Merge branch 'main' into reformat-code-samples-2

fern/pages/text-generation/structured-outputs.mdx

@@ -14,7 +14,7 @@ updatedAt: "Tue Jun 11 2024 02:43:00 GMT+0000 (Coordinated Universal Time)"
 
 ## Overview
 
-Structured Outputs is a feature for forcing the LLM's output to follow a specified format 100% of the time. This increases the reliability of LLM in enterprise applications where downstream applications expect the LLM output to be correctly formatted. By forcing the model to follow a structured schema, hallucinated fields and entries in structured data can be reliably eliminated.
+Structured Outputs is a feature that forces the LLM’s response to strictly follow a schema specified by the user. When Structured Outputs is turned on, the LLM will generate structured data that follows the desired schema, provided by the user, 100% of the time. This increases the reliability of LLMs in enterprise applications where downstream applications expect the LLM output to be correctly formatted. With Structured Outputs, hallucinated fields and entries in structured data can be reliably eliminated.
 
 Compatible models:
 - Command R 08 2024
@@ -75,8 +75,8 @@ By setting the `response_format` type to `"json_object"` in the Chat API, the ou
 When using  `{ "type": "json_object" }` your `message` should always explicitly instruct the model to generate a JSON (eg: _"Generate a JSON ..."_) . Otherwise the model may end up getting stuck generating an infinite stream of characters and eventually run out of context length.
 </Info>
 
-<Note title="Experimental"> 
-This feature is currently not supported in RAG mode.
+<Note title="Note"> 
+This feature is currently not supported in [RAG](https://docs.cohere.com/v1/docs/retrieval-augmented-generation-rag) mode.
 </Note>
 
 #### JSON Schema mode
@@ -125,15 +125,11 @@ In this schema, we defined three keys ("title," "author," "publication_year") an
 
 ```
 
-<Info title="Important"> 
-Note: Each schema provided will incur a latency overhead required for processing the schema. This is only applicable for the first few requests.
-</Info>
-
 ## Specifying a schema
 
 ### Generating nested objects
 
-The model can be configured to output objects with up to 5 levels of nesting. When a `schema` is specified, there are no limitations on the levels of nesting.
+In JSON Schema mode, there are no limitations on the levels of nesting. However, in JSON mode (no schema specified), nesting is limited to 5 levels.
 
 ### Schema constraints
 
@@ -164,3 +160,7 @@ We do not support the entirety of the [JSON Schema specification](https://json-s
 - Others:
     - `uniqueItems`
     - `additionalProperties`
+
+<Info title="Important"> 
+Note: Using Structured Outputs (in JSON Schema mode) will incur a latency overhead required for processing the structured schema. This increase in latency only applies for the first few requests, since the schema is cached afterwards.
+</Info>

fern/pages/text-generation/tools/tool-use.mdx

@@ -323,18 +323,6 @@ outputs = [{"number": 2343}] # Not Great
 outputs = [{"sum": 2343}] # Better
 ```
 
-## What's Next?
-
-Here, we'll preview some of the functionality we plan on adding in the coming months.
-
-### Cohere-hosted Tools
-
-The model can currently handle any tool provided by the developer. That having been said, Cohere has implemented some pre-defined tools that users can leverage out-of-the-box.
-
-Specifically we're going to roll out a **Python interpreter** tool and a **Web search** tool. 
-
-Please [reach out](mailto:[email protected]) to join the beta.
-
 ## Getting started
 
 Check out [this notebook](https://github.com/cohere-ai/cohere-developer-experience/blob/main/notebooks/agents/Vanilla_Tool_Use.ipynb) for a worked-out examples.

fern/pages/tutorials/build-things-with-cohere.mdx

@@ -38,7 +38,8 @@ Next, we'll import the `cohere` library and create a client to be used throughou
 ```python PYTHON
 import cohere
 
-co = cohere.Client(api_key="YOUR_COHERE_API_KEY") # Get your API key here: https://dashboard.cohere.com/api-keys
+# Get your API key here: https://dashboard.cohere.com/api-keys
+co = cohere.Client(api_key="YOUR_COHERE_API_KEY")  
 ```
 
 # Accessing Cohere from Other Platforms
@@ -93,8 +94,8 @@ For further information, read this documentation on [Cohere on Azure](/docs/cohe
 import cohere
 
 co = cohere.Client(
-  api_key="...",
-  base_url="...",
+    api_key="...",
+    base_url="...",
 )
 ```
 

fern/pages/tutorials/build-things-with-cohere/building-a-chatbot-with-cohere.mdx

@@ -30,7 +30,8 @@ To get started, first we need to install the `cohere` library and create a Coher
 
 import cohere
 
-co = cohere.Client("COHERE_API_KEY") # Get your API key: https://dashboard.cohere.com/api-keys
+# Get your API key: https://dashboard.cohere.com/api-keys
+co = cohere.Client("COHERE_API_KEY")  
 ```
 
 ## Creating a custom preamble
@@ -48,15 +49,14 @@ In the example below, the preamble provides context for the assistant's task (ta
 message = "I'm joining a new startup called Co1t today. Could you help me write a short introduction message to my teammates."
 
 # Create a custom preamble
-preamble="""## Task and Context
+preamble = """## Task and Context
 You are an assistant who assist new employees of Co1t with their first week.
 
 ## Style Guide
 Try to speak in rhymes as much as possible. Be professional."""
 
 # Generate the response
-response = co.chat(message=message,
-                   preamble=preamble)
+response = co.chat(message=message, preamble=preamble)
 
 print(response.text)
 ```
@@ -107,12 +107,11 @@ Here, we are also adding a custom preamble for generating concise response, just
 message = "I'm joining a new startup called Co1t today. Could you help me write a short introduction message to my teammates."
 
 # Create a custom preamble
-preamble="""## Task & Context
+preamble = """## Task & Context
 Generate concise responses, with maximum one-sentence."""
 
 # Generate the response
-response = co.chat(message=message,
-                   preamble=preamble)
+response = co.chat(message=message, preamble=preamble)
 
 print(response.text)
 ```
@@ -136,9 +135,11 @@ Looking at the response, we see that the model is able to get the context from t
 message = "Make it more upbeat and conversational."
 
 # Generate the response with the current chat history as the context
-response = co.chat(message=message,
-                   preamble=preamble,
-                   chat_history=response.chat_history)
+response = co.chat(
+    message=message,
+    preamble=preamble,
+    chat_history=response.chat_history,
+)
 
 print(response.text)
 ```
@@ -157,12 +158,16 @@ You can continue doing this for any number of turns by passing the most recent `
 
 ```python PYTHON
 # Add the user message
-message = "Thanks. Could you create another one for my DM to my manager."
+message = (
+    "Thanks. Could you create another one for my DM to my manager."
+)
 
 # Generate the response with the current chat history as the context
-response = co.chat(message=message,
-                   preamble=preamble,
-                   chat_history=response.chat_history)
+response = co.chat(
+    message=message,
+    preamble=preamble,
+    chat_history=response.chat_history,
+)
 
 print(response.text)
 ```
@@ -178,8 +183,8 @@ To look at the current chat history, you can print the `response.chat_history` o
 ```python PYTHON
 # View the chat history
 for turn in response.chat_history:
-    print("Role:",turn.role)
-    print("Message:",turn.message,"\n")
+    print("Role:", turn.role)
+    print("Message:", turn.message, "\n")
 ```
 
 ```