fix: revert

Author: VinciGit00

README.md

@@ -24,21 +24,9 @@ Just say which information you want to extract and the library will do it for yo
   <img src="https://raw.githubusercontent.com/VinciGit00/Scrapegraph-ai/main/docs/assets/sgai-hero.png" alt="ScrapeGraphAI Hero" style="width: 100%;">
 </p>
 
-## 🔗 ScrapeGraph API & SDKs
-If you are looking for a quick solution to integrate ScrapeGraph in your system, check out our powerful API [here!](https://dashboard.scrapegraphai.com/login)
+## News 📰
 
-<p align="center">
-  <img src="https://raw.githubusercontent.com/VinciGit00/Scrapegraph-ai/main/docs/assets/api-banner.png" alt="ScrapeGraph API Banner" style="width: 100%;">
-</p>
-
-We offer SDKs in both Python and Node.js, making it easy to integrate into your projects. Check them out below:
-
-| SDK       | Language | GitHub Link                                                                 |
-|-----------|----------|-----------------------------------------------------------------------------|
-| Python SDK | Python   | [scrapegraph-py](https://github.com/ScrapeGraphAI/scrapegraph-sdk/tree/main/scrapegraph-py) |
-| Node.js SDK | Node.js  | [scrapegraph-js](https://github.com/ScrapeGraphAI/scrapegraph-sdk/tree/main/scrapegraph-js) |
-
-The Official API Documentation can be found [here](https://docs.scrapegraphai.com/).
+- ScrapegraphAI has now his APIs! Check it out [here](https://scrapegraphai.com)!
 
 ## 🚀 Quick install
 
@@ -99,8 +87,8 @@ graph_config = {
 
 # Create the SmartScraperGraph instance
 smart_scraper_graph = SmartScraperGraph(
-    prompt="Extract me all the news from the website",
-    source="https://www.wired.com",
+    prompt="Find some information about what does the company do, the name and a contact email.",
+    source="https://scrapegraphai.com/",
     config=graph_config
 )
 
@@ -112,20 +100,10 @@ print(json.dumps(result, indent=4))
 The output will be a dictionary like the following:
 
 ```python
-"result": {
-    "news": [
-      {
-        "title": "The New Jersey Drone Mystery May Not Actually Be That Mysterious",
-        "link": "https://www.wired.com/story/new-jersey-drone-mystery-maybe-not-drones/",
-        "author": "Lily Hay Newman"
-      },
-      {
-        "title": "Former ByteDance Intern Accused of Sabotage Among Winners of Prestigious AI Award",
-        "link": "https://www.wired.com/story/bytedance-intern-best-paper-neurips/",
-        "author": "Louise Matsakis"
-      },
-    ...
-    ]
+{
+    "company": "ScrapeGraphAI",
+    "name": "ScrapeGraphAI Extracting content from websites and local documents using LLM",
+    "contact_email": "[email protected]"
 }
 ```
 There are other pipelines that can be used to extract information from multiple pages, generate Python scripts, or even generate audio files.
@@ -157,7 +135,8 @@ Try it directly on the web using Google Colab:
 ## 📖 Documentation
 
 The documentation for ScrapeGraphAI can be found [here](https://scrapegraph-ai.readthedocs.io/en/latest/).
-Check out also the Docusaurus [here](https://docs-oss.scrapegraphai.com/).
+
+Check out also the Docusaurus [here](https://scrapegraph-doc.onrender.com/).
 
