diff --git a/README.md b/README.md
index 09aa6d1..82d4c99 100644
--- a/README.md
+++ b/README.md
@@ -15,13 +15,13 @@
 [![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)](https://github.com/pre-commit/pre-commit)
 [![License MIT](https://img.shields.io/github/license/DS4SD/quackling)](https://opensource.org/licenses/MIT)
 
-Quackling enables document-native generative AI applications, such as RAG, based on [Docling](https://github.com/DS4SD/docling).
+Easily build document-native generative AI applications, such as RAG, leveraging [Docling](https://github.com/DS4SD/docling)'s efficient PDF extraction and rich data model — while still using your favorite framework, [🦙 LlamaIndex](https://docs.llamaindex.ai/en/stable/) or [🦜🔗 LangChain](https://python.langchain.com/).
 
 ## Features
 
 - 🧠 Enables rich gen AI applications by providing capabilities on native document level — not just plain text / Markdown!
 - ⚡️ Leverages Docling's conversion quality and speed.
-- ⚙️ Integrates with standard LLM application frameworks, such as LlamaIndex, for building powerful applications like RAG.
+- ⚙️ Plug-and-play integration with LlamaIndex and LangChain for building powerful applications like RAG.
 
 <p align="center">
   <a href="https://raw.githubusercontent.com/DS4SD/quackling/main/resources/doc_native_rag.png">
@@ -40,12 +40,11 @@ pip install quackling
 
 ## Usage
 
-Quackling offers core capabilities (`quackling.core`), as well as framework integration components
-e.g. for LlamaIndex (`quackling.llama_index`). Below you find examples of both.
+Quackling offers core capabilities (`quackling.core`), as well as framework integration components (`quackling.llama_index` and `quackling.langchain`). Below you find examples of both.
 
 ### Basic RAG
 
-Below you find a basic RAG pipeline using LlamaIndex.
+Here is a basic RAG pipeline using LlamaIndex:
 
 > [!NOTE]
 > To use as is, first `pip install llama-index-embeddings-huggingface llama-index-llms-huggingface-api`
@@ -67,7 +66,7 @@ QUESTION = "How many pages were human annotated?"
 EMBED_MODEL = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
 LLM = HuggingFaceInferenceAPI(
     token=os.getenv("HF_TOKEN"),
-    model_name="mistralai/Mixtral-8x7B-Instruct-v0.1",
+    model_name="mistralai/Mistral-7B-Instruct-v0.3",
 )
 
 index = VectorStoreIndex.from_documents(
@@ -105,13 +104,18 @@ chunks = list(HierarchicalChunker().chunk(doc))
 ```
 
 ## More examples
-Check out the [examples](examples) — showcasing different variants of RAG incl. vector ingestion & retrieval:
-- [[LlamaIndex] Milvus basic RAG (dense embeddings)](examples/basic_pipeline.ipynb)
-- [[LlamaIndex] Milvus hybrid RAG (dense & sparse embeddings combined e.g. via RRF) & reranker model usage](examples/hybrid_pipeline.ipynb)
-- [[LlamaIndex] Milvus RAG also fetching native document metadata for search results](examples/native_nodes.ipynb)
-- [[LlamaIndex] Local node transformations (e.g. embeddings)](examples/node_transformations.ipynb)
+
+### LlamaIndex
+
+- [Milvus basic RAG (dense embeddings)](examples/llama_index/basic_pipeline.ipynb)
+- [Milvus hybrid RAG (dense & sparse embeddings combined e.g. via RRF) & reranker model usage](examples/llama_index/hybrid_pipeline.ipynb)
+- [Milvus RAG also fetching native document metadata for search results](examples/llama_index/native_nodes.ipynb)
+- [Local node transformations (e.g. embeddings)](examples/llama_index/node_transformations.ipynb)
 - ...
