updated reqs

agent.py

@@ -175,7 +175,7 @@ def create_assistant_tools(cfg):
 
     ask_caselaw = vec_factory.create_rag_tool(
         tool_name = "ask_caselaw",
-        tool_description = "A tool for asking questions about case law in Alaska.",
+        tool_description = "A tool for asking questions about case law in Alaska. ",
         tool_args_schema = QueryCaselawArgs,
         reranker = "chain", rerank_k = 100,
         rerank_chain = [
@@ -195,7 +195,7 @@ def create_assistant_tools(cfg):
         n_sentences_before = 2, n_sentences_after = 2, lambda_val = 0.005,
         summary_num_results = 15,
         vectara_summarizer = summarizer,
-        include_citations = False,
+        include_citations = True,
     )
 
     tools_factory = ToolsFactory()
@@ -232,8 +232,9 @@ def initialize_agent(_cfg, agent_progress_callback=None):
       Do not use your own knowledge to answer questions.
     - If the ask_caselaw tool responds that it does not have enough information to answer the query,
       try to rephrase the query and call the tool again.
-    - When presenting the output from ask_caselaw tool, use the metadata provided in the tool's response (references). 
-      For example you can include citations, decision date, or case name.
+    - If the ask_caselaw tool returns a response along with a ist of references mentioned in its response (in [N] format), 
+      Format your response to focus on the main response, and use the metadata (such as citations, decision date, or case name) 
+      in each relevant reference to provide more context in your response.
     - Citations include 3 components: volume number, reporter, and first page. 
       Here are some examples: '253 P.2d 136', '10 Alaska 11', '6 C.M.A. 3'
     - Never use your internal knowledge to guess citations. Only use citations information provided by a tool or the user.
@@ -259,4 +260,5 @@ def initialize_agent(_cfg, agent_progress_callback=None):
         custom_instructions=legal_assistant_instructions,
         agent_progress_callback=agent_progress_callback,
     )
+    agent.report()
     return agent

requirements.txt

@@ -1,9 +1,10 @@
 omegaconf==2.3.0
 python-dotenv==1.0.1
-streamlit==1.39.0
+streamlit==1.41.1
 streamlit_pills==0.3.0
 streamlit-feedback==0.1.3
 uuid==1.30
 langdetect==1.0.9
 langcodes==3.4.0
-vectara-agentic==0.1.19
+vectara-agentic==0.1.21
+anthropic==0.40.0

utils.py

@@ -31,8 +31,11 @@ def thumbs_feedback(feedback, **kwargs):
 
 def send_amplitude_data(user_query, bot_response, demo_name, feedback=None):
     # Send query and response to Amplitude Analytics
+    amplitude_token = os.environ.get('AMPLITUDE_TOKEN', None)
+    if amplitude_token is None:
+        return
     data = {
-        "api_key": os.getenv('AMPLITUDE_TOKEN'),
+        "api_key": amplitude_token,
         "events": [{
             "device_id": st.session_state.device_id,
             "event_type": "submitted_query",