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feat: adding oss version of companion cookbook (#4564)

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Kartik
2026-03-28 21:11:59 +05:30
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docs/cookbooks/essentials/building-ai-companion.mdx
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@@ -10,6 +10,32 @@ Problem: LLMs are stateless. GPT doesn't remember conversations. You could stuff
The solution: Mem0. It extracts and stores what matters from conversations, then retrieves it when needed. Your companion remembers user preferences, past events, and history.
<Tabs>
<Tab title="Platform">
</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 Ray's chat replies.
## 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>
</Tab>
</Tabs>
In this cookbook we'll build a **fitness companion** that:
- Remembers user goals across sessions
@@ -25,6 +51,8 @@ By the end, you'll have a working fitness companion and know how to handle commo
Max wants to train for a marathon. He starts chatting with Ray, an AI running coach.
<Tabs>
<Tab title="Platform">
```python
from openai import OpenAI
from mem0 import MemoryClient
@@ -53,8 +81,73 @@ def chat(user_input, user_id):
], user_id=user_id)
return response
```
</Tab>
<Tab title="Open Source">
```python
from openai import OpenAI
from mem0 import Memory
OLLAMA_URL = "http://localhost:11434"
CHAT_MODEL = "llama3.1:latest"
memory = Memory.from_config({
"vector_store": {
"provider": "qdrant",
"config": {
"collection_name": "fitness_companion",
"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,
},
},
})
ollama_chat = OpenAI(base_url=f"{OLLAMA_URL}/v1", api_key="ollama")
def chat(user_input, user_id):
# Retrieve relevant memories
memories = memory.search(user_input, user_id=user_id, limit=5)
context = "\n".join(m["memory"] for m in memories["results"])
# Call LLM with memory context (Ollama via OpenAI-compatible API)
response = ollama_chat.chat.completions.create(
model=CHAT_MODEL,
messages=[
{"role": "system", "content": f"You're Ray, a running coach. Memories:\n{context}"},
{"role": "user", "content": user_input},
],
).choices[0].message.content
# Store the exchange
memory.add(
[
{"role": "user", "content": user_input},
{"role": "assistant", "content": response},
],
user_id=user_id,
)
return response
```
</Tab>
</Tabs>
**Session 1:**
@@ -62,7 +155,6 @@ def chat(user_input, user_id):
chat("I want to run a marathon in under 4 hours", user_id="max")
# Output: "That's a solid goal. What's your current weekly mileage?"
# Stored in Mem0: "Max wants to run sub-4 marathon"
```
**Session 2 (next day, app restarted):**
@@ -70,7 +162,6 @@ chat("I want to run a marathon in under 4 hours", user_id="max")
```python
chat("What should I focus on today?", user_id="max")
# Output: "Based on your sub-4 marathon goal, let's work on building your aerobic base..."
```
<Info>
@@ -87,6 +178,8 @@ Ray remembers. Restart the app, and the goal persists. From here on, we'll focus
Max mentions his knee hurts. That's different from his marathon goal - one is temporary, the other is long-term.
<Tabs>
<Tab title="Platform">
**Categories vs Metadata:**
- **Categories**: AI-assigned by Mem0 based on content (you can't force them)
@@ -100,7 +193,6 @@ mem0_client.project.update(custom_categories=[
{"constraints": "Injuries, limitations, recovery needs"},
{"preferences": "Training style, surfaces, schedules"}
])
```
<Note>
@@ -121,7 +213,6 @@ mem0_client.add(
[{"role": "user", "content": "My right knee flares up on downhills"}],
user_id="max"
)
```
Mem0 reads the content and intelligently picks which categories apply. You define the palette, it handles the tagging.
@@ -135,13 +226,50 @@ mem0_client.add(
user_id="max",
metadata={"workout_type": "speed", "forced_tag": "custom_label"}
)
```
</Tab>
<Tab title="Open Source">
**Categories via Metadata:**
In open source, model categories with a stable field in `metadata`—here we use `memory_bucket`:
```python
# Add goal
memory.add(
[{"role": "user", "content": "Sub-4 marathon is my A-race"}],
user_id="max",
metadata={"memory_bucket": "goals"},
)
# Add constraint
memory.add(
[{"role": "user", "content": "My right knee flares up on downhills"}],
user_id="max",
metadata={"memory_bucket": "constraints"},
)
```
<Note>
**Categories vs Metadata:** In open source, categories are modeled as `metadata` fields you set on each `add`. Filters only see what you put on `add`.
