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Add support for sarvam-m model (#2802)

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This commit is contained in:
Antaripa Saha
2025-05-26 23:19:37 +05:30
committed by GitHub
parent 5c6fbcaab0
commit 1ba9c71f54
8 changed files with 192 additions and 0 deletions
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6
docs/components/llms/config.mdx
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@@ -110,6 +110,12 @@ Here's a comprehensive list of all parameters that can be used across different
| `azure_kwargs` | Azure LLM args for initialization | AzureOpenAI |
| `deepseek_base_url` | Base URL for DeepSeek API | DeepSeek |
| `xai_base_url` | Base URL for XAI API | XAI |
| `sarvam_base_url` | Base URL for Sarvam API | Sarvam |
| `reasoning_effort` | Reasoning level (low, medium, high) | Sarvam |
| `frequency_penalty` | Penalize frequent tokens (-2.0 to 2.0) | Sarvam |
| `presence_penalty` | Penalize existing tokens (-2.0 to 2.0) | Sarvam |
| `seed` | Seed for deterministic sampling | Sarvam |
| `stop` | Stop sequences (max 4) | Sarvam |
| `lmstudio_base_url` | Base URL for LM Studio API | LM Studio |
</Tab>
<Tab title="TypeScript">

75
docs/components/llms/models/sarvam.mdx Normal file
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@@ -0,0 +1,75 @@
---
title: Sarvam AI
---
<Snippet file="paper-release.mdx" />
**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.
To use Sarvam AI's models, please set the `SARVAM_API_KEY` which you can get from their [platform](https://dashboard.sarvam.ai/).
## Usage
```python
import os
from mem0 import Memory
os.environ["OPENAI_API_KEY"] = "your-api-key" # used for embedding model
os.environ["SARVAM_API_KEY"] = "your-api-key"
config = {
"llm": {
"provider": "sarvam",
"config": {
"model": "sarvam-m",
"temperature": 0.7,
}
}
}
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."}
]
m.add(messages, user_id="alex")
```
## Advanced Usage with Sarvam-Specific Features
```python
import os
from mem0 import Memory
config = {
"llm": {
"provider": "sarvam",
"config": {
"model": {
"name": "sarvam-m",
"reasoning_effort": "high", # Enable advanced reasoning
"frequency_penalty": 0.1, # Reduce repetition
"seed": 42 # For deterministic outputs
},
"temperature": 0.3,
"max_tokens": 2000,
"api_key": "your-sarvam-api-key"
}
}
}
m = Memory.from_config(config)
# Example with Hindi conversation
messages = [
{"role": "user", "content": "मैं SBI में joint account खोलना चाहता हूँ।"},
{"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?"}
]
m.add(messages, user_id="rajesh", metadata={"language": "hindi", "topic": "banking"})
```
## Config
All available parameters for the `sarvam` config are present in [Master List of All Params in Config](../config).

1
docs/components/llms/overview.mdx
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@@ -34,6 +34,7 @@ To view all supported llms, visit the [Supported LLMs](./models).
<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="XAI" href="/components/llms/models/sarvam" />
<Card title="LM Studio" href="/components/llms/models/lmstudio" />
<Card title="Langchain" href="/components/llms/models/langchain" />
</CardGroup>

1
docs/docs.json
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@@ -115,6 +115,7 @@
"components/llms/models/gemini",
"components/llms/models/deepseek",
"components/llms/models/xAI",
"components/llms/models/sarvam",
"components/llms/models/lmstudio",
"components/llms/models/langchain"
]

