feat: add integrations for markdown files

README.md

@@ -12,7 +12,7 @@
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg?style=for-the-badge)](https://opensource.org/licenses/MIT)
 [![](https://dcbadge.vercel.app/api/server/gkxQDAjfeX)](https://discord.gg/gkxQDAjfeX)
 
-ScrapeGraphAI is a *web scraping* python library that uses LLM and direct graph logic to create scraping pipelines for websites and local documents (XML, HTML, JSON, etc.).
+ScrapeGraphAI is a *web scraping* python library that uses LLM and direct graph logic to create scraping pipelines for websites and local documents (XML, HTML, JSON, Markdown, etc.).
 
 Just say which information you want to extract and the library will do it for you!
 

examples/benchmarks/SmartScraper/Readme.md

@@ -1,16 +1,17 @@
 # Local models
+# Local models
 The two websites benchmark are:
 - Example 1:  https://perinim.github.io/projects
 - Example 2: https://www.wired.com (at 17/4/2024)
 
 Both are strored locally as txt file in .txt format  because in this way we do not have to think about the internet connection
 
-| Hardware           | Model                                   | Example 1 | Example 2 |
-| ------------------ | --------------------------------------- | --------- | --------- |
-| Macbook 14' m1 pro | Mistral on Ollama with nomic-embed-text | 11.60s    | 26.61s    |
-| Macbook m2 max     | Mistral on Ollama with nomic-embed-text | 8.05s     | 12.17s    |
-| Macbook 14' m1 pro | Llama3 on Ollama with nomic-embed-text  | 29.87s    | 35.32s    |
-| Macbook m2 max     | Llama3 on Ollama with nomic-embed-text  | 18.36s    | 78.32s    |
+| Hardware               | Model                                   | Example 1 | Example 2 |
+| ---------------------- | --------------------------------------- | --------- | --------- |
+| Macbook 14' m1 pro     | Mistral on Ollama with nomic-embed-text | 16.291s   | 38.74s    |
+| Macbook m2 max         | Mistral on Ollama with nomic-embed-text |           |           |
+| Macbook 14' m1 pro<br> | Llama3 on Ollama with nomic-embed-text  | 12.88s    | 13.84s    |
+| Macbook m2 max<br>     | Llama3 on Ollama with nomic-embed-text  |           |           |
 
 **Note**: the examples on Docker are not runned on other devices than the Macbook because the performance are to slow (10 times slower than Ollama). Indeed the results are the following:
 
@@ -22,20 +23,20 @@ Both are strored locally as txt file in .txt format  because in this way we do n
 **URL**: https://perinim.github.io/projects
 **Task**: List me all the projects with their description.
 
-| Name                        | Execution time (seconds) | total_tokens | prompt_tokens | completion_tokens | successful_requests | total_cost_USD |
-| --------------------------- | ------------------------ | ------------ | ------------- | ----------------- | ------------------- | -------------- |
-| gpt-3.5-turbo               | 25.22                    | 445          | 272           | 173               | 1                   | 0.000754       |
-| gpt-4-turbo-preview         | 9.53                     | 449          | 272           | 177               | 1                   | 0.00803        |
-| Grooq with nomic-embed-text | 1.99                     | 474          | 284           | 190               | 1                   | 0              |
+| Name                            | Execution time (seconds) | total_tokens | prompt_tokens | completion_tokens | successful_requests | total_cost_USD |
+| ------------------------------- | ------------------------ | ------------ | ------------- | ----------------- | ------------------- | -------------- |
+| gpt-3.5-turbo                   | 4.132s                   | 438          | 303           | 135               | 1                   | 0.000724       |
+| gpt-4-turbo-preview             | 6.965s                   | 442          | 303           | 139               | 1                   | 0.0072         |
+| gpt-4-o                         | 4.446s                   | 444          | 305           | 139               | 1                   | 0              |
+| Grooq with nomic-embed-text<br> | 1.335s                   | 648          | 482           | 166               | 1                   | 0              |
 
