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Add pdf support to file_search for Responses API

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This adds basic PDF support (using our existing `parse_pdf` function)
to the file_search tool and corresponding Vector Files API.

When a PDF file is uploaded and attached to a vector store, we parse
the pdf and then chunk its content as normal. This is not the best
solution long-term, but it does match what we've been doing so far for
PDF files in the memory tool.

Signed-off-by: Ben Browning <bbrownin@redhat.com>
This commit is contained in:
Ben Browning 2025-06-11 16:45:28 -04:00
parent 57eccf023d
commit 055885bd5a
4 changed files with 41 additions and 33 deletions
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25
llama_stack/providers/inline/vector_io/faiss/faiss.py
View file

@ -9,6 +9,7 @@ import base64
import io
import json
import logging
import mimetypes
import time
from typing import Any
@ -19,7 +20,6 @@ from numpy.typing import NDArray
from llama_stack.apis.files import Files
from llama_stack.apis.inference import InterleavedContent
from llama_stack.apis.inference.inference import Inference
from llama_stack.apis.tools.rag_tool import RAGDocument
from llama_stack.apis.vector_dbs import VectorDB
from llama_stack.apis.vector_io import (
Chunk,
@ -40,8 +40,8 @@ from llama_stack.providers.utils.memory.openai_vector_store_mixin import OpenAIV
from llama_stack.providers.utils.memory.vector_store import (
EmbeddingIndex,
VectorDBWithIndex,
content_from_doc,
make_overlapped_chunks,
parse_pdf,
)
from .config import FaissVectorIOConfig
@ -292,20 +292,23 @@ class FaissVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorDBsProtocolPr
chunk_overlap_tokens = 400
try:
file_response = await self.files_api.openai_retrieve_file(file_id)
mime_type, _ = mimetypes.guess_type(file_response.filename)
content_response = await self.files_api.openai_retrieve_file_content(file_id)
content = content_response.body
doc = RAGDocument(
document_id=file_id,
content=content,
metadata=attributes,
)
content = await content_from_doc(doc)
# TODO: We can't use content_from_doc directly from vector_store
# but should figure out how to centralize this logic near there
if mime_type == "application/pdf":
content = parse_pdf(content_response.body)
else:
content = content_response.body.decode("utf-8")
chunks = make_overlapped_chunks(
doc.document_id,
file_id,
content,
max_chunk_size_tokens,
chunk_overlap_tokens,
doc.metadata,
attributes,
)
if not chunks: