Is the training Qualiopi certified?

Yes, Learni is a Qualiopi-certified training organization. This certification guarantees the quality of our courses and enables funding through OPCO and other mechanisms.

How can I fund my training?

Our courses are eligible for OPCO funding. Learni supports you through the funding process. A personalized quote is available upon request.

What are the prerequisites for this training?

The prerequisites for the Training Hybrid Search - Mastering BM25 and Vectors for RAG training are: Proficiency in Python, NLP fundamentals, vector embeddings, and Elasticsearch or Pinecone. A preliminary interview validates your eligibility.

Is the training available in person?

Yes, the Training Hybrid Search - Mastering BM25 and Vectors for RAG training is available in remote. The format can be adapted to your needs (inter, intra, custom).

Training Hybrid Search - Mastering BM25 and Vect... | Learni

The story of Learni

Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.

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Methods, approaches, and teaching resources

The Training Hybrid Search - Mastering BM25 and Vectors for RAG training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.

The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.

Teaching resources provided

To ensure the quality of the Training Hybrid Search - Mastering BM25 and Vectors for RAG training, Learni provides the following teaching resources:

Mac or PC computers, high-speed fiber internet, whiteboard or flipchart, video projector or interactive touchscreen (for remote sessions)
Training environments installed on workstations or accessible online
Course materials, practical exercises, and supplementary resources
Post-training access to materials and teaching resources

For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.

* contact us for remote feasibility** ratio varies depending on the training followed

Assessment methods

The assessment of skills acquired during the Training Hybrid Search - Mastering BM25 and Vectors for RAG training is carried out through:

During training: case studies, practical exercises, and professional scenarios
At the end of training: self-assessment questionnaire and skills evaluation by the trainer
After training: training completion certificate detailing acquired skills

Training accessibility

Learni is committed to the accessibility of its professional training programs. All our training programs are accessible to people with disabilities. Our teams are available to adapt teaching methods to your specific needs. Do not hesitate to contact us for any accommodation request.

Training objectives

Master BM25 algorithms for professional lexical search

Develop high-performance vector indexes with advanced embeddings

Implement hybrid search fusion for maximum relevance

Design scalable hybrid RAG pipelines for enterprise use

Optimize hybrid search performance on massive datasets

Deploy production-ready certified hybrid search solutions

Acquire hybrid search expertise for critical AI projects

Training program

Module 1Hybrid Search: BM25 Fundamentals and Data Preparation (Elasticsearch, TF-IDF)

Dive into BM25 mechanisms to precisely score lexical queries, set up a hybrid Elasticsearch environment, prepare real enterprise datasets by cleaning and indexing millions of documents, perform practical exercises on BM25 parameter optimization, analyze real-world e-commerce search engine cases, and create your first functional BM25 index with recall and precision metrics.

Module 2Hybrid Search: Vector Search and Embeddings (Sentence Transformers, FAISS)

Generate dense embeddings using models like Sentence Transformers on prepared data, implement ANN vector indexes via FAISS or Pinecone for fast semantic searches, test cosine similarity on ambiguous queries, integrate vector augmentation techniques to enhance relevance, apply to an enterprise RAG use case with Q&A on internal documents, and validate performance through comparative benchmarks.

Module 3Hybrid Search: BM25-Vector Fusion and Reranking (Reciprocal Rank Fusion, Cohere Rerank)

Fuse BM25 and vector scores using RRF or hybrid models for superior relevance, set up reranking pipelines with Cohere or cross-encoders, experiment on datasets like MS MARCO to refine fusion weights, develop a complete hybrid search prototype connected to an API, simulate high-traffic enterprise scenarios, generate NDCG and MRR evaluation reports, and iterate improvements based on real feedback.

Module 4Hybrid Search: Optimization, Deployment, and Monitoring (Kubernetes, Prometheus)

Scale your hybrid search system with Docker and Kubernetes for cloud-native deployments, integrate monitoring via Prometheus and Grafana for latency and throughput, optimize costs with sparse-dense hybrid indexing, test robustness against adversarial attacks, deploy an end-to-end professional RAG project on AWS or GCP, master advanced troubleshooting, and conclude with skills certification and deliverable portfolio.