History summarizer

api/src/stampy_chat/chat.py

@@ -64,13 +64,14 @@ def format_messages(self, **kwargs: Any) -> List[BaseMessage]:
 class PrefixedPrompt(BaseChatPromptTemplate):
     """A prompt that will prefix any messages with a system prompt, but only if messages provided."""
 
+    transformer: Callable[[Any], BaseMessage] = lambda i: i
     messages_field: str
     prompt: str  # the system prompt to be used
 
     def format_messages(self, **kwargs: Any) -> List[BaseMessage]:
         history = kwargs[self.messages_field]
         if history and self.prompt:
-            return [SystemMessage(content=self.prompt)] + history
+            return [SystemMessage(content=self.prompt)] + [self.transformer(i) for i in history]
         return []
 
 
@@ -132,6 +133,12 @@ def prune(self) -> None:
                 pruned_memory, self.moving_summary_buffer
             )
 
+    def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:
+        """
+        Because of how wonderfully LangChain is written, this method was blowing up.
+        It's not needed, so it's getting the chop.
+        """
+
 
 class ModeratedChatPrompt(ChatPromptTemplate):
     """Wraps a prompt with an OpenAI moderation check which will raise an exception if fails."""
@@ -157,6 +164,49 @@ def get_model(**kwargs):
     return ChatOpenAI(openai_api_key=OPENAI_API_KEY, **kwargs)
 
 
+class LLMInputsChain(LLMChain):
+
+    inputs: Dict[str, Any] = {}
+
+    def _call(self, inputs: Dict[str, Any], run_manager=None):
+        self.inputs = inputs
+        return super()._call(inputs, run_manager)
+
+    def _acall(self, inputs: Dict[str, Any], run_manager=None):
+        self.inputs = inputs
+        return super()._acall(inputs, run_manager)
+
+    def create_outputs(self, llm_result) -> List[Dict[str, Any]]:
+        result = super().create_outputs(llm_result)
+        return [dict(self.inputs, **r) for r in result]
+
+
+def make_history_summary(settings):
+    model = get_model(
+        streaming=False,
+        max_tokens=settings.maxHistorySummaryTokens,
+        model=settings.completions
+    )
+    summary_prompt = PrefixedPrompt(
+        input_variables=['history'],
+        messages_field='history',
+        prompt=settings.history_summary_prompt,
+        transformer=lambda m: ChatMessage(**m)
+    )
+    return LLMInputsChain(
+        llm=model,
+        verbose=False,
+        output_key='history_summary',
+        prompt=ModeratedChatPrompt.from_messages([
+            summary_prompt,
+            ChatPromptTemplate.from_messages([
+                ChatMessagePromptTemplate.from_template(template='Q: {query}', role='user'),
+            ]),
+            SystemMessage(content="Reply in one sentence only"),
+        ]),
+    )
+
+
 def make_prompt(settings, chat_model, callbacks):
     """Create a proper prompt object will all the nessesery steps."""
     # 1. Create the context prompt from items fetched from pinecone
@@ -176,7 +226,7 @@ def make_prompt(settings, chat_model, callbacks):
     query_prompt = ChatPromptTemplate.from_messages(
         [
             SystemMessage(content=settings.question_prompt),
-            ChatMessagePromptTemplate.from_template(template='Q: {query}', role='user'),
+            ChatMessagePromptTemplate.from_template(template='Q: {history_summary}: {query}', role='user'),
         ]
     )
 
@@ -247,7 +297,7 @@ def run_query(session_id: str, query: str, history: List[Dict], settings: Settin
         model=settings.completions
     )
 
-    chain = LLMChain(
+    chain = make_history_summary(settings) | LLMChain(
         llm=chat_model,
         verbose=False,
         prompt=make_prompt(settings, chat_model, callbacks),

api/src/stampy_chat/followups.py

@@ -74,7 +74,7 @@ class Config:
 
     @property
     def input_keys(self) -> List[str]:
-        return ['query', 'text']
+        return ['query', 'text', 'history_summary']
 
     @property
     def output_keys(self) -> List[str]:

api/src/stampy_chat/settings.py

@@ -9,7 +9,7 @@
 
 SOURCE_PROMPT = (
     "You are a helpful assistant knowledgeable about AI Alignment and Safety. "
-    "Please give a clear and coherent answer to the user's questions.(written after \"Q:\") "
+    "Please give a clear and coherent answer to the user's questions. (written after \"Q:\") "
     "using the following sources. Each source is labeled with a letter. Feel free to "
     "use the sources in any order, and try to use multiple sources in your answers.\n\n"
 )
@@ -19,6 +19,13 @@
     "These sources only apply to the last question. Any sources used in previous answers "
     "are invalid."
 )
+HISTORY_SUMMARIZE_PROMPT = (
+    "You are a helpful assistant knowledgeable about AI Alignment and Safety. "
+    "Please summarize the following chat history (written after \"H:\") in one "
+    "sentence so as to put the current questions (written after \"Q:\") in context. "
+    "Please keep things as terse as possible."
+    "\nH:"
+)
 
