LLM Zoomcamp - a free online course about real-life applications of LLMs. In 10 weeks you will learn how to build an AI system that answers questions about your knowledge base. Register here 👇🏼
This report summarizes scanner evidence for DataTalksClub/llm-zoomcamp, a Jupyter Notebook project with 7,450 GitHub stars. SynthScan examined 47,989 lines of code across 230 source files, recording 177 pattern matches distributed across 16 syntactic categories. The legacy adjusted pattern signal is 9.9 (Moderate 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? 127 finding(s).
Are imports declared and package/API names verified? 10 finding(s).
Are wrappers, guards, and repeated structures necessary? 47 finding(s).
Which changes need focused human review? 177 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 177 distinct pattern matches across 16 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 |
|---|---|---|---|---|
| MEDIUM⚡ | README.md | 83 | ### [Module 1: Agentic RAG](01-agentic-rag/) | COMMENT |
| MEDIUM⚡ | README.md | 83 | ### [Module 1: Agentic RAG](01-agentic-rag/) | COMMENT |
| MEDIUM⚡ | README.md | 93 | ### [Module 3: Orchestration](03-orchestration/) | COMMENT |
| MEDIUM | README.md | 176 | - [Timur Kamaliev](https://www.linkedin.com/in/timurkamaliev/) - AI engineer building production LLM systems, RAG pipeli | CODE |
| MEDIUM | 06-best-practices/01-intro.md | 5 | # Best Practices for RAG | COMMENT |
| MEDIUM | 06-best-practices/01-intro.md | 12 | ## The RAG pipeline | COMMENT |
| MEDIUM | 03-orchestration/05-rag.md | 12 | ## What is RAG? | COMMENT |
| MEDIUM | 03-orchestration/05-rag.md | 18 | ## How RAG Works in Kestra | COMMENT |
| MEDIUM | 03-orchestration/05-rag.md | 52 | ### Step 1: Without RAG | COMMENT |
| MEDIUM | 03-orchestration/05-rag.md | 63 | ### Step 2: With RAG | COMMENT |
| MEDIUM | 03-orchestration/05-rag.md | 79 | ## Extending RAG with web search | COMMENT |
| MEDIUM | 03-orchestration/05-rag.md | 102 | ### Static RAG vs. web search RAG | COMMENT |
| MEDIUM | 03-orchestration/README.md | 28 | ## Part 3: RAG Workflows | COMMENT |
| MEDIUM | 02-vector-search/09-onnx-embedder.md | 141 | wget https://raw.githubusercontent.com/DataTalksClub/llm-zoomcamp/main/cohorts/2026/01-agentic-rag/code/ingest.py | CODE |
| MEDIUM | 02-vector-search/03-embeddings-dataset.md | 20 | wget https://raw.githubusercontent.com/DataTalksClub/llm-zoomcamp/main/cohorts/2026/01-agentic-rag/code/ingest.py | CODE |
| MEDIUM | 02-vector-search/08-pgvector.md | 250 | ## Using it in RAG | STRING |
| MEDIUM | 02-vector-search/06-rag-vector.md | 6 | # RAG with Vector Search | COMMENT |
| MEDIUM | 02-vector-search/06-rag-vector.md | 32 | wget https://raw.githubusercontent.com/DataTalksClub/llm-zoomcamp/main/cohorts/2026/01-agentic-rag/code/rag_helper.py | CODE |
| MEDIUM | 02-vector-search/06-rag-vector.md | 33 | wget https://raw.githubusercontent.com/DataTalksClub/llm-zoomcamp/main/cohorts/2026/01-agentic-rag/code/ingest.py | CODE |
| MEDIUM | 02-vector-search/07-sqlitesearch-vector.md | 183 | ## Using sqlitesearch vector search in RAG | COMMENT |
| MEDIUM | 04-evaluation/README.md | 25 | ## Part 2: RAG and Agent Evaluation | COMMENT |
| MEDIUM | 04-evaluation/12-rag-answers.md | 6 | # Generating RAG Answers | COMMENT |
| MEDIUM | 04-evaluation/12-rag-answers.md | 69 | ## Running RAG | COMMENT |
| MEDIUM | 04-evaluation/11-evaluation-intro.md | 6 | # RAG and Agent Evaluation | COMMENT |
| MEDIUM | 04-evaluation/13-llm-as-judge.md | 43 | ## Loading the RAG answers | COMMENT |
| MEDIUM | etc/chunking.md | 45 | ### Implementing Chunking for RAG | COMMENT |
| MEDIUM | 07-project-example/01-intro.md | 116 | ## The RAG flow | STRING |
| MEDIUM | 07-project-example/04-interface.md | 27 | rag.py # RAG flow + search + LLM | CODE |
