Zep

VectorStore Example for Zep - Fast, scalable building blocks for LLM Apps

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Zep is an open source platform for productionizing LLM apps. Go from a prototype built in LangChain or LlamaIndex, or a custom app, to production in minutes without rewriting code.

Fast, Scalable Building Blocks for LLM Apps

Zep is an open source platform for productionizing LLM apps. Go from a prototype built in LangChain or LlamaIndex, or a custom app, to production in minutes without rewriting code.

Key Features:

• Fast! Zep operates independently of the your chat loop, ensuring a snappy user experience.
• Chat History Memory, Archival, and Enrichment, populate your prompts with relevant chat history, sumamries, named entities, intent data, and more.
• Vector Search over Chat History and Documents Automatic embedding of documents, chat histories, and summaries. Use Zep's similarity or native MMR Re-ranked search to find the most relevant.
• Manage Users and their Chat Sessions Users and their Chat Sessions are first-class citizens in Zep, allowing you to manage user interactions with your bots or agents easily.
• Records Retention and Privacy Compliance Comply with corporate and regulatory mandates for records retention while ensuring compliance with privacy regulations such as CCPA and GDPR. Fulfill Right To Be Forgotten requests with a single API call

Note: The ZepVectorStore works with Documents and is intended to be used as a Retriever. It offers separate functionality to Zep's ZepMemory class, which is designed for persisting, enriching and searching your user's chat history.

Installation

Follow the Zep Quickstart Guide to install and get started with Zep.

Usage

You'll need your Zep API URL and optionally an API key to use the Zep VectorStore. See the Zep docs for more information.

In the examples below, we're using Zep's auto-embedding feature which automatically embed documents on the Zep server using low-latency embedding models.

Note

• These examples use Zep's async interfaces. Call sync interfaces by removing the a prefix from the method names.
• If you pass in an Embeddings instance Zep will use this to embed documents rather than auto-embed them. You must also set your document collection to isAutoEmbedded === false.
• If you set your collection to isAutoEmbedded === false, you must pass in an Embeddings instance.

Load or create a Collection from documents

from uuid import uuid4

from langchain.document_loaders import WebBaseLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import ZepVectorStore
from langchain.vectorstores.zep import CollectionConfig

ZEP_API_URL = "http://localhost:8000"  # this is the API url of your Zep instance
ZEP_API_KEY = "<optional_key>"  # optional API Key for your Zep instance
collection_name = f"babbage{uuid4().hex}"  # a unique collection name. alphanum only

# Collection config is needed if we're creating a new Zep Collection
config = CollectionConfig(
    name=collection_name,
    description="<optional description>",
    metadata={"optional_metadata": "associated with the collection"},
    is_auto_embedded=True,  # we'll have Zep embed our documents using its low-latency embedder
    embedding_dimensions=1536,  # this should match the model you've configured Zep to use.
)

# load the document
article_url = "https://www.gutenberg.org/cache/epub/71292/pg71292.txt"
loader = WebBaseLoader(article_url)
documents = loader.load()

# split it into chunks
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
docs = text_splitter.split_documents(documents)

# Instantiate the VectorStore. Since the collection does not already exist in Zep,
# it will be created and populated with the documents we pass in.
vs = ZepVectorStore.from_documents(
    docs,
    collection_name=collection_name,
    config=config,
    api_url=ZEP_API_URL,
    api_key=ZEP_API_KEY,
    embedding=None,  # we'll have Zep embed our documents using its low-latency embedder
)

# wait for the collection embedding to complete


async def wait_for_ready(collection_name: str) -> None:
    import time

    from zep_python import ZepClient

    client = ZepClient(ZEP_API_URL, ZEP_API_KEY)

    while True:
        c = await client.document.aget_collection(collection_name)
        print(
            "Embedding status: "
            f"{c.document_embedded_count}/{c.document_count} documents embedded"
        )
        time.sleep(1)
        if c.status == "ready":
            break


await wait_for_ready(collection_name)

