adding semanticscholar_apikey to prevent exceeding rate limit

chemcrow/agents/tools.py

@@ -13,6 +13,9 @@ def make_tools(llm: BaseLanguageModel, api_keys: dict = {}, verbose=True):
     chemspace_api_key = api_keys.get("CHEMSPACE_API_KEY") or os.getenv(
         "CHEMSPACE_API_KEY"
     )
+    semantic_scholar_api_key = api_keys.get("SEMANTIC_SCHOLAR_API_KEY") or os.getenv(
+        "SEMANTIC_SCHOLAR_API_KEY"
+    )
 
     all_tools = agents.load_tools(
         [
@@ -34,8 +37,12 @@ def make_tools(llm: BaseLanguageModel, api_keys: dict = {}, verbose=True):
         ExplosiveCheck(),
         ControlChemCheck(),
         SimilarControlChemCheck(),
-        Scholar2ResultLLM(llm=llm, api_key=openai_api_key),
         SafetySummary(llm=llm),
+        Scholar2ResultLLM(
+            llm=llm,
+            openai_api_key=openai_api_key,
+            semantic_scholar_api_key=semantic_scholar_api_key
+        ),
     ]
     if chemspace_api_key:
         all_tools += [GetMoleculePrice(chemspace_api_key)]

chemcrow/frontend/streamlit_callback_handler.py

@@ -79,9 +79,9 @@ def on_tool_start(
         )
 
         # Display note of potential long time
-        if serialized["name"] == "ReactionRetrosynthesis":
+        if serialized["name"] == "ReactionRetrosynthesis" or serialized["name"] == "LiteratureSearch":
             self._container.markdown(
-                f"‼️ Note: This tool can take up to 5 minutes to complete execution ‼️",
+                f"‼️ Note: This tool can take some time to complete execution ‼️",
                 unsafe_allow_html=True,
             )
 

chemcrow/tools/search.py

@@ -14,14 +14,18 @@
 from chemcrow.utils import is_multiple_smiles, split_smiles
 
 
-def paper_scraper(search: str, pdir: str = "query") -> dict:
+def paper_scraper(search: str, pdir: str = "query", semantic_scholar_api_key: str = None) -> dict:
     try:
-        return paperscraper.search_papers(search, pdir=pdir)
+        return paperscraper.search_papers(
+            search,
+            pdir=pdir,
+            semantic_scholar_api_key=semantic_scholar_api_key,
+        )
     except KeyError:
         return {}
 
 
-def paper_search(llm, query):
+def paper_search(llm, query, semantic_scholar_api_key=None):
     prompt = langchain.prompts.PromptTemplate(
         input_variables=["question"],
         template="""
@@ -37,14 +41,14 @@ def paper_search(llm, query):
         os.mkdir("query/")
     search = query_chain.run(query)
     print("\nSearch:", search)
-    papers = paper_scraper(search, pdir=f"query/{re.sub(' ', '', search)}")
+    papers = paper_scraper(search, pdir=f"query/{re.sub(' ', '', search)}", semantic_scholar_api_key=semantic_scholar_api_key)
     return papers
 
 
-def scholar2result_llm(llm, query, k=5, max_sources=2, openai_api_key=None):
+def scholar2result_llm(llm, query, k=5, max_sources=2, openai_api_key=None, semantic_scholar_api_key=None):
     """Useful to answer questions that require
     technical knowledge. Ask a specific question."""
-    papers = paper_search(llm, query)
+    papers = paper_search(llm, query, semantic_scholar_api_key=semantic_scholar_api_key)
     if len(papers) == 0:
         return "Not enough papers found"
     docs = paperqa.Docs(
@@ -59,7 +63,11 @@ def scholar2result_llm(llm, query, k=5, max_sources=2, openai_api_key=None):
         except (ValueError, FileNotFoundError, PdfReadError):
             not_loaded += 1
 
-    print(f"\nFound {len(papers.items())} papers but couldn't load {not_loaded}")
+    if not_loaded > 0:
+        print(f"\nFound {len(papers.items())} papers but couldn't load {not_loaded}.")
+    else:
+        print(f"\nFound {len(papers.items())} papers and loaded all of them.")
+
     answer = docs.query(query, k=k, max_sources=max_sources).formatted_answer
     return answer
 
@@ -71,15 +79,24 @@ class Scholar2ResultLLM(BaseTool):
         "knowledge. Ask a specific question."
     )
     llm: BaseLanguageModel = None
-    api_key: str = None
+    openai_api_key: str = None 
+    semantic_scholar_api_key: str = None
+
 
-    def __init__(self, llm, api_key):
+    def __init__(self, llm, openai_api_key, semantic_scholar_api_key):
         super().__init__()
         self.llm = llm
-        self.api_key = api_key
+        # api keys
+        self.openai_api_key = openai_api_key
+        self.semantic_scholar_api_key = semantic_scholar_api_key
 
     def _run(self, query) -> str:
-        return scholar2result_llm(self.llm, query, openai_api_key=self.api_key)
+        return scholar2result_llm(
+            self.llm,
+            query,
+            openai_api_key=self.openai_api_key,
+            semantic_scholar_api_key=self.semantic_scholar_api_key
+        )
 
     async def _arun(self, query) -> str:
         """Use the tool asynchronously."""