reformat prompt-engineering section (#341)

Co-authored-by: Max Shkutnyk <max@lightsonsoftware.com>
cohere-ai · Jan 9, 2025 · b6700cc · b6700cc
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diff --git a/...2/text-generation/prompt-engineering/advanced-prompt-engineering-techniques.mdx b/...2/text-generation/prompt-engineering/advanced-prompt-engineering-techniques.mdx
@@ -50,15 +50,16 @@ Using the Chat API, we could do the following:
 
 ```python PYTHON
 import cohere
+
 co = cohere.ClientV2(api_key="<YOUR API KEY>")
 
-example = '''On the issue of Albert's wellbeing after the accident, Angela testified that he
-gave a thumbs up when asked how he was feeling.'''
-message = f'''{example} Is there hearsay?'''
+example = """On the issue of Albert's wellbeing after the accident, Angela testified that he
+gave a thumbs up when asked how he was feeling."""
+message = f"""{example} Is there hearsay?"""
 
 response = co.chat(
-    messages=[{'role': 'user', 'content': message}],
-    model='command-r',
+    messages=[{"role": "user", "content": message}],
+    model="command-r",
     temperature=0.3,
 )
 

diff --git a/fern/pages/v2/text-generation/prompt-engineering/crafting-effective-prompts.mdx b/fern/pages/v2/text-generation/prompt-engineering/crafting-effective-prompts.mdx
@@ -30,6 +30,7 @@ Then use the Chat API to send a message to the model:
 
 ```python PYTHON
 import cohere
+
 co = cohere.ClientV2(api_key="<YOUR API KEY>")
 
 message = """
@@ -42,9 +43,9 @@ Summarize the text below.
 
 # get model response
 response = co.chat(
-  messages=[{'role': 'user', 'content': message}],
-  model="command-r-plus-08-2024",
-  temperature=0.3
+    messages=[{"role": "user", "content": message}],
+    model="command-r-plus-08-2024",
+    temperature=0.3,
 )
 ```
 
@@ -77,21 +78,21 @@ For the example above, we can chunk a news article into different sections and a
 ```python PYTHON
 # Sections from the original news article
 document_chunked = [
-  {
-    "data": {
-      "text": "Equipment rental in North America is predicted to “normalize” going into 2024, according to Josh Nickell, vice president of equipment rental for the American Rental Association (ARA)."
-    }
-  },
-  {
-    "data": {
-      "text": "“Rental is going back to ‘normal,’ but normal means that strategy matters again - geography matters, fleet mix matters, customer type matters,” Nickell said. “In late 2020 to 2022, you just showed up with equipment and you made money."
-    }
-  },
-  {
-    "data": {
-      "text": "“Everybody was breaking records, from the national rental chains to the smallest rental companies; everybody was having record years, and everybody was raising prices. The conversation was, ‘How much are you up?’ And now, the conversation is changing to ‘What’s my market like?’”"
-    }
-  }
+    {
+        "data": {
+            "text": "Equipment rental in North America is predicted to “normalize” going into 2024, according to Josh Nickell, vice president of equipment rental for the American Rental Association (ARA)."
+        }
+    },
+    {
+        "data": {
+            "text": "“Rental is going back to ‘normal,’ but normal means that strategy matters again - geography matters, fleet mix matters, customer type matters,” Nickell said. “In late 2020 to 2022, you just showed up with equipment and you made money."
+        }
+    },
+    {
+        "data": {
+            "text": "“Everybody was breaking records, from the national rental chains to the smallest rental companies; everybody was having record years, and everybody was raising prices. The conversation was, ‘How much are you up?’ And now, the conversation is changing to ‘What’s my market like?’”"
+        }
+    },
 ]
 
 # Add a system message for additional context
@@ -101,10 +102,14 @@ You will receive a series of text fragments from a document that are presented i
 # Call the model
 message = f"Summarize this text in one sentence."
 
-response = co.chat(model="command-r-plus-08-2024",
-                   documents=document_chunked,
-                   messages=[{"role": "system", "content": system_message},
-                             {'role': 'user', "content": message}])
+response = co.chat(
+    model="command-r-plus-08-2024",
+    documents=document_chunked,
+    messages=[
+        {"role": "system", "content": system_message},
+        {"role": "user", "content": message},
+    ],
+)
 
