context engine docstring

src/canopy/context_engine/context_engine.py

@@ -24,6 +24,26 @@ async def aquery(self, queries: List[Query], max_context_tokens: int, ) -> Conte
 
 
 class ContextEngine(BaseContextEngine):
+    """
+    ContextEngine is responsible for providing context to the LLM, given a set of search queries.
+    
+    Once called with a set of queries, the ContextEngine will go through the following steps:
+    1. Query the knowledge base for relevant documents
+    2. Build a context from the documents retrieved that can be injected into the LLM prompt
+    
+    The context engine considers token budgeting when building the context, and tries to maximize the amount of relevant information that can be provided to the LLM within the token budget.
+    
+    To create a context engine, you must provide a knowledge base and optionally a context builder.
+    
+    Example:
+    >>> from canopy.context_engine import ContextEngine
+    >>> from canopy.models.data_models import Query
+    >>> context_engine = ContextEngine(knowledge_base=knowledge_base)
+    >>> context_engine.query(Query(text="What is the capital of France?"), max_context_tokens=1000)
+    
+    To create a knowledge base, see the documentation for the knowledge base module (canopy.knowledge_base.knowledge_base).
+    """  # noqa: E501
+
 
     _DEFAULT_COMPONENTS = {
         'knowledge_base': KnowledgeBase,
@@ -36,6 +56,14 @@ def __init__(self,
                  context_builder: Optional[ContextBuilder] = None,
                  global_metadata_filter: Optional[dict] = None
                  ):
+        """
+        Initialize a new ContextEngine.
+
+        Args:
+            knowledge_base: The knowledge base to query for retrieving documents
+            context_builder: The context builder to use for building the context from the retrieved documents
+            global_metadata_filter: A metadata filter to apply to all queries. See: https://docs.pinecone.io/docs/metadata-filtering
+        """  # noqa: E501
 
         if not isinstance(knowledge_base, BaseKnowledgeBase):
             raise TypeError("knowledge_base must be an instance of BaseKnowledgeBase, "
@@ -55,6 +83,22 @@ def __init__(self,
         self.global_metadata_filter = global_metadata_filter
 
     def query(self, queries: List[Query], max_context_tokens: int, ) -> Context:
+        """
+        Query the knowledge base for relevant documents and build a context from the retrieved documents that can be injected into the LLM prompt.
+        
+        Args:
+            queries: A list of queries to use for retrieving documents from the knowledge base
+            max_context_tokens: The maximum number of tokens to use for the context
+        
+        Returns:
+            A Context object containing the retrieved documents and metadata
+            
+        Example:
+        >>> from canopy.context_engine import ContextEngine
+        >>> from canopy.models.data_models import Query
+        >>> context_engine = ContextEngine(knowledge_base=knowledge_base)
+        >>> context_engine.query(Query(text="What is the capital of France?"), max_context_tokens=1000)
+        """  # noqa: E501
         query_results = self.knowledge_base.query(
             queries,
             global_metadata_filter=self.global_metadata_filter)