fix: code block titles are consistent

fern/pages/changelog/2024-06-11-force-json-object-response-format.mdx

@@ -5,13 +5,14 @@ type: ""
 createdAt: "Tue Jun 11 2024 15:45:40 GMT+0000 (Coordinated Universal Time)"
 hidden: false
 ---
+
 Users can now force `command-nightly`to generate outputs in JSON objects by setting the `response_format` parameter in the Chat API. Users can also specify a JSON schema for the output.
 
 This feature is available across all of Cohere's SDKs (Python, Typescript, Java, Go).
 
-Example request for forcing JSON response format: 
+Example request for forcing JSON response format:
 
-```Text cURL
+```http
 POST https://api.cohere.ai/v1/chat
 {
     "message": "Generate a JSON that represents a person, with name and age",
@@ -24,9 +25,9 @@ POST https://api.cohere.ai/v1/chat
 
 <br />
 
-Example request for forcing JSON response format in user defined schema: 
+Example request for forcing JSON response format in user defined schema:
 
-```Text cURL
+```http
 POST https://api.cohere.ai/v1/chat
 {
     "message": "Generate a JSON that represents a person, with name and age",
@@ -46,4 +47,4 @@ POST https://api.cohere.ai/v1/chat
 
 ```
 
-Currently only compatible with \`command-nightly model.
+Currently only compatible with \`command-nightly model.

fern/pages/cohere-api/about.mdx

@@ -27,7 +27,7 @@ We support SDKs in 4 different languages. Please see the following installation
 python -m pip install cohere --upgrade
 ```
 
-```python 
+```python PYTHON
 import cohere
 
 co = cohere.Client("<<apiKey>>")
@@ -47,7 +47,7 @@ print(response)
 npm i -s cohere-ai
 ```
 
-```typescript
+```typescript TYPESCRIPT
 const { CohereClient } = require("cohere-ai");
 
 const cohere = new CohereClient({
@@ -103,7 +103,7 @@ public class ChatPost {
 go get github.com/cohere-ai/cohere-go/v2
 ```
 
-```go 
+```go GO
 package main
 
 import (

fern/pages/cohere-api/errors.mdx

@@ -4,12 +4,13 @@ slug: "reference/errors"
 
 hidden: false
 description: "This document explains how Cohere uses HTTP response codes to indicate the success or failure of an API request, with codes in the 2xx range indicating success, 4xx range indicating errors due to provided information, and 5xx range indicating server errors. It also provides code examples for error handling in different programming languages."
-image: "../../assets/images/72cf813-meta_docs_image_cohere.jpg"  
+image: "../../assets/images/72cf813-meta_docs_image_cohere.jpg"
 keywords: "RAG, retrieval, augmented, generation, LLM, connectors, connector, langchain"
 
 createdAt: "Tue Sep 06 2022 17:39:07 GMT+0000 (Coordinated Universal Time)"
 updatedAt: "Fri Mar 15 2024 04:29:33 GMT+0000 (Coordinated Universal Time)"
 ---
+
 Cohere uses conventional HTTP response codes to indicate the success or failure of an API request. In general:
 
 - Codes in the **2xx** range indicate success.
@@ -37,20 +38,23 @@ Here are code examples for how error handling might look in our SDKs:
       print(e.http_status)
       print(e.headers)
 ```
+
 ```javascript JAVASCRIPT
 (async () => {
-  const response = await cohere.generate({model: 'invalid-model'});
+  const response = await cohere.generate({ model: "invalid-model" });
 
   if (response.statusCode !== 200) {
-      console.log(response.body.message);
+    console.log(response.body.message);
   }
 })();
 ```
+
 ```go GO
 _, err := co.Generate(generateOptions)
 if err != nil {
   fmt.Println(err)
   return
 }
 ```
+
 </CodeBlocks>

fern/pages/cookbooks/agent-api-calls.mdx

@@ -165,7 +165,7 @@ response_1 = agent_executor.invoke(
         )
 ```
 
-````txt title="Output"
+````txt OUTPUT
 > Entering new AgentExecutor chain...
 