</Note>
```python
# Force tag using metadata
memory.add(
[{"role": "user", "content": "Some workout note"}],
user_id="max",
metadata={"memory_bucket": "goals", "workout_type": "speed", "forced_tag": "custom_label"},
)
```
</Tab>
</Tabs>
### Filtering by Category
Retrieve just constraints for workout planning:
<Tabs>
<Tab title="Platform">
```python
constraints = mem0_client.search(
query="injury concerns",
@@ -155,8 +283,21 @@ constraints = mem0_client.search(
)
print([m["memory"] for m in constraints["results"]])
# Output: ["Max's right knee flares up on downhills"]
```
</Tab>
<Tab title="Open Source">
```python
constraints = memory.search(
query="injury concerns",
user_id="max",
filters={"memory_bucket": {"in": ["constraints"]}},
threshold=0.0 # optional: widen recall for short phrases
)
print([m["memory"] for m in constraints["results"]])
# Output: ["Max's right knee flares up on downhills"]
```
</Tab>
</Tabs>
Ray can plan workouts that avoid aggravating Max's knee, without pulling in race goals or other unrelated memories.
@@ -168,12 +309,22 @@ Ray can plan workouts that avoid aggravating Max's knee, without pulling in race
Run the basic loop for a week and check what's stored:
<Tabs>
<Tab title="Platform">
```python
memories = mem0_client.get_all(filters={"AND": [{"user_id": "max"}]})
print([m["memory"] for m in memories["results"]])
# Output: ["Max wants to run marathon under 4 hours", "hey", "lol ok", "cool thanks", "gtg bye"]
```
</Tab>
<Tab title="Open Source">
```python
memories = memory.get_all(user_id="max")
print([m["memory"] for m in memories["results"]])
# Output: ["Max wants to run marathon under 4 hours", "hey", "lol ok", "cool thanks", "gtg bye"]
```
</Tab>
</Tabs>
<Warning>
Without filters, Mem0 stores everything—greetings, filler, and casual chat. This pollutes retrieval: instead of pulling "marathon goal," you get "lol ok." Set custom instructions to keep memory clean.
@@ -183,6 +334,8 @@ Noise. Greetings and filler clutter the memory.
### Custom Instructions
<Tabs>
<Tab title="Platform">
Tell Mem0 what matters:
```python
@@ -198,11 +351,38 @@ Exclude:
- Casual chatter
- Hypotheticals unless planning related
""")
```
</Tab>
<Tab title="Open Source">
Tell Mem0 what matters by including `custom_fact_extraction_prompt` in the config dict:
```python
MEMORY_CONFIG["custom_fact_extraction_prompt"] = """
Extract from running coach conversations:
- Training goals and race targets
- Physical constraints or injuries
- Training preferences (time of day, surfaces, weather)
- Progress milestones
Exclude:
- Greetings and filler
- Casual chatter
- Hypotheticals unless planning related
Return JSON with key "facts" as a list of strings (use [] if nothing to store).
"""
memory = Memory.from_config(MEMORY_CONFIG)
```
<Note>`custom_fact_extraction_prompt` is a top-level key in the config dictionary passed to `Memory.from_config()`. Make sure it's set before creating the Memory instance — not after.</Note>
</Tab>
</Tabs>
Now chat again:
<Tabs>
<Tab title="Platform">
```python
chat("hey how's it going", user_id="max")
chat("I prefer trail running over roads", user_id="max")
@@ -210,8 +390,19 @@ chat("I prefer trail running over roads", user_id="max")
memories = mem0_client.get_all(filters={"AND": [{"user_id": "max"}]})
print([m["memory"] for m in memories["results"]])
# Output: ["Max wants to run marathon under 4 hours", "Max prefers trail running over roads"]
```
</Tab>
<Tab title="Open Source">
```python
chat("hey how's it going", user_id="max")
chat("I prefer trail running over roads", user_id="max")
memories = memory.get_all(user_id="max")
print([m["memory"] for m in memories["results"]])
# Output: ["Max wants to run marathon under 4 hours", "Max prefers trail running over roads"]
```
</Tab>
</Tabs>
<Info>
**Expected output:** Only 2 memories stored—the marathon goal and trail preference. The greeting "hey how's it going" was filtered out automatically. Custom instructions are working.
