q

langchain/chains/classification/multiselection.py

@@ -132,8 +132,17 @@ def _write_records_to_string(
 T = TypeVar("T")
 
 
-def batch(iterable: Iterable[T], size) -> Iterator[List[T]]:
-    """Batch an iterable into chunks of size `size`."""
+def _batch(iterable: Iterable[T], size: int) -> Iterator[List[T]]:
+    """Batch an iterable into chunks of size `size`.
+
+    Args:
+        iterable: the iterable to batch
+        size: the size of each batch
+
+    Returns:
+        iterator over batches of size `size` except for last batch which will be up
+        to size `size`
+    """
     iterator = iter(iterable)
     while True:
         batch = list(islice(iterator, size))
@@ -167,12 +176,12 @@ class MultiSelectChain(Chain):
         question = inputs["question"]
         columns = inputs.get("columns", None)
 
-        selected = []
+        selected: List[Mapping[str, Any]] = []
         # TODO(): Balance choices into equal batches with constraint dependent
         # on context window and prompt
         max_choices = 30
 
-        for choice_batch in batch(choices, max_choices):
+        for choice_batch in _batch(choices, max_choices):
             records_with_ids = [
                 {**record, "id": idx} for idx, record in enumerate(choice_batch)
             ]
@@ -185,7 +194,7 @@ class MultiSelectChain(Chain):
                 self.llm_chain.predict_and_parse(
                     records=records_str,
                     question=question,
-                    callbacks=run_manager.get_child(),
+                    callbacks=run_manager.get_child() if run_manager else None,
                 ),
             )
             valid_indexes = [idx for idx in indexes if 0 <= idx < len(choice_batch)]
@@ -204,12 +213,12 @@ class MultiSelectChain(Chain):
         question = inputs["question"]
         columns = inputs.get("columns", None)
 
-        selected = []
+        selected: List[Mapping[str, Any]] = []
         # TODO(): Balance choices into equal batches with constraint dependent
         # on context window and prompt
         max_choices = 30
 
-        for choice_batch in batch(choices, max_choices):
+        for choice_batch in _batch(choices, max_choices):
             records_with_ids = [
                 {**record, "id": idx} for idx, record in enumerate(choice_batch)
             ]
@@ -222,7 +231,7 @@ class MultiSelectChain(Chain):
                 await self.llm_chain.apredict_and_parse(
                     records=records_str,
                     question=question,
-                    callbacks=run_manager.get_child(),
+                    callbacks=run_manager.get_child() if run_manager else None,
                 ),
             )
             valid_indexes = [idx for idx in indexes if 0 <= idx < len(choice_batch)]

langchain/chains/research/api.py

@@ -36,7 +36,8 @@ class Research(Chain):
 
     Limitations:
         * This research chain only implements a single hop at the moment; i.e.,
-          it goes from the questions to a list of URLs to documents to compiling answers.
+          it goes from the questions to a list of URLs to documents to compiling
+          answers.
         * The reader chain needs to match the task. For example, if using a QA refine
           chain, a task of collecting a list of entries from a long document will
           fail because the QA refine chain is not designed to handle such a task.
@@ -145,7 +146,8 @@ class Research(Chain):
             top_k_per_search: The number of documents to return per search.
             max_concurrency: The maximum number of concurrent reads.
             max_num_pages_per_doc: The maximum number of pages to read per document.
-            text_splitter: The text splitter to use to split the document into smaller chunks.
+            text_splitter: The text splitter to use to split the document into
+                           smaller chunks.
             download_handler: The download handler to use to download the documents.
                               Provide either a download handler or the name of a
                               download handler.

langchain/chains/research/download.py

@@ -70,7 +70,7 @@ class PlaywrightDownloadHandler(DownloadHandler):
         """
         self.timeout = timeout
 
-    def download(self, urls: Sequence[str]) -> List[Blob]:
+    def download(self, urls: Sequence[str]) -> List[MaybeBlob]:
         """Download list of urls synchronously."""
         return asyncio.run(self.adownload(urls))
 
@@ -87,7 +87,7 @@ class PlaywrightDownloadHandler(DownloadHandler):
             html_content = None
         return html_content
 
-    async def adownload(self, urls: Sequence[str]) -> List[Blob]:
+    async def adownload(self, urls: Sequence[str]) -> List[MaybeBlob]:
         """Download a batch of URLs asynchronously using playwright.
 
         Args:
@@ -112,7 +112,7 @@ class RequestsDownloadHandler(DownloadHandler):
         """Initialize the requests download handler."""
         self.web_downloader = web_downloader or WebBaseLoader(web_path=[])
 
-    def download(self, urls: Sequence[str]) -> str:
+    def download(self, urls: Sequence[str]) -> List[MaybeBlob]:
         """Download a batch of URLS synchronously."""
         return asyncio.run(self.adownload(urls))
 
@@ -160,7 +160,7 @@ class AutoDownloadHandler(DownloadHandler):
         must_redownload = [
             (idx, url)
             for idx, (url, blob) in enumerate(zip(urls, blobs))
-            if _is_javascript_required(blob.as_string())
+            if blob is not None and _is_javascript_required(blob.as_string())
         ]
         if must_redownload:
             indexes, urls_to_redownload = zip(*must_redownload)

langchain/chains/research/readers.py

@@ -59,7 +59,7 @@ class DocReadingChain(Chain):
 
         response = self.chain(
             {"input_documents": _sub_docs, "question": question},
-            callbacks=run_manager.get_child(),
+            callbacks=run_manager.get_child() if run_manager else None,
         )
         summary_doc = Document(
             page_content=response["output_text"],
@@ -83,7 +83,7 @@ class DocReadingChain(Chain):
 
         results = await self.chain.acall(
             {"input_documents": _sub_docs, "question": question},
-            callbacks=run_manager.get_child(),
+            callbacks=run_manager.get_child() if run_manager else None,
         )
         summary_doc = Document(
             page_content=results["output_text"],

langchain/chains/research/search.py

@@ -167,7 +167,7 @@ async def _arun_searches(
         a list of unique search results
     """
     wrapper = serpapi.SerpAPIWrapper()
-    tasks = [wrapper.results(query) for query in queries]
+    tasks = [wrapper.aresults(query) for query in queries]
     results = await asyncio.gather(*tasks)
 
     finalized_results = []
@@ -218,7 +218,8 @@ class GenericSearcher(Chain):
 
     1. Breaking a complex question into a series of simpler queries using an LLM.
     2. Running the queries against a search engine.
-    3. Selecting the most relevant urls using an LLM (can be replaced with tf-idf or other models).
+    3. Selecting the most relevant urls using an LLM (can be replaced with tf-idf
+       or other models).
 
     This chain is not meant to be used for questions requiring multiple hops to answer.
 
@@ -236,7 +237,7 @@ class GenericSearcher(Chain):
     """
 
     query_generator: LLMChain
-    """An LLM that is used to break down a complex question into a list of simpler queries."""
+    """An LLM used to break down a complex question into a list of simpler queries."""
     link_selection_model: Chain
     """An LLM that is used to select the most relevant urls from the search results."""
     top_k_per_search: int = -1