 I will use the regex_extractor tool to extract the codes from the user query.
@@ -202,7 +202,7 @@ The output of the tool is then used to generate the request.
 convert_to_json(response_1['output'])
 ```
 
-```python title="Output"
+```python OUTPUT
 [{'urn': ['urn:75f2b737-06dd-4399-9206-a6c11b65138e'],
   'objref': ['GLCMS004AGTCAMIS', 'GLCHL000CGUCHALE'],
   'nmgs': ['0000234GLCMS0100ANORAKCAA']}]
@@ -221,7 +221,7 @@ response_2 = agent_executor.invoke(
 )
 ```
 
-````txt title="Output"
+````txt OUTPUT
 > Entering new AgentExecutor chain...
 
 I will use the regex_extractor tool to extract the relevant information from the user request.
@@ -256,7 +256,7 @@ The Agent runs the tool to check if any target string was in the query, then it
 convert_to_json(response_2['output'])
 ```
 
-```python title="Output"
+```python OUTPUT
 [{'taxonomies': ['SPORT']}]
 ```
 
@@ -273,7 +273,7 @@ response_3 = agent_executor.invoke(
 )
 ```
 
-````txt title="Output"
+````txt OUTPUT
 > Entering new AgentExecutor chain...
 
 I will use the regex_extractor tool to extract the codes from the user query. Then, I will create a JSON for each of the key-value pairs in the dictionary.
@@ -310,7 +310,7 @@ Grounded answer: ```json JSON
 convert_to_json(response_3['output'])
 ```
 
-```python title="Output"
+```python OUTPUT
 [{'objref': ['GLBRL0000GACHALE'], 'nmgs': ['0000234GLCZD0000GUREDTOAA']},
   {'taxonomies': ['CARS']}]
 ```

fern/pages/cookbooks/agent-short-term-memory.mdx

@@ -117,7 +117,7 @@ a1=agent_executor.invoke({
 })
 ```
 
-```txt title="Output"
+```txt OUTPUT
 > Entering new AgentExecutor chain...
 
 I will use python to read the CSV file and extract the column names.
@@ -147,7 +147,7 @@ a2_no_mem = agent_executor.invoke({
 })
 ```
 
-````txt title="Output"
+````txt OUTPUT
 > Entering new AgentExecutor chain...
 Plan: I will ask the user for clarification on what data they would like to visualise.
 Action: ```json JSON
@@ -182,7 +182,7 @@ a2_mem_ai = agent_executor.invoke({
 })
 ```
 
-```txt title="Output"
+```txt OUTPUT
 > Entering new AgentExecutor chain...
 
 I will copy and paste the code from the previous conversation and add code to plot the revenue numbers.
@@ -214,7 +214,7 @@ a2_mem_ai_hum = agent_executor.invoke({
 })
 ```
 
-```txt title="Output"
+```txt OUTPUT
 > Entering new AgentExecutor chain...
 
 I will copy and paste the code from the previous conversation into this one, and then use it to plot the revenue numbers.
@@ -247,7 +247,7 @@ a3_mem_ai_hum = agent_executor.invoke({
 })
 ```
 
-```txt title="Output"
+```txt OUTPUT
 > Entering new AgentExecutor chain...
 
 I will copy and paste the previous code and make the changes requested by the user. Then I will execute the code to plot the graph with the changes applied.
@@ -312,7 +312,7 @@ Below, an example of the augmented memory object generated by the model. You can
 print(augmented_mem_obj_a2)
 ```
 
-```txt title="Output"
+```txt OUTPUT
 This is the sequence of tools you used in the previous turn:
 
 START TOOL 0 NAME:
@@ -340,7 +340,7 @@ a3_mem_ai_hum_amo = agent_executor.invoke({
 
 ```
 
-```txt title="Output"
+```txt OUTPUT
 > Entering new AgentExecutor chain...
 
 I will copy and paste the previous code, and modify the y axis limits as requested.

fern/pages/cookbooks/agentic-multi-stage-rag.mdx

@@ -55,7 +55,7 @@ from sklearn.metrics.pairwise import cosine_similarity
 print('cohere version:', cohere.__version__)
 ```
 
-```txt title="Output"
+```txt OUTPUT
 cohere version: 5.5.1
 ```
 
@@ -339,7 +339,7 @@ output = simple_rag(question1, db)
 print(output)
 ```
 
-```txt title="Output"
+```txt OUTPUT
 top_matched_document 1    Title: Bicycle helmet requirement\nBody: Curre...
 Name: combined, dtype: object
 There is currently no state law requiring the use of helmets when riding a bicycle. However, some cities and counties do require helmet use.
@@ -356,7 +356,7 @@ Use the provided documents to answer questions about an employee's specific situ
 output = cohere_agent(question1, preamble, tools, verbose=True)
 ```
 
-```txt title="Output"
+```txt OUTPUT
 running 0th step.
 I will search for 'state level law for wearing helmets' in the documents provided and write an answer based on what I find.
 
