using AIStudio.Provider;
using AIStudio.Settings;
using AIStudio.Tools.Databases.IndexStore;
using AIStudio.Tools.Databases.VectorStore;
using AIStudio.Tools.PluginSystem;

namespace AIStudio.Tools.Services.Indexing;

/// <summary>
/// One indexing run of a data source: what it works with, and what it does to the stores.
/// </summary>
/// <remarks>
/// Worked out once before the run starts, after the stores, the embedding provider and the stored
/// manifest were all found to be usable. Whatever kind of data source is indexed, these are the same
/// things, and so is cleaning up after a document. Keeping them here lets each kind of data source
/// bring only what is its own: how it finds its documents and how it reads them.
///
/// A document is addressed by its key, which is what its vectors and its row in the index store are
/// filed under. For a file, that is its full path.
/// </remarks>
/// <param name="dataSource">The data source being indexed.</param>
/// <param name="embeddingProvider">The embedding provider the data source points at.</param>
/// <param name="provider">The provider instance which embeds the text.</param>
/// <param name="vectorStore">The vector store, known to be available.</param>
/// <param name="indexStore">The index store, known to be available.</param>
/// <param name="manifest">What the index stores about the data source, made to match the current embedding configuration.</param>
/// <param name="settingsManager">The settings, which the provider reads when it embeds.</param>
/// <param name="publishStatus">Hands a new status of the data source to the embedding service.</param>
/// <param name="logger">The logger of the embedding service, so the log reads the same whoever writes it.</param>
internal sealed class IndexedRunContext(IIndexedDataSource dataSource, EmbeddingProvider embeddingProvider, IProvider provider, VectorStoreClient vectorStore, IndexStoreClient indexStore, DataSourceEmbeddingManifest manifest, SettingsManager settingsManager, Action<DataSourceEmbeddingStatus> publishStatus, ILogger logger)
{
    /// <summary>
    /// After how many stored chunks the collection is optimized while a run is still going.
    /// </summary>
    private const int VECTOR_STORE_OPTIMIZATION_CHUNK_THRESHOLD = 100_000;

    private long storedChunksSinceLastOptimization;
    private bool hasPendingChanges;

    private static string TB(string fallbackEN) => I18N.I.T(fallbackEN, typeof(IndexedRunContext).Namespace, nameof(IndexedRunContext));

    /// <summary>
    /// One chunk on its way to the stores, with the id it is stored under.
    /// </summary>
    private sealed record EmbeddingChunkDraft(string ChunkId, string Text, int ChunkIndex, int? PageNumber);

    public IIndexedDataSource DataSource => dataSource;

    public EmbeddingProvider EmbeddingProvider => embeddingProvider;

    public IProvider Provider => provider;

    public VectorStoreClient VectorStore => vectorStore;

    public IndexStoreClient IndexStore => indexStore;

    public DataSourceEmbeddingManifest Manifest => manifest;

    public string CollectionName { get; } = DataSourceEmbeddingNames.GetCollectionName(dataSource.Id);

    /// <summary>
    /// Tells the user interface about a new status of the data source.
    /// </summary>
    /// <param name="status">The status.</param>
    public void PublishStatus(DataSourceEmbeddingStatus status) => publishStatus(status);

    /// <summary>
    /// Embeds one document and stores it, in place of whatever was stored for it before.
    /// </summary>
    /// <remarks>
    /// The old vectors and the old index row go first, then the row is written anew with a chunk
    /// count of zero, so the chunks have something to point at while they arrive batch by batch.
    /// The final row, with the real chunk count, is written by whoever decides that the document
    /// was indexed: only the kind of data source knows whether it changed in the meantime.
    /// </remarks>
    /// <param name="document">The document to index.</param>
    /// <param name="reportBlockProgress">Told about every chunk, with its number and its page.</param>
    /// <param name="token">The cancellation token.</param>
    /// <returns>The number of chunks stored for the document.</returns>
    public async Task<int> IndexDocumentAsync(EmbeddingDocument document, Action<int, int?> reportBlockProgress, CancellationToken token)
    {
        logger.LogDebug(
            "Resetting stored embeddings for file '{FilePath}' in collection '{CollectionName}' before re-indexing.",
            document.Key,
            this.CollectionName);
        await this.DeleteDocumentPointsAsync(document.Key, token);
        await indexStore.DeleteFileAsync(dataSource.Id, document.Key, token);
        await indexStore.UpsertFileAsync(dataSource.Id, document.State, token);

