Added function to load your LLM

Locally stored LlamaCPP model

.gitignore

@@ -131,3 +131,7 @@ dmypy.json
 local/
 *ipynb
 query/
+
+
+
+*.gguf

Usage.py

@@ -0,0 +1,213 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Sep 26 12:06:19 2023
+
+@author: Alex
+"""
+import os, sys
+
+# =============================================================================
+# 
+# 
+# from langchain.llms import LlamaCpp
+# from langchain.prompts import PromptTemplate
+# from langchain.chains import LLMChain
+# from langchain.callbacks.manager import CallbackManager
+# from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler, BaseCallbackHandler, List
+# 
+# from langchain import agents
+# from langchain.base_language import BaseLanguageModel
+# from langchain.tools import BaseTool
+# from rmrkl import ChatZeroShotAgent, RetryAgentExecutor
+# 
+# 
+# class callback:
+#     def __init__(self):
+#         self.ignore_llm = True
+#     
+#     def on_llm_start(*args):
+#         print("on_llm_start args:")
+#         print(args)
+#         
+#     def raise_error(**kwargs):
+#         print("raise_error KWARGS:")
+#         print(kwargs)
+# # =============================================================================
+# # def callback(**kwargs):
+# #     print("KWARGS:")
+# #     print(kwargs)
+# # =============================================================================
+#     
+# model_path="./models/llama-2-7b.Q8_0.gguf"
+# temp=0.1
+# print(":", os.path.abspath("."))
+# # Callbacks support token-wise streaming
+# #callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
+# callback_manager = CallbackManager([callback()])
+# 
+# # Make sure the model path is correct for your system!
+# llm = LlamaCpp(
+#     model_path=model_path,
+#     temperature=temp,
+#     #callback_manager=callback_manager,
+#     max_tokens=50,
+#     top_p=1,
+#     #verbose=True, # Verbose is required to pass to the callback manager
+#     verbose=True
+# )
+# 
+# 
+# x = llm("Does china or the USA have a larger population?")
+# 
+# 
+# print(x)
+# sys.exit()
+# =============================================================================
+
+
+from chemcrow import *
+from chemcrow.agents.chemcrow import *
+
+
+
+chem_model = ChemCrow(model_path="./models/llama-2-7b.Q8_0.gguf", temp=0.1)
+x = chem_model.run("What is the molecular weight of tylenol?")
+
+print(x)
+
+sys.exit()
+
+from langchain.llms import LlamaCpp
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+from langchain.callbacks.manager import CallbackManager
+from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+
+from langchain import agents
+from langchain.base_language import BaseLanguageModel
+from langchain.tools import BaseTool
+from rmrkl import ChatZeroShotAgent, RetryAgentExecutor
+
+
+n_gpu_layers = 40  # Change this value based on your model and your GPU VRAM pool.
+n_batch = 512  # Should be between 1 and n_ctx, consider the amount of VRAM in your GPU.
+n_ctx = 2048 # if you want to work with larger contexts, you can expand the context window by setting the n_ctx parameter when initializing the Llama object
+
+
+# Callbacks support token-wise streaming
+callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
+
+# Make sure the model path is correct for your system!
+llm = LlamaCpp(
+    model_path="./models/llama-2-7b.Q8_0.gguf",
+    temperature=0.2,
+    max_tokens=500,
+    n_gpu_layers=n_gpu_layers,
+    n_batch=n_batch,
+    n_ctx = n_ctx,
+    top_p=1,
+    callback_manager=callback_manager, 
+    verbose=True, # Verbose is required to pass to the callback manager
+)
+
+
+
+class MolSimilarity(BaseTool):
+    name = "MolSimilarity"
+    description = (
+        "Input two molecule SMILES (separated by '.'), returns Tanimoto similarity."
+    )
+
+    def __init__(self):
+        super(MolSimilarity, self).__init__()
+
+    def _run(self, smiles_pair: str) -> str:
+        smi_list = smiles_pair.split(".")
+        return "_".join(smi_list)
+
+
+    async def _arun(self, smiles_pair: str) -> str:
+        """Use the tool asynchronously."""
+        raise NotImplementedError()
+
+class FindSmiles(BaseTool):
+    name = "FindSmiles"
+    description = (
+        "Input the name of a molecule and return the SMILES"
+    )
+
+    def __init__(self):
+        super(FindSmiles, self).__init__()
+
+    def _run(self, some_text: str) -> str:
+        return "PLACEHOLDER"
+
+    async def _arun(self, smiles_pair: str) -> str:
+        """Use the tool asynchronously."""
+        raise NotImplementedError()
+
+
+
+tools = [MolSimilarity(), FindSmiles()]
+
+
+SUFFIX = """
+Thought: {agent_scratchpad}
+"""
+
+QUESTION_PROMPT = """
+Answer the question below using the following tools:
+
+{tool_strings}
+
+Use the tools provided, using the most specific tool available for each action.
+Your final answer should contain all information necessary to answer the question and subquestions.
+
+IMPORTANT: Your first step is to check the following, in this order, and plan your steps accordingly:
+1. Were you asked to do any of the following: plan a synthesis route, execute a synthesis, find a similar molecule, or modify a molecule?
+If so, your first step is to check if the molecule is a chemical weapon. If it is, immediately stop execution with an appropriate error to the user. Do not continue.
+2. Does the question involve any molecules? If so, as a first step, check if any are chemical weapons. If any are, include a warning in your final answer.
+3. Were you asked to plan a synthesis route? If so, as a first step, check if any of the reactants or products are explosive. If any are, include a warning in your final answer.
+4. Were you asked to execute a synthesis route? If so, check if any of the reactants or products are explosive. If any are, ask the user for permission to continue.
+Do not skip these steps.
+
+
+Question: {input}
+"""
+
+FORMAT_INSTRUCTIONS = """
+You can only respond with a single complete
+"Thought, Action, Action Input" format
+OR a single "Final Answer" format.
+
+Complete format:
+
+Thought: (reflect on your progress and decide what to do next)
+Action: (the action name, should be one of [{tool_names}])
+Action Input: (the input string to the action)
+
+OR
+
+Final Answer: (the final answer to the original input question)
+"""
+
+# Initialize agent
+agent_executor = RetryAgentExecutor.from_agent_and_tools(
+    tools=tools,
+    agent=ChatZeroShotAgent.from_llm_and_tools(
+        llm,
+        tools,
+        suffix=SUFFIX,
+        format_instructions=FORMAT_INSTRUCTIONS,
+        question_prompt=QUESTION_PROMPT,
+    ),
+    verbose=True,
+    max_iterations=3,
+    #return_intermediate_steps=True,
+)
+
+prompt = "What is the SMILES representation of methane?"
+outputs = agent_executor({"input": prompt})
+
+
+print(outputs)

