From 5bc20bad9ccc7067cc7322c3441dc7217090f163 Mon Sep 17 00:00:00 2001 From: Parag Ghatage Date: Mon, 23 Jun 2025 22:08:09 +0530 Subject: [PATCH] built generate-perspective-node --- new-backend/app/modules/langgraph_builder.py | 32 +- .../app/modules/langgraph_nodes/fact_check.py | 3 +- .../langgraph_nodes/generate_perspective.py | 41 +- .../app/modules/langgraph_nodes/judge.py | 2 +- .../modules/langgraph_nodes/store_and_send.py | 9 +- new-backend/app/modules/pipeline.py | 50 +-- new-backend/app/utils/fact_check_utils.py | 31 ++ new-backend/app/utils/prompt_templates.py | 32 ++ new-backend/pyproject.toml | 2 + new-backend/uv.lock | 381 ++++++++++++++++++ 10 files changed, 528 insertions(+), 55 deletions(-) create mode 100644 new-backend/app/utils/fact_check_utils.py diff --git a/new-backend/app/modules/langgraph_builder.py b/new-backend/app/modules/langgraph_builder.py index 47705433..fe607106 100644 --- a/new-backend/app/modules/langgraph_builder.py +++ b/new-backend/app/modules/langgraph_builder.py @@ -8,13 +8,25 @@ error_handler ) +from typing_extensions import TypedDict + + +class MyState(TypedDict): + cleaned_text: str + facts: list[dict] + sentiment: str + perspective: str + score: int + retries: int + status: str + def build_langgraph(): - graph = StateGraph() + graph = StateGraph(MyState) graph.add_node( "sentiment_analysis", - sentiment.run_sentiment + sentiment.run_sentiment_sdk ) graph.add_node( "fact_checking", @@ -34,21 +46,21 @@ def build_langgraph(): ) graph.add_node( "error_handler", - error_handler + error_handler.error_handler ) graph.set_entry_point( - "sentiment_analysis" + "sentiment_analysis", ) - graph.set_conditional_edges( + graph.add_conditional_edges( "sentiment_analysis", lambda x: ( "error_handler" if x.get("status") == "error" else "fact_checking" ) ) - graph.set_conditional_edges( + graph.add_conditional_edges( "fact_checking", lambda x: ( "error_handler" @@ -57,7 +69,7 @@ def build_langgraph(): ) ) - graph.set_conditional_edges( + graph.add_conditional_edges( "generate_perspective", lambda x: ( "error_handler" @@ -66,7 +78,7 @@ def build_langgraph(): ) ) - graph.set_conditional_edges( + graph.add_conditional_edges( "judge_perspective", lambda state: ( "error_handler" @@ -80,12 +92,12 @@ def build_langgraph(): else "store_and_send" ) ) - graph.set_conditional_edges( + graph.add_conditional_edges( "store_and_send", lambda x: ( "error_handler" if x.get("status") == "error" - else None + else "__end__" ) ) diff --git a/new-backend/app/modules/langgraph_nodes/fact_check.py b/new-backend/app/modules/langgraph_nodes/fact_check.py index 05a7dde6..0d73188c 100644 --- a/new-backend/app/modules/langgraph_nodes/fact_check.py +++ b/new-backend/app/modules/langgraph_nodes/fact_check.py @@ -1,4 +1,5 @@ -from app.modules.pipeline import run_fact_check_pipeline +# from app.modules.pipeline import run_fact_check_pipeline\ +from app.utils.fact_check_utils import run_fact_check_pipeline def run_fact_check(state): diff --git a/new-backend/app/modules/langgraph_nodes/generate_perspective.py b/new-backend/app/modules/langgraph_nodes/generate_perspective.py index 94759fa9..b2a12e06 100644 --- a/new-backend/app/modules/langgraph_nodes/generate_perspective.py +++ b/new-backend/app/modules/langgraph_nodes/generate_perspective.py @@ -1,21 +1,27 @@ -from langchain.chains import LLMChain -from langchain.prompts import PromptTemplate +from app.utils.prompt_templates import generation_prompt +from langchain_groq import ChatGroq +from pydantic import BaseModel, Field -prompt = PromptTemplate( - input_variables=["text", "facts"], - template="""Given the following article: -{text} -And the following verified facts: -{facts} +prompt = generation_prompt -Generate a reasoned opposing perspective using chain-of-thought logic. -""" + +class PerspectiveOutput(BaseModel): + reasoning: str = Field(..., description="Chain-of-thought reasoning steps") + perspective: str = Field(..., description="Generated opposite perspective") + + +my_llm = "llama-3.3-70b-versatile" + +llm = ChatGroq( + model=my_llm, + temperature=0.7 ) -my_llm = "groq llm" +structured_llm = llm.with_structured_output(PerspectiveOutput) + -chain = LLMChain(prompt=prompt, llm=my_llm) +chain = prompt | structured_llm def generate_perspective(state): @@ -31,8 +37,15 @@ def generate_perspective(state): elif not facts: raise ValueError("Missing or empty 'facts' in state") - facts = "\n".join([f["snippet"] for f in state["facts"]]) - result = chain.run({"text": text, "facts": facts}) + facts_str = "\n".join([f"Claim: {f['original_claim']}\n" + "Verdict: {f['verdict']}\nExplanation: " + "{f['explanation']}" for f in state["facts"]]) + + result = chain.invoke({ + "cleaned_article": text, + "facts": facts_str, + "sentiment": state.get("sentiment", "neutral") + }) except Exception as e: print(f"some error occured in generate_perspective:{e}") return { diff --git a/new-backend/app/modules/langgraph_nodes/judge.py b/new-backend/app/modules/langgraph_nodes/judge.py index ef040068..ddecb190 100644 --- a/new-backend/app/modules/langgraph_nodes/judge.py +++ b/new-backend/app/modules/langgraph_nodes/judge.py @@ -6,7 +6,7 @@ def judge_perspective(state): if not perspective: raise ValueError("Missing or empty 'perspective' in state") - score = 85 if "reasoned" in perspective else 40 + score = 85 if "reasoning" in perspective else 40 except Exception as e: print(f"some error occured in judge_perspetive:{e}") return { diff --git a/new-backend/app/modules/langgraph_nodes/store_and_send.py b/new-backend/app/modules/langgraph_nodes/store_and_send.py index e19a0de9..7fe5b909 100644 --- a/new-backend/app/modules/langgraph_nodes/store_and_send.py +++ b/new-backend/app/modules/langgraph_nodes/store_and_send.py @@ -1,12 +1,13 @@ -from utils.vector_store import save_to_vector_db +# from app.utils.vector_store import save_to_vector_db def store_and_send(state): # to store data in vector db try: - save_to_vector_db({ - **state - }) + print(state) + # save_to_vector_db({ + # **state + # }) except Exception as e: print(f"some error occured in store_and_send:{e}") return { diff --git a/new-backend/app/modules/pipeline.py b/new-backend/app/modules/pipeline.py index 87dfb751..13386fcb 100644 --- a/new-backend/app/modules/pipeline.py +++ b/new-backend/app/modules/pipeline.py @@ -2,12 +2,12 @@ from app.modules.scraper.cleaner import clean_extracted_text from app.modules.scraper.keywords import extract_keywords from app.modules.langgraph_builder import build_langgraph -from app.modules.facts_check.llm_processing import run_claim_extractor_sdk -from app.modules.facts_check.llm_processing import run_fact_verifier_sdk -from app.modules.facts_check.web_search import search_duckduckgo +# from app.modules.facts_check.llm_processing import run_claim_extractor_sdk +# from app.modules.facts_check.llm_processing import run_fact_verifier_sdk +# from app.modules.facts_check.web_search import search_duckduckgo import json -import re -import time +# import re +# import time # Compile once when module loads _LANGGRAPH_WORKFLOW = build_langgraph() @@ -38,27 +38,27 @@ def run_langgraph_workflow(state: dict): return result -def run_fact_check_pipeline(state): +# def run_fact_check_pipeline(state): - result = run_claim_extractor_sdk(state) - # Step 1: Extract claims - raw_output = result["verifiable_claims"] +# result = run_claim_extractor_sdk(state) +# # Step 1: Extract claims +# raw_output = result["verifiable_claims"] - # Match any line that starts with *, -, or • followed by text - claims = re.findall(r"^[\*\-•]\s+(.