    Embedding status: 0/401 documents embedded
    Embedding status: 0/401 documents embedded
    Embedding status: 0/401 documents embedded
    Embedding status: 0/401 documents embedded
    Embedding status: 0/401 documents embedded
    Embedding status: 0/401 documents embedded
    Embedding status: 401/401 documents embedded

Simarility Search Query over the Collection

# query it
query = "what is the structure of our solar system?"
docs_scores = await vs.asimilarity_search_with_relevance_scores(query, k=3)

# print results
for d, s in docs_scores:
    print(d.page_content, " -> ", s, "\n====\n")

    the positions of the two principal planets, (and these the most
    necessary for the navigator,) Jupiter and Saturn, require each not less
    than one hundred and sixteen tables. Yet it is not only necessary to
    predict the position of these bodies, but it is likewise expedient to
    tabulate the motions of the four satellites of Jupiter, to predict the
    exact times at which they enter his shadow, and at which their shadows
    cross his disc, as well as the times at which they are interposed  ->  0.9003241539387915 
    ====
    
    furnish more than a small fraction of that aid to navigation (in the
    large sense of that term), which, with greater facility, expedition, and
    economy in the calculation and printing of tables, it might be made to
    supply.
    
    Tables necessary to determine the places of the planets are not less
    necessary than those for the sun, moon, and stars. Some notion of the
    number and complexity of these tables may be formed, when we state that  ->  0.8911165633479508 
    ====
    
    the scheme of notation thus applied, immediately suggested the
    advantages which must attend it as an instrument for expressing the
    structure, operation, and circulation of the animal system; and we
    entertain no doubt of its adequacy for that purpose. Not only the
    mechanical connexion of the solid members of the bodies of men and
    animals, but likewise the structure and operation of the softer parts,
    including the muscles, integuments, membranes, &c. the nature, motion,  ->  0.8899750214770481 
    ====
    

Search over Collection Re-ranked by MMR

Zep offers native, hardware-accelerated MMR re-ranking of search results.

query = "what is the structure of our solar system?"
docs = await vs.asearch(query, search_type="mmr", k=3)

for d in docs:
    print(d.page_content, "\n====\n")

    the positions of the two principal planets, (and these the most
    necessary for the navigator,) Jupiter and Saturn, require each not less
    than one hundred and sixteen tables. Yet it is not only necessary to
    predict the position of these bodies, but it is likewise expedient to
    tabulate the motions of the four satellites of Jupiter, to predict the
    exact times at which they enter his shadow, and at which their shadows
    cross his disc, as well as the times at which they are interposed 
    ====
    
    the scheme of notation thus applied, immediately suggested the
    advantages which must attend it as an instrument for expressing the
    structure, operation, and circulation of the animal system; and we
    entertain no doubt of its adequacy for that purpose. Not only the
    mechanical connexion of the solid members of the bodies of men and
    animals, but likewise the structure and operation of the softer parts,
    including the muscles, integuments, membranes, &c. the nature, motion, 
    ====
    
    resistance, economizing time, harmonizing the mechanism, and giving to
    the whole mechanical action the utmost practical perfection.
    
    The system of mechanical contrivances by which the results, here
    attempted to be described, are attained, form only one order of
    expedients adopted in this machinery;--although such is the perfection
    of their action, that in any ordinary case they would be regarded as
    having attained the ends in view with an almost superfluous degree of 
    ====
    

Filter by Metadata

Use a metadata filter to narrow down results. First, load another book: "Adventures of Sherlock Holmes"

# Let's add more content to the existing Collection
article_url = "https://www.gutenberg.org/files/48320/48320-0.txt"
loader = WebBaseLoader(article_url)
documents = loader.load()

# split it into chunks
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
docs = text_splitter.split_documents(documents)

await vs.aadd_documents(docs)

await wait_for_ready(collection_name)

    Embedding status: 401/1691 documents embedded
    Embedding status: 401/1691 documents embedded
    Embedding status: 401/1691 documents embedded
    Embedding status: 401/1691 documents embedded
    Embedding status: 401/1691 documents embedded
    Embedding status: 401/1691 documents embedded
    Embedding status: 901/1691 documents embedded
    Embedding status: 901/1691 documents embedded
    Embedding status: 901/1691 documents embedded
    Embedding status: 901/1691 documents embedded
    Embedding status: 901/1691 documents embedded
    Embedding status: 901/1691 documents embedded
    Embedding status: 1401/1691 documents embedded
    Embedding status: 1401/1691 documents embedded
    Embedding status: 1401/1691 documents embedded
    Embedding status: 1401/1691 documents embedded
    Embedding status: 1691/1691 documents embedded

