feat: add QA on given chunk refs (#181)

Signed-off-by: Panos Vagenas <[email protected]>

deepsearch/cps/queries/__init__.py

@@ -15,6 +15,7 @@
     SemanticBackendResource,
 )
 from deepsearch.cps.client.queries import Query, TaskCoordinates
+from deepsearch.cps.queries.results import ChunkRef
 
 
 def Wf(wf_query: Dict[str, Any], kg: TaskCoordinates) -> Query:
@@ -110,6 +111,7 @@ class _APISemanticRagParameters(_APISemanticRetrievalParameters):
     gen_ctx_window_size: int = 5000
     gen_ctx_window_lead_weight: float = 0.5
     return_prompt: bool = False
+    chunk_refs: Optional[List[ChunkRef]] = None
     gen_timeout: Optional[float] = None
 
 
@@ -129,6 +131,7 @@ def RAGQuery(
     gen_ctx_window_size: int = 5000,
     gen_ctx_window_lead_weight: float = 0.5,
     return_prompt: bool = False,
+    chunk_refs: Optional[List[ChunkRef]] = None,
     gen_timeout: Optional[float] = None,
 ) -> Query:
     """Create a RAG query
@@ -147,6 +150,7 @@ def RAGQuery(
         gen_ctx_window_size (int, optional): (relevant only if gen_ctx_extr_method=="window") max chars to use for extracted gen context (actual extraction quantized on doc item level); defaults to 5000
         gen_ctx_window_lead_weight (float, optional): (relevant only if gen_ctx_extr_method=="window") weight of leading text for distributing remaining window size after extracting the `main_path`; defaults to 0.5 (centered around `main_path`)
         return_prompt (bool, optional): whether to return the instantiated prompt; defaults to False
+        chunk_refs (Optional[List[ChunkRef]], optional): list of explicit chunk references to use instead of performing retrieval; defaults to None (i.e. retrieval-mode)
         gen_timeout (float, optional): timeout for LLM generation; defaults to None, i.e. determined by system
     """
 
@@ -181,6 +185,7 @@ def RAGQuery(
         gen_ctx_window_size=gen_ctx_window_size,
         gen_ctx_window_lead_weight=gen_ctx_window_lead_weight,
         return_prompt=return_prompt,
+        chunk_refs=chunk_refs,
         gen_timeout=gen_timeout,
     )
 

deepsearch/cps/queries/results.py

@@ -2,21 +2,24 @@
 
 from typing import List, Optional
 
-from pydantic.v1 import BaseModel, root_validator
+from pydantic.v1 import BaseModel
 
 from deepsearch.cps.client.components.queries import RunQueryResult
 
 
-class SearchResultItem(BaseModel):
+class ChunkRef(BaseModel):
     doc_hash: str
+    main_path: str  # the anchor path among the contributing group
+    path_group: List[str]  # the doc paths contributing to the encoding source
+
+
+class SearchResultItem(ChunkRef):
     chunk: str
-    main_path: str
-    path_group: List[str]
     source_is_text: bool
 
 
 class RAGGroundingInfo(BaseModel):
-    retr_items: List[SearchResultItem]
+    retr_items: Optional[List[SearchResultItem]] = None
     gen_ctx_paths: List[str]
 
 
@@ -45,26 +48,35 @@ def __init__(self, msg="Search returned no results", *args, **kwargs):
 
 class RAGResult(BaseModel):
     answers: List[RAGAnswerItem]
-    search_result_items: List[SearchResultItem]
+    search_result_items: Optional[List[SearchResultItem]] = None
 
     @classmethod
     def from_api_output(cls, data: RunQueryResult, raise_on_error=True):
         answers: List[RAGAnswerItem] = []
         try:
-            search_result_items = data.outputs["retrieval"]["items"]
-            if raise_on_error and len(search_result_items) == 0:
-                raise NoSearchResultsError()
+            retrieval_part = data.outputs["retrieval"]
+            if retrieval_part is not None:
+                search_result_items = retrieval_part["items"]
+                if raise_on_error and len(search_result_items) == 0:
+                    raise NoSearchResultsError()
+            else:
+                search_result_items = None
             for answer_item in data.outputs["answers"]:
                 if raise_on_error and (gen_err := answer_item.get("gen_err")):
                     raise GenerationError(gen_err)
+                retr_idxs = answer_item["grounding_info"]["retr_idxs"]
                 answers.append(
                     RAGAnswerItem(
                         answer=answer_item["answer"],
                         grounding=RAGGroundingInfo(
-                            retr_items=[
-                                SearchResultItem.parse_obj(search_result_items[i])
-                                for i in answer_item["grounding_info"]["retr_idxs"]
-                            ],
+                            retr_items=(
+                                [
+                                    SearchResultItem.parse_obj(search_result_items[i])
+                                    for i in retr_idxs
+                                ]
+                                if retr_idxs is not None and retrieval_part is not None
+                                else None
+                            ),
                             gen_ctx_paths=answer_item["grounding_info"][
                                 "gen_ctx_paths"
                             ],