fix actions impl

src/lmql/lib/actions.py

@@ -21,7 +21,7 @@ async def wiki(q: str, lookup = None):
         extract = list(pages.values())[0]["extract"][:280]
         return extract
     except:
-        return "No results"
+        return "No results (try differently)"
 
 async def calc(expr: str):
     """
@@ -117,8 +117,8 @@ async def inline_segment(fcts):
             return SEGMENT
         else:
             "[CALL]"
-            result = CALL.rsplit("|", 1)[-1].strip()
-            return SEGMENT[:-len(DELIMITER)] + CALL + DELIMITER_END + " " + result
+            result = CALL.split("|", 1)[1]
+            return SEGMENT[:-len(DELIMITER)] + CALL + DELIMITER_END
     where
         STOPS_AT(SEGMENT, DELIMITER) and fct_call(CALL, fcts)
     '''
@@ -147,11 +147,10 @@ async def inline_use(fcts):
         truncated = ""
         while True: 
             "[SEGMENT]"
-            if not SEGMENT.endswith(DELIMITER):
+            if not SEGMENT.endswith(DELIMITER_END):
                 " " # seems to be needed for now
-                return truncated + SEGMENT[:-len("END")]
-            truncated += SEGMENT[:-len("END")]
-        print("truncated", [truncated])
+                return truncated + SEGMENT
+            truncated += SEGMENT
         return truncated
     where
         inline_segment(SEGMENT, fcts)

src/lmql/runtime/interpreter.py

@@ -1063,7 +1063,7 @@ def subinterpreter(self, identifier, prompt, fct, captures):
         if key in self.subinterpreters.keys():
             return self.subinterpreters[key]
         else:
-            subinterpreter = SubInterpreter(fct, self, captures)
+            subinterpreter = SubInterpreter(fct, self, captures, model=self.model, model_identifier=self.model_identifier)
 
             # inherits interpreter attributes from parent
             subinterpreter.tokenizer = self.tokenizer
@@ -1165,7 +1165,7 @@ def __exit__(self, type, value, traceback):
 PromptInterpreter.main = None
 
 class SubInterpreter(PromptInterpreter):
-    def __init__(self, fct, parent_interpreter: PromptInterpreter, captures: Dict[str, Any]):
+    def __init__(self, fct, parent_interpreter: PromptInterpreter, captures: Dict[str, Any], model: str = None, model_identifier: str = None):
         super().__init__(context=parent_interpreter)
         self.query_fct = fct
         self.fct = fct.fct
@@ -1177,6 +1177,13 @@ def __init__(self, fct, parent_interpreter: PromptInterpreter, captures: Dict[st
         self.user_data_key = "head[sub-" + str(id(self)) + "]"
 
         self.initial_subprompt_ids = None
+
+        self.model = model
+        self.model_identifier = model_identifier
+
+    def set_model(self, model):
+        # ignore set_model for subinterpreters
+        pass
 
     def user_data_layer(self, *sqs):
         return UserDataLayer(sqs, self.user_data_mappings)

src/lmql/ui/playground/src/Explore.jsx

@@ -85,7 +85,7 @@ export const Dialog = styled.div`
 
 const ExploreDialog = styled(Dialog)`
   width: 800pt;
-  height: 550pt;
+  height: 700pt;
   max-height: 100vh;
   max-width: 100vh;
   overflow-y: auto;
@@ -142,12 +142,10 @@ const ExploreDialog = styled(Dialog)`
     top: 15pt;
     right: 15pt;
     font-size: 8pt;
-    /* italic */
-    font-style: italic;
   }
 
   div.highlight {
-    background-color: #d4d3d3;
+    background-color: #dedef8;
   }
 
   >div>div {
@@ -175,7 +173,6 @@ const ExploreDialog = styled(Dialog)`
 
   h2 {
     font-size: 12pt;
-    color: #373737;
   }
 
   h3 {
@@ -387,7 +384,7 @@ function BasicHighlighted(props) {
 }
 
 const Description = styled.p`
-  font-size: 12pt;
+  font-size: 14pt;
   color: #696969;
   padding: 0pt 20pt;
 `
@@ -486,7 +483,7 @@ export function Explore() {
     if (!visible) return null;
 
     let description = <>
-    LMQL is a programming language for interacting with large language models. This playground allows you to explore LMQL's capabilities. To get started, choose one of the example queries below, demonstrating <i>constrained model use</i>, <i>control-flow guided generation</i>, and tool-augmented LLMs.
+    This playground allows you to explore LMQL's capabilities. To get started, choose one of the example queries below.
     </>
 
