add interfaces and tests for nearest neighbors and embeddings

.github/workflows/linters.yml

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+name: Ruff
+on: [ push, pull_request ]
+jobs:
+  ruff:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: astral-sh/ruff-action@v1

.github/workflows/unit_tests.yml

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+name: Python Tests
+
+on:
+  push:
+    branches:
+      - '**'
+  pull_request:
+    branches:
+      - '**'
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    defaults:
+      run:
+        working-directory: ./oxen
+
+    steps:
+    - uses: actions/checkout@v3
+
+    - uses: robinraju/[email protected]
+      with:
+        repository: "Oxen-AI/Oxen"
+        latest: true
+        fileName: "oxen-server-ubuntu-22.04.deb"
+        out-file-path: "."
+
+    - uses: robinraju/[email protected]
+      with:
+        repository: "Oxen-AI/Oxen"
+        latest: true
+        fileName: "oxen-ubuntu-22.04.deb"
+        out-file-path: "."
+
+    - name: Install oxen-server
+      run: |
+        sudo dpkg -i /home/runner/work/oxen-release/oxen-release/oxen-server-ubuntu-22.04.deb
+        sudo dpkg -i /home/runner/work/oxen-release/oxen-release/oxen-ubuntu-22.04.deb
+
+    - name: Set up Python
+      uses: actions/setup-python@v4
+      with:
+        python-version: '3.12'
+
+    - name: Install Python Dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install -e .
+        pip install -r requirements.txt
+
+    - name: Run Tests
+      run: |
+        oxen config --name "Bessie Testington" --email "[email protected]"
+        oxen-server start &
+        pytest -s tests
+
+

examples/rag/03_search_and_retrieval.py

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+import oxen
+import os
+import argparse
+import pandas as pd
+from typing import List
+from oxen import Workspace, DataFrame, RemoteRepo
+
+
+HTTP_HOST = "localhost:3001"
+HTTP_SCHEME = "http"
+
+def load_data(
+    repo_id: str,
+    questions_path: str,
+    branch: str,
+    data_dir: str
+):
+    questions_file = os.path.join(data_dir, questions_path)
+    if not os.path.exists(questions_file):
+        parent = os.path.dirname(questions_file)
+        if not os.path.exists(parent):
+            os.makedirs(parent)
+
+        print(f"Downloading {questions_path}")
+        oxen.datasets.download(
+            repo_id,
+            questions_path,
+            dst=questions_file,
+            revision=branch,
+            host=HTTP_HOST,
+            scheme=HTTP_SCHEME
+        )
+
+    df = pd.read_parquet(questions_file)
+    return df
+
+
+def fetch_results(documents_df: DataFrame, embedding: List[float]):
+    results = documents_df.query(
+        embedding=embedding,
+        sort_by_similarity_to="chunk_embeddings"
+    )
+    return results
+
+def evaluate(
+    repo_id: str,
+    questions_path: str,
+    documents_path: str,
+    branch: str,
+    data_dir: str,
+    output_path: str
+):
+    remote_repo = RemoteRepo(repo_id, host=HTTP_HOST, scheme=HTTP_SCHEME)
+    workspace = Workspace(remote_repo, branch=branch)
+    documents_df = DataFrame(workspace, documents_path)
+    column = "chunk_embeddings"
+    if not documents_df.is_nearest_neighbors_enabled(column=column):
+        print("Enabling nearest neighbors for column", column)
+        documents_df.enable_nearest_neighbors(column=column)
+
+    df = load_data(repo_id, questions_path, branch, data_dir)
+    print(df)
+
+    correct = 0
+    incorrect = 0
+    results = []
+    for idx, row in df.iterrows():
+        print(f"\n\nQuestion {idx}: {row['question']}")
+        answer = row["answer"]
+        embedding = row["question_embeddings"]
+        documents = fetch_results(documents_df, embedding)
+        found_answer = False
+        print(f"Looking for answer '{answer}' in {len(documents)} documents")
+        documents_str = ""
+        for jdx, doc in enumerate(documents):
+            print(f"Document {jdx}: {doc['chunk']}")
+            documents_str += f"Document Id: {doc['document_id']}\nTitle: {doc['title']}\nText: {doc['chunk']}\n\n"
+            if answer.lower() in doc["chunk"].lower():
+                print(f"Answer '{answer}' found in document {jdx}")
+                correct += 1
+                found_answer = True
+
+        if not found_answer:
+            print(f"Answer '{answer}' not found in any document")
+            incorrect += 1
+        print("\n\n")
+
+        print(f"Correct: {correct}, Incorrect: {incorrect}")
+        print(f"Accuracy: {correct / (correct + incorrect)}")
+        results.append({
+            "id": row["id"],
+            "question": row["question"],
+            "answer": answer,
+            "search_results": documents_str
+        })
+
+    results_df = pd.DataFrame(results)
+    results_df.to_parquet(output_path, index=False)
+
+def main():
+    parser = argparse.ArgumentParser(description='Evaluate a set of questions and answers.')
+    parser.add_argument('-r', '--repository', help='The id of the repository ie: hallo/RAG')
+    parser.add_argument('-q', '--questions', help='Path to file with the questions and answers')
+    parser.add_argument('-d', '--documents', help='Path to file with the documents')
+    parser.add_argument('-b', '--branch', help="The branch to fetch the file from", default="main")
+    parser.add_argument('--data-dir', help='Where to store all the output data', default="data")
+    parser.add_argument('-o', '--output', help='Path to file to store the output', default="output.parquet")
+
+    args = parser.parse_args()
+
+    evaluate(
+        args.repository,
+        args.questions,
+        args.documents,
+        args.branch,
+        args.data_dir,
+        args.output
+    )
+
+
+if __name__ == "__main__":
+    main()