retrieval : minor

examples/retrieval/README.md

@@ -1,25 +1,31 @@
 # llama.cpp/examples/retrieval
 
-Demonstration of simple retrieval technique based on cosin similarity
+Demonstration of simple retrieval technique based on cosine similarity
 
 More info:
 https://github.com/ggerganov/llama.cpp/pull/6193
 
 ### How to use
+
 `retieval.cpp` has parameters of its own:
 - `--context-file`: file to be embedded - state this option multiple times to embed multiple files
 - `--chunk-size`: minimum size of each text chunk to be embedded
 - `--chunk-separator`: STRING to divide chunks by. newline by default
 
-`retrieval` example can be tested as below
+`retrieval` example can be tested as follows:
+
 ```bash
 make -j && ./retrieval --model ./models/bge-base-en-v1.5-f16.gguf --top-k 3 --context-file README.md --context-file License --chunk-size 100 --chunk-separator .
 ```
-which chunks & embeds all given files and starts a loop requesting query inputs:
+
+This chunks and embeds all given files and starts a loop requesting query inputs:
+
 ```
 Enter query:
 ```
-and on query input, top k chunks are shown along with file name, chunk position within file and original text
+
+On each query input, top k chunks are shown along with file name, chunk position within file and original text:
+
 ```
 Enter query: describe the mit license
 batch_decode: n_tokens = 6, n_seq = 1

examples/retrieval/retrieval.cpp

@@ -5,15 +5,15 @@
 #include <fstream>
 
 struct retrieval_params {
-    std::vector<std::string> context_files;// context files to embed
+    std::vector<std::string> context_files; // context files to embed
     int32_t chunk_size            = 64;     // chunk size for context embedding
     std::string chunk_separator   = "\n";   // chunk separator for context embedding
 };
 
-static void retrieval_params_print_usage(int argc, char** argv, gpt_params & gpt_params, retrieval_params & params) {
+static void retrieval_params_print_usage(int argc, char ** argv, gpt_params & gpt_params, retrieval_params & params) {
     gpt_print_usage(argc, argv, gpt_params);
     printf("retrieval options:\n");
-    printf("  --context-file FNAME files containing context to embed.\n");
+    printf("  --context-file FNAME  file containing context to embed.\n");
     printf("                        specify multiple files by providing --context-file option multiple times.\n");
     printf("  --chunk-size N        minimum length of embedded text chunk (default:%d)\n", params.chunk_size);
     printf("  --chunk-separator STRING\n");
@@ -24,7 +24,7 @@ static void retrieval_params_print_usage(int argc, char** argv, gpt_params & gpt
 static void retrieval_params_parse(int argc, char ** argv, gpt_params & gpt_params, retrieval_params & retrieval_params) {
     int i = 1;
     std::string arg;
-    while(i < argc) {
+    while (i < argc) {
         arg = argv[i];
         bool invalid_gpt_param = false;
         if(gpt_params_find_arg(argc, argv, argv[i], gpt_params, i, invalid_gpt_param)) {
@@ -36,7 +36,7 @@ static void retrieval_params_parse(int argc, char ** argv, gpt_params & gpt_para
             // option was parsed by gpt_params_find_arg
         } else if (arg == "--context-file") {
             if (++i >= argc) {
-                fprintf(stderr, "error: missing argument for --context-files\n");
+                fprintf(stderr, "error: missing argument for --context-file\n");
                 retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params);
                 exit(1);
             }
@@ -87,7 +87,7 @@ struct chunk {
 
 // chunk file data to chunks of size >= chunk_size
 // chunk_separator is the separator between chunks
-static std::vector<chunk> chunk_file(const std::string filename, int chunk_size, std::string chunk_separator) {
+static std::vector<chunk> chunk_file(const std::string & filename, int chunk_size, const std::string & chunk_separator) {
     std::vector<chunk> chunks;
     std::ifstream f(filename.c_str());
 
@@ -312,17 +312,20 @@ int main(int argc, char ** argv) {
 
         struct llama_batch query_batch = llama_batch_init(n_batch, 0, 1);
         batch_add_seq(query_batch, query_tokens, 0);
+
         std::vector<float> query_emb(n_embd, 0);
         batch_decode(ctx, query_batch, query_emb.data(), 1, n_embd);
-        std::vector<float> query_embedding(query_emb.data(), query_emb.data() + n_embd);
+
         llama_batch_clear(query_batch);
 
-        // similarities tuple vector (chunk index, similarity)
+        // compute cosine similarities
+        {
             std::vector<std::pair<int, float>> similarities;
             for (int i = 0; i < n_chunks; i++) {
-            float sim = llama_embd_similarity_cos(chunks[i].embedding.data(), query_embedding.data(), n_embd);
+                float sim = llama_embd_similarity_cos(chunks[i].embedding.data(), query_emb.data(), n_embd);
                 similarities.push_back(std::make_pair(i, sim));
             }
+
             // sort similarities
             std::sort(similarities.begin(), similarities.end(), [](const std::pair<int, float> & a, const std::pair<int, float> & b) {
                 return a.second > b.second;
@@ -336,7 +339,7 @@ int main(int argc, char ** argv) {
                 printf("textdata:\n%s\n", chunks[similarities[i].first].textdata.c_str());
                 printf("--------------------\n");
             }
-        similarities.clear();
+        }
     }
 
     // clean up