docs(readme): improve main readme

PeriniM · PeriniM · commit ae5655fdde81 · 2024-05-08T19:00:13.000+02:00
diff --git a/README.md b/README.md
@@ -48,11 +48,16 @@ The documentation for ScrapeGraphAI can be found [here](https://scrapegraph-ai.r
 Check out also the docusaurus [documentation](https://scrapegraph-doc.onrender.com/).
 
 ## 💻 Usage
-You can use the `SmartScraper` class to extract information from a website using a prompt.
+There are three main scraping pipelines that can be used to extract information from a website (or local file):
+- `SmartScraperGraph`: single-page scraper that only needs a user prompt and an input source;
+- `SearchGraph`: multi-page scraper that extracts information from the top n search results of a search engine;
+- `SpeechGraph`: single-page scraper that extracts information from a website and generates an audio file.
 
-The `SmartScraper` class is a direct graph implementation that uses the most common nodes present in a web scraping pipeline. For more information, please see the [documentation](https://scrapegraph-ai.readthedocs.io/en/latest/).
-### Case 1: Extracting information using Ollama
-Remember to download the model on Ollama separately!
+It is possible to use different LLM through APIs, such as **OpenAI**, **Groq**, **Azure** and **Gemini**, or local models using **Ollama**.
+
+### Case 1: SmartScraper using Local Models
+
+Remember to have [Ollama](https://ollama.com/) installed and download the models using the **ollama pull** command.
 
 ```python
 from scrapegraphai.graphs import SmartScraperGraph
@@ -67,11 +72,12 @@ graph_config = {
     "embeddings": {
         "model": "ollama/nomic-embed-text",
         "base_url": "http://localhost:11434",  # set Ollama URL
-    }
+    },
+    "verbose": True,
 }
 
 smart_scraper_graph = SmartScraperGraph(
-    prompt="List me all the articles",
+    prompt="List me all the projects with their descriptions",
     # also accepts a string with the already downloaded HTML code
     source="https://perinim.github.io/projects",
     config=graph_config
@@ -82,159 +88,77 @@ print(result)
 
 ```
 
-### Case 2: Extracting information using Docker
+The output will be a list of projects with their descriptions like the following:
 
-Note: before using the local model remember to create the docker container!
-```text
-    docker-compose up -d
-    docker exec -it ollama ollama pull stablelm-zephyr
-```
-You can use which models avaiable on Ollama or your own model instead of stablelm-zephyr
 ```python
-from scrapegraphai.graphs import SmartScraperGraph
-
-graph_config = {
-    "llm": {
-        "model": "ollama/mistral",
-        "temperature": 0,
-        "format": "json",  # Ollama needs the format to be specified explicitly
-        # "model_tokens": 2000, # set context length arbitrarily
-    },
-}
-
-smart_scraper_graph = SmartScraperGraph(
-    prompt="List me all the articles",
-    # 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)
+{'projects': [{'title': 'Rotary Pendulum RL', 'description': 'Open Source project aimed at controlling a real life rotary pendulum using RL algorithms'}, {'title': 'DQN Implementation from scratch', 'description': 'Developed a Deep Q-Network algorithm to train a simple and double pendulum'}, ...]}
 ```
 
+### Case 2: SearchGraph using Mixed Models
 
-### Case 3: Extracting information using Openai model
-```python
-from scrapegraphai.graphs import SmartScraperGraph
-OPENAI_API_KEY = "YOUR_API_KEY"
-
-graph_config = {
-    "llm": {
-        "api_key": OPENAI_API_KEY,
-        "model": "gpt-3.5-turbo",
-    },
-}
+We use **Groq** for the LLM and **Ollama** for the embeddings.
 