@@ -221,8 +412,6 @@ Only meaningful facts. Filler gets dropped automatically.
---
---
## Agent Memory for Personality
### Why Agents Need Memory Too
@@ -231,16 +420,30 @@ Max prefers direct feedback, not motivational fluff. Ray needs to remember how t
Store agent personality:
<Tabs>
<Tab title="Platform">
```python
mem0_client.add(
[{"role": "system", "content": "Max wants direct, data-driven feedback. Skip motivational language."}],
agent_id="ray_coach"
)
```
</Tab>
<Tab title="Open Source">
```python
memory.add(
[{"role": "user", "content": "Max wants direct, data-driven feedback. Skip motivational language."}],
agent_id="ray_coach",
infer=False,
)
```
</Tab>
</Tabs>
Retrieve agent style alongside user memories:
<Tabs>
<Tab title="Platform">
```python
# Get coach personality
agent_memories = mem0_client.search("coaching style", agent_id="ray_coach")
@@ -251,8 +454,26 @@ mem0_client.add([
{"role": "user", "content": "How'd my run look today?"},
{"role": "assistant", "content": "Pace was 8:15/mile. Heart rate 152, zone 2."}
], user_id="max", agent_id="ray_coach")
```
</Tab>
<Tab title="Open Source">
```python
# Get coach personality
agent_memories = memory.search("coaching style", agent_id="ray_coach")
# Output: ["Max wants direct, data-driven feedback. Skip motivational language."]
# Store conversations with agent_id
memory.add(
[
{"role": "user", "content": "How'd my run look today?"},
{"role": "assistant", "content": "Pace was 8:15/mile. Heart rate 152, zone 2."},
],
user_id="max",
agent_id="ray_coach",
)
```
</Tab>
</Tabs>
<Info>
**Expected behavior:** Ray's responses are now data-driven and direct. The agent memory stored the coaching style preference, so future responses adapt automatically without Max having to repeat his preference.
@@ -268,6 +489,8 @@ No "Great job!" or "Keep it up!" - just data. Ray adapts to Max's preference.
Don't send every single message to Mem0. Keep recent context in memory, let Mem0 handle the important long-term facts.
<Tabs>
<Tab title="Platform">
```python
# Store only meaningful exchanges in Mem0
mem0_client.add([
@@ -280,8 +503,27 @@ mem0_client.add([
# "cool thanks" → don't store
# Or rely on custom_instructions to filter automatically
```
</Tab>
<Tab title="Open Source">
```python
# Store only meaningful exchanges in Mem0
memory.add(
[
{"role": "user", "content": "I want to run a marathon"},
{"role": "assistant", "content": "Let's build a training plan"},
],
user_id="max",
)
# Skip storing filler
# "hey" → don't store
# "cool thanks" → don't store
# Or rely on custom_fact_extraction_prompt to filter automatically
```
</Tab>
</Tabs>
Last 10 messages in your app's buffer. Important facts in Mem0. Faster, cheaper, still works.
@@ -293,6 +535,8 @@ Last 10 messages in your app's buffer. Important facts in Mem0. Faster, cheaper,
Max tweaks his ankle. It'll heal in two weeks - the memory should expire too.
<Tabs>
<Tab title="Platform">
```python
from datetime import datetime, timedelta
@@ -303,10 +547,26 @@ mem0_client.add(
user_id="max",
expiration_date=expiration
)
```
In 14 days, this memory disappears automatically. Ray stops asking about the ankle.
</Tab>
<Tab title="Open Source">
```python
from datetime import datetime, timedelta
expiration = (datetime.now() + timedelta(days=14)).strftime("%Y-%m-%d")
memory.add(
[{"role": "user", "content": "Rolled my left ankle, needs rest"}],
user_id="max",
metadata={"memory_bucket": "constraints", "expires_on": expiration},
)
```
Store `expires_on` in metadata and prune expired memories in your app. Ray stops asking about the ankle once it's removed.
