Apply Byron's and Nathan's suggestions from code review

Co-authored-by: byronnlandry <[email protected]>

src/canopy/models/data_models.py

@@ -14,11 +14,11 @@ class Query(BaseModel):
     text: str = Field(description="The query text.")
     namespace: str = Field(
         default="",
-        description="The namespace of the query, to learn more about namespaces, see https://docs.pinecone.io/docs/namespaces",  # noqa: E501
+        description="The namespace of the query. To learn more about namespaces, see https://docs.pinecone.io/docs/namespaces",  # noqa: E501
     )
     metadata_filter: Optional[dict] = Field(
         default=None,
-        description="A pinecone metadata filter, to learn more about metadata filters, see https://docs.pinecone.io/docs/metadata-filtering",  # noqa: E501
+        description="A Pinecone metadata filter, to learn more about metadata filters, see https://docs.pinecone.io/docs/metadata-filtering",  # noqa: E501
     )
     top_k: Optional[int] = Field(
         default=None,
@@ -39,7 +39,7 @@ class Document(BaseModel):
     )
     metadata: Metadata = Field(
         default_factory=dict,
-        description="The document metadata, to learn more about metadata, see https://docs.pinecone.io/docs/manage-data",  # noqa: E501
+        description="The document metadata. To learn more about metadata, see https://docs.pinecone.io/docs/manage-data",  # noqa: E501
     )
 
     class Config:
@@ -89,7 +89,7 @@ class Role(Enum):
 
 
 class MessageBase(BaseModel):
-    role: Role = Field(description="The role of the messages author.")
+    role: Role = Field(description="The role of the message's author. Can be one of ['User', 'Assistant', 'System']")
     content: str = Field(description="The contents of the message.")
 
     def dict(self, *args, **kwargs):

src/canopy_server/__init__.py

@@ -1,7 +1,7 @@
 description = """
 Canopy is an open-source Retrieval Augmented Generation (RAG) framework and context engine built on top of the Pinecone vector database. Canopy enables you to quickly and easily experiment with and build applications using RAG. Start chatting with your documents or text data with a few simple commands.
 
-Canopy provides a configurable built-in server so you can effortlessly deploy a RAG-powered chat application to your existing chat UI or interface. Or you can build your own, custom RAG application using the Canopy lirbary.
+Canopy provides a configurable built-in server, so you can effortlessly deploy a RAG-powered chat application to your existing chat UI or interface. Or you can build your own custom RAG application using the Canopy library.
 
 ## Prerequisites
 

src/canopy_server/api_models.py

@@ -8,14 +8,14 @@
 class ChatRequest(BaseModel):
     model: str = Field(
         default="",
-        description="ID of the model to use. Currecntly this field is ignored and this should be configured on Canopy config.",  # noqa: E501
+        description="The ID of the model to use. This field is ignored; instead, configure this field in the Canopy config.",  # noqa: E501
     )
     messages: Messages = Field(
         description="A list of messages comprising the conversation so far."
     )
     stream: bool = Field(
         default=False,
-        description="""Whether or not to stream the chatbot's response. If set, the response will be server-sent events containing [chat.completion.chunk](https://platform.openai.com/docs/api-reference/chat/streaming) objects""",  # noqa: E501
+        description="""Whether or not to stream the chatbot's response. If set, the response is server-sent events containing [chat.completion.chunk](https://platform.openai.com/docs/api-reference/chat/streaming) objects""",  # noqa: E501
     )
     user: Optional[str] = Field(
         default=None,

src/canopy_server/app.py

@@ -77,7 +77,7 @@ async def chat(
     """
     Chat with Canopy, using the LLM and context engine, and return a response.
 
-    The request schema is following OpenAI's chat completion API schema: https://platform.openai.com/docs/api-reference/chat/create.
+    The request schema follows OpenAI's chat completion API schema: https://platform.openai.com/docs/api-reference/chat/create.
     Note that all fields other than `messages` and `stream` are currently ignored. The Canopy server uses the model parameters defined in the `ChatEngine` config for all underlying LLM calls.
 
     """  # noqa: E501
@@ -122,9 +122,9 @@ async def query(
 ) -> ContextContent:
     """
     Query the knowledge base for relevant context.
-    The returned text might be structured or unstructured, depending on the ContextEngine's configuration.
-    Query allows limiting the context length (in tokens), to control LLM costs.
-    This method does not pass through the LLM and uses only retieval and construction from Pinecone DB.
+    The returned text may be structured or unstructured, depending on the Canopy configuration.
+    Query allows limiting the context length in tokens to control LLM costs.
+    This method does not pass through the LLM and uses only retrieval and construction from Pinecone DB.
     """  # noqa: E501
     try:
         context: Context = await run_in_threadpool(
@@ -152,7 +152,7 @@ async def upsert(
     Upsert documents into the knowledge base. Upserting is a way to add new documents or update existing ones.
     Each document has a unique ID. If a document with the same ID already exists, it is updated.
 
-    The documents will be chunked and encoded, then the resulting encoded chunks will be sent to the Pinecone index in batches
+    The documents are chunked and encoded, then the resulting encoded chunks are sent to the Pinecone index in batches.
     """  # noqa: E501
     try:
         logger.info(f"Upserting {len(request.documents)} documents")