Embed4All: optionally count tokens, misc fixes (#2145)

Key changes:
* python: optionally return token count in Embed4All.embed
* python and docs: models2.json -> models3.json
* Embed4All: require explicit prefix for unknown models
* llamamodel: fix shouldAddBOS for Bert and Nomic Bert

Signed-off-by: Jared Van Bortel <jared@nomic.ai>

gpt4all-backend/llamamodel.cpp

@@ -476,7 +476,9 @@ const std::vector<LLModel::Token> &LLamaModel::endTokens() const
 bool LLamaModel::shouldAddBOS() const
 {
     int add_bos = llama_add_bos_token(d_ptr->model);
-    return add_bos != -1 ? bool(add_bos) : llama_vocab_type(d_ptr->model) == LLAMA_VOCAB_TYPE_SPM;
+    if (add_bos != -1) { return add_bos; }
+    auto vocab_type = llama_vocab_type(d_ptr->model);
+    return vocab_type == LLAMA_VOCAB_TYPE_SPM || vocab_type == LLAMA_VOCAB_TYPE_WPM;
 }
 
 int32_t LLamaModel::maxContextLength(std::string const &modelPath) const
@@ -638,6 +640,7 @@ static const EmbModelGroup EMBEDDING_MODEL_SPECS[] {
     {LLM_EMBEDDER_SPEC, {"llm-embedder"}},
     {BGE_SPEC,          {"bge-small-en", "bge-base-en", "bge-large-en",
                          "bge-small-en-v1.5", "bge-base-en-v1.5", "bge-large-en-v1.5"}},
+    // NOTE: E5 Mistral is not yet implemented in llama.cpp, so it's not in EMBEDDING_ARCHES
     {E5_SPEC,           {"e5-small", "e5-base", "e5-large",
                          "e5-small-unsupervised", "e5-base-unsupervised", "e5-large-unsupervised",
                          "e5-small-v2", "e5-base-v2", "e5-large-v2"}},
@@ -658,20 +661,20 @@ static const EmbModelSpec *getEmbedSpec(const std::string &modelName) {
 }
 
 void LLamaModel::embed(
-    const std::vector<std::string> &texts, float *embeddings, bool isRetrieval, int dimensionality, bool doMean,
+    const std::vector<std::string> &texts, float *embeddings, bool isRetrieval, int dimensionality, size_t *tokenCount,
-    bool atlas
+    bool doMean, bool atlas
 ) {
     const EmbModelSpec *spec;
     std::optional<std::string> prefix;
     if (d_ptr->model && (spec = getEmbedSpec(llama_model_name(d_ptr->model))))
         prefix = isRetrieval ? spec->queryPrefix : spec->docPrefix;
 
-    embed(texts, embeddings, prefix, dimensionality, doMean, atlas);
+    embed(texts, embeddings, prefix, dimensionality, tokenCount, doMean, atlas);
 }
 
 void LLamaModel::embed(
     const std::vector<std::string> &texts, float *embeddings, std::optional<std::string> prefix, int dimensionality,
-    bool doMean, bool atlas
+    size_t *tokenCount, bool doMean, bool atlas
 ) {
     if (!d_ptr->model)
         throw std::logic_error("no model is loaded");
@@ -698,12 +701,9 @@ void LLamaModel::embed(
     }
 
     if (!prefix) {
-        if (spec) {
+        if (!spec)
-            prefix = spec->docPrefix;
+            throw std::invalid_argument("unknown model "s + modelName + ", specify a prefix if applicable or an empty string");
-        } else {
+        prefix = spec->docPrefix;
-            std::cerr << __func__ << ": warning: assuming no prefix\n";
-            prefix = "";
-        }
     } else if (spec && prefix != spec->docPrefix && prefix != spec->queryPrefix &&
                std::find(spec->otherPrefixes.begin(), spec->otherPrefixes.end(), *prefix) == spec->otherPrefixes.end())
     {
@@ -712,7 +712,7 @@ void LLamaModel::embed(
         throw std::invalid_argument(ss.str());
     }
 
-    embedInternal(texts, embeddings, *prefix, dimensionality, doMean, atlas, spec);
+    embedInternal(texts, embeddings, *prefix, dimensionality, tokenCount, doMean, atlas, spec);
 }
 
