pip install langchain langchain-community
pip install langchain-openai
pip install pymysql
pip install tidb-vector

# Here we useimport getpass
import getpass
import os

if "OPENAI_API_KEY" not in os.environ:
    os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
# copy from tidb cloud console
tidb_connection_string_template = "mysql+pymysql://<USER>:<PASSWORD>@<HOST>:4000/<DB>?ssl_ca=/etc/ssl/cert.pem&ssl_verify_cert=true&ssl_verify_identity=true"
# tidb_connection_string_template = "mysql+pymysql://root:<PASSWORD>@34.212.137.91:4000/test"
tidb_password = getpass.getpass("Input your TiDB password:")
tidb_connection_string = tidb_connection_string_template.replace(
    "<PASSWORD>", tidb_password
)

from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import TiDBVectorStore
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter

loader = TextLoader("../../how_to/state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)

embeddings = OpenAIEmbeddings()

TABLE_NAME = "semantic_embeddings"
db = TiDBVectorStore.from_documents(
    documents=docs,
    embedding=embeddings,
    table_name=TABLE_NAME,
    connection_string=tidb_connection_string,
    distance_strategy="cosine",  # default, another option is "l2"
)

query = "What did the president say about Ketanji Brown Jackson"
docs_with_score = db.similarity_search_with_score(query, k=3)

for doc, score in docs_with_score:
    print("-" * 80)
    print("Score: ", score)
    print(doc.page_content)
    print("-" * 80)

--------------------------------------------------------------------------------
Score:  0.18459301498220004
Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections.

Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.

One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.

And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
Score:  0.2172729943284636
A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.

And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system.

We can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.

We’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.

We’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster.

We’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
Score:  0.2262166799003692
And for our LGBTQ+ Americans, let’s finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong.

As I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential.

While it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice.

And soon, we’ll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things.

So tonight I’m offering a Unity Agenda for the Nation. Four big things we can do together.

First, beat the opioid epidemic.
--------------------------------------------------------------------------------

docs_with_relevance_score = db.similarity_search_with_relevance_scores(query, k=2)
for doc, score in docs_with_relevance_score:
    print("-" * 80)
    print("Score: ", score)
    print(doc.page_content)
    print("-" * 80)

--------------------------------------------------------------------------------
Score:  0.8154069850178
Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections.

Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.

One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.

And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
Score:  0.7827270056715364
A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.

And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system.

We can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.

We’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.

We’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster.

We’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.
--------------------------------------------------------------------------------

{
    "page": 12,
    "book_tile": "Siddhartha"
}

{
    "page": 12,
    "book_tile": "Siddhartha"
}

{"page": 12}

{"page":{"$eq": 12}}

{"page":{"$in": [11, 12, 13]}}

{"page":{"$nin": [13]}}

{"page":{"$lt": 11}}

{
    "$or": [{"page": 11}, {"page": 12}],
    "$and": [{"page": 12}, {"page": 13}],
}

db.add_texts(
    texts=[
        "TiDB Vector offers advanced, high-speed vector processing capabilities, enhancing AI workflows with efficient data handling and analytics support.",
        "TiDB Vector, starting as low as $10 per month for basic usage",
    ],
    metadatas=[
        {"title": "TiDB Vector functionality"},
        {"title": "TiDB Vector Pricing"},
    ],
)

[UUID('c782cb02-8eec-45be-a31f-fdb78914f0a7'),
 UUID('08dcd2ba-9f16-4f29-a9b7-18141f8edae3')]

docs_with_score = db.similarity_search_with_score(
    "Introduction to TiDB Vector", filter={"title": "TiDB Vector functionality"}, k=4
)
for doc, score in docs_with_score:
    print("-" * 80)
    print("Score: ", score)
    print(doc.page_content)
    print("-" * 80)

--------------------------------------------------------------------------------
Score:  0.12761409169211535
TiDB Vector offers advanced, high-speed vector processing capabilities, enhancing AI workflows with efficient data handling and analytics support.
--------------------------------------------------------------------------------

retriever = db.as_retriever(
    search_type="similarity_score_threshold",
    search_kwargs={"k": 3, "score_threshold": 0.8},
)
docs_retrieved = retriever.invoke(query)
for doc in docs_retrieved:
    print("-" * 80)
    print(doc.page_content)
    print("-" * 80)

--------------------------------------------------------------------------------
Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections.

Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.

One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.

