Refactor Elasticsearch ingestion and document processing functions for improved clarity and functionality

scripts/pipelines/flows/elasticsearch_ingestion.py

@@ -2,31 +2,28 @@ import typer
 import logging
 
 from loguru import logger
-from chonkie import FileFetcher
 
-from src.config import settings
+from scripts.pipelines.tasks.chunk import fetch_documents, process_documents, ingest_documents
-from scripts.pipelines.tasks.chunk import process_documents, ingest_documents
 
 app = typer.Typer()
 
 
 @app.command()
 def elasticsearch_ingestion(
-    docs_folder_path: str = "docs/LRM",
+    docs_folder_path: str = "docs/samples",
-    docs_extension: str = ".md",
+    docs_extension: list[str] = [".md", ".avap"],
     es_index: str = "avap-docs-test-v3",
     es_request_timeout: int = 120,
     es_max_retries: int = 5,
     es_retry_on_timeout: bool = True,
-    delete_es_index: bool = False
+    delete_es_index: bool = True
 ):  
     logger.info("Starting Elasticsearch ingestion pipeline...")
     logger.info(f"Fetching files from {docs_folder_path}...")
-    fetcher = FileFetcher()
+    docs_path = fetch_documents(docs_folder_path, docs_extension)
-    docs_path = fetcher.fetch(dir=f"{settings.proj_root}/{docs_folder_path}")
 
     logger.info("Processing docs...")
-    chunked_docs = process_documents(docs_path, docs_extension)
+    chunked_docs = process_documents(docs_path)
 
     logger.info(f"Ingesting chunks in Elasticsearch index: {es_index}...")
     ingest_documents(chunked_docs, es_index, es_request_timeout, es_max_retries, 

scripts/pipelines/tasks/chunk.py

@@ -5,9 +5,11 @@ from pathlib import Path
 from chonkie import (
     Chunk,
     ElasticHandshake,
+    FileFetcher,
     MarkdownChef,
     TextChef,
     TokenChunker,
+    MarkdownDocument
 )
 from elasticsearch import Elasticsearch
 from loguru import logger
@@ -27,16 +29,16 @@ def _get_text(element) -> str:
     )
 
 
-def _merge_markdown_document(doc):
+def _merge_markdown_document(processed_doc: MarkdownDocument) -> MarkdownDocument:
     elements = []
 
-    for chunk in doc.chunks:
+    for chunk in processed_doc.chunks:
         elements.append(("chunk", chunk.start_index, chunk.end_index, chunk))
 
-    for code in doc.code:
+    for code in processed_doc.code:
         elements.append(("code", code.start_index, code.end_index, code))
 
-    for table in doc.tables:
+    for table in processed_doc.tables:
         elements.append(("table", table.start_index, table.end_index, table))
 
     elements.sort(key=lambda item: (item[1], item[2]))
@@ -87,41 +89,60 @@ def _merge_markdown_document(doc):
 
     flush()
 
-    new_doc = deepcopy(doc)
+    fused_processed_doc = deepcopy(processed_doc)
-    new_doc.chunks = merged_chunks
+    fused_processed_doc.chunks = merged_chunks
-    new_doc.code = doc.code
+    fused_processed_doc.code = processed_doc.code
-    new_doc.tables = doc.tables
+    fused_processed_doc.tables = processed_doc.tables
 
-    return new_doc
+    return fused_processed_doc
 
 
-def process_documents(docs_path: list[Path], docs_extension: str) -> list[Chunk]:
+def fetch_documents(docs_folder_path: str, docs_extension: list[str]) -> list[Path]:
+    """
+    Fetch files from a folder that match the specified extensions.
+
+    Args:
+        docs_folder_path (str): Path to the folder containing documents
+        docs_extension (list[str]): List of file extensions to filter by (e.g., [".md", ".avap"])
+
+    Returns:
+        List of Paths to the fetched documents
+    """
+    fetcher = FileFetcher()
+    docs_path = fetcher.fetch(dir=f"{settings.proj_root}/{docs_folder_path}", ext=docs_extension)
+    return docs_path
+
+
+def process_documents(docs_path: list[Path]) -> list[Chunk]:
+    """
+    Process documents by applying appropriate chefs and chunking strategies based on file type.
+
+    Args:
+        docs_path (list[Path]): List of Paths to the documents to be processed
+
+    Returns:
+        List of processed documents ready for ingestion
+    """
     processed_docs = []
-    chunked_docs = []
     custom_tokenizer = AutoTokenizer.from_pretrained(settings.hf_emb_model_name)
-
+    chef_md = MarkdownChef(tokenizer=custom_tokenizer)
-    if docs_extension == ".md":
+    chef_txt = TextChef()
-        chef = MarkdownChef(tokenizer=custom_tokenizer)
-        for doc in docs_path:
-            processed_doc = chef.process(doc)
-            processed_docs.append((processed_doc, doc.name))
-
-        for processed_doc, filename in processed_docs:
-            fused_doc = _merge_markdown_document(processed_doc)
-            chunked_docs.extend(fused_doc.chunks)
-
-    elif docs_extension == ".avap":
-        chef = TextChef()
     chunker = TokenChunker(tokenizer=custom_tokenizer)
-        for doc in docs_path:
-            processed_doc = chef.process(doc)
-            processed_docs.append((processed_doc, doc.name))
 
-        for processed_doc, filename in processed_docs:
+    for doc_path in docs_path:
+        doc_extension = doc_path.suffix.lower()
+        
+        if doc_extension == ".md":
+            processed_doc = chef_md.process(doc_path)
+            fused_doc = _merge_markdown_document(processed_doc)
+            processed_docs.extend(fused_doc.chunks)
+
+        elif doc_extension == ".avap":
+            processed_doc = chef_txt.process(doc_path)
             chunked_doc = chunker.chunk(processed_doc.content)
-            chunked_docs.extend(chunked_doc)
+            processed_docs.extend(chunked_doc)
 
-    return chunked_docs
+    return processed_docs
 
 
 def ingest_documents(