Add examples/ Llama 4 Maverick Crawler

examples/llama-4-maverick-web-crawler/.env.example

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+# Firecrawl API Key
+FIRECRAWL_API_KEY=your_firecrawl_api_key_here
+
+# Together AI API Key
+TOGETHER_API_KEY=your_together_api_key_here 

examples/llama-4-maverick-web-crawler/.gitignore

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+# Dependencies
+node_modules/
+venv/
+.env
+.env.local
+.env.*.local
+
+# Build outputs
+dist/
+build/
+*.pyc
+__pycache__/
+.cache/
+.pytest_cache/
+
+# IDE and editor files
+.idea/
+.vscode/
+*.swp
+*.swo
+.DS_Store
+Thumbs.db
+
+# Logs
+*.log
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*
+
+# Coverage and test reports
+coverage/
+.coverage
+htmlcov/
+
+# Temporary files
+*.tmp
+*.temp
+.tmp/
+temp/
+
+# System files
+.DS_Store
+.DS_Store?
+._*
+.Spotlight-V100
+.Trashes
+ehthumbs.db
+Thumbs.db 

examples/llama-4-maverick-web-crawler/README.md

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+# Llama 4 Maverick Web Crawler
+
+This project combines the power of Firecrawl for web crawling and Llama 4 Maverick (via Together AI) for intelligent content analysis. It helps you find specific information on websites by crawling pages and analyzing their content using advanced language models.
+
+## Features
+
+- Intelligent URL mapping and relevance ranking
+- Content analysis using Llama 4 Maverick model
+- Automatic extraction of relevant information
+- Color-coded console output for better readability
+
+## Prerequisites
+
+- Python 3.8 or higher
+- Firecrawl API key
+- Together AI API key
+
+## Installation
+
+1. Clone this repository
+2. Install the required packages:
+   ```bash
+   pip install -r requirements.txt
+   ```
+3. Copy the `.env.example` file to `.env`:
+   ```bash
+   cp .env.example .env
+   ```
+4. Add your API keys to the `.env` file:
+   ```
+   FIRECRAWL_API_KEY=your_firecrawl_api_key_here
+   TOGETHER_API_KEY=your_together_api_key_here
+   ```
+
+## Usage
+
+Run the script using:
+
+```bash
+python llama4-maverick-web-crawler.py
+```
+
+You will be prompted to:
+
+1. Enter the website URL to crawl
+2. Specify your objective/what information you're looking for
+
+The script will then:
+
+1. Map the website and find relevant pages
+2. Analyze the content using Llama 4 Maverick
+3. Extract and return the requested information in JSON format
+
+## Example
+
+```bash
+Enter the website to crawl: https://example.com
+Enter your objective: Find the company's contact information
+```
+
+## Error Handling
+
+The script includes comprehensive error handling and will provide clear feedback if:
+
+- API keys are missing
+- Website is inaccessible
+- No relevant information is found
+- Any other errors occur during execution
+
+## Dependencies
+
+- firecrawl: For web crawling and content extraction
+- together: For accessing the Llama 4 Maverick model
+- python-dotenv: For environment variable management
+
+## License
+
+[Your chosen license]

