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notebooks/claim_svm.jl

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+## Write an SVM classifier to take embeddings, and use them to predict the label 
+
+# Get the data 
+import OstreaCultura as OC 
+dat = CSV.read("data/Climate Misinformation Library with counterclaims.csv", DataFrame)
+## Stack claims and counter claims into a single column, label as 1 for claim, 0 for counter claim
+dat = stack(select(dat, r"laims"), [:Claims, :Counterclaims], variable_name=:Type, value_name=:text)
+dropmissing!(dat)
+dat.label = ifelse.(dat.Type .== "Claims", 1, 0)
+## Embeddings of text 
+model = "multilingual-e5-large"
+embeds = OC.multi_embeddings(dat)
+#Features 
+features = convert(Array, embeds.Embeddings)
+y = convert(Array, dat.label)
+# Generate resnet svm (resvm)
+@sk_import calibration: CalibratedClassifierCV
+import ScikitLearn: CrossValidation
+@sk_import svm: LinearSVC
+import ScikitLearn: CrossValidation
+using ScikitLearn.CrossValidation: cross_val_score
+resvm = LinearSVC(C=.5, loss="squared_hinge", penalty="l2", multi_class="ovr", random_state = 35552, max_iter=2000)
+cv = ScikitLearn.CrossValidation.KFold(189, n_folds=5, random_state = 134, shuffle=true)
+out = cross_val_score(resvm, features, y, cv = cv)
+## get precision and recall 
+using ScikitLearn: metrics
+y_pred = ScikitLearn.CrossValidation.cross_val_predict(resvm, features, y, cv=cv)
+## roll your own precision 
+pre = sum((y .== 1) .& (y_pred .== 1)) / sum(y_pred .== 1)
+## roll your own recall
+rec = sum((y .== 1) .& (y_pred .== 1)) / sum(y .== 1)
+
+## pull out the support vector embeddings 
+fit!(resvm, features, y)
+# 
+sv = resvm.coef_
+histogram(sv[1, :])
+
+calsvm = CalibratedClassifierCV(resvm)
+calsvm.fit(features, y)
+
+prob_preds = calsvm.predict_proba(features)
+## Get indices of top highest probabilities in the first column 
+top_k = 5
+top_indices = sortperm(prob_preds[:, 1], rev=true)[1:top_k]
+prob_preds[top_indices, :]
+dat[top_indices, :]
+## Get the indices of the top k highest probabilities in second column
+top_indices = sortperm(prob_preds[:, 2], rev=true)[1:top_k]
+prob_preds[top_indices, :]
+dat[top_indices, :]

notebooks/embedding_notebook.jl

@@ -0,0 +1,2321 @@
+### A Pluto.jl notebook ###
+# v0.19.45
+
+using Markdown
+using InteractiveUtils
+
+# ╔═╡ cdb6a532-59d8-11ef-253f-a1fb2ac43958
+## 
+using CSV, JLD2, DataFrames, OpenAI, StatsBase, Distances, TidierPlots, Markdown, PlutoUI, MLJ, InteractiveUtils
+
+# ╔═╡ 1872c213-90af-4924-9abc-c670857e2cb5
+cd("/home/swojcik/github/misinfo_detection")
+
+# ╔═╡ 91f5f6b3-3cc8-4bdf-8386-715d49e6d83b
+md"""
+## Text Embedding Distance as Misinfo Detection
+
+#### Method: 
+- Take narrative summaries *as is* --> embed
+- Take text from the labeled data --> embed
+- Assign a misinfo narrative to each based on proximity to one of the narrative summaries (all text gets a misinfo narrative)
+- Plot the distribution of distances by misinfo label 
+- Select a semi-arbitrary cutoff point 
+- Calculate model scores 
+"""
+
+# ╔═╡ 8aa21d7a-24ab-41c2-accd-cb3983108ea6
+begin
+	## Analysis: 
+	# What is the distribution of distances by assigned narrative and label? 
