From 293d40f91c7a92422dab23efedbc495aeed34291 Mon Sep 17 00:00:00 2001 From: Alexey Rybak Date: Tue, 23 Sep 2025 09:25:57 -0700 Subject: [PATCH] api and provider codegen fixes --- llama_stack/apis/files/files.py | 2 +- llama_stack/providers/registry/vector_io.py | 2 +- scripts/provider_codegen.py | 7 +++++++ 3 files changed, 9 insertions(+), 2 deletions(-) diff --git a/llama_stack/apis/files/files.py b/llama_stack/apis/files/files.py index d39e96e96..124e4bc8e 100644 --- a/llama_stack/apis/files/files.py +++ b/llama_stack/apis/files/files.py @@ -119,7 +119,7 @@ class Files(Protocol): The file upload should be a multipart form request with: - file: The File object (not file name) to be uploaded. - purpose: The intended purpose of the uploaded file. - - expires_after: Optional form values describing expiration for the file. Expected expires_after[anchor] = "created_at", expires_after[seconds] = . Seconds must be between 3600 and 2592000 (1 hour to 30 days). + - expires_after: Optional form values describing expiration for the file. Expected expires_after[anchor] = "created_at", expires_after[seconds] = {integer}. Seconds must be between 3600 and 2592000 (1 hour to 30 days). :param file: The uploaded file object containing content and metadata (filename, content_type, etc.). :param purpose: The intended purpose of the uploaded file (e.g., "assistants", "fine-tune"). diff --git a/llama_stack/providers/registry/vector_io.py b/llama_stack/providers/registry/vector_io.py index e8237bc62..9816838e7 100644 --- a/llama_stack/providers/registry/vector_io.py +++ b/llama_stack/providers/registry/vector_io.py @@ -410,7 +410,7 @@ There are three implementations of search for PGVectoIndex available: - How it works: - Uses PostgreSQL's vector extension (pgvector) to perform similarity search - Compares query embeddings against stored embeddings using Cosine distance or other distance metrics - - Eg. SQL query: SELECT document, embedding <=> %s::vector AS distance FROM table ORDER BY distance + - Eg. SQL query: SELECT document, embedding <=> %s::vector AS distance FROM table ORDER BY distance -Characteristics: - Semantic understanding - finds documents similar in meaning even if they don't share keywords diff --git a/scripts/provider_codegen.py b/scripts/provider_codegen.py index 80b22198e..88776cba8 100755 --- a/scripts/provider_codegen.py +++ b/scripts/provider_codegen.py @@ -226,6 +226,13 @@ def generate_provider_docs(progress, provider_spec: Any, api_name: str) -> str: field_type = field_info["type"].replace("|", "\\|") required = "Yes" if field_info["required"] else "No" default = str(field_info["default"]) if field_info["default"] is not None else "" + + # Handle multiline default values and escape problematic characters for MDX + if "\n" in default: + default = default.replace("\n", "
").replace("<", "<").replace(">", ">").replace("{", "{").replace("}", "}") + else: + default = default.replace("<", "<").replace(">", ">").replace("{", "{").replace("}", "}") + description_text = field_info["description"] or "" md_lines.append(f"| `{field_name}` | `{field_type}` | {required} | {default} | {description_text} |")