Building AI agents using Python, llama Index and using Gemini Api and Deepseek model.
Creating a basic AI Agents using DeepSeek API or locally downloaded LLMs using Ollama/Hugging Face to extract information using available dataset and PDF file. The following are the steps to run this repo:
git clone https://github.com/usman619/ai_agents_RAG.git
cd ai_agents_RAG
code .
python -m venv env
source env/bin/activate
pip install -r requirements.txt
python main.py
❯ python main.py
> Pandas Instructions:
```python
df[df['Country'] == 'Pakistan']['Population2023'].iloc[0]
```
> Pandas Output: 240485658
❯ python main.py
Enter a prompt (q to quite): Save a note saying "I love coding in Python"
> Running step 687679b4-d0ca-4a21-a61b-f4c8c5e99ad3. Step input: Save a note saying "I love coding in Python"
Thought: The current language of the user is: English. I need to use the note_saver tool to save the note.
Action: note_saver
Action Input: {'note': AttributedDict([('title', 'Coding Note'), ('content', 'I love coding in Python')])}
Observation: Note Saved
> Running step f5e96726-60cc-48f9-a21f-d8f40a0f818f. Step input: None
Thought: I can answer without using any more tools. I'll use the user's language to answer
Answer: Note saved successfully.
Save a note saying "I love coding in Python"
Enter a prompt (q to quite): What is the current population of Pakistan?
> Running step 36e1d99c-c7f1-4eb9-89b5-d2859ec4703c. Step input: What is the current population of Pakistan?
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把一队分工 Agent 织成一条写小说的流水线,做成桌面客户端;写作指纹让它越写越像你(BYO DeepSeek key,纯本地)。
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