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patchy631--ai-engineering-hub/database-memory-agent/ingest_data.py
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2026-07-13 12:37:47 +08:00

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Python

from config import vector_collection, voyage_client, VOYAGE_MODEL
from pymongo.operations import SearchIndexModel
from langchain_community.document_loaders import PyPDFLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
import time
def get_embedding(data, input_type = "document"):
embeddings = voyage_client.embed(
data, model = VOYAGE_MODEL, input_type = input_type
).embeddings
return embeddings[0]
def ingest_data():
loader = PyPDFLoader("https://investors.mongodb.com/node/13176/pdf")
data = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=400, chunk_overlap=20)
documents = text_splitter.split_documents(data)
print(f"Successfully split PDF into {len(documents)} chunks.")
print("Generating embeddings and ingesting documents...")
docs_to_insert = []
for i, doc in enumerate(documents):
embedding = get_embedding(doc.page_content)
if embedding:
docs_to_insert.append({
"text": doc.page_content,
"embedding": embedding
})
if docs_to_insert:
result = vector_collection.insert_many(docs_to_insert)
print(f"Inserted {len(result.inserted_ids)} documents into the collection.")
else:
print("No documents were inserted. Check embedding generation process.")
index_name = "vector_index"
search_index_model = SearchIndexModel(
definition = {
"fields": [
{
"type": "vector",
"numDimensions": 1024,
"path": "embedding",
"similarity": "cosine"
}
]
},
name=index_name,
type="vectorSearch"
)
try:
vector_collection.create_search_index(model=search_index_model)
print(f"Search index '{index_name}' creation initiated.")
except Exception as e:
print(f"Error creating search index: {e}")
return
print("Polling to check if the index is ready. This may take up to a minute.")
predicate=None
if predicate is None:
predicate = lambda index: index.get("queryable") is True
while True:
indices = list(vector_collection.list_search_indexes(index_name))
if len(indices) and predicate(indices[0]):
break
time.sleep(5)
print(index_name + " is ready for querying.")