7
mem0/configs/llms/base.py
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@@ -39,6 +39,8 @@ class BaseLlmConfig(ABC):
deepseek_base_url: Optional[str] = None,
# XAI specific
xai_base_url: Optional[str] = None,
# Sarvam specific
sarvam_base_url: Optional[str] = "https://api.sarvam.ai/v1",
# LM Studio specific
lmstudio_base_url: Optional[str] = "http://localhost:1234/v1",
# AWS Bedrock specific
@@ -89,6 +91,8 @@ class BaseLlmConfig(ABC):
:type deepseek_base_url: Optional[str], optional
:param xai_base_url: XAI base URL to be use, defaults to None
:type xai_base_url: Optional[str], optional
:param sarvam_base_url: Sarvam base URL to be use, defaults to "https://api.sarvam.ai/v1"
:type sarvam_base_url: Optional[str], optional
:param lmstudio_base_url: LM Studio base URL to be use, defaults to "http://localhost:1234/v1"
:type lmstudio_base_url: Optional[str], optional
"""
@@ -125,6 +129,9 @@ class BaseLlmConfig(ABC):
# XAI specific
self.xai_base_url = xai_base_url
# Sarvam specific
self.sarvam_base_url = sarvam_base_url
# LM Studio specific
self.lmstudio_base_url = lmstudio_base_url

1
mem0/llms/configs.py
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@@ -24,6 +24,7 @@ class LlmConfig(BaseModel):
"gemini",
"deepseek",
"xai",
"sarvam",
"lmstudio",
"langchain",
):

100
mem0/llms/sarvam.py Normal file
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@@ -0,0 +1,100 @@
import os
import requests
from typing import Dict, List, Optional
from mem0.configs.llms.base import BaseLlmConfig
from mem0.llms.base import LLMBase
class SarvamLLM(LLMBase):
def __init__(self, config: Optional[BaseLlmConfig] = None):
super().__init__(config)
# Set default model if not provided
if not self.config.model:
self.config.model = "sarvam-m"
# Get API key from config or environment variable
self.api_key = self.config.api_key or os.getenv("SARVAM_API_KEY")
if not self.api_key:
raise ValueError(
"Sarvam API key is required. Set SARVAM_API_KEY environment variable "
"or provide api_key in config."
)
# Set base URL - use config value or environment or default
self.base_url = (
getattr(self.config, 'sarvam_base_url', None) or
os.getenv("SARVAM_API_BASE") or
"https://api.sarvam.ai/v1"
)
def generate_response(
self,
messages: List[Dict[str, str]],
response_format=None
) -> str:
"""
Generate a response based on the given messages using Sarvam-M.
Args:
messages (list): List of message dicts containing 'role' and 'content'.
response_format (str or object, optional): Format of the response.
Currently not used by Sarvam API.
Returns:
str: The generated response.
"""
url = f"{self.base_url}/chat/completions"
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
}
# Prepare the request payload
params = {
"messages": messages,
"model": self.config.model if isinstance(self.config.model, str) else "sarvam-m",
}
# Add standard parameters that already exist in BaseLlmConfig
if self.config.temperature is not None:
params["temperature"] = self.config.temperature
if self.config.max_tokens is not None:
params["max_tokens"] = self.config.max_tokens
if self.config.top_p is not None:
params["top_p"] = self.config.top_p
# Handle Sarvam-specific parameters if model is passed as dict
if isinstance(self.config.model, dict):
# Extract model name
params["model"] = self.config.model.get("name", "sarvam-m")
# Add Sarvam-specific parameters
sarvam_specific_params = [
'reasoning_effort', 'frequency_penalty', 'presence_penalty',
'seed', 'stop', 'n'
]
for param in sarvam_specific_params:
if param in self.config.model:
params[param] = self.config.model[param]
try:
response = requests.post(url, headers=headers, json=params, timeout=30)
response.raise_for_status()
result = response.json()
if 'choices' in result and len(result['choices']) > 0:
return result['choices'][0]['message']['content']
else:
raise ValueError("No response choices found in Sarvam API response")
except requests.exceptions.RequestException as e:
raise RuntimeError(f"Sarvam API request failed: {e}")
except KeyError as e:
raise ValueError(f"Unexpected response format from Sarvam API: {e}")

1
mem0/utils/factory.py
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@@ -27,6 +27,7 @@ class LlmFactory:
"gemini": "mem0.llms.gemini.GeminiLLM",
"deepseek": "mem0.llms.deepseek.DeepSeekLLM",
"xai": "mem0.llms.xai.XAILLM",
"sarvam": "mem0.llms.sarvam.SarvamLLM",
"lmstudio": "mem0.llms.lmstudio.LMStudioLLM",
"langchain": "mem0.llms.langchain.LangchainLLM",
}