SynapsIA ✍️

SynapsIA is the digital scribe of the new era. Its name comes from the fusion of Synapse, the neural connections where knowledge resides, and AI, the intelligence that brings it to life.

It is not a simple reader; it is a craftsman of knowledge. Just as an ancient scribe forged texts that would last for centuries, SynapsIA ingests your documents and forges an intricate network of synaptic connections. The result is a coherent, living digital mind, ready to be interrogated by other AI systems.

SynapsIA: Forging knowledge, one synapse at a time.

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🚀 Features

• Ollama-Powered: Leverages local Ollama services for all embedding tasks, keeping your data private.
• RAG-Ready: Processes your documents into a persistent, optimized vector index, creating the "knowledge base" for any RAG application (like Kondoo).
• Tunable Ingestion: Provides fine-grained control over chunk-size and chunk-overlap so you can optimize your knowledge base for Q&A, summarization, or other tasks.
• Built-in Query Tool: Includes a companion script, synapsia_query.py, to immediately test and debug your new knowledge base with your local LLMs.

⚡ Prerequisites

Before you begin, ensure you have the following installed and running:

1. Python 3.9+: A recent version of Python. We highly recommend using a virtual environment (python -m venv .venv).
2. Python Dependencies: Install the required libraries:

   pip install -r requirements.txt

3. Ollama Service: The script needs to connect to a running Ollama instance. You can launch one using Podman:

   # Launch the Ollama container in the background
   # This uses a persistent volume to save your models
   podman run -d --rm -p 11434:11434 --name ollama-synapsia -v synapsia_storage:/root/.ollama ollama/ollama

4. Embedding Model: After starting the container, pull your desired embedding model:

   # Tell the running 'ollama-synapsia' container to download the model
   podman exec -it ollama-synapsia ollama pull mxbai-embed-large

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✍️ Ingesting Knowledge (synapsia.py)

This is the main script for processing your documents.

Command Syntax:

python synapsia.py --docs <path_to_docs> --knowledge <path_to_knowledge_base> [OPTIONS]

Required Arguments

• --docs <path>: The relative or absolute path to the directory containing your source documents.

• --knowledge <path>: The relative or absolute path to the output directory where the knowledge base will be saved.

Options

• --embed-model <model_name>: The name of the embedding model to use from Ollama. Default: mxbai-embed-large

• --ollama-url <url>: The base URL of the Ollama API service. Default: http://localhost:11434

• --embed-batch-size <number>: Number of chunks to process at a time. Default: 5

• --chunk-size <number>: The size of text chunks in tokens. Default: 1024

• --chunk-overlap <number>: The number of overlapping tokens between consecutive chunks. Default: 20

• -h, --help: Show the help message and exit.

Ingestion Examples

1. Basic Usage

Process documents from ./my_docs/ and save the index to ./my_kb/.

python synapsia.py --docs ./my_docs/ --knowledge ./my_kb/

2. Advanced Usage (Custom Model)

Use a different embedding model.

python synapsia.py --docs ./my_docs/ --knowledge ./my_kb/ --embed-model nomic-embed-text

3. Advanced Usage (Tuned for Q&A)

Optimized for dense, factual documents. Uses a smaller chunk size for more precise answers.

python synapsia.py \
    --docs ./faq_docs/ \
    --knowledge ./faq_kb/ \
    --chunk-size 256 \
    --chunk-overlap 25

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❓ Querying Knowledge (synapsia_query.py)

Use this script to test your new knowledge base. It loads the index, sends your query to a local LLM (via Ollama), and provides a RAG-generated answer.

Command Syntax:

python synapsia_query.py --knowledge <path_to_knowledge_base> --query "Your question here" [OPTIONS]

Required Arguments

• --knowledge <path>: The path to the directory where the knowledge base was saved.

• --query <question>: The question you want to ask, enclosed in quotes.

Options

• --llm-model <model_name>: The name of the LLM to use from Ollama for generating the answer. Default: llama3

• --top-k <number>: The number of relevant text chunks to retrieve. Default: 3

• --show-context: A flag that displays the source text chunks and their relevance scores. Extremely useful for debugging.

• --embed-model, --ollama-url: These should match the values used during the ingestion process.

Query Examples

1. Basic Question

Get a direct answer from the knowledge base.

python synapsia_query.py \
    --knowledge ./my_kb/ \
    --query "What is the main purpose of Ansible?"

2. Debugging Query

Get an answer and see the top 2 source chunks used as context.

python synapsia_query.py \
    --knowledge ./faq_kb/ \
    --query "How do I reset my password?" \
    --top-k 2 \
    --show-context

⚖️ License

This project is licensed under the MIT License. See the LICENSE file for details.