 
+### LangChain
+- [Milvus basic RAG (dense embeddings)](examples/langchain/basic_pipeline.ipynb)
+
 ## Contributing
 
 Please read [Contributing to Quackling](./CONTRIBUTING.md) for details.
diff --git a/examples/langchain/basic_pipeline.ipynb b/examples/langchain/basic_pipeline.ipynb
new file mode 100644
index 0000000..9416f4b
--- /dev/null
+++ b/examples/langchain/basic_pipeline.ipynb
@@ -0,0 +1,350 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# requirements for this example:\n",
+    "%pip install -qq \\\n",
+    "    quackling \\\n",
+    "    python-dotenv \\\n",
+    "    langchain-text-splitters \\\n",
+    "    langchain-huggingface \\\n",
+    "    langchain-milvus"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "from dotenv import load_dotenv\n",
+    "\n",
+    "load_dotenv()\n",
+    "\n",
+    "FILE_PATH = \"https://arxiv.org/pdf/2206.01062\"  # DocLayNet paper"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(action=\"ignore\", category=UserWarning, module=\"pydantic|torch\")\n",
+    "warnings.filterwarnings(action=\"ignore\", category=FutureWarning, module=\"easyocr\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Loader and splitter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from quackling.langchain.loaders import DoclingPDFLoader"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Below we set up:\n",
+    "- a `Loader` which will be used to create LangChain documents, and\n",
+    "- a splitter, which will be used to split these documents"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Using JSON"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To leverage Docling's rich document structure format, we namely set the parse type to JSON and use a `HierarchicalJSONSplitter` accordingly:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "37a35b6883bd444293bae3a589be56e5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Fetching 7 files:   0%|          | 0/7 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from quackling.langchain.loaders import DoclingPDFLoader\n",
+    "from quackling.langchain.splitters import HierarchicalJSONSplitter\n",
+    "\n",
+    "loader = DoclingPDFLoader(\n",
+    "    file_path=FILE_PATH,\n",
+    "    parse_type=DoclingPDFLoader.ParseType.JSON,\n",
+    ")\n",
+    "text_splitter = HierarchicalJSONSplitter()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Using Markdown"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Alternatively, to just use the flat Markdown export instead of the native document format, one can uncomment and use the following:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
+    "\n",
+    "# loader = DoclingPDFLoader(\n",
+    "#     file_path=FILE_PATH,\n",
+    "#     parse_type=DoclingPDFLoader.ParseType.MARKDOWN,\n",
+    "# )\n",
+    "# text_splitter = RecursiveCharacterTextSplitter(\n",
+    "#     chunk_size=1000,\n",
+    "#     chunk_overlap=200,\n",
+    "# )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We now used the above-defined objects to get the document splits:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "docs = loader.load()\n",
+    "splits = text_splitter.split_documents(docs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Embeddings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_huggingface.embeddings import HuggingFaceEmbeddings\n",
+    "\n",
+    "HF_EMBED_MODEL_ID = \"BAAI/bge-small-en-v1.5\"\n",
+    "embeddings = HuggingFaceEmbeddings(model_name=HF_EMBED_MODEL_ID)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Vector store"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tempfile import TemporaryDirectory\n",
+    "\n",
+    "from langchain_milvus import Milvus\n",
+    "\n",
+    "MILVUS_URI = os.environ.get(\n",
+    "    \"MILVUS_URL\", f\"{(tmp_dir := TemporaryDirectory()).name}/milvus_demo.db\"\n",
+    ")\n",
+    "\n",
+    "vectorstore = Milvus.from_documents(\n",
+    "    splits,\n",
+    "    embeddings,\n",
+    "    connection_args={\"uri\": MILVUS_URI},\n",
