feat(rag): add temporary chat attachments

fastapi_modules/fastapi_leaudit/rag_engine/generator.py

@@ -33,16 +33,34 @@ async def generate_stream(
 
     max_context_chars = 8000
     if context_chunks:
-        parts: list[str] = []
+        attachment_parts: list[str] = []
+        formal_parts: list[str] = []
         total_len = 0
         for chunk in context_chunks:
-            part = f"[来源: {chunk.get('source', '未知')}]\\n{chunk.get('text', '')}"
+            scope = str(chunk.get("source_scope") or chunk.get("data_source_type") or "formal_kb")
+            if scope == "chat_attachment":
+                label = "上传文件事实"
+            else:
+                label = "正式知识库依据"
+            part = f"[{label} | 来源: {chunk.get('source', '未知')}]\\n{chunk.get('text', '')}"
             if total_len + len(part) > max_context_chars:
                 break
-            parts.append(part)
+            if scope == "chat_attachment":
+                attachment_parts.append(part)
+            else:
+                formal_parts.append(part)
             total_len += len(part)
-        context_text = "\\n\\n---\\n\\n".join(parts)
-        user_content = f"知识库内容:\\n{context_text}\\n\\n用户问题: {query}"
+        context_sections: list[str] = []
+        if attachment_parts:
+            context_sections.append("【上传文件事实】\n" + "\\n\\n---\\n\\n".join(attachment_parts))
+        if formal_parts:
+            context_sections.append("【正式知识库依据】\n" + "\\n\\n---\\n\\n".join(formal_parts))
+        context_text = "\\n\\n======\\n\\n".join(context_sections)
+        user_content = (
+            "请严格区分上下文来源：上传文件事实只能用于判断用户材料中的事实；正式知识库依据只能用于法律、处罚、裁量或案例依据。"
+            "如果依据不足，请明确说明，不要编造。\n\n"
+            f"{context_text}\\n\\n用户问题: {query}"
+        )
     else:
         user_content = query
 
@@ -115,7 +133,7 @@ async def generate_stream(
             "dataset_name": dataset_name,
             "document_id": "",
             "document_name": chunk.get("source", ""),
-            "data_source_type": "upload_file",
+            "data_source_type": chunk.get("data_source_type") or chunk.get("source_scope") or "upload_file",
             "segment_id": chunk.get("id", ""),
             "retriever_from": "rag",
             "score": round(chunk.get("score", 0.0), 4),
@@ -125,6 +143,8 @@ async def generate_stream(
             "index_node_hash": "",
             "content": chunk.get("text", "")[:500],
             "page": None,
+            "source_scope": chunk.get("source_scope") or "",
+            "attachment_id": chunk.get("attachment_id") or "",
         }
         for i, chunk in enumerate(context_chunks)
     ]

fastapi_modules/fastapi_leaudit/rag_engine/retriever.py

@@ -183,10 +183,15 @@ class RagRetriever:
                     "source": meta.get("source") or meta.get("document_name") or dataset_name,
                     "score": score,
                     "chunk_index": int(meta.get("chunk_index") or idx),
-                    "document_name": document_name,
-                    "document_id": meta.get("document_id"),
-                    "page": meta.get("page"),
-                }
+                "document_name": document_name,
+                "document_id": meta.get("document_id"),
+                "page": meta.get("page"),
+                "source_scope": meta.get("source_scope"),
+                "attachment_id": meta.get("attachment_id"),
+                "conversation_id": meta.get("conversation_id"),
+                "tenant_code": meta.get("tenant_code"),
+                "user_id": meta.get("user_id"),
+            }
             )
         return chunks
 
@@ -285,6 +290,11 @@ class RagRetriever:
                     "document_name": document_name,
                     "document_id": meta.get("document_id"),
                     "page": meta.get("page"),
+                    "source_scope": meta.get("source_scope"),
+                    "attachment_id": meta.get("attachment_id"),
+                    "conversation_id": meta.get("conversation_id"),
+                    "tenant_code": meta.get("tenant_code"),
+                    "user_id": meta.get("user_id"),
                 }
             )
 
@@ -392,10 +402,10 @@ class RagRetriever:
             {
                 "position": index + 1,
                 "dataset_id": str(chunk.get("dataset_id") or ""),
-                "dataset_name": dataset_name,
+                "dataset_name": chunk.get("dataset_name") or dataset_name,
                 "document_id": str(chunk.get("document_id") or ""),
                 "document_name": chunk.get("document_name") or chunk.get("source", ""),
-                "data_source_type": "upload_file",
+                "data_source_type": chunk.get("data_source_type") or chunk.get("source_scope") or "upload_file",
                 "segment_id": chunk.get("id", ""),
                 "retriever_from": "rag",
                 "score": round(float(chunk.get("score") or 0.0), 4),
@@ -405,6 +415,8 @@ class RagRetriever:
                 "index_node_hash": "",
                 "content": chunk.get("text", "")[:500],
                 "page": None,
+                "source_scope": chunk.get("source_scope") or "",
+                "attachment_id": chunk.get("attachment_id") or "",
             }
             for index, chunk in enumerate(context_chunks)
         ]