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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>
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@ -476,7 +476,9 @@ const std::vector<LLModel::Token> &LLamaModel::endTokens() const
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bool LLamaModel::shouldAddBOS() const
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{
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int add_bos = llama_add_bos_token(d_ptr->model);
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return add_bos != -1 ? bool(add_bos) : llama_vocab_type(d_ptr->model) == LLAMA_VOCAB_TYPE_SPM;
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if (add_bos != -1) { return add_bos; }
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auto vocab_type = llama_vocab_type(d_ptr->model);
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return vocab_type == LLAMA_VOCAB_TYPE_SPM || vocab_type == LLAMA_VOCAB_TYPE_WPM;
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}
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int32_t LLamaModel::maxContextLength(std::string const &modelPath) const
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@ -638,6 +640,7 @@ static const EmbModelGroup EMBEDDING_MODEL_SPECS[] {
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{LLM_EMBEDDER_SPEC, {"llm-embedder"}},
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{BGE_SPEC, {"bge-small-en", "bge-base-en", "bge-large-en",
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"bge-small-en-v1.5", "bge-base-en-v1.5", "bge-large-en-v1.5"}},
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// NOTE: E5 Mistral is not yet implemented in llama.cpp, so it's not in EMBEDDING_ARCHES
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{E5_SPEC, {"e5-small", "e5-base", "e5-large",
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"e5-small-unsupervised", "e5-base-unsupervised", "e5-large-unsupervised",
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"e5-small-v2", "e5-base-v2", "e5-large-v2"}},
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@ -658,20 +661,20 @@ static const EmbModelSpec *getEmbedSpec(const std::string &modelName) {
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}
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void LLamaModel::embed(
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const std::vector<std::string> &texts, float *embeddings, bool isRetrieval, int dimensionality, bool doMean,
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bool atlas
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const std::vector<std::string> &texts, float *embeddings, bool isRetrieval, int dimensionality, size_t *tokenCount,
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bool doMean, bool atlas
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) {
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const EmbModelSpec *spec;
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std::optional<std::string> prefix;
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if (d_ptr->model && (spec = getEmbedSpec(llama_model_name(d_ptr->model))))
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prefix = isRetrieval ? spec->queryPrefix : spec->docPrefix;
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embed(texts, embeddings, prefix, dimensionality, doMean, atlas);
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embed(texts, embeddings, prefix, dimensionality, tokenCount, doMean, atlas);
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}
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void LLamaModel::embed(
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const std::vector<std::string> &texts, float *embeddings, std::optional<std::string> prefix, int dimensionality,
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bool doMean, bool atlas
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size_t *tokenCount, bool doMean, bool atlas
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) {
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if (!d_ptr->model)
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throw std::logic_error("no model is loaded");
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@ -698,12 +701,9 @@ void LLamaModel::embed(
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}
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if (!prefix) {
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if (spec) {
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prefix = spec->docPrefix;
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} else {
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std::cerr << __func__ << ": warning: assuming no prefix\n";
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prefix = "";
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}
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if (!spec)
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throw std::invalid_argument("unknown model "s + modelName + ", specify a prefix if applicable or an empty string");
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prefix = spec->docPrefix;
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} else if (spec && prefix != spec->docPrefix && prefix != spec->queryPrefix &&
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std::find(spec->otherPrefixes.begin(), spec->otherPrefixes.end(), *prefix) == spec->otherPrefixes.end())
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{
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@ -712,7 +712,7 @@ void LLamaModel::embed(
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throw std::invalid_argument(ss.str());
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}
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embedInternal(texts, embeddings, *prefix, dimensionality, doMean, atlas, spec);
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embedInternal(texts, embeddings, *prefix, dimensionality, tokenCount, doMean, atlas, spec);
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}
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// MD5 hash of "nomic empty"
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@ -730,7 +730,7 @@ double getL2NormScale(T *start, T *end) {
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void LLamaModel::embedInternal(
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const std::vector<std::string> &texts, float *embeddings, std::string prefix, int dimensionality,
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bool doMean, bool atlas, const EmbModelSpec *spec
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size_t *tokenCount, bool doMean, bool atlas, const EmbModelSpec *spec
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) {
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typedef std::vector<LLModel::Token> TokenString;
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static constexpr int32_t atlasMaxLength = 8192;
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@ -796,6 +796,7 @@ void LLamaModel::embedInternal(
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// split into max_len-sized chunks
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struct split_batch { unsigned idx; TokenString batch; };
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std::vector<split_batch> batches;
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size_t totalTokens = 0;
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for (unsigned i = 0; i < inputs.size(); i++) {
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auto &input = inputs[i];
