GraphIteratorNode and MergeAnswersNode

examples/openai/custom_graph_openai.py

@@ -4,6 +4,8 @@
 
 import os
 from dotenv import load_dotenv
+
+from langchain_openai import OpenAIEmbeddings
 from scrapegraphai.models import OpenAI
 from scrapegraphai.graphs import BaseGraph
 from scrapegraphai.nodes import FetchNode, ParseNode, RAGNode, GenerateAnswerNode, RobotsNode
@@ -20,7 +22,7 @@
         "api_key": openai_key,
         "model": "gpt-3.5-turbo",
         "temperature": 0,
-        "streaming": True
+        "streaming": False
     },
 }
 
@@ -29,33 +31,50 @@
 # ************************************************
 
 llm_model = OpenAI(graph_config["llm"])
+embedder = OpenAIEmbeddings(api_key=llm_model.openai_api_key)
 
 # define the nodes for the graph
 robot_node = RobotsNode(
     input="url",
     output=["is_scrapable"],
-    node_config={"llm_model": llm_model}
+    node_config={
+        "llm_model": llm_model,
+        "verbose": True,
+        }
 )
 
 fetch_node = FetchNode(
     input="url | local_dir",
     output=["doc"],
-    node_config={"headless": True, "verbose": True}
+    node_config={
+        "verbose": True,
+        "headless": True,
+    }
 )
 parse_node = ParseNode(
     input="doc",
     output=["parsed_doc"],
-    node_config={"chunk_size": 4096}
+    node_config={
+        "chunk_size": 4096,
+        "verbose": True,
+    }
 )
 rag_node = RAGNode(
     input="user_prompt & (parsed_doc | doc)",
     output=["relevant_chunks"],
-    node_config={"llm_model": llm_model},
+    node_config={
+        "llm_model": llm_model,
+        "embedder_model": embedder,
+        "verbose": True,
+    }
 )
 generate_answer_node = GenerateAnswerNode(
     input="user_prompt & (relevant_chunks | parsed_doc | doc)",
     output=["answer"],
-    node_config={"llm_model": llm_model},
+    node_config={
+        "llm_model": llm_model,
+        "verbose": True,
+    }
 )
 
 # ************************************************

examples/openai/search_graph_multi.py

@@ -0,0 +1,98 @@
+"""
+Example of custom graph using existing nodes
+"""
+
+import os
+from dotenv import load_dotenv
+from langchain_openai import OpenAIEmbeddings
+from scrapegraphai.models import OpenAI
+from scrapegraphai.graphs import BaseGraph, SmartScraperGraph
+from scrapegraphai.nodes import SearchInternetNode, GraphIteratorNode, MergeAnswersNode
+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",
+    },
+}
+
+# ************************************************
+# Create a SmartScraperGraph instance
+# ************************************************
+
+smart_scraper_graph = SmartScraperGraph(
+    prompt="",
+    source="",
+    config=graph_config
+)
+
+# ************************************************
+# Define the graph nodes
+# ************************************************
+
+llm_model = OpenAI(graph_config["llm"])
+embedder = OpenAIEmbeddings(api_key=llm_model.openai_api_key)
+
+search_internet_node = SearchInternetNode(
+    input="user_prompt",
+    output=["urls"],
+    node_config={
+        "llm_model": llm_model,
+        "max_results": 5,  # num of search results to fetch
+        "verbose": True,
+    }
+)
+
+graph_iterator_node = GraphIteratorNode(
+    input="user_prompt & urls",
+    output=["results"],
+    node_config={
+        "graph_instance": smart_scraper_graph,
+        "verbose": True,
+    }
+)
+
+merge_answers_node = MergeAnswersNode(
+    input="user_prompt & results",
+    output=["answer"],
+    node_config={
+        "llm_model": llm_model,
+        "verbose": True,
+    }
+)
+
+# ************************************************
+# Create the graph by defining the connections
+# ************************************************
+
+graph = BaseGraph(
+    nodes=[
+        search_internet_node,
+        graph_iterator_node,
+        merge_answers_node
+    ],
+    edges=[
+        (search_internet_node, graph_iterator_node),
+        (graph_iterator_node, merge_answers_node)
+    ],
+    entry_point=search_internet_node
+)
+
+# ************************************************
+# Execute the graph
+# ************************************************
+
+result, execution_info = graph.execute({
+    "user_prompt": "List me all the typical Chioggia dishes."
+})
+
+# get the answer from the result
+result = result.get("answer", "No answer found.")
+print(result)

examples/openai/search_graph_openai.py

@@ -19,14 +19,16 @@
         "api_key": openai_key,
         "model": "gpt-3.5-turbo",
     },
+    "max_results": 5,
+    "verbose": True,
 }
 
 # ************************************************
 # Create the SearchGraph instance and run it
 # ************************************************
 
 search_graph = SearchGraph(
-    prompt="List me top 5 eyeliner products for a gift.",
+    prompt="List me the best escursions near Trento",
     config=graph_config
 )
 

examples/openai/smart_scraper_openai.py

@@ -21,7 +21,7 @@
         "api_key": openai_key,
         "model": "gpt-3.5-turbo",
     },
-    "verbose": True,
+    "verbose": False,
 }
 
 # ************************************************

scrapegraphai/graphs/__init__.py

@@ -2,6 +2,7 @@
 __init__.py file for graphs folder
 """
 
+from .abstract_graph import AbstractGraph
 from .base_graph import BaseGraph
 from .smart_scraper_graph import SmartScraperGraph
 from .speech_graph import SpeechGraph

scrapegraphai/graphs/abstract_graph.py

@@ -56,7 +56,7 @@ def __init__(self, prompt: str, config: dict, source: Optional[str] = None):
         self.execution_info = None
 
