Unified framework for building enterprise RAG pipelines with small, specialized models
This report summarizes scanner evidence for llmware-ai/llmware, a Python project with 14,820 GitHub stars. SynthScan examined 83,207 lines of code across 323 source files, recording 1005 pattern matches distributed across 19 syntactic categories. The legacy adjusted pattern signal is 20.2 (Elevated pattern signal); it is not an authorship estimate.
Use the findings to ask whether maintainers can explain intent, verify dependencies, simplify unnecessary scaffolding, and focus review effort. Risk Payload v1 adds ownership, supply-chain, over-engineering, and review-effort vectors when available. These are prompts for human verification, not probabilities, security findings, or measured maintenance costs.
Use each vector to guide manual verification of the underlying findings.
Can maintainers explain intent, assumptions, and trade-offs? 819 finding(s).
Are imports declared and package/API names verified? 35 finding(s).
Are wrappers, guards, and repeated structures necessary? 502 finding(s).
Which changes need focused human review? 1005 finding(s).
This chart tracks the legacy adjusted pattern score across scan runs. Changes can reflect source changes, scan coverage, or detector/scoring versions; they do not measure AI adoption or prove that a team refactored code. Compare runs only when their scoring versions are compatible. The dashed line tracks the raw pattern-hit count.
Groups findings by configured severity weight. Severity is not a confidence estimate, security rating, or proof that a package is missing. Verify imports against the project's manifests and inspect each finding in its local design context.
This horizontal bar chart shows where configured pattern scores accumulate across directories. Treat the bars as locations to inspect, not as evidence of authorship or code quality. Use the file-level findings to decide whether a directory needs a focused ownership, dependency, complexity, or review check.
The scanner identified 1005 distinct pattern matches across 19 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a detector match to inspect in context. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.
Reading the findings table: The Severity column indicates the configured finding weight (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — legacy comment-context matches receive a ×1.5 score weight. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier under the legacy scoring model.
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | README.md | 0 | this 'hello world' example demonstrates how to get started using local bling models with provided context, using both py | STRING |
| HIGH | docs/components/rag_optimized_models.md | 0 | this 'hello world' example demonstrates how to get started using local bling models with provided context, using both py | STRING |
| HIGH | docs/getting_started/overview.md | 0 | this 'hello world' example demonstrates how to get started using local bling models with provided context, using both py | STRING |
| HIGH | tests/embeddings/test_install_embeddings.py | 0 | note: this setup_library method is provided to enable a self-contained example to create a test library | STRING |
| HIGH | solutions/embeddings/using_milvus_lite.py | 0 | note: this setup_library method is provided to enable a self-contained example to create a test library | STRING |
| HIGH | solutions/rag/example-2-build_embeddings.py | 0 | note: this setup_library method is provided to enable a self-contained example to create a test library | STRING |
| HIGH | docs/examples/embedding.md | 0 | note: this setup_library method is provided to enable a self-contained example to create a test library | STRING |
| HIGH | tests/models/test_whisper_cpp_model_load.py | 0 | execute a basic inference on voice-to-text model passing a file_path string | STRING |
| HIGH | solutions/gguf/using-whisper-cpp-sample-files.py | 0 | execute a basic inference on voice-to-text model passing a file_path string | STRING |
| HIGH | …slim_agents/agents-7-using-whisper-cpp-sample-files.py | 0 | execute a basic inference on voice-to-text model passing a file_path string | STRING |
| HIGH | docs/components/whisper_cpp.md | 0 | execute a basic inference on voice-to-text model passing a file_path string | STRING |
| HIGH | solutions/ui/integrating_pywebio_ui.py | 0 | simple inference loop that loads a model and runs through a series of test questions. | STRING |
| HIGH | solutions/gguf/bling_fast_start.py | 0 | simple inference loop that loads a model and runs through a series of test questions. | STRING |
| HIGH | solutions/models/bling_fast_start.py | 0 | simple inference loop that loads a model and runs through a series of test questions. | STRING |
| HIGH | …_agents/agents-2-llmware_model_sampler_bling_dragon.py | 0 | simple inference loop that loads a model and runs through a series of test questions. | STRING |
| HIGH | docs/examples/models.md | 0 | simple inference loop that loads a model and runs through a series of test questions. | STRING |
| HIGH | solutions/embeddings/using_milvus_lite.py | 0 | this method is the core example of installing an embedding on a library. -- two inputs - (1) a pre-created library objec | STRING |
| HIGH | solutions/rag/example-2-build_embeddings.py | 0 | this method is the core example of installing an embedding on a library. -- two inputs - (1) a pre-created library objec | STRING |
| HIGH | docs/examples/embedding.md | 0 | this method is the core example of installing an embedding on a library. -- two inputs - (1) a pre-created library objec | STRING |
| HIGH | …t_start_marimo_examples/llmware_fast_start_example2.py | 0 | this method is the core example of installing an embedding on a library. -- two inputs - (1) a pre-created library objec | STRING |
| HIGH | …lutions/embeddings/using_semantic_reranker_with_rag.py | 0 | executes a rag process in memory using semantic reranker model and bling-phi-3-gguf to answer the question. | STRING |
| HIGH | solutions/onnxruntime/using_onnx_reranker_models.py | 0 | executes a rag process in memory using semantic reranker model and bling-phi-3-gguf to answer the question. | STRING |
| HIGH | solutions/openvino/using_openvino_reranker_model.py | 0 | executes a rag process in memory using semantic reranker model and bling-phi-3-gguf to answer the question. | STRING |
| HIGH | …m_agents/agents-10-using_semantic_reranker_with_rag.py | 0 | executes a rag process in memory using semantic reranker model and bling-phi-3-gguf to answer the question. | STRING |
| HIGH | solutions/onnxruntime/using_local_foundry_models.py | 0 | this example shows how to use windows local foundry models. pre-reqs: 1. install local foundry, e.g., `winget install mi | STRING |
| HIGH | solutions/gguf/using_local_foundry_models.py | 0 | this example shows how to use windows local foundry models. pre-reqs: 1. install local foundry, e.g., `winget install mi | STRING |
| HIGH | solutions/openvino/using_local_foundry_models.py | 0 | this example shows how to use windows local foundry models. pre-reqs: 1. install local foundry, e.g., `winget install mi | STRING |
| HIGH | solutions/models/using_local_foundry_models.py | 0 | this example shows how to use windows local foundry models. pre-reqs: 1. install local foundry, e.g., `winget install mi | STRING |
| HIGH | solutions/gguf/using-whisper-cpp-sample-files.py | 0 | this example shows how to use llmware provided sample files for testing with whispercpp, integrated as of llmware 0.2.11 | STRING |
| HIGH | …slim_agents/agents-7-using-whisper-cpp-sample-files.py | 0 | this example shows how to use llmware provided sample files for testing with whispercpp, integrated as of llmware 0.2.11 | STRING |
| HIGH | docs/components/whisper_cpp.md | 0 | this example shows how to use llmware provided sample files for testing with whispercpp, integrated as of llmware 0.2.11 | STRING |
| HIGH | solutions/gguf/using-phi-3-function-calls.py | 0 | this example shows how to use 7 different slim function calling models fine-tuned on top of phi-3: -- extraction - slim- | STRING |
| HIGH | solutions/models/using-phi-3-function-calls.py | 0 | this example shows how to use 7 different slim function calling models fine-tuned on top of phi-3: -- extraction - slim- | STRING |
| HIGH | …ons/slim_agents/agents-8-using-phi-3-function-calls.py | 0 | this example shows how to use 7 different slim function calling models fine-tuned on top of phi-3: -- extraction - slim- | STRING |
| HIGH | solutions/gguf/using_function_calls.py | 0 | discover a list of slim tools in the model catalog. -- slims are available in both traditional pytorch and quantized ggu | STRING |
| HIGH | solutions/models/using_function_calls.py | 0 | discover a list of slim tools in the model catalog. -- slims are available in both traditional pytorch and quantized ggu | STRING |
| HIGH | solutions/rag/example-7-function-calls.py | 0 | discover a list of slim tools in the model catalog. -- slims are available in both traditional pytorch and quantized ggu | STRING |
| HIGH | docs/components/slim_models.md | 0 | discover a list of slim tools in the model catalog. -- slims are available in both traditional pytorch and quantized ggu | STRING |
| HIGH | solutions/gguf/using_function_calls.py | 0 | slim models can be identified in the modelcatalog like any llmware model. instead of using inference method, slim models | STRING |
| HIGH | solutions/models/using_function_calls.py | 0 | slim models can be identified in the modelcatalog like any llmware model. instead of using inference method, slim models | STRING |
| HIGH | solutions/rag/example-7-function-calls.py | 0 | slim models can be identified in the modelcatalog like any llmware model. instead of using inference method, slim models | STRING |
| HIGH | docs/components/slim_models.md | 0 | slim models can be identified in the modelcatalog like any llmware model. instead of using inference method, slim models | STRING |
| HIGH | solutions/gguf/using_function_calls.py | 0 | this example shows two key elements of function calling slim models - 1. using logit information to indicate confidence | STRING |
| HIGH | solutions/models/using_function_calls.py | 0 | this example shows two key elements of function calling slim models - 1. using logit information to indicate confidence | STRING |
| HIGH | solutions/rag/example-7-function-calls.py | 0 | this example shows two key elements of function calling slim models - 1. using logit information to indicate confidence | STRING |
| HIGH | docs/components/slim_models.md | 0 | this example shows two key elements of function calling slim models - 1. using logit information to indicate confidence | STRING |
| HIGH | solutions/gguf/using_slim_extract.py | 0 | this example illustrates how to use the slim-extract model to extract custom keys from selected text. we have included a | STRING |
| HIGH | solutions/models/using_slim_extract.py | 0 | this example illustrates how to use the slim-extract model to extract custom keys from selected text. we have included a | STRING |
| HIGH | solutions/slim_agents/agents-3-using_slim_extract.py | 0 | this example illustrates how to use the slim-extract model to extract custom keys from selected text. we have included a | STRING |
| HIGH | solutions/gguf/bling_fast_start.py | 0 | this example demonstrates prompting local bling models with provided context - easy to select among different bling mode | STRING |
| HIGH | solutions/models/bling_fast_start.py | 0 | this example demonstrates prompting local bling models with provided context - easy to select among different bling mode | STRING |
| HIGH | docs/examples/models.md | 0 | this example demonstrates prompting local bling models with provided context - easy to select among different bling mode | STRING |
| HIGH | solutions/gguf/gguf_streaming.py | 0 | this example illustrates how to use the stream method for gguf models for fast streaming of inference, especially for re | STRING |
| HIGH | solutions/slim_agents/agents-11-gguf_streaming.py | 0 | this example illustrates how to use the stream method for gguf models for fast streaming of inference, especially for re | STRING |
| HIGH | docs/components/gguf.md | 0 | this example illustrates how to use the stream method for gguf models for fast streaming of inference, especially for re | STRING |
| HIGH | solutions/models/document_summarizer.py | 0 | this example shows a packaged 'document_summarizer' prompt using the slim-summary-tool. it shows a variety of techniques | STRING |
| HIGH | solutions/slim_agents/agents-9-document_summarizer.py | 0 | this example shows a packaged 'document_summarizer' prompt using the slim-summary-tool. it shows a variety of techniques | STRING |
| HIGH | docs/examples/prompts.md | 0 | this example shows a packaged 'document_summarizer' prompt using the slim-summary-tool. it shows a variety of techniques | STRING |
| HIGH | …ions/rag/example-9-function-calls-with-web-services.py | 0 | end-to-end example generating 30 output key:value pairs | STRING |
| HIGH | solutions/use_cases/web_services_slim_fx.py | 0 | end-to-end example generating 30 output key:value pairs | STRING |
| 13 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | README.md | 35 | # 300+ Models in Catalog with 50+ RAG-optimized BLING, DRAGON and Industry BERT models | COMMENT |
| MEDIUM | README.md | 82 | # to install an embedding on a library - pick an embedding model and vector_db | COMMENT |
| MEDIUM | README.md | 150 | # including in batches, if needed, to fit the model context window | COMMENT |
| MEDIUM | README.md | 413 | # check out the model card on Huggingface for RAG benchmark test performance results and other useful information | COMMENT |
| MEDIUM | README.md | 502 | # using a RAG-optimized LLM running locally | COMMENT |
| MEDIUM | README.md | 588 | # use local cpu model - try the newest - RAG finetune of Phi-3 quantized and packaged in GGUF | COMMENT |
| MEDIUM | README.md | 600 | ### OpenVINO Encoders - ideal for on-device, efficient RAG: | COMMENT |
| MEDIUM | README.md | 629 | ### Industry-specific Embedding Models for Specialized RAG | COMMENT |
| MEDIUM | llmware/library.py | 870 | # step 2 - pass loaded embedding model to EmbeddingHandler, which will route to the appropriate resource | COMMENT |
| MEDIUM | llmware/models.py | 1378 | # main case - load embedding model from Catalog | COMMENT |
| MEDIUM | llmware/models.py | 3290 | # adjusted to the actual model context window (if available) | COMMENT |
| MEDIUM | llmware/models.py | 6839 | # Claude/Anthropic model - 8000 max token context window | COMMENT |
| MEDIUM | llmware/models.py | 7167 | # Google Gemini model - 8000 max token context window | COMMENT |
| MEDIUM | llmware/models.py | 8218 | # must have elements for embedding model | COMMENT |
| MEDIUM | llmware/models.py | 8391 | # default for HF embedding model -> will be over-ridden by model card / configs, if available | COMMENT |
| MEDIUM | llmware/models.py | 8482 | # option to set smaller size than model context window | COMMENT |
| MEDIUM | llmware/models.py | 8588 | # default for HF embedding model -> will be over-ridden by model card / configs, if available | COMMENT |
| MEDIUM | llmware/models.py | 8679 | # option to set smaller size than model context window | COMMENT |
| MEDIUM | llmware/models.py | 10403 | # stop if combined input + output at context window size | COMMENT |
| MEDIUM | llmware/models.py | 11040 | # stop if combined input + output at context window size | COMMENT |
| MEDIUM | llmware/models.py | 14200 | # default for HF embedding model -> will be over-ridden by model card / configs, if available | COMMENT |
| MEDIUM | llmware/models.py | 14250 | # option to set smaller size than model context window | COMMENT |
| MEDIUM | llmware/models.py | 15613 | # stop if combined input + output at context window size | COMMENT |
| MEDIUM | llmware/models.py | 16083 | # stop if combined input + output at context window size | COMMENT |
| MEDIUM | llmware/models.py | 16195 | # stop if combined input + output at context window size | COMMENT |
| MEDIUM | llmware/util.py | 1948 | # note: the tokenizer is used primarily for 'counting' against the context window, so if the | COMMENT |
| MEDIUM | llmware/util.py | 1950 | # relative to the context window, but there should be any other detrimental impacts | COMMENT |
| MEDIUM | llmware/prompts.py | 439 | # and 'inject' real-time, up-to-date fact set into the prompt to minimize hallucination risk | COMMENT |
| MEDIUM | llmware/prompts.py | 1000 | # note: when loading model, context window is automatically set based on model | COMMENT |
| MEDIUM | llmware/retrieval.py | 131 | # explicitly pass name of embedding model, if multiple embeddings on library | COMMENT |
| MEDIUM | llmware/retrieval.py | 409 | # check that embedding model is available, and if not, flip to text search | COMMENT |
| MEDIUM | llmware/retrieval.py | 715 | # confirm that embedding model exists, or catch and raise error | COMMENT |
| MEDIUM | llmware/retrieval.py | 778 | # confirm that embedding model exists, or catch and raise error | COMMENT |
| MEDIUM | llmware/retrieval.py | 825 | # confirm that embedding model exists, or catch and raise error | COMMENT |
| MEDIUM | tests/embeddings/test_install_embeddings.py | 54 | # select embedding model | COMMENT |
| MEDIUM | solutions/ui/integrating_pywebio_ui.py | 396 | # please note that by default, we recommend setting temperature=0.0 and sample=False for fact-based RAG | COMMENT |
| MEDIUM | solutions/embeddings/embeddings_fast_start.py | 29 | # To create vector embeddings you just need to specify the embedding model and the vector embedding DB | COMMENT |
| MEDIUM | solutions/embeddings/using_pinecone.py | 22 | # To create vector embeddings you just need to specify the embedding model and the vector embedding DB | COMMENT |
| MEDIUM⚡ | solutions/embeddings/using_milvus_lite.py | 124 | # Step 2 - Select any embedding model in the LLMWare catalog | COMMENT |
| MEDIUM | solutions/embeddings/using_sentence_transformer.py | 89 | # use directly now as an embedding model | COMMENT |
| MEDIUM | solutions/embeddings/using_multiple_vector_db.py | 92 | # use directly now as an embedding model | COMMENT |
| MEDIUM | solutions/embeddings/using_multiple_vector_db.py | 108 | # 2. if embedding_model_name only passed, then looks up the first instance of that embedding model | COMMENT |
| MEDIUM | solutions/embeddings/using_multiple_embeddings.py | 96 | # use directly now as an embedding model | COMMENT |
| MEDIUM | solutions/embeddings/using_multiple_embeddings.py | 113 | # 2. if embedding_model_name only passed, then looks up the first instance of that embedding model | COMMENT |
| MEDIUM | solutions/embeddings/using_lance.py | 23 | # To create vector embeddings you just need to specify the embedding model and the vector embedding DB | COMMENT |
| MEDIUM | solutions/onnxruntime/adding_openvino_or_onnx_model.py | 65 | # or if using in conjunction with building a RAG prompt | COMMENT |
| MEDIUM | …lutions/gguf/dragon_rag_benchmark_tests_huggingface.py | 25 | # Pull a 200 question RAG benchmark test dataset from llmware HuggingFace repo | COMMENT |
| MEDIUM | solutions/gguf/dragon_rag_benchmark_tests_llmware.py | 26 | # Pull a 200 question RAG benchmark test dataset from llmware HuggingFace repo | COMMENT |
| MEDIUM | solutions/gguf/huggingface_integration.py | 44 | # *** use any HF embedding model, e.g., BERT, Roberta, etc. | COMMENT |
| MEDIUM | solutions/gguf/dragon_gguf_fast_start.py | 27 | # Pull a 200 question RAG benchmark test dataset from llmware HuggingFace repo | COMMENT |
| MEDIUM | solutions/gguf/using-qwen2-models.py | 24 | # 2 - RAG FINETUNE - DRAGON + BLING | COMMENT |
| MEDIUM | solutions/gguf/using-qwen2-models.py | 26 | print("\nExample #2 - RAG finetuned Qwen2 for fact-based question answering with context passage.") | CODE |
| MEDIUM | solutions/gguf/llmware_model_fast_start.py | 85 | # Pull a 200 question RAG benchmark test dataset from llmware HuggingFace repo | COMMENT |
| MEDIUM⚡ | solutions/openvino/README.md | 108 | Explore a wide range of [examples in the llmware repository](https://llmware-ai.github.io/llmware/examples). Because Ope | CODE |
| MEDIUM | solutions/openvino/adding_openvino_or_onnx_model.py | 65 | # or if using in conjunction with building a RAG prompt | COMMENT |
| MEDIUM | …tions/models/dragon_rag_benchmark_tests_huggingface.py | 25 | # Pull a 200 question RAG benchmark test dataset from llmware HuggingFace repo | COMMENT |
| MEDIUM | solutions/models/dragon_rag_benchmark_tests_llmware.py | 26 | # Pull a 200 question RAG benchmark test dataset from llmware HuggingFace repo | COMMENT |
| MEDIUM | solutions/models/huggingface_integration.py | 44 | # *** use any HF embedding model, e.g., BERT, Roberta, etc. | COMMENT |
| MEDIUM | solutions/models/prompt_with_pdf_source.py | 23 | # 3. batch according to the model context window, and make available for any future inferences | COMMENT |
| MEDIUM | solutions/models/dragon_gguf_fast_start.py | 27 | # Pull a 200 question RAG benchmark test dataset from llmware HuggingFace repo | COMMENT |
| 50 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | README.md | 353 | def bling_meets_llmware_hello_world (model_name): | CODE |
| LOW | README.md | 510 | def contract_analysis_on_laptop (model_name): | CODE |
| LOW | llmware/configs.py | 263 | def get_supported_collection_db(cls): | CODE |
| LOW | llmware/configs.py | 434 | def get_logging_level_by_module(cls, module): | CODE |
| LOW | llmware/configs.py | 441 | def set_logging_level_by_module(cls, module, level): | CODE |
| LOW | llmware/configs.py | 799 | def get_uri_string_experimental_db(cls): | CODE |
| LOW⚡ | llmware/configs.py | 1143 | def get_auth_credentials_provider(cls): | CODE |
| LOW⚡ | llmware/configs.py | 1147 | def get_auth_credentials_file(cls): | CODE |
| LOW⚡ | llmware/configs.py | 1155 | def get_auth_token_transport_header(cls): | CODE |
| LOW | llmware/library.py | 454 | def set_incremental_docs_blocks_images(self, added_docs=0, added_blocks=0, added_images=0, added_pages=0, | CODE |
| LOW | llmware/library.py | 627 | def export_library_to_txt_file(self, output_fp=None, output_fn=None, include_text=True, include_tables=True, | CODE |
| LOW | llmware/library.py | 684 | def export_library_to_jsonl_file(self, output_fp, output_fn, include_text=True, include_tables=True, | CODE |
| LOW | llmware/library.py | 757 | def pull_files_from_cloud_bucket (self, aws_access_key=None, aws_secret_key=None, bucket_name=None): | CODE |
| LOW | llmware/library.py | 1133 | def delete_installed_embedding(self, embedding_model_name, vector_db, vector_db_api_key=None): | CODE |
| LOW | llmware/library.py | 1210 | def expand_text_result_before(self, block, window_size=400): | CODE |
| LOW | llmware/library.py | 1424 | def set_incremental_docs_blocks_images(self, added_docs=0, added_blocks=0, added_images=0, added_pages=0, | CODE |
| LOW⚡ | llmware/models.py | 2699 | def get_global_inference_count(cls): | CODE |
| LOW⚡ | llmware/models.py | 2703 | def increment_global_inference_count(cls): | CODE |
| LOW⚡ | llmware/models.py | 2708 | def reset_global_inference_count(cls): | CODE |
| LOW | llmware/models.py | 178 | def get_tokenizer_bos_eos_lookup(cls): | CODE |
| LOW | llmware/models.py | 202 | def load_prompt_wrappers_from_file(cls, new_wrapper_registry): | CODE |
| LOW | llmware/models.py | 214 | def load_tokenizer_configs_from_file(cls, new_tokenizer_configs): | CODE |
| LOW | llmware/models.py | 370 | def create_new_foundry_manager(cls): | CODE |
| LOW | llmware/models.py | 602 | def load_prompt_wrapper_registry(self, fp=None, fn="prompt_wrappers.json"): | CODE |
| LOW | llmware/models.py | 615 | def save_prompt_wrapper_registry(self, fp=None, fn="prompt_wrappers.json"): | CODE |
| LOW | llmware/models.py | 630 | def get_tokenizer_bos_eos_configs(self): | CODE |
| LOW | llmware/models.py | 636 | def save_tokenizer_bos_eos_configs(self, fp=None, fn="tokenizer_bos_eos_configs.json"): | CODE |
| LOW | llmware/models.py | 651 | def load_tokenizer_bos_eos_configs(self, fp=None, fn="tokenizer_bos_eos_configs.json"): | CODE |
| LOW | llmware/models.py | 664 | def add_model_cards_from_file(self, fp=None, fn="custom_models_manifest.json"): | CODE |
| LOW | llmware/models.py | 703 | def register_new_finetune_wrapper(self, name, main_start="", main_stop="", llm_start="", | CODE |
| LOW | llmware/models.py | 730 | def get_list_registered_finetune_wrappers(self): | CODE |
| LOW | llmware/models.py | 736 | def register_new_hf_generative_model(self, hf_model_name, llmware_lookup_name=None, display_name=None, | CODE |
| LOW | llmware/models.py | 778 | def register_sentence_transformer_model(self, model_name, embedding_dims, context_window, | CODE |
| LOW | llmware/models.py | 920 | def setup_custom_llmware_inference_server(self, uri_string, secret_key=None): | CODE |
| LOW | llmware/models.py | 953 | def _instantiate_model_class_from_string(self, model_class, model_name, model_card, api_key=None, | CODE |
| LOW | llmware/models.py | 1325 | def load_sentence_transformer_model(self,model, model_name): | CODE |
| LOW | llmware/models.py | 1442 | def list_generative_local_models(self): | CODE |
| LOW | llmware/models.py | 1468 | def list_intel_npu_optimized_models(self): | CODE |
| LOW | llmware/models.py | 1757 | def list_function_call_models(self): | CODE |
| LOW | llmware/models.py | 1997 | def remediate_function_call_string(self,input_string, dedupe_values=True): | CODE |
| LOW | llmware/models.py | 2444 | def parse_instruction_for_user_vars(self, prompt_card, inference_dict=None): | CODE |
| LOW | llmware/models.py | 3935 | def cleanup_stream_gen_on_early_stop(self): | CODE |
| LOW | llmware/models.py | 3942 | def inference_over_api_endpoint(self, prompt, context=None, inference_dict=None, get_logits=False): | CODE |
| LOW | llmware/models.py | 4003 | def function_call_over_api_endpoint(self, context="", tool_type="", model_name="", params="", prompt="", | CODE |
| LOW | llmware/models.py | 4422 | def load_ov_external_tokenizer(self): | CODE |
| LOW | llmware/models.py | 4480 | def ov_text_to_image_pipeline(self): | CODE |
| LOW | llmware/models.py | 4950 | def inference_over_api_endpoint(self, prompt, context=None, inference_dict=None, get_logits=False): | CODE |
| LOW | llmware/models.py | 5145 | def function_call_over_api_endpoint(self, context="", tool_type="", model_name="", params="", prompt="", | CODE |
| LOW | llmware/models.py | 5526 | def load_ov_external_tokenizer(self): | CODE |
| LOW | llmware/models.py | 5842 | def prompt_engineer_completion (self, query, context, inference_dict=None): | CODE |
| LOW | llmware/models.py | 7978 | def cleanup_stream_gen_on_early_stop(self): | CODE |
| LOW | llmware/models.py | 9705 | def inference_over_api_endpoint(self, prompt, context=None, inference_dict=None, get_logits=False): | CODE |
| LOW | llmware/models.py | 9767 | def function_call_over_api_endpoint(self, context="", tool_type="", model_name="", params="", prompt="", | CODE |
| LOW | llmware/models.py | 10161 | def _load_llama_cpp_shared_library(self): | CODE |
| LOW | llmware/models.py | 11063 | def function_call_over_api_endpoint(self, context="", tool_type="", model_name="", params="", prompt="", | CODE |
| LOW | llmware/models.py | 11151 | def inference_over_api_endpoint(self, prompt, context=None, inference_dict=None, get_logits=False): | CODE |
| LOW | llmware/models.py | 12599 | def create_model_catalog_extension(self): | CODE |
| LOW | llmware/models.py | 12738 | def prepare_foundry_manager_and_model(self): | CODE |
| LOW | llmware/models.py | 13751 | def cleanup_stream_gen_on_early_stop(self): | CODE |
| LOW | llmware/models.py | 14934 | def _load_clip_model_for_inference(self, model_repo_path, model_card=None, **kwargs): | CODE |
| 222 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | llmware/models.py | 1137 | # Step 1 - resolve local path | COMMENT |
| LOW⚡ | tests/embeddings/test_install_embeddings.py | 17 | # Step 1 - Create library which is the main 'organizing construct' in llmware | COMMENT |
| LOW⚡ | tests/embeddings/test_install_embeddings.py | 26 | # Step 2 - Pull down the sample files from S3 through the .load_sample_files() command | COMMENT |
| LOW⚡ | tests/embeddings/test_install_embeddings.py | 32 | # Step 3 - point ".add_files" method to the folder of documents that was just created | COMMENT |
| LOW⚡ | solutions/embeddings/using_chromadb.py | 45 | # Step 1 - Create library which is the main 'organizing construct' in llmware | COMMENT |
| LOW⚡ | solutions/embeddings/using_chromadb.py | 50 | # Step 2 - Pull down the sample files from S3 through the .load_sample_files() command | COMMENT |
| LOW⚡ | solutions/embeddings/using_chromadb.py | 56 | # Step 3 - point ".add_files" method to the folder of documents that was just created | COMMENT |
| LOW⚡ | solutions/embeddings/using_redis.py | 34 | # Step 1 - Create library which is the main 'organizing construct' in llmware | COMMENT |
| LOW⚡ | solutions/embeddings/using_redis.py | 39 | # Step 2 - Pull down the sample files from S3 through the .load_sample_files() command | COMMENT |
| LOW⚡ | solutions/embeddings/using_redis.py | 45 | # Step 3 - point ".add_files" method to the folder of documents that was just created | COMMENT |
| LOW⚡ | solutions/embeddings/using_milvus_lite.py | 28 | # Step 1 - Create library which is the main 'organizing construct' in llmware | COMMENT |
| LOW⚡ | solutions/embeddings/using_milvus_lite.py | 37 | # Step 2 - Pull down the sample files from S3 through the .load_sample_files() command | COMMENT |
| LOW⚡ | solutions/embeddings/using_milvus_lite.py | 43 | # Step 3 - point ".add_files" method to the folder of documents that was just created | COMMENT |
| LOW⚡ | solutions/embeddings/using_milvus_lite.py | 121 | # Step 1 - create library | COMMENT |
| LOW⚡ | solutions/embeddings/using_milvus_lite.py | 124 | # Step 2 - Select any embedding model in the LLMWare catalog | COMMENT |
| LOW⚡ | solutions/embeddings/using_neo4j.py | 47 | # Step 1 - Create library which is the main 'organizing construct' in llmware | COMMENT |
| LOW⚡ | solutions/embeddings/using_neo4j.py | 52 | # Step 2 - Pull down the sample files from S3 through the .load_sample_files() command | COMMENT |
| LOW⚡ | solutions/embeddings/using_neo4j.py | 58 | # Step 3 - point ".add_files" method to the folder of documents that was just created | COMMENT |
| LOW⚡ | solutions/embeddings/using_sentence_transformer.py | 26 | # Step 1 - Create library which is the main 'organizing construct' in llmware | COMMENT |
| LOW⚡ | solutions/embeddings/using_sentence_transformer.py | 31 | # Step 2 - Pull down the sample files from S3 through the .load_sample_files() command | COMMENT |
| LOW⚡ | solutions/embeddings/using_sentence_transformer.py | 37 | # Step 3 - point ".add_files" method to the folder of documents that was just created | COMMENT |
| LOW⚡ | solutions/embeddings/using_multiple_vector_db.py | 34 | # Step 1 - Create library which is the main 'organizing construct' in llmware | COMMENT |
| LOW⚡ | solutions/embeddings/using_multiple_vector_db.py | 39 | # Step 2 - Pull down the sample files from S3 through the .load_sample_files() command | COMMENT |
| LOW⚡ | solutions/embeddings/using_multiple_vector_db.py | 45 | # Step 3 - point ".add_files" method to the folder of documents that was just created | COMMENT |
| LOW⚡ | solutions/embeddings/using_pg_vector.py | 46 | # Step 1 - Create library which is the main 'organizing construct' in llmware | COMMENT |
| LOW⚡ | solutions/embeddings/using_pg_vector.py | 51 | # Step 2 - Pull down the sample files from S3 through the .load_sample_files() command | COMMENT |
| LOW⚡ | solutions/embeddings/using_pg_vector.py | 57 | # Step 3 - point ".add_files" method to the folder of documents that was just created | COMMENT |
| LOW⚡ | solutions/embeddings/using_multiple_embeddings.py | 39 | # Step 1 - Create library which is the main 'organizing construct' in llmware | COMMENT |
| LOW⚡ | solutions/embeddings/using_multiple_embeddings.py | 44 | # Step 2 - Pull down the sample files from S3 through the .load_sample_files() command | COMMENT |
| LOW⚡ | solutions/embeddings/using_multiple_embeddings.py | 50 | # Step 3 - point ".add_files" method to the folder of documents that was just created | COMMENT |
| LOW⚡ | solutions/embeddings/using_qdrant.py | 31 | # Step 1 - Create library which is the main 'organizing construct' in llmware | COMMENT |
| LOW⚡ | solutions/embeddings/using_qdrant.py | 36 | # Step 2 - Pull down the sample files from S3 through the .load_sample_files() command | COMMENT |
| LOW⚡ | solutions/embeddings/using_qdrant.py | 42 | # Step 3 - point ".add_files" method to the folder of documents that was just created | COMMENT |
| LOW⚡ | solutions/embeddings/docs2vecs_with_milvus-rag.py | 23 | # Step 0 - Configuration - we will use these in Step 4 to install the embeddings | COMMENT |
| LOW⚡ | solutions/embeddings/docs2vecs_with_milvus-rag.py | 27 | # Step 1 - Create library which is the main 'organizing construct' in llmware | COMMENT |
| LOW⚡ | solutions/embeddings/docs2vecs_with_milvus-rag.py | 32 | # Step 2 - Pull down the sample files from S3 through the .load_sample_files() command | COMMENT |
| LOW⚡ | solutions/embeddings/docs2vecs_with_milvus-rag.py | 39 | # Step 3 - point ".add_files" method to the folder of documents that was just created | COMMENT |
| LOW⚡ | solutions/embeddings/docs2vecs_with_milvus-rag.py | 45 | # Step 4 - Install the embeddings | COMMENT |
| LOW⚡ | …ons/embeddings/docs2vecs_with_milvus-un_resolutions.py | 21 | # Step 0 - Configuration - we will use these in Step 4 to install the embeddings | COMMENT |
| LOW⚡ | …ons/embeddings/docs2vecs_with_milvus-un_resolutions.py | 25 | # Step 1 - Create library which is the main 'organizing construct' in llmware | COMMENT |
| LOW⚡ | …ons/embeddings/docs2vecs_with_milvus-un_resolutions.py | 30 | # Step 2 - Pull down the sample files from S3 through the .load_sample_files() command | COMMENT |
| LOW⚡ | …ons/embeddings/docs2vecs_with_milvus-un_resolutions.py | 36 | # Step 3 - point ".add_files" method to the folder of documents that was just created | COMMENT |
| LOW⚡ | …ons/embeddings/docs2vecs_with_milvus-un_resolutions.py | 42 | # Step 4 - Install the embeddings | COMMENT |
| LOW⚡ | …ons/embeddings/docs2vecs_with_milvus-un_resolutions.py | 52 | # Step 5 - start using the new vector embeddings with Query | COMMENT |
| LOW⚡ | solutions/embeddings/docs2vecs_with_milvus-contracts.py | 22 | # Step 0 - Configuration - we will use these in Step 4 to install the embeddings | COMMENT |
| LOW⚡ | solutions/embeddings/docs2vecs_with_milvus-contracts.py | 26 | # Step 1 - Create library which is the main 'organizing construct' in llmware | COMMENT |
| LOW⚡ | solutions/embeddings/docs2vecs_with_milvus-contracts.py | 31 | # Step 2 - Pull down the sample files from S3 through the .load_sample_files() command | COMMENT |
| LOW⚡ | solutions/embeddings/docs2vecs_with_milvus-contracts.py | 37 | # Step 3 - point ".add_files" method to the folder of documents that was just created | COMMENT |
| LOW⚡ | solutions/embeddings/docs2vecs_with_milvus-contracts.py | 43 | # Step 4 - Install the embeddings | COMMENT |
| LOW⚡ | solutions/embeddings/docs2vecs_with_milvus-contracts.py | 53 | # Step 5 - start using the new vector embeddings with Query | COMMENT |
| LOW | solutions/gguf/using-custom-gguf-model.py | 16 | # Step 1 - register new gguf model - we will pick the popular LLama-2-13B-chat-GGUF | COMMENT |
| LOW | solutions/gguf/using-custom-gguf-model.py | 23 | # Step 2- if the prompt_wrapper is a standard, e.g., Meta's <INST>, then no need to do anything else | COMMENT |
| LOW | solutions/gguf/using-ollama-models.py | 8 | # Step 1 - register your Ollama models in llmware ModelCatalog | COMMENT |
| LOW | solutions/gguf/using-ollama-models.py | 22 | # Step 2 - start using the Ollama model like any other model in llmware | COMMENT |
| LOW | solutions/gguf/using-ollama-models.py | 66 | # Step 3 - using the ollama discovery API - optional | COMMENT |
| LOW | solutions/models/using-ollama-models.py | 8 | # Step 1 - register your Ollama models in llmware ModelCatalog | COMMENT |
| LOW | solutions/models/using-ollama-models.py | 22 | # Step 2 - start using the Ollama model like any other model in llmware | COMMENT |
| LOW | solutions/models/using-ollama-models.py | 66 | # Step 3 - using the ollama discovery API - optional | COMMENT |
| LOW⚡ | solutions/rag/example-2-build_embeddings.py | 56 | # Step 1 - Create library which is the main 'organizing construct' in llmware | COMMENT |
| LOW⚡ | solutions/rag/example-2-build_embeddings.py | 65 | # Step 2 - Pull down the sample files from S3 through the .load_sample_files() command | COMMENT |
| 44 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | llmware/configs.py | 403 | CODE | |
| LOW | llmware/models.py | 257 | CODE | |
| LOW | llmware/models.py | 993 | CODE | |
| LOW | llmware/models.py | 1119 | CODE | |
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| LOW | llmware/models.py | 15408 | CODE | |
| LOW | llmware/util.py | 242 | CODE | |
| LOW | llmware/util.py | 578 | CODE | |
| LOW | llmware/util.py | 641 | CODE | |
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| LOW | llmware/util.py | 1920 | CODE | |
| LOW | llmware/util.py | 1990 | CODE | |
| LOW | llmware/web_services.py | 191 | CODE | |
| LOW | llmware/web_services.py | 480 | CODE | |
| 107 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | llmware/embeddings.py | 940 | # Creating an index will accelerate the search process and it needs to be done once table has some vectors a | COMMENT |
| MEDIUM | tests/models/test_agent_llmfx_process.py | 9 | # Create an agent using LLMfx class | COMMENT |
| MEDIUM | tests/models/test_cohere_command_r_model.py | 13 | # Define a simple prompt for testing | COMMENT |
| MEDIUM | solutions/embeddings/using_mongo_atlas.py | 30 | # Create a library and populate it with some sample documents | COMMENT |
| MEDIUM | solutions/gguf/huggingface_integration.py | 139 | # Create a library and add some documents so we can do some vector embeddings | COMMENT |
| MEDIUM | solutions/gguf/huggingface_integration.py | 172 | # Create a library and add some documents so we can do some vector embeddings | COMMENT |
| MEDIUM | solutions/gguf/cohere_command_r_example.py | 13 | # Define a prompt for inference | COMMENT |
| MEDIUM | solutions/models/huggingface_integration.py | 139 | # Create a library and add some documents so we can do some vector embeddings | COMMENT |
| MEDIUM | solutions/models/huggingface_integration.py | 172 | # Create a library and add some documents so we can do some vector embeddings | COMMENT |
| MEDIUM | solutions/models/prompt_state.py | 11 | # Create a new prompter with state persistence | COMMENT |
| MEDIUM | solutions/models/prompt_state.py | 20 | # Define a list of prompts | COMMENT |
| MEDIUM | solutions/models/cohere_command_r_example.py | 13 | # Define a prompt for inference | COMMENT |
| MEDIUM⚡ | solutions/sources/semantic_retrieval.py | 39 | # Create a Query instance | COMMENT |
| MEDIUM | solutions/sources/bulk_retrieval.py | 28 | # Create a Query instance | COMMENT |
| MEDIUM | solutions/sources/bulk_retrieval.py | 31 | # Create a list of keys of interest. This can be omitted if you want all keys | COMMENT |
| MEDIUM | solutions/sources/query_state.py | 29 | # Create a Query instance with history persistence | COMMENT |
| MEDIUM⚡ | solutions/sources/text_retrieval.py | 29 | # Create a Query instance | COMMENT |
| MEDIUM | solutions/sources/bibliography.py | 28 | # Create a Query instance | COMMENT |
| MEDIUM⚡ | solutions/sources/dual_pass_with_custom_filter.py | 19 | # Create a new library or load if it exists | COMMENT |
| MEDIUM | solutions/use_cases/lecture_tool/pages/4_Summarizer.py | 65 | # Create a list of only the unique points generated by the model | COMMENT |
| MEDIUM | solutions/slim_agents/youtube_topics_extraction.py | 21 | # Create a LLMfx object | COMMENT |
| MEDIUM | tutorials/getting_started/working_with_libraries.py | 47 | # Create a library | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | llmware/models.py | 8453 | except: | CODE |
| HIGH | llmware/models.py | 8650 | except: | CODE |
| HIGH | llmware/models.py | 2388 | except: | CODE |
| HIGH | llmware/util.py | 1620 | except: | CODE |
| HIGH | llmware/util.py | 1942 | except: | CODE |
| HIGH | llmware/prompts.py | 1776 | except: | CODE |
| HIGH | llmware/embeddings.py | 2525 | except neo4j.DatabaseError: # Index did not exist yet | CODE |
| HIGH | llmware/retrieval.py | 31 | except: | CODE |
| HIGH | llmware/resources.py | 35 | except ImportError: | CODE |
| HIGH | llmware/resources.py | 43 | except ImportError: | CODE |
| HIGH | llmware/resources.py | 52 | except ImportError: | CODE |
| HIGH | llmware/resources.py | 57 | except ImportError: | CODE |
| HIGH | solutions/openvino/using_openvino_streamer.py | 20 | except: | CODE |
| HIGH | solutions/openvino/using-openvino-vision-models.py | 19 | except: | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | llmware/models.py | 2358 | except Exception as e: | CODE |
| LOW | llmware/models.py | 6010 | except Exception as e: | CODE |
| LOW | llmware/models.py | 6295 | except Exception as e: | CODE |
| LOW | llmware/models.py | 6610 | except Exception as e: | CODE |
| LOW | llmware/models.py | 6789 | except Exception as e: | CODE |
| LOW | llmware/models.py | 7016 | except Exception as e: | CODE |
| LOW | llmware/models.py | 7114 | except Exception as e: | CODE |
| LOW | llmware/models.py | 7338 | except Exception as e: | CODE |
| LOW | llmware/models.py | 7458 | except Exception as e: | CODE |
| LOW | llmware/models.py | 7835 | except Exception as e: | CODE |
| LOW | llmware/models.py | 10276 | except Exception as e: | CODE |
| LOW | llmware/models.py | 11425 | except Exception as e: | CODE |
| LOW | llmware/models.py | 13598 | except Exception as e: | CODE |
| LOW | llmware/models.py | 13643 | except Exception as e: | CODE |
| LOW | llmware/models.py | 13715 | except Exception as e: | CODE |
| LOW | llmware/models.py | 14373 | except Exception as e: | CODE |
| LOW | llmware/models.py | 15165 | except Exception as e: | CODE |
| LOW | llmware/models.py | 15341 | except Exception as e: | CODE |
| LOW | llmware/web_services.py | 452 | except Exception as e: | CODE |
| LOW | llmware/web_services.py | 1176 | except Exception as e: | CODE |
| LOW | llmware/web_services.py | 1205 | except Exception as e: | CODE |
| LOW | llmware/web_services.py | 1233 | except Exception as e: | CODE |
| LOW | llmware/web_services.py | 1274 | except Exception as e: | CODE |
| LOW | llmware/web_services.py | 1316 | except Exception as e: | CODE |
| LOW | llmware/web_services.py | 1351 | except Exception as e: | CODE |
| MEDIUM | llmware/web_services.py | 357 | def __init__(self, url_or_fp, link="/", save_images=True, reset_img_folder=False | CODE |
| LOW | llmware/parsers.py | 624 | except Exception as e: | CODE |
| LOW | llmware/embeddings.py | 1034 | except Exception as e: | CODE |
| LOW | llmware/embeddings.py | 2383 | except Exception as err: | CODE |
| MEDIUM | llmware/embeddings.py | 1019 | def search_index(self, query_embedding_vector, sample_count=10): | CODE |
| LOW | tests/run-tests.py | 24 | except Exception as e: | CODE |
| LOW | tests/run-tests.py | 41 | except Exception as e: | CODE |
| LOW | tests/run-tests.py | 54 | except Exception as e: | CODE |
| LOW | tests/run-tests.py | 64 | except Exception as e: | CODE |
| LOW | tests/run-tests.py | 85 | except Exception as e: | CODE |
| LOW | tests/run-tests.py | 100 | except Exception as e: | CODE |
| LOW | tests/models/test_prompt_benchmark_test.py | 46 | except Exception as e: | CODE |
| LOW | tests/models/test_prompt_benchmark_test.py | 102 | except Exception as e: | CODE |
| LOW⚡ | solutions/gguf/chat_models_gguf_fast_start.py | 26 | except Exception as e: | CODE |
| LOW⚡ | solutions/gguf/chat_models_gguf_fast_start.py | 36 | except Exception as e: | CODE |
| LOW⚡ | solutions/models/chat_models_gguf_fast_start.py | 26 | except Exception as e: | CODE |
| LOW⚡ | solutions/models/chat_models_gguf_fast_start.py | 36 | except Exception as e: | CODE |
| MEDIUM⚡ | solutions/sources/dual_pass_with_custom_filter.py | 24 | print(f"Error: Failed to create or load library '{library_name}'") | CODE |
| MEDIUM⚡ | solutions/sources/dual_pass_with_custom_filter.py | 30 | print(f"Error: Input folder path '{input_folder_path}' does not exist.") | CODE |
| MEDIUM⚡ | solutions/sources/dual_pass_with_custom_filter.py | 40 | print(f"Error: Embedding generation failed for model '{embedding_model}' with vector db '{vector_db}'") | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | llmware/library.py | 516 | CODE | |
| LOW | llmware/library.py | 783 | CODE | |
| LOW | llmware/library.py | 1208 | CODE | |
| LOW | llmware/library.py | 1231 | CODE | |
| LOW | llmware/library.py | 1254 | CODE | |
| LOW | llmware/models.py | 880 | CODE | |
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| LOW | llmware/models.py | 16215 | CODE | |
| LOW | llmware/web_services.py | 1249 | CODE | |
| LOW | llmware/parsers.py | 62 | CODE | |
| LOW | llmware/parsers.py | 1198 | CODE | |
| LOW | llmware/parsers.py | 3059 | CODE | |
| LOW | llmware/parsers.py | 3325 | CODE | |
| LOW | llmware/parsers.py | 3459 | CODE | |
| LOW | llmware/parsers.py | 4337 | CODE | |
| LOW | llmware/prompts.py | 103 | CODE | |
| LOW | llmware/prompts.py | 216 | CODE | |
| LOW | llmware/prompts.py | 690 | CODE | |
| LOW | llmware/prompts.py | 697 | CODE | |
| LOW | llmware/prompts.py | 711 | CODE | |
| LOW | llmware/prompts.py | 720 | CODE | |
| LOW | llmware/prompts.py | 731 | CODE | |
| LOW | llmware/prompts.py | 746 | CODE | |
| LOW | llmware/prompts.py | 760 | CODE | |
| LOW | llmware/prompts.py | 773 | CODE | |
| LOW | llmware/prompts.py | 1245 | CODE | |
| LOW | llmware/prompts.py | 1253 | CODE | |
| LOW | llmware/prompts.py | 1261 | CODE | |
| LOW | llmware/prompts.py | 1962 | CODE | |
| LOW | llmware/prompts.py | 1971 | CODE | |
| LOW | llmware/agents.py | 1370 | CODE | |
| LOW | llmware/retrieval.py | 116 | CODE | |
| LOW | llmware/retrieval.py | 1684 | CODE | |
| LOW | llmware/retrieval.py | 1719 | CODE | |
| LOW | llmware/retrieval.py | 1741 | CODE | |
| LOW | llmware/retrieval.py | 1748 | CODE | |
| LOW | llmware/resources.py | 808 | CODE | |
| LOW | llmware/resources.py | 3559 | CODE | |
| LOW | llmware/resources.py | 3659 | CODE | |
| LOW | llmware/resources.py | 3704 | CODE | |
| 9 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | llmware/models.py | 9131 | # explicit check and setting to facilitate debugging | COMMENT |
| MEDIUM | llmware/models.py | 9164 | # explicit check and setting to facilitate debugging, if needed | COMMENT |
| MEDIUM | llmware/models.py | 9187 | # explicit check and setting to facilitate debugging, if needed | COMMENT |
| MEDIUM | llmware/models.py | 9471 | # explicit check and setting to facilitate debugging | COMMENT |
| MEDIUM | llmware/models.py | 9504 | # explicit check and setting to facilitate debugging, if needed | COMMENT |
| MEDIUM | llmware/models.py | 9522 | # explicit check and setting to facilitate debugging, if needed | COMMENT |
| LOW | llmware/web_services.py | 850 | # this is an external link - just use the source | COMMENT |
| MEDIUM | llmware/prompts.py | 568 | # this method is intended to be used in conjunction with sources as follows: | COMMENT |
| MEDIUM | llmware/agents.py | 1585 | #TODO: build more robust type checking, e.g., float/decimal/currency | COMMENT |
| LOW | llmware/embeddings.py | 1444 | # if wait longer than 5 mins, then time out and just return | COMMENT |
| MEDIUM | solutions/onnxruntime/adding_openvino_or_onnx_model.py | 65 | # or if using in conjunction with building a RAG prompt | COMMENT |
| MEDIUM | solutions/openvino/adding_openvino_or_onnx_model.py | 65 | # or if using in conjunction with building a RAG prompt | COMMENT |
| LOW | solutions/slim_agents/agents-1-start_here.py | 62 | # Discovering other models is easy -> to invoke a model, simply use the 'model_name' and pass in .load_model() | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | setup.py | 3 | CODE | |
| LOW | setup.py | 4 | CODE | |
| LOW | setup.py | 6 | CODE | |
| LOW | setup.py | 7 | CODE | |
| LOW | setup.py | 7 | CODE | |
| LOW | llmware/models.py | 18 | CODE | |
| LOW | llmware/models.py | 32 | CODE | |
| LOW | llmware/util.py | 31 | CODE | |
| LOW | llmware/web_services.py | 364 | CODE | |
| LOW | llmware/parsers.py | 48 | CODE | |
| LOW | llmware/gguf_configs.py | 36 | CODE | |
| LOW | tests/run-tests.py | 48 | CODE | |
| LOW | tests/embeddings/test_all_embedding_dbs.py | 6 | CODE | |
| LOW | tests/embeddings/test_all_embedding_dbs.py | 14 | CODE | |
| LOW | tests/embeddings/test_all_embedding_dbs.py | 15 | CODE | |
| LOW | tests/library/test_library.py | 11 | CODE | |
| LOW | tests/models/test_prompt_benchmark_test.py | 14 | CODE | |
| LOW | tests/models/test_hf_model_load_prompt.py | 3 | CODE | |
| LOW | tests/configs/test_account_overrides.py | 11 | CODE | |
| LOW | solutions/gguf/huggingface_integration.py | 17 | CODE | |
| LOW | solutions/gguf/using_gguf.py | 3 | CODE | |
| LOW | solutions/models/huggingface_integration.py | 17 | CODE | |
| LOW | solutions/models/prompt_with_sources.py | 10 | CODE | |
| LOW | solutions/rag/example-5-rag-semantic-query.py | 36 | CODE | |
| LOW | solutions/sources/parse_images.py | 9 | CODE | |
| LOW | solutions/sources/parse_images.py | 9 | CODE | |
| LOW | solutions/sources/parse_images.py | 10 | CODE | |
| LOW | solutions/sources/parse_web_sources_in_memory.py | 16 | CODE | |
| LOW | solutions/use_cases/agent_with_custom_tables.py | 27 | CODE | |
| LOW | solutions/use_cases/agent_with_custom_tables.py | 31 | CODE | |
| LOW | …tions/slim_agents/document_unique_topics_extraction.py | 8 | CODE | |
| LOW | …ns/slim_agents/agents-5-agent-llmfx-getting-started.py | 12 | CODE | |
| LOW | solutions/slim_agents/agent-llmfx-getting-started.py | 12 | CODE | |
| LOW | solutions/slim_agents/youtube_topics_extraction.py | 8 | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | llmware/parsers.py | 741 | # char * input_fp | COMMENT |