+	
+	### UTILITIES ####
+	# Define count function 
+	function table(df::DataFrame, cols::Vector{Symbol})
+	    combine(groupby(df, cols), nrow)
+	end
+	#########
+	
+	"""
+	## Embeddings to recover narratives 
+	narrative_embeddings = create_narrative_embeddings()
+	"""
+	function create_narrative_embeddings(regenerate=false)
+	    if !regenerate && isfile("data/narrative_embeddings.jld2")
+	        return load_object("data/narrative_embeddings.jld2")
+	    end
+	    @info "Regenerating narrative embeddings..."
+	    narratives = CSV.read("data/Modified Misinformation Library.csv", DataFrame)
+	    ## narrative Embeddings 
+	    n_embeddings = create_embeddings(ENV["OPENAI_API_KEY"], narratives[!, "Misinformation Narrative"])
+	    ## Add vector of embeddings to dataset 
+	    narratives[!, "Embeddings"] = [x["embedding"] for x in n_embeddings.response["data"]]
+	    # Save the embeddings
+	    save_object("data/narrative_embeddings.jld2", narratives)
+	    return narratives
+	end
+	
+	"""
+	# This is the testing data
+	target_embeddings = create_test_embeddings()
+	"""
+	function create_test_embeddings(regenerate=false)
+	    if !regenerate && isfile("data/test_embeddings.jld2")
+	        return load_object("data/test_embeddings.jld2")
+	    end
+	    @info "Regenerating test embeddings..."
+	    df_test = CSV.read("data/Indicator_Test.csv", DataFrame)
+	    ## narrative Embeddings 
+	    n_embeddings = create_embeddings(ENV["OPENAI_API_KEY"], df_test[!, "text"])
+	    ## Add vector of embeddings to dataset 
+	    df_test[!, "Embeddings"] = [x["embedding"] for x in n_embeddings.response["data"]]
+	    # Save the embeddings
+	    save_object("data/test_embeddings.jld2", df_test)
+	    return df_test
+	end
+	
+	"""
+	### The embeddings for each example are along the rows, so they can be compared column-wise (fast)
+	narrative_embeddings = create_narrative_embeddings()
+	target_embeddings = create_test_embeddings()
+	one_shot_classification!(narrative_embeddings, target_embeddings)
+	## Show the results - text, closest narrative
+	target_embeddings[:, ["text", "Closest Narrative", "label"]] |> first(5)
+	"""
+	function one_shot_classification!(narrative_embeddings, target_embeddings)
+	    ## Matrix of embeddings
+	    narrative_matrix = hcat(narrative_embeddings[:, "Embeddings"]...)
+	    target_matrix = hcat(target_embeddings[:, "Embeddings"]...)
+	    # Create a search function
+	    function search(narrative_matrix, target_matrix)
+	        distances = pairwise(CosineDist(), target_matrix, narrative_matrix, dims=2)
+	        # get the index of the column with the smallest distance
+	        narrative_index = argmin(distances, dims=2)
+	        return narrative_index
+	    end
+	    # Search for the closest narrative for each test data
+	    narrative_assignment = search(narrative_matrix, target_matrix)
+	    target_embeddings[:, "Closest Narrative"] = [narrative_embeddings[x[2], "Misinformation Narrative"] for x in narrative_assignment[:, 1]]
+	    return target_embeddings
+	end
+	
+	function get_distances!(narrative_embeddings, target_embeddings)
+	    ## Matrix of embeddings
+	    narrative_matrix = hcat(narrative_embeddings[:, "Embeddings"]...)
+	    target_matrix = hcat(target_embeddings[:, "Embeddings"]...)
