feat(multiple_search): working multiple example

Author: PeriniM

examples/openai/multiple_search_openai.py

@@ -10,6 +10,36 @@
 load_dotenv()
 
 
+schema= """{ 
+    "Job Postings": { 
+        "Company x": [ 
+            { 
+                "title": "...", 
+                "description": "...", 
+                "location": "...", 
+                "date_posted": "..", 
+                "requirements": ["...", "...", "..."] 
+            }, 
+            { 
+                "title": "...", 
+                "description": "...", 
+                "location": "...", 
+                "date_posted": "..", 
+                "requirements": ["...", "...", "..."] 
+            } 
+        ], 
+        "Company y": [ 
+            { 
+                "title": "...", 
+                "description": "...", 
+                "location": "...", 
+                "date_posted": "..", 
+                "requirements": ["...", "...", "..."] 
+            } 
+        ] 
+    } 
+}"""
+
 # ************************************************
 # Define the configuration for the graph
 # ************************************************
@@ -19,47 +49,23 @@
 graph_config = {
     "llm": {
         "api_key": openai_key,
-        "model": "gpt-4o",
+        "model": "gpt-3.5-turbo",
     },
     "verbose": True,
     "headless": False,
+    "schema": schema,
 }
 
-schema= """{ 
-    "Job Postings": { 
-        "Company A": [ 
-            { 
-                "title": "Software Engineer", 
-                "description": "Develop and maintain software applications.", 
-                "location": "New York, NY", 
-                "date_posted": "2024-05-01", 
-                "requirements": ["Python", "Django", "REST APIs"] 
-            }, 
-            { 
-                "title": "Data Scientist", 
-                "description": "Analyze and interpret complex data.", 
-                "location": "San Francisco, CA", 
-                "date_posted": "2024-05-05", 
-                "requirements": ["Python", "Machine Learning", "SQL"] 
-            } 
-        ], 
-        "Company B": [ 
-            { 
-                "title": "Project Manager", 
-                "description": "Manage software development projects.", 
-                "location": "Boston, MA", 
-                "date_posted": "2024-04-20", 
-                "requirements": ["Project Management", "Agile", "Scrum"] 
-            } 
-        ] 
-    } 
-}"""
+
 
 multiple_search_graph = MultipleSearchGraph(
     prompt="List me all the projects with their description",
-    source= ["https://perinim.github.io/projects/", "https://perinim.github.io/projects/"],
+    source= [
+        "https://www.linkedin.com/jobs/machine-learning-engineer-offerte-di-lavoro/?currentJobId=3889037104&originalSubdomain=it",
+        "https://www.glassdoor.com/Job/italy-machine-learning-engineer-jobs-SRCH_IL.0,5_IN120_KO6,31.html",
+        "https://it.indeed.com/jobs?q=ML+engineer&vjk=3c2e6d27601ffaaa"
+        ],
     config=graph_config,
-    schema = schema
 )
 
 result = multiple_search_graph.run()

scrapegraphai/graphs/abstract_graph.py

@@ -40,12 +40,11 @@ class AbstractGraph(ABC):
         >>> result = my_graph.run()
     """
 
-    def __init__(self, prompt: str, config: dict, source: Optional[str] = None, schema: Optional[dict]=None):
+    def __init__(self, prompt: str, config: dict, source: Optional[str] = None):
 
         self.prompt = prompt
         self.source = source
         self.config = config
-        self.schema = schema
         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(
@@ -62,13 +61,15 @@ def __init__(self, prompt: str, config: dict, source: Optional[str] = None, sche
         self.headless = True if config is None else config.get(
             "headless", True)
         self.loader_kwargs = config.get("loader_kwargs", {})
+        self.schema = config.get("schema", None)
 
         common_params = {"headless": self.headless,
                          "verbose": self.verbose,
                          "loader_kwargs": self.loader_kwargs,
                          "llm_model": self.llm_model,
                          "embedder_model": self.embedder_model,
                          "schema": self.schema}
+        
         self.set_common_params(common_params, overwrite=False)
 
     def set_common_params(self, params: dict, overwrite=False):

scrapegraphai/graphs/multiple_search_graph.py

@@ -14,6 +14,8 @@
 from .smart_scraper_graph import SmartScraperGraph
 
 from typing import List, Optional
+
+
 class MultipleSearchGraph(AbstractGraph):
     """ 
     MultipleSearchGraph is a scraping pipeline that searches the internet for answers to a given prompt.
@@ -39,7 +41,7 @@ class MultipleSearchGraph(AbstractGraph):
         >>> result = search_graph.run()
     """
 
-    def __init__(self, prompt: str, source: List[str], config: dict, schema:Optional[dict]= None):
+    def __init__(self, prompt: str, source: List[str], config: dict):
 
         self.max_results = config.get("max_results", 3)
 
@@ -48,7 +50,7 @@ def __init__(self, prompt: str, source: List[str], config: dict, schema:Optional
         else:
             self.copy_config = deepcopy(config)
 
-        super().__init__(prompt, config)
+        super().__init__(prompt, config, source)
 
     def _create_graph(self) -> BaseGraph:
         """
@@ -65,7 +67,7 @@ def _create_graph(self) -> BaseGraph:
         smart_scraper_instance = SmartScraperGraph(
             prompt="",
             source="",
-            config=self.copy_config
+            config=self.copy_config,
         )
 
         # ************************************************
@@ -85,6 +87,7 @@ def _create_graph(self) -> BaseGraph:
             output=["answer"],
             node_config={
                 "llm_model": self.llm_model,
+                "schema": self.config.get("schema", None),
             }
         )
 

scrapegraphai/graphs/smart_scraper_graph.py

@@ -81,7 +81,8 @@ def _create_graph(self) -> BaseGraph:
             input="user_prompt & (relevant_chunks | parsed_doc | doc)",
             output=["answer"],
             node_config={
-                "llm_model": self.llm_model
+                "llm_model": self.llm_model,
+                "schema": self.config.get("schema", None),
             }
         )
 

scrapegraphai/nodes/generate_answer_node.py

@@ -35,7 +35,7 @@ class GenerateAnswerNode(BaseNode):
 
     def __init__(self, input: str, output: List[str], node_config: Optional[dict] = None,
                  node_name: str = "GenerateAnswer"):
-        print(node_config)
+
         super().__init__(node_name, "node", input, output, 2, node_config)
 
         self.llm_model = node_config["llm_model"]

scrapegraphai/nodes/merge_answers_node.py

@@ -79,6 +79,8 @@ def execute(self, state: dict) -> dict:
         You need to merge the content from the different websites into a single answer without repetitions (if there are any). \n
         The scraped contents are in a JSON format and you need to merge them based on the context and providing a correct JSON structure.\n
         OUTPUT INSTRUCTIONS: {format_instructions}\n
+        You must format the output with the following schema, if not None:\n
+        SCHEMA: {schema}\n
         USER PROMPT: {user_prompt}\n
         WEBSITE CONTENT: {website_content}
         """
@@ -89,6 +91,7 @@ def execute(self, state: dict) -> dict:
             partial_variables={
                 "format_instructions": format_instructions,
                 "website_content": answers_str,
+                "schema": self.node_config.get("schema", None),
             },
         )