         # Set common configuration parameters
-        self.verbose = True 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,

scrapegraphai/graphs/search_graph.py

@@ -5,12 +5,11 @@
 from .base_graph import BaseGraph
 from ..nodes import (
     SearchInternetNode,
-    FetchNode,
-    ParseNode,
-    RAGNode,
-    GenerateAnswerNode
+    GraphIteratorNode,
+    MergeAnswersNode
 )
 from .abstract_graph import AbstractGraph
+from .smart_scraper_graph import SmartScraperGraph
 
 
 class SearchGraph(AbstractGraph):
@@ -38,6 +37,11 @@ class SearchGraph(AbstractGraph):
         >>> result = search_graph.run()
     """
 
+    def __init__(self, prompt: str, config: dict):
+
+        self.max_results = config.get("max_results", 3)
+        super().__init__(prompt, config)
+
     def _create_graph(self) -> BaseGraph:
         """
         Creates the graph of nodes representing the workflow for web scraping and searching.
@@ -46,53 +50,53 @@ def _create_graph(self) -> BaseGraph:
             BaseGraph: A graph instance representing the web scraping and searching workflow.
         """
 
+        # ************************************************
+        # Create a SmartScraperGraph instance
+        # ************************************************
+
+        smart_scraper_instance = SmartScraperGraph(
+            prompt="",
+            source="",
+            config=self.config
+        )
+
+        # ************************************************
+        # Define the graph nodes
+        # ************************************************
+
         search_internet_node = SearchInternetNode(
             input="user_prompt",
-            output=["url"],
-            node_config={
-                "llm_model": self.llm_model
-            }
-        )
-        fetch_node = FetchNode(
-            input="url | local_dir",
-            output=["doc"]
-        )
-        parse_node = ParseNode(
-            input="doc",
-            output=["parsed_doc"],
+            output=["urls"],
             node_config={
-                "chunk_size": self.model_token
+                "llm_model": self.llm_model,
+                "max_results": self.max_results
             }
         )
-        rag_node = RAGNode(
-            input="user_prompt & (parsed_doc | doc)",
-            output=["relevant_chunks"],
+        graph_iterator_node = GraphIteratorNode(
+            input="user_prompt & urls",
+            output=["results"],
             node_config={
-                "llm_model": self.llm_model,
-                "embedder_model": self.embedder_model
+                "graph_instance": smart_scraper_instance,
             }
         )
-        generate_answer_node = GenerateAnswerNode(
-            input="user_prompt & (relevant_chunks | parsed_doc | doc)",
+
+        merge_answers_node = MergeAnswersNode(
+            input="user_prompt & results",
             output=["answer"],
             node_config={
-                "llm_model": self.llm_model
+                "llm_model": self.llm_model,
             }
         )
 
         return BaseGraph(
             nodes=[
                 search_internet_node,
-                fetch_node,
-                parse_node,
-                rag_node,
-                generate_answer_node,
+                graph_iterator_node,
+                merge_answers_node
             ],
             edges=[
-                (search_internet_node, fetch_node),
-                (fetch_node, parse_node),
-                (parse_node, rag_node),
-                (rag_node, generate_answer_node)
+                (search_internet_node, graph_iterator_node),
+                (graph_iterator_node, merge_answers_node)
             ],
             entry_point=search_internet_node
         )

scrapegraphai/nodes/__init__.py

@@ -17,3 +17,5 @@
 from .robots_node import RobotsNode
 from .generate_answer_csv_node import GenerateAnswerCSVNode
 from .generate_answer_pdf_node import GenerateAnswerPDFNode
+from .graph_iterator_node import GraphIteratorNode
+from .merge_answers_node import MergeAnswersNode

scrapegraphai/nodes/generate_answer_csv_node.py

@@ -2,7 +2,7 @@
 Module for generating the answer node
 """
 # Imports from standard library
-from typing import List
+from typing import List, Optional
 from tqdm import tqdm
 
 # Imports from Langchain
@@ -39,7 +39,7 @@ class GenerateAnswerCSVNode(BaseNode):
                         updating the state with the generated answer under the 'answer' key.
     """
 
-    def __init__(self, input: str, output: List[str], node_config: dict,
+    def __init__(self, input: str, output: List[str], node_config: Optional[dict] = None,
                  node_name: str = "GenerateAnswer"):
         """
         Initializes the GenerateAnswerNodeCsv with a language model client and a node name.

scrapegraphai/nodes/generate_answer_node.py

@@ -3,7 +3,7 @@
 """
 
 # Imports from standard library
-from typing import List
+from typing import List, Optional
 from tqdm import tqdm
 
 # Imports from Langchain
@@ -33,7 +33,7 @@ class GenerateAnswerNode(BaseNode):
         node_name (str): The unique identifier name for the node, defaulting to "GenerateAnswer".
     """
 
-    def __init__(self, input: str, output: List[str], node_config: dict,
+    def __init__(self, input: str, output: List[str], node_config: Optional[dict]=None,
                  node_name: str = "GenerateAnswer"):
         super().__init__(node_name, "node", input, output, 2, node_config)
 

scrapegraphai/nodes/generate_answer_node_csv.py

@@ -2,7 +2,7 @@
 Module for generating the answer node
 """
 # Imports from standard library
-from typing import List
+from typing import List, Optional
 from tqdm import tqdm
 
 # Imports from Langchain
@@ -39,7 +39,7 @@ class GenerateAnswerCSVNode(BaseNode):
                         updating the state with the generated answer under the 'answer' key.
     """
 
-    def __init__(self, input: str, output: List[str], node_config: dict,
+    def __init__(self, input: str, output: List[str], node_config: Optional[dict] = None,
                  node_name: str = "GenerateAnswer"):
         """
         Initializes the GenerateAnswerNodeCsv with a language model client and a node name.