diff --git a/backend/AssetIngestion.py b/backend/AssetIngestion.py
new file mode 100644
index 00000000..9196ea6a
--- /dev/null
+++ b/backend/AssetIngestion.py
@@ -0,0 +1,105 @@
+from sklearn.metrics.pairwise import cosine_similarity
+# from langchain_community.embeddings import HuggingFaceEmbeddings
+from llama_index.readers.file import PyMuPDFReader
+from llama_index.core.node_parser import SentenceSplitter
+from llama_index.core.schema import TextNode
+import pymupdf
+from llama_index.core import Document, VectorStoreIndex
+#from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+from langchain.embeddings import HuggingFaceEmbeddings
+from llama_index.embeddings.langchain import LangchainEmbedding
+# build index
+# index = VectorStoreIndex.from_documents(documents)
+
+# def write_pdf_into_txt(pdf_filename="test_chunking.pdf", txt_filename="test_chunking.txt"):
+#     doc = pymupdf.open(str(pdf_filename))  # open a document
+#     out = open(str(txt_filename), "wb")  # create a text output
+#     for page in doc:  # iterate the document pages
+#         text = page.get_text().encode("utf8")  # get plain text (is in UTF-8)
+#         out.write(text)  # write text of page
+#         out.write(bytes((12,)))  # write page delimiter (form feed 0x0C)
+#     out.close()
+#
+#
+# def read_txt_file(filename='test_chunking.txt'):
+#     with open(filename, encoding="utf8") as file:
+#         essay = file.read()
+#     return essay
+
+
+# create chunks from PDF
+def chunk_pdf():
+    loader = PyMuPDFReader()
+    documents = loader.load(file_path="test_chunking.pdf")
+
+    text_parser = SentenceSplitter(
+        chunk_size=1024,
+        # separator=" ",
+    )
+    text_chunks = []
+    doc_idxs = []
+    for doc_idx, doc in enumerate(documents):
+        cur_text_chunks = text_parser.split_text(doc.text)
+        text_chunks.extend(cur_text_chunks)
+        # for node metadata
+        doc_idxs.extend([doc_idx] * len(cur_text_chunks))
+    return text_chunks, doc_idxs, documents
+
+
+# building an index by creating for each chunk of text a Textnode
+def create_nodes(text_chunks,documents,doc_idxs):
+    nodes = []
+    for idx, text_chunk in enumerate(text_chunks):
+        node = TextNode(
+            text=text_chunk,
+        )
+        src_doc = documents[doc_idxs[idx]]
+        node.metadata = src_doc.metadata
+        nodes.append(node)
+    return nodes
+
+
+
+
+def get_nodes_embedding(nodes):
+    for node in nodes:
+        node_embedding = embed_model.get_text_embedding(
+            node.get_content(metadata_mode="all")
+        )
+        node.embedding = node_embedding
+    return nodes
+
+
+# write_pdf_into_txt()
+
+
+def calculate_cosine_distances(embedding_parent, embedding_child):
+    similarity = cosine_similarity(embedding_parent, embedding_child)[0][0]
+    return similarity
+
+
+def compute_child_nodes_similarity(nodes):
+    for node in nodes:
+        node_embedding = embed_model.get_text_embedding(
+            node.get_content(metadata_mode="all")
+        )
+
+        for idx, child_node in enumerate(node.child_nodes):
+            child_embedding = embed_model.get_text_embedding(
+                child_node.get_content(metadata_mode="all")
+            )
+            similarity = calculate_cosine_distances(node_embedding, child_embedding)
+            # print similarities
+            print("similarity between node"+str(node.node_id)+" and node "+str(child_node.node_id)+" is "+similarity)
+            node.node_info.update({"related_node_similarity " + str(idx): float(similarity)})
+    return nodes
+
+
+if __name__ == "__main__":
+    #embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
+    lc_embed_model  = HuggingFaceEmbeddings(model_name="BAAI/bge-small-en-v1.5")
+    embed_model = LangchainEmbedding(lc_embed_model)
+    text_chunks, doc_idx, documents = chunk_pdf()
+    nodes = create_nodes(text_chunks,documents,doc_idx)
+    nodes = get_nodes_embedding(nodes)
+    compute_child_nodes_similarity(nodes)