 ### Example 2: Wired
 **URL**: https://www.wired.com
 **Task**: List me all the articles with their description.
 
-| Name                        | Execution time (seconds) | total_tokens | prompt_tokens | completion_tokens | successful_requests | total_cost_USD |
-| --------------------------- | ------------------------ | ------------ | ------------- | ----------------- | ------------------- | -------------- |
-| gpt-3.5-turbo               | 25.89                    | 445          | 272           | 173               | 1                   | 0.000754       |
-| gpt-4-turbo-preview         | 64.70                    | 3573         | 2199          | 1374              | 1                   | 0.06321        |
-| Grooq with nomic-embed-text | 3.82                     | 2459         | 2192          | 267               | 1                   | 0              |
-
-
+| Name                            | Execution time (seconds) | total_tokens | prompt_tokens | completion_tokens | successful_requests | total_cost_USD |
+| ------------------------------- | ------------------------ | ------------ | ------------- | ----------------- | ------------------- | -------------- |
+| gpt-3.5-turbo                   | 8.836s                   | 1167         | 726           | 441               | 1                   | 0.001971       |
+| gpt-4-turbo-preview             | 21.53s                   | 1205         | 726           | 479               | 1                   | 0.02163        |
+| gpt-4-o                         | 15.27s                   | 1400         | 715           | 685               | 1                   | 0              |
+| Grooq with nomic-embed-text<br> | 3.82s                    | 2459         | 2192          | 267               | 1                   | 0              |

examples/benchmarks/SmartScraper/benchmark_openai_gpt4o.py

@@ -0,0 +1,53 @@
+""" 
+Basic example of scraping pipeline using SmartScraper from text
+"""
+
+import os
+from dotenv import load_dotenv
+from scrapegraphai.graphs import SmartScraperGraph
+from scrapegraphai.utils import prettify_exec_info
+load_dotenv()
+
+# ************************************************
+# Read the text file
+# ************************************************
+files = ["inputs/example_1.txt", "inputs/example_2.txt"]
+tasks = ["List me all the projects with their description.",
+         "List me all the articles with their description."]
+
+
+# ************************************************
+# Define the configuration for the graph
+# ************************************************
+
+openai_key = os.getenv("OPENAI_APIKEY")
+
+graph_config = {
+    "llm": {
+        "api_key": openai_key,
+        "model": "gpt-4o",
+    },
+}
+
+# ************************************************
+# Create the SmartScraperGraph instance and run it
+# ************************************************
+
+for i in range(0, 2):
+    with open(files[i], 'r', encoding="utf-8") as file:
+        text = file.read()
+
+    smart_scraper_graph = SmartScraperGraph(
+        prompt=tasks[i],
+        source=text,
+        config=graph_config
+    )
+
+    result = smart_scraper_graph.run()
+    print(result)
+    # ************************************************
+    # Get graph execution info
+    # ************************************************
+
+    graph_exec_info = smart_scraper_graph.get_execution_info()
+    print(prettify_exec_info(graph_exec_info))

examples/extras/example.yml

@@ -0,0 +1,15 @@
+{
+    "llm": {
+        "model": "ollama/llama3",
+        "temperature": 0,
+        "format": "json",
+        # "base_url": "http://localhost:11434", 
+    },
+    "embeddings": {
+        "model": "ollama/nomic-embed-text",
+        "temperature": 0,
+        # "base_url": "http://localhost:11434",  
+    },
+    "verbose": true,
+    "headless": false
+}

examples/extras/force_mode.py

@@ -0,0 +1,54 @@
+""" 
+Basic example of scraping pipeline using SmartScraper
+"""
+
+import os
+from dotenv import load_dotenv
+from scrapegraphai.graphs import SmartScraperGraph
+from scrapegraphai.utils import prettify_exec_info
+
+load_dotenv()
+
+
+# ************************************************
+# Define the configuration for the graph
+# ************************************************
+
+openai_key = os.getenv("OPENAI_APIKEY")
+
+graph_config = {
+  "llm": {
+        "model": "ollama/llama3",
+        "temperature": 0,
+        # "format": "json",  # Ollama needs the format to be specified explicitly
+        # "base_url": "http://localhost:11434",  # set ollama URL arbitrarily
+    },
+    "embeddings": {
+        "model": "ollama/nomic-embed-text",
+        "temperature": 0,
+        # "base_url": "http://localhost:11434",  # set ollama URL arbitrarily
+    },
+    "force": True,
+    "caching": True
+}
+
+# ************************************************
+# Create the SmartScraperGraph instance and run it
+# ************************************************
+
+smart_scraper_graph = SmartScraperGraph(
+    prompt="List me all the projects with their description.",
+    # also accepts a string with the already downloaded HTML code
+    source="https://perinim.github.io/projects/",
+    config=graph_config
+)
+
+result = smart_scraper_graph.run()
+print(result)
+
+# ************************************************
+# Get graph execution info
+# ************************************************
+
+graph_exec_info = smart_scraper_graph.get_execution_info()
+print(prettify_exec_info(graph_exec_info))

