Chat optimization around tokens and keeping important messages in history

alphastats/gui/pages/06_LLM.py

@@ -20,7 +20,7 @@
     init_session_state,
     sidebar_info,
 )
-from alphastats.llm.llm_integration import LLMIntegration, Models
+from alphastats.llm.llm_integration import LLMIntegration, MessageKeys, Models, Roles
 from alphastats.llm.prompts import get_initial_prompt, get_system_message
 from alphastats.plots.plot_utils import PlotlyObject
 
@@ -82,6 +82,14 @@ def llm_config():
                 else:
                     st.error(f"Connection to {model_name} failed: {str(error)}")
 
+        st.number_input(
+            "Maximal number of tokens",
+            value=st.session_state[StateKeys.MAX_TOKENS],
+            min_value=1,
+            max_value=100000,
+            key=StateKeys.MAX_TOKENS,
+        )
+
         if current_model != st.session_state[StateKeys.MODEL_NAME]:
             st.rerun(scope="app")
 
@@ -216,6 +224,7 @@ def llm_config():
             base_url=OLLAMA_BASE_URL,
             dataset=st.session_state[StateKeys.DATASET],
             genes_of_interest=list(regulated_genes_dict.keys()),
+            max_tokens=st.session_state[StateKeys.MAX_TOKENS],
         )
 
         st.session_state[StateKeys.LLM_INTEGRATION][model_name] = llm_integration
@@ -226,7 +235,7 @@ def llm_config():
         )
 
         with st.spinner("Processing initial prompt..."):
-            llm_integration.chat_completion(initial_prompt)
+            llm_integration.chat_completion(initial_prompt, pinned=True)
 
         st.rerun(scope="app")
     except AuthenticationError:
@@ -237,7 +246,11 @@ def llm_config():
 
 
 @st.fragment
-def llm_chat(llm_integration: LLMIntegration, show_all: bool = False):
+def llm_chat(
+    llm_integration: LLMIntegration,
+    show_all: bool = False,
+    show_inidvidual_tokens: bool = False,
+):
     """The chat interface for the LLM analysis."""
 
     # TODO dump to file -> static file name, plus button to do so
@@ -246,10 +259,28 @@ def llm_chat(llm_integration: LLMIntegration, show_all: bool = False):
     # Alternatively write it all in one pdf report using e.g. pdfrw and reportlab (I have code for that combo).
 
     # no. tokens spent
+    total_tokens = 0
+    pinned_tokens = 0
     for message in llm_integration.get_print_view(show_all=show_all):
-        with st.chat_message(message["role"]):
-            st.markdown(message["content"])
-            for artifact in message["artifacts"]:
+        with st.chat_message(message[MessageKeys.ROLE]):
+            st.markdown(message[MessageKeys.CONTENT])
+            tokens = llm_integration.estimate_tokens([message])
+            if message[MessageKeys.IN_CONTEXT]:
+                total_tokens += tokens
+            if message[MessageKeys.PINNED]:
+                pinned_tokens += tokens
+            if message[MessageKeys.PINNED] or show_inidvidual_tokens:
+                token_message = ""
+                if message[MessageKeys.PINNED]:
+                    token_message += ":pushpin: "
+                if show_inidvidual_tokens:
+                    token_message += f"*estimated tokens: {str(tokens)}*"
+                st.markdown(token_message)
+            if not message[MessageKeys.IN_CONTEXT]:
+                st.markdown(
+                    "**This message is no longer in context due to token limitations.**"
+                )
+            for artifact in message[MessageKeys.ARTIFACTS]:
                 if isinstance(artifact, pd.DataFrame):
                     st.dataframe(artifact)
                 elif isinstance(
@@ -260,7 +291,16 @@ def llm_chat(llm_integration: LLMIntegration, show_all: bool = False):
                     st.warning("Don't know how to display artifact:")
                     st.write(artifact)
 
+    st.markdown(
+        f"*total tokens used: {str(total_tokens)}, tokens used for pinned messages: {str(pinned_tokens)}*"
+    )
+
     if prompt := st.chat_input("Say something"):
+        with st.chat_message(Roles.USER):
+            st.markdown(prompt)
+            st.markdown(
+                f"*estimated tokens: {str(llm_integration.estimate_tokens([{MessageKeys.CONTENT:prompt}]))}*"
+            )
         with st.spinner("Processing prompt..."):
             llm_integration.chat_completion(prompt)
         st.rerun(scope="fragment")
@@ -273,10 +313,22 @@ def llm_chat(llm_integration: LLMIntegration, show_all: bool = False):
     )
 
 
-show_all = st.checkbox(
-    "Show system messages",
-    key="show_system_messages",
-    help="Show all messages in the chat interface.",
-)
+c1, c2 = st.columns((1, 2))
+with c1:
+    show_all = st.checkbox(
+        "Show system messages",
+        key="show_system_messages",
+        help="Show all messages in the chat interface.",
+    )
+with c2:
+    show_inidvidual_tokens = st.checkbox(
+        "Show individual token estimates",
+        key="show_individual_tokens",
+        help="Show individual token estimates for each message.",
+    )
 
-llm_chat(st.session_state[StateKeys.LLM_INTEGRATION][model_name], show_all)
+llm_chat(
+    st.session_state[StateKeys.LLM_INTEGRATION][model_name],
+    show_all,
+    show_inidvidual_tokens,
+)

alphastats/gui/utils/ui_helper.py

@@ -136,6 +136,9 @@ def init_session_state() -> None:
             DefaultStates.SELECTED_UNIPROT_FIELDS.copy()
         )
 
+    if StateKeys.MAX_TOKENS not in st.session_state:
+        st.session_state[StateKeys.MAX_TOKENS] = 10000
+
 
 class StateKeys(metaclass=ConstantsClass):
     USER_SESSION_ID = "user_session_id"
@@ -152,5 +155,6 @@ class StateKeys(metaclass=ConstantsClass):
     LLM_INTEGRATION = "llm_integration"
     ANNOTATION_STORE = "annotation_store"
     SELECTED_UNIPROT_FIELDS = "selected_uniprot_fields"
+    MAX_TOKENS = "max_tokens"
 
     ORGANISM = "organism"  # TODO this is essentially a constant