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Update Docs (#3520)

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This commit is contained in:
Deshraj Yadav
2025-09-30 08:41:36 -07:00
committed by GitHub
parent 135883935f
commit d68ed11d58
134 changed files with 2193 additions and 2090 deletions
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docs/api-reference/memory/create-memory-export.mdx
+1 -1
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@@ -3,4 +3,4 @@ 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.
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.
docs/api-reference/memory/v2-search-memories.mdx
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@@ -13,96 +13,96 @@ The v2 search API is powerful and flexible, allowing for more precise memory ret
- `icontains`: Case-insensitive containment check
- `*`: Wildcard character that matches everything
<CodeGroup>
```python Code
related_memories = m.search(
query="What are Alice's hobbies?",
version="v2",
filters={
"OR": [
{
<CodeGroup>
```python Code
related_memories = m.search(
query="What are Alice's hobbies?",
version="v2",
filters={
"OR": [
{
"user_id": "alice"
},
{
"agent_id": {"in": ["travel-agent", "sports-agent"]}
}
]
},
)
```
```json Output
{
"memories": [
{
"id": "ea925981-272f-40dd-b576-be64e4871429",
"memory": "Likes to play cricket and plays cricket on weekends.",
"metadata": {
"category": "hobbies"
},
"score": 0.32116443111457704,
"created_at": "2024-07-26T10:29:36.630547-07:00",
"updated_at": null,
"user_id": "alice",
"agent_id": "sports-agent"
}
],
}
```
</CodeGroup>
<CodeGroup>
```python Wildcard Example
# Using wildcard to match all run_ids for a specific user
all_memories = m.search(
query="What are Alice's hobbies?",
version="v2",
filters={
"AND": [
{
"user_id": "alice"
},
{
"agent_id": {"in": ["travel-agent", "sports-agent"]}
}
]
},
)
```
},
{
"run_id": "*"
}
]
},
)
```
</CodeGroup>
```json Output
{
"memories": [
{
"id": "ea925981-272f-40dd-b576-be64e4871429",
"memory": "Likes to play cricket and plays cricket on weekends.",
"metadata": {
"category": "hobbies"
},
"score": 0.32116443111457704,
"created_at": "2024-07-26T10:29:36.630547-07:00",
"updated_at": null,
"user_id": "alice",
"agent_id": "sports-agent"
}
],
}
```
</CodeGroup>
<CodeGroup>
```python Categories Filter Examples
# Example 1: Using 'contains' for partial matching
finance_memories = m.search(
query="What are my financial goals?",
version="v2",
filters={
"AND": [
{ "user_id": "alice" },
{
"categories": {
"contains": "finance"
}
}
]
},
)
<CodeGroup>
```python Wildcard Example
# Using wildcard to match all run_ids for a specific user
all_memories = m.search(
query="What are Alice's hobbies?",
version="v2",
filters={
"AND": [
{
"user_id": "alice"
},
{
"run_id": "*"
}
]
},
)
```
</CodeGroup>
<CodeGroup>
```python Categories Filter Examples
# Example 1: Using 'contains' for partial matching
finance_memories = m.search(
query="What are my financial goals?",
version="v2",
filters={
"AND": [
{ "user_id": "alice" },
{
"categories": {
"contains": "finance"
}
}
]
},
)
# Example 2: Using 'in' for exact matching
personal_memories = m.search(
query="What personal information do you have?",
version="v2",
filters={
"AND": [
{ "user_id": "alice" },
{
"categories": {
"in": ["personal_information"]
}
}
]
},
)
```
</CodeGroup>
# Example 2: Using 'in' for exact matching
personal_memories = m.search(
query="What personal information do you have?",
version="v2",
filters={
"AND": [
{ "user_id": "alice" },
{
"categories": {
"in": ["personal_information"]
}
}
]
},
)
```
</CodeGroup>