Add v1 of function-driven implementation for Ansari

agents/ansari.py

@@ -45,7 +45,6 @@ def update_message_history(self, inp):
 
         else: 
             print(f'In else clause {inp}')
-            )
             if ' flag ' in inp:
                 print(f'In flag clause {inp}')
                 inp = inp + '\nIt seems the user asked to flag something. Ask them what they want to flag and why.\n'

agents/ansari_fn.py

@@ -0,0 +1,58 @@
+from hermetic.agents.openai_chat_agent import OpenAIChatAgent
+from hermetic.core.prompt_mgr import PromptMgr
+from tools.kalemat import Kalemat
+import json
+
+NAME = 'ansarifn'
+class AnsariFn(OpenAIChatAgent): 
+    def __init__(self, env):
+        super().__init__(model = 'gpt-4',
+            environment = env, id=NAME)
+        env.add_agent(NAME, self)
+        self.kalemat = self.env.tools['kalemat']
+        self.pm = self.env.prompt_mgr
+        sys_msg = self.pm.bind('system_msg')
+        functions = [
+            {"name": "search_quran",
+            "description": "Search the Qur'an for relevant verses. This should be extracted from the question.",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "query": {
+                        "type": "string",
+                        "description": "The topic to search the Qur'an for ",
+                    },
+                },
+                "required": ["query"],
+            },
+          }
+        ]
+        self.functions = functions
+
+        self.message_history = [{
+            'role': 'system',
+            'content': sys_msg.render()
+        }]
+
+    def greet(self):
+        self.greeting = self.pm.bind('greeting')
+        return self.greeting.render()
+
+    def process_fn_call(self, orig_question, function_name, function_arguments):
+        if function_name == 'search_quran':
+            args = json.loads(function_arguments)
+            query = args['query']
+            qt =  self.pm.bind('quran_result')
+            results = qt.render(kalemat_results=self.kalemat.run_as_string(query), 
+                                orig_question=orig_question)
+            # print(f'Function call received! Results: {results}')
+            return results
+
+
+    def update_message_history(self, inp):
+        # First stage is to pass in the functions. 
+        #kalemat = self.env.tools['kalemat']
+        self.message_history.append({
+            'role': 'user', 
+            'content': inp
+        })

agents/quran_filter.py

@@ -0,0 +1,14 @@
+from hermetic.agents.openai_chat_agent import OpenAIChatAgent
+from hermetic.core.prompt_mgr import PromptMgr
+
+MODEL = 'gpt-4'
+NAME = 'quran_filter'
+class QuranFilter(OpenAIChatAgent): 
+    def __init__(self, env):
+        super().__init__(model = MODEL, environment = env, id=NAME)
+
+        self.env = env
+        self.env.add_agent(NAME, self)
+        self.filter = self.env.prompt_mgr.bind('filter_relevant_verses')
+
+    def process_input

main_stdio.py

@@ -1,11 +1,22 @@
 from hermetic.presenters.stdio_presenter import StdioPresenter
-from agents.ansari import Ansari
 from hermetic.core.prompt_mgr import PromptMgr
 from hermetic.core.environment import Environment
+from agents.ansari_fn import AnsariFn
+from tools.kalemat import Kalemat
+from hermetic.core.environment import Environment
+from hermetic.stores.file_store import FileStore
+from hermetic.core.prompt_mgr import PromptMgr
+
+env = Environment(store =  FileStore(root_dir = 'ansari-stores'), 
+                  prompt_mgr = PromptMgr(hot_reload=True))
+
+# This work involves 3 agents, with Ansari as primary. 
+kalemat = Kalemat(env)
+ansari = AnsariFn(env)
+
+
+env.set_primary_agent('ansarifn')
 
-env = Environment(prompt_mgr=PromptMgr())
-aa = Ansari(env)
-env.set_primary_agent(aa)
 sp = StdioPresenter()
 
 sp.present(env)

resources/prompts/filter_relevant_verses.txt

@@ -0,0 +1,7 @@
+Below are ayahs from the Qur'an. Some of them are relevant
+to answering the question: {question}. 
+
+In your output include all the ones that are relevant 
+and drop any that are irrelevant.
+
+{ayahs}

resources/prompts/quran_result.txt

@@ -0,0 +1,7 @@
+Here are the results from the quran: 
+
+{kalemat_results}
+
+Not all answers may be relevant. 
+
+Would you like me to answer your original question: \"{orig_question}\" from these results?'

resources/prompts/system_msg_fn.txt

@@ -0,0 +1,29 @@
+You are a helpful assistant. Your name is Ansari. You help Muslims become stronger in their faith. 
+
+Respond to questions with information drawn from the Hadith and Qur'an and opinions
+from respected classical and modern scholars in the Sunni tradition. 
+
+Respected classical scholars of the Sunni Islamic tradition inclue
+Al Ghazali, Ibn Al Qayyim, Ibn Taymiyah, Imam Shafiee, Imam Nawawi, Imam Abu Hanifah, 
+Ibn Hajr al Asqalani, Imam Ahmad bin Hanbal, Imam Malik, Ibn Hazm and others. 
+
+You also draw from the work of modern Islamic scholars including Yusuf
+Al Qaradawi, Yasir Qadhi, Ma'in Al Qudah, Shu'aib Al Arnaout, Hamza Yusuf, Zaid Shakir,
+Taqiuddin Usmani, Muhammad Shinqeeti, Ismail Menk, Omar Suleiman, Salman Al-Awdah, 
+Jamaaluddin Zarabozo and Yaser Birjas. 
+
+Be gentle, forbearing and non-judgemental. 
+
+Be particularly careful about something is obligatory or prohibited. Evidences 
+are required to say something is obligatory or prohibited. 
+The evidence must directly support the assertion. 
+
+Do not say 'Some scholars say' but rather be specific about which scholars say something. 
+
+Be concise. 
+
+When presenting the Qur'an, present it as in the following example: 
+
+Ayah: 55:22
+Arabic: مِنْهُمَا يَخْرُجُ اللُّؤْلُؤُ وَالْمَرْجَانُ
+English: From both of them emerge pearl and coral.

tools/kalemat.py

@@ -30,12 +30,12 @@ def run(self, query: str, numResults: int=5,getText: int=1):
 
         return response.json()
 
-    def run_as_string(self, query: str, numResults: int=5,getText: int=1):
+    def run_as_string(self, query: str, numResults: int=10, getText: int=1):
         def pp_ayah(ayah):
                 ayah_num = ayah['id']
                 ayah_ar = ayah['text']
                 ayah_en = ayah['en_text']
-                result = f'Ayah: {ayah_num}\nArabic Text: {ayah_ar}\nEnglish Text: {ayah_en}\n'
+                result = f'Ayah: {ayah_num}\nArabic Text: {ayah_ar}\nEnglish Text: {ayah_en}\n\n'
                 return result
         results =  self.run(query, numResults, getText)
         rstring = '\n'.join([pp_ayah(r) for r in results])