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Offline Document Interaction: PRIVATE GPT
The power of LLMs has revolutionized offline document interaction, allowing users to ask questions and receive answers without the need for an internet connection. By ensuring complete privacy and keeping data within your execution environment, LLMs enable you to explore your documents with confidence and peace of mind. Embrace this innovative technology, break free from online dependencies, and unlock the full potential of offline document interaction with the power of LLMs.
Ask questions to your documents without an internet connection, using the power of LLMs. 100% private, no data leaves your execution environment at any point. You can ingest documents and ask questions without an internet connection!
In order to set your environment up to run the code here, first install all requirements:
pip3 install -r requirements.txt
Then, download the LLM model and place it in a directory of your choice:
- LLM: default to ggml-gpt4all-j-v1.3-groovy.bin. If you prefer a different GPT4All-J compatible model, just download it and reference it in your
.envfile.
Copy the example.env template into .env
cp example.env .env
and edit the variables appropriately in the .env file.
The power of LLMs has revolutionized offline document interaction, allowing users to ask questions and receive answers without the need for an internet connection. By ensuring complete privacy and keeping data within your execution environment, LLMs enable you to explore your documents with confidence and peace of mind. Embrace this innovative technology, break free from online dependencies, and unlock the full potential of offline document interaction with the power of LLMs.
The supported extensions are:
.csv: CSV,.docx: Word Document,.doc: Word Document,.enex: EverNote,.eml: Email,.epub: EPub,.html: HTML File,.md: Markdown,.msg: Outlook Message,.odt: Open Document Text,.pdf: Portable Document Format (PDF),.pptx: PowerPoint Document,.ppt: PowerPoint Document,.txt: Text file (UTF-8),
Run the following command to ingest all the data.
python ingest.py
Output should look like this:
Creating new vectorstore
Loading documents from source_documents
Loading new documents: 100%|██████████████████████| 1/1 [00:01<00:00, 1.73s/it]
Loaded 1 new documents from source_documents
Split into 90 chunks of text (max. 500 tokens each)
Creating embeddings. May take some minutes...
Using embedded DuckDB with persistence: data will be stored in: db
Ingestion complete! You can now run privateGPT.py to query your documents
It will create a db folder containing the local vectorstore. Will take 20-30 seconds per document, depending on the size of the document. You can ingest as many documents as you want, and all will be accumulated in the local embeddings database. If you want to start from an empty database, delete the db folder.
Note: during the ingest process no data leaves your local environment. You could ingest without an internet connection, except for the first time you run the ingest script, when the embeddings model is down
01.Requirements
pip3 install -r requirements.txt
02. Usage
python privateGPT.py
- Offline Document Interaction: Traditionally, asking questions and obtaining answers required internet access. However, with the advent of LLMs, you can now interact with your documents offline. This means you have the freedom to ask questions and receive responses without relying on an internet connection, ensuring uninterrupted access to critical information.
- Ensuring Complete Privacy: Privacy has become a significant concern in the digital age. With LLMs, you can rest assured that your data remains confidential. The entire question-answering process takes place within your execution environment, without any data leaving your device. This guarantees a 100% private experience, safeguarding sensitive information and protecting your privacy.
- Seamless Document Ingestion and Questioning: Using LLMs for offline document interaction is straightforward. You can easily ingest your documents into the execution environment, allowing the LLM to comprehend their content. Once ingested, you can ask questions about the documents, and the LLM will analyze the context to provide accurate and relevant answers. This streamlined process enables efficient and convenient document exploration.
- Unleashing the Potential: The ability to ask questions to your documents without an internet connection opens up a world of possibilities. Whether you're conducting research, studying, or working in environments with limited connectivity, LLMs empower you to access and extract valuable insights from your documents anytime, anywhere. It allows for enhanced productivity, improved decision-making, and the convenience of offline information retrieval.
Document Interaction: Ask Questions with LLMs in a 100% Private Environment
- Category : LLM
- Time Read:10 Min
- Source: PrivateGPT
- Author: Partener Link
- Date: June 18, 2023, 11:19 p.m.
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