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Add support: MemoryExport API (#2129)

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Co-authored-by: Deshraj Yadav <deshrajdry@gmail.com>
This commit is contained in:
Dev Khant
2025-01-09 20:43:01 +05:30
committed by GitHub
parent 09bf7ad916
commit 21854c6a24
6 changed files with 456 additions and 4 deletions
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6
docs/api-reference/memory/create-memory-export.mdx Normal file
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@@ -0,0 +1,6 @@
---
title: 'Create Memory Export'
openapi: post /v1/exports/
---
Submit a job to create a structured export of memories using a customizable Pydantic schema. This process may take some time to complete, especially if you’re exporting a large number of memories. You can tailor the export by applying various filters (e.g., user_id, agent_id, run_id, or session_id) and by modifying the Pydantic schema to ensure the final data matches your exact needs.

6
docs/api-reference/memory/get-memory-export.mdx Normal file
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@@ -0,0 +1,6 @@
---
title: 'Get Memory Export'
openapi: get /v1/exports/
---
Retrieve the latest structured memory export after submitting an export job. You can filter the export by `user_id`, `run_id`, `session_id`, or `app_id` to get the most recent export matching your filters.

4
docs/mint.json
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@@ -160,7 +160,9 @@
"api-reference/memory/v2-search-memories",
"api-reference/memory/history-memory",
"api-reference/memory/batch-update",
"api-reference/memory/batch-delete"
"api-reference/memory/batch-delete",
"api-reference/memory/create-memory-export",
"api-reference/memory/get-memory-export"
]
},
{

194
docs/openapi.json
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@@ -394,6 +394,200 @@
}
}
},
"/v1/exports/": {
"get": {
"tags": [
"exports"
],
"summary": "Export data based on filters",
"description": "Get the latest memory export.",
"operationId": "exports_list",
"parameters": [
{
"name": "user_id",
"in": "query",
"schema": {
"type": "string"
},
"description": "Filter exports by user ID"
},
{
"name": "run_id",
"in": "query",
"schema": {
"type": "string"
},
"description": "Filter exports by run ID"
},
{
"name": "session_id",
"in": "query",
"schema": {
"type": "string"
},
"description": "Filter exports by session ID"
},
{
"name": "app_id",
"in": "query",
"schema": {
"type": "string"
},
"description": "Filter exports by app ID"
},
{
"name": "org_id",
"in": "query",
"schema": {
"type": "string"
},
"description": "Filter exports by organization ID"
},
{
"name": "project_id",
"in": "query",
"schema": {
"type": "string"
},
"description": "Filter exports by project ID"
}
],
"responses": {
"200": {
"description": "Successful export",
"content": {
"application/json": {
"schema": {
"type": "object",
"description": "Export data response"
}
}
}
},
"400": {
"description": "Bad Request",
"content": {
"application/json": {
"schema": {
"type": "object",
"properties": {
"message": {
"type": "string",
"example": "One of the filters: app_id, user_id, agent_id, run_id is required!"
}
}
}
}
}
},
"404": {
"description": "Not Found",
"content": {
"application/json": {
"schema": {
"type": "object",
"properties": {
"error": {
"type": "string",
"example": "No memory export request found"
}
}
}
}
}
}
}
},
"post": {
"tags": [
"exports"
],
"summary": "Create an export job with schema",
"description": "Create a structured export of memories based on a provided schema.",
"operationId": "exports_create",
"requestBody": {
"content": {
"application/json": {
"schema": {
"type": "object",
"required": ["schema"],
"properties": {
"schema": {
"type": "object",
"description": "Schema definition for the export"
},
"user_id": {
"type": "string",
"description": "Filter exports by user ID"
},
"run_id": {
"type": "string",
"description": "Filter exports by run ID"
},
"session_id": {
"type": "string",
"description": "Filter exports by session ID"
},
"app_id": {
"type": "string",
"description": "Filter exports by app ID"
},
"org_id": {
"type": "string",
"description": "Filter exports by organization ID"
},
"project_id": {
"type": "string",
"description": "Filter exports by project ID"
}
}
}
}
},
"required": true
},
"responses": {
"201": {
"description": "Export created successfully",
"content": {
"application/json": {
"schema": {
"type": "object",
"properties": {
"message": {
"type": "string",
"example": "Memory export request received. The export will be ready in a few seconds."
},
"id": {
"type": "string",
"format": "uuid",
"example": "550e8400-e29b-41d4-a716-446655440000"
}
},
"required": ["message", "id"]
}
}
}
},
"400": {
"description": "Bad Request",
"content": {
"application/json": {
"schema": {
"type": "object",
"properties": {
"message": {
"type": "string",
"example": "Schema is required and must be a valid object"
}
}
}
}
}
}
}
}
},
"/v1/memories/": {
"get": {
"tags": [