@@ -388,7 +388,7 @@ output = simple_rag(question2, db)
 print(output)
 ```
 
-```txt title="Output"
+```txt OUTPUT
 top_matched_document 1    Title: Bicycle helmet requirement\nBody: Curre...
 Name: combined, dtype: object
 In the state of Washington, there is no law requiring you to wear a helmet when riding a bike. However, some cities and counties do require helmet use, so it is worth checking your local laws.
@@ -407,7 +407,7 @@ Use the provided documents to answer questions about an employee's specific situ
 output = cohere_agent(question2, preamble, tools, verbose=True)
 ```
 
-```txt title="Output"
+```txt OUTPUT
 running 0th step.
 I will search for 'helmet with a bike' and then write an answer.
 
@@ -505,7 +505,7 @@ Please follow these steps:
 output = cohere_agent(question2, preamble2, tools, verbose=True)
 ```
 
-```txt title="Output"
+```txt OUTPUT
 running 0th step.
 I will search for 'Orting' and 'bike helmet' to find the relevant information.
 

fern/pages/cookbooks/agentic-rag-mixed-data.mdx

@@ -136,7 +136,7 @@ text_elements = [e for e in categorized_elements if e.type == "text"]
 print(len(text_elements))
 ```
 
-```txt title="Output"
+```txt OUTPUT
 14
 24
 ```
@@ -292,7 +292,7 @@ print(final_answer_docs)
 chat_history=[{'role':"USER", 'message':query},{'role':"CHATBOT", 'message':f'The final answer is: {final_answer}.' + final_answer_docs}]
 ```
 
-```txt title="Output"
+```txt OUTPUT
 The charges for services in 2022 were $5,266 million.
 The final answer is from the documents below:
 
@@ -327,7 +327,7 @@ else:
     print(response.text)
 ```
 
-```txt title="Output"
+```txt OUTPUT
     RAG is not needed
     Final answer:
     The result of dividing the charges for services in 2022 by two is $2,633.
@@ -555,7 +555,7 @@ rag_object=RAG_pipeline(paths=["city_ny_popular_fin_report.pdf"])
 
     This function will be deprecated in a future release and `unstructured` will simply use the DEFAULT_MODEL from `unstructured_inference.model.base` to set default model name
 
-```txt title="Output"
+```txt OUTPUT
 PDFs parsed
 14
 24
@@ -663,7 +663,7 @@ agent_object=react_agent(rag_retriever=rag_object)
 step1_response=agent_object.run_agent("what are the charges for services in 2022 and 2023")
 ```
 
-```txt title="Output"
+```txt OUTPUT
 > Entering new AgentExecutor chain...
 
 I will search for the charges for services in 2022 and 2023.
@@ -695,7 +695,7 @@ AIMessage(content=step1_response['output'])
 agent_object.run_agent("what is the mean of the two values",history=chat_history)
 ```
 
-```txt title="Output"
+```txt OUTPUT
 > Entering new AgentExecutor chain...
 
 
@@ -714,7 +714,7 @@ Grounded answer: The mean of the two values is <co: 0="">5518 million</co:>.
 > Finished chain.
 ```
 
-````python title="Output"
+````python OUTPUT
 {'input': 'what is the mean of the two values',
 'preamble': "\n## Task &amp; Context\nYou help people answer their questions and other requests interactively. You will be asked a very wide array of requests on all kinds of topics. You will be equipped with a wide range of search engines or similar tools to help you, which you use to research your answer. You should focus on serving the user's needs as best you can, which will be wide-ranging.\n\n## Style Guide\nUnless the user asks for a different style of answer, you should answer in full sentences, using proper grammar and spelling.\n\n## Guidelines\nYou are an expert who answers the user's question. \nYou have access to a vectorsearch tool that will use your query to search through documents and find the relevant answer.\nYou also have access to a python interpreter tool which you can use to run code for mathematical operations.\n",
 'chat_history': [HumanMessage(content='what are the charges for services in 2022 and 2023'),