JDSS/mem0
2
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

fix: updating docs with v3 integrations updates (#4898)

Browse Source
This commit is contained in:
Kartik
2026-04-20 18:54:21 +05:30
committed by GitHub
parent 00695e3113
commit 5520226b5b
10 changed files with 48 additions and 52 deletions
Download Patch File Download Diff File Expand all files Collapse all files
docs/cookbooks/companions/travel-assistant.mdx
+1 -2
View File
@@ -54,7 +54,6 @@ config = {
"embedding_model_dims": 3072,
}
},
"version": "v1.1",
}
class PersonalTravelAssistant:
@@ -154,7 +153,7 @@ class PersonalTravelAssistant:
return answer
def get_memories(self, user_id):
memories = self.memory.get_all(user_id=user_id)
memories = self.memory.get_all(filters={"user_id": user_id})
return [m['memory'] for m in memories.get('results', [])]
def search_memories(self, query, user_id):
docs/cookbooks/integrations/aws-bedrock.mdx
+1 -1
View File
@@ -42,7 +42,7 @@ This sets up Mem0 with:
```python
import boto3
from opensearchpy import RequestsHttpConnection, AWSV4SignerAuth
from mem0.memory.main import Memory
from mem0 import Memory
region = 'us-west-2'
service = 'aoss'
docs/integrations/aws-bedrock.mdx
+1 -1
View File
@@ -49,7 +49,7 @@ Import necessary modules and configure Mem0:
```python
import boto3
from opensearchpy import OpenSearch, RequestsHttpConnection, AWSV4SignerAuth
from mem0.memory.main import Memory
from mem0 import Memory
region = 'us-west-2'
service = 'aoss'
docs/integrations/keywords.mdx
+2 -2
View File
@@ -24,7 +24,7 @@ You can get your Mem0 API key from the <a href="https://app.mem0.ai/" rel="nofol
Install the necessary libraries:
```bash
pip install mem0 keywordsai-sdk
pip install mem0ai keywordsai-sdk
```
Set up your environment variables:
@@ -65,7 +65,7 @@ config = {
}
# Initialize Memory
memory = Memory.from_config(config_dict=config)
memory = Memory.from_config(config)
# Add a memory
result = memory.add(
docs/integrations/llama-index.mdx
-1
View File
@@ -92,7 +92,6 @@ config = {
"provider": "openai",
"config": {"model": "text-embedding-3-small"},
},
"version": "v1.1",
}
```
docs/integrations/openai-agents-sdk.mdx
+1 -1
View File
@@ -214,7 +214,7 @@ Customize memory behavior:
# Configure memory search
memories = mem0.search(
query="travel preferences",
user_id="alex",
filters={"user_id": "alex"},
top_k=5 # Number of memories to retrieve
)
docs/open-source/features/custom-instructions.mdx
+1 -3
View File
@@ -115,17 +115,15 @@ config = {
}
},
"custom_instructions": custom_instructions,
"version": "v1.1"
}
m = Memory.from_config(config_dict=config)
m = Memory.from_config(config)
```
```ts TypeScript
import { Memory } from "mem0ai/oss";
const config = {
version: "v1.1",
llm: {
provider: "openai",
config: {
docs/open-source/features/metadata-filtering.mdx
+26 -21
View File
@@ -51,8 +51,7 @@ m = Memory()
# Search with simple metadata filters
results = m.search(
"What are my preferences?",
user_id="alice",
filters={"category": "preferences"}
filters={"user_id": "alice", "category": "preferences"}
)
```
@@ -68,8 +67,8 @@ Layer greater-than/less-than comparisons to rank results by score, confidence, o
# Greater than / Less than
results = m.search(
"recent activities",
user_id="alice",
filters={
"user_id": "alice",
"score": {"gt": 0.8},
"priority": {"gte": 5},
"confidence": {"lt": 0.9},
@@ -80,8 +79,8 @@ results = m.search(
# Equality operators
results = m.search(
"specific content",
user_id="alice",
filters={
"user_id": "alice",
"status": {"eq": "active"},
"archived": {"ne": True}
}
@@ -96,8 +95,8 @@ Use `in` and `nin` when you want to pre-approve or exclude specific values witho
# In / Not in operators
results = m.search(
"multi-category search",
user_id="alice",
filters={
"user_id": "alice",
"category": {"in": ["food", "travel", "entertainment"]},
"status": {"nin": ["deleted", "archived"]}
}
@@ -116,8 +115,8 @@ results = m.search(
# Text matching operators
results = m.search(
"content search",
user_id="alice",
filters={
"user_id": "alice",
"title": {"contains": "meeting"},
"description": {"icontains": "important"},
"tags": {"contains": "urgent"}
@@ -133,8 +132,8 @@ Allow any value for a field while still requiring the field to exist—handy whe
# Match any value for a field
results = m.search(
"all with category",
user_id="alice",
filters={
"user_id": "alice",
"category": "*"
}
)
@@ -148,9 +147,9 @@ Combine filters with `AND`, `OR`, and `NOT` to express complex decision trees. N
# Logical AND
results = m.search(
"complex query",
user_id="alice",
filters={
"AND": [
{"user_id": "alice"},
{"category": "work"},
{"priority": {"gte": 7}},
{"status": {"ne": "completed"}}
@@ -161,34 +160,42 @@ results = m.search(
# Logical OR
results = m.search(
"flexible query",
user_id="alice",
filters={
"AND": [
{"user_id": "alice"},
{
"OR": [
{"category": "urgent"},
{"priority": {"gte": 9}},
{"deadline": {"contains": "today"}}
]
}
]
}
)
# Logical NOT
results = m.search(
"exclusion query",
user_id="alice",
filters={
"AND": [
{"user_id": "alice"},
{
"NOT": [
{"category": "archived"},
{"status": "deleted"}
]
}
]
}
)
