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Refactoring from gemini to google ai (#3244)

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This commit is contained in:
Parshva Daftari
2025-08-02 00:26:42 +05:30
committed by GitHub
parent fbe8a2e90f
commit 89b67e0834
8 changed files with 52 additions and 95 deletions
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docs/components/embedders/models/gemini.mdx → docs/components/embedders/models/google_AI.mdx
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@@ -1,8 +1,8 @@
---
title: Gemini
title: Google AI
---
To use Gemini embedding models, set the `GOOGLE_API_KEY` environment variables. You can obtain the Gemini API key from [here](https://aistudio.google.com/app/apikey).
To use Google AI embedding models, set the `GOOGLE_API_KEY` environment variables. You can obtain the Gemini API key from [here](https://aistudio.google.com/app/apikey).
### Usage
@@ -40,4 +40,4 @@ Here are the parameters available for configuring Gemini embedder:
| --- | --- | --- |
| `model` | The name of the embedding model to use | `models/text-embedding-004` |
| `embedding_dims` | Dimensions of the embedding model (output_dimensionality will be considered as embedding_dims, so please set embedding_dims accordingly) | `768` |
| `api_key` | The Gemini API key | `None` |
| `api_key` | The Google API key | `None` |
docs/components/embedders/overview.mdx
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@@ -21,7 +21,7 @@ See the list of supported embedders below.
<Card title="Azure OpenAI" href="/components/embedders/models/azure_openai"></Card>
<Card title="Ollama" href="/components/embedders/models/ollama"></Card>
<Card title="Hugging Face" href="/components/embedders/models/huggingface"></Card>
<Card title="Gemini" href="/components/embedders/models/gemini"></Card>
<Card title="Google AI" href="/components/embedders/models/google_AI"></Card>
<Card title="Vertex AI" href="/components/embedders/models/vertexai"></Card>
<Card title="Together" href="/components/embedders/models/together"></Card>
<Card title="LM Studio" href="/components/embedders/models/lmstudio"></Card>
docs/components/llms/models/gemini.mdx
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@@ -1,76 +0,0 @@
---
title: Gemini
---
<Snippet file="blank-notif.mdx" />
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
<CodeGroup>
```python Python
import os
from mem0 import Memory
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-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 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"})
```
```typescript TypeScript
import { Memory } from "mem0ai/oss";
const config = {
llm: {
// You can also use "google" as provider ( for backward compatibility )
provider: "gemini",
config: {
model: "gemini-2.0-flash-001",
temperature: 0.1
}
}
}
const memory = new Memory(config);
const messages = [
{ role: "user", content: "I'm planning to watch a movie tonight. Any recommendations?" },
{ 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." }
]
await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });
```
</CodeGroup>
## Config
All available parameters for the `Gemini` config are present in [Master List of All Params in Config](../config).
docs/components/llms/models/google_AI.mdx
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@@ -4,38 +4,73 @@ title: Google AI
<Snippet file="blank-notif.mdx" />
To use Google AI model, you have to set the `GOOGLE_API_KEY` environment variable. You can obtain the Google API key from the [Google Maker Suite](https://makersuite.google.com/app/apikey)
To use the Gemini model, set the `GOOGLE_API_KEY` environment variable. You can obtain the Google/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
```python
<CodeGroup>
```python Python
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["GOOGLE_API_KEY"] = "your-gemini-api-key"
config = {
"llm": {
"provider": "litellm",
"provider": "gemini",
"config": {
"model": "gemini/gemini-pro",
"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"})
```
```typescript TypeScript
import { Memory } from "mem0ai/oss";
const config = {
llm: {
// You can also use "google" as provider ( for backward compatibility )
provider: "gemini",
config: {
model: "gemini-2.0-flash-001",
temperature: 0.1
}
}
}
const memory = new Memory(config);
const messages = [
{ role: "user", content: "I'm planning to watch a movie tonight. Any recommendations?" },
{ 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." }
]
await memory.add(messages, { userId: "alice", metadata: { category: "movies" } });
```
</CodeGroup>
## Config
All available parameters for the `litellm` config are present in [Master List of All Params in Config](../config).