+	    # Create a search function
+	    function embedding_distances(narrative_matrix, target_matrix)
+	        distances = pairwise(CosineDist(), target_matrix, narrative_matrix, dims=2)
+	        # get the index of the column with the smallest distance
+	        return distances[argmin(distances, dims=2)][:, 1]
+	    end
+	    # Search for the closest narrative for each test data
+	    target_embeddings[:, "Dist"] = embedding_distances(narrative_matrix, target_matrix)
+	    return target_embeddings
+	end
+	
+end
+
+# ╔═╡ 2ba50521-2fed-4fc0-a140-6af8d2c57115
+begin
+	# Get the embeddings for the narratives
+	narrative_embeddings = create_narrative_embeddings()
+	target_embeddings = create_test_embeddings()
+	one_shot_classification!(narrative_embeddings, target_embeddings)
+	get_distances!(narrative_embeddings, target_embeddings)
+	
+	
+end
+
+# ╔═╡ ae0f994c-1e54-4a3f-9252-9e7d0df3cc04
+md"""
+## Distribution of distances by label
+"""
+
+# ╔═╡ 476da329-5c95-4825-98a9-70a490b0609c
+begin
+	# Plot the distribution of distances by narrative and label	
+	## By Label 
+	ggplot(target_embeddings, @aes(x = label, y = Dist)) +
+	    geom_violin() + labs(x="Misinfo Label", y="Distance") #+ geom_hline()
+	
+	
+end
+
+# ╔═╡ 8deeb8eb-3072-4f1b-ba46-1ac6abb78091
+md"""
+!!! question "What Threshold?"
+	*Distance threshold is set to .2 based on the histogram above*
+"""
+
+# ╔═╡ f5c1511a-7307-43e2-a69f-d01ffa15f700
+begin
+	### Assign MisinfoPred = true if distance is less than .2 
+	
+	target_embeddings[!, "MisinfoPred"] = target_embeddings[!, "Dist"] .< 0.2
+	
+	## Precision and Recall
+	y_true = target_embeddings[!, "label"]
+	y_pred = target_embeddings[!, "MisinfoPred"]
+	#confusion_matrix(y_pred, y_true)
+	model_scores = DataFrame(
+	accuracy = accuracy(y_true, y_pred),
+	true_positive_rate = true_positive_rate(y_true, y_pred),
+	false_positive_rate = false_positive_rate(y_true, y_pred),
+		
+	)
+	
+	
+end
+
+# ╔═╡ 3905c150-ae5c-4173-9e9d-f3eed67a3642
+md"""
+### Text with the Smallest Distances
+"""
+
+# ╔═╡ c64f8299-3470-475a-b7f3-c72186df3973
+## Top 10 closest narratives
+target_embeddings |> 
+    (data -> filter(:label => x -> x .== 1.0, data)) |> 
+    (data -> sort(data, :Dist)) |>
+    (data -> first(data, 10)) |> 
+    (data -> select(data, ["text", "Closest Narrative", "Dist"]))
+
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+uuid = "981d1d27-644d-49a2-9326-4793e63143c3"
+version = "0.1.0"
+
+[[deps.URIs]]
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+uuid = "5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4"
+version = "1.5.1"
+
+[[deps.UUIDs]]
+deps = ["Random", "SHA"]
+uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
+
+[[deps.UnPack]]
+git-tree-sha1 = "387c1f73762231e86e0c9c5443ce3b4a0a9a0c2b"
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+version = "1.0.2"
+
+[[deps.Unicode]]
+uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"
+
+[[deps.UnicodeFun]]
+deps = ["REPL"]
+git-tree-sha1 = "53915e50200959667e78a92a418594b428dffddf"
+uuid = "1cfade01-22cf-5700-b092-accc4b62d6e1"
+version = "0.4.1"
+
+[[deps.Unitful]]