 // MD5 hash of "nomic empty"
@@ -730,7 +730,7 @@ double getL2NormScale(T *start, T *end) {
 
 void LLamaModel::embedInternal(
     const std::vector<std::string> &texts, float *embeddings, std::string prefix, int dimensionality,
-    bool doMean, bool atlas, const EmbModelSpec *spec
+    size_t *tokenCount, bool doMean, bool atlas, const EmbModelSpec *spec
 ) {
     typedef std::vector<LLModel::Token> TokenString;
     static constexpr int32_t atlasMaxLength = 8192;
@@ -796,6 +796,7 @@ void LLamaModel::embedInternal(
     // split into max_len-sized chunks
     struct split_batch { unsigned idx; TokenString batch; };
     std::vector<split_batch> batches;
+    size_t totalTokens = 0;
     for (unsigned i = 0; i < inputs.size(); i++) {
         auto &input = inputs[i];
         for (auto it = input.begin(); it < input.end(); it += max_len) {
@@ -805,6 +806,7 @@ void LLamaModel::embedInternal(
             auto &batch = batches.back().batch;
             batch = prefixTokens;
             batch.insert(batch.end(), it, end);
+            totalTokens += end - it;
             batch.push_back(eos_token);
             if (!doMean) { break; /* limit text to one chunk */ }
         }
@@ -889,6 +891,8 @@ void LLamaModel::embedInternal(
         std::transform(embd, embd_end, embeddings, product(scale));
         embeddings += dimensionality;
     }
+
+    if (tokenCount) { *tokenCount = totalTokens; }
 }
 
 #if defined(_WIN32)

gpt4all-backend/llamamodel_impl.h

@@ -39,10 +39,10 @@ public:
     size_t embeddingSize() const override;
     // user-specified prefix
     void embed(const std::vector<std::string> &texts, float *embeddings, std::optional<std::string> prefix,
-               int dimensionality = -1, bool doMean = true, bool atlas = false) override;
+               int dimensionality = -1, size_t *tokenCount = nullptr, bool doMean = true, bool atlas = false) override;
     // automatic prefix
     void embed(const std::vector<std::string> &texts, float *embeddings, bool isRetrieval, int dimensionality = -1,
-               bool doMean = true, bool atlas = false) override;
+               size_t *tokenCount = nullptr, bool doMean = true, bool atlas = false) override;
 
 private:
     std::unique_ptr<LLamaPrivate> d_ptr;
@@ -61,7 +61,7 @@ protected:
     int32_t layerCount(std::string const &modelPath) const override;
 
     void embedInternal(const std::vector<std::string> &texts, float *embeddings, std::string prefix, int dimensionality,
-                       bool doMean, bool atlas, const EmbModelSpec *spec);
+                       size_t *tokenCount, bool doMean, bool atlas, const EmbModelSpec *spec);
 };
 
 #endif // LLAMAMODEL_H

gpt4all-backend/llmodel.h

@@ -110,10 +110,10 @@ public:
     }
     // user-specified prefix
     virtual void embed(const std::vector<std::string> &texts, float *embeddings, std::optional<std::string> prefix,
-                       int dimensionality = -1, bool doMean = true, bool atlas = false);
+                       int dimensionality = -1, size_t *tokenCount = nullptr, bool doMean = true, bool atlas = false);
     // automatic prefix
     virtual void embed(const std::vector<std::string> &texts, float *embeddings, bool isRetrieval,
-                       int dimensionality = -1, bool doMean = true, bool atlas = false);
+                       int dimensionality = -1, size_t *tokenCount = nullptr, bool doMean = true, bool atlas = false);
 
     virtual void setThreadCount(int32_t n_threads) { (void)n_threads; }
     virtual int32_t threadCount() const { return 1; }

gpt4all-backend/llmodel_c.cpp

@@ -158,7 +158,7 @@ void llmodel_prompt(llmodel_model model, const char *prompt,
 
 float *llmodel_embed(
     llmodel_model model, const char **texts, size_t *embedding_size, const char *prefix, int dimensionality,
-    bool do_mean, bool atlas, const char **error
+    size_t *token_count, bool do_mean, bool atlas, const char **error
 ) {
     auto *wrapper = static_cast<LLModelWrapper *>(model);
 
