Dear Antonio,
Since you published the MCP source for Harbor
I am trying to understand it, but I do not see what the AI
from Ollama to the MCP scheduled to request, for example,
data from a database, this is obviously because of my
ignorance of the matter.
There is some documentation/example (in any language) where you can see this implementation.
My interest comes that I have an idea of
generation of an assistant, which depending on the request
made, should consult 1 or 2 external data sources, one that
is in a database and another that is a document file, each
with its own form of consultation and this MCP comes very
well for this, if I understand how to use it.
Greetings,
--- Spanish ----
Estimado Antonio,
Desde que publicaste el fuente del MCP para harbour estoy
tratando de entenderlo, pero no veo de que forma se
comunicara la IA desde ollama al MCP programado para
solicitar, por ejemplo, datos de una base de datos, esto
obviamente es por mi desconocimiento del asunto.
Hay alguna documentacion/ejemplo ( en cualquier lenguaje )
donde poder ver esta implementacion.
Mi interes viene sobre que tengo una idea de generacion de
un asistente, que dependiendo de la solicitud que se le
haga, debera consultar 1 o 2 fuentes externas de datos, una
que esta en una base de datos y otra que es un archivo de
documentos, cada uno con su forma propia de consulta y esto
del MCP viene muy bien para esto, si es que entiendo como
usarlo.
Saludos,
-------------------------
Lautaro Moreira
Hi Antonio,
I appreciate your continued support of the Harbour community.
I'm working on a project that utilizes Node.js, Ollama, and LangChain to build a system for generating PRG code.
I'm leveraging SQLite3 for local embedding storage and feeding the system with various source files (PRG, JSON, TS, HTML, CSS, MD).
The aim is to fine-tune a model to produce practical PRG code.
At the moment I'm using Mistral 7B
Instruct Q4 but it can be any other that is suited for a local
usage.
I'd be interested in any insights or recommendations anyone might have.
Best regards,
Lorenzo
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Dear Antonio,
Many thanks for the links
Saludos,
Lautaro Moreira
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Hi Francesco,
I'm currently experimenting with Retrieval Augmented Generation
(RAG).
I've observed that applying RAG to relational business data is
challenging due to the normalized structure of tables, relations,
keys, and values.
This complexity makes it difficult to produce data directly usable
by an LLM.
However, RAG or fine-tuning for code generation yields more
promising results.
The generated code can be easily linted, compiled, and tested, and
the source code's text format is readily suitable for
tokenization, embedding, and retrieval.
Actually my goal is to setup a AI environment for RAG (and/or
fine-tuning): find out the tools and models to use and understand
hardware and resources required.
I'm still in the experimental phase, and these are my initial findings.
Best regards,
Lorenzo
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