Metadata-Version: 2.4
Name: mypackage_anshita
Version: 0.1.4
Summary: A flexible chatbot package with vector embeddings and prompt handling using Ollama and FAISS
Home-page: https://github.com/yourusername/mypackage_anshita
Author: Anshita Bhatnagar
Author-email: your_email@example.com
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: ollama
Requires-Dist: faiss-cpu
Requires-Dist: numpy
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

## mypackage_anshita

`mypackage_anshita` is a Python package built with Ollama models to provide:

- Prompt-based text generation using models like `gemma:2b`, `llama2`, and others.
- Vector-based document embedding and semantic search with FAISS.
- Flexibility to use different models for generation and embeddings.




## Installation

Install the package via PyPI:

```bash
pip install mypackage-anshita```




##Features

PromptRunner: Run prompts on various Ollama models.
DocumentEmbedder: Embed documents and perform vector search.
Integration with Ollama, FAISS, and NumPy.
Supports multiple Ollama models, including Gemma and LLaMA2.




##Usage Example

Generate Text
```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)```




##Author

Anshita Bhatnagar
Built during internship using LangChain, Ollama, FAISS, and Streamlit.




##License

MIT License
