mypackage-anshita 0.1.1

A flexible chatbot package with vector embeddings and prompt handling using Ollama and FAISS

🧠 mypackage_anshita

mypackage_anshita is a Python package built with ❤️ using Ollama models. It supports:

• 💬 Prompt-based text generation using models like gemma:2b, llama2, and more
• 🔍 Vector-based document embedding + semantic search using FAISS
• ⚙️ Model flexibility for generation and embeddings

🚀 Installation

Install directly from PyPI:

pip install mypackage-anshita

📌 **Why this?** → Shows users how to install your package easily.

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### ✅ 3. Features

```markdown
## 🔥 Features

- `PromptRunner` class for prompt-based responses from various models
- `DocumentEmbedder` class for embedding documents and performing vector-based search
- Uses Ollama + FAISS + NumPy
- Tested with Gemma, LLaMA2, and other local models via Ollama
## 💡 Usage Example

### Generate Text with PromptRunner

```python
from mypackage_anshita import PromptRunner

runner = PromptRunner(model='gemma:2b')
response = runner.run_prompt("What is the theory of relativity?")
print(response)
from mypackage_anshita import DocumentEmbedder

docs = ["Marie Curie won two Nobel Prizes.", "Einstein created the theory of relativity."]
embedder = DocumentEmbedder()
embedder.embed_text(docs)

results = embedder.search("radioactivity")
print(results)

📌 **Why this?** → Shows users how to *actually use* your package in code.

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### ✅ 5. Author Info

```markdown
## 👩‍💻 Author

**Anshita Bhatnagar**  
Made with 💕 during internship  
Built using LangChain, Ollama, FAISS and Streamlit
## 📄 License

MIT License