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docs: add content-writing cookbook to operations (#4566)

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Kartik
2026-03-28 20:52:54 +05:30
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@@ -16,6 +16,8 @@ Integrating Mem0 into your writing workflow helps you:
## Setup
<Tabs>
<Tab title="Platform">
```python
import os
from openai import OpenAI
@@ -32,13 +34,81 @@ openai = OpenAI()
USER_ID = "content_writer"
RUN_ID = "smart_editing_session"
```
</Tab>
<Tab title="Open Source">
Here we use **Mem0 open source** (`Memory`): all local, no API keys needed for memory. Vectors in **Qdrant**, LLM and embeddings via **Ollama**. The **OpenAI** Python SDK calls Ollama's **OpenAI-compatible** `/v1` endpoint for content rewriting.
### Installation
Install the required dependencies:
```bash
pip install mem0ai qdrant-client openai ollama
```
Then start Qdrant and pull the Ollama models:
```bash
docker run -d -p 6333:6333 qdrant/qdrant
ollama pull llama3.1:latest
ollama pull nomic-embed-text:latest
```
<Note>You can swap `nomic-embed-text` for any Ollama-supported embedding model (e.g., `snowflake-arctic-embed`, `mxbai-embed-large`). Just update the `model` in the `embedder` config and set `embedding_model_dims` in the Qdrant config to match the model's output dimensions (768 for `nomic-embed-text`).</Note>
```python
from openai import OpenAI
from mem0 import Memory
OLLAMA_URL = "http://localhost:11434"
CHAT_MODEL = "llama3.1:latest"
# Set up Mem0 with local providers
memory = Memory.from_config({
"vector_store": {
"provider": "qdrant",
"config": {
"collection_name": "content_writing",
"host": "localhost",
"port": 6333,
"embedding_model_dims": 768,
},
},
"llm": {
"provider": "ollama",
"config": {
"model": CHAT_MODEL,
"temperature": 0,
"max_tokens": 2000,
"ollama_base_url": OLLAMA_URL,
},
},
"embedder": {
"provider": "ollama",
"config": {
"model": "nomic-embed-text:latest",
"ollama_base_url": OLLAMA_URL,
},
},
})
# OpenAI SDK pointed at Ollama for content rewriting
ollama_chat = OpenAI(base_url=f"{OLLAMA_URL}/v1", api_key="ollama")
USER_ID = "content_writer"
RUN_ID = "smart_editing_session"
```
</Tab>
</Tabs>
## Storing Your Writing Preferences in Mem0
<Tabs>
<Tab title="Platform">
```python
def store_writing_preferences():
"""Store your writing preferences in Mem0."""
preferences = """My writing preferences:
1. Use headings and sub-headings for structure.
2. Keep paragraphs concise (8–10 sentences max).
@@ -61,9 +131,41 @@ def store_writing_preferences():
return response
```
</Tab>
<Tab title="Open Source">
```python
def store_writing_preferences():
"""Store your writing preferences in Mem0."""
preferences = """My writing preferences:
1. Use headings and sub-headings for structure.
2. Keep paragraphs concise (8–10 sentences max).
3. Incorporate specific numbers and statistics.
4. Provide concrete examples.
5. Use bullet points for clarity.
6. Avoid jargon and buzzwords."""
messages = [
{"role": "user", "content": "Here are my writing style preferences."},
{"role": "assistant", "content": preferences},
]
response = memory.add(
messages,
user_id=USER_ID,
run_id=RUN_ID,
metadata={"type": "preferences", "category": "writing_style"},
)
return response
```
</Tab>
</Tabs>
## Editing Content Using Stored Preferences
<Tabs>
<Tab title="Platform">
```python
def apply_writing_style(original_content):
"""Use preferences stored in Mem0 to guide content rewriting."""
@@ -107,29 +209,73 @@ Preferences:
return clean_response
```
</Tab>
<Tab title="Open Source">
```python
def apply_writing_style(original_content):
"""Use preferences stored in Mem0 to guide content rewriting."""
results = memory.search(
query="What are my writing style preferences?",
user_id=USER_ID,
run_id=RUN_ID,
)
if not results:
print("No preferences found.")
return None
preferences = "\n".join(r["memory"] for r in results.get("results", []))
system_prompt = f"""
You are a writing assistant.
Apply the following writing style preferences to improve the user's content:
Preferences:
{preferences}
"""
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": f"""Original Content:
{original_content}"""},
]
# Ollama via OpenAI-compatible API
response = ollama_chat.chat.completions.create(
model=CHAT_MODEL,
messages=messages,
)
clean_response = response.choices[0].message.content.strip()
return clean_response
```
</Tab>
</Tabs>
## Complete Workflow: Content Editing
```python
def content_writing_workflow(content):
"""Automated workflow for editing a document based on writing preferences."""
# Store writing preferences (if not already stored)
store_writing_preferences() # Ideally done once, or with a conditional check
# Edit the document with Mem0 preferences
edited_content = apply_writing_style(content)
if not edited_content:
return "Failed to edit document."
# Display results
print("\n=== ORIGINAL DOCUMENT ===\n")
print(content)
print("\n=== EDITED DOCUMENT ===\n")
print(edited_content)
return edited_content
```
@@ -138,8 +284,8 @@ def content_writing_workflow(content):
```python
# Define your document
original_content = """Project Proposal
The following proposal outlines our strategy for the Q3 marketing campaign.
The following proposal outlines our strategy for the Q3 marketing campaign.
We believe this approach will significantly increase our market share.
Increase brand awareness
@@ -160,8 +306,8 @@ Your document will be transformed into a structured, well-formatted version base
### Original Document
```
Project Proposal
The following proposal outlines our strategy for the Q3 marketing campaign.
The following proposal outlines our strategy for the Q3 marketing campaign.
We believe this approach will significantly increase our market share.
Increase brand awareness
@@ -204,7 +350,7 @@ This proposal outlines our strategy for the Q3 marketing campaign. We aim to sig
### Conclusion
We believe this strategy will effectively increase our market share. To achieve these goals, we need your support and collaboration. Let’s work together to make this campaign a success. Please review the proposal and provide your feedback by the end of the week.
We believe this strategy will effectively increase our market share. To achieve these goals, we need your support and collaboration. Let's work together to make this campaign a success. Please review the proposal and provide your feedback by the end of the week.
```
Mem0 enables a seamless, intelligent content-writing workflow, perfect for content creators, marketers, and technical writers looking to scale their personal tone and structure across work.