+deps = ["Dates", "LinearAlgebra", "Random"]
+git-tree-sha1 = "d95fe458f26209c66a187b1114df96fd70839efd"
+uuid = "1986cc42-f94f-5a68-af5c-568840ba703d"
+version = "1.21.0"
+weakdeps = ["ConstructionBase", "InverseFunctions"]
+
+    [deps.Unitful.extensions]
+    ConstructionBaseUnitfulExt = "ConstructionBase"
+    InverseFunctionsUnitfulExt = "InverseFunctions"
+
+[[deps.UnsafeAtomics]]
+git-tree-sha1 = "6331ac3440856ea1988316b46045303bef658278"
+uuid = "013be700-e6cd-48c3-b4a1-df204f14c38f"
+version = "0.2.1"
+
+[[deps.UnsafeAtomicsLLVM]]
+deps = ["LLVM", "UnsafeAtomics"]
+git-tree-sha1 = "4073c836c2befcb041e5fe306cb6abf621eb3140"
+uuid = "d80eeb9a-aca5-4d75-85e5-170c8b632249"
+version = "0.2.0"
+
+[[deps.WeakRefStrings]]
+deps = ["DataAPI", "InlineStrings", "Parsers"]
+git-tree-sha1 = "b1be2855ed9ed8eac54e5caff2afcdb442d52c23"
+uuid = "ea10d353-3f73-51f8-a26c-33c1cb351aa5"
+version = "1.4.2"
+
+[[deps.WoodburyMatrices]]
+deps = ["LinearAlgebra", "SparseArrays"]
+git-tree-sha1 = "c1a7aa6219628fcd757dede0ca95e245c5cd9511"
+uuid = "efce3f68-66dc-5838-9240-27a6d6f5f9b6"
+version = "1.0.0"
+
+[[deps.WorkerUtilities]]
+git-tree-sha1 = "cd1659ba0d57b71a464a29e64dbc67cfe83d54e7"
+uuid = "76eceee3-57b5-4d4a-8e66-0e911cebbf60"
+version = "1.6.1"
+
+[[deps.XML2_jll]]
+deps = ["Artifacts", "JLLWrappers", "Libdl", "Libiconv_jll", "Zlib_jll"]
+git-tree-sha1 = "d9717ce3518dc68a99e6b96300813760d887a01d"
+uuid = "02c8fc9c-b97f-50b9-bbe4-9be30ff0a78a"
+version = "2.13.1+0"
+
+[[deps.XSLT_jll]]
+deps = ["Artifacts", "JLLWrappers", "Libdl", "Libgcrypt_jll", "Libgpg_error_jll", "Libiconv_jll", "XML2_jll", "Zlib_jll"]
+git-tree-sha1 = "a54ee957f4c86b526460a720dbc882fa5edcbefc"
+uuid = "aed1982a-8fda-507f-9586-7b0439959a61"
+version = "1.1.41+0"
+
+[[deps.Xorg_libX11_jll]]
+deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_libxcb_jll", "Xorg_xtrans_jll"]
+git-tree-sha1 = "afead5aba5aa507ad5a3bf01f58f82c8d1403495"
+uuid = "4f6342f7-b3d2-589e-9d20-edeb45f2b2bc"
+version = "1.8.6+0"
+
+[[deps.Xorg_libXau_jll]]
+deps = ["Artifacts", "JLLWrappers", "Libdl"]
+git-tree-sha1 = "6035850dcc70518ca32f012e46015b9beeda49d8"
+uuid = "0c0b7dd1-d40b-584c-a123-a41640f87eec"
+version = "1.0.11+0"
+
+[[deps.Xorg_libXdmcp_jll]]
+deps = ["Artifacts", "JLLWrappers", "Libdl"]
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+
+[[deps.Xorg_libXext_jll]]
+deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_libX11_jll"]
+git-tree-sha1 = "d2d1a5c49fae4ba39983f63de6afcbea47194e85"
+uuid = "1082639a-0dae-5f34-9b06-72781eeb8cb3"
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+
+[[deps.Xorg_libXrender_jll]]
+deps = ["Artifacts", "JLLWrappers", "Libdl", "Xorg_libX11_jll"]
+git-tree-sha1 = "47e45cd78224c53109495b3e324df0c37bb61fbe"
+uuid = "ea2f1a96-1ddc-540d-b46f-429655e07cfa"
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+
+[[deps.Xorg_libpthread_stubs_jll]]
+deps = ["Artifacts", "JLLWrappers", "Libdl"]
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+uuid = "14d82f49-176c-5ed1-bb49-ad3f5cbd8c74"
+version = "0.1.1+0"
+
+[[deps.Xorg_libxcb_jll]]
