced_source_code": [
+        {
+          "qualified_name": "chatterbot.trainers.Trainer",
+          "reference_file": "chatterbot/trainers.py",
+          "reference_start_line": 14,
+          "reference_end_line": 77
+        }
+      ]
+    },
+    {
+      "name": "Corpus Data Loader",
+      "description": "Dedicated to loading and managing conversational corpus data. It provides a standardized way to access and prepare datasets that are used by the Training Module to train the chatbot, primarily through functions like `load_corpus` and `list_corpus_files`.",
+      "referenced_source_code": [
+        {
+          "qualified_name": "chatterbot.corpus",
+          "reference_file": "<file_path>",
+          "reference_start_line": 1,
+          "reference_end_line": 10
+        }
+      ]
+    },
+    {
+      "name": "Unclassified",
+      "description": "Component for all unclassified files and utility functions (Utility functions/External Libraries/Dependencies)",
+      "referenced_source_code": []
+    }
+  ],
+  "components_relations": [
+    {
+      "relation": "depends on",
+      "src_name": "Training Module",
+      "dst_name": "Corpus Data Loader"
+    },
+    {
+      "relation": "writes to",
+      "src_name": "Training Module",
+      "dst_name": "Storage Adapters"
+    },
+    {
+      "relation": "reads from",
+      "src_name": "Training Module",
+      "dst_name": "Storage Adapters"
+    }
+  ]
+}

.codeboarding/Data_Storage_Training.rst

@@ -0,0 +1,61 @@
+Data Storage Training
+=====================
+
+.. mermaid::
+
+   graph LR
+      Storage_Adapters["Storage Adapters"]
+      Training_Module["Training Module"]
+      Corpus_Data_Loader["Corpus Data Loader"]
+      Unclassified["Unclassified"]
+      Training_Module -- "depends on" --> Corpus_Data_Loader
+      Training_Module -- "writes to" --> Storage_Adapters
+      Training_Module -- "reads from" --> Storage_Adapters
+
+| |codeboarding-badge| |demo-badge| |contact-badge|
+
+.. |codeboarding-badge| image:: https://img.shields.io/badge/Generated%20by-CodeBoarding-9cf?style=flat-square
+   :target: https://github.com/CodeBoarding/CodeBoarding
+.. |demo-badge| image:: https://img.shields.io/badge/Try%20our-Demo-blue?style=flat-square
+   :target: https://www.codeboarding.org/demo
+.. |contact-badge| image:: https://img.shields.io/badge/Contact%20us%20-%20contact@codeboarding.org-lightgrey?style=flat-square
+   :target: mailto:contact@codeboarding.org
+
+Details
+-------
+
+The ChatterBot system is structured around three core components: the `Storage Adapters`, `Training Module`, and `Corpus Data Loader`. The `Corpus Data Loader` is responsible for providing raw conversational data, which is then consumed by the `Training Module`. The `Training Module` processes this data to learn and update the chatbot's knowledge base, persisting and retrieving conversational statements through the `Storage Adapters`. This design ensures a clear separation of concerns, allowing for flexible data storage and diverse training methodologies.
+
+Storage Adapters
+^^^^^^^^^^^^^^^^
+
+Provides an abstract interface for all data persistence and retrieval operations within ChatterBot. It allows for interchangeable storage backends (e.g., SQL, NoSQL, in-memory) without affecting the core chatbot logic, managing the storage of statements, responses, and other conversational data.
+
+**Related Classes/Methods**:
+
+* chatterbot.storage.StorageAdapter
+
+Training Module
+^^^^^^^^^^^^^^^
+
+Responsible for the entire lifecycle of training the chatbot. It takes raw conversational data (corpus) and processes it to populate and update the chatbot's knowledge base, making it capable of generating responses.
+
+**Related Classes/Methods**:
+
+* chatterbot.trainers.Trainer:14-77
+
+Corpus Data Loader
+^^^^^^^^^^^^^^^^^^
+
+Dedicated to loading and managing conversational corpus data. It provides a standardized way to access and prepare datasets that are used by the Training Module to train the chatbot, primarily through functions like `load_corpus` and `list_corpus_files`.
+
+**Related Classes/Methods**:
+
+* chatterbot.corpus:1-10
+
+Unclassified
+^^^^^^^^^^^^
+
+Component for all unclassified files and utility functions (Utility functions/External Libraries/Dependencies)
+
+**Related Classes/Methods**: *None*