All available parameters for the `Gemini` config are present in [Master List of All Params in Config](../config).
docs/components/llms/overview.mdx
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@@ -33,7 +33,6 @@ See the list of supported LLMs below.
<Card title="Mistral AI" href="/components/llms/models/mistral_ai" />
<Card title="Google AI" href="/components/llms/models/google_ai" />
<Card title="AWS bedrock" href="/components/llms/models/aws_bedrock" />
<Card title="Gemini" href="/components/llms/models/gemini" />
<Card title="DeepSeek" href="/components/llms/models/deepseek" />
<Card title="xAI" href="/components/llms/models/xAI" />
<Card title="Sarvam AI" href="/components/llms/models/sarvam" />
docs/components/vectordbs/dbs/vertex_ai.mdx
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@@ -12,7 +12,7 @@ To use Google Cloud Vertex AI Vector Search with `mem0`, you need to configure t
import os
from mem0 import Memory
os.environ["GEMINI_API_KEY"] = = "sk-xx"
os.environ["GOOGLE_API_KEY"] = "sk-xx"
config = {
"vector_store": {
docs/docs.json
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@@ -121,7 +121,6 @@
"components/llms/models/mistral_AI",
"components/llms/models/google_AI",
"components/llms/models/aws_bedrock",
"components/llms/models/gemini",
"components/llms/models/deepseek",
"components/llms/models/xAI",
"components/llms/models/sarvam",
@@ -177,7 +176,7 @@
"components/embedders/models/ollama",
"components/embedders/models/huggingface",
"components/embedders/models/vertexai",
"components/embedders/models/gemini",
"components/embedders/models/google_AI",
"components/embedders/models/lmstudio",
"components/embedders/models/together",
"components/embedders/models/langchain",
docs/llms.txt
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@@ -84,7 +84,7 @@
[OpenAI](https://docs.mem0.ai/components/llms/models/openai): Integrate OpenAI LLM models by setting OPENAI_API_KEY and configuring the Memory client with provider settings - supports both standard models (like gpt-4) and structured-outputs, with options for temperature, max tokens and Openrouter integration.
[Anthropic](https://docs.mem0.ai/components/llms/models/anthropic): Integrate Anthropic LLM models by setting ANTHROPIC_API_KEY from your Account Settings Page and configuring the Memory client with provider settings - supports models like claude-3-7-sonnet-latest with customizable temperature and max_tokens parameters.
[Google AI](https://docs.mem0.ai/components/llms/models/google_AI): Integrate Gemini LLM models by setting GEMINI_API_KEY from Google Maker Suite and configuring the Memory client with litellm provider - supports models like gemini-pro with customizable temperature and max_tokens parameters.
[Google AI](https://docs.mem0.ai/components/llms/models/google_AI): Integrate Gemini LLM models by setting GOOGLE_API_KEY from Google Maker Suite and configuring the Memory client with litellm provider - supports models like gemini-pro with customizable temperature and max_tokens parameters.
[Groq](https://docs.mem0.ai/components/llms/models/groq): Integrate Groq's Language Processing Unit (LPU) optimized models by setting GROQ_API_KEY and configuring the Memory client with provider settings - supports models like mixtral-8x7b-32768 with customizable temperature and max_tokens parameters for high-performance AI inference.
[Together](https://docs.mem0.ai/components/llms/models/together): Integrate Together LLM models by setting TOGETHER_API_KEY and configuring the Memory client with provider settings - supports both standard models (like together-llama-3-8b-instant) and structured-outputs, with options for temperature, max tokens and Openrouter integration.
[Deepseek](https://docs.mem0.ai/components/llms/models/deepseek): Integrate Deepseek LLM models by setting DEEPSEEK_API_KEY and configuring the Memory client with provider settings - supports both standard models (like deepseek-chat) and structured-outputs, with options for temperature, max tokens and Openrouter integration.