Simple Search Agent

This is a simple search agent that (kind of) does what Perplexity AI does.

How It Works

The user asks the agent a question.
The agent performs a web search using the question as the query.
The agent extracts the most relevant snippets and information from the top search results.
The extracted web results are passed as context to a large language model.
The LLM uses the web search context to generate a final answer to the original question.
The agent returns the generated answer to the user.

Setup and Installation

Clone this repo
Install the required dependencies:
```
pip install -r requirements.txt
```
Set up API keys:
- You will need API keys for the web search API and LLM API.
- Add your API keys to the .env file. Use dotenv.sample to create this file.

Usage

➜ python ./search_agent.py  --provider groq -o text "Write a linkedin post on how Sequoia Capital AI Ascent 2024 is interesting"
[21:41:34] Using mixtral-8x7b-32768 on groq with temperature 0.0                                                                                      search_agent.py:78
[21:41:35] Optimized search query: Sequoia Capital AI Ascent 2024 interest                                                                           search_agent.py:248
[21:41:36] Found 10 sources                                                                                                                          search_agent.py:252
[21:41:37] Managed to extract content from 7 sources                                                                                                 search_agent.py:256
[21:41:42] Filtered 21 relevant content extracts                                                                                                     search_agent.py:263
────────────────────────────────────────────────────────────────────────── Response from groq ──────────────────────────────────────────────────────────────────────────
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#AI #ArtificialIntelligence #GenerativeAI #SequoiaCapital #AIascent2024
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License

This project is licensed under the Apache License Version 2.0. See the LICENSE file for details.

Let me know if you have any other questions! The key components are using a web search API to find relevant information, extracting the key snippets from the search results, passing that as context to a large language model, and having the LLM generate a natural language answer based on the web search context.