updated

agent.py

@@ -17,9 +17,16 @@ languages = {'English': 'en', 'Spanish': 'es', 'French': 'fr', 'German': 'de', '
              'Hebrew': 'he', 'Hindi': 'hi', 'Italian': 'it', 'Japanese': 'ja', 'Korean': 'ko', 'Portuguese': 'pt'}
 initial_prompt = "How can I help you today?"
 
-def create_assistant_tools(cfg, agent_config):        
+class AgentTools:
+    def __init__(self, _cfg, agent_config):
+        self.tools_factory = ToolsFactory()
+        self.agent_config = agent_config
+        self.cfg = _cfg
+        self.vec_factory = VectaraToolFactory(vectara_api_key=_cfg.api_key,
+                                              vectara_corpus_key=_cfg.corpus_key)
 
     def adjust_response_to_student(
+            self,
             text: str = Field(description='the text to adjust. may include citations in markdown format.'), 
             age: int = Field(description='the age of the student. An integer'), 
             style: str = Field(description='teaching style'), 
@@ -38,44 +45,45 @@ def create_assistant_tools(cfg, agent_config):
         For example, in the inquiry-based teaching style, choose to ask questions that encourage the student to think critically instead of repsonding directly with the answer.
         Or in the socratic teaching style, choose to ask questions that lead the student to the answer.
         Always respond in the {language} language.''' \
-            .replace("{style}", cfg.style) \
-            .replace("{language}", cfg.language) \
-            .replace("{student_age}", str(cfg.student_age))
+            .replace("{style}", self.cfg.style) \
+            .replace("{language}", self.cfg.language) \
+            .replace("{student_age}", str(self.cfg.student_age))
 
-        rephrase_text = ToolsCatalog(agent_config).rephrase_text
+        rephrase_text = ToolsCatalog(self.cfg).rephrase_text
         return rephrase_text(text, instructions)
 
 
-    class JusticeHarvardArgs(BaseModel):
-        query: str = Field(..., description="The user query.")
-
-    vec_factory = VectaraToolFactory(vectara_api_key=cfg.api_key,vectara_corpus_key=cfg.corpus_key)
-    summarizer = 'vectara-summary-ext-24-05-med-omni'
-    query_tool = vec_factory.create_rag_tool(
-        tool_name = "ask_about_justice_harvard",
-        tool_description = """
-        Answer questions about the justice, morality, politics and related topics,
-        based on transcripts of recordings from the Justice Harvard class that includes a lot of content on these topics.
-        """,
-        tool_args_schema = JusticeHarvardArgs,
-        reranker = "multilingual_reranker_v1", rerank_k = 100,
-        n_sentences_before = 2, n_sentences_after = 2, lambda_val = 0.005,
-        summary_num_results = 10,
-        vectara_summarizer = summarizer,
-        include_citations = True,
-    )
-
-    tools_factory = ToolsFactory()
-    return (
-            [tools_factory.create_tool(tool) for tool in
-                [
-                    adjust_response_to_student,
-                ]
-            ] +
-            tools_factory.standard_tools() + 
-            tools_factory.guardrail_tools() +
-            [query_tool]
-    )
+    def get_tools(self):
+        class JusticeHarvardArgs(BaseModel):
+            query: str = Field(..., description="The user query.")
+
+        vec_factory = VectaraToolFactory(vectara_api_key=self.cfg.api_key,vectara_corpus_key=self.cfg.corpus_key)
+        summarizer = 'vectara-summary-ext-24-05-med-omni'
+        query_tool = vec_factory.create_rag_tool(
+            tool_name = "ask_about_justice_harvard",
+            tool_description = """
+            Answer questions about the justice, morality, politics and related topics,
+            based on transcripts of recordings from the Justice Harvard class that includes a lot of content on these topics.
+            """,
+            tool_args_schema = JusticeHarvardArgs,
+            reranker = "multilingual_reranker_v1", rerank_k = 100,
+            n_sentences_before = 2, n_sentences_after = 2, lambda_val = 0.005,
+            summary_num_results = 10,
+            vectara_summarizer = summarizer,
+            include_citations = True,
+        )
+
+        tools_factory = ToolsFactory()
+        return (
+                [tools_factory.create_tool(tool) for tool in
+                    [
+                        self.adjust_response_to_student,
+                    ]
+                ] +
+                tools_factory.standard_tools() + 
+                tools_factory.guardrail_tools() +
+                [query_tool]
+        )
 
 def initialize_agent(_cfg, agent_progress_callback=None):
     bot_instructions = f"""
@@ -94,7 +102,7 @@ def initialize_agent(_cfg, agent_progress_callback=None):
     agent_config = AgentConfig()
     agent = Agent(
         agent_config=agent_config,
-        tools=create_assistant_tools(_cfg, agent_config=agent_config),
+        tools=AgentTools(_cfg, agent_config).get_tools(),
         topic="justice, morality, politics, and philosophy",
         custom_instructions=bot_instructions,
         agent_progress_callback=agent_progress_callback

requirements.txt

@@ -6,4 +6,4 @@ streamlit_feedback==0.1.3
 uuid==1.30
 langdetect==1.0.9
 langcodes==3.4.0
-vectara-agentic==0.2.0
+vectara-agentic==0.2.1