Need help for vector embedding search with Open Ai embeddding and elastic search cosine similarity

Hello everyone,
Overview: I am creating a search engine using the Open Ai embeddings .I am using the dividing the documents in chunks and then converting the chunks to embedding and storing in Elastic Search(7.10).Now when i am sending a prompt i again convert to the same and do a script base cosine similarity search.
here is an example of elastic search query.

                    "script_score": {
                        "script": {
                            "source": "doc['associated_questions_vector_field'].size() == 0 ? 0 : (1  + cosineSimilarity(params.prompt_vector, doc['associated_questions_vector_field']) + (1 + cosineSimilarity(params.prompt_vector, doc['vector_field']))) / 2,
                            "params": {
                                "prompt_vector": query_vector

Problem : Earlier it seemed to be working fine but now as the documents have increased i am getting wrong documents results any idea why that is happening and has anyone faced similar issue.

The new documents may just have content that affects the specific space area of the test queries you were using.

Just in case though, review the process you followed with the new documents to ensure you haven’t introduced an issue. Review the documents themselves after chunking, to ensure the content is well understood and there isn’t a lot of hidden text that is trhowing matching off.

Finally, it is normal that a match that worked in a small controlled subset will not work once you populate the space with many more chunks. What you can do to continuously improve, is to have an adapter that fine-tunes your embedding model as per your ideal pairs of questions and answers. I recommend Chroma and’s course on this: (it’s just one hour) Short Courses | Learn Generative AI from DeepLearning.AI