 ## 🏆 Sponsors
 <div style="text-align: center;">

examples/anthropic/.env.example

@@ -0,0 +1 @@
+ANTHROPIC_API_KEY="YOUR ANTHROPIC API KEY"

examples/anthropic/code_generator_graph_anthropic.py

@@ -0,0 +1,59 @@
+""" 
+Basic example of scraping pipeline using Code Generator with schema
+"""
+import os, json
+from typing import List
+from dotenv import load_dotenv
+from pydantic import BaseModel, Field
+from scrapegraphai.graphs import CodeGeneratorGraph
+
+load_dotenv()
+
+# ************************************************
+# Define the output schema for the graph
+# ************************************************
+
+class Project(BaseModel):
+    title: str = Field(description="The title of the project")
+    description: str = Field(description="The description of the project")
+
+class Projects(BaseModel):
+    projects: List[Project]
+
+# ************************************************
+# Define the configuration for the graph
+# ************************************************
+
+anthropic_key = os.getenv("ANTHROPIC_API_KEY")
+
+graph_config = {
+    "llm": {
+        "api_key":anthropic_key,
+        "model": "anthropic/claude-3-haiku-20240307",
+    },
+    "verbose": True,
+    "headless": False,
+    "reduction": 2,
+    "max_iterations": {
+        "overall": 10,
+        "syntax": 3,
+        "execution": 3,
+        "validation": 3,
+        "semantic": 3
+    },
+    "output_file_name": "extracted_data.py"
+}
+
+# ************************************************
+# Create the SmartScraperGraph instance and run it
+# ************************************************
+
+code_generator_graph = CodeGeneratorGraph(
+    prompt="List me all the projects with their description",
+    source="https://perinim.github.io/projects/",
+    schema=Projects,
+    config=graph_config
+)
+
+result = code_generator_graph.run()
+print(result)

examples/anthropic/csv_scraper_anthropic.py

@@ -0,0 +1,60 @@
+"""
+Basic example of scraping pipeline using CSVScraperGraph from CSV documents
+"""
+import os
+from dotenv import load_dotenv
+import pandas as pd
+from scrapegraphai.graphs import CSVScraperGraph
+from scrapegraphai.utils import convert_to_csv, convert_to_json, prettify_exec_info
+
+load_dotenv()
+
+# ************************************************
+# Read the CSV file
+# ************************************************
+
+FILE_NAME = "inputs/username.csv"
+curr_dir = os.path.dirname(os.path.realpath(__file__))
+file_path = os.path.join(curr_dir, FILE_NAME)
+
+text = pd.read_csv(file_path)
+
+# ************************************************
+# Define the configuration for the graph
+# ************************************************
+
+# required environment variables in .env
+# HUGGINGFACEHUB_API_TOKEN
+# ANTHROPIC_API_KEY
+load_dotenv()
+
+graph_config = {
+    "llm": {
+        "api_key": os.getenv("ANTHROPIC_API_KEY"),
+        "model": "anthropic/claude-3-haiku-20240307",
+    },
+}
+
+# ************************************************
+# Create the CSVScraperGraph instance and run it
+# ************************************************
+
+csv_scraper_graph = CSVScraperGraph(
+    prompt="List me all the last names",
+    source=str(text),  # Pass the content of the file, not the file object
+    config=graph_config
+)
+
+result = csv_scraper_graph.run()
+print(result)
+
+# ************************************************
+# Get graph execution info
+# ************************************************
+
+graph_exec_info = csv_scraper_graph.get_execution_info()
+print(prettify_exec_info(graph_exec_info))
+
+# Save to json or csv
+convert_to_csv(result, "result")
+convert_to_json(result, "result")

examples/anthropic/csv_scraper_graph_multi_anthropic.py

@@ -0,0 +1,54 @@
+"""
+Basic example of scraping pipeline using CSVScraperMultiGraph from CSV documents
+"""
+import os
+from dotenv import load_dotenv
+import pandas as pd
+from scrapegraphai.graphs import CSVScraperMultiGraph
+from scrapegraphai.utils import convert_to_csv, convert_to_json, prettify_exec_info
+
+load_dotenv()
+# ************************************************
+# Read the CSV file
+# ************************************************
+
+FILE_NAME = "inputs/username.csv"
+curr_dir = os.path.dirname(os.path.realpath(__file__))
+file_path = os.path.join(curr_dir, FILE_NAME)
+
+text = pd.read_csv(file_path)
+
+# ************************************************
+# Define the configuration for the graph
+# ************************************************
+
+graph_config = {
+    "llm": {
+        "api_key": os.getenv("ANTHROPIC_API_KEY"),
+        "model": "anthropic/claude-3-haiku-20240307",
+    },
+}
+
+# ************************************************
+# Create the CSVScraperMultiGraph instance and run it
+# ************************************************
+
+csv_scraper_graph = CSVScraperMultiGraph(
+    prompt="List me all the last names",
+    source=[str(text), str(text)],
+    config=graph_config
+)
+
+result = csv_scraper_graph.run()
+print(result)
+
+# ************************************************
+# Get graph execution info
+# ************************************************
+
+graph_exec_info = csv_scraper_graph.get_execution_info()
+print(prettify_exec_info(graph_exec_info))
+
+# Save to json or csv
+convert_to_csv(result, "result")
+convert_to_json(result, "result")