+    "    drop_old=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### LLM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.\n",
+      "Token is valid (permission: write).\n",
+      "Your token has been saved to /Users/pva/.cache/huggingface/token\n",
+      "Login successful\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain_huggingface import HuggingFaceEndpoint\n",
+    "\n",
+    "HF_API_KEY = os.environ.get(\"HF_API_KEY\")\n",
+    "HF_LLM_MODEL_ID = \"mistralai/Mistral-7B-Instruct-v0.3\"\n",
+    "\n",
+    "llm = HuggingFaceEndpoint(\n",
+    "    repo_id=HF_LLM_MODEL_ID,\n",
+    "    huggingfacehub_api_token=HF_API_KEY,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## RAG"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import Iterable\n",
+    "\n",
+    "from langchain_core.documents import Document as LCDocument\n",
+    "from langchain_core.output_parsers import StrOutputParser\n",
+    "from langchain_core.prompts import PromptTemplate\n",
+    "from langchain_core.runnables import RunnablePassthrough\n",
+    "\n",
+    "\n",
+    "def format_docs(docs: Iterable[LCDocument]):\n",
+    "    return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
+    "\n",
+    "\n",
+    "retriever = vectorstore.as_retriever()\n",
+    "\n",
+    "prompt = PromptTemplate.from_template(\n",
+    "    \"Context information is below.\\n---------------------\\n{context}\\n---------------------\\nGiven the context information and not prior knowledge, answer the query.\\nQuery: {question}\\nAnswer:\\n\"\n",
+    ")\n",
+    "\n",
+    "rag_chain = (\n",
+    "    {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
+    "    | prompt\n",
+    "    | llm\n",
+    "    | StrOutputParser()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'The number of pages annotated for DocLayNet is 80,863.'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rag_chain.invoke(\"How many pages were human annotated for DocLayNet?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/basic_pipeline.ipynb b/examples/llama_index/basic_pipeline.ipynb
similarity index 100%
rename from examples/basic_pipeline.ipynb
rename to examples/llama_index/basic_pipeline.ipynb
diff --git a/examples/hybrid_pipeline.ipynb b/examples/llama_index/hybrid_pipeline.ipynb
similarity index 100%
rename from examples/hybrid_pipeline.ipynb
rename to examples/llama_index/hybrid_pipeline.ipynb
diff --git a/examples/native_nodes.ipynb b/examples/llama_index/native_nodes.ipynb
similarity index 100%
rename from examples/native_nodes.ipynb
rename to examples/llama_index/native_nodes.ipynb
diff --git a/examples/node_transformations.ipynb b/examples/llama_index/node_transformations.ipynb
similarity index 100%
rename from examples/node_transformations.ipynb
rename to examples/llama_index/node_transformations.ipynb
diff --git a/examples/prev_next_augmentation.ipynb b/examples/llama_index/prev_next_augmentation.ipynb
similarity index 100%
rename from examples/prev_next_augmentation.ipynb
rename to examples/llama_index/prev_next_augmentation.ipynb
diff --git a/poetry.lock b/poetry.lock
index 55c4d31..30e18c1 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -2231,6 +2231,20 @@ files = [
 [package.dependencies]
 attrs = ">=19.2.0"
 
+[[package]]
+name = "jsonpatch"
+version = "1.33"
+description = "Apply JSON-Patches (RFC 6902)"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*"
+files = [
+    {file = "jsonpatch-1.33-py2.py3-none-any.whl", hash = "sha256:0ae28c0cd062bbd8b8ecc26d7d164fbbea9652a1a3693f3b956c1eae5145dade"},
+    {file = "jsonpatch-1.33.tar.gz", hash = "sha256:9fcd4009c41e6d12348b4a0ff2563ba56a2923a7dfee731d004e212e1ee5030c"},
+]
+
+[package.dependencies]
+jsonpointer = ">=1.9"
+
 [[package]]
 name = "jsonpath-ng"
 version = "1.6.1"
@@ -2245,6 +2259,17 @@ files = [
 [package.dependencies]
 ply = "*"
 
+[[package]]
+name = "jsonpointer"
+version = "3.0.0"
+description = "Identify specific nodes in a JSON document (RFC 6901)"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "jsonpointer-3.0.0-py2.py3-none-any.whl", hash = "sha256:13e088adc14fca8b6aa8177c044e12701e6ad4b28ff10e65f2267a90109c9942"},
