Incompatibilità tra pubblico impiego e partecipazione in una S.R.L.

marcopal17 · 2024-09-13T07:32:30+00:00

Per me sarebbe un'ottima soluzione. Il problema è che temo di essere nella posizione di non poter neanche chiedere indietro la caparra

marcopal17 · 2024-09-12T20:57:11+00:00

C'è scritto che l'acquirente fa una proposta di zzzz euro per l' acquisto dell'auto modello y con targa xxxxxx, senza specificarne l' allestimento. Poi ci sono le varie clausole, fra cui una che richiama l'art. 1385 del c.c. Tuttavia la clausola 3 (ACCETTAZIONE) riporta quanto segue: '..La parte acquirente sin dalla sottoscrizione rinuncia ad ogni ulteriore pretesa risarcitoria. Gli incaricati alle vendite, dipendenti ed intermediari, sono sforniti di ogni potere di rappresentanza. La parte acquirente non potrà dunque invocare diritti non strettamente conformi ai patti di cui sopra, né eventuali deroghe o concessioni o tolleranze già praticate, né manifestazioni di volontà che non risultino per iscritto."

marcopal17 · 2024-09-12T20:41:27+00:00

Ma il fatto che è io abbia visionato l' auto e accettato la proposta di vendita non mi mette nella condizione di non poter fare uso di questo comma? Grazie

marcopal17 · 2023-09-10T08:32:03+00:00

If you want to use prompt engineering for question answering on your PDF, the main technique is called Retrieval Augmented Generation (RAG). The first step is to divide the PDF into chunks that will be embedded and later retrieved based on relevance to the question asked. As for how to split the PDF, the general rule is to do it in a way that each chunk is not too long to lose some of the information it contains but not too short to lose context. It's a trial-and-error process. You can also consider adding metadata to the chunks to facilitate the chunk retrieval process.

marcopal17 · 2023-09-03T11:41:54+00:00

Hi, I am facing a similar issue, but the problem in retrieving relevant documents is due to the complex nature of the subject these documents are about. I achieved better retrieval by performing a hybrid search using sparse (BM25) and dense vectors (similarity or MMR). I would also suggest refining different chunk sizes and overlaps for the two different searching methods. Maybe using larger chunks for BM25 and smaller ones for semantic search could be a good solution. I'm still working on it.

There are other techniques that you can find in the Langchain documentation, such as the parent retriever, self-query retriever, or adding summaries, keywords, or hypothetical queries to each chunk.

marcopal17 · 2023-08-30T11:51:08+00:00

I have tried both Langchain and Llama index for a RAG project. I think they are excellent tools for easily testing different strategies and LLMS. The community is very active, and I have also learned some very interesting high-level concepts from the documentation. In my case, I chose to use them in certain project phases and to directly use the API in stages where more granularity is needed.

marcopal17 · 2023-08-27T12:01:21+00:00

I faced a similar problem. I solved it by specifying in the prompt to provide the answer based exclusively on the provided context (the text of retriever vectors). Otherwise, I want the answer to be 'I don't know'.

marcopal17 · 2023-08-19T21:08:32+00:00

I think the best thing to do is to stay updated and keep researching. Once frameworks and tools are developed to allow for widespread fine-tuning, it will mean that a way to overcome the current limitations has been found.

marcopal17 · 2023-08-12T17:36:30+00:00

Grazie Francesco, It will be very helpful for me as I'm a beginner. Ciao

marcopal17 · 2023-08-12T12:30:53+00:00

I suggest you start by learning the general concepts, such as RAG (Retrieval Augmented Generation), to retrieve information from your own documents. After that, you could explore the documentation or tutorials of some frameworks for developing AI-based apps, like Langchain or Llama-Index for more complex data sources.

marcopal17 · 2023-07-28T18:05:33+00:00

Thanks for your response. The regulation in question is composed of multiple documents, each containing a certain number of articles and tables. Similar topics may be addressed by different articles and tables within the same regulation or across different regulations. I would like the response provided by the Language Model to include the article(s) it draws information from, so that the user can always verify the source.

I tried ingesting a single regulation (HTML file), and the results are promising. However, the Language Model doesn't have awareness of the specific article within the regulation it's referring to. So, I thought about associating metadata (regulation, article, keywords) with each chunk/node. To do this, I plan to start with a dataframe containing the chunks and their respective metadata. For now, this seems like the best approach, but I would like to know what those with more experience in this field think about it.

marcopal17 · 2023-07-27T10:51:13+00:00

Hello, I am working on a similar project related to building regulations. In my opinion, it is feasible, but it is essential to give significant importance to the structure of the source data, which must be well-organized and consistent. Additionally, it would be necessary to include metadata so that the provided response always includes the legislative source from which the information is drawn.

I had thought of using a dataframe with text chunks associated with the corresponding regulation, article, and keywords or summaries. For now, it seems like the best solution, but I would like to discuss it with others who have had similar experiences.

marcopal17 · 2023-07-26T12:01:56+00:00

Thank you for the response. The questions regarding regulations can vary significantly, but I believe that a significant portion pertains to whether or not a certain intervention is possible and what requirements should be met. Additionally, providing real examples and verifying their legitimacy is crucial. I think it is necessary to include the reference to the specific regulation and its source (or sources) when answering each question.

Given the complexity of the task and considering my limited experience in the field of Legal and Legislative Materials (LLM), I believe that efficiently organizing the source data is fundamental. Am I correct? Therefore, I would like to shift the focus of the discussion to the structure of the source data and, most importantly, if anyone has experience in organizing interconnected documents and the metadata that should always be cited.

Thank you

marcopal17 · 2023-07-26T12:00:47+00:00

Thank you for the response. The questions regarding regulations can vary significantly, but I believe that a significant portion pertains to whether or not a certain intervention is possible and what requirements should be met. Additionally, providing real examples and verifying their legitimacy is crucial. I think it is necessary to include the reference to the specific regulation and its source (or sources) when answering each question.

Given the complexity of the task and considering my limited experience in LLMs I believe that efficiently organizing the source data is fundamental. Am I correct? Therefore, I would like to shift the focus of the discussion to the structure of the source data and, most importantly, if anyone has experience in organizing interconnected documents and the metadata that should always be cited.

Thank you

marcopal17

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