*)", raw_output, re.MULTILINE) - claims = [claim.strip() for claim in claims if claim.strip()] +# # Match any line that starts with *, -, or • followed by text +# claims = re.findall(r"^[\*\-•]\s+(.*)", raw_output, re.MULTILINE) +# claims = [claim.strip() for claim in claims if claim.strip()] - # Step 2: Search each claim with polite delay - search_results = [] - for claim in claims: - print(f"\n🔍Searching for claim...: {claim}") - try: - val = search_duckduckgo(claim) - val[0]["claim"] = claim - search_results.append(val[0]) - except Exception as e: - print(f"❌ Search failed for: {claim} -> {e}") - time.sleep(4) # Add 4 second delay to prevent rate-limit +# # Step 2: Search each claim with polite delay +# search_results = [] +# for claim in claims: +# print(f"\n🔍Searching for claim...: {claim}") +# try: +# val = search_duckduckgo(claim) +# val[0]["claim"] = claim +# search_results.append(val[0]) +# except Exception as e: +# print(f"❌ Search failed for: {claim} -> {e}") +# time.sleep(4) # Add 4 second delay to prevent rate-limit - final = run_fact_verifier_sdk(search_results) - return final["verifications"] +# final = run_fact_verifier_sdk(search_results) +# return final["verifications"] diff --git a/new-backend/app/utils/fact_check_utils.py b/new-backend/app/utils/fact_check_utils.py new file mode 100644 index 00000000..b64fa3ca --- /dev/null +++ b/new-backend/app/utils/fact_check_utils.py @@ -0,0 +1,31 @@ +from app.modules.facts_check.web_search import search_duckduckgo +from app.modules.facts_check.llm_processing import run_claim_extractor_sdk +from app.modules.facts_check.llm_processing import run_fact_verifier_sdk +import re +import time + + +def run_fact_check_pipeline(state): + + result = run_claim_extractor_sdk(state) + # Step 1: Extract claims + raw_output = result["verifiable_claims"] + + # Match any line that starts with *, -, or • followed by text + claims = re.findall(r"^[\*\-•]\s+(.*)", raw_output, re.MULTILINE) + claims = [claim.strip() for claim in claims if claim.strip()] + + # Step 2: Search each claim with polite delay + search_results = [] + for claim in claims: + print(f"\n🔍Searching for claim...: {claim}") + try: + val = search_duckduckgo(claim) + val[0]["claim"] = claim + search_results.append(val[0]) + except Exception as e: + print(f"❌ Search failed for: {claim} -> {e}") + time.sleep(4) # Add 4 second delay to prevent rate-limit + + final = run_fact_verifier_sdk(search_results) + return final["verifications"] diff --git a/new-backend/app/utils/prompt_templates.py b/new-backend/app/utils/prompt_templates.py index e69de29b..9d17f70d 100644 --- a/new-backend/app/utils/prompt_templates.py +++ b/new-backend/app/utils/prompt_templates.py @@ -0,0 +1,32 @@ +from langchain.prompts import ChatPromptTemplate + +generation_prompt = ChatPromptTemplate.from_template(""" +You are an AI assistant that generates a well-reasoned ' +'counter-perspective to a given article. + +## Article: +{cleaned_article} + +## Sentiment: +{sentiment} + +## Verified Facts: +{facts} + +--- + +Generate a logical and respectful *opposite perspective* to the article. +Use *step-by-step reasoning* and return your output in this JSON format: + +```json +{{ + "counter_perspective": "", + "reasoning_steps": [ + "", + "", + "", + "...", + "" + ] +}} +""") diff --git 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