And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.
--------------------------------------------------------------------------------

# create table to store airplan data
db.tidb_vector_client.execute(
    """CREATE TABLE airplan_routes (
        id INT AUTO_INCREMENT PRIMARY KEY,
        airport_code VARCHAR(10),
        airline_code VARCHAR(10),
        destination_code VARCHAR(10),
        route_details TEXT,
        duration TIME,
        frequency INT,
        airplane_type VARCHAR(50),
        price DECIMAL(10, 2),
        layover TEXT
    );"""
)

# insert some data into Routes and our vector table
db.tidb_vector_client.execute(
    """INSERT INTO airplan_routes (
        airport_code,
        airline_code,
        destination_code,
        route_details,
        duration,
        frequency,
        airplane_type,
        price,
        layover
    ) VALUES
    ('JFK', 'DL', 'LAX', 'Non-stop from JFK to LAX.', '06:00:00', 5, 'Boeing 777', 299.99, 'None'),
    ('LAX', 'AA', 'ORD', 'Direct LAX to ORD route.', '04:00:00', 3, 'Airbus A320', 149.99, 'None'),
    ('EFGH', 'UA', 'SEA', 'Daily flights from SFO to SEA.', '02:30:00', 7, 'Boeing 737', 129.99, 'None');
    """
)
db.add_texts(
    texts=[
        "Clean lounges and excellent vegetarian dining options. Highly recommended.",
        "Comfortable seating in lounge areas and diverse food selections, including vegetarian.",
        "Small airport with basic facilities.",
    ],
    metadatas=[
        {"airport_code": "JFK"},
        {"airport_code": "LAX"},
        {"airport_code": "EFGH"},
    ],
)

[UUID('6dab390f-acd9-4c7d-b252-616606fbc89b'),
 UUID('9e811801-0e6b-4893-8886-60f4fb67ce69'),
 UUID('f426747c-0f7b-4c62-97ed-3eeb7c8dd76e')]

retriever = db.as_retriever(
    search_type="similarity_score_threshold",
    search_kwargs={"k": 3, "score_threshold": 0.85},
)
semantic_query = "Could you recommend a US airport with clean lounges and good vegetarian dining options?"
reviews = retriever.invoke(semantic_query)
for r in reviews:
    print("-" * 80)
    print(r.page_content)
    print(r.metadata)
    print("-" * 80)

--------------------------------------------------------------------------------
Clean lounges and excellent vegetarian dining options. Highly recommended.
{'airport_code': 'JFK'}
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
Comfortable seating in lounge areas and diverse food selections, including vegetarian.
{'airport_code': 'LAX'}
--------------------------------------------------------------------------------

# Extracting airport codes from the metadata
airport_codes = [review.metadata["airport_code"] for review in reviews]

# Executing a query to get the airport details
search_query = "SELECT * FROM airplan_routes WHERE airport_code IN :codes"
params = {"codes": tuple(airport_codes)}

airport_details = db.tidb_vector_client.execute(search_query, params)
airport_details.get("result")

[(1, 'JFK', 'DL', 'LAX', 'Non-stop from JFK to LAX.', datetime.timedelta(seconds=21600), 5, 'Boeing 777', Decimal('299.99'), 'None'),
 (2, 'LAX', 'AA', 'ORD', 'Direct LAX to ORD route.', datetime.timedelta(seconds=14400), 3, 'Airbus A320', Decimal('149.99'), 'None')]

search_query = f"""
    SELECT
        VEC_Cosine_Distance(se.embedding, :query_vector) as distance,
        ar.*,
        se.document as airport_review
    FROM
        airplan_routes ar
    JOIN
        {TABLE_NAME} se ON ar.airport_code = JSON_UNQUOTE(JSON_EXTRACT(se.meta, '$.airport_code'))
    ORDER BY distance ASC
    LIMIT 5;
"""
query_vector = embeddings.embed_query(semantic_query)
params = {"query_vector": str(query_vector)}
airport_details = db.tidb_vector_client.execute(search_query, params)
airport_details.get("result")

[(0.1219207353407008, 1, 'JFK', 'DL', 'LAX', 'Non-stop from JFK to LAX.', datetime.timedelta(seconds=21600), 5, 'Boeing 777', Decimal('299.99'), 'None', 'Clean lounges and excellent vegetarian dining options. Highly recommended.'),
 (0.14613754359804654, 2, 'LAX', 'AA', 'ORD', 'Direct LAX to ORD route.', datetime.timedelta(seconds=14400), 3, 'Airbus A320', Decimal('149.99'), 'None', 'Comfortable seating in lounge areas and diverse food selections, including vegetarian.'),
 (0.19840519342700513, 3, 'EFGH', 'UA', 'SEA', 'Daily flights from SFO to SEA.', datetime.timedelta(seconds=9000), 7, 'Boeing 737', Decimal('129.99'), 'None', 'Small airport with basic facilities.')]

# clean up
db.tidb_vector_client.execute("DROP TABLE airplan_routes")

{'success': True, 'result': 0, 'error': None}

db.drop_vectorstore()