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JibolaJibola
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Merge branch 'main' into update-langchain-test
2 parents 75cc8b8 + 5672f3c commit f3f890e

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‎.evergreen/config.yml

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- func: "fetch repo"
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- func: "execute tests"
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- name: test-docarray
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commands:
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- func: "fetch repo"
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- func: "execute tests"
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buildvariants:
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- name: test-semantic-kernel-python-rhel
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display_name: Semantic-Kernel RHEL Python
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- rhel87-small
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tasks:
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- name: test-llama-index
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- name: test-docarray-rhel
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display_name: DocArray RHEL
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expansions:
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DIR: docarray
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REPO_NAME: docarray
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# TODO - Update CLONE_URL once pull-request is merged
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CLONE_URL: -b mongodb-atlas-backend --single-branch https://github.com/caseyclements/docarray.git
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DATABASE: docarray_test_db
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run_on:
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- rhel87-small
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tasks:
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- name: test-docarray

‎docarray/database/flatschema.json

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[]

‎docarray/database/mydoc__docs.json

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[]

‎docarray/database/mydoc__list_docs__docs.json

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[]

‎docarray/database/nesteddoc.json

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[]

‎docarray/database/simpleschema.json

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[]

‎docarray/indexes/text_index_simpleschema.json

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{
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"mappings": {
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"dynamic": false,
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"fields": {
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"text": [
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{
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"type": "string"
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}
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]
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}
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},
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"name": "text_index",
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"type": "search",
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"database": "docarray_test_db",
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"collectionName": "simpleschema"
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}

‎docarray/indexes/vector_index_flatschema_1.json

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{
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"fields": [
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{
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"numDimensions": 10,
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"path": "embedding1",
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"similarity": "cosine",
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"type": "vector"
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}
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],
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"name": "vector_index_1",
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"type": "vectorSearch",
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"database": "docarray_test_db",
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"collectionName": "flatschema"
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}

‎docarray/indexes/vector_index_flatschema_2.json

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{
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"fields": [
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{
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"numDimensions": 50,
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"path": "embedding2",
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"similarity": "cosine",
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"type": "vector"
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}
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],
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"name": "vector_index_2",
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"type": "vectorSearch",
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"database": "docarray_test_db",
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"collectionName": "flatschema"
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}

‎docarray/indexes/vector_index_mydoc__docs.json

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{
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"fields": [
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{
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"numDimensions": 10,
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"path": "simple_tens",
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"similarity": "euclidean",
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"type": "vector"
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}
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],
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"name": "vector_index",
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"type": "vectorSearch",
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"database": "docarray_test_db",
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"collectionName": "mydoc__docs"
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}

‎docarray/indexes/vector_index_mydoc__list_docs__docs.json

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{
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"fields": [
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{
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"numDimensions": 10,
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"path": "simple_tens",
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"similarity": "euclidean",
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"type": "vector"
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}
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],
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"name": "vector_index",
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"type": "vectorSearch",
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"database": "docarray_test_db",
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"collectionName": "mydoc__list_docs__docs"
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}
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‎docarray/indexes/vector_index_nesteddoc_1.json

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{
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"fields": [
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{
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"numDimensions": 10,
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"path": "d__embedding",
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"similarity": "cosine",
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"type": "vector"
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}
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],
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"name": "vector_index_1",
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"type": "vectorSearch",
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"database": "docarray_test_db",
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"collectionName": "nesteddoc"
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}

‎docarray/indexes/vector_index_nesteddoc_2.json

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{
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"fields": [
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{
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"numDimensions": 10,
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"path": "embedding",
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"similarity": "cosine",
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"type": "vector"
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}
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],
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"name": "vector_index",
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"type": "vectorSearch",
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"database": "docarray_test_db",
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"collectionName": "nesteddoc"
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}

‎docarray/indexes/vector_index_simpleschema.json

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{
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"fields": [
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{
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"numDimensions": 10,
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"path": "embedding",
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"similarity": "cosine",
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"type": "vector"
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},
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{
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"path": "number",
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"type": "filter"
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},
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{
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"path": "text",
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"type": "filter"
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}
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],
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"name": "vector_index",
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"type": "vectorSearch",
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"database": "docarray_test_db",
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"collectionName": "simpleschema"
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}

‎docarray/run.sh

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#!/bin/sh
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# Sets up a virtual environment (poetry)
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# Runs the mongodb tests of the upstream repo
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set -x
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. $workdir/src/.evergreen/utils.sh
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PYTHON_BINARY=$(find_python3)
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$PYTHON_BINARY -c "import sys; print(f'Python version found: {sys.version_info}')"
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# Create and activate an isolated python venv environment
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$PYTHON_BINARY -m venv venv
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source venv/bin/activate
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# Install Poetry
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pip install -U pip poetry
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# Recreate the poetry lock file
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poetry lock --no-update
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# Install from pyproject.toml into package specific environment
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poetry install --with dev --extras mongo
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# Run tests. Sensitive variables in Evergreen come from Evergeen project: ai-ml-pipeline-testing/
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MONGODB_URI=$docarray_mongodb_uri \
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MONGODB_DATABASE="docarray_test_db" \
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pytest -v tests/index/mongo_atlas

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