examples/anthropic/custom_graph_anthropic.py

@@ -0,0 +1,94 @@
+"""
+Example of custom graph using existing nodes
+"""
+import os
+from dotenv import load_dotenv
+from langchain_anthropic import ChatAnthropic
+from scrapegraphai.graphs import BaseGraph
+from scrapegraphai.nodes import FetchNode, ParseNode, GenerateAnswerNode, RobotsNode
+load_dotenv()
+
+# ************************************************
+# Define the configuration for the graph
+# ************************************************
+
+graph_config = {
+    "llm": {
+        "api_key": os.getenv("ANTHROPIC_API_KEY"),
+        "model": "claude-3-haiku-20240307",
+    },
+}
+
+# ************************************************
+# Define the graph nodes
+# ************************************************
+
+llm_model = ChatAnthropic(graph_config["llm"])
+
+# define the nodes for the graph
+robot_node = RobotsNode(
+    input="url",
+    output=["is_scrapable"],
+    node_config={
+        "llm_model": llm_model,
+        "force_scraping": True,
+        "verbose": True,
+        }
+)
+
+fetch_node = FetchNode(
+    input="url | local_dir",
+    output=["doc"],
+    node_config={
+        "verbose": True,
+        "headless": True,
+    }
+)
+parse_node = ParseNode(
+    input="doc",
+    output=["parsed_doc"],
+    node_config={
+        "chunk_size": 4096,
+        "verbose": True,
+    }
+)
+generate_answer_node = GenerateAnswerNode(
+    input="user_prompt & (relevant_chunks | parsed_doc | doc)",
+    output=["answer"],
+    node_config={
+        "llm_model": llm_model,
+        "verbose": True,
+    }
+)
+
+# ************************************************
+# Create the graph by defining the connections
+# ************************************************
+
+graph = BaseGraph(
+    nodes=[
+        robot_node,
+        fetch_node,
+        parse_node,
+        generate_answer_node,
+    ],
+    edges=[
+        (robot_node, fetch_node),
+        (fetch_node, parse_node),
+        (parse_node, generate_answer_node)
+    ],
+    entry_point=robot_node
+)
+
+# ************************************************
+# Execute the graph
+# ************************************************
+
+result, execution_info = graph.execute({
+    "user_prompt": "Describe the content",
+    "url": "https://example.com/"
+})
+
+# get the answer from the result
+result = result.get("answer", "No answer found.")
+print(result)

examples/anthropic/depth_search_graph_anthropic.py

@@ -0,0 +1,28 @@
+"""
+depth_search_graph_opeani example
+"""
+import os
+from dotenv import load_dotenv
+from scrapegraphai.graphs import DepthSearchGraph
+
+load_dotenv()
+
+graph_config = {
+    "llm": {
+        "api_key": os.getenv("ANTHROPIC_API_KEY"),
+        "model": "anthropic/claude-3-haiku-20240307",
+    },
+    "verbose": True,
+    "headless": False,
+    "depth": 2,
+    "only_inside_links": False,
+}
+
+search_graph = DepthSearchGraph(
+    prompt="List me all the projects with their description",
+    source="https://perinim.github.io",
+    config=graph_config
+)
+
+result = search_graph.run()
+print(result)