 response_text = response.message.content[0].text
 
@@ -136,21 +141,30 @@ These can easily be rendered into the text to show the source of each piece of i
 ```python PYTHON
 # Function to insert inline citations into the text
 def insert_inline_citations(text, citations):
-    sorted_citations = sorted(citations, key=lambda c: c.start, reverse=True)
-
+    sorted_citations = sorted(
+        citations, key=lambda c: c.start, reverse=True
+    )
+
     for citation in sorted_citations:
-        source_ids = [source.id.split(':')[-1] for source in citation.sources]
+        source_ids = [
+            source.id.split(":")[-1] for source in citation.sources
+        ]
         citation_text = f"[{','.join(source_ids)}]"
-        text = text[:citation.end] + citation_text + text[citation.end:]
-
+        text = (
+            text[: citation.end]
+            + citation_text
+            + text[citation.end :]
+        )
+
     return text
 
+
 # Function to list source documents
 def list_sources(citations):
     unique_sources = {}
     for citation in citations:
         for source in citation.sources:
-            source_id = source.id.split(':')[-1]
+            source_id = source.id.split(":")[-1]
             if source_id not in unique_sources:
                 unique_sources[source_id] = source.document
 
@@ -160,11 +174,14 @@ def list_sources(citations):
         for key, value in document.items():
             footnote += f"{key}: {value}, "
         footnotes.append(footnote.rstrip(", "))
-    
+
     return "\n".join(footnotes)
 
+
 # Use the functions
-cited_text = insert_inline_citations(response.message.content[0].text, response.message.citations)
+cited_text = insert_inline_citations(
+    response.message.content[0].text, response.message.citations
+)
 
 # Print the result with inline citations
 print(cited_text)

diff --git a/fern/pages/v2/text-generation/prompt-engineering/preambles.mdx b/fern/pages/v2/text-generation/prompt-engineering/preambles.mdx
@@ -32,12 +32,21 @@ To set a custom system message, use the `system` role in the `messages` paramete
 
 ```python PYTHON
 import cohere
+
 co = cohere.ClientV2(api_key="<YOUR API KEY>")
 
 response = co.chat(
     model="command-r-plus-08-2024",
-    messages=[{'role': 'system', 'content': 'You are an overly enthusiastic model that responds to everything with a lot of punctuation'},
-              {'role': 'user', 'content': 'Come up with a great name for a cat'}]
+    messages=[
+        {
+            "role": "system",
+            "content": "You are an overly enthusiastic model that responds to everything with a lot of punctuation",
+        },
+        {
+            "role": "user",
+            "content": "Come up with a great name for a cat",
+        },
+    ],
 )
 
 print(response.message.content[0].text)
@@ -64,56 +73,71 @@ The Command R model responds particularly well to system messages that follow a
 Copy this template for best results in your custom system message.
 
 ```python PYTHON
-system_message_template = '''
+system_message_template = """
 
 ## Task and Context
 ----> TELL THE MODEL WHO IT IS AND WHAT IT DOES  <----
 
 ## Style Guide
 ----> ADD INSTRUCTIONS FOR STYLISTIC CHOICES THE MODEL SHOULD MAKE <----
-'''
+"""
 co.chat(
     model="command-r-plus-08-2024",
-    messages=[{'role': 'system', 'content': system_message_template},
-              {'role': 'user', 'content': 'Where can I find the best burger in San Francisco?'}]
+    messages=[
+        {"role": "system", "content": system_message_template},
+        {
+            "role": "user",
+            "content": "Where can I find the best burger in San Francisco?",
+        },
+    ],
 )
 ```
 
 ### Example System Message 1
 
 ```python PYTHON
-tour_guide_system_message = '''
+tour_guide_system_message = """
 
 ## Task and Context
 You are a tour guide in Toronto. You give walking tours peppered with fun facts about the history of the city. If someone asks you a question unrelated to Toronto, subtly yet firmly change the topic back to fun facts about Toronto.
 
 ## Style Guide
 Use British/Canadian spelling of words, and try to speak in sonnets as much as possible. Be professional.
-'''
+"""
 
 co.chat(
     model="command-r-plus-08-2024",
-    messages=[{'role': 'system', 'content': tour_guide_system_message},
-              {'role': 'user', 'content': 'Where can I find the best burger in San Francisco?'}]
+    messages=[
+        {"role": "system", "content": tour_guide_system_message},
+        {
+            "role": "user",
+            "content": "Where can I find the best burger in San Francisco?",
+        },
+    ],
 )
 ```
 
 ### Example System Message 2
 
 ```python PYTHON
-pirate_system_message='''
+pirate_system_message = """
 
 ## Task and Context
 You are a chatbot who talks with users about various nautical themes
 
 ## Style Guide
 Always answer with ooh arrr. Talk like Pirate. Be as chatty and verbose as possible
-'''
+"""
 
 co.chat(
     model="command-r-plus-08-2024",
-    messages=[{'role': 'system', 'content': pirate_system_message},
-              {'role': 'user', 'content': 'What is the most dangerous thing about sailing?'}]
+    messages=[
+        {"role": "system", "content": pirate_system_message},
+        {
+            "role": "user",
+            "content": "What is the most dangerous thing about sailing?",
+        },
+    ],
 )
 ```
 

diff --git a/...t-generation/prompt-engineering/prompt-library/add-a-docstring-to-your-code.mdx b/...t-generation/prompt-engineering/prompt-library/add-a-docstring-to-your-code.mdx
@@ -50,15 +50,19 @@ co = cohere.ClientV2(api_key="<YOUR API KEY>")
 