| MEDIUM | 07-project-example/04-interface.md | 75 | ## RAG module | COMMENT |
| MEDIUM | 07-project-example/05-monitoring.md | 15 | ## Adding evaluation to the RAG flow | COMMENT |
| MEDIUM | 07-project-example/03-evaluating-rag.md | 5 | # Evaluating RAG | COMMENT |
| MEDIUM | 07-project-example/03-evaluating-rag.md | 15 | ## LLM-as-a-Judge for RAG | COMMENT |
| MEDIUM | cohorts/2025/pre-course-q-a-stream-summary.md | 24 | ## 4. Will the course help me implement RAG from a live database? | COMMENT |
| MEDIUM | cohorts/2025/pre-course-q-a-stream-summary.md | 28 | ## 5. Are we going to do any agentic AI development in this course? | COMMENT |
| MEDIUM | cohorts/2025/pre-course-q-a-stream-summary.md | 47 | ## 9. Do we discuss evaluating LLM-based applications, generating metrics, and setting up guardrails? | COMMENT |
| MEDIUM | cohorts/2025/pre-course-q-a-stream-summary.md | 58 | ## 11. Is the RAG pipeline included in the course? | COMMENT |
| MEDIUM | cohorts/2025/02-vector-search/README.md | 51 | ## 2.5 RAG with Vector Search | COMMENT |
| MEDIUM | cohorts/2025/02-vector-search/homework.md | 150 | ## Q5. Selecting the embedding model | COMMENT |
| MEDIUM | cohorts/2025/03-evaluation/README.md | 1 | # RAG and LLM Evaluation | COMMENT |
| MEDIUM | cohorts/2025/03-evaluation/README.md | 78 | ## 3.5 Offline vs Online (RAG) evaluation | COMMENT |
| MEDIUM | cohorts/2025/03-evaluation/README.md | 89 | ## 3.6 Generating data for offline RAG evaluation | COMMENT |
| MEDIUM | cohorts/2025/03-evaluation/README.md | 108 | ## 3.7 Offline RAG evaluation: cosine similarity | COMMENT |
| MEDIUM | cohorts/2025/03-evaluation/README.md | 130 | ## 3.8 Offline RAG evaluation: LLM as a judge | COMMENT |
| MEDIUM | cohorts/2025/01-intro/README.md | 15 | ## 1.1 Introduction to LLM and RAG | COMMENT |
| MEDIUM | cohorts/2025/01-intro/README.md | 85 | ## 1.5 Cleaned RAG flow | COMMENT |
| MEDIUM | cohorts/2024/04-monitoring/README.md | 25 | ## 4.2 Offline vs Online (RAG) evaluation | COMMENT |
| MEDIUM | cohorts/2024/04-monitoring/README.md | 36 | ## 4.3 Generating data for offline RAG evaluation | COMMENT |
| MEDIUM | cohorts/2024/04-monitoring/README.md | 49 | ## 4.4 Offline RAG evaluation: cosine similarity | COMMENT |
| MEDIUM | cohorts/2024/04-monitoring/README.md | 71 | ## 4.5 Offline RAG evaluation: LLM as a judge | COMMENT |
| MEDIUM | cohorts/2024/05-orchestration/README.md | 1 | # Data Preparation in RAG | COMMENT |
| MEDIUM | cohorts/2024/05-orchestration/homework.md | 58 | ## Creating a RAG pipeline | COMMENT |
| MEDIUM | cohorts/2024/02-open-source/README.md | 190 | ## 2.9 UI for RAG | COMMENT |
| MEDIUM | cohorts/2026/homework/03-orchestration/homework.md | 30 | ## Question 2: RAG vs No RAG | COMMENT |
| MEDIUM | cohorts/2026/homework/01-agentic-rag/homework.md | 1 | # Homework 1: Agentic RAG | COMMENT |
| MEDIUM | cohorts/2026/homework/01-agentic-rag/homework.md | 118 | ## Q3. RAG | COMMENT |
| MEDIUM | cohorts/2026/homework/01-agentic-rag/homework.md | 124 | wget https://raw.githubusercontent.com/DataTalksClub/llm-zoomcamp/main/cohorts/2026/01-agentic-rag/code/rag_helper.py | CODE |
| MEDIUM | cohorts/2026/homework/01-agentic-rag/homework.md | 199 | ## Q5. RAG with chunking | COMMENT |
| MEDIUM | 05-monitoring/04-metrics.md | 69 | ## Instrumented RAG | COMMENT |
| MEDIUM | 05-monitoring/13-docker-compose.md | 33 | ├── assistant.py # RAG pipeline + LLM | CODE |
| MEDIUM | 01-agentic-rag/09-data-ingestion.md | 147 | ## RAG with sqlitesearch | COMMENT |
| 13 more matches not shown… | ||||
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | 04-evaluation/14-agent-evaluation.md | 0 | search the faq database for entries matching the given query. | STRING |
| HIGH | cohorts/2026/workshops/dlt/homework/agent.py | 0 | search the faq database for entries matching the given query. | STRING |