@@ -184,7 +184,7 @@ float *llmodel_embed(
         if (prefix) { prefixStr = prefix; }
 
         embedding = new float[embd_size];
-        wrapper->llModel->embed(textsVec, embedding, prefixStr, dimensionality, do_mean, atlas);
+        wrapper->llModel->embed(textsVec, embedding, prefixStr, dimensionality, token_count, do_mean, atlas);
     } catch (std::exception const &e) {
         llmodel_set_error(error, e.what());
         return nullptr;

gpt4all-backend/llmodel_c.h

@@ -193,6 +193,7 @@ void llmodel_prompt(llmodel_model model, const char *prompt,
  * @param prefix The model-specific prefix representing the embedding task, without the trailing colon. NULL for no
  * prefix.
  * @param dimensionality The embedding dimension, for use with Matryoshka-capable models. Set to -1 to for full-size.
+ * @param token_count Return location for the number of prompt tokens processed, or NULL.
  * @param do_mean True to average multiple embeddings if the text is longer than the model can accept, False to
  * truncate.
  * @param atlas Try to be fully compatible with the Atlas API. Currently, this means texts longer than 8192 tokens with
@@ -202,7 +203,7 @@ void llmodel_prompt(llmodel_model model, const char *prompt,
  * be responsible for lifetime of this memory. NULL if an error occurred.
  */
 float *llmodel_embed(llmodel_model model, const char **texts, size_t *embedding_size, const char *prefix,
-                     int dimensionality, bool do_mean, bool atlas, const char **error);
+                     int dimensionality, size_t *token_count, bool do_mean, bool atlas, const char **error);
 
 /**
  * Frees the memory allocated by the llmodel_embedding function.

gpt4all-backend/llmodel_shared.cpp

@@ -270,25 +270,27 @@ void LLModel::generateResponse(std::function<bool(int32_t, const std::string&)>
 
 void LLModel::embed(
     const std::vector<std::string> &texts, float *embeddings, std::optional<std::string> prefix, int dimensionality,
-    bool doMean, bool atlas
+    size_t *tokenCount, bool doMean, bool atlas
 ) {
     (void)texts;
     (void)embeddings;
     (void)prefix;
     (void)dimensionality;
+    (void)tokenCount;
     (void)doMean;
     (void)atlas;
     throw std::logic_error(std::string(implementation().modelType()) + " does not support embeddings");
 }
 
 void LLModel::embed(
-    const std::vector<std::string> &texts, float *embeddings, bool isRetrieval, int dimensionality, bool doMean,
+    const std::vector<std::string> &texts, float *embeddings, bool isRetrieval, int dimensionality, size_t *tokenCount,
-    bool atlas
+    bool doMean, bool atlas
 ) {
     (void)texts;
     (void)embeddings;
     (void)isRetrieval;
     (void)dimensionality;
+    (void)tokenCount;
     (void)doMean;
     (void)atlas;
     throw std::logic_error(std::string(implementation().modelType()) + " does not support embeddings");

gpt4all-bindings/python/docs/gpt4all_chat.md

@@ -7,7 +7,7 @@ It is optimized to run 7-13B parameter LLMs on the CPU's of any computer running
 ## Running LLMs on CPU
 The GPT4All Chat UI supports models from all newer versions of `llama.cpp` with `GGUF` models including the `Mistral`, `LLaMA2`, `LLaMA`, `OpenLLaMa`, `Falcon`, `MPT`, `Replit`, `Starcoder`, and `Bert` architectures
 
-GPT4All maintains an official list of recommended models located in [models2.json](https://github.com/nomic-ai/gpt4all/blob/main/gpt4all-chat/metadata/models2.json). You can pull request new models to it and if accepted they will show up in the official download dialog.
+GPT4All maintains an official list of recommended models located in [models3.json](https://github.com/nomic-ai/gpt4all/blob/main/gpt4all-chat/metadata/models3.json). You can pull request new models to it and if accepted they will show up in the official download dialog.
 
