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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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The Training: Mastering Haystack - Augmented Document Retrieval with NLP and Generative AI 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.
To ensure the quality of the Training: Mastering Haystack - Augmented Document Retrieval with NLP and Generative AI training, Learni provides the following 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.
The assessment of skills acquired during the Training: Mastering Haystack - Augmented Document Retrieval with NLP and Generative AI training is carried out through:
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Learni training programs are available for inter-company and intra-company settings, both in-person and remote. Registration is possible up to 48 business hours before the start of training. Our programs are eligible for OPCO, Pôle emploi, and FNE-Formation funding. Contact us to discuss your training project and funding possibilities.
Overview of document retrieval challenges, limitations of traditional engines, modern NLP concepts, and the Haystack ecosystem. Installation of the working environment (Python, Docker, Elasticsearch, Haystack).
Exploration of core components (DocumentStore, Retriever, Reader, Generator, Pipelines). Document import, structuring, and indexing in Elasticsearch. Setting up a question-answering API via FastAPI/FAISS. Hands-on exercises on a thematic document collection.
Implementation of advanced pipelines (retrieval-augmented generation, multi-hop QA, metadata filters). Use of Transformer models (BERT, RoBERTa, LLMs via OpenAI or Hugging Face APIs). Monitoring and performance evaluation, adaptation to business cases. Deployment strategies, scalability management, and security best practices.
Target audience
Developers, data scientists, engineers, or project managers wishing to implement intelligent document retrieval systems in their organizations
Prerequisites
Basic knowledge of Python and natural language processing (NLP). Proficiency in Docker and familiarity with REST APIs are a plus.
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