docstrings: docstore and client (#6783)

updated docstrings in `docstore/` and `client/`

@baskaryan

langchain/client/__init__.py

@@ -1,9 +1,16 @@
 """LangChain + Client."""
 from langchain.client.runner_utils import (
+    InputFormatError,
     arun_on_dataset,
     arun_on_examples,
     run_on_dataset,
     run_on_examples,
 )
 
-__all__ = ["arun_on_dataset", "run_on_dataset", "arun_on_examples", "run_on_examples"]
+__all__ = [
+    "InputFormatError",
+    "arun_on_dataset",
+    "run_on_dataset",
+    "arun_on_examples",
+    "run_on_examples",
+]

langchain/client/runner_utils.py

@@ -51,8 +51,7 @@ class InputFormatError(Exception):
 
 
 def _get_prompts(inputs: Dict[str, Any]) -> List[str]:
-    """
-    Get prompts from inputs.
+    """Get prompts from inputs.
 
     Args:
         inputs: The input dictionary.
@@ -99,8 +98,7 @@ def _get_prompts(inputs: Dict[str, Any]) -> List[str]:
 
 
 def _get_messages(inputs: Dict[str, Any]) -> List[List[BaseMessage]]:
-    """
-    Get Chat Messages from inputs.
+    """Get Chat Messages from inputs.
 
     Args:
         inputs: The input dictionary.
@@ -143,8 +141,7 @@ async def _arun_llm(
     callbacks: Callbacks = None,
     input_mapper: Optional[Callable[[Dict], Any]] = None,
 ) -> Union[LLMResult, ChatResult]:
-    """
-    Asynchronously run the language model.
+    """Asynchronously run the language model.
 
     Args:
         llm: The language model to run.
@@ -203,8 +200,7 @@ async def _arun_llm_or_chain(
     callbacks: Optional[List[BaseCallbackHandler]] = None,
     input_mapper: Optional[Callable[[Dict], Any]] = None,
 ) -> Union[List[dict], List[str], List[LLMResult], List[ChatResult]]:
-    """
-    Asynchronously run the Chain or language model.
+    """Asynchronously run the Chain or language model.
 
     Args:
         example: The example to run.
@@ -264,8 +260,7 @@ async def _gather_with_concurrency(
         [Sequence[BaseCallbackHandler], Dict], Coroutine[Any, Any, Any]
     ],
 ) -> List[Any]:
-    """
-    Run coroutines with a concurrency limit.
+    """Run coroutines with a concurrency limit.
 
     Args:
         n: The maximum number of concurrent tasks.
@@ -503,7 +498,8 @@ def run_llm_or_chain(
         callbacks: Optional callbacks to use during the run.
 
     Returns:
-        A list of outputs.
+        Union[List[dict], List[str], List[LLMResult], List[ChatResult]]:
+          The outputs of the model or chain.
     """
     if callbacks:
         previous_example_ids = [
@@ -670,8 +666,8 @@ async def arun_on_dataset(
         project_name: Name of the project to store the traces in.
             Defaults to {dataset_name}-{chain class name}-{datetime}.
         verbose: Whether to print progress.
-        client: Client to use to read the dataset. If not provided, a new
-            client will be created using the credentials in the environment.
+        client: Client to use to read the dataset. If not provided,
+            a new client will be created using the credentials in the environment.
         tags: Tags to add to each run in the project.
         run_evaluators: Evaluators to run on the results of the chain.
         input_mapper: A function to map to the inputs dictionary from an Example
@@ -725,15 +721,14 @@ def run_on_dataset(
         llm_or_chain_factory: Language model or Chain constructor to run
             over the dataset. The Chain constructor is used to permit
             independent calls on each example without carrying over state.
-        concurrency_level: Number of async workers to run in parallel.
         num_repetitions: Number of times to run the model on each example.
             This is useful when testing success rates or generating confidence
             intervals.
         project_name: Name of the project to store the traces in.
             Defaults to {dataset_name}-{chain class name}-{datetime}.
         verbose: Whether to print progress.
-        client: Client to use to access the dataset. If None, a new client
-            will be created using the credentials in the environment.
+        client: Client to use to access the dataset. If None,
+            a new client will be created using the credentials in the environment.
         tags: Tags to add to each run in the project.
         run_evaluators: Evaluators to run on the results of the chain.
         input_mapper: A function to map to the inputs dictionary from an Example

langchain/docstore/arbitrary_fn.py

@@ -5,8 +5,7 @@ from langchain.schema import Document
 
 
 class DocstoreFn(Docstore):
-    """
-    Langchain Docstore via arbitrary lookup function.
+    """Langchain Docstore via arbitrary lookup function.
 
     This is useful when:
      * it's expensive to construct an InMemoryDocstore/dict
@@ -21,6 +20,14 @@ class DocstoreFn(Docstore):
         self._lookup_fn = lookup_fn
 
     def search(self, search: str) -> Document:
+        """Search for a document.
+
+        Args:
+            search: search string
+
+        Returns:
+            Document if found, else error message.
+        """
         r = self._lookup_fn(search)
         if isinstance(r, str):
             # NOTE: assume the search string is the source ID

langchain/docstore/in_memory.py

@@ -13,14 +13,28 @@ class InMemoryDocstore(Docstore, AddableMixin):
         self._dict = _dict if _dict is not None else {}
 
     def add(self, texts: Dict[str, Document]) -> None:
-        """Add texts to in memory dictionary."""
+        """Add texts to in memory dictionary.
+
+        Args:
+            texts: dictionary of id -> document.
+
+        Returns:
+            None
+        """
         overlapping = set(texts).intersection(self._dict)
         if overlapping:
             raise ValueError(f"Tried to add ids that already exist: {overlapping}")
         self._dict = {**self._dict, **texts}
 
     def search(self, search: str) -> Union[str, Document]:
-        """Search via direct lookup."""
+        """Search via direct lookup.
+
+        Args:
+            search: id of a document to search for.
+
+        Returns:
+            Document if found, else error message.
+        """
         if search not in self._dict:
             return f"ID {search} not found."
         else:

langchain/docstore/wikipedia.py

@@ -25,6 +25,11 @@ class Wikipedia(Docstore):
 
         If page exists, return the page summary, and a PageWithLookups object.
         If page does not exist, return similar entries.
+
+        Args:
+            search: search string.
+
+        Returns: a Document object or error message.
         """
         import wikipedia