 #### Sideloading any GGUF model
 If a model is compatible with the gpt4all-backend, you can sideload it into GPT4All Chat by:

gpt4all-bindings/python/docs/gpt4all_faq.md

@@ -61,12 +61,12 @@ or `allowDownload=true` (default), a model is automatically downloaded into `.ca
 unless it already exists.
 
 In case of connection issues or errors during the download, you might want to manually verify the model file's MD5
-checksum by comparing it with the one listed in [models2.json].
+checksum by comparing it with the one listed in [models3.json].
 
 As an alternative to the basic downloader built into the bindings, you can choose to download from the 
 <https://gpt4all.io/> website instead. Scroll down to 'Model Explorer' and pick your preferred model.
 
-[models2.json]: https://github.com/nomic-ai/gpt4all/blob/main/gpt4all-chat/metadata/models2.json
+[models3.json]: https://github.com/nomic-ai/gpt4all/blob/main/gpt4all-chat/metadata/models3.json
 
 #### I need the chat GUI and bindings to behave the same
 
@@ -93,7 +93,7 @@ The chat GUI and bindings are based on the same backend. You can make them behav
 - Next you'll have to compare the templates, adjusting them as necessary, based on how you're using the bindings.
     - Specifically, in Python:
         - With simple `generate()` calls, the input has to be surrounded with system and prompt templates.
-        - When using a chat session, it depends on whether the bindings are allowed to download [models2.json]. If yes,
+        - When using a chat session, it depends on whether the bindings are allowed to download [models3.json]. If yes,
           and in the chat GUI the default templates are used, it'll be handled automatically. If no, use
           `chat_session()` template parameters to customize them.
 

gpt4all-bindings/python/docs/index.md

@@ -38,7 +38,7 @@ The GPT4All software ecosystem is compatible with the following Transformer arch
 - `MPT` (including `Replit`)
 - `GPT-J`
 
-You can find an exhaustive list of supported models on the [website](https://gpt4all.io) or in the [models directory](https://raw.githubusercontent.com/nomic-ai/gpt4all/main/gpt4all-chat/metadata/models2.json)
+You can find an exhaustive list of supported models on the [website](https://gpt4all.io) or in the [models directory](https://raw.githubusercontent.com/nomic-ai/gpt4all/main/gpt4all-chat/metadata/models3.json)
 
 
 GPT4All models are artifacts produced through a process known as neural network quantization.

gpt4all-bindings/python/gpt4all/_pyllmodel.py

@@ -9,13 +9,15 @@ import sys
 import threading
 from enum import Enum
 from queue import Queue
-from typing import Any, Callable, Iterable, overload
+from typing import Any, Callable, Generic, Iterable, TypedDict, TypeVar, overload
 
 if sys.version_info >= (3, 9):
     import importlib.resources as importlib_resources
 else:
     import importlib_resources
 
+EmbeddingsType = TypeVar('EmbeddingsType', bound='list[Any]')
+
 
 # TODO: provide a config file to make this more robust
 MODEL_LIB_PATH = importlib_resources.files("gpt4all") / "llmodel_DO_NOT_MODIFY" / "build"
@@ -25,7 +27,7 @@ def load_llmodel_library():
     ext = {"Darwin": "dylib", "Linux": "so", "Windows": "dll"}[platform.system()]
 
     try:
-        # Linux, Windows, MinGW
+        # macOS, Linux, MinGW
         lib = ctypes.CDLL(str(MODEL_LIB_PATH / f"libllmodel.{ext}"))
     except FileNotFoundError:
         if ext != 'dll':
@@ -108,6 +110,7 @@ llmodel.llmodel_embed.argtypes = [
     ctypes.POINTER(ctypes.c_size_t),
     ctypes.c_char_p,
     ctypes.c_int,
+    ctypes.POINTER(ctypes.c_size_t),
     ctypes.c_bool,
     ctypes.c_bool,
     ctypes.POINTER(ctypes.c_char_p),
@@ -157,6 +160,11 @@ class Sentinel(Enum):
     TERMINATING_SYMBOL = 0
 
 
+class EmbedResult(Generic[EmbeddingsType], TypedDict):
+    embeddings: EmbeddingsType
+    n_prompt_tokens: int
+
+
 class LLModel:
     """
     Base class and universal wrapper for GPT4All language models
@@ -188,7 +196,7 @@ class LLModel:
             raise RuntimeError(f"Unable to instantiate model: {'null' if s is None else s.decode()}")
         self.model = model
 