| HIGH | 01-agentic-rag/15-frameworks.md | 0 | search the faq database for entries matching the given query. | STRING |
| HIGH | 04-evaluation/code/rag_helper.py | 0 | your task is to answer questions from the course participants based on the provided context. use the context to find rel | STRING |
| HIGH | 05-monitoring/rag_helper.py | 0 | your task is to answer questions from the course participants based on the provided context. use the context to find rel | STRING |
| HIGH | 05-monitoring/code/rag_helper.py | 0 | your task is to answer questions from the course participants based on the provided context. use the context to find rel | STRING |
| HIGH | 01-agentic-rag/08-rag-helper.md | 0 | your task is to answer questions from the course participants based on the provided context. use the context to find rel | STRING |
| HIGH | 01-agentic-rag/06-building-prompt.md | 0 | your task is to answer questions from the course participants based on the provided context. use the context to find rel | STRING |
| HIGH | 01-agentic-rag/code/rag_helper.py | 0 | your task is to answer questions from the course participants based on the provided context. use the context to find rel | STRING |
| HIGH | 01-agentic-rag/03-rag.md | 0 | your task is to answer questions from the course participants based on the provided context. use the context to find rel | STRING |
| HIGH | 07-project-example/05-monitoring.md | 0 | create table conversations ( id text primary key, question text not null, answer text not null, course text not null, mo | STRING |
| HIGH | cohorts/2024/04-monitoring/code.md | 0 | create table conversations ( id text primary key, question text not null, answer text not null, course text not null, mo | STRING |
| HIGH | cohorts/2024/04-monitoring/app/db.py | 0 | create table conversations ( id text primary key, question text not null, answer text not null, course text not null, mo | STRING |
| HIGH | 07-project-example/05-monitoring.md | 0 | create table feedback ( id serial primary key, conversation_id text references conversations(id), feedback integer not n | STRING |
| HIGH | cohorts/2024/04-monitoring/code.md | 0 | create table feedback ( id serial primary key, conversation_id text references conversations(id), feedback integer not n | STRING |
| HIGH | cohorts/2024/04-monitoring/app/db.py | 0 | create table feedback ( id serial primary key, conversation_id text references conversations(id), feedback integer not n | STRING |
| HIGH | 07-project-example/05-monitoring.md | 0 | insert into conversations (id, question, answer, course, model_used, response_time, relevance, relevance_explanation, pr | STRING |
| HIGH | cohorts/2024/04-monitoring/code.md | 0 | insert into conversations (id, question, answer, course, model_used, response_time, relevance, relevance_explanation, pr | STRING |
| HIGH | cohorts/2024/04-monitoring/app/db.py | 0 | insert into conversations (id, question, answer, course, model_used, response_time, relevance, relevance_explanation, pr | STRING |
| HIGH | cohorts/2025/01-intro/homework.md | 0 | you're a course teaching assistant. answer the question based on the context from the faq database. use only the facts f | STRING |
| HIGH | cohorts/2024/04-monitoring/code.md | 0 | you're a course teaching assistant. answer the question based on the context from the faq database. use only the facts f | STRING |
| HIGH | cohorts/2024/04-monitoring/app/assistant.py | 0 | you're a course teaching assistant. answer the question based on the context from the faq database. use only the facts f | STRING |
| HIGH | cohorts/2024/01-intro/homework.md | 0 | you're a course teaching assistant. answer the question based on the context from the faq database. use only the facts f | STRING |
