For advanced question answering based on the search query, this app can integrate with the Gemini API. After retrieving relevant documents using semantic search, you can send the query and context to Gemini for generating precise answers.

Example Workflow:

1. User submits a question.
2. Semantic search retrieves relevant documents.
3. The question and retrieved context are sent to the Gemini API.
4. Gemini returns a concise, context-aware answer.

Refer to the Gemini API documentation for integration details.

This application implements semantic search capabilities, allowing users to find relevant information based on meaning rather than exact keyword matches. It leverages natural language processing (NLP) and vector-based search techniques to deliver more accurate and context-aware results.

• Semantic Querying: Search using natural language queries to retrieve contextually relevant results.
• Vector Embeddings: Converts text data into vector representations for similarity comparison.
• Fast Retrieval: Efficiently searches large datasets using vector indexes.
• Customizable Models: Supports integration with various NLP models (e.g., BERT, Sentence Transformers).
• Ollama Integration: Supports local embedding generation using models like all-MiniLM-L6-v2 via Ollama.
• User Interface: Provides a simple UI for entering queries and viewing results (if applicable).

This app is designed to work seamlessly with the all-MiniLM-L6-v2 embedding model, which is lightweight, fast, and provides high-quality semantic embeddings. You can run this model locally using Ollama.

1. Install Ollama:
   • Download and install from Ollama's website.
2. Pull the model:

   ollama pull all-minilm

3. Run the model:

   ollama run all-minilm

4. Generate embeddings:
   • Use the Ollama API to generate embeddings for your documents and queries.
   • Integrate these embeddings into your semantic search workflow.

For more details, see the Ollama documentation.

1. Data Ingestion: Text data is collected and preprocessed.
2. Embedding Generation: Each document or data entry is converted into a vector using an NLP model.
3. Indexing: Vectors are stored in a vector database or index for fast similarity search.
4. Query Processing: User queries are embedded and compared to indexed vectors to find the most semantically similar results.
5. Result Ranking: Results are ranked by similarity score and presented to the user.

• Knowledge base search
• Document retrieval
• FAQ and support bots
• Code or content recommendation

1. Clone the repository:

   git clone <repo-url>

2. Install dependencies: (Instructions depend on the tech stack, e.g., .NET, Python, etc.)
3. Run the application: (Provide command or instructions here)

• Model Selection: Choose or configure the NLP model for embeddings.
• Index Settings: Adjust parameters for the vector index (e.g., distance metric, index type).

Contributions are welcome! Please open issues or submit pull requests for improvements.

This project is licensed under the MIT License.