     if (configuration.NEXT_MODE) {
@@ -508,7 +505,7 @@ export function Explore() {
             <div className={c.highlight ? "highlight" : ""} key={c.category + "-container"}>
             <h2 key={c.category}>{c.category}</h2>
             {c.highlight && <span class="sidenote">
-              <a href="https://github.com/eth-sri/lmql/issues" target="_blank" rel="noreferrer"> Please report any issues you find.</a>
+              <a href="https://github.com/eth-sri/lmql/issues" target="_blank" rel="noreferrer"> Report Issues</a>
             </span>}
             <div key={c.category + "-div"}>
             {c.queries.map((q,i) => <Tile key={c.category + "-" + i} onClick={() => onClickTile(q)}>

src/lmql/ui/playground/src/queries.js

@@ -136,40 +136,37 @@ where
           // hello world
           name: "❤️ Sentiment Constraints",
           description: "Affect sentiment with in-context prompting.",
-          code: `@lmql.query
+          code: `@lmql.query(cache="mood.tokens", model="chatgpt")
 async def mood_description(m: str):
-  '''lmql
-  argmax(cache="mood.tokens")
-      print("Generating mood for", m)
-      """Provide a one sentence instruction that prompts a model to write text that 
-      is written in a {m} tone, addressing some previously provided question.\\n"""
-      "[SUMMARY]\\n"
-      return SUMMARY.strip();
-  from
-      "chatgpt"
-  '''
+    '''lmql
+    print("Generating mood for", m)
+    """Provide a one sentence instruction that prompts a model to write text that 
+    is written in a {m} tone, addressing some previously provided question.\\n"""
+    "[SUMMARY]\\n"
+    return SUMMARY.strip();
+    '''
 
 @lmql.query
 async def mood(m: str):
-  '''lmql
-  incontext
-      """
-      Instruction: {mood_description(m)}
-      Answer: [RESPONSE]
-      """
-      return RESPONSE.strip(); 
-  where
-      stops_at(RESPONSE, ".") and stops_at(RESPONSE, "\\n")
-  '''
+    '''lmql
+    """
+    Instruction: {await mood_description(m)}
+    Answer: [RESPONSE]
+    """ where stops_at(RESPONSE, ".") and stops_at(RESPONSE, "\\n")
+
+    return RESPONSE.strip(); 
+    '''
 
+# main query
 argmax
-  for q in ["Hi", "Who are you", "How is your day going?"]:
-      "Q: {q}\\n"
-      "A: [RESPONSE]\\n"
+    for q in ["Hi", "Who are you", "How is your day going?"]:
+        "Q: {q}\\n"
+        "A: [RESPONSE]\\n"
 from 
-  "chatgpt" 
+    "chatgpt" 
 where 
-  mood(RESPONSE, "loving like a partner")
+    mood(RESPONSE, "loving like a partner")
+          
 `,
           state: ''
        },
@@ -179,38 +176,32 @@ where
           description: "Insert dynamic instructions with incontext.",
           code: `@lmql.query
 async def rhyme():
- '''
- incontext
+    '''
     """
     Above is the beginning of the poem. Generate the next verse that rhymes with the last line and has the same number of syllables.
     [VERSE]
-    """
+    """ where stops_before(VERSE, "\\n")
     return VERSE
- where
-    stops_before(VERSE, "\\n")
- '''
+    '''
 
 @lmql.query
 async def first_verse():
- '''
- incontext
+    '''
     """
     Generate a verse that would be perfect for the start of a beautiful rhyme. 
     [VERSE]
-    """
+    """ where stops_before(VERSE, "\\n")
     return VERSE
- where
-    stops_before(VERSE, "\\n")
- '''
+    '''
 
 argmax
-  "[FIRST_VERSE]\\n"
-  for i in range(5):
-     "[VERSE]\\n"
+    "[FIRST_VERSE]\\n"
+    for i in range(5):
+        "[VERSE]\\n"
 from 
-  "chatgpt" 
+    "chatgpt" 
 where 
-  rhyme(VERSE) and first_verse(FIRST_VERSE)
+    rhyme(VERSE) and first_verse(FIRST_VERSE)
 `,
           state: ''
        }

web/chat/chat.css

@@ -0,0 +1,29 @@
+.side-by-side .query {
+    background-color: white;
+    color: black;
+}
+
+.side-by-side .query h3 {
+    color: black;
+}
+
+.side-by-side .query h3 a {
+    display: none;
+}
+
+.lmql-str {
+    color: #7ea464;
+}
+
+.lmql-kw {
+    color: #a866bd;
+}
+
+.side-by-side .query pre {
+    font-size: 8pt;
+    color: rgb(88, 87, 87);
+}
+
+.query anchor {
+    border-bottom-color: rgb(62, 57, 57);
+}