expanding llm providers, updating litsearch

mdagent/tools/base_tools/util_tools/search_tools.py

@@ -1,3 +1,4 @@
+import logging
 import os
 import re
 from typing import Optional
@@ -14,6 +15,16 @@
 from mdagent.utils import PathRegistry
 
 
+def configure_logging(path):
+    # to log all runtime errors from paperscraper, which can be VERY noisy
+    log_file = os.path.join(path, "scraping_errors.log")
+    logging.basicConfig(
+        filename=log_file,
+        level=logging.ERROR,
+        format="%(asctime)s:%(levelname)s:%(message)s",
+    )
+
+
 def paper_scraper(search: str, pdir: str = "query") -> dict:
     try:
         return paperscraper.search_papers(search, pdir=pdir)
@@ -36,6 +47,7 @@ def paper_search(llm, query, path_registry):
     query_chain = prompt | llm | StrOutputParser()
     if not os.path.isdir(path):
         os.mkdir(path)
+    configure_logging(path)
     search = query_chain.invoke(query)
     print("\nSearch:", search)
     papers = paper_scraper(search, pdir=f"{path}/{re.sub(' ', '', search)}")
@@ -45,10 +57,14 @@ def paper_search(llm, query, path_registry):
 def scholar2result_llm(llm, query, path_registry, k=5, max_sources=2):
     """Useful to answer questions that require
     technical knowledge. Ask a specific question."""
+    if llm.model_name.startswith("gpt"):
+        docs = paperqa.Docs(llm=llm.model_name)
+    else:
+        docs = paperqa.Docs()  # default gpt model
+
     papers = paper_search(llm, query, path_registry)
     if len(papers) == 0:
         return "Failed. Not enough papers found"
-    docs = paperqa.Docs(llm=llm.model_name)
     not_loaded = 0
     for path, data in papers.items():
         try:

mdagent/tools/maketools.py

@@ -1,3 +1,5 @@
+import os
+
 import streamlit as st
 from dotenv import load_dotenv
 from langchain import agents
@@ -70,8 +72,10 @@ def make_all_tools(
         # all_tools += [PythonREPLTool()]
         all_tools += [
             ModifyBaseSimulationScriptTool(path_registry=path_instance, llm=llm),
-            Scholar2ResultLLM(llm=llm, path_registry=path_instance),
         ]
+        if "OPENAI_API_KEY" in os.environ:
+            # LiteratureSearch only works with OpenAI
+            all_tools += [Scholar2ResultLLM(llm=llm, path_registry=path_instance)]
         if human:
             all_tools += [agents.load_tools(["human"], llm)[0]]
 

mdagent/utils/makellm.py

@@ -1,8 +1,9 @@
 from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
-from langchain_openai import ChatOpenAI
 
 
 def _make_llm(model, temp, verbose):
+    from langchain_openai import ChatOpenAI
+
     if model.startswith("gpt-3.5-turbo") or model.startswith("gpt-4"):
         llm = ChatOpenAI(
             temperature=temp,
@@ -11,25 +12,27 @@ def _make_llm(model, temp, verbose):
             streaming=True if verbose else False,
             callbacks=[StreamingStdOutCallbackHandler()] if verbose else None,
         )
-    elif model.startswith("llama"):
+    elif model.startswith("accounts/fireworks"):
         from langchain_fireworks import ChatFireworks
 
         llm = ChatFireworks(
             temperature=temp,
-            model_name=f"accounts/fireworks/models/{model}",
+            model_name=model,
+            request_timeout=1000,
+            streaming=True if verbose else False,
+            callbacks=[StreamingStdOutCallbackHandler()] if verbose else None,
+        )
+    elif model.startswith("together/"):
+        # user needs to add 'together/' prefix to use TogetherAI provider
+        from langchain_together import ChatTogether
+
+        llm = ChatTogether(
+            temperature=temp,
+            model=model.replace("together/", ""),
             request_timeout=1000,
             streaming=True if verbose else False,
             callbacks=[StreamingStdOutCallbackHandler()] if verbose else None,
         )
-    # elif model.startswith("Meta-Llama"):
-    # from langchain_together import ChatTogether
-    # llm = ChatTogether(
-    #     temperature=temp,
-    #     model=f"meta-llama/{model}",
-    #     request_timeout=1000,
-    #     streaming=True if verbose else False,
-    #     callbacks=[StreamingStdOutCallbackHandler()] if verbose else None,
-    # )
     else:
-        raise ValueError(f"Invalid or Unsupported model name: {model}")
+        raise ValueError(f"Unrecognized or Unsupported model name: {model}")
     return llm

setup.py

@@ -24,7 +24,7 @@
         "langchain-community",
         "langchain-fireworks==0.1.7",
         "langchain-openai==0.1.19",
-        # "langchain-together==0.1.4",
+        "langchain-together==0.1.4",
         "matplotlib",
         "nbformat",
         "openai",