Timescale Vector (Postgres)

%pip install --upgrade --quiet  lark

%pip install --upgrade --quiet  timescale-vector

# Get openAI api key by reading local .env file
# The .env file should contain a line starting with `OPENAI_API_KEY=sk-`
import os

from dotenv import find_dotenv, load_dotenv

_ = load_dotenv(find_dotenv())

OPENAI_API_KEY = os.environ["OPENAI_API_KEY"]
# Alternatively, use getpass to enter the key in a prompt
# import os
# import getpass
# os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")

# Get the service url by reading local .env file
# The .env file should contain a line starting with `TIMESCALE_SERVICE_URL=postgresql://`
_ = load_dotenv(find_dotenv())
TIMESCALE_SERVICE_URL = os.environ["TIMESCALE_SERVICE_URL"]

# Alternatively, use getpass to enter the key in a prompt
# import os
# import getpass
# TIMESCALE_SERVICE_URL = getpass.getpass("Timescale Service URL:")

from langchain_community.vectorstores.timescalevector import TimescaleVector
from langchain_core.documents import Document
from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings()

docs = [
    Document(
        page_content="A bunch of scientists bring back dinosaurs and mayhem breaks loose",
        metadata={"year": 1993, "rating": 7.7, "genre": "science fiction"},
    ),
    Document(
        page_content="Leo DiCaprio gets lost in a dream within a dream within a dream within a ...",
        metadata={"year": 2010, "director": "Christopher Nolan", "rating": 8.2},
    ),
    Document(
        page_content="A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea",
        metadata={"year": 2006, "director": "Satoshi Kon", "rating": 8.6},
    ),
    Document(
        page_content="A bunch of normal-sized women are supremely wholesome and some men pine after them",
        metadata={"year": 2019, "director": "Greta Gerwig", "rating": 8.3},
    ),
    Document(
        page_content="Toys come alive and have a blast doing so",
        metadata={"year": 1995, "genre": "animated"},
    ),
    Document(
        page_content="Three men walk into the Zone, three men walk out of the Zone",
        metadata={
            "year": 1979,
            "director": "Andrei Tarkovsky",
            "genre": "science fiction",
            "rating": 9.9,
        },
    ),
]

COLLECTION_NAME = "langchain_self_query_demo"
vectorstore = TimescaleVector.from_documents(
    embedding=embeddings,
    documents=docs,
    collection_name=COLLECTION_NAME,
    service_url=TIMESCALE_SERVICE_URL,
)

from langchain.chains.query_constructor.schema import AttributeInfo
from langchain.retrievers.self_query.base import SelfQueryRetriever
from langchain_openai import OpenAI

# Give LLM info about the metadata fields
metadata_field_info = [
    AttributeInfo(
        name="genre",
        description="The genre of the movie",
        type="string or list[string]",
    ),
    AttributeInfo(
        name="year",
        description="The year the movie was released",
        type="integer",
    ),
    AttributeInfo(
        name="director",
        description="The name of the movie director",
        type="string",
    ),
    AttributeInfo(
        name="rating", description="A 1-10 rating for the movie", type="float"
    ),
]
document_content_description = "Brief summary of a movie"

# Instantiate the self-query retriever from an LLM
llm = OpenAI(temperature=0)
retriever = SelfQueryRetriever.from_llm(
    llm, vectorstore, document_content_description, metadata_field_info, verbose=True
)

# This example only specifies a relevant query
retriever.invoke("What are some movies about dinosaurs")

# This example only specifies a filter
retriever.invoke("I want to watch a movie rated higher than 8.5")

# This example specifies a query and a filter
retriever.invoke("Has Greta Gerwig directed any movies about women")

# This example specifies a composite filter
retriever.invoke("What's a highly rated (above 8.5) science fiction film?")

# This example specifies a query and composite filter
retriever.invoke(
    "What's a movie after 1990 but before 2005 that's all about toys, and preferably is animated"
)

retriever = SelfQueryRetriever.from_llm(
    llm,
    vectorstore,
    document_content_description,
    metadata_field_info,
    enable_limit=True,
    verbose=True,
)

# This example specifies a query with a LIMIT value
retriever.invoke("what are two movies about dinosaurs")