LangChain Integration

import agentbasis
from agentbasis.frameworks.langchain import get_callback_handler

# Initialize AgentBasis first
agentbasis.init(api_key="your-api-key", agent_id="your-agent-id")

# Get a callback handler
handler = get_callback_handler()

from langchain_openai import ChatOpenAI

handler = get_callback_handler()

llm = ChatOpenAI(model="gpt-4")

response = llm.invoke(
    "Hello world", 
    config={"callbacks": [handler]}
)

from agentbasis.frameworks.langchain import get_callback_config

config = get_callback_config()

response = llm.invoke("Hello world", config=config)

from agentbasis.frameworks.langchain import instrument

# Returns the same handler instance every time
handler = instrument()

# Use throughout your application
llm.invoke("Hello", config={"callbacks": [handler]})

from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from agentbasis.frameworks.langchain import get_callback_config

chain = LLMChain(
    llm=llm, 
    prompt=PromptTemplate.from_template("Answer this: {query}")
)

result = chain.invoke(
    {"query": "What is the capital of France?"}, 
    config=get_callback_config()
)

from langchain.agents import create_openai_functions_agent, AgentExecutor
from langchain.tools import tool
from agentbasis.frameworks.langchain import get_callback_handler

@tool
def search(query: str) -> str:
    """Search for information."""
    return f"Results for: {query}"

handler = get_callback_handler()

agent_executor = AgentExecutor(
    agent=agent,
    tools=[search],
    callbacks=[handler]  # Can also pass to constructor
)

result = agent_executor.invoke(
    {"input": "Search for Python tutorials"}
)

from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAIEmbeddings
from agentbasis.frameworks.langchain import get_callback_config

vectorstore = FAISS.from_texts(["Document 1", "Document 2"], OpenAIEmbeddings())
retriever = vectorstore.as_retriever()

docs = retriever.invoke(
    "search query",
    config=get_callback_config()
)

langchain.chain.RetrievalQA
├── langchain.retriever.VectorStoreRetriever
│   └── query: "What is machine learning?"
│   └── documents: [...]
└── langchain.llm.ChatOpenAI
    └── model: "gpt-4"
    └── prompt: [...]
    └── completion: "..."

import agentbasis
from agentbasis.frameworks.langchain import get_callback_config

# Set user context
agentbasis.set_user("user-123")
agentbasis.set_session("session-456")

# All traces will include user context
result = chain.invoke(
    {"query": "Hello"},
    config=get_callback_config()
)