-    def __del__(self):
+    def __del__(self, llmodel=llmodel):
         if hasattr(self, 'model'):
             llmodel.llmodel_model_destroy(self.model)
 
@@ -291,20 +299,20 @@ class LLModel:
 
     @overload
     def generate_embeddings(
-        self, text: str, prefix: str, dimensionality: int, do_mean: bool, atlas: bool,
+        self, text: str, prefix: str, dimensionality: int, do_mean: bool, count_tokens: bool, atlas: bool,
-    ) -> list[float]: ...
+    ) -> EmbedResult[list[float]]: ...
     @overload
     def generate_embeddings(
         self, text: list[str], prefix: str | None, dimensionality: int, do_mean: bool, atlas: bool,
-    ) -> list[list[float]]: ...
+    ) -> EmbedResult[list[list[float]]]: ...
     @overload
     def generate_embeddings(
         self, text: str | list[str], prefix: str | None, dimensionality: int, do_mean: bool, atlas: bool,
-    ) -> Any: ...
+    ) -> EmbedResult[list[Any]]: ...
 
     def generate_embeddings(
         self, text: str | list[str], prefix: str | None, dimensionality: int, do_mean: bool, atlas: bool,
-    ) -> Any:
+    ) -> EmbedResult[list[Any]]:
         if not text:
             raise ValueError("text must not be None or empty")
 
@@ -313,6 +321,7 @@ class LLModel:
 
         # prepare input
         embedding_size = ctypes.c_size_t()
+        token_count = ctypes.c_size_t()
         error = ctypes.c_char_p()
         c_prefix = ctypes.c_char_p() if prefix is None else prefix.encode()
         c_texts = (ctypes.c_char_p * (len(text) + 1))()
@@ -321,8 +330,8 @@ class LLModel:
 
         # generate the embeddings
         embedding_ptr = llmodel.llmodel_embed(
-            self.model, c_texts, ctypes.byref(embedding_size), c_prefix, dimensionality, do_mean, atlas,
+            self.model, c_texts, ctypes.byref(embedding_size), c_prefix, dimensionality, ctypes.byref(token_count),
-            ctypes.byref(error),
+            do_mean, atlas, ctypes.byref(error),
         )
 
         if not embedding_ptr:
@@ -337,7 +346,8 @@ class LLModel:
         ]
         llmodel.llmodel_free_embedding(embedding_ptr)
 
-        return embedding_array[0] if single_text else embedding_array
+        embeddings = embedding_array[0] if single_text else embedding_array
+        return {'embeddings': embeddings, 'n_prompt_tokens': token_count.value}
 
     def prompt_model(
         self,

gpt4all-bindings/python/gpt4all/gpt4all.py

@@ -18,6 +18,7 @@ from tqdm import tqdm
 from urllib3.exceptions import IncompleteRead, ProtocolError
 
 from . import _pyllmodel
+from ._pyllmodel import EmbedResult as EmbedResult
 
 if TYPE_CHECKING:
     from typing import TypeAlias
@@ -49,35 +50,69 @@ class Embed4All:
             model_name = 'all-MiniLM-L6-v2.gguf2.f16.gguf'
         self.gpt4all = GPT4All(model_name, n_threads=n_threads, **kwargs)
 