-smart_scraper_graph = SmartScraperGraph(
-    prompt="List me all the articles",
-    # 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)
-```
-
-### Case 4: Extracting information using Groq
 ```python
-from scrapegraphai.graphs import SmartScraperGraph
-from scrapegraphai.utils import prettify_exec_info
-
-groq_key = os.getenv("GROQ_APIKEY")
+from scrapegraphai.graphs import SearchGraph
 
+# Define the configuration for the graph
 graph_config = {
     "llm": {
         "model": "groq/gemma-7b-it",
-        "api_key": groq_key,
+        "api_key": "GROQ_API_KEY",
         "temperature": 0
     },
     "embeddings": {
         "model": "ollama/nomic-embed-text",
-        "temperature": 0,
-        "base_url": "http://localhost:11434", 
+        "base_url": "http://localhost:11434",  # set ollama URL arbitrarily
     },
-    "headless": False
+    "max_results": 5,
 }
 
-smart_scraper_graph = SmartScraperGraph(
-    prompt="List me all the projects with their description and the author.",
-    source="https://perinim.github.io/projects",
+# Create the SearchGraph instance
+search_graph = SearchGraph(
+    prompt="List me all the traditional recipes from Chioggia",
     config=graph_config
 )
 
-result = smart_scraper_graph.run()
+# Run the graph
+result = search_graph.run()
 print(result)
 ```
 
+The output will be a list of recipes like the following:
 
-### Case 5: Extracting information using Azure
 ```python
-from langchain_openai import AzureChatOpenAI
-from langchain_openai import AzureOpenAIEmbeddings
-
-lm_model_instance = AzureChatOpenAI(
-    openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"],
-    azure_deployment=os.environ["AZURE_OPENAI_CHAT_DEPLOYMENT_NAME"]
-)
-
-embedder_model_instance = AzureOpenAIEmbeddings(
-    azure_deployment=os.environ["AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME"],
-    openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"],
-)
-graph_config = {
-    "llm": {"model_instance": llm_model_instance},
-    "embeddings": {"model_instance": embedder_model_instance}
-}
-
-smart_scraper_graph = SmartScraperGraph(
-    prompt="""List me all the events, with the following fields: company_name, event_name, event_start_date, event_start_time, 
-    event_end_date, event_end_time, location, event_mode, event_category, 
-    third_party_redirect, no_of_days, 
-    time_in_hours, hosted_or_attending, refreshments_type, 
-    registration_available, registration_link""",
-    source="https://www.hmhco.com/event",
-    config=graph_config
-)
+{'recipes': [{'name': 'Sarde in Saòre'}, {'name': 'Bigoli in salsa'}, {'name': 'Seppie in umido'}, {'name': 'Moleche frite'}, {'name': 'Risotto alla pescatora'}, {'name': 'Broeto'}, {'name': 'Bibarasse in Cassopipa'}, {'name': 'Risi e bisi'}, {'name': 'Smegiassa Ciosota'}]}
 ```
+### Case 3: SpeechGraph using OpenAI
+
+You just need to pass the OpenAI API key and the model name.
 
-### Case 6: Extracting information using Gemini 
 ```python
 from scrapegraphai.graphs import SmartScraperGraph
-GOOGLE_APIKEY = "YOUR_API_KEY"
 
 # Define the configuration for the graph
 graph_config = {
     "llm": {
-        "api_key": GOOGLE_APIKEY,
-        "model": "gemini-pro",
+        "api_key": "OPENAI_API_KEY",
+        "model": "gpt-3.5-turbo",
     },
 }
 
 # Create the SmartScraperGraph instance
 smart_scraper_graph = SmartScraperGraph(
-    prompt="List me all the articles",
+    prompt="Make a detailed audio summary of the projects on this page",
     source="https://perinim.github.io/projects",
     config=graph_config
 )
 
+# Run the graph
 result = smart_scraper_graph.run()
 print(result)
 ```
 
-The output for all 3 the cases will be a dictionary with the extracted information, for example:
-
-```bash
-{
-    'titles': [
-        'Rotary Pendulum RL'
-        ],
-    'descriptions': [
-        'Open Source project aimed at controlling a real life rotary pendulum using RL algorithms'
-        ]
-}
-```
+The output will be an audio file with the summary of the projects on the page.
 
 ## 🤝 Contributing
 