 response = co.chat(
     model="command-r-plus-08-2024",
-    messages=[{"role": "user", "content": """
+    messages=[
+        {
+            "role": "user",
+            "content": """
     You are a Python expert. For the given Python function, add mypy typing and a docstring. Return the Python function only.
     ```py
     def add(a,b): 
         return a + b 
     ```
-    """}]
+    """,
+        }
+    ],
 )
 
 print(response.message.content[0].text)
-
 ````
diff --git a/...es/v2/text-generation/prompt-engineering/prompt-library/book-an-appointment.mdx b/...es/v2/text-generation/prompt-engineering/prompt-library/book-an-appointment.mdx
@@ -57,8 +57,12 @@ import cohere
 
 co = cohere.ClientV2(api_key="<YOUR API KEY>")
 
-response = co.chat(model="command-r-plus-08-2024",
-    messages=[{"role": "user", "content": """
+response = co.chat(
+    model="command-r-plus-08-2024",
+    messages=[
+        {
+            "role": "user",
+            "content": """
     # Customer 
     I want to book an appointment for a haircut next Friday at 3pm. 
 
@@ -81,7 +85,9 @@ response = co.chat(model="command-r-plus-08-2024",
     next_available_time: "%Y-%m-%d %H"
     }
     ```
-    """}]
+    """,
+        }
+    ],
 )
 
 print(response.message.content[0].text)

diff --git a/...ion/prompt-engineering/prompt-library/create-a-markdown-table-from-raw-data.mdx b/...ion/prompt-engineering/prompt-library/create-a-markdown-table-from-raw-data.mdx
@@ -45,7 +45,10 @@ co = cohere.ClientV2(api_key="<YOUR API KEY>")
 
 response = co.chat(
     model="command-r-plus-08-2024",
-    messages=[{"role": "user", "content": """
+    messages=[
+        {
+            "role": "user",
+            "content": """
     You are an expert in data formatting. For the following csv data, output it as a markdown table.
     Output the table only. 
 
@@ -55,8 +58,10 @@ response = co.chat(
     Bob Johnson,42,Software Developer
     Emily Davis,37,Product Manager
     ```
-    """}]
-    )
+    """,
+        }
+    ],
+)
 
 print(response.message.content[0].text)
 ````
diff --git a/...generation/prompt-engineering/prompt-library/create-csv-data-from-json-data.mdx b/...generation/prompt-engineering/prompt-library/create-csv-data-from-json-data.mdx
@@ -58,7 +58,10 @@ co = cohere.ClientV2(api_key="<YOUR API KEY>")
 
 response = co.chat(
     model="command-r-plus-08-2024",
-    messages=[{"role": "user", "content": """
+    messages=[
+        {
+            "role": "user",
+            "content": """
     You are an expert in data formatting. Convert the following JSON object into a CSV format.
 
     ```
@@ -80,7 +83,9 @@ response = co.chat(
         },
     ]
     ```
-    """}]
+    """,
+        }
+    ],
 )
 
 print(response.message.content[0].text)

diff --git a/...ext-generation/prompt-engineering/prompt-library/evaluate-your-llm-response.mdx b/...ext-generation/prompt-engineering/prompt-library/evaluate-your-llm-response.mdx
@@ -44,7 +44,10 @@ co = cohere.ClientV2(api_key="<YOUR API KEY>")
 
 response = co.chat(
     model="command-r-plus-08-2024",
-    messages=[{"role": "user", "content": """
+    messages=[
+        {
+            "role": "user",
+            "content": """
     You are an AI grader that given an output and a criterion, grades the completion based on
     the prompt and criterion. Below is a prompt, a completion, and a criterion with which to grade
     the completion. You need to respond according to the criterion instructions.
@@ -57,7 +60,9 @@ response = co.chat(
     ## Criterion 
     Rate the ouput text with a score between 0 and 1. 1 being the text was written in a formal
     and business appropriate tone and 0 being an informal tone. Respond only with the score.
-    """}]
+    """,
+        }
+    ],
 )
 
 print(response.message.content[0].text)

diff --git a/...ges/v2/text-generation/prompt-engineering/prompt-library/meeting-summarizer.mdx b/...ges/v2/text-generation/prompt-engineering/prompt-library/meeting-summarizer.mdx
@@ -111,10 +111,15 @@ co = cohere.ClientV2(api_key="<YOUR API KEY>")
 
 response = co.chat(
     model="command-r-plus-08-2024",
-    messages=[{"role": "user", "content": """
+    messages=[
+        {
+            "role": "user",
+            "content": """
 	... <content skipped> ...
 	Summarize the key events from this meeting in 2 clear and concise bullet points. Each bullet point should be very concise, with no more than 20 words. Use the active voice only. Do not use the passive voice. Order the bullets such that more general bullets come first. Do not include any action items that resulted from the meeting. Do not include speaker names unless it is important to differentiate.
-    """}]
+    """,
+        }
+    ],
 )
 
 print(response.message.content[0].text)