Build with Sarvam AI in LangChain

1
import os
2
3
from dotenv import load_dotenv
4
from langchain_sarvam import ChatSarvam
5
6
load_dotenv()
7
8
if not os.getenv("SARVAM_API_KEY"):
9
    raise RuntimeError("Set SARVAM_API_KEY in your environment or .env file before running.")
10
11
llm = ChatSarvam(model="sarvam-105b")

1
import os
2
3
from dotenv import load_dotenv
4
from langchain_sarvam import ChatSarvam
5
6
load_dotenv()
7
8
if not os.getenv("SARVAM_API_KEY"):
9
    raise RuntimeError("Set SARVAM_API_KEY in your environment or .env file before running.")
10
11
llm = ChatSarvam(model="sarvam-30b")
12
13
try:
14
    response = llm.invoke("Translate this to malayalam: 'Keep cooking, guys'")
15
    print(response.content)  # പാചകം തുടരൂ, സുഹൃത്തുക്കളേ
16
except Exception as e:
17
    print(f"Error: {e}")

1
import os
2
3
from dotenv import load_dotenv
4
from langchain_sarvam import ChatSarvam
5
6
load_dotenv()
7
8
if not os.getenv("SARVAM_API_KEY"):
9
    raise RuntimeError("Set SARVAM_API_KEY in your environment or .env file before running.")
10
11
llm = ChatSarvam(model="sarvam-105b", streaming=True)
12
13
try:
14
    for chunk in llm.stream("Count from 1 to 5."):
15
        print(chunk.content, end="", flush=True)
16
except Exception as e:
17
    print(f"Error: {e}")

1
import asyncio
2
import os
3
4
from dotenv import load_dotenv
5
from langchain_sarvam import ChatSarvam
6
7
load_dotenv()
8
9
if not os.getenv("SARVAM_API_KEY"):
10
    raise RuntimeError("Set SARVAM_API_KEY in your environment or .env file before running.")
11
12
llm = ChatSarvam(model="sarvam-105b")
13
14
15
async def main():
16
    try:
17
        response = await llm.ainvoke("What is the capital of India?")
18
        print(response.content)
19
20
        async for chunk in llm.astream("Count from 1 to 3."):
21
            print(chunk.content, end="", flush=True)
22
    except Exception as e:
23
        print(f"Error: {e}")
24
25
26
asyncio.run(main())

1
import os
2
3
from dotenv import load_dotenv
4
from langchain_core.tools import tool
5
from langchain_sarvam import ChatSarvam
6
7
load_dotenv()
8
9
if not os.getenv("SARVAM_API_KEY"):
10
    raise RuntimeError("Set SARVAM_API_KEY in your environment or .env file before running.")
11
12
13
@tool
14
def get_weather(city: str) -> str:
15
    """Get the weather for a city."""
16
    return f"Sunny in {city}"
17
18
19
llm = ChatSarvam(model="sarvam-105b")
20
llm_with_tools = llm.bind_tools([get_weather])
21
22
try:
23
    response = llm_with_tools.invoke("What is the weather in Mumbai?")
24
    print(response.tool_calls)
25
except Exception as e:
26
    print(f"Error: {e}")

1
import os
2
3
from dotenv import load_dotenv
4
from pydantic import BaseModel
5
from langchain_sarvam import ChatSarvam
6
7
load_dotenv()
8
9
if not os.getenv("SARVAM_API_KEY"):
10
    raise RuntimeError("Set SARVAM_API_KEY in your environment or .env file before running.")
11
12
13
class CapitalCity(BaseModel):
14
    city: str
15
    country: str
16
17
18
llm = ChatSarvam(model="sarvam-105b")
19
structured_llm = llm.with_structured_output(CapitalCity)
20
21
try:
22
    result = structured_llm.invoke("What is the capital of France?")
23
    print(result)  # CapitalCity(city='Paris', country='France')
24
except Exception as e:
25
    print(f"Error: {e}")

1
import os
2
3
from dotenv import load_dotenv
4
from langchain_sarvam import ChatSarvam
5
6
load_dotenv()
7
8
if not os.getenv("SARVAM_API_KEY"):
9
    raise RuntimeError("Set SARVAM_API_KEY in your environment or .env file before running.")
10
11
reasoning_llm = ChatSarvam(model="sarvam-105b", reasoning_effort="high")
12
13
try:
14
    response = reasoning_llm.invoke("Explain the Riemann hypothesis.")
15
    print(response.additional_kwargs.get("reasoning_content"))
16
    print(response.content)
17
except Exception as e:
18
    print(f"Error: {e}")