LangChat

from langchat import LangChat
from langchat.providers import OpenAI, Pinecone, Supabase

LangChat(
    *,
    llm,
    vector_db,
    db,
    reranker=None,
    prompt_template=None,
    standalone_question_prompt=None,
    verbose=False,
    max_chat_history=20,
)

lc = LangChat(
    llm=OpenAI("gpt-4o-mini"),
    vector_db=Pinecone("my-index"),
    db=Supabase(),
    max_chat_history=10,
)

async def chat(
    self,
    query: str,
    user_id: str,
    platform: str = "default",
) -> ChatResponse

response = await lc.chat(
    query="What is our return policy?",
    user_id="alice",
    platform="web",
)

if response:                                    # True when status == "success"
    print(response.text)
    print(f"{response.response_time:.2f}s")
else:
    print(f"Error: {response.error}")

def chat_sync(
    self,
    query: str,
    user_id: str,
    platform: str = "default",
) -> ChatResponse

# Useful in scripts, notebooks, and synchronous frameworks
response = lc.chat_sync(query="Hello!", user_id="alice")
print(response)

def index(
    self,
    paths: str | list[str],
    *,
    chunk_size: int = 1000,
    chunk_overlap: int = 200,
    namespace: str | None = None,
    prevent_duplicates: bool = True,
) -> dict

result = lc.index(["docs/faq.pdf", "docs/manual.pdf"], namespace="support")
print(f"Indexed {result['chunks_indexed']} chunks")

def get_session(
    self,
    user_id: str,
    platform: str = "default",
) -> UserSession

session = lc.get_session("alice", platform="web")
print(session.chat_history)   # list of (query, response) tuples

@classmethod
def load_env(cls, path: str = ".env") -> None

LangChat.load_env()            # loads .env from current directory
LangChat.load_env(".env.prod") # loads a specific file