mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
270 lines
8.4 KiB
Python
270 lines
8.4 KiB
Python
import inspect
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import sys
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from pathlib import Path
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from langchain_astradb import AstraDBVectorStore
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from langchain_chroma import Chroma
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from langchain_community import vectorstores
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from langchain_core.vectorstores import VectorStore
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from langchain_couchbase import CouchbaseVectorStore
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from langchain_milvus import Milvus
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from langchain_mongodb import MongoDBAtlasVectorSearch
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from langchain_pinecone import PineconeVectorStore
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from langchain_qdrant import QdrantVectorStore
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vectorstore_list = [
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"FAISS",
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"ElasticsearchStore",
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"PGVector",
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"Redis",
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"Clickhouse",
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"InMemoryVectorStore",
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]
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from_partners = [
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("Chroma", Chroma),
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("AstraDBVectorStore", AstraDBVectorStore),
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("QdrantVectorStore", QdrantVectorStore),
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("PineconeVectorStore", PineconeVectorStore),
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("Milvus", Milvus),
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("MongoDBAtlasVectorSearch", MongoDBAtlasVectorSearch),
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("CouchbaseVectorStore", CouchbaseVectorStore),
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]
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VECTORSTORE_TEMPLATE = """\
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---
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sidebar_position: 1
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sidebar_class_name: hidden
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keywords: [compatibility]
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custom_edit_url:
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---
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# Vectorstores
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## Features
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The table below lists the features for some of our most popular vector stores.
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{table}
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"""
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def get_vectorstore_table():
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vectorstore_feat_table = {
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"FAISS": {
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"Delete by ID": True,
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"Filtering": True,
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"similarity_search_by_vector": True,
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"similarity_search_with_score": True,
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"asearch": True,
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"Passes Standard Tests": False,
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"Multi Tenancy": False,
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"Local/Cloud": "Local",
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"IDs in add Documents": True,
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},
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"ElasticsearchStore": {
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"Delete by ID": True,
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"Filtering": True,
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"similarity_search_by_vector": True,
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"similarity_search_with_score": True,
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"asearch": True,
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"Passes Standard Tests": False,
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"Multi Tenancy": False,
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"Local/Cloud": "Local",
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"IDs in add Documents": True,
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},
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"PGVector": {
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"Delete by ID": True,
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"Filtering": True,
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"similarity_search_by_vector": True,
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"similarity_search_with_score": True,
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"asearch": True,
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"Passes Standard Tests": False,
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"Multi Tenancy": False,
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"Local/Cloud": "Local",
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"IDs in add Documents": True,
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},
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"Redis": {
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"Delete by ID": True,
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"Filtering": True,
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"similarity_search_by_vector": True,
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"similarity_search_with_score": True,
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"asearch": True,
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"Passes Standard Tests": False,
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"Multi Tenancy": False,
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"Local/Cloud": "Local",
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"IDs in add Documents": True,
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},
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"Clickhouse": {
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"Delete by ID": True,
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"Filtering": True,
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"similarity_search_by_vector": True,
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"similarity_search_with_score": True,
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"asearch": True,
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"Passes Standard Tests": False,
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"Multi Tenancy": False,
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"Local/Cloud": "Local",
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"IDs in add Documents": True,
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},
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"InMemoryVectorStore": {
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"Delete by ID": True,
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"Filtering": True,
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"similarity_search_by_vector": True,
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"similarity_search_with_score": True,
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"asearch": True,
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"Passes Standard Tests": False,
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"Multi Tenancy": False,
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"Local/Cloud": "Local",
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"IDs in add Documents": True,
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},
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"Chroma": {
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"Delete by ID": True,
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"Filtering": True,
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"similarity_search_by_vector": True,
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"similarity_search_with_score": True,
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"asearch": True,
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"Passes Standard Tests": False,
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"Multi Tenancy": False,
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"Local/Cloud": "Local",
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"IDs in add Documents": True,
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},
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"AstraDBVectorStore": {
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"Delete by ID": True,
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"Filtering": True,
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"similarity_search_by_vector": True,
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"similarity_search_with_score": True,
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"asearch": True,