</Tab>
</Tabs>
---
@@ -314,6 +574,8 @@ In 14 days, this memory disappears automatically. Ray stops asking about the ank
Here's the Mem0 setup combining everything:
<Tabs>
<Tab title="Platform">
```python
from mem0 import MemoryClient
from datetime import datetime, timedelta
@@ -332,11 +594,55 @@ mem0_client.project.update(
{"name": "preferences", "description": "Training style"}
]
)
```
</Tab>
<Tab title="Open Source">
```python
from mem0 import Memory
from datetime import datetime, timedelta
MEMORY_CONFIG = {
"vector_store": {
"provider": "qdrant",
"config": {
"collection_name": "fitness_companion",
"host": "localhost",
"port": 6333,
"embedding_model_dims": 768,
},
},
"llm": {
"provider": "ollama",
"config": {
"model": "llama3.1:latest",
"temperature": 0,
"max_tokens": 2000,
"ollama_base_url": "http://localhost:11434",
},
},
"embedder": {
"provider": "ollama",
"config": {
"model": "nomic-embed-text:latest",
"ollama_base_url": "http://localhost:11434",
},
},
"custom_fact_extraction_prompt": """
Extract: goals, constraints, preferences, progress
Exclude: greetings, filler, casual chat
Return JSON with key "facts" as a list of strings.
""",
}
memory = Memory.from_config(MEMORY_CONFIG)
```
</Tab>
</Tabs>
**Week 1 - Store goals and preferences:**
<Tabs>
<Tab title="Platform">
```python
mem0_client.add([
{"role": "user", "content": "I want to run a sub-4 marathon"},
@@ -346,11 +652,33 @@ mem0_client.add([
mem0_client.add([
{"role": "user", "content": "I prefer trail running over roads"}
], user_id="max", categories=["preferences"])
```
</Tab>
<Tab title="Open Source">
```python
memory.add(
[
{"role": "user", "content": "I want to run a sub-4 marathon"},
{"role": "assistant", "content": "Got it. Let's build a training plan."},
],
user_id="max",
agent_id="ray",
metadata={"memory_bucket": "goals"},
)
memory.add(
[{"role": "user", "content": "I prefer trail running over roads"}],
user_id="max",
metadata={"memory_bucket": "preferences"},
)
```
</Tab>
</Tabs>
**Week 3 - Temporary injury with expiration:**
<Tabs>
<Tab title="Platform">
```python
expiration = (datetime.now() + timedelta(days=14)).strftime("%Y-%m-%d")
mem0_client.add(
@@ -359,16 +687,36 @@ mem0_client.add(
categories=["constraints"],
expiration_date=expiration
)
```
</Tab>
<Tab title="Open Source">
```python
expiration = (datetime.now() + timedelta(days=14)).strftime("%Y-%m-%d")
memory.add(
[{"role": "user", "content": "Rolled ankle, need light workouts"}],
user_id="max",
metadata={"memory_bucket": "constraints", "expires_on": expiration},
)
```
</Tab>
</Tabs>
**Retrieve for context:**
<Tabs>
<Tab title="Platform">
```python
memories = mem0_client.search("training plan", user_id="max", limit=5)
# Gets: marathon goal, trail preference, ankle injury (if still valid)
```
</Tab>
<Tab title="Open Source">
```python
memories = memory.search("training plan", user_id="max", limit=5)
# Gets: marathon goal, trail preference, ankle injury (if still valid / not pruned)
```
</Tab>
</Tabs>
Ray remembers goals, preferences, and personality. Handles temporary injuries. Works across sessions.
@@ -380,6 +728,8 @@ Ray remembers goals, preferences, and personality. Handles temporary injuries. W
Training for Boston is different from training for New York. Separate the memory threads:
<Tabs>
<Tab title="Platform">
```python
mem0_client.add(messages, user_id="max", run_id="boston-2025")
mem0_client.add(messages, user_id="max", run_id="nyc-2025")
@@ -390,8 +740,22 @@ boston_memories = mem0_client.search(
user_id="max",
run_id="boston-2025"
)
```
</Tab>
<Tab title="Open Source">
```python
memory.add(messages, user_id="max", run_id="boston-2025")
memory.add(messages, user_id="max", run_id="nyc-2025")
# Retrieve only Boston memories
boston_memories = memory.search(
"training plan",
user_id="max",
run_id="boston-2025",
)
```
</Tab>
</Tabs>
Each race gets its own episodic boundary. No cross-contamination.
@@ -399,6 +763,8 @@ Each race gets its own episodic boundary. No cross-contamination.
