Upload 2 files

agents.py

@@ -2,20 +2,25 @@ import os
 import json
 import time
 import traceback
+from dotenv import load_dotenv
 
-from langchain_core.prompts import PromptTemplate
-from langchain_core.output_parsers import JsonOutputParser
-from pydantic import BaseModel, Field
-
+from langfuse import Langfuse
 from langchain_groq import ChatGroq
-from dotenv import load_dotenv
+from langfuse.callback import CallbackHandler
+from langchain_core.prompts import PromptTemplate
 
 load_dotenv()
 
+langfuse_handler = CallbackHandler(
+    secret_key=os.getenv("LANGFUSE_SECRET_KEY"),
+    public_key=os.getenv("LANGFUSE_PUBLIC_KEY"),
+    host="https://cloud.langfuse.com", # 🇪🇺 EU region
+)
+langfuse = Langfuse()
+
 analysis_llm = ChatGroq(
     model="llama-3.1-8b-instant",
     temperature=0.8,
-    max_tokens=800,
     timeout=None,
     max_retries=2,
     api_key=os.getenv("GROQ_ANALYSIS_API_KEY"),
@@ -30,21 +35,21 @@ post_content_llm = ChatGroq(
 
     
 def basic_analysis(news):
-    global analysis_llm_indx
-    prompt = PromptTemplate.from_file(
-        template_file="prompts/news_selector.yml",
-        input_variables=["news_object"],
-    )
+    prompt = langfuse.get_prompt("news_selector")
 
     for _ in range(5):
         try:
             response = analysis_llm.invoke(
-                prompt.invoke({"news_object": news})
+                prompt.compile(news_object = news), 
+                config={"callbacks": [langfuse_handler]}
             )
             print("################ BASIC ANALYSIS AGENT RESPONSE ################")
             print(response.content)
             print("################ BASIC ANALYSIS END AGENT RESPONSE ################")
 
+            if "</think>" in response.content:
+                response.content = response.content.split("</think>")[1]
+
             start_index = response.content.find("{")
             end_index = response.content.rfind("}")
 
@@ -75,11 +80,11 @@ def get_text_post_content(details, reference):
             template_file="prompts/post_generator_without_source.yml",
             input_variables=["NEWS_CONTENT", "CHAR_LENGTH"],
         )
+        prompt = langfuse.get_prompt("post_generator")
 
-        user_query = prompt.invoke(
-            {"NEWS_CONTENT": details, "CHAR_LENGTH": 490- len(reference)}
-        )
-        response = post_content_llm.invoke(user_query)
+
+        user_query = prompt.compile(NEWS_CONTENT = details, CHAR_LENGTH = 490- len(reference))
+        response = post_content_llm.invoke(user_query, config={"callbacks": [langfuse_handler]})
 
         print("POST CONTENT RESPONSE:", response)
         

requirements.txt

@@ -13,4 +13,5 @@ fastapi===0.115.8
 gunicorn===23.0.0
 uvicorn===0.34.0
 pydantic==2.9.2
-certifi
+certifi==2025.1.31
+langfuse==2.60.3