SurfSense/surfsense_backend/app/utils/document_converters.py
2025-03-14 18:53:14 -07:00

136 lines
4.5 KiB
Python

async def convert_element_to_markdown(element) -> str:
"""
Convert an Unstructured element to markdown format based on its category.
Args:
element: The Unstructured API element object
Returns:
str: Markdown formatted string
"""
element_category = element.metadata["category"]
content = element.page_content
if not content:
return ""
markdown_mapping = {
"Formula": lambda x: f"```math\n{x}\n```",
"FigureCaption": lambda x: f"*Figure: {x}*",
"NarrativeText": lambda x: f"{x}\n\n",
"ListItem": lambda x: f"- {x}\n",
"Title": lambda x: f"# {x}\n\n",
"Address": lambda x: f"> {x}\n\n",
"EmailAddress": lambda x: f"`{x}`",
"Image": lambda x: f"![{x}]({x})",
"PageBreak": lambda x: "\n---\n",
"Table": lambda x: f"```html\n{element.metadata['text_as_html']}\n```",
"Header": lambda x: f"## {x}\n\n",
"Footer": lambda x: f"*{x}*\n\n",
"CodeSnippet": lambda x: f"```\n{x}\n```",
"PageNumber": lambda x: f"*Page {x}*\n\n",
"UncategorizedText": lambda x: f"{x}\n\n"
}
converter = markdown_mapping.get(element_category, lambda x: x)
return converter(content)
async def convert_document_to_markdown(elements):
"""
Convert all document elements to markdown.
Args:
elements: List of Unstructured API elements
Returns:
str: Complete markdown document
"""
markdown_parts = []
for element in elements:
markdown_text = await convert_element_to_markdown(element)
if markdown_text:
markdown_parts.append(markdown_text)
return "".join(markdown_parts)
def convert_chunks_to_langchain_documents(chunks):
"""
Convert chunks from hybrid search results to LangChain Document objects.
Args:
chunks: List of chunk dictionaries from hybrid search results
Returns:
List of LangChain Document objects
"""
try:
from langchain_core.documents import Document as LangChainDocument
except ImportError:
raise ImportError(
"LangChain is not installed. Please install it with `pip install langchain langchain-core`"
)
langchain_docs = []
for chunk in chunks:
# Extract content from the chunk
content = chunk.get("content", "")
# Create metadata dictionary
metadata = {
"chunk_id": chunk.get("chunk_id"),
"score": chunk.get("score"),
"rank": chunk.get("rank") if "rank" in chunk else None,
}
# Add document information to metadata
if "document" in chunk:
doc = chunk["document"]
metadata.update({
"document_id": doc.get("id"),
"document_title": doc.get("title"),
"document_type": doc.get("document_type"),
})
# Add document metadata if available
if "metadata" in doc:
# Prefix document metadata keys to avoid conflicts
doc_metadata = {f"doc_meta_{k}": v for k, v in doc.get("metadata", {}).items()}
metadata.update(doc_metadata)
# Add source URL if available in metadata
if "url" in doc.get("metadata", {}):
metadata["source"] = doc["metadata"]["url"]
elif "sourceURL" in doc.get("metadata", {}):
metadata["source"] = doc["metadata"]["sourceURL"]
# Ensure source_id is set for citation purposes
# Use document_id as the source_id if available
if "document_id" in metadata:
metadata["source_id"] = metadata["document_id"]
# Update content for citation mode - format as XML with explicit source_id
new_content = f"""
<document>
<metadata>
<source_id>{metadata.get("source_id", metadata.get("document_id", "unknown"))}</source_id>
</metadata>
<content>
<text>
{content}
</text>
</content>
</document>
"""
# Create LangChain Document
langchain_doc = LangChainDocument(
page_content=new_content,
metadata=metadata
)
langchain_docs.append(langchain_doc)
return langchain_docs