| HIGH | cohorts/2024/02-open-source/qa_faq.py | 0 | you're a course teaching assistant. answer the question based on the context from the faq database. use only the facts f | STRING |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | 06-best-practices/documents-with-ids.json | 6212 | "text": "Problem: when running the preprocess_data.py file you get the following error:\n\nwandb: ERROR api_key not | CODE |
| HIGH | …3-evaluation/search_evaluation/documents-with-ids.json | 6212 | "text": "Problem: when running the preprocess_data.py file you get the following error:\n\nwandb: ERROR api_key not | CODE |
| HIGH | cohorts/2025/01-intro/documents.json | 4451 | "text": "Problem: when running the preprocess_data.py file you get the following error:\n\nwandb: ERROR api_key | CODE |
| HIGH | cohorts/2024/04-monitoring/code.md | 1485 | OPENAI_API_KEY = os.getenv('OPENAI_API_KEY', 'your-api-key-here') | STRING |
| HIGH | cohorts/2024/04-monitoring/code.md | 1893 | OPENAI_API_KEY = os.getenv('OPENAI_API_KEY', 'your-api-key-here') | STRING |
| HIGH | cohorts/2024/04-monitoring/app/assistant.py | 13 | OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", "your-api-key-here") | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | 03-orchestration/05-rag.md | 52 | ### Step 1: Without RAG | COMMENT |
| LOW | 03-orchestration/05-rag.md | 63 | ### Step 2: With RAG | COMMENT |
| LOW | 03-orchestration/03-setup.md | 14 | ## Step 1: Start Kestra | COMMENT |
| LOW | 03-orchestration/03-setup.md | 31 | ## Step 2: Obtain API Keys | COMMENT |
| LOW | 03-orchestration/03-setup.md | 56 | ## Step 3: Configure API Keys in Kestra | COMMENT |
| LOW | 03-orchestration/03-setup.md | 78 | ## Step 4: Import Example Flows | COMMENT |
| LOW | 03-orchestration/03-setup.md | 94 | ## Step 5: Run Your First Agent | COMMENT |
| LOW⚡ | …rts/2024/02-open-source/serving-hugging-face-models.md | 5 | ### Step 1: Authenticate and Accept the User Agreement | COMMENT |
| LOW⚡ | …rts/2024/02-open-source/serving-hugging-face-models.md | 12 | ### Step 2: Install Hugging Face Transformers and Other Dependencies | COMMENT |
| LOW⚡ | …rts/2024/02-open-source/serving-hugging-face-models.md | 20 | ### Step 3: Authenticate with Hugging Face CLI | COMMENT |
| LOW⚡ | …rts/2024/02-open-source/serving-hugging-face-models.md | 30 | ### Step 4: Download the Model | COMMENT |
| LOW | …rts/2024/02-open-source/serving-hugging-face-models.md | 48 | ### Step 5: Load the Model Locally | COMMENT |
| LOW | …rts/2024/02-open-source/serving-hugging-face-models.md | 63 | ### Step 6: Serve the Model | COMMENT |
| LOW | …rts/2024/02-open-source/serving-hugging-face-models.md | 97 | ### Step 7: Deploy the Service | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | 06-best-practices/documents-with-ids.json | 549 | "text": "error: could not change permissions of directory \"/var/lib/postgresql/data\": Operation not permitted vol | CODE |
| MEDIUM | 06-best-practices/documents-with-ids.json | 1529 | "text": "There are multiple benefits of using Cloud Functions to automate tasks in Google Cloud.\nUse below Cloud Fu | CODE |
| MEDIUM | 06-best-practices/documents-with-ids.json | 3041 | "text": "Docker Commands\n# Create a Docker Image from a base image\nDocker run -it ubuntu bash\n#List docker images | CODE |
| MEDIUM | …3-evaluation/search_evaluation/documents-with-ids.json | 549 | "text": "error: could not change permissions of directory \"/var/lib/postgresql/data\": Operation not permitted vol | CODE |
| MEDIUM | …3-evaluation/search_evaluation/documents-with-ids.json | 1529 | "text": "There are multiple benefits of using Cloud Functions to automate tasks in Google Cloud.\nUse below Cloud Fu | CODE |
| MEDIUM | …3-evaluation/search_evaluation/documents-with-ids.json | 3041 | "text": "Docker Commands\n# Create a Docker Image from a base image\nDocker run -it ubuntu bash\n#List docker images | CODE |
| MEDIUM | cohorts/2025/01-intro/documents.json | 396 | "text": "error: could not change permissions of directory \"/var/lib/postgresql/data\": Operation not permitted | CODE |
| MEDIUM | cohorts/2025/01-intro/documents.json | 1096 | "text": "There are multiple benefits of using Cloud Functions to automate tasks in Google Cloud.\nUse below Clou | CODE |
| MEDIUM | cohorts/2025/01-intro/documents.json | 2176 | "text": "Docker Commands\n# Create a Docker Image from a base image\nDocker run -it ubuntu bash\n#List docker im | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | 06-best-practices/documents-with-ids.json | 724 | "text": "ImportError: no pq wrapper available.\nAttempts made:\n- couldn't import \\dt\nopg 'c' implementation: No m | CODE |
| MEDIUM | 06-best-practices/documents-with-ids.json | 1277 | "text": "It means your container consumed all available RAM allocated to it. It can happen in particular when workin | CODE |
| MEDIUM | 06-best-practices/documents-with-ids.json | 5008 | "text": "Problem: When trying to install tflite_runtime with\n!pip install --extra-index-url https://google-coral.gi | CODE |
| LOW | …3-evaluation/search_evaluation/documents-with-ids.json | 724 | "text": "ImportError: no pq wrapper available.\nAttempts made:\n- couldn't import \\dt\nopg 'c' implementation: No m | CODE |
| MEDIUM | …3-evaluation/search_evaluation/documents-with-ids.json | 1277 | "text": "It means your container consumed all available RAM allocated to it. It can happen in particular when workin | CODE |
| MEDIUM | …3-evaluation/search_evaluation/documents-with-ids.json | 5008 | "text": "Problem: When trying to install tflite_runtime with\n!pip install --extra-index-url https://google-coral.gi | CODE |
| LOW | cohorts/2025/01-intro/documents.json | 521 | "text": "ImportError: no pq wrapper available.\nAttempts made:\n- couldn't import \\dt\nopg 'c' implementation: | CODE |
| MEDIUM | cohorts/2025/01-intro/documents.json | 916 | "text": "It means your container consumed all available RAM allocated to it. It can happen in particular when wo | CODE |
| MEDIUM | cohorts/2025/01-intro/documents.json | 3586 | "text": "Problem: When trying to install tflite_runtime with\n!pip install --extra-index-url https://google-cora | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | cohorts/2025/03-evaluation/README.md | 55 | * [queries generated by OpenAI (pickle)](search_evaluation/results.bin) | COMMENT |
| HIGH | cohorts/2024/03-vector-search/README.md | 57 | * [queries generated by OpenAI (pickle)](eval/results.bin) | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | 04-evaluation/code/evaluation_utils.py | 64 | except Exception: | CODE |
| LOW | cohorts/2024/04-monitoring/code.md | 863 | except Exception as e: | STRING |
| MEDIUM | cohorts/2024/04-monitoring/code.md | 864 | print(f"An error occurred during database initialization: {e}") | STRING |
| LOW | cohorts/2024/04-monitoring/code.md | 879 | except Exception as e: | STRING |
| MEDIUM | cohorts/2024/04-monitoring/code.md | 880 | print(f"An error occurred while saving the conversation: {e}") | STRING |
| LOW | cohorts/2024/04-monitoring/code.md | 895 | except Exception as e: | STRING |
| MEDIUM | cohorts/2024/04-monitoring/code.md | 896 | print(f"An error occurred while saving feedback: {e}") | STRING |
| LOW | cohorts/2024/04-monitoring/code.md | 981 | except Exception as e: | STRING |
| MEDIUM | cohorts/2024/04-monitoring/code.md | 982 | print(f"An error occurred during database initialization: {e}") | STRING |
| LOW | cohorts/2024/04-monitoring/code.md | 997 | except Exception as e: | STRING |
