feat: add integration for infos

examples/extras/custom_prompt.py

@@ -0,0 +1,50 @@
+""" 
+Basic example of scraping pipeline using SmartScraper
+"""
+import os
+import json
+from dotenv import load_dotenv
+from scrapegraphai.graphs import SmartScraperGraph
+from scrapegraphai.utils import prettify_exec_info
+
+load_dotenv()
+
+
+# ************************************************
+# Define the configuration for the graph
+# ************************************************
+
+openai_key = os.getenv("OPENAI_APIKEY")
+
+prompt = "Some more info"
+
+graph_config = {
+    "llm": {
+        "api_key": openai_key,
+        "model": "gpt-3.5-turbo",
+    },
+    "additional_info": prompt,
+    "verbose": True,
+    "headless": False,
+}
+
+# ************************************************
+# Create the SmartScraperGraph instance and run it
+# ************************************************
+
+smart_scraper_graph = SmartScraperGraph(
+    prompt="List me all the projects with their description",
+    # 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(json.dumps(result, indent=4))
+
+# ************************************************
+# Get graph execution info
+# ************************************************
+
+graph_exec_info = smart_scraper_graph.get_execution_info()
+print(prettify_exec_info(graph_exec_info))

scrapegraphai/graphs/csv_scraper_graph.py

@@ -50,6 +50,7 @@ class CSVScraperGraph(AbstractGraph):
             output=["answer"],
             node_config={
                 "llm_model": self.llm_model,
+                "additional_info": self.config.get("additional_info"),
                 "schema": self.schema,
             }
         )

scrapegraphai/graphs/deep_scraper_graph.py

@@ -95,6 +95,7 @@ class DeepScraperGraph(AbstractGraph):
             output=["answer"],
             node_config={
                 "llm_model": self.llm_model,
+                "additional_info": self.config.get("additional_info"),
                 "schema": self.schema
             }
         )

scrapegraphai/graphs/json_scraper_graph.py

@@ -75,6 +75,7 @@ class JSONScraperGraph(AbstractGraph):
             output=["answer"],
             node_config={
                 "llm_model": self.llm_model,
+                "additional_info": self.config.get("additional_info"),
                 "schema": self.schema
             }
         )

scrapegraphai/graphs/markdown_scraper_graph.py

@@ -76,6 +76,7 @@ class MDScraperGraph(AbstractGraph):
             output=["answer"],
             node_config={
                 "llm_model": self.llm_model,
+                "additional_info": self.config.get("additional_info"),
                 "schema": self.schema,
             }
         )

scrapegraphai/graphs/omni_scraper_graph.py

@@ -18,7 +18,6 @@ from ..nodes import (
 
 from ..models import OpenAIImageToText
 
-
 class OmniScraperGraph(AbstractGraph):
     """
     OmniScraper is a scraping pipeline that automates the process of 
@@ -61,7 +60,6 @@ class OmniScraperGraph(AbstractGraph):
 
         self.input_key = "url" if source.startswith("http") else "local_dir"
 
-
     def _create_graph(self) -> BaseGraph:
         """
         Creates the graph of nodes representing the workflow for web scraping.
@@ -104,6 +102,7 @@ class OmniScraperGraph(AbstractGraph):
             output=["answer"],
             node_config={
                 "llm_model": self.llm_model,
+                "additional_info": self.config.get("additional_info"),
                 "schema": self.schema
             }
         )

scrapegraphai/graphs/pdf_scraper_graph.py

@@ -89,6 +89,7 @@ class PDFScraperGraph(AbstractGraph):
             output=["answer"],
             node_config={
                 "llm_model": self.llm_model,
+                "additional_info": self.config.get("additional_info"),
                 "schema": self.schema
             }
         )

scrapegraphai/graphs/script_creator_graph.py

@@ -84,6 +84,7 @@ class ScriptCreatorGraph(AbstractGraph):
             output=["answer"],
             node_config={
                 "llm_model": self.llm_model,
+                "additional_info": self.config.get("additional_info"),
                 "schema": self.schema,
             },
             library=self.library,

scrapegraphai/graphs/smart_scraper_graph.py

@@ -91,6 +91,7 @@ class SmartScraperGraph(AbstractGraph):
             output=["answer"],
             node_config={
                 "llm_model": self.llm_model,
+                "additional_info": self.config.get("additional_info"),
                 "schema": self.schema,
             }
         )

scrapegraphai/graphs/speech_graph.py

@@ -84,6 +84,7 @@ class SpeechGraph(AbstractGraph):
             output=["answer"],
             node_config={
                 "llm_model": self.llm_model,
+                "additional_info": self.config.get("additional_info"),
                 "schema": self.schema
             }
         )

scrapegraphai/graphs/xml_scraper_graph.py

@@ -77,6 +77,7 @@ class XMLScraperGraph(AbstractGraph):
             output=["answer"],
             node_config={
                 "llm_model": self.llm_model,
+                "additional_info": self.config.get("additional_info"),
                 "schema": self.schema
             }
         )

scrapegraphai/nodes/generate_answer_csv_node.py

@@ -60,10 +60,13 @@ class GenerateAnswerCSVNode(BaseNode):
         super().__init__(node_name, "node", input, output, 2, node_config)
 
         self.llm_model = node_config["llm_model"]
+
         self.verbose = (
             False if node_config is None else node_config.get("verbose", False)
         )
         