| LOW | llmware/parsers.py | 1021 | # if any issue loading module, will be captured at .get_module_office_parser() | COMMENT |
| LOW | llmware/parsers.py | 1041 | # int unique_doc_num | COMMENT |
| LOW | llmware/parsers.py | 2421 | # char * write_to_filename | COMMENT |
| LOW | llmware/parsers.py | 2601 | # char * input_filename | COMMENT |
| LOW | llmware/parsers.py | 3601 | # char * input_account_name | COMMENT |
| LOW | llmware/parsers.py | 3801 | # char * input_account_name | COMMENT |
| LOW | tests/set-env.sh | 1 | # LLM API KEYS | COMMENT |
| LOW | tests/set-env.sh | 21 | #export NEO4J_PASSWORD=<an alternative Neo4j password> | COMMENT |
| LOW | tests/embeddings/test_all_embedding_dbs.py | 81 | sample_files_path = Setup().load_sample_files() | COMMENT |
| LOW | solutions/embeddings/using_multiple_vector_db.py | 101 | COMMENT | |
| LOW | solutions/embeddings/using_multiple_embeddings.py | 101 | print("\nupdate: Embedding record of the Library") | COMMENT |
| LOW | solutions/gguf/using-llama-3.py | 1 | COMMENT | |
| LOW | solutions/models/set_model_api_keys.py | 21 | COMMENT | |
| LOW | solutions/models/using-llama-3.py | 1 | COMMENT | |
| LOW | solutions/rag/example-2-build_embeddings.py | 161 | # alternatively, to use this example as self-contained, then create a new library from scratch: | COMMENT |
| LOW | solutions/sources/parse_jsonl_custom.py | 41 | # All three text databases supported (mongo, postgres, and sqlite) | COMMENT |
| LOW | solutions/sources/pdf_parser_new_configs.py | 21 | # Measured in string len characters, e.g., 400 characters as target text chunk size, with max of 600 characters | COMMENT |
| LOW | solutions/sources/pdf_parser_new_configs.py | 41 | # ENCODING | COMMENT |
| LOW | solutions/sources/pdf_parser_new_configs.py | 61 | lib.add_files(input_folder_path=fp, strip_header=True) | COMMENT |
| LOW | solutions/sources/parse_csv_custom.py | 101 | # if "doc_ID" or "block_ID" mapping provided, then will "over-write" the default doc_ID and block_ID and | COMMENT |
| LOW | solutions/sources/office_parser_new_configs.py | 21 | COMMENT | |
| LOW | solutions/sources/office_parser_new_configs.py | 61 | COMMENT | |
| LOW | solutions/use_cases/lecture_tool/pages/3_Prompts.py | 41 | COMMENT | |
| LOW | solutions/slim_agents/agents-1-start_here.py | 141 | COMMENT | |
| LOW | docs/components/model_catalog.md | 161 | # Add a LM Studio Model | COMMENT |
| LOW | scripts/aws/launch-llmware-ami.sh | 1 | #! /bin/bash | COMMENT |
| LOW | tutorials/getting_started/loading_sample_files.py | 21 | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM⚡ | solutions/openvino/README.md | 114 | # --------------------------- | COMMENT |
| MEDIUM⚡ | solutions/openvino/README.md | 116 | # --------------------------- | COMMENT |
| MEDIUM⚡ | solutions/openvino/README.md | 124 | # --------------------------- | COMMENT |
| MEDIUM⚡ | solutions/openvino/README.md | 126 | # --------------------------- | COMMENT |
| MEDIUM⚡ | solutions/openvino/README.md | 134 | # --------------------------- | COMMENT |
| MEDIUM⚡ | solutions/openvino/README.md | 136 | # --------------------------- | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | llmware/models.py | 14972 | # Check if vision is supported | COMMENT |
| LOW | llmware/models.py | 15214 | # Check if vision is supported | COMMENT |
| LOW | tests/models/test_prompt_benchmark_test.py | 71 | # Print results | COMMENT |
| LOW | …lutions/gguf/dragon_rag_benchmark_tests_huggingface.py | 88 | # Print results | COMMENT |
| LOW | solutions/gguf/dragon_rag_benchmark_tests_llmware.py | 54 | # Print results | COMMENT |
| LOW | solutions/gguf/using-custom-gguf-model.py | 46 | # Print results | COMMENT |
| LOW⚡ | solutions/gguf/chat_models_gguf_fast_start.py | 40 | # Print results | COMMENT |
| LOW | solutions/gguf/dragon_gguf_fast_start.py | 54 | # Print results | COMMENT |
| LOW | solutions/gguf/llmware_model_fast_start.py | 148 | # Print results | COMMENT |
| LOW | …tions/models/dragon_rag_benchmark_tests_huggingface.py | 88 | # Print results | COMMENT |
| LOW | solutions/models/dragon_rag_benchmark_tests_llmware.py | 54 | # Print results | COMMENT |
| LOW⚡ | solutions/models/chat_models_gguf_fast_start.py | 40 | # Print results | COMMENT |
| LOW | solutions/models/dragon_gguf_fast_start.py | 54 | # Print results | COMMENT |
| LOW | solutions/models/llmware_model_fast_start.py | 148 | # Print results | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | solutions/gguf/adjusting_sampling_settings.py | 22 | # -- temperature: range between 0.0 - 1.0 (for GGUF models, you can also try setting to negative) | COMMENT |
| LOW | solutions/gguf/using-azure-openai.py | 20 | # Make sure to replace the deployment names with your deployments in the | COMMENT |
| MEDIUM | solutions/gguf/using-azure-openai.py | 67 | # reset so you can use the standard OpenAI client | COMMENT |
| MEDIUM | solutions/openvino/using_openvino_streamer.py | 31 | # here is a simple example that will stream the text to local host | COMMENT |
| MEDIUM | solutions/openvino/using-openvino-vision-models.py | 30 | # here is a simple example that will stream the text to local host | COMMENT |
| MEDIUM | solutions/models/adjusting_sampling_settings.py | 22 | # -- temperature: range between 0.0 - 1.0 (for GGUF models, you can also try setting to negative) | COMMENT |
| LOW | solutions/models/using-azure-openai.py | 20 | # Make sure to replace the deployment names with your deployments in the | COMMENT |
| MEDIUM | solutions/models/using-azure-openai.py | 67 | # reset so you can use the standard OpenAI client | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | llmware/library.py | 35 | logger = logging.getLogger(__name__) | CODE |
| LOW | llmware/models.py | 60 | logger = logging.getLogger(__name__) | CODE |
| LOW | llmware/models.py | 14006 | def set_request_tensor(self, request, tensor, key=None) -> None: | CODE |
| LOW | llmware/models.py | 14020 | def update_tensor(self, inputs: Any, request, key=None) -> None: | CODE |
| LOW | llmware/util.py | 48 | logger = logging.getLogger(__name__) | CODE |
| LOW | llmware/web_services.py | 27 | logger = logging.getLogger(__name__) | CODE |
| LOW | llmware/parsers.py | 56 | logger = logging.getLogger(__name__) | CODE |
| LOW | llmware/prompts.py | 40 | logger = logging.getLogger(__name__) | CODE |
| LOW | llmware/agents.py | 39 | logger = logging.getLogger(__name__) | CODE |
| LOW | llmware/setup.py | 32 | logger = logging.getLogger(__name__) | CODE |
| LOW | llmware/embeddings.py | 73 | logger = logging.getLogger(__name__) | CODE |
| LOW | llmware/retrieval.py | 40 | logger = logging.getLogger(__name__) | CODE |
| LOW | llmware/resources.py | 60 | logger = logging.getLogger(__name__) | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | llmware/retrieval.py | 1516 | # edge case - but return empty match if query is null | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | llmware/resources.py | 4103 | # TODO: implement cursor option - see DBCursor class for more details. | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | tests/models/test_prompt_benchmark_test.py | 121 | # Example usage | COMMENT |