Update the python examples

Author: RobinPicard

examples/babyagi.py

@@ -78,15 +78,15 @@ def one_cycle(objective: str, task_list, next_task_id: int):
     task = task_list.popleft()
 
     prompt = perform_task_ppt(objective=objective, task=task)
-    result = complete(prompt)
+    result = complete(prompt).content
 
     prompt = create_tasks_ppt(
         objective=objective,
         task=first_task["task_name"],
         result=result,
         previous_tasks=[first_task["task_name"]],
     )
-    new_tasks = complete(prompt)
+    new_tasks = complete(prompt).content
 
     new_tasks = create_tasks_fmt(new_tasks)
 
@@ -99,7 +99,7 @@ def one_cycle(objective: str, task_list, next_task_id: int):
         tasks=[task["task_name"] for task in task_list],
         next_task_id=next_task_id,
     )
-    prioritized_tasks = complete(prompt)
+    prioritized_tasks = complete(prompt).content
 
     prioritized_tasks = prioritize_tasks_fmt(prioritized_tasks)
 

examples/beam-cloud/app.py

@@ -42,5 +42,5 @@ def predict(context, **inputs):
     model = context.on_start_value
     # Inference
     generator = outlines.Generator(model, Literal["Positive", "Negative"])
-    answer = generator(prompt)
+    answer = generator(prompt).content
     return {"answer": answer}

examples/bentoml/service.py

@@ -77,6 +77,6 @@ async def generate(
         import outlines
 
         generator = outlines.Generator(self.model, outlines.json_schema(json_schema))
-        character = generator(prompt)
+        character = generator(prompt).content
 
         return character

examples/cerebrium/main.py

@@ -43,7 +43,7 @@ def generate(
     character = model(
         f"<s>[INST]Give me a character description. Describe {prompt}.[/INST]",
         outlines.json_schema(schema),
-    )
+    ).content
 
     print(character)
     return character

examples/dating_profile.py

@@ -96,7 +96,7 @@ class Example:
 
 dating_profile_prompt = Template.from_file("prompts/dating_profile.txt")
 prompt = dating_profile_prompt(description=new_description, examples=samples)
-profile = model(prompt, outlines.json_schema(DatingProfile), max_tokens=500)  # type: ignore
+profile = model(prompt, outlines.json_schema(DatingProfile), max_tokens=500).content  # type: ignore
 print(profile)
 
 # Sample generated profiles

examples/llamacpp_example.py

@@ -42,4 +42,4 @@ class Character(BaseModel):
     prompt = "Instruct: You are a leading role play gamer. You have seen thousands of different characters and their attributes.\nPlease return a JSON object with common attributes of an RPG character. Give me a character description\nOutput:"
 
     sequence = generator(prompt, seed=seed, max_tokens=512)
-    print(sequence)
+    print(sequence.content)

examples/math_generate_code.py

@@ -39,6 +39,6 @@ def execute_code(code):
 
 prompt = answer_with_code_prompt(question=question, examples=examples)
 model = outlines.from_openai(openai.OpenAI(), "gpt-4o-mini")
-answer = model(prompt)
+answer = model(prompt).content
 result = execute_code(answer)
 print(f"It takes Carla {result:.0f} minutes to download the file.")

examples/meta_prompting.py

@@ -31,12 +31,12 @@ def split_into_steps(question, model_name: str):
     model = outlines.from_openai(client, model_name)
 
     prompt = solve(question=question)
-    answer = model(prompt, max_tokens=500)
+    answer = model(prompt, max_tokens=500).content
     prompt += (
         answer
         + "\n what is the only option that displays the same type of relationship as : :?"
     )
-    answer = model(prompt, max_tokens=500)
+    answer = model(prompt, max_tokens=500).content
     completed = prompt + answer
 
     return completed
@@ -55,9 +55,9 @@ def fill_in_the_blanks(question, model_name: str):
     model = outlines.from_openai(client, model_name)
 
     prompt = determine_goal(question=question)
-    answer = model(prompt, stop=["."])
+    answer = model(prompt, stop=["."]).content
     prompt = solve(memory=prompt + answer)
-    answer = model(prompt, max_tokens=500)
+    answer = model(prompt, max_tokens=500).content
     completed = prompt + answer
 
     return completed
@@ -94,9 +94,9 @@ def ask_an_expert(question, model_name: str):
     model = outlines.from_openai(client, model_name)
 
     prompt = find_expert(question=question)
-    expert = model(prompt, stop=['"'])
+    expert = model(prompt, stop=['"']).content
     prompt = get_answer(question=question, expert=expert, memory=prompt+expert)
-    answer = model(prompt, max_tokens=500)
+    answer = model(prompt, max_tokens=500).content
     completed = prompt + answer
 
     return completed
@@ -121,9 +121,9 @@ def ask_an_expert_simple(question, model_name: str):
     model = outlines.from_openai(client, model_name)
 
     prompt = find_expert(question=question)
-    expert = model(prompt, stop=["\n", "."])
+    expert = model(prompt, stop=["\n", "."]).content
     prompt = get_answer(expert=expert, memory=prompt+expert)
-    answer = model(prompt, max_tokens=500)
+    answer = model(prompt, max_tokens=500).content
     completed = prompt + answer
 
     return completed

examples/modal_example.py

@@ -76,7 +76,7 @@ def generate(
     character = model(
         f"<s>[INST]Give me a character description. Describe {prompt}.[/INST]",
         outlines.json_schema(schema),
-    )
+    ).content
 
     print(character)
 

examples/pick_odd_one_out.py

@@ -39,8 +39,8 @@
 
 prompt = build_ooo_prompt(options=options)
 reasoning = gen_text(prompt, stop=["Pick the odd word", "So the odd one"])
-prompt += reasoning
+prompt += reasoning.content
 raw_result = gen_choice(prompt)
-result = json.loads(raw_result)["result"]
+result = json.loads(raw_result.content)["result"]
 prompt += result
 print(result)