+        self.additional_info = node_config.get("additional_info")
+
     def execute(self, state):
         """
         Generates an answer by constructing a prompt from the user's input and the scraped
@@ -100,6 +103,11 @@ class GenerateAnswerCSVNode(BaseNode):
         else:
             output_parser = JsonOutputParser()
    
+        if self.additional_info is not None:
+            template_no_chunks_csv += self.additional_info
+            template_chunks_csv += self.additional_info
+            template_merge_csv += self.additional_info
+
         format_instructions = output_parser.get_format_instructions()
 
         chains_dict = {}

scrapegraphai/nodes/generate_answer_node.py

@@ -54,6 +54,7 @@ class GenerateAnswerNode(BaseNode):
             False if node_config is None else node_config.get("script_creator", False)
         )
 
+        self.additional_info = node_config.get("additional_info")
 
     def execute(self, state: dict) -> dict:
         """
@@ -98,6 +99,11 @@ class GenerateAnswerNode(BaseNode):
             template_chunks_prompt = template_chunks
             template_merge_prompt = template_merge
 
+        if self.additional_info is not None:
+            template_no_chunks_prompt += self.additional_info
+            template_chunks_prompt += self.additional_info
+            template_merge_prompt += self.additional_info
+
         chains_dict = {}
 
         # Use tqdm to add progress bar
@@ -118,7 +124,6 @@ class GenerateAnswerNode(BaseNode):
                     partial_variables={"context": chunk.page_content,
                                         "chunk_id": i + 1,
                                         "format_instructions": format_instructions})
-
             # Dynamically name the chains based on their index
             chain_name = f"chunk{i+1}"
             chains_dict[chain_name] = prompt | self.llm_model | output_parser

scrapegraphai/nodes/generate_answer_omni_node.py

@@ -51,6 +51,8 @@ class GenerateAnswerOmniNode(BaseNode):
             False if node_config is None else node_config.get("verbose", False)
         )
 
+        self.additional_info = node_config.get("additional_info")
+
     def execute(self, state: dict) -> dict:
         """
         Generates an answer by constructing a prompt from the user's input and the scraped
@@ -86,6 +88,11 @@ class GenerateAnswerOmniNode(BaseNode):
         else:
             output_parser = JsonOutputParser()
 
+        if self.additional_info is not None:
+            template_no_chunk_omni += self.additional_info
+            template_chunks_omni += self.additional_info
+            template_merge_omni += self.additional_info
+
         format_instructions = output_parser.get_format_instructions()
 
 

scrapegraphai/nodes/generate_answer_pdf_node.py

@@ -61,10 +61,13 @@ class GenerateAnswerPDFNode(BaseNode):
         self.llm_model = node_config["llm_model"]
         if isinstance(node_config["llm_model"], Ollama):
             self.llm_model.format="json"
+   
         self.verbose = (
             False if node_config is None else node_config.get("verbose", False)
         )
 
+        self.additional_info = node_config.get("additional_info")
+
     def execute(self, state):
         """
         Generates an answer by constructing a prompt from the user's input and the scraped
@@ -101,6 +104,11 @@ class GenerateAnswerPDFNode(BaseNode):
         else:
             output_parser = JsonOutputParser()
   
+        if self.additional_info is not None:
+            template_no_chunks_pdf += self.additional_info
+            template_chunks_pdf += self.additional_info
+            template_merge_pdf += self.additional_info
+
         format_instructions = output_parser.get_format_instructions()
 
         chains_dict = {}

scrapegraphai/nodes/generate_scraper_node.py

@@ -54,6 +54,8 @@ class GenerateScraperNode(BaseNode):
             False if node_config is None else node_config.get("verbose", False)
         )
 
+        self.additional_info = node_config.get("additional_info")
+
     def execute(self, state: dict) -> dict:
         """
         Generates a python script for scraping a website using the specified library.
@@ -106,6 +108,8 @@ class GenerateScraperNode(BaseNode):
         USER QUESTION: {question}
         SCHEMA INSTRUCTIONS: {schema_instructions}
         """
+        if self.additional_info is not None:
+            template_no_chunks += self.additional_info
 
         if len(doc) > 1:
             raise NotImplementedError(