Generate answer parallel

pyproject.toml

@@ -3,19 +3,15 @@ name = "scrapegraphai"
 
 version = "1.11.0b1"
 
-
-
 description = "A web scraping library based on LangChain which uses LLM and direct graph logic to create scraping pipelines."
 authors = [
     { name = "Marco Vinciguerra", email = "[email protected]" },
     { name = "Marco Perini", email = "[email protected]" },
     { name = "Lorenzo Padoan", email = "[email protected]" }
 ]
+
 dependencies = [
     "langchain>=0.2.10",
-
-    "langchain-fireworks>=0.1.3",
-    "langchain_community>=0.2.9",
     "langchain-google-genai>=1.0.7",
     "langchain-google-vertexai",
     "langchain-openai>=0.1.17",
@@ -37,6 +33,8 @@ dependencies = [
     "google>=3.0.0",
     "undetected-playwright>=0.3.0",
     "semchunk>=1.0.1",
+    "langchain-fireworks>=0.1.3",
+    "langchain-community>=0.2.9"
 ]
 
 license = "MIT"

requirements.txt

@@ -1,7 +1,5 @@
 langchain>=0.2.10
-langchain_community>=0.2.9
 langchain-google-genai>=1.0.7
-langchain-fireworks>=0.1.3
 langchain-google-vertexai
 langchain-openai>=0.1.17
 langchain-groq>=0.1.3
@@ -22,4 +20,5 @@ playwright>=1.43.0
 google>=3.0.0
 undetected-playwright>=0.3.0
 semchunk>=1.0.1
-
+langchain-fireworks>=0.1.3
+langchain-community>=0.2.9

scrapegraphai/nodes/generate_answer_node.py

@@ -1,7 +1,7 @@
 """
 GenerateAnswerNode Module
 """
-
+import asyncio
 from typing import List, Optional
 from langchain.prompts import PromptTemplate
 from langchain_core.output_parsers import JsonOutputParser
@@ -107,44 +107,43 @@ def execute(self, state: dict) -> dict:
             template_chunks_prompt = self.additional_info + template_chunks_prompt
             template_merge_prompt = self.additional_info + template_merge_prompt
 
-        chains_dict = {}
+        if len(doc) == 1:
+            prompt = PromptTemplate(
+                template=template_no_chunks_prompt,
+                input_variables=["question"],
+                partial_variables={"context": doc,
+                                    "format_instructions": format_instructions})
+            chain =  prompt | self.llm_model | output_parser
+            answer = chain.invoke({"question": user_prompt})
+
+            state.update({self.output[0]: answer})
+            return state
 
-        # Use tqdm to add progress bar
+        chains_dict = {}
         for i, chunk in enumerate(tqdm(doc, desc="Processing chunks", disable=not self.verbose)):
-            if len(doc) == 1:
-                prompt = PromptTemplate(
-                    template=template_no_chunks_prompt,
-                    input_variables=["question"],
-                    partial_variables={"context": chunk,
-                                       "format_instructions": format_instructions})
-                chain =  prompt | self.llm_model | output_parser
-                answer = chain.invoke({"question": user_prompt})
-                break
 
             prompt = PromptTemplate(
-                    template=template_chunks_prompt,
-                    input_variables=["question"],
-                    partial_variables={"context": chunk,
-                                        "chunk_id": i + 1,
-                                        "format_instructions": format_instructions})
-            # Dynamically name the chains based on their index
+                template=template_chunks,
+                input_variables=["question"],
+                partial_variables={"context": chunk,
+                                "chunk_id": i + 1,
+                                "format_instructions": format_instructions})
+            # Add chain to dictionary with dynamic name
             chain_name = f"chunk{i+1}"
             chains_dict[chain_name] = prompt | self.llm_model | output_parser
 
-        if len(chains_dict) > 1:
-            # Use dictionary unpacking to pass the dynamically named chains to RunnableParallel
-            map_chain = RunnableParallel(**chains_dict)
-            # Chain
-            answer = map_chain.invoke({"question": user_prompt})
-            # Merge the answers from the chunks
-            merge_prompt = PromptTemplate(
+        async_runner = RunnableParallel(**chains_dict)
+
+        batch_results =  async_runner.invoke({"question": user_prompt})
+
+        merge_prompt = PromptTemplate(
                 template = template_merge_prompt,
                 input_variables=["context", "question"],
                 partial_variables={"format_instructions": format_instructions},
             )
-            merge_chain = merge_prompt | self.llm_model | output_parser
-            answer = merge_chain.invoke({"context": answer, "question": user_prompt})
 
-        # Update the state with the generated answer
+        merge_chain = merge_prompt | self.llm_model | output_parser
+        answer = merge_chain.invoke({"context": batch_results, "question": user_prompt})
+
         state.update({self.output[0]: answer})
         return state