Add support for sarvam-m model (#2802)
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@@ -110,6 +110,12 @@ Here's a comprehensive list of all parameters that can be used across different
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| `azure_kwargs` | Azure LLM args for initialization | AzureOpenAI |
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| `deepseek_base_url` | Base URL for DeepSeek API | DeepSeek |
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| `xai_base_url` | Base URL for XAI API | XAI |
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| `sarvam_base_url` | Base URL for Sarvam API | Sarvam |
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| `reasoning_effort` | Reasoning level (low, medium, high) | Sarvam |
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| `frequency_penalty` | Penalize frequent tokens (-2.0 to 2.0) | Sarvam |
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| `presence_penalty` | Penalize existing tokens (-2.0 to 2.0) | Sarvam |
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| `seed` | Seed for deterministic sampling | Sarvam |
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| `stop` | Stop sequences (max 4) | Sarvam |
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| `lmstudio_base_url` | Base URL for LM Studio API | LM Studio |
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</Tab>
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<Tab title="TypeScript">
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75
docs/components/llms/models/sarvam.mdx
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75
docs/components/llms/models/sarvam.mdx
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@@ -0,0 +1,75 @@
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---
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title: Sarvam AI
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---
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<Snippet file="paper-release.mdx" />
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**Sarvam AI** is an Indian AI company developing language models with a focus on Indian languages and cultural context. Their latest model **Sarvam-M** is designed to understand and generate content in multiple Indian languages while maintaining high performance in English.
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To use Sarvam AI's models, please set the `SARVAM_API_KEY` which you can get from their [platform](https://dashboard.sarvam.ai/).
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## Usage
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```python
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import os
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from mem0 import Memory
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os.environ["OPENAI_API_KEY"] = "your-api-key" # used for embedding model
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os.environ["SARVAM_API_KEY"] = "your-api-key"
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config = {
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"llm": {
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"provider": "sarvam",
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"config": {
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"model": "sarvam-m",
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"temperature": 0.7,
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}
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}
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}
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m = Memory.from_config(config)
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messages = [
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{"role": "user", "content": "I'm planning to watch a movie tonight. Any recommendations?"},
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{"role": "assistant", "content": "How about a thriller movies? They can be quite engaging."},
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{"role": "user", "content": "I'm not a big fan of thriller movies but I love sci-fi movies."},
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{"role": "assistant", "content": "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future."}
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]
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m.add(messages, user_id="alex")
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```
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## Advanced Usage with Sarvam-Specific Features
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```python
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import os
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from mem0 import Memory
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config = {
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"llm": {
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"provider": "sarvam",
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"config": {
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"model": {
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"name": "sarvam-m",
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"reasoning_effort": "high", # Enable advanced reasoning
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"frequency_penalty": 0.1, # Reduce repetition
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"seed": 42 # For deterministic outputs
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},
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"temperature": 0.3,
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"max_tokens": 2000,
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"api_key": "your-sarvam-api-key"
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}
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}
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}
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m = Memory.from_config(config)
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# Example with Hindi conversation
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messages = [
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{"role": "user", "content": "मैं SBI में joint account खोलना चाहता हूँ।"},
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{"role": "assistant", "content": "SBI में joint account खोलने के लिए आपको कुछ documents की जरूरत होगी। क्या आप जानना चाहते हैं कि कौन से documents चाहिए?"}
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]
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m.add(messages, user_id="rajesh", metadata={"language": "hindi", "topic": "banking"})
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```
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## Config
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All available parameters for the `sarvam` config are present in [Master List of All Params in Config](../config).
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@@ -34,6 +34,7 @@ To view all supported llms, visit the [Supported LLMs](./models).
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<Card title="Gemini" href="/components/llms/models/gemini" />
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<Card title="DeepSeek" href="/components/llms/models/deepseek" />
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<Card title="xAI" href="/components/llms/models/xAI" />
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<Card title="XAI" href="/components/llms/models/sarvam" />
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<Card title="LM Studio" href="/components/llms/models/lmstudio" />
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<Card title="Langchain" href="/components/llms/models/langchain" />
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</CardGroup>
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@@ -115,6 +115,7 @@
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"components/llms/models/gemini",
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"components/llms/models/deepseek",
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"components/llms/models/xAI",
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"components/llms/models/sarvam",
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"components/llms/models/lmstudio",
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"components/llms/models/langchain"
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]
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