Fixed readme

README.md

@@ -20,39 +20,42 @@ This is a simple search agent that (kind of) does what [Perplexity AI](https://w
    ```
 3. Set up API keys:
    - You will need API keys for the web search API and LLM API. 
-   - Add your API keys to the `config.py` file.
-4. Run the agent:
-   ```
-   python agent.py
-   ```
+   - Add your API keys to the `.env` file. Use `dotenv.sample` to create this file.
 
 ## Usage
 
-To use the agent, simply run the `agent.py` script and enter your question when prompted. The agent will return a generated answer based on web search results.
-
-Example:
 ```
-$ python agent.py
-Enter a question: What is the capital of France?
+➜ 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 ──────────────────────────────────────────────────────────────────────────
+🚀 Sequoia Capital's AI Ascent 2024 conference brought together some of the brightest minds in AI, including founders, researchers, and industry leaders. The event was
+a unique opportunity to discuss the state of AI and its future, focusing on the promise of generative AI to revolutionize industries and provide amazing productivity
+gains.
 
-Based on the information from web searches, the capital of France is Paris. Paris is the largest city in France and has been the country's capital since the 12th century. It is located in north-central France along the Seine River. As the capital, Paris is the seat of France's national government. Key landmarks include the Eiffel Tower, the Louvre museum, and the Notre-Dame cathedral. Paris is also a major global city known for its art, fashion, cuisine and culture.
-```
+🌟 Highlights of the conference included talks by Sam Altman of OpenAI, Dylan Field of Figma, Alfred Mensch of Mistral, Daniela Amodei of Anthropic, Andrew Ng of AI
+Fund, CJ Desai of ServiceNow, and independent researcher Andrej Karpathy. Sessions covered a wide range of topics, from the merits of large and small models to the rise
+of reasoning agents, the future of compute, and the evolving AI ecosystem.
+
+💡 One key takeaway from the event is the recognition that we are in a 'primordial soup' phase of AI development. This is a crucial moment for the technology to
+transition from being an idea to solving real-world problems efficiently. Factors like cheap compute power, fast networks, ubiquitous supercomputers, and readily
+available data are enabling AI as the next significant technology wave.
 
-## Configuration
+🔜 As we move forward, we can expect AI to become an even more significant part of our lives, revolutionizing various sectors and offering unprecedented value creation
+potential. Stay tuned for the upcoming advancements in AI, and let's continue to explore and harness its vast capabilities!
 
-- `config.py`: Contains configuration settings for the agent, including API keys.
-- `agent.py`: The main agent script that accepts user input, performs web searches, and generates answers.
-- `search.py`: Handles making web search requests and extracting relevant snippets from the results.
-- `llm.py`: Interfaces with the language model API to generate answer text based on the web search information.
+_For more information, check out the [Sequoia Capital AI Ascent 2024 conference recap](https://www.sequoiacap.com/article/ai-ascent-2024/)._
 
-## Dependencies 
+#AI #ArtificialIntelligence #GenerativeAI #SequoiaCapital #AIascent2024
+───────────────────────────────────────────────────────────────────────────────────  ──────────────────────────────────────────────────────────────────────────────────
 
-- `requests`: For making HTTP requests to web search and LLM APIs
-- `openai`: For interfacing with the OpenAI language model API
-- `googlesearch-python`: For performing Google web searches
+```
 
 ## License
 
-This project is licensed under the MIT License. See the `LICENSE` file for details.
+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.