171
docs/platform/quickstart.mdx
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@@ -1630,6 +1630,177 @@ curl -X DELETE "https://api.mem0.ai/v1/memories/batch/" \
```
</CodeGroup>
### 4.11 Create Memory Export
Submit a job to create a structured export of memories using a customizable Pydantic schema. This process may take some time to complete, especially if you’re exporting a large number of memories. You can tailor the export by applying various filters (e.g., `user_id`, `agent_id`, `run_id`, or `session_id`) and by modifying the Pydantic schema to ensure the final data matches your exact needs.
For example, if you want to extract professional profile information from memories, you can define a schema and export the memories in that structured format:
<CodeGroup>
```python Python
response = client.create_memory_export(schema=json_schema, user_id="user123")
print(response)
```
```bash cURL
curl -X POST "https://api.mem0.ai/v1/memories/export/" \
-H "Authorization: Token your-api-key" \
-H "Content-Type: application/json" \
-d '{
"schema":{json_schema},
"user_id": "user123"
}'
```
```json json_schema
{
"$defs": {
"EducationLevel": {
"enum": ["high_school", "bachelors", "masters"],
"title": "EducationLevel",
"type": "string"
},
"EmploymentStatus": {
"enum": ["full_time", "part_time", "student"],
"title": "EmploymentStatus",
"type": "string"
}
},
"example": {
"current_role": "Senior Software Engineer",
"education_level": "masters",
"employment_status": "full_time",
"full_name": "John Doe",
"skills": ["Python", "AWS", "Machine Learning"],
"years_experience": 8
},
"properties": {
"full_name": {
"anyOf": [
{
"maxLength": 100,
"minLength": 2,
"type": "string"
},
{
"type": "null"
}
],
"default": None,
"description": "The professional's full name",
"title": "Full Name"
},
"current_role": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": None,
"description": "Current job title or role",
"title": "Current Role"
},
"years_experience": {
"anyOf": [
{
"maximum": 70,
"minimum": 0,
"type": "integer"
},
{
"type": "null"
}
],
"default": None,
"description": "Years of professional experience",
"title": "Years Experience"
},
"employment_status": {
"anyOf": [
{
"$ref": "#/$defs/EmploymentStatus"
},
{
"type": "null"
}
],
"default": None,
"description": "Current employment status"
},
"education_level": {
"anyOf": [
{
"$ref": "#/$defs/EducationLevel"
},
{
"type": "null"
}
],
"default": None,
"description": "Highest level of education completed"
},
"skills": {
"anyOf": [
{
"items": {
"type": "string"
},
"type": "array"
},
{
"type": "null"
}
],
"default": None,
"description": "Professional skills and competencies",
"title": "Skills"
}
},
"title": "ProfessionalProfile",
"type": "object"
}
```
```json Output
{
"message": "Memory export request received. The export will be ready in a few seconds.",
"id": "550e8400-e29b-41d4-a716-446655440000"
}
```
</CodeGroup>
### 4.12 Get Memory Export
Retrieve the structured export of memories after you have submitted a export job using the previous endpoint. It return the latest memory export.
<CodeGroup>
```python Python
response = client.get_memory_export(user_id="user123")
print(response)
```
```bash cURL
curl -X GET "https://api.mem0.ai/v1/memories/export/?user_id=user123" \
-H "Authorization: Token your-api-key"
```
```json Output
{
"full_name": "John Doe",
"current_role": "Senior Software Engineer",
"years_experience": 8,
"employment_status": "full_time",
"education_level": "masters",
