LangChain Python SDK - Friendli Docs

You can use LangChain Python SDK to interact with FriendliAI. This makes migration of existing applications already using LangChain particularly easy.

How to use

Before you start, ensure you’ve already obtained the API_KEY from the Friendli Suite > Personal Settings > API Keys. Our products are entirely compatible with OpenAI, so we use the langchain-openai package by referring to the FriendliAI baseURL.

pip install -qU langchain-openai langchain

Instantiation

Now we can instantiate our model object and generate chat completions. We provide usage examples for each type of endpoint. Choose the one that best suits your needs:

import os
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="meta-llama-3.1-8b-instruct",
    base_url="https://api.friendli.ai/serverless/v1",
    api_key=os.environ["API_KEY"],
)

Runnable interface

We support both synchronous and asynchronous runnable methods to generate a response.

Synchronous methods:

result = llm.invoke("Tell me a joke.")
print(result.content)

Asynchronous methods:

result = await llm.ainvoke("Tell me a joke.")
print(result.content)

Chaining

We can chain our model with a prompt template. Prompt templates convert raw user input to better input to the LLM.

from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a world class technical documentation writer."),
    ("user", "{input}")
])

chain = prompt | llm

print(chain.invoke({"input": "how can langsmith help with testing?"}))

To get the string value instead of the message, we can add an output parser to the chain.

from langchain_core.output_parsers import StrOutputParser

output_parser = StrOutputParser()

chain = prompt | llm | output_parser

print(chain.invoke({"input": "how can langsmith help with testing?"}))

Tool calling

Describe tools and their parameters, and let the model return a tool to invoke with the input arguments. Tool calling is extremely useful for enhancing the model’s capability to provide more comprehensive and actionable responses.

Define tools to use

The @tool decorator is used to define a tool. If you set parse_docstring=True, the tool will parse the docstring to extract the information of arguments.

from langchain_core.tools import tool

@tool
def add(a: int, b: int) -> int:
    """Adds a and b."""
    return a + b

@tool
def multiply(a: int, b: int) -> int:
    """Multiplies a and b."""
    return a * b

tools = [add, multiply]

Bind tools to the model

Now models can generate a tool calling response.

import os
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="meta-llama-3.1-8b-instruct",
    base_url="https://api.friendli.ai/serverless/v1",
    api_key=os.environ["API_KEY"],
)

llm_with_tools = llm.bind_tools(tools)

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

print(llm_with_tools.invoke(query).tool_calls)

Generate a tool assisted message

Use the tool call results to generate a message.

from langchain_core.messages import HumanMessage, ToolMessage

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

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"]))

print(llm_with_tools.invoke(messages))

For more information on how to use tools, check out the LangChain documentation.

OpenAI

LangChain

Weaviate

Documentation Index

​How to use

​Instantiation

​Runnable interface

​Synchronous methods:

​Asynchronous methods:

​Chaining

​Tool calling

​Define tools to use

​Bind tools to the model

​Generate a tool assisted message