langchain-yugabytedb 0.0.4

An integration package connecting YugabyteDB and LangChain

langchain-yugabytedb

The langchain-yugabytedb package implementations of core LangChain abstractions using YugabyteDB Distributed SQL Database.

The package is released under the MIT license.

Feel free to use the abstraction as provided or else modify them / extend them as appropriate for your own application.

Requirements

The package supports the asyncpg and psycopg3 drivers.

Installation

pip install -U langchain-yugabytedb

Documentation

• Quickstart

Example

from langchain_core.documents import Document
from langchain_core.embeddings import DeterministicFakeEmbedding
from langchain_yugabytedb import YBEngine, YugabyteDBVectorStore

# Replace the connection string with your own YugabyteDB connection string
CONNECTION_STRING = "postgresql+psycopg://yugabyte:@localhost:5433/yugabyte"
engine = YBEngine.from_connection_string(url=CONNECTION_STRING)

# Replace the vector size with your own vector size
VECTOR_SIZE = 768
embedding = DeterministicFakeEmbedding(size=VECTOR_SIZE)

TABLE_NAME = "my_doc_collection"

engine.init_vectorstore_table(
    table_name=TABLE_NAME,
    vector_size=VECTOR_SIZE,
)

store = YugabyteDBVectorStore.create_sync(
    engine=engine,
    table_name=TABLE_NAME,
    embedding_service=embedding,
)

docs = [
    Document(page_content="Apples and oranges"),
    Document(page_content="Cars and airplanes"),
    Document(page_content="Train")
]

store.add_documents(docs)

query = "I'd like a fruit."
docs = store.similarity_search(query)
print(docs)

[!TIP] All synchronous functions have corresponding asynchronous functions

ChatMessageHistory

The chat message history abstraction helps to persist chat message history in a YugabyteDB table.

YugabyteDBChatMessageHistory is parameterized using a table_name and a session_id.

The table_name is the name of the table in the database where the chat messages will be stored.

The session_id is a unique identifier for the chat session. It can be assigned by the caller using uuid.uuid4().

import uuid

from langchain_core.messages import SystemMessage, AIMessage, HumanMessage
from langchain_yugabytedb import YugabyteDBChatMessageHistory
import psycopg

# Establish a synchronous connection to the database
# (or use psycopg.AsyncConnection for async)
conn_info = ... # Fill in with your connection info
sync_connection = psycopg.connect(conn_info)

# Create the table schema (only needs to be done once)
table_name = "chat_history"
YugabyteDBChatMessageHistory.create_tables(sync_connection, table_name)

session_id = str(uuid.uuid4())

# Initialize the chat history manager
chat_history = YugabyteDBChatMessageHistory(
    table_name,
    session_id,
    sync_connection=sync_connection
)

# Add messages to the chat history
chat_history.add_messages([
    SystemMessage(content="Meow"),
    AIMessage(content="woof"),
    HumanMessage(content="bark"),
])

print(chat_history.messages)