        var embeddingBatchSize = Math.Max(1, embeddingProvider.EffectiveEmbeddingBatchSize);
        var batch = new List<EmbeddingChunkDraft>(embeddingBatchSize);
        var totalChunkCount = 0;

        await foreach (var chunk in document.StreamChunks(token))
        {
            batch.Add(new(IndexedDocumentIds.CreateChunkId(dataSource.Id, document.State.Fingerprint, totalChunkCount), chunk.Text, totalChunkCount, chunk.PageNumber));
            totalChunkCount++;
            reportBlockProgress(totalChunkCount, chunk.PageNumber);

            if (batch.Count >= embeddingBatchSize)
                await this.FlushBatchAsync(document, batch, token);
        }

        if (batch.Count > 0)
            await this.FlushBatchAsync(document, batch, token);

        //
        // The extraction itself did not report a failure, but nothing usable came out of it. For
        // the index this is the same case as a scanned page without a text layer, which is why it
        // carries a code of its own instead of an unclassified exception:
        //
        if (totalChunkCount == 0)
            throw new FileExtractionException(FileExtractionErrorCode.NO_CONTENT, string.Format(TB("No text could be read from '{0}'."), document.Key));

        logger.LogDebug(
            "Generated {ChunkCount} chunks for file '{FilePath}' in data source '{DataSourceName}' ({DataSourceId}).",
            totalChunkCount,
            document.Key,
            dataSource.Name,
            dataSource.Id);

        return totalChunkCount;
    }

    /// <summary>
    /// Removes the vectors of one document from the collection.
    /// </summary>
    /// <param name="documentKey">The key of the document.</param>
    /// <param name="token">The cancellation token.</param>
    public async Task DeleteDocumentPointsAsync(string documentKey, CancellationToken token)
    {
        await vectorStore.DeleteEmbeddingByFile(this.CollectionName, documentKey, token);
        this.hasPendingChanges = true;
    }

    /// <summary>
    /// Removes whatever a failed attempt left behind of one document.
    /// </summary>
    /// <remarks>
    /// Never throws for a store which refuses: the document already failed, and that failure is the
    /// one the user has to hear about.
    /// </remarks>
    /// <param name="documentKey">The key of the document.</param>
    /// <param name="token">The cancellation token.</param>
    public async Task CleanupFailedDocumentAsync(string documentKey, CancellationToken token)
    {
        try
        {
            await this.DeleteDocumentPointsAsync(documentKey, token);
        }
        catch (OperationCanceledException) when (token.IsCancellationRequested)
        {
            throw;
        }
        catch (Exception exception)
        {
            logger.LogWarning(
                exception,
                "Could not remove vector points while cleaning up failed embedding for file '{FilePath}' in data source '{DataSourceName}' ({DataSourceId}).",
                documentKey,
                dataSource.Name,
                dataSource.Id);
        }

        try
        {
            await indexStore.DeleteFileAsync(dataSource.Id, documentKey, token);
        }
        catch (OperationCanceledException) when (token.IsCancellationRequested)
        {
            throw;
        }
        catch (Exception exception)
        {
            logger.LogWarning(
                exception,
                "Could not remove embedding state while cleaning up failed embedding for file '{FilePath}' in data source '{DataSourceName}' ({DataSourceId}).",
                documentKey,
                dataSource.Name,
                dataSource.Id);
        }
    }