Max has 6 months of training logs to backfill:
<Tabs>
<Tab title="Platform">
```python
old_logs = [
[{"role": "user", "content": "Completed 20-mile long run"}],
@@ -407,13 +773,27 @@ old_logs = [
for log in old_logs:
mem0_client.add(log, user_id="max")
```
</Tab>
<Tab title="Open Source">
```python
old_logs = [
[{"role": "user", "content": "Completed 20-mile long run"}],
[{"role": "user", "content": "Hit 8:00 pace on tempo run"}],
]
for log in old_logs:
memory.add(log, user_id="max")
```
</Tab>
</Tabs>
### Handling Contradictions
Max changes his goal from sub-4 to sub-3:45:
<Tabs>
<Tab title="Platform">
```python
# Find the old memory
memories = mem0_client.get_all(filters={"AND": [{"user_id": "max"}]})
@@ -421,8 +801,19 @@ goal_memory = [m for m in memories["results"] if "sub-4" in m["memory"]][0]
# Update it
mem0_client.update(goal_memory["id"], "Max wants to run sub-3:45 marathon")
```
</Tab>
<Tab title="Open Source">
```python
# Find the old memory
memories = memory.get_all(user_id="max")
goal_memory = [m for m in memories["results"] if "sub-4" in m["memory"]][0]
# Update it
memory.update(goal_memory["id"], "Max wants to run sub-3:45 marathon")
```
</Tab>
</Tabs>
Update instead of creating duplicates.
@@ -430,11 +821,20 @@ Update instead of creating duplicates.
Max works with Ray for running and Jordan for strength training:
<Tabs>
<Tab title="Platform">
```python
chat("easy run today", user_id="max", agent_id="ray")
chat("leg day workout", user_id="max", agent_id="jordan")
```
</Tab>
<Tab title="Open Source">
```python
chat("easy run today", user_id="max", agent_id="ray")
chat("leg day workout", user_id="max", agent_id="jordan")
```
</Tab>
</Tabs>
Each coach maintains separate personality memory while sharing user context.
@@ -442,19 +842,44 @@ Each coach maintains separate personality memory while sharing user context.
Prioritize recent training over old data:
<Tabs>
<Tab title="Platform">
```python
recent = mem0_client.search(
"training progress",
user_id="max",
filters={"created_at": {"gte": "2025-10-01"}}
)
```
</Tab>
<Tab title="Open Source">
```python
# Qdrant range filters require numbers — store an epoch timestamp in metadata
from datetime import datetime
epoch = int(datetime(2025, 10, 15).timestamp())
memory.add(
[{"role": "user", "content": "Completed 18-mile long run"}],
user_id="max",
metadata={"logged_epoch": epoch},
)
cutoff = int(datetime(2025, 10, 1).timestamp())
recent = memory.search(
"training progress",
user_id="max",
filters={"logged_epoch": {"gte": cutoff}},
)
```
</Tab>
</Tabs>
### Metadata Tagging
Tag workouts by type:
<Tabs>
<Tab title="Platform">
```python
mem0_client.add(
[{"role": "user", "content": "10x400m intervals"}],
@@ -468,20 +893,48 @@ speed_sessions = mem0_client.search(
user_id="max",
filters={"metadata": {"workout_type": "speed"}}
)
```
</Tab>
<Tab title="Open Source">
```python
memory.add(
[{"role": "user", "content": "10x400m intervals"}],
user_id="max",
metadata={"workout_type": "speed", "intensity": "high"},
)
# Later, find all speed workouts
speed_sessions = memory.search(
"speed work",
user_id="max",
filters={"workout_type": "speed"},
)
```
</Tab>
</Tabs>
### Pruning Old Memories
Delete irrelevant memories:
<Tabs>
<Tab title="Platform">
```python
mem0_client.delete(memory_id="mem_xyz")
# Or clear an entire run_id
mem0_client.delete_all(user_id="max", run_id="old-training-cycle")
```
</Tab>
<Tab title="Open Source">
```python
memory.delete(memory_id="mem_xyz")
# Or clear an entire run_id
memory.delete_all(user_id="max", run_id="old-training-cycle")
```
</Tab>
</Tabs>
---
@@ -515,7 +968,7 @@ Before launching:
- Define 2-3 categories (goals, constraints, preferences)
- Add expiration strategy for time-bound facts
- Implement error handling for API calls
- Monitor memory quality in Mem0 dashboard
- Monitor memory quality (Mem0 dashboard or `get_all` / Qdrant when local)
- Clear test data from production project
---