Draft

Author: RobinPicard

docs/examples/chain_of_density.md

@@ -67,13 +67,13 @@ model = outlines.from_transformers(
     transformers.AutoTokenizer.from_pretrained(MODEL_NAME)
 )
 prompt = chain_of_density(article=article)
-result = model(prompt, Summaries, max_new_tokens=2000)
+result = model(prompt, Summaries, max_new_tokens=2000).content
 ```
 
 We can now check the results:
 
 ```python
-print(result)
+print(result.content)
 # {'summaries': [
 #     {
 #       'missing_entities': 'English mathematician, cryptanalyst, philosopher',

docs/examples/chain_of_thought.md

@@ -111,7 +111,7 @@ We obtain a series of intermediate reasoning steps as well as the conclusion:
 ```python
 import json
 
-json_response = json.loads(response)
+json_response = json.loads(response.content)
 
 print(json_response["reasoning"])
 print(json_response["conclusion"])

docs/examples/classification.md

@@ -52,7 +52,7 @@ prompts = [customer_support(request=request) for request in requests]
 We can now ask the model to classify the requests:
 
 ```python
-labels = generator(prompts)
+labels = generator(prompts).content
 print(labels)
 # ['URGENT', 'STANDARD']
 ```
@@ -79,7 +79,7 @@ We can then create a generator with the Pydantic model we just defined and call
 
 ```python
 generator = outlines.Generator(model, Classification)
-labels = generator(prompts)
+labels = generator(prompts).content
 print(labels)
 # ['{"label":"URGENT"}', '{ "label": "STANDARD" }']
 ```

docs/examples/dating_profiles.md

@@ -165,7 +165,7 @@ it's  a good excuse for a date. I watch the latest series because I'm paying,
 with my hard-earned money, for every streaming service."""
 
 prompt = dating_profile_prompt(description=new_description, examples=samples)
-profile = model(prompt, DatingProfile)
+profile = model(prompt, DatingProfile).content
 parsed_profile = DatingProfile.model_validate_json(json.loads(profile))
 ```
 

docs/examples/deploy-using-bentoml.md

@@ -154,7 +154,7 @@ We then need to define an HTTP endpoint using `@bentoml.api` to decorate the met
         from outlines.types import JsonSchema
 
         generator = outlines.Generator(self.model, JsonSchema(json_schema))
-        character = generator(prompt)
+        character = generator(prompt).content
 
         return json.loads(character)
 ```
@@ -200,7 +200,7 @@ with bentoml.SyncHTTPClient("http://localhost:3000") as client:
     response = client.generate(
         prompt="Give me a character description"
     )
-    print(response)
+    print(response.content)
 ```
 
 </details>

docs/examples/deploy-using-cerebrium.md

@@ -110,7 +110,7 @@ def generate(
 
     character = generator(
         f"<s>[INST]Give me a character description. Describe {prompt}.[/INST]"
-    )
+    ).content
 
     return character
 ```

docs/examples/deploy-using-modal.md

@@ -161,7 +161,7 @@ def generate(
     # by models, so make sure to check the model's documentation.
     character = generator(
         f"<s>[INST]Give me a character description. Describe {prompt}.[/INST]"
-    )
+    ).content
 
     # Print out the generated character.
     print(character)

docs/examples/earnings-reports.md

@@ -246,7 +246,7 @@ Provide the prompt to the model and run it:
 csv_data = csv_extractor(
     extract_financial_data_prompt(columns_to_extract, income_statement),
     max_new_tokens=1024,
-)
+).content
 
 print(csv_data)
 ```

docs/examples/extract_event_details.py

@@ -45,7 +45,7 @@ class Event(BaseModel):
 prompt = prompt_template(now=now, message=message)
 
 # Extract the event information
-event = generator(prompt)
+event = generator(prompt).content # type: ignore
 
 # Print the current date and time
 print(f"Today: {now}")

docs/examples/extraction.md

@@ -79,7 +79,7 @@ prompts = [take_order(order=order) for order in orders]
 generator = outlines.Generator(model, Order)
 
 results = generator(prompts)
-print(results)
+print(results.content)
 # ['{"pizza": "Pepperoni", "number": 2}',
 # '{"pizza": "Margherita", "number": 12}']
 ```