+    # return_dict=False
     @overload
     def embed(
-        self, text: str, prefix: str | None = ..., dimensionality: int | None = ..., long_text_mode: str = ...,
+        self, text: str, *, prefix: str | None = ..., dimensionality: int | None = ..., long_text_mode: str = ...,
-        atlas: bool = ...,
+        return_dict: Literal[False] = ..., atlas: bool = ...,
     ) -> list[float]: ...
     @overload
     def embed(
-        self, text: list[str], prefix: str | None = ..., dimensionality: int | None = ..., long_text_mode: str = ...,
+        self, text: list[str], *, prefix: str | None = ..., dimensionality: int | None = ..., long_text_mode: str = ...,
-        atlas: bool = ...,
+        return_dict: Literal[False] = ..., atlas: bool = ...,
     ) -> list[list[float]]: ...
+    @overload
+    def embed(
+        self, text: str | list[str], *, prefix: str | None = ..., dimensionality: int | None = ...,
+        long_text_mode: str = ..., return_dict: Literal[False] = ..., atlas: bool = ...,
+    ) -> list[Any]: ...
+
+    # return_dict=True
+    @overload
+    def embed(
+        self, text: str, *, prefix: str | None = ..., dimensionality: int | None = ..., long_text_mode: str = ...,
+        return_dict: Literal[True], atlas: bool = ...,
+    ) -> EmbedResult[list[float]]: ...
+    @overload
+    def embed(
+        self, text: list[str], *, prefix: str | None = ..., dimensionality: int | None = ..., long_text_mode: str = ...,
+        return_dict: Literal[True], atlas: bool = ...,
+    ) -> EmbedResult[list[list[float]]]: ...
+    @overload
+    def embed(
+        self, text: str | list[str], *, prefix: str | None = ..., dimensionality: int | None = ...,
+        long_text_mode: str = ..., return_dict: Literal[True], atlas: bool = ...,
+    ) -> EmbedResult[list[Any]]: ...
+
+    # return type unknown
+    @overload
+    def embed(
+        self, text: str | list[str], *, prefix: str | None = ..., dimensionality: int | None = ...,
+        long_text_mode: str = ..., return_dict: bool = ..., atlas: bool = ...,
+    ) -> Any: ...
 
     def embed(
-        self, text: str | list[str], prefix: str | None = None, dimensionality: int | None = None,
+        self, text: str | list[str], *, prefix: str | None = None, dimensionality: int | None = None,
-        long_text_mode: str = "mean", atlas: bool = False,
+        long_text_mode: str = "mean", return_dict: bool = False, atlas: bool = False,
-    ) -> list[Any]:
+    ) -> Any:
         """
         Generate one or more embeddings.
 
         Args:
             text: A text or list of texts to generate embeddings for.
             prefix: The model-specific prefix representing the embedding task, without the trailing colon. For Nomic
-                Embed this can be `search_query`, `search_document`, `classification`, or `clustering`.
+                Embed, this can be `search_query`, `search_document`, `classification`, or `clustering`. Defaults to
+                `search_document` or equivalent if known; otherwise, you must explicitly pass a prefix or an empty
+                string if none applies.
             dimensionality: The embedding dimension, for use with Matryoshka-capable models. Defaults to full-size.
             long_text_mode: How to handle texts longer than the model can accept. One of `mean` or `truncate`.
+            return_dict: Return the result as a dict that includes the number of prompt tokens processed.
             atlas: Try to be fully compatible with the Atlas API. Currently, this means texts longer than 8192 tokens
                 with long_text_mode="mean" will raise an error. Disabled by default.
 
         Returns:
-            An embedding or list of embeddings of your text(s).
+            With return_dict=False, an embedding or list of embeddings of your text(s).
+            With return_dict=True, a dict with keys 'embeddings' and 'n_prompt_tokens'.
         """
         if dimensionality is None:
             dimensionality = -1
@@ -93,7 +128,8 @@ class Embed4All:
             do_mean = {"mean": True, "truncate": False}[long_text_mode]
         except KeyError:
             raise ValueError(f"Long text mode must be one of 'mean' or 'truncate', got {long_text_mode!r}")
-        return self.gpt4all.model.generate_embeddings(text, prefix, dimensionality, do_mean, atlas)
+        result = self.gpt4all.model.generate_embeddings(text, prefix, dimensionality, do_mean, atlas)
+        return result if return_dict else result['embeddings']
 
 
 class GPT4All:
@@ -157,12 +193,12 @@ class GPT4All:
     @staticmethod
     def list_models() -> list[ConfigType]:
         """
-        Fetch model list from https://gpt4all.io/models/models2.json.
+        Fetch model list from https://gpt4all.io/models/models3.json.
 
         Returns:
             Model list in JSON format.
         """
-        resp = requests.get("https://gpt4all.io/models/models2.json")
+        resp = requests.get("https://gpt4all.io/models/models3.json")
         if resp.status_code != 200:
             raise ValueError(f'Request failed: HTTP {resp.status_code} {resp.reason}')
         return resp.json()