Add interpret method and batching in collections

Makefile

@@ -54,8 +54,9 @@ callgrind-colbert: build-conda
 
 py-docs:
 	rm -rf docs/build
-	sphinx-apidoc -o docs/source/ ./build/lintdb/python/lintdb
+	sphinx-apidoc -o docs/source/ ./builds/python/lintdb/python/lintdb
 	cd docs && make html
+	cp icon.svg docs/build/html/icon.svg
 
 debug-conda:
 	conda debug lintdb --python 3.10 --output-id 'lintdb-*-py*' 

docs/requirements.txt

@@ -1,2 +1,3 @@
 chardet = "^5.2.0"
-sphinx-immaterial = "^0.11.11"
+sphinx-immaterial = "^0.11.11"
+myst-parser

docs/source/benchmarks.rst

@@ -2,7 +2,7 @@ Benchmarks
 =============
 
 Mac OS
-=======
+-------
 
 Benchmark: LoTTE Lifestyle 40k
 -------------------------------
@@ -36,7 +36,7 @@ lintdb:
 
 
 Linux
-======
+------
 
 Benchmark: LoTTE Lifestyle 40k
 -------------------------------

docs/source/conf.py

@@ -1,7 +1,7 @@
 
 import os
 import sys
-sys.path.insert(0, os.path.abspath(os.path.join('..', '..', '_build_python/lintdb/python/build/lib')))
+sys.path.insert(0, os.path.abspath(os.path.join('..', '..', 'builds/python/lintdb/python/build/lib')))
 
 # Configuration file for the Sphinx documentation builder.
 #
@@ -27,7 +27,7 @@
 author = 'DeployQL'
 
 # The full version, including alpha/beta/rc tags
-release = '0.1'
+release = '0.3.0'
 
 
 # -- General configuration ---------------------------------------------------
@@ -119,19 +119,19 @@
         },
     ],
     # BEGIN: version_dropdown
-    # "version_dropdown": True,
-    # "version_info": [
-    #     {
-    #         "version": "https://sphinx-immaterial.rtfd.io",
-    #         "title": "ReadTheDocs",
-    #         "aliases": [],
-    #     },
-    #     {
-    #         "version": "https://jbms.github.io/sphinx-immaterial",
-    #         "title": "Github Pages",
-    #         "aliases": [],
-    #     },
-    # ],
+    "version_dropdown": True,
+    "version_info": [
+        {
+            "version": "LintDB/v0.3.0",
+            "title": "v0.3.0",
+            "aliases": [],
+        },
+         {
+            "version": "LintDB/v0.2.1",
+            "title": "v0.2.1",
+            "aliases": [],
+        },
+    ],
     # END: version_dropdown
     "toc_title_is_page_title": True
     # BEGIN: social icons

docs/source/index.rst

@@ -8,7 +8,7 @@
    :parser: myst_parser.sphinx_
 
 .. toctree::
-   :maxdepth: 2
+   :maxdepth: 3
    :caption: Contents:
 
    installation

docs/docs/source/lintdb.rst → docs/source/python.rst

@@ -1,21 +1,21 @@
-lintdb package
+python package
 ==============
 
 Submodules
 ----------
 
-lintdb.lintdb module
---------------------
+python.setup module
+-------------------
 
-.. automodule:: lintdb.lintdb
+.. automodule:: python.setup
    :members:
    :undoc-members:
    :show-inheritance:
 
 Module contents
 ---------------
 
-.. automodule:: lintdb
+.. automodule:: python
    :members:
    :undoc-members:
    :show-inheritance:

lintdb/CMakeLists.txt

@@ -28,9 +28,9 @@ set(
     Collection.h
     constants.h
     cf.h
-    RawPassage.h
     EmbeddingBlock.h
     Encoder.h
+    Passages.h
     quantizers/Binarizer.h
     quantizers/Quantizer.h
     quantizers/ProductEncoder.h

lintdb/Collection.cpp

@@ -1,5 +1,5 @@
 #include "lintdb/Collection.h"
-#include "lintdb/RawPassage.h"
+#include "lintdb/EmbeddingBlock.h"
 #include <glog/logging.h>
 #include <iostream>
 #include "lintdb/utils/progress_bar.h"
@@ -29,11 +29,48 @@ namespace lintdb {
         input.attention_mask = attn;
 
         auto output = model->encode(input);
-        
-        auto passage = RawPassage(output.data(), ids.size(), model->get_dims(), id, metadata);
+
+        auto passage = EmbeddingPassage(output.data(), ids.size(), model->get_dims(), id, metadata);
         index->add(tenant, {passage});
     }
 
+    void Collection::add_batch(
+        const uint64_t tenant, 
+        const std::vector<TextPassage> passages
+        ) const {
+
+        std::vector<ModelInput> inputs;
+        for(auto passage: passages) {
+            auto ids = tokenizer->encode(passage.data);
+
+            ModelInput input;
+            input.input_ids = ids;
+
+            std::vector<int32_t> attn;
+            for(auto id: ids) {
+                if(id == 0) {
+                    attn.push_back(0);
+                } else {
+                    attn.push_back(1);
+                }
+            }
+            input.attention_mask = attn;
+
+            inputs.push_back(input);
+        }
+
+        auto output = model->encode(inputs);
+
+        std::vector<EmbeddingPassage> embedded_passages;
+        for (size_t i = 0; i < passages.size(); i++) {
+            auto encoded_vector = output.get(i);
+            auto passage = EmbeddingPassage(encoded_vector.data(), inputs[i].input_ids.size(), model->get_dims(), passages[i].id, passages[i].metadata);
+            embedded_passages.push_back(passage);
+        }
+
+        index->add(tenant, embedded_passages);
+    }
+
     std::vector<SearchResult> Collection::search(
             const uint64_t tenant, 
             const std::string& text, 
@@ -58,6 +95,27 @@ namespace lintdb {
         return index->search(tenant, output.data(), ids.size(), model->get_dims(), opts.n_probe, k, opts);
     }
 