examples/extras/load_yml.py

@@ -0,0 +1,32 @@
+""" 
+Basic example of scraping pipeline using SmartScraper
+"""
+import yaml
+from scrapegraphai.graphs import SmartScraperGraph
+from scrapegraphai.utils import prettify_exec_info
+
+# ************************************************
+# Define the configuration for the graph
+# ************************************************
+with open("example.yml", 'r') as file:
+    graph_config = yaml.safe_load(file)
+
+# ************************************************
+# Create the SmartScraperGraph instance and run it
+# ************************************************
+
+smart_scraper_graph = SmartScraperGraph(
+    prompt="List me all the titles",
+    source="https://sport.sky.it/nba?gr=www",
+    config=graph_config
+)
+
+result = smart_scraper_graph.run()
+print(result)
+
+# ************************************************
+# Get graph execution info
+# ************************************************
+
+graph_exec_info = smart_scraper_graph.get_execution_info()
+print(prettify_exec_info(graph_exec_info))

examples/extras/no_cut.py

@@ -0,0 +1,43 @@
+""" 
+This example shows how to do not process the html code in the fetch phase
+"""
+
+import os, json
+from scrapegraphai.graphs import SmartScraperGraph
+from scrapegraphai.utils import prettify_exec_info
+
+
+# ************************************************
+# Define the configuration for the graph
+# ************************************************
+
+
+graph_config = {
+    "llm": {
+        "api_key": "s",
+        "model": "gpt-3.5-turbo",
+    },
+    "cut": False,
+    "verbose": True,
+    "headless": False,
+}
+
+# ************************************************
+# Create the SmartScraperGraph instance and run it
+# ************************************************
+
+smart_scraper_graph = SmartScraperGraph(
+    prompt="Extract me the python code inside the page",
+    source="https://www.exploit-db.com/exploits/51447",
+    config=graph_config
+)
+
+result = smart_scraper_graph.run()
+print(json.dumps(result, indent=4))
+
+# ************************************************
+# Get graph execution info
+# ************************************************
+
+graph_exec_info = smart_scraper_graph.get_execution_info()
+print(prettify_exec_info(graph_exec_info))

examples/extras/proxy_rotation.py

@@ -0,0 +1,48 @@
+""" 
+Basic example of scraping pipeline using SmartScraper
+"""
+
+from scrapegraphai.graphs import SmartScraperGraph
+from scrapegraphai.utils import prettify_exec_info
+
+
+# ************************************************
+# Define the configuration for the graph
+# ************************************************
+
+graph_config = {
+    "llm": {
+        "api_key": "API_KEY",
+        "model": "gpt-3.5-turbo",
+    },
+    "loader_kwargs": {
+        "proxy" : {
+            "server": "http:/**********",
+            "username": "********",
+            "password": "***",
+        },
+     },
+    "verbose": True,
+    "headless": False,
+}
+
+# ************************************************
+# Create the SmartScraperGraph instance and run it
+# ************************************************
+
+smart_scraper_graph = SmartScraperGraph(
+    prompt="List me all the projects with their description",
+    # also accepts a string with the already downloaded HTML code
+    source="https://perinim.github.io/projects/",
+    config=graph_config
+)
+
+result = smart_scraper_graph.run()
+print(result)
+
+# ************************************************
+# Get graph execution info
+# ************************************************
+
+graph_exec_info = smart_scraper_graph.get_execution_info()
+print(prettify_exec_info(graph_exec_info))

examples/extras/rag_caching.py

@@ -0,0 +1,46 @@
+""" 
+Basic example of scraping pipeline using SmartScraper
+"""
+
+import os
+from dotenv import load_dotenv
+from scrapegraphai.graphs import SmartScraperGraph
+from scrapegraphai.utils import prettify_exec_info
+
+load_dotenv()
+
+
+# ************************************************
+# Define the configuration for the graph
+# ************************************************
+
+openai_key = os.getenv("OPENAI_APIKEY")
+
+graph_config = {
+    "llm": {
+        "api_key": openai_key,
+        "model": "gpt-3.5-turbo",
+    },
+    "caching": True
+}
+
+# ************************************************
+# Create the SmartScraperGraph instance and run it
+# ************************************************
+
+smart_scraper_graph = SmartScraperGraph(
+    prompt="List me all the projects with their description.",
+    # also accepts a string with the already downloaded HTML code
+    source="https://perinim.github.io/projects/",
+    config=graph_config
+)
+
+result = smart_scraper_graph.run()
+print(result)
+
+# ************************************************
+# Get graph execution info
+# ************************************************
+
+graph_exec_info = smart_scraper_graph.get_execution_info()
+print(prettify_exec_info(graph_exec_info))