examples/llama-4-maverick-web-crawler/llama4-maverick-web-crawler.py

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+import os
+from firecrawl import FirecrawlApp
+import json
+from dotenv import load_dotenv
+from together import Together
+
+# ANSI color codes
+class Colors:
+    CYAN = '\033[96m'
+    YELLOW = '\033[93m'
+    GREEN = '\033[92m'
+    RED = '\033[91m'
+    MAGENTA = '\033[95m'
+    BLUE = '\033[94m'
+    RESET = '\033[0m'
+
+# Load environment variables
+load_dotenv()
+
+# Retrieve API keys from environment variables
+firecrawl_api_key = os.getenv("FIRECRAWL_API_KEY")
+together_api_key = os.getenv("TOGETHER_API_KEY")
+
+# Initialize the FirecrawlApp and Together client
+app = FirecrawlApp(api_key=firecrawl_api_key)
+client = Together(api_key=together_api_key)
+
+# Find the page that most likely contains the objective
+def find_relevant_page_via_map(objective, url, app, client):
+    try:
+        print(f"{Colors.CYAN}Understood. The objective is: {objective}{Colors.RESET}")
+        print(f"{Colors.CYAN}Initiating search on the website: {url}{Colors.RESET}")
+        
+        map_prompt = f"""
+        The map function generates a list of URLs from a website and it accepts a search parameter. Based on the objective of: {objective}, come up with a 1-2 word search parameter that will help us find the information we need. Only respond with 1-2 words nothing else.
+        """
+
+        print(f"{Colors.YELLOW}Analyzing objective to determine optimal search parameter...{Colors.RESET}")
+        completion = client.chat.completions.create(
+            model="meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
+            messages=[
+                {
+                    "role": "user",
+                    "content": map_prompt
+                }
+            ]
+        )
+
+        map_search_parameter = completion.choices[0].message.content
+        print(f"{Colors.GREEN}Optimal search parameter identified: {map_search_parameter}{Colors.RESET}")
+
+        print(f"{Colors.YELLOW}Mapping website using the identified search parameter...{Colors.RESET}")
+        map_website = app.map_url(url, params={"search": map_search_parameter})
+        
+        # Debug print to see the response structure
+        print(f"{Colors.MAGENTA}Debug - Map response structure: {json.dumps(map_website, indent=2)}{Colors.RESET}")
+        
+        print(f"{Colors.GREEN}Website mapping completed successfully.{Colors.RESET}")
+        
+        # Handle the response based on its structure
+        if isinstance(map_website, dict):
+            # Assuming the links are in a 'urls' or similar key
+            links = map_website.get('urls', []) or map_website.get('links', [])
+        elif isinstance(map_website, str):
+            try:
+                parsed = json.loads(map_website)
+                links = parsed.get('urls', []) or parsed.get('links', [])
+            except json.JSONDecodeError:
+                links = []
+        else:
+            links = map_website if isinstance(map_website, list) else []
+
+        if not links:
+            print(f"{Colors.RED}No links found in map response.{Colors.RESET}")
+            return None
+
+        rank_prompt = f"""
+        Given this list of URLs and the objective: {objective}
+        Analyze each URL and rank the top 3 most relevant ones that are most likely to contain the information we need.
+        
+        IMPORTANT: You must ONLY return a JSON array with exactly 3 objects. Do not include ANY explanation text.
+        Do not include markdown formatting or ```json blocks. Return ONLY the raw JSON array.
+        
+        Each object in the array must have exactly these fields:
+        - "url": the full URL
+        - "relevance_score": number between 0-100
+        - "reason": brief explanation of why this URL is relevant
+
+        URLs to analyze:
+        {json.dumps(links, indent=2)}
+        """
+
+        print(f"{Colors.YELLOW}Ranking URLs by relevance to objective...{Colors.RESET}")
+        completion = client.chat.completions.create(
+            model="meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
+            messages=[
+                {
+                    "role": "user", 
+                    "content": rank_prompt
+                }
+            ]
+        )
+
+        # Debug print to see LLM's raw response
+        print(f"{Colors.MAGENTA}Debug - LLM raw response:{Colors.RESET}")
+        print(f"{Colors.MAGENTA}{completion.choices[0].message.content}{Colors.RESET}")
+
+        try:
+            # Try to clean the response by stripping any potential markdown or extra whitespace
+            cleaned_response = completion.choices[0].message.content.strip()
+            if cleaned_response.startswith("```json"):
+                cleaned_response = cleaned_response.split("```json")[1]
+            if cleaned_response.endswith("```"):
+                cleaned_response = cleaned_response.rsplit("```", 1)[0]
+            cleaned_response = cleaned_response.strip()
+            
+            ranked_results = json.loads(cleaned_response)
+            
+            # Validate the structure of the results
+            if not isinstance(ranked_results, list):
+                raise ValueError("Response is not a list")
+            
+            for result in ranked_results:
+                if not all(key in result for key in ["url", "relevance_score", "reason"]):
+                    raise ValueError("Response items missing required fields")
+            
+            links = [result["url"] for result in ranked_results]
