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@github-actions github-actions released this 09 Sep 22:23
· 26 commits to main since this release
libs/astradb/v0.4.0
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What's Changed

Full Changelog: libs/astradb/v0.3.5...libs/astradb/v0.4.0

Vector Store "autodetect mode". A short guide

Summary

The newly-introduced "autodetect" mode for the Astra DB Vector Store assumes
a collection exists already, possibly populated by external means with vector
documents of some (uniform) shape.

Upon initialization, the vector store class figures out the collection and
"schema" settings and works seamlessly on it.

Tested scenarios:

All the following have been tested to work with this init mode.
(See a section below for more extended details/instructions.)

  • "native" non-vectorize store (i.e. the usual store as this class has always produced)
  • "native" vectorize-based store (same)
  • non-vectorize collection with an imported CSV (Astra UI) containing embedding vectors
  • vectorize collection with an imported CSV (Astra UI) containing a "$vectorize" column
  • vectorize collection with an imported CSV (Astra UI) one of whose columns is marked as text-to-embed
  • vectorize collection with an ingested PDF file (Astra UI)

Note that the vector stores created through LangFlow fall in the first two cases,
if using the Astra DB Vector Store component
(e.g. when connecting a "Load from file" component to a Vector Store in LangFlow).

Usage tips

If you anticipate the collection to be populated by ingestion pipelines other than
the AstraDBVectorStore itself, you should assume that, when the latter component
initializes, the collection exists already. In this case, you can make use of the
autodetect mode.

Note: autodetect will fall back to "native" mode, also for back-compatibility, in these
cases: (1) empty collection, (2) collection populated with documents compliant with "native".

Typical usage

You can initialize the store with the autodetect_collection parameter like this:

store = AstraDBVectorStore(
    collection_name="my_collection",
    token=os.environ["ASTRA_DB_APPLICATION_TOKEN"],
    api_endpoint=os.environ["ASTRA_DB_API_ENDPOINT"],
    autodetect_collection=True,
)

Note that most other init parameters can also be passed (see below for details).

Additional parameters:

  • content_field: specifies what root-level field in the documents carry the textual content. Cannot be passed for vectorize collection, where it is fixed to $vectorize. For autodetect, it can also be passed as "*", meaning that the autodetect procedure should also guess it by looking at a handful of documents on the collection. For non-vectorize collections, defaults to "content".
  • ignore_invalid_documents: by default, malformed documents from the DB (e.g. missing metadata, missing textual content) trigger a runtime error. This flag sets a more permissive behaviour, by which bad documents are logged and ignored without compromising the working of the store. Keep in mind this is a post-filtering, so one may end up with a lower number of matches from a vector search.

(Note: both parameters can also be used outside of the autodetect mode -- except for "*" to the first one. Probably there are not many cases where this is useful, but there's nothing blocking you from passing them.)

Forbidden parameters:

The following parameters, pertaining to how the collection should be created, are not permitted
on autodetect: metric, setup_mode, metadata_indexing_include, metadata_indexing_exclude, collection_indexing_policy, collection_vector_service_options.

Tested scenarios, details

Here is a minimal script to try a basic similarity search with an autodetected store:

import os
import logging
logging.basicConfig(level=5)

from langchain_astradb import AstraDBVectorStore

ad_store = AstraDBVectorStore(
    collection_name=os.environ["AUTODETECT_COLLECTION_NAME"],
    token=os.environ["ASTRA_DB_APPLICATION_TOKEN"],
    api_endpoint=os.environ["ASTRA_DB_API_ENDPOINT"],
    autodetect_collection=True,
)


for doc, sco in ad_store.similarity_search_with_score(
    "Tell me so and so",
    k=2,
):
    print(f"\n\n{'=' * 80}")
    print(f"Score = {sco}")
    print(f"Page content = {doc.page_content}")

The following outlines the preparation steps preliminary to running the above
in the various tested cases.

"native" non-vectorize store

Simply use a regular vector store with client-side embeddings and have it add_texts(...)
with a handful of entries.

Afterwards, try the autodetect script above on the same collection (but see the following Note).

Note 1: in this case, you need to create the correct embedding, e.g.:

from langchain_openai import OpenAIEmbeddings
embedding = OpenAIEmbeddings(
    api_key=os.environ["OPENAI_API_KEY"],
    model="text-embedding-ada-002",
)

and then pass it as an additional argument (embedding=embedding) when creating ad_store in the script.

(Note 2: in this case, content_field should never be needed.)

"native" vectorize-based store

Use a regular vectorize-based vector store and have it add_texts(...) with some entries.

Afterwards, try the autodetect script above on the same collection.

non-vectorize with imported CSV

You can create a Collection in the Astra UI with "Bring your own embedding", setting
the desired dimensionality, and then import a CSV with a "$vector" field into it.

An example CSV is as follows (requires a dimension of 1536):

