reformatted the code to pass the tests

Signed-off-by: Peter Staar <[email protected]>

docs/examples/advanced_chunking_with_merging.ipynb

@@ -16,20 +16,16 @@
    ],
    "source": [
     "from typing import Iterator\n",
+    "\n",
+    "import lancedb\n",
     "import semchunk\n",
-    "from docling_core.transforms.chunker import (\n",
-    "    BaseChunk,\n",
-    "    BaseChunker,\n",
-    "    HierarchicalChunker\n",
-    ")\n",
+    "from docling_core.transforms.chunker import BaseChunk, BaseChunker, HierarchicalChunker\n",
     "from docling_core.types import DoclingDocument\n",
     "from pydantic import PositiveInt\n",
-    "\n",
-    "from docling.document_converter import DocumentConverter\n",
-    "import lancedb\n",
-    "\n",
+    "from sentence_transformers import SentenceTransformer\n",
     "from transformers import AutoTokenizer\n",
-    "from sentence_transformers import SentenceTransformer"
+    "\n",
+    "from docling.document_converter import DocumentConverter"
    ]
   },
   {
@@ -321,9 +317,7 @@
     "        return t1 + \"\\n\" + t2\n",
     "\n",
     "\n",
-    "def split_by_doc_items(\n",
-    "    doc_chunk: DocChunk, tokenizer, chunk_size: int\n",
-    "):\n",
+    "def split_by_doc_items(doc_chunk: DocChunk, tokenizer, chunk_size: int):\n",
     "    if doc_chunk.meta.doc_items == None or len(doc_chunk.meta.doc_items) <= 1:\n",
     "        return [doc_chunk]\n",
     "    length = doc_chunk_length(doc_chunk, tokenizer)\n",
@@ -618,9 +612,7 @@
     "def adjust_chunks_for_fixed_size(doc, original_chunks, tokenizer, splitter, chunk_size):\n",
     "    chunks_after_splitting_by_items = []\n",
     "    for chunk in original_chunks:\n",
-    "        chunk_split_by_doc_items = split_by_doc_items(\n",
-    "            chunk, tokenizer, chunk_size\n",
-    "        )\n",
+    "        chunk_split_by_doc_items = split_by_doc_items(chunk, tokenizer, chunk_size)\n",
     "        chunks_after_splitting_by_items.extend(chunk_split_by_doc_items)\n",
     "    chunks_after_splitting_recursively = []\n",
     "    for chunk in chunks_after_splitting_by_items:\n",
@@ -828,11 +820,11 @@
     "    output = \"\"\n",
     "    if chunk.meta.headings != None:\n",
     "        for h in chunk.meta.headings:\n",
-    "            output += h + '\\n'\n",
+    "            output += h + \"\\n\"\n",
     "    if chunk.meta.captions != None:\n",
     "        for c in chunk.meta.captions:\n",
-    "            output += c + '\\n'\n",
-    "    output += chunk.text        \n",
+    "            output += c + \"\\n\"\n",
+    "    output += chunk.text\n",
     "    return output"
    ]
   },
@@ -854,7 +846,6 @@
     }
    ],
    "source": [
-    "\n",
     "print(make_text_for_embedding(chunks[19]))"
    ]
   },
@@ -874,7 +865,7 @@
     "            \"vector\": embeddings,\n",
     "            \"text\": chunk.text,\n",
     "            \"headings\": chunk.meta.headings,\n",
-    "            \"captions\": chunk.meta.captions\n",
+    "            \"captions\": chunk.meta.captions,\n",
     "        }\n",
     "        data.append(data_item)\n",
     "    tbl = db.create_table(index_name, data=data, exist_ok=True)\n",

docs/examples/rag_langchain.ipynb

@@ -85,6 +85,7 @@
     "\n",
     "from docling.document_converter import DocumentConverter\n",
     "\n",
+    "\n",
     "class DoclingPDFLoader(BaseLoader):\n",
     "\n",
     "    def __init__(self, file_path: str | list[str]) -> None:\n",