+            
+            # Print detailed ranking info
+            print(f"{Colors.CYAN}Top 3 ranked URLs:{Colors.RESET}")
+            for result in ranked_results:
+                print(f"{Colors.GREEN}URL: {result['url']}{Colors.RESET}")
+                print(f"{Colors.YELLOW}Relevance Score: {result['relevance_score']}{Colors.RESET}")
+                print(f"{Colors.BLUE}Reason: {result['reason']}{Colors.RESET}")
+                print("---")
+
+            if not links:
+                print(f"{Colors.RED}No relevant links identified.{Colors.RESET}")
+                return None
+
+        except (json.JSONDecodeError, KeyError) as e:
+            print(f"{Colors.RED}Error parsing ranked results: {str(e)}{Colors.RESET}")
+            return None
+            
+        print(f"{Colors.GREEN}Located {len(links)} relevant links.{Colors.RESET}")
+        return links
+    
+    except Exception as e:
+        print(f"{Colors.RED}Error encountered during relevant page identification: {str(e)}{Colors.RESET}")
+        return None
+    
+# Scrape the top 3 pages and see if the objective is met, if so return in json format else return None
+def find_objective_in_top_pages(map_website, objective, app, client):
+    try:
+        # Get top 3 links from the map result
+        if not map_website:
+            print(f"{Colors.RED}No links found to analyze.{Colors.RESET}")
+            return None
+            
+        top_links = map_website[:3]
+        print(f"{Colors.CYAN}Proceeding to analyze top {len(top_links)} links: {top_links}{Colors.RESET}")
+        
+        for link in top_links:
+            print(f"{Colors.YELLOW}Initiating scrape of page: {link}{Colors.RESET}")
+            scrape_result = app.scrape_url(link, params={'formats': ['markdown']})
+            print(f"{Colors.GREEN}Page scraping completed successfully.{Colors.RESET}")
+     
+            check_prompt = f"""
+            Given the following scraped content and objective, determine if the objective is met.
+            
+            IMPORTANT: You must ONLY return one of two possible responses:
+            1. If objective is NOT met, respond with exactly: Objective not met
+            2. If objective IS met, respond with ONLY a JSON object containing the relevant information.
+               Do not include ANY explanation text, markdown formatting, or ```json blocks.
+               Return ONLY the raw JSON object.
+
+            Objective: {objective}
+            Scraped content: {scrape_result['markdown']}
+            """
+        
+            completion = client.chat.completions.create(
+                model="meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
+                messages=[{"role": "user", "content": check_prompt}]
+            )
+            
+            result = completion.choices[0].message.content.strip()
+            
+            # Clean up the response if it contains markdown formatting
+            if result.startswith("```json"):
+                result = result.split("```json")[1]
+            if result.endswith("```"):
+                result = result.rsplit("```", 1)[0]
+            result = result.strip()
+            
+            if result == "Objective not met":
+                print(f"{Colors.YELLOW}Objective not met on this page. Proceeding to next link...{Colors.RESET}")
+                continue
+                
+            try:
+                json_result = json.loads(result)
+                print(f"{Colors.GREEN}Objective fulfilled. Relevant information found.{Colors.RESET}")
+                return json_result
+            except json.JSONDecodeError as e:
+                print(f"{Colors.RED}Error parsing JSON response: {str(e)}{Colors.RESET}")
+                print(f"{Colors.MAGENTA}Raw response: {result}{Colors.RESET}")
+                continue
+
+        print(f"{Colors.RED}All available pages analyzed. Objective not fulfilled in examined content.{Colors.RESET}")
+        return None
+    
+    except Exception as e:
+        print(f"{Colors.RED}Error encountered during page analysis: {str(e)}{Colors.RESET}")
+        return None
+
+# Main function to execute the process
+def main():
+    # Get user input
+    url = input(f"{Colors.BLUE}Enter the website to crawl : {Colors.RESET}")
+    objective = input(f"{Colors.BLUE}Enter your objective: {Colors.RESET}")
+    
+    print(f"{Colors.YELLOW}Initiating web crawling process...{Colors.RESET}")
+    # Find the relevant page
+    map_website = find_relevant_page_via_map(objective, url, app, client)
+    
+    if map_website:
+        print(f"{Colors.GREEN}Relevant pages identified. Proceeding with detailed analysis using Llama 4 Maverick...{Colors.RESET}")
+        # Find objective in top pages
+        result = find_objective_in_top_pages(map_website, objective, app, client)
+        
+        if result:
+            print(f"{Colors.GREEN}Objective successfully fulfilled. Extracted information :{Colors.RESET}")
+            print(f"{Colors.MAGENTA}{json.dumps(result, indent=2)}{Colors.RESET}")
+        else:
+            print(f"{Colors.RED}Unable to fulfill the objective with the available content.{Colors.RESET}")
+    else:
+        print(f"{Colors.RED}No relevant pages identified. Consider refining the search parameters or trying a different website.{Colors.RESET}")
+
+if __name__ == "__main__":
+    main()

examples/llama-4-maverick-web-crawler/requirements.txt

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+firecrawl>=0.1.0
+together>=0.2.0
+python-dotenv>=0.19.0