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for (auto it = input.begin(); it < input.end(); it += max_len) {
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@ -805,6 +806,7 @@ void LLamaModel::embedInternal(
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auto &batch = batches.back().batch;
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batch = prefixTokens;
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batch.insert(batch.end(), it, end);
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totalTokens += end - it;
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batch.push_back(eos_token);
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if (!doMean) { break; /* limit text to one chunk */ }
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}
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@ -889,6 +891,8 @@ void LLamaModel::embedInternal(
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std::transform(embd, embd_end, embeddings, product(scale));
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embeddings += dimensionality;
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}
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if (tokenCount) { *tokenCount = totalTokens; }
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}
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#if defined(_WIN32)
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@ -39,10 +39,10 @@ public:
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size_t embeddingSize() const override;
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// user-specified prefix
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void embed(const std::vector<std::string> &texts, float *embeddings, std::optional<std::string> prefix,
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int dimensionality = -1, bool doMean = true, bool atlas = false) override;
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int dimensionality = -1, size_t *tokenCount = nullptr, bool doMean = true, bool atlas = false) override;
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// automatic prefix
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void embed(const std::vector<std::string> &texts, float *embeddings, bool isRetrieval, int dimensionality = -1,
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bool doMean = true, bool atlas = false) override;
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size_t *tokenCount = nullptr, bool doMean = true, bool atlas = false) override;
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private:
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std::unique_ptr<LLamaPrivate> d_ptr;
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@ -61,7 +61,7 @@ protected:
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int32_t layerCount(std::string const &modelPath) const override;
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void embedInternal(const std::vector<std::string> &texts, float *embeddings, std::string prefix, int dimensionality,
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bool doMean, bool atlas, const EmbModelSpec *spec);
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size_t *tokenCount, bool doMean, bool atlas, const EmbModelSpec *spec);
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};
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#endif // LLAMAMODEL_H
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@ -110,10 +110,10 @@ public:
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}
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// user-specified prefix
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virtual void embed(const std::vector<std::string> &texts, float *embeddings, std::optional<std::string> prefix,
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int dimensionality = -1, bool doMean = true, bool atlas = false);
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int dimensionality = -1, size_t *tokenCount = nullptr, bool doMean = true, bool atlas = false);
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// automatic prefix
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virtual void embed(const std::vector<std::string> &texts, float *embeddings, bool isRetrieval,
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int dimensionality = -1, bool doMean = true, bool atlas = false);
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int dimensionality = -1, size_t *tokenCount = nullptr, bool doMean = true, bool atlas = false);
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virtual void setThreadCount(int32_t n_threads) { (void)n_threads; }
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virtual int32_t threadCount() const { return 1; }
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@ -158,7 +158,7 @@ void llmodel_prompt(llmodel_model model, const char *prompt,
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float *llmodel_embed(
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llmodel_model model, const char **texts, size_t *embedding_size, const char *prefix, int dimensionality,
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bool do_mean, bool atlas, const char **error
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size_t *token_count, bool do_mean, bool atlas, const char **error
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) {
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auto *wrapper = static_cast<LLModelWrapper *>(model);
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@ -184,7 +184,7 @@ float *llmodel_embed(
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if (prefix) { prefixStr = prefix; }
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embedding = new float[embd_size];
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wrapper->llModel->embed(textsVec, embedding, prefixStr, dimensionality, do_mean, atlas);
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wrapper->llModel->embed(textsVec, embedding, prefixStr, dimensionality, token_count, do_mean, atlas);
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} catch (std::exception const &e) {
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llmodel_set_error(error, e.what());
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return nullptr;
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@ -193,6 +193,7 @@ void llmodel_prompt(llmodel_model model, const char *prompt,
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* @param prefix The model-specific prefix representing the embedding task, without the trailing colon. NULL for no
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* prefix.
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* @param dimensionality The embedding dimension, for use with Matryoshka-capable models. Set to -1 to for full-size.
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* @param token_count Return location for the number of prompt tokens processed, or NULL.
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* @param do_mean True to average multiple embeddings if the text is longer than the model can accept, False to
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* truncate.
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* @param atlas Try to be fully compatible with the Atlas API. Currently, this means texts longer than 8192 tokens with
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@ -202,7 +203,7 @@ void llmodel_prompt(llmodel_model model, const char *prompt,
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* be responsible for lifetime of this memory. NULL if an error occurred.