+    {file = "jsonpointer-3.0.0.tar.gz", hash = "sha256:2b2d729f2091522d61c3b31f82e11870f60b68f43fbc705cb76bf4b832af59ef"},
+]
+
 [[package]]
 name = "jsonref"
 version = "1.1.0"
@@ -2480,6 +2505,100 @@ files = [
     {file = "kiwisolver-1.4.5.tar.gz", hash = "sha256:e57e563a57fb22a142da34f38acc2fc1a5c864bc29ca1517a88abc963e60d6ec"},
 ]
 
+[[package]]
+name = "langchain-core"
+version = "0.2.38"
+description = "Building applications with LLMs through composability"
+optional = false
+python-versions = "<4.0,>=3.8.1"
+files = [
+    {file = "langchain_core-0.2.38-py3-none-any.whl", hash = "sha256:8a5729bc7e68b4af089af20eff44fe4e7ca21d0e0c87ec21cef7621981fd1a4a"},
+    {file = "langchain_core-0.2.38.tar.gz", hash = "sha256:eb69dbedd344f2ee1f15bcea6c71a05884b867588fadc42d04632e727c1238f3"},
+]
+
+[package.dependencies]
+jsonpatch = ">=1.33,<2.0"
+langsmith = ">=0.1.75,<0.2.0"
+packaging = ">=23.2,<25"
+pydantic = [
+    {version = ">=1,<3", markers = "python_full_version < \"3.12.4\""},
+    {version = ">=2.7.4,<3.0.0", markers = "python_full_version >= \"3.12.4\""},
+]
+PyYAML = ">=5.3"
+tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<9.0.0"
+typing-extensions = ">=4.7"
+
+[[package]]
+name = "langchain-huggingface"
+version = "0.0.3"
+description = "An integration package connecting Hugging Face and LangChain"
+optional = true
+python-versions = "<4.0,>=3.8.1"
+files = [
+    {file = "langchain_huggingface-0.0.3-py3-none-any.whl", hash = "sha256:d6827adf3c7c8fcc0bca8c43c7e900c3bf68af9a1532a83d4b8ace137e02887e"},
+    {file = "langchain_huggingface-0.0.3.tar.gz", hash = "sha256:0637acf484c47323cf3dcc46745a93467f6955989af9b7c01e2382fe1b630aaf"},
+]
+
+[package.dependencies]
+huggingface-hub = ">=0.23.0"
+langchain-core = ">=0.1.52,<0.3"
+sentence-transformers = ">=2.6.0"
+tokenizers = ">=0.19.1"
+transformers = ">=4.39.0"
+
+[[package]]
+name = "langchain-milvus"
+version = "0.1.4"
+description = "An integration package connecting Milvus and LangChain"
+optional = true
+python-versions = "<4.0,>=3.8.1"
+files = [
+    {file = "langchain_milvus-0.1.4-py3-none-any.whl", hash = "sha256:f5c1f2d023c6853d1acc22dc8d0b61ca4d99015c1b095b0cf84ec84a9ba2936e"},
+    {file = "langchain_milvus-0.1.4.tar.gz", hash = "sha256:1cd67f127d60c73ffb07cd789705766479137630d43f8ff547c69eee4775dae8"},
+]
+
+[package.dependencies]
+langchain-core = ">=0.2.20,<0.3.0"
+pymilvus = ">=2.4.3,<3.0.0"
+scipy = [
+    {version = ">=1.7,<2.0", markers = "python_version < \"3.12\""},
+    {version = ">=1.9,<2.0", markers = "python_version >= \"3.12\""},
+]
+
+[[package]]
+name = "langchain-text-splitters"
+version = "0.2.4"
+description = "LangChain text splitting utilities"
+optional = true
+python-versions = "<4.0,>=3.8.1"
+files = [
+    {file = "langchain_text_splitters-0.2.4-py3-none-any.whl", hash = "sha256:2702dee5b7cbdd595ccbe43b8d38d01a34aa8583f4d6a5a68ad2305ae3e7b645"},
+    {file = "langchain_text_splitters-0.2.4.tar.gz", hash = "sha256:f7daa7a3b0aa8309ce248e2e2b6fc8115be01118d336c7f7f7dfacda0e89bf29"},
+]
+
+[package.dependencies]
+langchain-core = ">=0.2.38,<0.3.0"
+
+[[package]]
+name = "langsmith"
+version = "0.1.116"
+description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
+optional = false
+python-versions = "<4.0,>=3.8.1"
+files = [
+    {file = "langsmith-0.1.116-py3-none-any.whl", hash = "sha256:4b5ea64c81ba5ca309695c85dc3fb4617429a985129ed5d9eca00d1c9d6483f4"},
+    {file = "langsmith-0.1.116.tar.gz", hash = "sha256:5ccd7f5c1840f7c507ab3ee56334a1391de28c8bf72669782e2d82cafeefffa7"},
+]
+
+[package.dependencies]
+httpx = ">=0.23.0,<1"
+orjson = ">=3.9.14,<4.0.0"
+pydantic = [
+    {version = ">=1,<3", markers = "python_full_version < \"3.12.4\""},
+    {version = ">=2.7.4,<3.0.0", markers = "python_full_version >= \"3.12.4\""},
+]
+requests = ">=2,<3"
+
 [[package]]
 name = "lazy-loader"
 version = "0.4"
@@ -3706,6 +3825,72 @@ numpy = [
     {version = ">=1.26.0", markers = "python_version >= \"3.12\""},