# Complex nested logic
results = m.search(
"advanced query",
user_id="alice",
filters={
"AND": [
{"user_id": "alice"},
{
"OR": [
{"category": "work"},
@@ -288,16 +295,15 @@ Vector store support varies. Confirm operator coverage before shipping:
# Before (v0.x) - simple key-value filtering only
results = m.search(
"query",
user_id="alice",
filters={"category": "work", "status": "active"}
filters={"user_id": "alice", "category": "work", "status": "active"}
)
# After (v1.0.0) - enhanced filtering with operators
results = m.search(
"query",
user_id="alice",
filters={
"AND": [
{"user_id": "alice"},
{"category": "work"},
{"status": {"ne": "archived"}},
{"priority": {"gte": 5}}
@@ -320,9 +326,9 @@ results = m.search(
# Find high-priority active tasks
results = m.search(
"What tasks need attention?",
user_id="project_manager",
filters={
"AND": [
{"user_id": "project_manager"},
{"project": {"in": ["alpha", ""]}},
{"priority": {"gte": 8}},
{"status": {"ne": "completed"}},
@@ -347,9 +353,9 @@ results = m.search(
# Find recent unresolved tickets
results = m.search(
"pending support issues",
agent_id="support_bot",
filters={
"AND": [
{"agent_id": "support_bot"},
{"ticket_status": {"ne": "resolved"}},
{"priority": {"in": ["high", "critical"]}},
{"created_date": {"gte": "2024-01-01"}},
@@ -364,7 +370,7 @@ results = m.search(
```
<Tip>
Pair `agent_id` filters with ticket-specific metadata so shared support bots return only the tickets they can act on in the current session.
Pair agent ID filters with ticket-specific metadata so shared support bots return only the tickets they can act on in the current session.
</Tip>
### Content recommendation filtering
@@ -373,9 +379,9 @@ results = m.search(
# Personalized content filtering
results = m.search(
"recommend content",
user_id="reader123",
filters={
"AND": [
{"user_id": "reader123"},
{
"OR": [
{"genre": {"in": ["sci-fi", "fantasy"]}},
@@ -400,8 +406,8 @@ results = m.search(
try:
results = m.search(
"test query",
user_id="alice",
filters={
"user_id": "alice",
"invalid_operator": {"unknown": "value"}
}
)
@@ -409,8 +415,7 @@ except ValueError as e:
print(f"Filter error: {e}")
results = m.search(
"test query",
user_id="alice",
filters={"category": "general"}
filters={"user_id": "alice", "category": "general"}
)
```
docs/open-source/features/reranker-search.mdx
+8 -10
View File
@@ -189,7 +189,7 @@ async_memory = AsyncMemory.from_config(config)
async def search_with_rerank():
return await async_memory.search(
"What are my preferences?",
user_id="alice",
filters={"user_id": "alice"},
rerank=True
)
@@ -272,7 +272,7 @@ results = m.search("query", filters={"user_id": "alice"})
```python
results = m.search(
"What are my food preferences?",
user_id="alice"
filters={"user_id": "alice"}
)
for result in results["results"]:
@@ -289,13 +289,13 @@ for result in results["results"]:
```python
results_with_rerank = m.search(
"What movies do I like?",
user_id="alice",
filters={"user_id": "alice"},
rerank=True
)
results_without_rerank = m.search(
"What movies do I like?",
user_id="alice",
filters={"user_id": "alice"},
rerank=False
)
```
@@ -313,9 +313,9 @@ results_without_rerank = m.search(
```python
results = m.search(
"important work tasks",
user_id="alice",
filters={
"AND": [
{"user_id": "alice"},
{"category": "work"},
{"priority": {"gte": 7}}
]
@@ -348,8 +348,7 @@ m = Memory.from_config(config)
results = m.search(
"customer having login issues with mobile app",
agent_id="support_bot",
filters={"category": "technical_support"},
filters={"agent_id": "support_bot", "category": "technical_support"},
rerank=True
)
```
@@ -363,8 +362,7 @@ results = m.search(
```python
results = m.search(
"science fiction books with space exploration themes",
user_id="reader123",
filters={"content_type": "book_recommendation"},
filters={"user_id": "reader123", "content_type": "book_recommendation"},
rerank=True,
top_k=10
)
@@ -383,9 +381,9 @@ for result in results["results"]:
```python
results = m.search(
"What restaurants did I enjoy last month that had good vegetarian options?",
user_id="foodie_user",
filters={
"AND": [
{"user_id": "foodie_user"},
{"category": "dining"},
{"rating": {"gte": 4}},
{"date": {"gte": "2024-01-01"}}
docs/open-source/node-quickstart.mdx
-3
View File
@@ -76,7 +76,6 @@ By default the Node SDK uses local-friendly settings (OpenAI `gpt-5-mini`, `text
import { Memory } from "mem0ai/oss";
const memory = new Memory({
version: "v1.1",
embedder: {
provider: "openai",
config: {
@@ -221,7 +220,6 @@ Mem0 offers granular configuration across vector stores, LLMs, embedders, and hi
| Parameter | Description | Default |
| --- | --- | --- |
| `historyDbPath` | Path to history database | `"{mem0_dir}/history.db"` |
| `version` | API version | `"v1.0"` |
| `customInstructions` | Custom processing prompt | `undefined` |
</Accordion>
<Accordion title="History store">
@@ -234,7 +232,6 @@ Mem0 offers granular configuration across vector stores, LLMs, embedders, and hi
<Accordion title="Complete config example">
```ts
const config = {
version: "v1.1",
embedder: {
provider: "openai",
config: {