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*/
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float *llmodel_embed(llmodel_model model, const char **texts, size_t *embedding_size, const char *prefix,
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int dimensionality, bool do_mean, bool atlas, const char **error);
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int dimensionality, size_t *token_count, bool do_mean, bool atlas, const char **error);
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/**
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* Frees the memory allocated by the llmodel_embedding function.
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@ -270,25 +270,27 @@ void LLModel::generateResponse(std::function<bool(int32_t, const std::string&)>
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void LLModel::embed(
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const std::vector<std::string> &texts, float *embeddings, std::optional<std::string> prefix, int dimensionality,
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bool doMean, bool atlas
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size_t *tokenCount, bool doMean, bool atlas
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) {
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(void)texts;
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(void)embeddings;
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(void)prefix;
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(void)dimensionality;
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(void)tokenCount;
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(void)doMean;
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(void)atlas;
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throw std::logic_error(std::string(implementation().modelType()) + " does not support embeddings");
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}
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void LLModel::embed(
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const std::vector<std::string> &texts, float *embeddings, bool isRetrieval, int dimensionality, bool doMean,
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bool atlas
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const std::vector<std::string> &texts, float *embeddings, bool isRetrieval, int dimensionality, size_t *tokenCount,
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bool doMean, bool atlas
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) {
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(void)texts;
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(void)embeddings;
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(void)isRetrieval;
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(void)dimensionality;
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(void)tokenCount;
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(void)doMean;
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(void)atlas;
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throw std::logic_error(std::string(implementation().modelType()) + " does not support embeddings");
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@ -7,7 +7,7 @@ It is optimized to run 7-13B parameter LLMs on the CPU's of any computer running
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## Running LLMs on CPU
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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
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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.
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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.
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#### Sideloading any GGUF model
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If a model is compatible with the gpt4all-backend, you can sideload it into GPT4All Chat by:
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@ -61,12 +61,12 @@ or `allowDownload=true` (default), a model is automatically downloaded into `.ca
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unless it already exists.
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In case of connection issues or errors during the download, you might want to manually verify the model file's MD5
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checksum by comparing it with the one listed in [models2.json].
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checksum by comparing it with the one listed in [models3.json].
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As an alternative to the basic downloader built into the bindings, you can choose to download from the
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<https://gpt4all.io/> website instead. Scroll down to 'Model Explorer' and pick your preferred model.
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[models2.json]: https://github.com/nomic-ai/gpt4all/blob/main/gpt4all-chat/metadata/models2.json
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[models3.json]: https://github.com/nomic-ai/gpt4all/blob/main/gpt4all-chat/metadata/models3.json
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#### I need the chat GUI and bindings to behave the same
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@ -93,7 +93,7 @@ The chat GUI and bindings are based on the same backend. You can make them behav
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- Next you'll have to compare the templates, adjusting them as necessary, based on how you're using the bindings.
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- Specifically, in Python:
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- With simple `generate()` calls, the input has to be surrounded with system and prompt templates.
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- When using a chat session, it depends on whether the bindings are allowed to download [models2.json]. If yes,
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- When using a chat session, it depends on whether the bindings are allowed to download [models3.json]. If yes,
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and in the chat GUI the default templates are used, it'll be handled automatically. If no, use
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`chat_session()` template parameters to customize them.
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@ -38,7 +38,7 @@ The GPT4All software ecosystem is compatible with the following Transformer arch
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- `MPT` (including `Replit`)
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- `GPT-J`
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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)
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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)
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GPT4All models are artifacts produced through a process known as neural network quantization.