We see results from both books. Note the source metadata

query = "Was he interested in astronomy?"
docs = await vs.asearch(query, search_type="similarity", k=3)

for d in docs:
    print(d.page_content, " -> ", d.metadata, "\n====\n")

    or remotely, for this purpose. But in addition to these, a great number
    of tables, exclusively astronomical, are likewise indispensable. The
    predictions of the astronomer, with respect to the positions and motions
    of the bodies of the firmament, are the means, and the only means, which
    enable the mariner to prosecute his art. By these he is enabled to
    discover the distance of his ship from the Line, and the extent of his  ->  {'source': 'https://www.gutenberg.org/cache/epub/71292/pg71292.txt'} 
    ====
    
    possess all knowledge which is likely to be useful to him in his work,
    and this I have endeavored in my case to do. If I remember rightly, you
    on one occasion, in the early days of our friendship, defined my limits
    in a very precise fashion.”
    
    “Yes,” I answered, laughing. “It was a singular document. Philosophy,
    astronomy, and politics were marked at zero, I remember. Botany
    variable, geology profound as regards the mud-stains from any region  ->  {'source': 'https://www.gutenberg.org/files/48320/48320-0.txt'} 
    ====
    
    of astronomy, and its kindred sciences, with the various arts dependent
    on them. In none are computations more operose than those which
    astronomy in particular requires;--in none are preparatory facilities
    more needful;--in none is error more detrimental. The practical
    astronomer is interrupted in his pursuit, and diverted from his task of
    observation by the irksome labours of computation, or his diligence in
    observing becomes ineffectual for want of yet greater industry of  ->  {'source': 'https://www.gutenberg.org/cache/epub/71292/pg71292.txt'} 
    ====
    

Let's try again using a filter for only the Sherlock Holmes document.

filter = {
    "where": {
        "jsonpath": (
            "$[*] ? (@.source == 'https://www.gutenberg.org/files/48320/48320-0.txt')"
        )
    },
}

docs = await vs.asearch(query, search_type="similarity", metadata=filter, k=3)

for d in docs:
    print(d.page_content, " -> ", d.metadata, "\n====\n")

    possess all knowledge which is likely to be useful to him in his work,
    and this I have endeavored in my case to do. If I remember rightly, you
    on one occasion, in the early days of our friendship, defined my limits
    in a very precise fashion.”
    
    “Yes,” I answered, laughing. “It was a singular document. Philosophy,
    astronomy, and politics were marked at zero, I remember. Botany
    variable, geology profound as regards the mud-stains from any region  ->  {'source': 'https://www.gutenberg.org/files/48320/48320-0.txt'} 
    ====
    
    the light shining upon his strong-set aquiline features. So he sat as I
    dropped off to sleep, and so he sat when a sudden ejaculation caused me
    to wake up, and I found the summer sun shining into the apartment. The
    pipe was still between his lips, the smoke still curled upward, and the
    room was full of a dense tobacco haze, but nothing remained of the heap
    of shag which I had seen upon the previous night.
    
    “Awake, Watson?” he asked.
    
    “Yes.”
    
    “Game for a morning drive?”  ->  {'source': 'https://www.gutenberg.org/files/48320/48320-0.txt'} 
    ====
    
    “I glanced at the books upon the table, and in spite of my ignorance
    of German I could see that two of them were treatises on science, the
    others being volumes of poetry. Then I walked across to the window,
    hoping that I might catch some glimpse of the country-side, but an oak
    shutter, heavily barred, was folded across it. It was a wonderfully
    silent house. There was an old clock ticking loudly somewhere in the
    passage, but otherwise everything was deadly still. A vague feeling of  ->  {'source': 'https://www.gutenberg.org/files/48320/48320-0.txt'} 
    ====