Brought pre-commit to pass

paperqa/__init__.py

@@ -21,29 +21,29 @@
 from .version import __version__
 
 __all__ = [
-    "Docs",
     "Answer",
-    "PromptCollection",
-    "__version__",
-    "Doc",
-    "Text",
+    "AnthropicLLMModel",
     "Context",
-    "LLMModel",
+    "Doc",
+    "Docs",
     "EmbeddingModel",
-    "OpenAIEmbeddingModel",
     "HybridEmbeddingModel",
-    "SparseEmbeddingModel",
-    "OpenAILLMModel",
-    "AnthropicLLMModel",
+    "LLMModel",
+    "LLMResult",
+    "LangchainEmbeddingModel",
     "LangchainLLMModel",
+    "LangchainVectorStore",
     "LlamaEmbeddingModel",
-    "SentenceTransformerEmbeddingModel",
-    "LangchainEmbeddingModel",
     "NumpyVectorStore",
-    "LangchainVectorStore",
+    "OpenAIEmbeddingModel",
+    "OpenAILLMModel",
+    "PromptCollection",
+    "SentenceTransformerEmbeddingModel",
+    "SparseEmbeddingModel",
+    "Text",
+    "__version__",
+    "embedding_model_factory",
+    "llm_model_factory",
     "print_callback",
-    "LLMResult",
     "vector_store_factory",
-    "llm_model_factory",
-    "embedding_model_factory",
 ]

paperqa/docs.py

@@ -142,7 +142,7 @@ def config_summary_llm_config(cls, data: Any) -> Any:
             if (
                 data.summary_llm_model is None
                 and data.llm == "default"
-                and type(data.llm_model) == OpenAILLMModel
+                and isinstance(data.llm_model, OpenAILLMModel)
             ):
                 data.summary_llm_model = OpenAILLMModel(
                     config={"model": "gpt-3.5-turbo", "temperature": 0.1}
@@ -530,8 +530,8 @@ async def adoc_match(
             if (
                 rerank is None
                 and (
-                    type(self.llm_model) == OpenAILLMModel
-                    and cast(OpenAILLMModel, self).config["model"].startswith("gpt-4")
+                    isinstance(self.llm_model, OpenAILLMModel)
+                    and self.config["model"].startswith("gpt-4")
                 )
                 or rerank is True
             ):
@@ -703,33 +703,34 @@ async def process(match):  # noqa: C901, PLR0912
                 if self.strip_citations:
                     # remove citations that collide with our grounded citations (for the answer LLM)
                     context = strip_citations(context)
-            c = Context(
-                context=context,
-                text=Text(
-                    text=match.text,
-                    name=match.name,
-                    doc=match.doc.__class__(
-                        **match.doc.model_dump(exclude="embedding")
+            return (
+                Context(
+                    context=context,
+                    text=Text(
+                        text=match.text,
+                        name=match.name,
+                        doc=match.doc.__class__(
+                            **match.doc.model_dump(exclude="embedding")
+                        ),
                     ),
+                    score=score,
+                    **extras,
                 ),
-                score=score,
-                **extras,
+                llm_result,
             )
-            return c, llm_result
 
         results = await gather_with_concurrency(
             self.max_concurrent, [process(m) for m in matches]
         )
         # update token counts
         [answer.add_tokens(r[1]) for r in results]
 
-        # filter out failures
-        contexts = [c for c, r in results if c is not None]
-
+        # filter out failures, sort by score, limit to max_sources
         answer.contexts = sorted(
-            contexts + answer.contexts, key=lambda x: x.score, reverse=True
-        )
-        answer.contexts = answer.contexts[:max_sources]
+            [c for c, r in results if c is not None] + answer.contexts,
+            key=lambda x: x.score,
+            reverse=True,
+        )[:max_sources]
         context_str = "\n\n".join(
             [
                 f"{c.text.name}: {c.context}"

paperqa/llms.py

@@ -81,13 +81,11 @@ def guess_model_type(model_name: str) -> str:  # noqa: PLR0911
 def is_anyscale_model(model_name: str) -> bool:
     # compares prefixes with anyscale models
     # https://docs.anyscale.com/endpoints/text-generation/query-a-model/
-    if (
+    return bool(
         os.environ.get("ANYSCALE_API_KEY")
         and os.environ.get("ANYSCALE_BASE_URL")
         and model_name.startswith(ANYSCALE_MODEL_PREFIXES)
-    ):
-        return True
-    return False
+    )
 
 
 def is_openai_model(model_name: str) -> bool:
@@ -296,8 +294,8 @@ async def execute(
                     )
                 result.prompt = messages
                 result.prompt_count = sum(
-                    [self.count_tokens(m["content"]) for m in messages]
-                ) + sum([self.count_tokens(m["role"]) for m in messages])
+                    self.count_tokens(m["content"]) for m in messages
+                ) + sum(self.count_tokens(m["role"]) for m in messages)
 
                 if callbacks is None:
                     output = await self.achat(client, messages)
@@ -612,14 +610,16 @@ async def similarity_search(
     def clear(self) -> None:
         pass
 
-    async def max_marginal_relevance_search(  # noqa: D417
+    async def max_marginal_relevance_search(
         self, client: Any, query: str, k: int, fetch_k: int
     ) -> tuple[Sequence[Embeddable], list[float]]:
         """Vectorized implementation of Maximal Marginal Relevance (MMR) search.
 