+deps = ["Artifacts", "JLLWrappers", "Libdl", "XSLT_jll", "Xorg_libXau_jll", "Xorg_libXdmcp_jll", "Xorg_libpthread_stubs_jll"]
+git-tree-sha1 = "bcd466676fef0878338c61e655629fa7bbc69d8e"
+uuid = "c7cfdc94-dc32-55de-ac96-5a1b8d977c5b"
+version = "1.17.0+0"
+
+[[deps.Xorg_xtrans_jll]]
+deps = ["Artifacts", "JLLWrappers", "Libdl"]
+git-tree-sha1 = "e92a1a012a10506618f10b7047e478403a046c77"
+uuid = "c5fb5394-a638-5e4d-96e5-b29de1b5cf10"
+version = "1.5.0+0"
+
+[[deps.Zlib_jll]]
+deps = ["Libdl"]
+uuid = "83775a58-1f1d-513f-b197-d71354ab007a"
+version = "1.2.13+1"
+
+[[deps.isoband_jll]]
+deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
+git-tree-sha1 = "51b5eeb3f98367157a7a12a1fb0aa5328946c03c"
+uuid = "9a68df92-36a6-505f-a73e-abb412b6bfb4"
+version = "0.2.3+0"
+
+[[deps.libaom_jll]]
+deps = ["Artifacts", "JLLWrappers", "Libdl"]
+git-tree-sha1 = "1827acba325fdcdf1d2647fc8d5301dd9ba43a9d"
+uuid = "a4ae2306-e953-59d6-aa16-d00cac43593b"
+version = "3.9.0+0"
+
+[[deps.libass_jll]]
+deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "HarfBuzz_jll", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"]
+git-tree-sha1 = "5982a94fcba20f02f42ace44b9894ee2b140fe47"
+uuid = "0ac62f75-1d6f-5e53-bd7c-93b484bb37c0"
+version = "0.15.1+0"
+
+[[deps.libblastrampoline_jll]]
+deps = ["Artifacts", "Libdl"]
+uuid = "8e850b90-86db-534c-a0d3-1478176c7d93"
+version = "5.8.0+1"
+
+[[deps.libfdk_aac_jll]]
+deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
+git-tree-sha1 = "daacc84a041563f965be61859a36e17c4e4fcd55"
+uuid = "f638f0a6-7fb0-5443-88ba-1cc74229b280"
+version = "2.0.2+0"
+
+[[deps.libpng_jll]]
+deps = ["Artifacts", "JLLWrappers", "Libdl", "Zlib_jll"]
+git-tree-sha1 = "d7015d2e18a5fd9a4f47de711837e980519781a4"
+uuid = "b53b4c65-9356-5827-b1ea-8c7a1a84506f"
+version = "1.6.43+1"
+
+[[deps.libsixel_jll]]
+deps = ["Artifacts", "JLLWrappers", "JpegTurbo_jll", "Libdl", "Pkg", "libpng_jll"]
+git-tree-sha1 = "d4f63314c8aa1e48cd22aa0c17ed76cd1ae48c3c"
+uuid = "075b6546-f08a-558a-be8f-8157d0f608a5"
+version = "1.10.3+0"
+
+[[deps.libvorbis_jll]]
+deps = ["Artifacts", "JLLWrappers", "Libdl", "Ogg_jll", "Pkg"]
+git-tree-sha1 = "490376214c4721cdaca654041f635213c6165cb3"
+uuid = "f27f6e37-5d2b-51aa-960f-b287f2bc3b7a"
+version = "1.3.7+2"
+
+[[deps.nghttp2_jll]]
+deps = ["Artifacts", "Libdl"]
+uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"
+version = "1.52.0+1"
+
+[[deps.oneTBB_jll]]
+deps = ["Artifacts", "JLLWrappers", "Libdl"]
+git-tree-sha1 = "7d0ea0f4895ef2f5cb83645fa689e52cb55cf493"
+uuid = "1317d2d5-d96f-522e-a858-c73665f53c3e"
+version = "2021.12.0+0"
+
+[[deps.p7zip_jll]]
+deps = ["Artifacts", "Libdl"]
+uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0"
+version = "17.4.0+2"
+
+[[deps.x264_jll]]
+deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
+git-tree-sha1 = "4fea590b89e6ec504593146bf8b988b2c00922b2"
+uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a"
+version = "2021.5.5+0"
+
+[[deps.x265_jll]]
+deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
+git-tree-sha1 = "ee567a171cce03570d77ad3a43e90218e38937a9"
+uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76"
