https://github.com/sahasourav17/intellianswer
A RAG-based question-answering system that processes user queries using local documents. It extracts relevant information to answer questions, falling back to a large language model when local sources are insufficient, ensuring accurate and contextual responses.
https://github.com/sahasourav17/intellianswer
chromadb generative-qa gqa llm local-rag ollama openai rag
Last synced: 3 months ago
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A RAG-based question-answering system that processes user queries using local documents. It extracts relevant information to answer questions, falling back to a large language model when local sources are insufficient, ensuring accurate and contextual responses.
- Host: GitHub
- URL: https://github.com/sahasourav17/intellianswer
- Owner: sahasourav17
- Created: 2024-08-25T17:19:54.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-20T15:06:33.000Z (10 months ago)
- Last Synced: 2025-07-20T22:06:06.919Z (3 months ago)
- Topics: chromadb, generative-qa, gqa, llm, local-rag, ollama, openai, rag
- Language: Python
- Homepage:
- Size: 8.74 MB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# IntelliAnswer
## Goal:
A system that processes user-provided question files and supplementary documents. It extracts questions, answers them using information from the supplementary files when available, and falls back to an LLM for answers when necessary.
## Installation
1. Create a virtual environment (optional but recommended)
```bash
python -m venv llmrag
```
2. Install all the dependencies
```bash
pip install -r requirements.txt
```
If you want to use Ollama with local models then follow this steps:
3. Download `Ollama` from here [https://ollama.com/download]
4. Run `ollama` after installing
5. In terminal you need to pull the `llama` and `nomic-embed-text`. Although you can use any of the model available in the ollama repository.
```bash
ollama run llama3
ollama pull nomic-embed-text
```
6. Verify your installation
```bash
ollama list
```
7. Now run the python file. For instance, you can use the following command to run the `langchain_ollama_llama3_rag_for_docx.py` script.
```bash
python3 langchain_ollama_llama3_rag_for_docx.py
```
If you want to run the webapp, you can use the following command. Make sure you have the `OPENAI_API_KEY` set to your `.env` file
```bash
streamlit run QEAG_webapp.py
```
**Note:**
- Before running the script, you must specify the filepath in the `main` function.
- If your docx file is large enough, then try to tweak the `chunk_size` and `chunk_overlap` parameters accordingly.
```python
def split_documents(documents):
text_splitter = RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=1000)
chunks = text_splitter.split_documents(documents)
document = chunks[0]
print(document.page_content)
print(document.metadata)
print(f"Split into {len(chunks)} chunks")
return chunks
```