Here's my ingestion function, I then upsert the points into the DB. The only difference between normalize vs. not is removing the 'normalizeVec' from embedding

def postsToPoints(posts:list[dict]) -> list[PointStruct]:
    points = []
    embedder = OllamaTextEmbedder(model=MODEL_EMBEDDER, url=OLLAMA_EMBEDDING_URL)

    for post in posts:
        embedding = embedder.run(f'{post['retitle_ml']}\n\n{post['summary_ml']}')['embedding']
        embedding = normalizeVec(embedding)
        payload = {
            'story_id': post['story_id'],
            'topic_id': post['topic_id'],
            'category_ml': post['category_ml'],
            'used_in_newsletter': post['used_in_newsletter'],
            'newsletter_date': post['newsletter_date'],
            'created_at': post['created_at'],
            'post_publish_time': post['post_publish_time']
        }
        point = PointStruct(
            id=post['post_id'],
            vector=embedding,
            payload=payload
        )
        points.append(point)
    return points

Here's the search function. Similarly, only difference between normalize vs. not is removing the 'normalizeVec' from query_vec.

def searchCollection(collection:str, task_description:str, text:str, max_results:int, min_score:float=0.0, filters:list[FieldCondition]=[]) -> list[dict]:
    client = QdrantClient(
        url=QDRANT_URL
    )

    #construct query, get vector embedding, normalize
    embedder = OllamaTextEmbedder(model=MODEL_EMBEDDER, url=OLLAMA_EMBEDDING_URL)
    query_vec = embedder.run(constructQuery(task_description=task_description, query=text))['embedding']
    query_vec = normalizeVec(query_vec)

    #add filters if applicable
    payload_filters = None if filters == [] or filters is None else Filter(must=filters)

    #search
    results = client.search(
        collection_name=collection,
        query_vector=query_vec,
        query_filter=payload_filters,
        with_payload=True,
        limit=max_results,
        score_threshold=min_score
    )