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@ -9,13 +9,15 @@ import sys
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import threading
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from enum import Enum
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from queue import Queue
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from typing import Any, Callable, Iterable, overload
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from typing import Any, Callable, Generic, Iterable, TypedDict, TypeVar, overload
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if sys.version_info >= (3, 9):
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import importlib.resources as importlib_resources
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else:
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import importlib_resources
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EmbeddingsType = TypeVar('EmbeddingsType', bound='list[Any]')
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# TODO: provide a config file to make this more robust
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MODEL_LIB_PATH = importlib_resources.files("gpt4all") / "llmodel_DO_NOT_MODIFY" / "build"
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@ -25,7 +27,7 @@ def load_llmodel_library():
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ext = {"Darwin": "dylib", "Linux": "so", "Windows": "dll"}[platform.system()]
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try:
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# Linux, Windows, MinGW
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# macOS, Linux, MinGW
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lib = ctypes.CDLL(str(MODEL_LIB_PATH / f"libllmodel.{ext}"))
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except FileNotFoundError:
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if ext != 'dll':
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@ -108,6 +110,7 @@ llmodel.llmodel_embed.argtypes = [
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ctypes.POINTER(ctypes.c_size_t),
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ctypes.c_char_p,
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ctypes.c_int,
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ctypes.POINTER(ctypes.c_size_t),
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ctypes.c_bool,
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ctypes.c_bool,
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ctypes.POINTER(ctypes.c_char_p),
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@ -157,6 +160,11 @@ class Sentinel(Enum):
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TERMINATING_SYMBOL = 0
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class EmbedResult(Generic[EmbeddingsType], TypedDict):
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embeddings: EmbeddingsType
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n_prompt_tokens: int
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class LLModel:
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"""
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Base class and universal wrapper for GPT4All language models
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@ -188,7 +196,7 @@ class LLModel:
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raise RuntimeError(f"Unable to instantiate model: {'null' if s is None else s.decode()}")
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self.model = model
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def __del__(self):
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def __del__(self, llmodel=llmodel):
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if hasattr(self, 'model'):
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llmodel.llmodel_model_destroy(self.model)
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@ -291,20 +299,20 @@ class LLModel:
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@overload
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def generate_embeddings(
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self, text: str, prefix: str, dimensionality: int, do_mean: bool, atlas: bool,
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) -> list[float]: ...
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self, text: str, prefix: str, dimensionality: int, do_mean: bool, count_tokens: bool, atlas: bool,
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) -> EmbedResult[list[float]]: ...
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@overload
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def generate_embeddings(
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self, text: list[str], prefix: str | None, dimensionality: int, do_mean: bool, atlas: bool,
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) -> list[list[float]]: ...
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) -> EmbedResult[list[list[float]]]: ...
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@overload
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def generate_embeddings(
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self, text: str | list[str], prefix: str | None, dimensionality: int, do_mean: bool, atlas: bool,
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) -> Any: ...
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) -> EmbedResult[list[Any]]: ...
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def generate_embeddings(
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self, text: str | list[str], prefix: str | None, dimensionality: int, do_mean: bool, atlas: bool,
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) -> Any:
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) -> EmbedResult[list[Any]]:
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if not text:
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raise ValueError("text must not be None or empty")
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@ -313,6 +321,7 @@ class LLModel:
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# prepare input
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embedding_size = ctypes.c_size_t()
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token_count = ctypes.c_size_t()
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error = ctypes.c_char_p()
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c_prefix = ctypes.c_char_p() if prefix is None else prefix.encode()
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c_texts = (ctypes.c_char_p * (len(text) + 1))()
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@ -321,8 +330,8 @@ class LLModel:
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# generate the embeddings
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embedding_ptr = llmodel.llmodel_embed(
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self.model, c_texts, ctypes.byref(embedding_size), c_prefix, dimensionality, do_mean, atlas,
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ctypes.byref(error),
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self.model, c_texts, ctypes.byref(embedding_size), c_prefix, dimensionality, ctypes.byref(token_count),
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do_mean, atlas, ctypes.byref(error),
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)
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if not embedding_ptr:
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@ -337,7 +346,8 @@ class LLModel:
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]
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llmodel.llmodel_free_embedding(embedding_ptr)
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return embedding_array[0] if single_text else embedding_array
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embeddings = embedding_array[0] if single_text else embedding_array
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return {'embeddings': embeddings, 'n_prompt_tokens': token_count.value}
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def prompt_model(
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self,
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|
@ -18,6 +18,7 @@ from tqdm import tqdm
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from urllib3.exceptions import IncompleteRead, ProtocolError
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from . import _pyllmodel
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from ._pyllmodel import EmbedResult as EmbedResult
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if TYPE_CHECKING:
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from typing import TypeAlias
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@ -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 = ...,
|
||||
atlas: bool = ...,
|
||||
self, text: str, *, prefix: str | None = ..., dimensionality: int | None = ..., long_text_mode: str = ...,
|
||||
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 = ...,
|
||||
atlas: bool = ...,
|
||||
self, text: list[str], *, prefix: str | None = ..., dimensionality: int | None = ..., long_text_mode: str = ...,
|
||||
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,
|
||||
long_text_mode: str = "mean", atlas: bool = False,
|
||||
) -> list[Any]:
|
||||
self, text: str | list[str], *, prefix: str | None = None, dimensionality: int | None = None,
|
||||
long_text_mode: str = "mean", return_dict: bool = False, atlas: bool = False,
|
||||
) -> 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()
|
||||
|
Loading…
Reference in New Issue
Block a user