ChatPremAI

pip install premai langchain

from langchain_community.chat_models import ChatPremAI
from langchain.messages import HumanMessage, SystemMessage

import getpass
import os

# First step is to set up the env variable.
# you can also pass the API key while instantiating the model but this
# comes under a best practices to set it as env variable.

if os.environ.get("PREMAI_API_KEY") is None:
    os.environ["PREMAI_API_KEY"] = getpass.getpass("PremAI API Key:")

# By default it will use the model which was deployed through the platform
# in my case it will is "gpt-4o"

chat = ChatPremAI(project_id=1234, model_name="gpt-4o")

human_message = HumanMessage(content="Who are you?")

response = chat.invoke([human_message])
print(response.content)

I am an AI language model created by OpenAI, designed to assist with answering questions and providing information based on the context provided. How can I help you today?

system_message = SystemMessage(content="You are a friendly assistant.")
human_message = HumanMessage(content="Who are you?")

chat.invoke([system_message, human_message])

AIMessage(content="I'm your friendly assistant! How can I help you today?", response_metadata={'document_chunks': [{'repository_id': 1985, 'document_id': 1306, 'chunk_id': 173899, 'document_name': '[D] Difference between sparse and dense informati…', 'similarity_score': 0.3209080100059509, 'content': "with the difference or anywhere\nwhere I can read about it?\n\n\n      17                  9\n\n\n      u/ScotiabankCanada        •  Promoted\n\n\n                       Accelerate your study permit process\n                       with Scotiabank's Student GIC\n                       Program. We're here to help you tur…\n\n\n                       startright.scotiabank.com         Learn More\n\n\n                            Add a Comment\n\n\nSort by:   Best\n\n\n      DinosParkour      • 1y ago\n\n\n     Dense Retrieval (DR) m"}]}, id='run-510bbd0e-3f8f-4095-9b1f-c2d29fd89719-0')

chat.invoke([system_message, human_message], temperature=0.7, max_tokens=10, top_p=0.95)

/home/anindya/prem/langchain/libs/community/langchain_community/chat_models/premai.py:355: UserWarning: WARNING: Parameter top_p is not supported in kwargs.
  warnings.warn(f"WARNING: Parameter {key} is not supported in kwargs.")

AIMessage(content="Hello! I'm your friendly assistant. How can I", response_metadata={'document_chunks': [{'repository_id': 1985, 'document_id': 1306, 'chunk_id': 173899, 'document_name': '[D] Difference between sparse and dense informati…', 'similarity_score': 0.3209080100059509, 'content': "with the difference or anywhere\nwhere I can read about it?\n\n\n      17                  9\n\n\n      u/ScotiabankCanada        •  Promoted\n\n\n                       Accelerate your study permit process\n                       with Scotiabank's Student GIC\n                       Program. We're here to help you tur…\n\n\n                       startright.scotiabank.com         Learn More\n\n\n                            Add a Comment\n\n\nSort by:   Best\n\n\n      DinosParkour      • 1y ago\n\n\n     Dense Retrieval (DR) m"}]}, id='run-c4b06b98-4161-4cca-8495-fd2fc98fa8f8-0')

query = "Which models are used for dense retrieval"
repository_ids = [
    1985,
]
repositories = dict(ids=repository_ids, similarity_threshold=0.3, limit=3)

import json

response = chat.invoke(query, max_tokens=100, repositories=repositories)

print(response.content)
print(json.dumps(response.response_metadata, indent=4))

Dense retrieval models typically include:

1. **BERT-based Models**: Such as DPR (Dense Passage Retrieval) which uses BERT for encoding queries and passages.
2. **ColBERT**: A model that combines BERT with late interaction mechanisms.
3. **ANCE (Approximate Nearest Neighbor Negative Contrastive Estimation)**: Uses BERT and focuses on efficient retrieval.
4. **TCT-ColBERT**: A variant of ColBERT that uses a two-tower
{
    "document_chunks": [
        {
            "repository_id": 1985,
            "document_id": 1306,
            "chunk_id": 173899,
            "document_name": "[D] Difference between sparse and dense informati\u2026",
            "similarity_score": 0.3209080100059509,
            "content": "with the difference or anywhere\nwhere I can read about it?\n\n\n      17                  9\n\n\n      u/ScotiabankCanada        \u2022  Promoted\n\n\n                       Accelerate your study permit process\n                       with Scotiabank's Student GIC\n                       Program. We're here to help you tur\u2026\n\n\n                       startright.scotiabank.com         Learn More\n\n\n                            Add a Comment\n\n\nSort by:   Best\n\n\n      DinosParkour      \u2022 1y ago\n\n\n     Dense Retrieval (DR) m"
        }
    ]
}

Say hello to my name and say a feel-good quote
from my age. My name is: {name} and age is {age}

human_messages = [
    HumanMessage(content="Shawn", id="name"),
    HumanMessage(content="22", id="age"),
]

template_id = "78069ce8-xxxxx-xxxxx-xxxx-xxx"
response = chat.invoke([human_messages], template_id=template_id)
print(response.content)

import sys

for chunk in chat.stream("hello how are you"):
    sys.stdout.write(chunk.content)
    sys.stdout.flush()

It looks like your message got cut off. If you need information about Dense Retrieval (DR) or any other topic, please provide more details or clarify your question.

import sys

# For some experimental reasons if you want to override the system prompt then you
# can pass that here too. However it is not recommended to override system prompt
# of an already deployed model.

for chunk in chat.stream(
    "hello how are you",
    system_prompt="act like a dog",
    temperature=0.7,
    max_tokens=200,
):
    sys.stdout.write(chunk.content)
    sys.stdout.flush()

Woof! 🐾 How can I help you today? Want to play fetch or maybe go for a walk 🐶🦴

from langchain.tools import tool
from pydantic import BaseModel, Field


# Define the schema for function arguments
class OperationInput(BaseModel):
    a: int = Field(description="First number")
    b: int = Field(description="Second number")


# Now define the function where schema for argument will be OperationInput
@tool("add", args_schema=OperationInput, return_direct=True)
def add(a: int, b: int) -> int:
    """Adds `a` and `b`.

    Args:
        a: First int
        b: Second int
    """
    return a + b


@tool("multiply", args_schema=OperationInput, return_direct=True)
def multiply(a: int, b: int) -> int:
    """Multiplies a and b.

    Args:
        a: First int
        b: Second int
    """
    return a * b

tools = [add, multiply]
llm_with_tools = chat.bind_tools(tools)

query = "What is 3 * 12? Also, what is 11 + 49?"

messages = [HumanMessage(query)]
ai_msg = llm_with_tools.invoke(messages)

ai_msg.tool_calls

[{'name': 'multiply',
  'args': {'a': 3, 'b': 12},
  'id': 'call_A9FL20u12lz6TpOLaiS6rFa8'},
 {'name': 'add',
  'args': {'a': 11, 'b': 49},
  'id': 'call_MPKYGLHbf39csJIyb5BZ9xIk'}]

messages.append(ai_msg)

from langchain.messages import ToolMessage

for tool_call in ai_msg.tool_calls:
    selected_tool = {"add": add, "multiply": multiply}[tool_call["name"].lower()]
    tool_output = selected_tool.invoke(tool_call["args"])
    messages.append(ToolMessage(tool_output, tool_call_id=tool_call["id"]))

response = llm_with_tools.invoke(messages)
print(response.content)

The final answers are:

- 3 * 12 = 36
- 11 + 49 = 60

from langchain_core.output_parsers.openai_tools import PydanticToolsParser


class add(BaseModel):
    """Add two integers together."""

    a: int = Field(..., description="First integer")
    b: int = Field(..., description="Second integer")


class multiply(BaseModel):
    """Multiply two integers together."""

    a: int = Field(..., description="First integer")
    b: int = Field(..., description="Second integer")


tools = [add, multiply]

chain = llm_with_tools | PydanticToolsParser(tools=[multiply, add])
chain.invoke(query)

[multiply(a=3, b=12), add(a=11, b=49)]