 ]
 
+[[package]]
+name = "orjson"
+version = "3.10.7"
+description = "Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy"
+optional = false
+python-versions = ">=3.8"
+files = [
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+    {file = "orjson-3.10.7.tar.gz", hash = "sha256:75ef0640403f945f3a1f9f6400686560dbfb0fb5b16589ad62cd477043c4eee3"},
+]
+
 [[package]]
 name = "packaging"
 version = "24.1"
@@ -7480,9 +7665,9 @@ test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools",
 type = ["pytest-mypy"]
 
 [extras]
-examples = ["flagembedding", "jsonpath-ng", "llama-index-embeddings-huggingface", "llama-index-llms-huggingface-api", "llama-index-postprocessor-flag-embedding-reranker", "llama-index-vector-stores-milvus", "peft", "python-dotenv"]
+examples = ["flagembedding", "jsonpath-ng", "langchain-huggingface", "langchain-milvus", "langchain-text-splitters", "llama-index-embeddings-huggingface", "llama-index-llms-huggingface-api", "llama-index-postprocessor-flag-embedding-reranker", "llama-index-vector-stores-milvus", "peft", "python-dotenv"]
 
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "9341adde375afd06a61daae54b8b87383fb98868f8e5b2c7f32c9d3cb3bcf897"
+content-hash = "417e7168718425cb9f2c3d487329d75c7ffbd47733ce88861209f0ac5f61d509"
diff --git a/pyproject.toml b/pyproject.toml
index 1eaf8c8..ca1576a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -36,6 +36,7 @@ python = "^3.10"
 docling-core = "^1.1.2"
 
 llama-index-core = "^0.11.1"
+langchain-core = "^0.2.38"
 docling = "^1.8.2"
 
 #########
@@ -49,6 +50,9 @@ llama-index-postprocessor-flag-embedding-reranker  = {version = "^0.2.0", option
 flagembedding = { version = "^1.2.10", optional = true }
 peft = { version = "^0.12.0", optional = true }  # TODO: remove once we can update FlagEmbedding past 1.2.11 to include https://github.com/FlagOpen/FlagEmbedding/commit/1613625417e293bf98311cb8ae0819a0a3af5297
 jsonpath-ng = { version = "^1.6.1", optional = true }
+langchain-huggingface = { version = "^0.0.3", optional = true}
+langchain-milvus = { version = "^0.1.4", optional = true }
+langchain-text-splitters = { version = "^0.2.4", optional = true }
 
 ##############
 # constraints:
@@ -65,6 +69,7 @@ torchvision = [
 [tool.poetry.extras]
 examples = [
     "python-dotenv",
+    # LlamaIndex examples:
     "llama-index-embeddings-huggingface",
     "llama-index-llms-huggingface-api",
     "llama-index-vector-stores-milvus",
@@ -72,6 +77,10 @@ examples = [
     "flagembedding",
     "peft",  # TODO: remove once we can update FlagEmbedding past 1.2.11 to include https://github.com/FlagOpen/FlagEmbedding/commit/1613625417e293bf98311cb8ae0819a0a3af5297
     "jsonpath-ng",
+    # LangChain examples:
+    "langchain-huggingface",
+    "langchain-milvus",
+    "langchain-text-splitters",
 ]
 
 [tool.poetry.group.dev.dependencies]
diff --git a/quackling/langchain/__init__.py b/quackling/langchain/__init__.py
new file mode 100644
index 0000000..bacc58a
--- /dev/null
+++ b/quackling/langchain/__init__.py
@@ -0,0 +1,4 @@
+#
+# Copyright IBM Corp. 2024 - 2024
+# SPDX-License-Identifier: MIT
+#
diff --git a/quackling/langchain/loaders/__init__.py b/quackling/langchain/loaders/__init__.py
new file mode 100644
index 0000000..31e2d18
--- /dev/null
+++ b/quackling/langchain/loaders/__init__.py
@@ -0,0 +1,6 @@
+#
+# Copyright IBM Corp. 2024 - 2024
+# SPDX-License-Identifier: MIT
+#
+
+from quackling.langchain.loaders.docling_pdf_loader import DoclingPDFLoader  # noqa
diff --git a/quackling/langchain/loaders/base.py b/quackling/langchain/loaders/base.py
new file mode 100644
index 0000000..aa756bd
--- /dev/null
+++ b/quackling/langchain/loaders/base.py
@@ -0,0 +1,43 @@
+#
+# Copyright IBM Corp. 2024 - 2024
+# SPDX-License-Identifier: MIT
+#
+
+from enum import Enum
+
+from docling.document_converter import DocumentConverter
+from docling_core.types import Document as DLDocument
+from langchain_core.document_loaders import BaseLoader
+from langchain_core.documents import Document as LCDocument