title,reviewid,creationdate,criticname,originalscore,reviewstate,reviewtext,embedding
Beavers,1145982,2003-05-23,Ivan M. Lincoln,3.5/4,fresh,"Timed to be just long enough for most youngsters' brief attention spans -- and it's packed with plenty of interesting activity, both on land and under the water.","[-0.007622489705681801, 0.005060779396444559, 0.02063874900341034, -0.007668646518141031, -0.029461318626999855, 0.013121760450303555, -0.02066512405872345, -0.028063422068953514, -0.013530578464269638, -0.019385917112231255, -0.0017160492716357112, 0.038217950612306595, -0.0042167664505541325, -0.009692958556115627, 0.011486485600471497, -0.00171934615354985, 0.025531385093927383, -0.016128554940223694, 0.004793728236109018, -0.0242785532027483, -0.00812362227588892, 0.010451250709593296, 0.006785070989280939, -0.007695022039115429, -0.011202950030565262, 0.003709040116518736, 0.006274047773331404, -0.008407157845795155, 0.005575099494308233, -0.004233251325786114, 0.02990970015525818, -0.011156792752444744, 0.005911386106163263, -0.013781145215034485, 0.001370696467347443, 0.002546874340623617, 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Blood Mask,1636744,2007-06-02,The Foywonder,1/5,rotten,"It doesn't matter if a movie costs 300 million or only 300 dollars; good is good and bad is bad, and Bloodmask: The Possession of Nicole Lameroux is just plain bad.","[-0.006344131659716368, -0.011165671981871128, 0.023076578974723816, -0.036413710564374924, -0.0017474469495937228, -0.02380896545946598, -0.0052487654611468315, -0.05463341623544693, -0.003780781291425228, -0.016780629754066467, 0.02422012761235237, 0.029526714235544205, 0.017757143825292587, -0.01185308676213026, -0.007323858328163624, 0.011377678252756596, 0.05324573442339897, -0.018656566739082336, 0.008807903155684471, 0.0032893118914216757, -0.019221916794776917, 0.004696263466030359, -0.02527373656630516, 0.01069668773561716, -0.005637443624436855, -0.01213576178997755, 2.5609915610402822e-05, -0.013568411581218243, 0.022562623023986816, -0.0003800456179305911, -0.007555138319730759, -0.009212642908096313, -0.006080729886889458, 0.00010630436008796096, 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City Hunter: Shinjuku Private Eyes,2590987,2019-05-28,Reuben Baron,,fresh,"The choreography is so precise and lifelike at points one might wonder whether the movie was rotoscoped, but no live-action reference footage was used. The quality is due to the skill of the animators and Kodama's love for professional wrestling.","[-0.006635915022343397, -0.023613516241312027, -0.021492144092917442, -0.019940892234444618, 0.01454465277493, 0.009115267544984818, -0.022433502599596977, -0.023812394589185715, 0.0074049122631549835, -0.021425852552056313, 0.02529735490679741, -0.0028257330413907766, 0.0385294072329998, -0.019688978791236877, -0.0017617327393963933, -0.00715299928560853, 0.0338093563914299, 0.0065563637763261795, 0.008299865759909153, -0.02529735490679741, -0.0008874956401996315, 0.007477834355086088, -0.018283570185303688, -0.021863384172320366, -0.01833660528063774, 0.007438058499246836, 0.024011272937059402, -0.02679557353258133, -0.0018661438953131437, -0.027445243671536446, 0.0069938963279128075, -0.027312656864523888, 0.0054227556101977825, -0.025655336678028107, -0.020046960562467575, -0.03099854104220867, -0.006828164216130972, -0.02695467509329319, -0.0010913461446762085, 0.0035234654787927866, 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Once the collection is created and populated, try the autodetect script given above, but check the following Note.

Note 1: in this case, you need to create the correct embedding, e.g.:

from langchain_openai import OpenAIEmbeddings
embedding = OpenAIEmbeddings(
    api_key=os.environ["OPENAI_API_KEY"],
    model="text-embedding-ada-002",
)

and then pass it as an additional argument (embedding=embedding) when creating ad_store in the script.

Note 2: if you leave content_field unspecified (or pass "*"), the store will try to guess the field
for the textual content. You may want to provide it explicitly (e.g. content_field="reviewtext" for the example CSV).

vectorize collection with imported CSV, "$vectorize" column

Create an empty collection with vectorize and upload a CSV with a column explicitly named "$vectorize". For example:

title,reviewid,creationdate,criticname,originalscore,reviewstate,$vectorize
Beavers,1145982,2003-05-23,Ivan M. Lincoln,3.5/4,fresh,"Timed to be just long enough for most youngsters' brief attention spans -- and it's packed with plenty of interesting activity, both on land and under the water."
Blood Mask,1636744,2007-06-02,The Foywonder,1/5,rotten,"It doesn't matter if a movie costs 300 million or only 300 dollars; good is good and bad is bad, and Bloodmask: The Possession of Nicole Lameroux is just plain bad."
City Hunter: Shinjuku Private Eyes,2590987,2019-05-28,Reuben Baron,,fresh,"The choreography is so precise and lifelike at points one might wonder whether the movie was rotoscoped, but no live-action reference footage was used. The quality is due to the skill of the animators and Kodama's love for professional wrestling."

Then you can simply run the autodetect mini script above as is.

vectorize collection with imported CSV, marked column

Create an empty collection with vectorize and upload a CSV with no "$vectorize" column.
During the import wizard, instead, mark one of the other columns as the one to vectorize.

In this example it would be "reviewtext":

title,reviewid,creationdate,criticname,originalscore,reviewstate,reviewtext
Beavers,1145982,2003-05-23,Ivan M. Lincoln,3.5/4,fresh,"Timed to be just long enough for most youngsters' brief attention spans -- and it's packed with plenty of interesting activity, both on land and under the water."
Blood Mask,1636744,2007-06-02,The Foywonder,1/5,rotten,"It doesn't matter if a movie costs 300 million or only 300 dollars; good is good and bad is bad, and Bloodmask: The Possession of Nicole Lameroux is just plain bad."
City Hunter: Shinjuku Private Eyes,2590987,2019-05-28,Reuben Baron,,fresh,"The choreography is so precise and lifelike at points one might wonder whether the movie was rotoscoped, but no live-action reference footage was used. The quality is due to the skill of the animators and Kodama's love for professional wrestling."

Then you can simply run the autodetect mini script above as is.

vectorize collection with PDF

Create an empty collection with vectorize and choose the PDF format in the Upload wizard.
(this option is available only for vectorize collections).

Then you can simply run the autodetect mini script above as is.

Contributors

hemidactylus
Assets 2
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