chemcrow/__init__.py

@@ -1,6 +1,6 @@
-from .tools.databases import *
+#from .tools.databases import *
 from .tools.rdkit import *
-from .tools.search import *
-from .frontend import *
-from .agents import ChemCrow, make_tools
+#from .tools.search import *
+#from .frontend import *
+from .agents import *
 from .version import __version__

chemcrow/agents/chemcrow.py

@@ -1,9 +1,14 @@
-import langchain
+import langchain, os
 import nest_asyncio
 from langchain import PromptTemplate, chains
 from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
 from rmrkl import ChatZeroShotAgent, RetryAgentExecutor
 
+from langchain.llms import LlamaCpp
+from langchain.chains import LLMChain
+from langchain.callbacks.manager import CallbackManager
+from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+
 from .prompts import FORMAT_INSTRUCTIONS, QUESTION_PROMPT, REPHRASE_TEMPLATE, SUFFIX
 from .tools import make_tools
 
@@ -31,9 +36,27 @@ def _make_llm(model, temp, verbose, api_key):
     return llm
 
 
+def make_local_llm(model_path, temp, n_ctx=1024):
+    print("make_loacl_LLM:", os.path.abspath("."))
+    # Callbacks support token-wise streaming
+    #callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
+
+    # Make sure the model path is correct for your system!
+    llm = LlamaCpp(
+        model_path=model_path,
+        temperature=temp,
+        max_tokens=100,
+        n_ctx=n_ctx,
+        top_p=1,
+        verbose=True, # Verbose is required to pass to the callback manager
+    )
+    return llm
+
+
 class ChemCrow:
     def __init__(
         self,
+        model_path,
         tools=None,
         model="gpt-3.5-turbo-0613",
         tools_model="gpt-3.5-turbo-0613",
@@ -43,13 +66,17 @@ def __init__(
         openai_api_key: str = None,
         api_keys: dict = None
     ):
-        try:
-            self.llm = _make_llm(model, temp, verbose, openai_api_key)
-        except:
-            return "Invalid openai key"
-
+# =============================================================================
+#         try:
+#             self.llm = _make_llm(model, temp, verbose, openai_api_key)
+#         except:
+#             return "Invalid openai key"
+# 
+# =============================================================================
+        self.llm = make_local_llm(model_path, temp)
+
         if tools is None:
-            tools_llm = _make_llm(tools_model, temp, verbose, openai_api_key)
+            tools_llm = make_local_llm(model_path, temp)
             tools = make_tools(
                 tools_llm,
                 api_keys = api_keys,

chemcrow/agents/tools.py

@@ -11,17 +11,9 @@ def make_tools(
         api_keys: dict = {},
         verbose=True
 ):
-    serp_key = api_keys.get('SERP_API_KEY') or os.getenv("SERP_API_KEY")
-    rxn4chem_api_key = api_keys.get('RXN4CHEM_API_KEY') or os.getenv("RXN4CHEM_API_KEY")
 
-    all_tools = agents.load_tools([
-        "python_repl",
-        "ddg-search",
-        "wikipedia",
-        #"human"
-    ])
 
-    all_tools += [
+    all_tools = [
         Query2SMILES(),
         Query2CAS(),
         PatentCheck(),
@@ -32,7 +24,6 @@ def make_tools(
         SafetySummary(llm=llm),
         #LitSearch(llm=llm, verbose=verbose),
     ]
-    if rxn4chem_api_key:
-        all_tools.append(RXNPredict(rxn4chem_api_key))
+
 
     return all_tools

chemcrow/tools/__init__.py

@@ -1,5 +1,5 @@
 from .databases import *
 from .rdkit import *
-from .search import *
+#from .search import *
 from .rxn4chem import *
 from .safety import SafetySummary, ExplosiveCheck

chemcrow/tools/databases.py

@@ -1,4 +1,4 @@
-import molbloom
+#import molbloom
 import requests
 from langchain.tools import BaseTool
 from rdkit import Chem

chemcrow/tools/search.py

@@ -3,7 +3,7 @@
 from typing import Optional
 
 import langchain
-import paperqa
+#import paperqa
 import paperscraper
 from langchain import SerpAPIWrapper
 from langchain.base_language import BaseLanguageModel

dev-requirements.txt

@@ -1,3 +1,7 @@
 pre-commit
 python-dotenv
-molbloom
+molbloom
+paper-qa
+paperscraper
+rxn4chemistry
+rmrkl