+version = "3.5.0+0"
+"""
+
+# ╔═╡ Cell order:
+# ╠═cdb6a532-59d8-11ef-253f-a1fb2ac43958
+# ╠═1872c213-90af-4924-9abc-c670857e2cb5
+# ╟─91f5f6b3-3cc8-4bdf-8386-715d49e6d83b
+# ╟─8aa21d7a-24ab-41c2-accd-cb3983108ea6
+# ╠═2ba50521-2fed-4fc0-a140-6af8d2c57115
+# ╠═ae0f994c-1e54-4a3f-9252-9e7d0df3cc04
+# ╟─476da329-5c95-4825-98a9-70a490b0609c
+# ╟─8deeb8eb-3072-4f1b-ba46-1ac6abb78091
+# ╠═f5c1511a-7307-43e2-a69f-d01ffa15f700
+# ╟─3905c150-ae5c-4173-9e9d-f3eed67a3642
+# ╠═c64f8299-3470-475a-b7f3-c72186df3973
+# ╟─00000000-0000-0000-0000-000000000001
+# ╟─00000000-0000-0000-0000-000000000002

notebooks/hybrid_example.py

@@ -0,0 +1,210 @@
+### Example code 
+from pinecone.grpc import PineconeGRPC as Pinecone
+import os
+import pandas as pd
+import numpy as np
+from pinecone import ServerlessSpec
+from pinecone_text.sparse import BM25Encoder
+import sys
+sys.path.append('src/python')
+import DataLoader
+
+####### VERY MINIMAL NONSENSE DATA
+data = {
+    'id': ['vec1', 'vec2'],
+    'values': [[0.1, 0.2, 0.3], [0.2, 0.3, 0.4]],
+    'metadata': [{'text': 'drama'}, {'text': 'action'}],
+    'sparse_indices': [[10, 45, 16], [12, 34, 56]],
+    'sparse_values': [[0.5, 0.5, 0.2], [0.3, 0.4, 0.1]]
+}
+
+pc.create_index(
+  name="oc-hybrid-index",
+  dimension=3,
+  metric="dotproduct",
+  spec=ServerlessSpec(
+    cloud="aws",
+    region="us-east-1"
+  )
+)
+
+index = pc.index("oc-hybrid-index")
+
+vecs = create_sparse_dense_dict(df)
+
+index.upsert(vecs, namespace="example-namespace")
+
+######################## Indicator Test Data
+
+pc = Pinecone("5faec954-a6c5-4af5-a577-89dbd2e4e5b0")
+pc.delete_index("oc-hybrid-index")
+
+pc.create_index(
+  name="oc-hybrid-index",
+  dimension=1024,
+  metric="dotproduct",
+  spec=ServerlessSpec(
+    cloud="aws",
+    region="us-east-1"
+  )
+)
+
+index = pc.Index("oc-hybrid-index")
+
+## Upsert Indicator Test Data 
+df = pd.read_csv('data/Indicator_Test.csv')
+## get top three rows 
+df = df.head(3)
+# get text and MessageID
+# Example usage
+df = pd.read_csv('data/Indicator_Test.csv')
+df = df.head(3)
+bm25, newdf = create_sparse_embeds(df)
+metadata = df[['text', 'label']].to_dict(orient='records')
+newdf['metadata'] = metadata
+vecs = create_sparse_dense_dict(newdf)
+index.upsert(vecs, namespace="example-namespace")
+## Query the hybrid index
+querytext = "immigrants are invading the border"
+queryembed = query_embed(pc, "multilingual-e5-large", querytext)
+query_sparse_vector = bm25.encode_documents(querytext)
+
+query_response = index.query(
+    top_k=1,
+    namespace="example-namespace",
+    vector=queryembed,
+    sparse_vector=query_sparse_vector,
+    include_metadata=True
+)
+
+
+## Now create embeddings 
+from pinecone import Pinecone
+pc = Pinecone("5faec954-a6c5-4af5-a577-89dbd2e4e5b0")
+model = "multilingual-e5-large"
+DataLoader.chunk_and_embed(pc, model, df)
+#df['Embeddings'] = [np.random.random(4) for x in range(len(df))]
+# rename embeddings to values 
+df.rename(columns={'Embeddings': 'values'}, inplace=True)
+#df['id'] = [sqids.encode([i, i+1, i+2]) for i in range(len(df))]
