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Fix: Add Google Genai library support (#2941)

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This commit is contained in:
Akshat Jain
2025-06-17 17:47:09 +05:30
committed by GitHub
parent e0003247c3
commit c70dc7614b
7 changed files with 589 additions and 276 deletions
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22
docs/components/llms/models/gemini.mdx
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@@ -4,7 +4,11 @@ title: Gemini
<Snippet file="paper-release.mdx" />
To use Gemini model, you have to set the `GEMINI_API_KEY` environment variable. You can obtain the Gemini API key from the [Google AI Studio](https://aistudio.google.com/app/apikey)
To use the Gemini model, set the `GEMINI_API_KEY` environment variable. You can obtain the Gemini API key from [Google AI Studio](https://aistudio.google.com/app/apikey).
> **Note:** As of the latest release, Mem0 uses the new `google.genai` SDK instead of the deprecated `google.generativeai`. All message formatting and model interaction now use the updated `types` module from `google.genai`.
> **Note:** Some Gemini models are being deprecated and will retire soon. It is recommended to migrate to the latest stable models like `"gemini-2.0-flash-001"` or `"gemini-2.0-flash-lite-001"` to ensure ongoing support and improvements.
## Usage
@@ -12,28 +16,32 @@ To use Gemini model, you have to set the `GEMINI_API_KEY` environment variable.
import os
from mem0 import Memory
os.environ["OPENAI_API_KEY"] = "your-api-key" # used for embedding model
os.environ["GEMINI_API_KEY"] = "your-api-key"
os.environ["OPENAI_API_KEY"] = "your-openai-api-key" # Used for embedding model
os.environ["GEMINI_API_KEY"] = "your-gemini-api-key"
config = {
"llm": {
"provider": "gemini",
"config": {
"model": "gemini-1.5-flash-latest",
"model": "gemini-2.0-flash-001",
"temperature": 0.2,
"max_tokens": 2000,
"top_p": 1.0
}
}
}
m = Memory.from_config(config)
messages = [
{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
{"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
{"role": "user", "content": "I’m not a big fan of thriller movies but I love sci-fi movies."},
{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
{"role": "assistant", "content": "How about thriller movies? They can be quite engaging."},
{"role": "user", "content": "I’m not a big fan of thrillers, but I love sci-fi movies."},
{"role": "assistant", "content": "Got it! I'll avoid thrillers and suggest sci-fi movies instead."}
]
m.add(messages, user_id="alice", metadata={"category": "movies"})
```
## Config

78
docs/open-source/graph_memory/overview.mdx
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@@ -238,16 +238,24 @@ The Mem0's graph supports the following operations:
### Add Memories
<Note>
If you are using Mem0 with Graph Memory, it is recommended to pass `user_id`. Use `userId` in NodeSDK.
Mem0 with Graph Memory supports both `user_id` and `agent_id` parameters. You can use either or both to organize your memories. Use `userId` and `agentId` in NodeSDK.
</Note>
<CodeGroup>
```python Python
# Using only user_id
m.add("I like pizza", user_id="alice")
# Using both user_id and agent_id
m.add("I like pizza", user_id="alice", agent_id="food-assistant")
```
```typescript TypeScript
// Using only userId
memory.add("I like pizza", { userId: "alice" });
// Using both userId and agentId
memory.add("I like pizza", { userId: "alice", agentId: "food-assistant" });
```
```json Output
@@ -260,11 +268,19 @@ memory.add("I like pizza", { userId: "alice" });
<CodeGroup>
```python Python
# Get all memories for a user
m.get_all(user_id="alice")
# Get all memories for a specific agent belonging to a user
m.get_all(user_id="alice", agent_id="food-assistant")
```
```typescript TypeScript
// Get all memories for a user
memory.getAll({ userId: "alice" });
// Get all memories for a specific agent belonging to a user
memory.getAll({ userId: "alice", agentId: "food-assistant" });
```
```json Output
@@ -277,7 +293,8 @@ memory.getAll({ userId: "alice" });
'metadata': None,
'created_at': '2024-08-20T14:09:27.588719-07:00',
'updated_at': None,
'user_id': 'alice'
'user_id': 'alice',
'agent_id': 'food-assistant'
}
],
'entities': [
@@ -295,11 +312,19 @@ memory.getAll({ userId: "alice" });
<CodeGroup>
```python Python
# Search memories for a user
m.search("tell me my name.", user_id="alice")
# Search memories for a specific agent belonging to a user
m.search("tell me my name.", user_id="alice", agent_id="food-assistant")
```
```typescript TypeScript
// Search memories for a user
memory.search("tell me my name.", { userId: "alice" });
// Search memories for a specific agent belonging to a user
memory.search("tell me my name.", { userId: "alice", agentId: "food-assistant" });
```
```json Output
@@ -312,7 +337,8 @@ memory.search("tell me my name.", { userId: "alice" });
'metadata': None,
'created_at': '2024-08-20T14:09:27.588719-07:00',
'updated_at': None,
'user_id': 'alice'
'user_id': 'alice',
'agent_id': 'food-assistant'
}
],
'entities': [
@@ -331,11 +357,19 @@ memory.search("tell me my name.", { userId: "alice" });
<CodeGroup>
```python Python
# Delete all memories for a user
m.delete_all(user_id="alice")
# Delete all memories for a specific agent belonging to a user
m.delete_all(user_id="alice", agent_id="food-assistant")
```
```typescript TypeScript
// Delete all memories for a user
memory.deleteAll({ userId: "alice" });
// Delete all memories for a specific agent belonging to a user
memory.deleteAll({ userId: "alice", agentId: "food-assistant" });
```
</CodeGroup>
@@ -516,6 +550,42 @@ memory.search("Who is spiderman?", { userId: "alice123" });
> **Note:** The Graph Memory implementation is not standalone. You will be adding/retrieving memories to the vector store and the graph store simultaneously.
## Using Multiple Agents with Graph Memory
When working with multiple agents, you can use the `agent_id` parameter to organize memories by both user and agent. This allows you to:
1. Create agent-specific knowledge graphs
2. Share common knowledge between agents
3. Isolate sensitive or specialized information to specific agents
### Example: Multi-Agent Setup
<CodeGroup>
```python Python
# Add memories for different agents
m.add("I prefer Italian cuisine", user_id="bob", agent_id="food-assistant")
m.add("I'm allergic to peanuts", user_id="bob", agent_id="health-assistant")
m.add("I live in Seattle", user_id="bob") # Shared across all agents
# Search within specific agent context
food_preferences = m.search("What food do I like?", user_id="bob", agent_id="food-assistant")
health_info = m.search("What are my allergies?", user_id="bob", agent_id="health-assistant")
location = m.search("Where do I live?", user_id="bob") # Searches across all agents
```
```typescript TypeScript
// Add memories for different agents
memory.add("I prefer Italian cuisine", { userId: "bob", agentId: "food-assistant" });
memory.add("I'm allergic to peanuts", { userId: "bob", agentId: "health-assistant" });
memory.add("I live in Seattle", { userId: "bob" }); // Shared across all agents
// Search within specific agent context
const foodPreferences = memory.search("What food do I like?", { userId: "bob", agentId: "food-assistant" });
const healthInfo = memory.search("What are my allergies?", { userId: "bob", agentId: "health-assistant" });
const location = memory.search("Where do I live?", { userId: "bob" }); // Searches across all agents
```
</CodeGroup>
If you want to use a managed version of Mem0, please check out [Mem0](https://mem0.dev/pd). If you have any questions, please feel free to reach out to us using one of the following methods:
<Snippet file="get-help.mdx" />
<Snippet file="get-help.mdx" />