Update agent.py

agent.py

@@ -7,7 +7,7 @@ import os
 from dotenv import load_dotenv
 from langgraph.checkpoint.memory import InMemorySaver
 from langgraph.prebuilt import create_react_agent
-from tools import (search_academic_source, download_pdf, save_papers, get_paper_batch,
+from tools import (search_academic_source, save_papers, get_paper_batch,
                    save_output, read_output, cluster_and_visualize,
                    get_pajais_taxonomy, read_pdf_text, enrich_doi,
                    read_word_text, import_from_scratch)
@@ -15,7 +15,7 @@ from prompt import build_prompt
 
 load_dotenv()
 
-TOOLS = [search_academic_source, download_pdf, save_papers, get_paper_batch, save_output,
+TOOLS = [search_academic_source, save_papers, get_paper_batch, save_output,
          read_output, cluster_and_visualize, get_pajais_taxonomy,
          read_pdf_text, enrich_doi, read_word_text, import_from_scratch]
 
@@ -23,7 +23,12 @@ def _llm():
     provider = os.getenv("LLM_PROVIDER", "groq").lower()
     if provider == "mistral":
         from langchain_mistralai import ChatMistralAI
-        return ChatMistralAI(model=os.getenv("MISTRAL_BIG", "mistral-small-latest"), temperature=0, max_retries=6)
+        return ChatMistralAI(model=os.getenv("MISTRAL_BIG", "mistral-small-latest"), temperature=0)
+    elif provider == "huggingface":
+        from langchain_huggingface import HuggingFaceEndpoint
+        repo_id = os.getenv("HF_MODEL_ID", "mistralai/Mistral-7B-Instruct-v0.3")
+        token = os.getenv("HF_TOKEN") or os.getenv("HUGGINGFACEHUB_API_TOKEN")
+        return HuggingFaceEndpoint(repo_id=repo_id, huggingfacehub_api_token=token, temperature=0.01)
     from langchain_groq import ChatGroq
     return ChatGroq(model=os.getenv("GROQ_BIG", "llama-3.3-70b-versatile"), temperature=0)