         Args:
+            client: TODOC.
             query: Query vector.
             k: Number of results to return.
+            fetch_k: Number of results to fetch from the vector store.
 
         Returns:
             List of tuples (doc, score) of length k.

paperqa/readers.py

@@ -116,7 +116,7 @@ def parse_text(
         "parsing_libraries": ["tiktoken (cl100k_base)"] if use_tiktoken else [],
         "paperqa_version": str(pqa_version),
         "total_parsed_text_length": (
-            len(text) if isinstance(text, str) else sum([len(t) for t in text])
+            len(text) if isinstance(text, str) else sum(len(t) for t in text)
         ),
         "parse_type": "txt" if not html else "html",
     }
@@ -254,7 +254,7 @@ def read_doc(
 ) -> tuple[list[Text], ParsedMetadata]: ...
 
 
-def read_doc(  # noqa: PLR0912
+def read_doc(
     path: Path,
     doc: Doc,
     parsed_text_only: bool = False,

paperqa/utils.py

@@ -17,9 +17,7 @@
 def name_in_text(name: str, text: str) -> bool:
     sname = name.strip()
     pattern = rf"\b({re.escape(sname)})\b(?!\w)"
-    if re.search(pattern, text):
-        return True
-    return False
+    return bool(re.search(pattern, text))
 
 
 def maybe_is_text(s: str, thresh: float = 2.5) -> bool:
@@ -33,9 +31,7 @@ def maybe_is_text(s: str, thresh: float = 2.5) -> bool:
             entropy += -p * math.log2(p)
 
     # Check if the entropy is within a reasonable range for text
-    if entropy > thresh:
-        return True
-    return False
+    return entropy > thresh
 
 
 def maybe_is_pdf(file: BinaryIO) -> bool:
@@ -47,7 +43,7 @@ def maybe_is_pdf(file: BinaryIO) -> bool:
 def maybe_is_html(file: BinaryIO) -> bool:
     magic_number = file.read(4)
     file.seek(0)
-    return magic_number in (b"<htm", b"<!DO", b"<xsl", b"<!X")
+    return magic_number in {b"<htm", b"<!DO", b"<xsl", b"<!X"}
 
 
 def strings_similarity(s1: str, s2: str) -> float:

tests/test_paperqa.py

@@ -1030,7 +1030,7 @@ def test_langchain_llm():
     assert docs2.summary_llm == "babbage-002"
     docs2.get_evidence(
         Answer(question="What is Frederick Bates's greatest accomplishment?"),
-        get_callbacks=lambda x: [lambda y: print(y)],  # noqa: ARG005
+        get_callbacks=lambda x: [print],  # noqa: ARG005
     )
 
 
@@ -1117,8 +1117,7 @@ async def test_langchain_vector_store():
     index.add_texts_and_embeddings(some_texts)
     assert index._store is not None
     # check search returns Text obj
-    data, score = await index.similarity_search(None, "test", k=1)  # type: ignore[unreachable]
-    print(data)
+    data, _ = await index.similarity_search(None, "test", k=1)  # type: ignore[unreachable]
     assert isinstance(data[0], Text)
 
     # now try with convenience
@@ -1386,7 +1385,7 @@ def test_parser_only_reader():
     assert any("pypdf" in t for t in parsed_text.metadata.parsing_libraries)
     assert parsed_text.metadata.chunk_metadata is None
     assert parsed_text.metadata.total_parsed_text_length == sum(
-        [len(t) for t in parsed_text.content.values()]  # type: ignore[misc,union-attr]
+        len(t) for t in parsed_text.content.values()  # type: ignore[misc,union-attr]
     )
 
 
@@ -1491,12 +1490,12 @@ def test_citation():
         f.write(r.text)
     docs = Docs()
     docs.add(doc_path)  # type: ignore[arg-type]
-    assert next(iter(docs.docs.values())).docname in (
+    assert next(iter(docs.docs.values())).docname in {
         "Wikipedia2024",
         "Frederick2024",
         "Wikipedia",
         "Frederick",
-    )
+    }
 
 
 def test_dockey_filter():