+## now, create metadata column to capture any column not including id, values, indices, and sparse_values
+df['metadata'] = df.drop(columns=['id', 'values', 'indices', 'sparse_values']).to_dict(orient='records')
+# only keep ids, values, metadata, indices, and sparse_values
+df = df[['id', 'values', 'metadata', 'indices', 'sparse_values']]
+
+vecs = create_sparse_dense_dict(df)
+
+pc.create_index(
+  name="oc-hybrid-indexv2",
+  dimension=1024,
+  metric="dotproduct",
+  spec=ServerlessSpec(
+    cloud="aws",
+    region="us-east-1"
+  )
+)
+
+index = pc.Index("oc-hybrid-indexv2")
+index.upsert(vecs, namespace="example-namespace")
+
+## QUERY 
+query_response = index.query(
+    top_k=10,
+    vector=[0.1, 0.2, 0.3],
+    sparse_vector={
+        'indices': [10, 45, 16],
+        'values':  [0.5, 0.5, 0.2]
+    }
+)
+
+################
+query = "test border patrol"
+query_sparse_vector = encode_query(bm25, query)
+
+query_response = index.query(
+    top_k=1,
+    namespace="example-namespace",
+    vector=np.random.random(1024).tolist(),
+    sparse_vector=query_sparse_vector
+)
+################
+query = "ireland"
+query_sparse_vector = encode_query(bm25, query)
+
+query_response = index.query(
+    top_k=1,
+    namespace="example-namespace",
+    vector=np.random.random(1024).tolist(),
+    sparse_vector=query_sparse_vector
+)
+
+
+################## Function to create sparse and dense vectors
+from tqdm.auto import tqdm
+
+# Remove columns you dont want to encode
+df = pd.read_csv('data/Indicator_Test.csv')
+metadata = df
+
+batch_size = 200
+
+# convert all columns to string
+metadata = metadata.astype(str)
+
+#cols_to_remove = ['channelID', 'MessageID', 'AccountID', 'label', 'contexts', 'topics', 'weak topics', 'indicators']
+
+for i in tqdm(range(0, len(df), batch_size)):
+    # find end of batch
+    i_end = min(i+batch_size, len(df))
+    # extract metadata batch
+    meta_batch = metadata.iloc[i:i_end]
+    meta_dict = meta_batch.to_dict(orient="records")
+    # concatenate all metadata field except for id and year to form a single string
+    meta_batch = [" ".join(x) for x in meta_batch.loc[:, ~meta_batch.columns.isin(cols_to_remove)].values.tolist()]
+    # extract image batch
+    img_batch = images[i:i_end]
+    # create sparse BM25 vectors
+    sparse_embeds = bm25.encode_documents([text for text in meta_batch])
+    # create dense vectors
+    dense_embeds = model.encode(img_batch).tolist()
+    # create unique IDs
+    ids = [str(x) for x in range(i, i_end)]
+
+    upserts = []
+    # loop through the data and create dictionaries for uploading documents to pinecone index
+    for _id, sparse, dense, meta in zip(ids, sparse_embeds, dense_embeds, meta_dict):
+        upserts.append({
+            'id': _id,
+            'sparse_values': sparse,
+            'values': dense,
+            'metadata': meta
+        })
+    # upload the documents to the new hybrid index
+    index.upsert(upserts)
+
+# Create an upsert function for hybrid vectors
+def upsert_hybrid_vectors(index, df, model, bm25, batch_size=200, cols_to_remove=['id', 'year']):