+    std::vector<TokenScore> Collection::interpret(
+        const std::string& text,
+        const std::vector<float> scores
+    ) {
+        auto ids = tokenizer->encode(text);
+        std::vector<std::string> tokens;
+        for(auto id: ids) {
+            tokens.push_back(tokenizer->decode({id}));
+        }
+
+        std::vector<TokenScore> results;
+        for(size_t i = 0; i < ids.size(); i++) {
+            if (tokenizer->is_special(ids[i])) {
+                continue;
+            }
+            results.push_back({tokens[i], scores[i]});
+        }
+
+        return results;
+    }
+
     void Collection::train(const std::vector<std::string> texts) {
         std::vector<float> embeddings;
         size_t num_embeddings = 0;

lintdb/Collection.h

@@ -3,6 +3,7 @@
 #include "lintdb/index.h"
 #include "lintdb/index_builder/EmbeddingModel.h"
 #include "lintdb/index_builder/Tokenizer.h"
+#include "lintdb/Passages.h"
 #include "lintdb/api.h"
 #include <string>
 #include <vector>
@@ -14,6 +15,12 @@ namespace lintdb {
         std::string tokenizer_file;
         size_t max_length = 512;
     };
+
+    struct TokenScore {
+        std::string token;
+        float score;
+    };
+
     /**
      * Collection is a collection of documents. Instead of dealing directly with vectors, this
      * class allows you to add and search for documents by text.
@@ -27,9 +34,20 @@ namespace lintdb {
          * @param tenant The tenant id.
          * @param id The document id.
          * @param text The text to add.
+         * @param metadata a dictionary of metadata to store with the document. only accepts strings.
         */
         void add(const uint64_t tenant, const uint64_t id, const std::string& text, const std::map<std::string, std::string>& metadata) const;
-
+
+        /**
+         * Add a batch of texts to the index.
+         * 
+         * @param tenant The tenant id.
+         * @param passages A list of EmbeddingPassage objects to add.
+        */
+        void add_batch(
+            const uint64_t tenant, 
+            const std::vector<TextPassage> passages
+        ) const;
         /**
          * Search the index for similar documents.
          * 
@@ -44,6 +62,11 @@ namespace lintdb {
             const size_t k, 
             const SearchOptions& opts=SearchOptions()) const;
 
+        std::vector<TokenScore> interpret(
+            const std::string& text,
+            const std::vector<float> scores
+        );
+
         void train(const std::vector<std::string> texts);
 
         private:

lintdb/Encoder.cpp

@@ -56,7 +56,7 @@
     // for normalized vectors, this should be the same as IP.
     this->coarse_quantizer = std::make_unique<faiss::IndexFlatIP>(dim);
     auto quantizer_config = QuantizerConfig{
             .nlist = nlist,
             .nbits = nbits,
             .niter = niter,
             .dim = dim,
@@ -66,7 +66,7 @@
 }
 
 std::unique_ptr<EncodedDocument> DefaultEncoder::encode_vectors(
-        const RawPassage& doc) {
+        const EmbeddingPassage& doc) {
     LINTDB_THROW_IF_NOT(nlist <= std::numeric_limits<code_t>::max());
     auto num_tokens = doc.embedding_block.num_tokens;
 
@@ -294,7 +294,7 @@
     encoder->coarse_quantizer = std::move(coarse_quantizer);
 
     auto quantizer_config = QuantizerConfig{
             .nlist = config.nlist,
             .nbits = config.nbits,
             .niter = config.niter,
             .dim = config.dim,

lintdb/Encoder.h

@@ -6,7 +6,8 @@
 #include <memory>
 #include <string>
 #include <vector>
-#include "lintdb/RawPassage.h"
+#include "lintdb/Passages.h"
+#include "lintdb/EmbeddingBlock.h"
 #include "lintdb/SearchOptions.h"
 #include "lintdb/api.h"
 #include "lintdb/invlists/EncodedDocument.h"
@@ -38,11 +39,11 @@ struct Encoder {
     virtual size_t get_num_centroids() const = 0;
     virtual size_t get_nbits() const = 0;
     /**
-     * Encode vectors translates the embeddings given to us in RawPassage to
+     * Encode vectors translates the embeddings given to us in EmbeddingPassage to
      * the internal representation that we expect to see in the inverted lists.
      */
     virtual std::unique_ptr<EncodedDocument> encode_vectors(
-            const RawPassage& doc) = 0;
+            const EmbeddingPassage& doc) = 0;
 
     /**
      * Decode vectors translates out of our internal representation.
@@ -138,7 +139,7 @@ struct DefaultEncoder : public Encoder {
     }
 
     std::unique_ptr<EncodedDocument> encode_vectors(
-            const RawPassage& doc) override;
+            const EmbeddingPassage& doc) override;
 
     std::vector<float> decode_vectors(
             gsl::span<const code_t> codes,