| MEDIUM | cohorts/2024/04-monitoring/code.md | 998 | print(f"An error occurred while saving the conversation: {e}") | STRING |
| LOW | cohorts/2024/04-monitoring/code.md | 1013 | except Exception as e: | STRING |
| MEDIUM | cohorts/2024/04-monitoring/code.md | 1014 | print(f"An error occurred while saving feedback: {e}") | STRING |
| LOW | cohorts/2024/05-orchestration/code/06_retrieval.py | 64 | except Exception as e: | CODE |
| LOW | 05-monitoring/code/evaluation_utils.py | 64 | except Exception: | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| MEDIUM | 06-best-practices/documents-with-ids.json | 3041 | "text": "Docker Commands\n# Create a Docker Image from a base image\nDocker run -it ubuntu bash\n#List docker images | CODE |
| MEDIUM | …3-evaluation/search_evaluation/documents-with-ids.json | 3041 | "text": "Docker Commands\n# Create a Docker Image from a base image\nDocker run -it ubuntu bash\n#List docker images | CODE |
| MEDIUM | cohorts/2025/01-intro/documents.json | 2176 | "text": "Docker Commands\n# Create a Docker Image from a base image\nDocker run -it ubuntu bash\n#List docker im | CODE |
| MEDIUM | cohorts/2025/01-intro/documents-llm.json | 176 | "text": "You can limit the amount of memory used in the ElasticSearch container by adding the next line to the e | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| HIGH | cohorts/2026/workshops/dlt/code/filesystem_pipeline.py | 72 | except json.JSONDecodeError: | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | 06-best-practices/documents-with-ids.json | 1501 | "text": "Reference:\nhttps://cloud.google.com/bigquery/docs/external-data-cloud-storage\nSolution:\nfrom google.clou | CODE |
| LOW | …3-evaluation/search_evaluation/documents-with-ids.json | 1501 | "text": "Reference:\nhttps://cloud.google.com/bigquery/docs/external-data-cloud-storage\nSolution:\nfrom google.clou | CODE |
| LOW | cohorts/2025/01-intro/documents.json | 1076 | "text": "Reference:\nhttps://cloud.google.com/bigquery/docs/external-data-cloud-storage\nSolution:\nfrom google. | CODE |
| LOW | cohorts/2025/01-intro/documents-llm.json | 116 | "text": "You can store your different API keys in a yaml file that you will add in your .gitignore file. Be care | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | 06-best-practices/03-reranking.md | 38 | def elastic_search_hybrid_rrf(field, query, vector, course): | CODE |
| LOW | 06-best-practices/03-reranking.md | 99 | def elastic_search_hybrid_rrf(field, query, vector, course, k=60): | CODE |
| LOW | 04-evaluation/04-search-evaluation.md | 164 | def compute_relevance_total_text(ground_truth): | CODE |
| LOW | cohorts/2025/0a-agents/mcp_client.py | 185 | def convert_mcp_tool_to_function_format(mcp_tool): | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | cohorts/2024/04-monitoring/app/generate_data.py | 6 | CODE | |
| LOW | cohorts/2024/05-orchestration/code/06_retrieval.py | 1 | CODE | |
| LOW | cohorts/2024/02-open-source/qa_faq.py | 2 | CODE | |
| LOW | 05-monitoring/code/judge.py | 1 | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | cohorts/2025/0a-agents/mcp_client.py | 159 | CODE | |
| LOW | 05-monitoring/rag_helper.py | 64 | CODE | |
| LOW | 05-monitoring/code/metrics.py | 51 | CODE |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | cohorts/2026/workshops/dlt/homework/agent.py | 41 | # In Module 1, the search function used a global `index` variable directly. | COMMENT |
| LOW | cohorts/2026/workshops/dlt/homework/agent.py | 61 | # Pydantic AI replaces all three: | COMMENT |
| LOW | cohorts/2026/workshops/dlt/homework/agent.py | 81 | # | COMMENT |
| Severity | File | Line | Snippet | Context |
|---|---|---|---|---|
| LOW | cohorts/2026/workshops/dlt/code/filesystem_pipeline.py | 47 | CODE | |
| LOW | cohorts/2026/workshops/dlt/code/filesystem_pipeline.py | 51 | CODE |