+    metadata = df.remove_columns("image")
+
+    for i in tqdm(range(0, len(df), batch_size)):
+        i_end = min(i+batch_size, len(df))
+        meta_batch = metadata.iloc[i:i_end]
+        meta_dict = meta_batch.to_dict(orient="records")
+        meta_batch = [" ".join(x) for x in meta_batch.loc[:, ~meta_batch.columns.isin(cols_to_remove)].values.tolist()]
+        text_batch = df
+        sparse_embeds = bm25.encode_documents([text for text in meta_batch])
+        dense_embeds = model.encode(text_batch).tolist()
+        ids = [str(x) for x in range(i, i_end)]
+
+        upserts = []
+        for _id, sparse, dense, meta in zip(ids, sparse_embeds, dense_embeds, meta_dict):
+            upserts.append({
+                'id': _id,
+                'sparse_values': sparse,
+                'values': dense,
+                'metadata': meta
+            })
+        index.upsert(upserts)
+
+# show index description after uploading the documents
+index.describe_index_stats()

notebooks/upsert_300K.py

@@ -0,0 +1,66 @@
+## Upload Telegram 300K to hybrid-sparse 
+from pinecone.grpc import PineconeGRPC as Pinecone
+import os
+import pandas as pd
+import numpy as np
+from pinecone import ServerlessSpec
+from pinecone_text.sparse import BM25Encoder
+import sys
+sys.path.append('src/python')
+import DataLoader
+
+pc = Pinecone("5faec954-a6c5-4af5-a577-89dbd2e4e5b0")
+pc.delete_index("oc-hybrid-300k-index")
+
+pc.create_index(
+  name="oc-hybrid-300k-index",
+  dimension=1024,
+  metric="dotproduct",
+  spec=ServerlessSpec(
+    cloud="aws",
+    region="us-east-1"
+  )
+)
+
+## Upsert Indicator Test Data 
+df = pd.read_csv('data/random_300k.csv')
+## get top three rows 
+#df = df.head(3)
+# get text and MessageID
+bm25, newdf = DataLoader.create_sparse_embeds(pc, df)
+metadata = df[['text']].to_dict(orient='records')
+newdf.loc[:, 'metadata'] = metadata
+## Taka look at rows where sparse values is an empty array
+sparse_lengths = [len(x) for x in newdf['sparse_values']]
+# Pull out the values where sparse length is 0 
+#edf = newdf[pd.Series(sparse_lengths) == 0]
+## Drop newdf rows where sparse length is 0
+newdf = newdf[pd.Series(sparse_lengths) != 0]
+
+vecs = DataLoader.create_sparse_dense_dict(newdf)
+index = pc.Index("oc-hybrid-300k-index")
+
+# Batch upsert the vectors
+for i in range(0, len(vecs), 400):
+    end_index = min(i + 400, len(vecs))
+    index.upsert(vecs[i:end_index], namespace="telegram-300k")
+
+
+################# Querying the index
+df = pd.read_csv('data/random_300k.csv')
+corpus = df['text'].tolist()
+vector, bm25 = DataLoader.encode_documents(corpus)
+index = pc.Index("oc-hybrid-300k-index")
+
+querytext = "satanic"
+queryembed = DataLoader.query_embed(pc, "multilingual-e5-large", querytext)
+query_sparse_vector = bm25.encode_documents(querytext)
+
+query_response = index.query(
+    top_k=5,
+    namespace="telegram-300k",
+    vector=queryembed,
+    sparse_vector=query_sparse_vector,
+    include_metadata=True
+)
+query_response

test/runtests.jl

@@ -0,0 +1,8 @@
+## runtests 
+
+using Test
+
+@testset "runtests" begin
+    @test 1 == 1
+end
+