 QUESTION_PROMPT = (
     "In your answer, please cite any claims you make back to each source "
@@ -47,6 +54,7 @@
 DEFAULT_PROMPTS = {
     'context': SOURCE_PROMPT,
     'history': HISTORY_PROMPT,
+    'history_summary': HISTORY_SUMMARIZE_PROMPT,
     'question': QUESTION_PROMPT,
     'modes': PROMPT_MODES,
 }
@@ -72,8 +80,9 @@ def __init__(
             topKBlocks=None,
             maxNumTokens=None,
             min_response_tokens=10,
-            tokensBuffer=50,
+            tokensBuffer=100,
             maxHistory=10,
+            maxHistorySummaryTokens=200,
             historyFraction=0.25,
             contextFraction=0.5,
             **_kwargs,
@@ -93,6 +102,9 @@ def __init__(
         self.maxHistory = maxHistory
         """the max number of previous interactions to use as the history"""
 
+        self.maxHistorySummaryTokens = maxHistorySummaryTokens
+        """the max number of tokens to be used on the history summary"""
+
         self.historyFraction = historyFraction
         """the (approximate) fraction of num_tokens to use for history text before truncating"""
 
@@ -153,6 +165,10 @@ def context_prompt(self):
     def history_prompt(self):
         return self.prompts['history']
 
+    @property
+    def history_summary_prompt(self):
+        return self.prompts['history_summary']
+
     @property
     def mode_prompt(self):
         return self.prompts['modes'].get(self.mode, '')
@@ -174,7 +190,7 @@ def history_tokens(self):
     @property
     def max_response_tokens(self):
         available_tokens = (
-            self.maxNumTokens -
+            self.maxNumTokens - self.maxHistorySummaryTokens -
             self.context_tokens - len(self.encoder.encode(self.context_prompt)) -
             self.history_tokens - len(self.encoder.encode(self.history_prompt)) -
             len(self.encoder.encode(self.question_prompt))

web/src/components/chat.tsx

@@ -63,6 +63,8 @@ export const ChatResponse = ({
       return <p>Loading: Sending query...</p>;
     case "semantic":
       return <p>Loading: Performing semantic search...</p>;
+    case "history":
+      return <p>Loading: Processing history...</p>;
     case "context":
       return <p>Loading: Creating context...</p>;
     case "prompt":

web/src/components/settings.tsx

@@ -122,6 +122,14 @@ export const ChatSettings = ({
         max={settings.maxNumTokens}
         updater={updateNum("tokensBuffer")}
       />
+      <NumberInput
+        field="maxHistorySummaryTokens"
+        value={settings.maxHistorySummaryTokens}
+        label="The max number of tokens to use for the history summary"
+        min="0"
+        max={settings.maxNumTokens}
+        updater={updateNum("maxHistorySummaryTokens")}
+      />
 
       <SectionHeader text="Prompt options" />
       <NumberInput
@@ -178,6 +186,14 @@ export const ChatPrompts = ({
 
   return (
     <div className="chat-prompts mx-5 w-[400px] flex-none border-2 p-5 outline-black">
+      <details>
+        <summary>History summary prompt</summary>
+        <TextareaAutosize
+          className="border-gray w-full border px-1"
+          value={settings?.prompts?.history_summary}
+          onChange={updatePrompt("history_summary")}
+        />
+      </details>
       <details open>
         <summary>Source prompt</summary>
         <TextareaAutosize

web/src/hooks/useSettings.ts

@@ -21,6 +21,12 @@ const DEFAULT_PROMPTS = {
     'Before the question ("Q: "), there will be a history of previous questions and answers. ' +
     "These sources only apply to the last question. any sources used in previous answers " +
     "are invalid.",
+  history_summary:
+    "You are a helpful assistant knowledgeable about AI Alignment and Safety. " +
+    'Please summarize the following chat history (written after "H:") in one ' +
+    'sentence so as to put the current questions (written after "Q:") in context. ' +
+    "Please keep things as terse as possible." +
+    "\nH:",
   question:
     "In your answer, please cite any claims you make back to each source " +
     "using the format: [a], [b], etc. If you use multiple sources to make a claim " +
@@ -131,6 +137,7 @@ const SETTINGS_PARSERS = {
   maxNumTokens: withDefault(MODELS["gpt-3.5-turbo"]?.maxNumTokens),
   tokensBuffer: withDefault(50), //  the number of tokens to leave as a buffer when calculating remaining tokens
   maxHistory: withDefault(10), //  the max number of previous items to use as history
+  maxHistorySummaryTokens: withDefault(200), //  the max number of tokens to use in the history summary
   historyFraction: withDefault(0.25), //  the (approximate) fraction of num_tokens to use for history text before truncating
   contextFraction: withDefault(0.5), //  the (approximate) fraction of num_tokens to use for context text before truncating
 };