"skills": ["Python", "AWS", "Machine Learning"]
}
```
</CodeGroup>
If you have any questions, please feel free to reach out to us using one of the following methods:
<Snippet file="get-help.mdx" />

79
mem0/client/main.py
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@@ -48,6 +48,11 @@ class MemoryClient:
api_key (str): The API key for authenticating with the Mem0 API.
host (str): The base URL for the Mem0 API.
client (httpx.Client): The HTTP client used for making API requests.
organization (str, optional): (Deprecated) Organization name.
project (str, optional): (Deprecated) Project name.
org_id (str, optional): Organization ID.
project_id (str, optional): Project ID.
user_id (str): Unique identifier for the user.
"""
def __init__(
@@ -324,7 +329,19 @@ class MemoryClient:
@api_error_handler
def batch_update(self, memories: List[Dict[str, Any]]) -> Dict[str, Any]:
"""Batch update memories."""
"""Batch update memories.
Args:
memories: List of memory dictionaries to update. Each dictionary must contain:
- memory_id (str): ID of the memory to update
- text (str): New text content for the memory
Returns:
str: Message indicating the success of the batch update.
Raises:
APIError: If the API request fails.
"""
response = self.client.put("/v1/batch/", json={"memories": memories})
response.raise_for_status()
@@ -333,7 +350,18 @@ class MemoryClient:
@api_error_handler
def batch_delete(self, memories: List[Dict[str, Any]]) -> Dict[str, Any]:
"""Batch delete memories."""
"""Batch delete memories.
Args:
memories: List of memory dictionaries to delete. Each dictionary must contain:
- memory_id (str): ID of the memory to delete
Returns:
str: Message indicating the success of the batch deletion.
Raises:
APIError: If the API request fails.
"""
response = self.client.request(
"DELETE",
"/v1/batch/",
@@ -344,6 +372,43 @@ class MemoryClient:
capture_client_event("client.batch_delete", self)
return response.json()
@api_error_handler
def create_memory_export(self, schema: str, **kwargs) -> Dict[str, Any]:
"""Create a memory export with the provided schema.
Args:
schema: JSON schema defining the export structure
**kwargs: Optional filters like user_id, run_id, etc.
Returns:
Dict containing export request ID and status message
"""
response = self.client.post(
"/v1/exports/",
json={"schema": schema, **self._prepare_params(kwargs)}
)
response.raise_for_status()
capture_client_event("client.create_memory_export", self, {"schema": schema, "keys": list(kwargs.keys())})
return response.json()
@api_error_handler
def get_memory_export(self, **kwargs) -> Dict[str, Any]:
"""Get a memory export.
Args:
**kwargs: Filters like user_id to get specific export
Returns:
Dict containing the exported data
"""
response = self.client.get(
"/v1/exports/",
params=self._prepare_params(kwargs)
)
response.raise_for_status()
capture_client_event("client.get_memory_export", self, {"keys": list(kwargs.keys())})
return response.json()
def chat(self):
"""Start a chat with the Mem0 AI. (Not implemented)
@@ -414,7 +479,15 @@ class MemoryClient:
class AsyncMemoryClient:
"""Asynchronous client for interacting with the Mem0 API."""
"""Asynchronous client for interacting with the Mem0 API.
This class provides asynchronous versions of all MemoryClient methods.
It uses httpx.AsyncClient for making non-blocking API requests.
Attributes:
sync_client (MemoryClient): Underlying synchronous client instance.
async_client (httpx.AsyncClient): Async HTTP client for making API requests.
"""
def __init__(
self,