    /// <summary>
    /// Drops the mark which keeps a document out of the index, in the store as well as in the manifest.
    /// </summary>
    /// <remarks>
    /// Called whenever a document was read, and whenever it failed for a reason outside of itself. The
    /// state heals on its own that way: a document which becomes readable, or a drive which comes
    /// back, leaves nothing behind.
    /// </remarks>
    /// <param name="documentKey">The key of the document.</param>
    /// <param name="token">The cancellation token.</param>
    public async Task ForgetPermanentFailureAsync(string documentKey, CancellationToken token)
    {
        if (!manifest.PermanentFailures.Remove(documentKey))
            return;

        await indexStore.DeletePermanentFailureAsync(dataSource.Id, documentKey, token);
        logger.LogDebug(
            "Removed the permanent indexing failure of file '{FilePath}' from data source '{DataSourceName}' ({DataSourceId}).",
            documentKey,
            dataSource.Name,
            dataSource.Id);
    }

    /// <summary>
    /// Counts chunks which reached the collection, and optimizes it once enough of them came together.
    /// </summary>
    /// <param name="chunkCount">How many chunks were just stored.</param>
    /// <param name="token">The cancellation token.</param>
    public async Task RecordStoredChunksAsync(int chunkCount, CancellationToken token)
    {
        if (chunkCount > 0)
        {
            this.hasPendingChanges = true;
            this.storedChunksSinceLastOptimization += chunkCount;
        }

        if (this.storedChunksSinceLastOptimization >= VECTOR_STORE_OPTIMIZATION_CHUNK_THRESHOLD)
            await this.OptimizeCollectionIfNeededAsync("stored chunk threshold reached", token);
    }

    /// <summary>
    /// Optimizes the collection when anything in it changed since the last time.
    /// </summary>
    /// <param name="reason">Why it is asked for now, for the log.</param>
    /// <param name="token">The cancellation token.</param>
    public async Task OptimizeCollectionIfNeededAsync(string reason, CancellationToken token)
    {
        if (!this.hasPendingChanges)
            return;

        logger.LogInformation(
            "Optimizing embedding collection '{CollectionName}' for data source '{DataSourceName}' ({DataSourceId}). Reason='{Reason}', StoredChunksSinceLastOptimization={StoredChunksSinceLastOptimization}, ChunkThreshold={ChunkThreshold}.",
            this.CollectionName,
            dataSource.Name,
            dataSource.Id,
            reason,
            this.storedChunksSinceLastOptimization,
            VECTOR_STORE_OPTIMIZATION_CHUNK_THRESHOLD);

        await vectorStore.OptimizeVectorStore(this.CollectionName, token);
        this.storedChunksSinceLastOptimization = 0;
        this.hasPendingChanges = false;
    }

    private async Task FlushBatchAsync(EmbeddingDocument document, List<EmbeddingChunkDraft> batch, CancellationToken token)
    {
        logger.LogDebug(
            "Requesting embeddings for batch of {ChunkCount} chunks from file '{FilePath}' in data source '{DataSourceName}' ({DataSourceId}).",
            batch.Count,
            document.Key,
            dataSource.Name,
            dataSource.Id);

        var texts = batch.Select(item => item.Text).ToList();
        IReadOnlyList<IReadOnlyList<float>> vectors;
        try
        {
            vectors = await provider.EmbedTextAsync(embeddingProvider.Model, settingsManager, token, texts);
            token.ThrowIfCancellationRequested();
        }
        catch (OperationCanceledException) when (token.IsCancellationRequested)
        {
            throw;
        }
        catch (ProviderRequestException)
        {
            //
            // The provider already named the cause and what to do about it. Wrapping that in a
            // sentence about a batch of chunks would replace the one thing the user can act on
            // with the fact that something failed:
            //
            throw;
        }
        catch (Exception exception)
        {
            //
            // The messages of these exceptions end up in the log, never in front of the user, so
            // they name the document by its key: the name a user reads may be the subject of a mail.
            //
            throw new InvalidOperationException(string.Format(TB("The embedding provider was not able to embed {0} part(s) of '{1}'. The provider reported: {2}"), batch.Count, document.Key, exception.Message), exception);
        }

        if (vectors.Count != batch.Count)
            throw new InvalidOperationException(string.Format(TB("The embedding provider answered with {0} vectors for {1} parts of '{2}'. Please select another embedding model or provider."), vectors.Count, batch.Count, document.Key));

        var vectorSize = vectors.FirstOrDefault()?.Count ?? 0;
        if (vectorSize <= 0)
            throw new InvalidOperationException(TB("The embedding provider answered with an empty vector. Please select another embedding model or provider."));

        if (vectors.Any(vector => vector.Count != vectorSize))
            throw new InvalidOperationException(TB("The embedding provider answered with vectors of different sizes. Please select another embedding model or provider."));

        if (vectors.Any(vector => vector.Any(value => !float.IsFinite(value))))
            throw new InvalidOperationException(TB("The embedding provider answered with a vector containing an invalid number. Please select another embedding model or provider."));

        if (manifest.VectorSize > 0 && manifest.VectorSize != vectorSize)
            throw new InvalidOperationException(string.Format(TB("The size of the embedding vectors changed from {0} to {1}. Please save the data source again to index it from scratch."), manifest.VectorSize, vectorSize));

        if (manifest.VectorSize == 0)
        {
            token.ThrowIfCancellationRequested();
            var ensureResult = await vectorStore.EnsureVectorStoreExists(this.CollectionName, dataSource.Name, vectorSize, token);
            if (!ensureResult.Created)
            {
                logger.LogWarning(
                    "Vector store '{CollectionName}' exists for data source '{DataSourceName}' ({DataSourceId}) although no persisted embedding state exists. Replacing the orphaned store before indexing.",
                    this.CollectionName,
                    dataSource.Name,
                    dataSource.Id);
                await vectorStore.DeleteVectorStore(this.CollectionName, token);
                ensureResult = await vectorStore.EnsureVectorStoreExists(this.CollectionName, dataSource.Name, vectorSize, token);
                if (!ensureResult.Created)
                    throw new InvalidOperationException(string.Format(TB("The local index '{0}' could not be created again. Please restart AI Studio and try once more."), this.CollectionName));
            }

            await indexStore.UpdateVectorSizeAsync(dataSource.Id, vectorSize, token);
            manifest.VectorSize = vectorSize;
            logger.LogInformation(
                "Created embedding collection '{CollectionName}' with vector size {VectorSize} for data source '{DataSourceName}' ({DataSourceId}).",
                this.CollectionName,
                vectorSize,
                dataSource.Name,
                dataSource.Id);
        }

        token.ThrowIfCancellationRequested();
        var embeddedAtUtc = DateTimeOffset.UtcNow;
        var state = document.State;
        var points = batch.Select((item, index) => new VectorStoragePoint(
            item.ChunkId,
            vectors[index],
            dataSource.Id,
            dataSource.Type.ToString(),
            item.ChunkId,
            state.ParentFileId,
            document.Key,
            state.AbsolutePath,
            state.FileName,
            state.RelativePath,
            state.FileType,
            item.PageNumber,
            item.ChunkIndex,
            item.Text,
            state.Fingerprint,
            state.CreationUtc,
            state.LastWriteUtc,
            embeddedAtUtc)).ToList();

        await vectorStore.InsertEmbedding(this.CollectionName, points, token);
        token.ThrowIfCancellationRequested();

        var chunks = batch
            .Select(chunk => new EmbeddingStateChunk(chunk.ChunkId, state.ParentFileId, chunk.PageNumber, chunk.ChunkIndex, chunk.Text, embeddedAtUtc))
            .ToList();

        await indexStore.UpsertChunksAsync(dataSource.Id, chunks, token);
        await this.RecordStoredChunksAsync(batch.Count, token);

        logger.LogDebug(
            "Stored {ChunkCount} embedded chunks for file '{FilePath}' in collection '{CollectionName}'.",
            batch.Count,
            document.Key,
            this.CollectionName);

        batch.Clear();
    }
}