## Semantic code chunking

Semantic code file chunking in Python involves splitting a code file into meaningful, self-contained units based on its structure and semantics, rather than just arbitrary character counts. This approach aims to create chunks that represent logical components like functions, classes, or distinct blocks of code, improving the effectiveness of operations like embedding for RAG pipelines or code analysis. 

Here's how you can achieve this in Python: 

1. **Using Language-Specific Text Splitters:** 
    Libraries like LangChain offer specialized text splitters for different programming languages. 
    ```python
    from langchain_experimental.text_splitter import PythonCodeTextSplitter  
    
    # Initialize the splitter
    python_splitter = PythonCodeTextSplitter()

    # Split the code
    code_chunks = python_splitter.split_text(your_python_code_string)
    ```  
    This `PythonCodeTextSplitter` is designed to understand Python syntax and split based on elements like function definitions, class definitions, and other structural components. Similar splitters exist for other languages.  

2. **Utilizing Tree-Sitter for AST-based Chunking:**
    Tree-sitter is a parsing library that can generate Abstract Syntax Trees (ASTs) for various programming languages. You can leverage this to identify semantic boundaries more precisely. 

    ```python
    # Example using a conceptual tree-sitter based approach
    # (Requires a tree-sitter parser for Python)
    from tree_sitter import Language, Parser

    # Load the Python language parser (you'd need to compile it first)
    Language.build_library('build/my-languages.so', ['path/to/tree-sitter-python'])
    PYTHON_LANGUAGE = Language('build/my-languages.so', 'python')

    parser = Parser()
    parser.set_language(PYTHON_LANGUAGE)

    tree = parser.parse(bytes(your_python_code_string, 'utf8'))

    # Traverse the AST to identify meaningful nodes (e.g., function definitions, class definitions)
    # and extract their corresponding code snippets as chunks.
    ```  
    This method offers fine-grained control over chunking based on the exact structure of the code, but requires more manual implementation to define how AST nodes translate into chunks.  
3. **Combining Semantic and Heuristic Approaches:** 
    You can also combine semantic understanding with more traditional heuristic rules, such as splitting by multiple newlines or specific keywords, to create robust chunking strategies.  

Considerations for Semantic Code Chunking: 
• **Granularity:**  Decide on the appropriate level of granularity for your chunks (e.g., entire functions, individual statements, or logical blocks within functions). 
• **Context:** Ensure that each chunk retains enough context to be meaningful on its own, especially for tasks like embedding and retrieval. 
• **Language Specificity:** The best chunking strategy often depends on the specific programming language and its conventions. 

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## Semantic file chunking

Semantic file chunking in Python involves splitting a document into meaningful segments based on the semantic relatedness of its content, rather than fixed-size or character-based methods. This approach aims to keep semantically coherent information together within a single chunk, which can be beneficial for tasks like Retrieval Augmented Generation (RAG) in Large Language Models (LLMs). 

Here's how you can perform semantic chunking in Python:

1. **Using LlamaIndex's Semantic Splitter:**  
    LlamaIndex provides a SemanticSplitterNodeParser designed for semantic chunking.  
    ```python
    from llama_index.node_parser import SemanticSplitterNodeParser
    from llama_index.embeddings import OpenAIEmbedding

    # Initialize the embedding model (e.g., OpenAIEmbeddings)
    embed_model = OpenAIEmbedding()

    # Initialize the semantic splitter
    # `buffer_size` determines how many sentences to consider for similarity comparison
    # `breakpoint_percentile_threshold` controls the sensitivity of splitting
    splitter = SemanticSplitterNodeParser(
        buffer_size=1, breakpoint_percentile_threshold=95, embed_model=embed_model
    )

    # Load your document (e.g., from a file)
    # Example: text = "Your long document text here..."
    # Or use LlamaIndex's SimpleDirectoryReader to load documents from a directory

    # Parse the document into nodes (chunks)
    nodes = splitter.get_nodes_from_documents([document]) # Replace 'document' with your LlamaIndex Document object

    # Access the content of the semantic chunks
    for node in nodes:
        print(node.text)
    ```  
2. **Using LangChain's Semantic Chunking (Experimental):** 
   LangChain also offers an experimental SemanticChunker within langchain_experimental.  
   ```python
    from langchain_experimental.text_splitter import SemanticChunker
    from langchain_openai.embeddings import OpenAIEmbeddings

    # Initialize the embedding model
    embeddings = OpenAIEmbeddings()

    # Initialize the semantic chunker
    semantic_chunker = SemanticChunker(embeddings, breakpoint_threshold_type="percentile")

    # Split your text into semantic chunks
    text = "Your long document text here..."
    chunks = semantic_chunker.split_text(text)

    for chunk in chunks:
        print(chunk)
    ```

Key Concepts in Semantic Chunking: 
• **Embeddings:** Text is converted into numerical vector representations (embeddings) that capture its semantic meaning. 
• **Similarity Measurement:** The similarity between embeddings of adjacent sentences or segments is calculated (e.g., using cosine similarity). 
• **Breakpoint Threshold:** A threshold is used to identify points where the semantic similarity drops significantly, indicating a natural break point for a new chunk. This can be based on percentiles, standard deviation, or interquartile range of similarity scores. 
• **Adaptive Chunk Sizes:** Unlike fixed-size chunking, semantic chunking results in chunks of varying lengths, as the splits are determined by semantic coherence. 

By using these methods, you can create more semantically meaningful chunks, which can lead to improved performance in downstream applications like RAG by ensuring that relevant contextual information remains together. 

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