Premai 集成

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

import os
import getpass

if "PREMAI_API_KEY" not in os.environ:
    os.environ["PREMAI_API_KEY"] = getpass.getpass("PremAI API Key:")

chat = ChatPremAI(project_id=1234, model_name="gpt-4.1")

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

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

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

chat.invoke([system_message, human_message])

chat.invoke(
    [system_message, human_message],
    temperature = 0.7, max_tokens = 20, top_p = 0.95
)

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 information\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"
        }
    ]
}

import sys

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

import sys

for chunk in chat.stream(
    "hello how are you",
    system_prompt = "You are an helpful assistant", temperature = 0.7, max_tokens = 20
):
    sys.stdout.write(chunk.content)
    sys.stdout.flush()

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_message], template_id=template_id)

import os
import getpass
from langchain_community.embeddings import PremEmbeddings


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

model = "text-embedding-3-large"
embedder = PremEmbeddings(project_id=8, model=model)

query = "Hello, this is a test query"
query_result = embedder.embed_query(query)

# 打印查询嵌入向量的前五个元素

print(query_result[:5])

documents = [
    "This is document1",
    "This is document2",
    "This is document3"
]

doc_result = embedder.embed_documents(documents)

# 与前面的结果类似，打印第一个文档向量的前五个元素

print(doc_result[0][:5])

print(f"Dimension of embeddings: {len(query_result)}")

doc_result[:5]

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

# 定义函数参数的模式
class OperationInput(BaseModel):
    a: int = Field(description="First number")
    b: int = Field(description="Second number")


# 现在定义函数，参数模式将是 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)]