+from pydantic import BaseModel
+
+
+class DocumentMetadata(BaseModel):
+    dl_doc_hash: str
+    # source: str
+
+
+class BaseDoclingLoader(BaseLoader):
+    class ParseType(str, Enum):
+        MARKDOWN = "markdown"
+        JSON = "json"
+
+    def __init__(self, file_path: str | list[str], parse_type: ParseType) -> None:
+        self._file_paths = file_path if isinstance(file_path, list) else [file_path]
+        self._parse_type = parse_type
+        self._converter = DocumentConverter()
+
+    def _create_lc_doc_from_dl_doc(self, dl_doc: DLDocument) -> LCDocument:
+        if self._parse_type == self.ParseType.MARKDOWN:
+            text = dl_doc.export_to_markdown()
+        elif self._parse_type == self.ParseType.JSON:
+            text = dl_doc.model_dump_json()
+        else:
+            raise RuntimeError(f"Unexpected parse type encountered: {self._parse_type}")
+        lc_doc = LCDocument(
+            page_content=text,
+            metadata=DocumentMetadata(
+                dl_doc_hash=dl_doc.file_info.document_hash,
+            ).model_dump(),
+        )
+        return lc_doc
diff --git a/quackling/langchain/loaders/docling_pdf_loader.py b/quackling/langchain/loaders/docling_pdf_loader.py
new file mode 100644
index 0000000..70a4eec
--- /dev/null
+++ b/quackling/langchain/loaders/docling_pdf_loader.py
@@ -0,0 +1,19 @@
+#
+# Copyright IBM Corp. 2024 - 2024
+# SPDX-License-Identifier: MIT
+#
+
+from typing import Iterator
+
+from langchain_core.documents import Document as LCDocument
+
+from quackling.langchain.loaders.base import BaseDoclingLoader
+
+
+class DoclingPDFLoader(BaseDoclingLoader):
+
+    def lazy_load(self) -> Iterator[LCDocument]:
+        for source in self._file_paths:
+            dl_doc = self._converter.convert_single(source).output
+            lc_doc = self._create_lc_doc_from_dl_doc(dl_doc=dl_doc)
+            yield lc_doc
diff --git a/quackling/langchain/splitters/__init__.py b/quackling/langchain/splitters/__init__.py
new file mode 100644
index 0000000..2ea44be
--- /dev/null
+++ b/quackling/langchain/splitters/__init__.py
@@ -0,0 +1,8 @@
+#
+# Copyright IBM Corp. 2024 - 2024
+# SPDX-License-Identifier: MIT
+#
+
+from quackling.langchain.splitters.hier_json_splitter import (  # noqa
+    HierarchicalJSONSplitter,
+)
diff --git a/quackling/langchain/splitters/hier_json_splitter.py b/quackling/langchain/splitters/hier_json_splitter.py
new file mode 100644
index 0000000..d4ff65e
--- /dev/null
+++ b/quackling/langchain/splitters/hier_json_splitter.py
@@ -0,0 +1,48 @@
+#
+# Copyright IBM Corp. 2024 - 2024
+# SPDX-License-Identifier: MIT
+#
+
+from typing import Iterable, List
+
+from docling_core.types import Document as DLDocument
+from langchain_core.documents import Document as LCDocument
+from pydantic import BaseModel
+
+from quackling.core.chunkers.base import BaseChunker
+from quackling.core.chunkers.hierarchical_chunker import HierarchicalChunker
+
+
+class ChunkDocMetadata(BaseModel):
+    dl_doc_id: str
+    path: str
+
+
+class HierarchicalJSONSplitter:
+
+    def __init__(
+        self,
+        chunker: BaseChunker | None = None,
+    ) -> None:
+        self.chunker: BaseChunker = chunker or HierarchicalChunker()
+
+    def split_documents(self, documents: Iterable[LCDocument]) -> List[LCDocument]:
+
+        all_chunk_docs: list[LCDocument] = []
+        for doc in documents:
+            lc_doc: LCDocument = LCDocument.parse_obj(doc)
+            dl_doc: DLDocument = DLDocument.model_validate_json(lc_doc.page_content)
+            chunk_iter = self.chunker.chunk(dl_doc=dl_doc)
+            chunk_docs = [
+                LCDocument(
+                    page_content=chunk.text,
+                    metadata=ChunkDocMetadata(
+                        dl_doc_id=dl_doc.file_info.document_hash,
+                        path=chunk.path,
+                    ).model_dump(),
+                )
+                for chunk in chunk_iter
+            ]
+            all_chunk_docs.extend(chunk_docs)
+
+        return all_chunk_docs