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"Passes Standard Tests": False,
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"Multi Tenancy": False,
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"Local/Cloud": "Local",
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"IDs in add Documents": True,
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},
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"QdrantVectorStore": {
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"Delete by ID": True,
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"Filtering": True,
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"similarity_search_by_vector": True,
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"similarity_search_with_score": True,
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"asearch": True,
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"Passes Standard Tests": False,
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"Multi Tenancy": False,
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"Local/Cloud": "Local",
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"IDs in add Documents": True,
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},
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"PineconeVectorStore": {
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"Delete by ID": True,
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"Filtering": True,
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"similarity_search_by_vector": True,
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"similarity_search_with_score": True,
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"asearch": True,
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"Passes Standard Tests": False,
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"Multi Tenancy": False,
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"Local/Cloud": "Local",
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"IDs in add Documents": True,
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},
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"Milvus": {
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"Delete by ID": True,
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"Filtering": True,
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"similarity_search_by_vector": True,
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"similarity_search_with_score": True,
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"asearch": True,
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"Passes Standard Tests": False,
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"Multi Tenancy": False,
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"Local/Cloud": "Local",
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"IDs in add Documents": True,
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},
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"MongoDBAtlasVectorSearch": {
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"Delete by ID": True,
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"Filtering": True,
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"similarity_search_by_vector": True,
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"similarity_search_with_score": True,
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"asearch": True,
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"Passes Standard Tests": False,
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"Multi Tenancy": False,
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"Local/Cloud": "Local",
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"IDs in add Documents": True,
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},
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"CouchbaseVectorStore": {
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"Delete by ID": True,
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"Filtering": True,
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"similarity_search_by_vector": True,
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"similarity_search_with_score": True,
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"asearch": True,
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"Passes Standard Tests": False,
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"Multi Tenancy": False,
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"Local/Cloud": "Local",
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"IDs in add Documents": True,
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},
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}
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for vs in vectorstore_list + from_partners:
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if isinstance(vs, tuple):
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cls = vs[1]
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vs_name = vs[0]
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else:
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cls = getattr(vectorstores, vs)
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vs_name = vs
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for feat in (
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"similarity_search_with_score",
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"similarity_search_by_vector",
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"asearch",
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):
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feat, name = feat, feat
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if getattr(cls, feat) != getattr(VectorStore, feat):
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vectorstore_feat_table[vs_name][name] = True
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else:
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vectorstore_feat_table[vs_name][name] = False
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if "filter" not in [
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key
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for key, _ in inspect.signature(
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getattr(cls, "similarity_search")
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).parameters.items()
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]:
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vectorstore_feat_table[vs_name]["Filtering"] = False
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header = [
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"Vectorstore",
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"Delete by ID",
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"Filtering",
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"similarity_search_by_vector",
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"similarity_search_with_score",
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"asearch",
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"Passes Standard Tests",
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"Multi Tenancy",
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"Local/Cloud",
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"IDs in add Documents",
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]
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title = [
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"Vectorstore",
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"Delete by ID",
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"Filtering",
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"Search by Vector",
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"Search with score",
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"Async",
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"Passes Standard Tests",
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"Multi Tenancy",
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"Local/Cloud",
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"IDs in add Documents",
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]
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rows = [title, [":-"] + [":-:"] * (len(title) - 1)]
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for vs, feats in sorted(vectorstore_feat_table.items()):
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rows += [
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[vs, "✅"]
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+ [
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("✅" if feats.get(h) else "❌") if h != "Local/Cloud" else feats.get(h)
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for h in header[1:]
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]
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]
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return "\n".join(["|".join(row) for row in rows])
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if __name__ == "__main__":
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output_dir = Path(sys.argv[1])
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output_integrations_dir = output_dir / "integrations"
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output_integrations_dir_vectorstore = output_integrations_dir / "vectorstores"
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output_integrations_dir_vectorstore.mkdir(parents=True, exist_ok=True)
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vectorstore_page = VECTORSTORE_TEMPLATE.format(table=get_vectorstore_table())
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with open(output_integrations_dir / "vectorstores" / "index.mdx", "w") as f:
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f.write(vectorstore_page)
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