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Generate multiple code snippets from a single source file (#2146)
* Support generating multiple snippets from one source file * Convert Python code snippets from one source file * Add code snippets for C#, Go, Java, Rust and TS * Make intro less Python-oriented * Add client installation instructions for all languages * Cleanup python code --------- Co-authored-by: xzfc <xzfcpw@gmail.com>
This commit is contained in:
Abdon Pijpelink
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# @hide-start
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# mypy: disable-error-code="arg-type"
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QDRANT_URL=""
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QDRANT_API_KEY=""
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# @hide-end
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# @block-start client-connection
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from qdrant_client import QdrantClient, models
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client = QdrantClient(
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url=QDRANT_URL,
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api_key=QDRANT_API_KEY,
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cloud_inference=True
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)
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# @block-end client-connection
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# @block-start create-collection
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COLLECTION_NAME="my_books"
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client.create_collection(
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collection_name=COLLECTION_NAME,
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vectors_config=models.VectorParams(
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size=384, # Vector size is defined by used model
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distance=models.Distance.COSINE,
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),
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)
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# @block-end create-collection
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# @block-start upload-data
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documents = [
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{
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"name": "The Time Machine",
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"description": "A man travels through time and witnesses the evolution of humanity.",
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"author": "H.G. Wells",
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"year": 1895,
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},
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{
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"name": "Ender's Game",
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"description": "A young boy is trained to become a military leader in a war against an alien race.",
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"author": "Orson Scott Card",
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"year": 1985,
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},
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{
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"name": "Brave New World",
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"description": "A dystopian society where people are genetically engineered and conditioned to conform to a strict social hierarchy.",
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"author": "Aldous Huxley",
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"year": 1932,
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},
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{
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"name": "The Hitchhiker's Guide to the Galaxy",
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"description": "A comedic science fiction series following the misadventures of an unwitting human and his alien friend.",
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"author": "Douglas Adams",
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"year": 1979,
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},
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{
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"name": "Dune",
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"description": "A desert planet is the site of political intrigue and power struggles.",
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"author": "Frank Herbert",
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"year": 1965,
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},
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{
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"name": "Foundation",
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"description": "A mathematician develops a science to predict the future of humanity and works to save civilization from collapse.",
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"author": "Isaac Asimov",
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"year": 1951,
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},
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{
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"name": "Snow Crash",
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"description": "A futuristic world where the internet has evolved into a virtual reality metaverse.",
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"author": "Neal Stephenson",
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"year": 1992,
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},
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{
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"name": "Neuromancer",
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"description": "A hacker is hired to pull off a near-impossible hack and gets pulled into a web of intrigue.",
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"author": "William Gibson",
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"year": 1984,
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},
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{
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"name": "The War of the Worlds",
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"description": "A Martian invasion of Earth throws humanity into chaos.",
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"author": "H.G. Wells",
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"year": 1898,
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},
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{
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"name": "The Hunger Games",
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"description": "A dystopian society where teenagers are forced to fight to the death in a televised spectacle.",
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"author": "Suzanne Collins",
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"year": 2008,
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},
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{
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"name": "The Andromeda Strain",
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"description": "A deadly virus from outer space threatens to wipe out humanity.",
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"author": "Michael Crichton",
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"year": 1969,
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},
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{
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"name": "The Left Hand of Darkness",
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"description": "A human ambassador is sent to a planet where the inhabitants are genderless and can change gender at will.",
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"author": "Ursula K. Le Guin",
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"year": 1969,
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},
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{
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"name": "The Three-Body Problem",
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"description": "Humans encounter an alien civilization that lives in a dying system.",
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"author": "Liu Cixin",
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"year": 2008,
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},
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]
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# @block-end upload-data
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# @block-start upload-points
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EMBEDDING_MODEL="sentence-transformers/all-minilm-l6-v2"
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client.upload_points(
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collection_name=COLLECTION_NAME,
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points=[
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models.PointStruct(
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id=idx,
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vector=models.Document(
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text=doc["description"],
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model=EMBEDDING_MODEL
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),
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payload=doc
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)
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for idx, doc in enumerate(documents)
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],
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)
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# @block-end upload-points
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# @block-start query-engine
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hits = client.query_points(
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collection_name=COLLECTION_NAME,
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query=models.Document(
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text="alien invasion",
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model=EMBEDDING_MODEL
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),
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limit=3,
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).points
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for hit in hits:
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print(hit.payload, "score:", hit.score)
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# @block-end query-engine
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# @block-start create-payload-index
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client.create_payload_index(
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collection_name=COLLECTION_NAME,
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field_name="year",
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field_schema=models.PayloadSchemaType.INTEGER,
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)
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# @block-end create-payload-index
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# @block-start query-with-filter
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hits = client.query_points(
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collection_name=COLLECTION_NAME,
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query=models.Document(
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text="alien invasion",
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model=EMBEDDING_MODEL
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),
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query_filter=models.Filter(
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must=[models.FieldCondition(key="year", range=models.Range(gte=2000))]
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),
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limit=1,
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).points
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for hit in hits:
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print(hit.payload, "score:", hit.score)
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# @block-end query-with-filter
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