Merge pull request #307 from ohdearquant/merge_direct

Merge direct， vote

lionagi/core/direct/__init__.py

@@ -1,9 +1,11 @@
 from .predict import predict
 from .select import select
 from .score import score
+from .vote import vote
 
 __all__ = [
     "predict",
     "select",
     "score",
+    "vote"
 ]

lionagi/core/direct/score.py

@@ -1,5 +1,5 @@
 from pydantic import Field
-
+import numpy as np
 from lionagi.libs import func_call, convert
 from ..prompt.prompt_template import ScoredTemplate
 from ..branch import Branch
@@ -55,7 +55,7 @@ def __init__(
             "precision": num_digit if num_digit != 0 else None,
         }
 
-async def score(
+async def _score(
     sentence,
     instruction=None,
     score_range=(1, 10),
@@ -80,6 +80,11 @@ async def score(
     tool_manager=None,
     **kwargs,
 ):
+
+    if "temperature" not in kwargs:
+        kwargs["temperature"] = 0.1
+
+    instruction = instruction or ""
 
     branch = Branch(
         name=branch_name,
@@ -116,3 +121,73 @@ async def score(
     )
 
     return _template
+
+# async def group_score(sentence, *args, num_instances=5, **kwargs):
+#     sentences = [sentence for _ in range(num_instances)]
+
+#     outs_ = await func_call.alcall(sentences, _score, *args, **kwargs)
+
+#     return np.mean([i.answer for i in outs_])
+
+
+async def score(
+    sentence,
+    num_instances=1,
+    instruction=None,
+    score_range=(1, 10),
+    inclusive=True,
+    num_digit=0,
+    confidence_score=False,
+    reason=False,
+    retries=2,
+    delay=0.5,
+    backoff_factor=2,
+    default_value=None,
+    timeout=None,
+    branch_name=None,
+    system=None,
+    messages=None,
+    service=None,
+    sender=None,
+    llmconfig=None,
+    tools=None,
+    datalogger=None,
+    persist_path=None,
+    tool_manager=None,
+    return_template=True,
+    **kwargs,
+):
+    async def _inner(i=0):
+        return await _score(
+            sentence=sentence, 
+            instruction=instruction,
+            score_range=score_range,
+            inclusive=inclusive,
+            num_digit=num_digit,
+            confidence_score=confidence_score,
+            reason=reason,
+            retries=retries,
+            delay=delay,
+            backoff_factor=backoff_factor,
+            default_value=default_value,
+            timeout=timeout,
+            branch_name=branch_name,
+            system=system,
+            messages=messages,
+            service=service,
+            sender=sender,
+            llmconfig=llmconfig,
+            tools=tools,
+            datalogger=datalogger,
+            persist_path=persist_path,
+            tool_manager=tool_manager,
+            **kwargs
+        )
+
+    if num_instances == 1:
+        _out = await _inner()
+        return _out if return_template else _out.answer
+
+    elif num_instances > 1:
+        _outs = await func_call.alcall(range(num_instances), _inner)
+        return _outs if return_template else np.mean([i.answer for i in _outs])

lionagi/core/direct/select.py

@@ -17,7 +17,6 @@ def __init__(
         self,
         sentence=None,
         choices=None,
-        num_choices=1,
         instruction=None,
         reason=False, 
         confidence_score=False,
@@ -27,7 +26,7 @@ def __init__(
 
         self.sentence = sentence
         self.choices = choices
-        self.task = f"select {num_choices} item(s), from provided choices {choices}."
+        self.task = f"select 1 item, from provided choices {choices}."
         if instruction:
             self.task += f"objetive {instruction}."
 
@@ -41,7 +40,6 @@ def __init__(
 async def select(
     sentence,
     choices=None,
-    num_choices=1,
     instruction=None,
     confidence_score=False,
     reason=False,
@@ -79,7 +77,6 @@ async def select(
     _template = SelectTemplate(
         sentence=sentence,
         choices=choices,
-        num_choices=num_choices,
         instruction=instruction,
         confidence_score=confidence_score,
         reason=reason,
@@ -97,6 +94,7 @@ async def select(
     )
 
     ans = _template.answer
+
     if ans not in _template.choices:
         _template.answer = StringMatch.choose_most_similar(ans, _template.choices)
 

lionagi/core/direct/vote.py

@@ -0,0 +1,30 @@
+from lionagi.libs import func_call
+import numpy as np
+from .predict import predict
+from .score import score
+
+# for example, directive=predict
+
+async def vote(sentence, directive=predict, num_generations=5, num_output=1, num_scorer=5, score_range=(0,100), num_digit=2, scorer_instruction=None, **kwargs):
+
+    async def _inner(i):
+        out_ = await directive(sentence, **kwargs)
+        score_ = await score(
+            out_.answer,
+            context=sentence, 
+            instruction=scorer_instruction, 
+            score_range=score_range, 
+            num_digit=num_digit, 
+            num_instances=num_scorer, 
+            return_template=False
+        )
+
+        out_.__setattr__('score', score_)
+        return out_
+
+    _outs = await func_call.alcall(list(range(num_generations)), _inner)
+
+    top_index = np.argsort([i.score for i in _outs])[-num_output:]
+    final_output = list(np.array(_outs)[top_index])
+
+    return final_output[0] if len(final_output) == 1 else final_output

lionagi/version.py

@@ -1 +1 @@
-__version__ = "0.0.310"
+__version__ = "0.0.311"

notebooks/lion_agent_with_tool.ipynb

@@ -330,7 +330,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.6"
+   "version": "3.12.1"
   }
  },
  "nbformat": 4,