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LLM API
Popular Providers – OpenAI (GPT 3 series), Cohere, AI21 Labs, Google AI.
Considerations – The choice of LLM provider influences capabilities, cost structure, fine-tuning options, and privacy features. Focaloid can help you evaluate the best fit for your needs.
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Knowledge Content Management System
Structured Data – Traditional databases (e.g., PostgreSQL, MySQL or data lake solutions (e.g., AWS S3, Azure Data Lake Storage) for well-defined data.
Unstructured Data – Search-oriented options like Elasticsearch or Solr, offering flexibility and fast text-based retrieval.
Hybrid Approaches – A combination of the above may be optimal for diverse enterprise knowledge types.
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Data Preprocessing Pipelines
Tools – Python libraries (Pandas, Numpy, Spacy), dedicated data preparation platforms (e.g., Trifacta, Alteryx), or cloud services (e.g., AWS Glue, Azure Data Factory).
Considerations – BThe complexity of your data, desired transformations, and team skillsets will guide the tool selection.
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Knowledge Graph (Optional)
Graph Databases – Neo4j, Amazon Neptune, TigerGraph, and others provide specialized storage and querying for highly interconnected data.
Alternatives – For simpler relationship modeling, RDF triplestores or property graphs within traditional databases can suffice.
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Conversational UI Frameworks
Popular Options – Rasa, Bot Framework, Dialogflow, Lex.
Factors – Desired level of customization, development team’s expertise, and integration needs will influence the choice.
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Cloud Infrastructure (e.g., AWS, Azure, GCP)
Benefits – Scalability, managed services, and security features offered by cloud platforms are highly advantageous for many deployments.
On-Premise Considerations – In specific scenarios with strict data sovereignty or connectivity constraints, on-premise solutions may be necessary.