add new search graph examples

Author: VinciGit00

examples/azure/search_graph_azure.py

@@ -0,0 +1,64 @@
+"""
+Example of Search Graph
+"""
+
+import os
+from dotenv import load_dotenv
+from langchain_openai import AzureChatOpenAI
+from langchain_openai import AzureOpenAIEmbeddings
+from scrapegraphai.graphs import SearchGraph
+from scrapegraphai.utils import convert_to_csv, convert_to_json, prettify_exec_info
+load_dotenv()
+
+FILE_NAME = "inputs/example.json"
+curr_dir = os.path.dirname(os.path.realpath(__file__))
+file_path = os.path.join(curr_dir, FILE_NAME)
+
+with open(file_path, 'r', encoding="utf-8") as file:
+    text = file.read()
+
+# ************************************************
+# Initialize the model instances
+# ************************************************
+
+llm_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"],
+)
+
+# ************************************************
+# Create the JSONScraperGraph instance and run it
+# ************************************************
+
+graph_config = {
+    "llm": {"model_instance": llm_model_instance},
+    "embeddings": {"model_instance": embedder_model_instance}
+}
+
+# ************************************************
+# Create the SearchGraph instance and run it
+# ************************************************
+
+search_graph = SearchGraph(
+    prompt="List me the best escursions near Trento",
+    config=graph_config
+)
+
+result = search_graph.run()
+print(result)
+
+# ************************************************
+# Get graph execution info
+# ************************************************
+
+graph_exec_info = search_graph.get_execution_info()
+print(prettify_exec_info(graph_exec_info))
+
+# Save to json and csv
+convert_to_csv(result, "result")
+convert_to_json(result, "result")

examples/gemini/search_graph_gemini.py

@@ -21,6 +21,8 @@
         "temperature": 0,
         "streaming": True
     },
+    "max_results": 5,
+    "verbose": True,
 }
 
 # ************************************************

examples/local_models/Docker/search_graph_docker.py

@@ -3,7 +3,7 @@
 """
 
 from scrapegraphai.graphs import SearchGraph
-from scrapegraphai.utils import convert_to_csv, convert_to_json
+from scrapegraphai.utils import convert_to_csv, convert_to_json, prettify_exec_info
 
 # ************************************************
 # Define the configuration for the graph
@@ -20,21 +20,30 @@
     "embeddings": {
         "model": "ollama/nomic-embed-text",
         "temperature": 0,
-    }
+    },
+    "max_results": 5,
+    "verbose": True,
 }
 
 # ************************************************
 # Create the SearchGraph instance and run it
 # ************************************************
 
 search_graph = SearchGraph(
-    prompt="List me all the regions of Italy.",
+    prompt="List me the best escursions near Trento",
     config=graph_config
 )
 
 result = search_graph.run()
 print(result)
 
+# ************************************************
+# Get graph execution info
+# ************************************************
+
+graph_exec_info = search_graph.get_execution_info()
+print(prettify_exec_info(graph_exec_info))
+
 # Save to json and csv
 convert_to_csv(result, "result")
 convert_to_json(result, "result")

examples/local_models/Ollama/search_graph_ollama.py

@@ -0,0 +1,50 @@
+"""
+Example of Search Graph
+"""
+from scrapegraphai.graphs import SearchGraph
+from scrapegraphai.utils import convert_to_csv, convert_to_json, prettify_exec_info
+
+# ************************************************
+# Define the configuration for the graph
+# ************************************************
+
+
+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,
+        "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
+    },
+    "max_results": 5,
+    "verbose": True,
+}
+
+# ************************************************
+# Create the SearchGraph instance and run it
+# ************************************************
+
+search_graph = SearchGraph(
+    prompt="List me the best escursions near Trento",
+    config=graph_config
+)
+
+result = search_graph.run()
+print(result)
+
+# ************************************************
+# Get graph execution info
+# ************************************************
+
+graph_exec_info = search_graph.get_execution_info()
+print(prettify_exec_info(graph_exec_info))
+
+# Save to json and csv
+convert_to_csv(result, "result")
+convert_to_json(result, "result")

examples/openai/search_graph_multi.py

@@ -45,7 +45,7 @@ def __init__(self, prompt: str, config: dict, source: Optional[str] = None):
         self.config = config
         self.llm_model = self._create_llm(config["llm"], chat=True)
         self.embedder_model = self._create_default_embedder(llm_config=config["llm"]
-        ) if "embeddings" not in config else self._create_embedder(
+                                                            ) if "embeddings" not in config else self._create_embedder(
             config["embeddings"])
 
         # Create the graph
@@ -54,7 +54,8 @@ def __init__(self, prompt: str, config: dict, source: Optional[str] = None):
         self.execution_info = None
 
         # Set common configuration parameters
-        self.verbose = False if config is None else config.get("verbose", False)
+        self.verbose = False if config is None else config.get(
+            "verbose", False)
         self.headless = True if config is None else config.get(
             "headless", True)
         common_params = {"headless": self.headless,
@@ -63,11 +64,10 @@ def __init__(self, prompt: str, config: dict, source: Optional[str] = None):
                          "embedder_model": self.embedder_model}
         self.set_common_params(common_params, overwrite=False)
 
-
     def set_common_params(self, params: dict, overwrite=False):
         """
         Pass parameters to every node in the graph unless otherwise defined in the graph.
-        
+
         Args:
             params (dict): Common parameters and their values.
         """
@@ -89,7 +89,6 @@ def _set_model_token(self, llm):
                     self.model_token = models_tokens['mistral'][llm.repo_id]
                 except KeyError:
                     raise KeyError("Model not supported")
-                
         elif 'Google' in str(type(llm)):
             try:
                 if 'gemini' in llm.model:
@@ -216,7 +215,8 @@ def _create_default_embedder(self, llm_config=None) -> object:
             ValueError: If the model is not supported.
         """
         if isinstance(self.llm_model, Gemini):
-            return GoogleGenerativeAIEmbeddings(google_api_key=llm_config['api_key'], model="models/embedding-001")
+            return GoogleGenerativeAIEmbeddings(google_api_key=llm_config['api_key'],
+                                                model="models/embedding-001")
         if isinstance(self.llm_model, OpenAI):
             return OpenAIEmbeddings(api_key=self.llm_model.openai_api_key)
         elif isinstance(self.llm_model, AzureOpenAIEmbeddings):