Method find_relevant_docs

src/rag/vector_store.py:247–356

Finds relevant documentation based on the provided queries. Args: queries (List[Dict]): List of query dictionaries with 'type' and 'query' keys k (int, optional): Number of results to return per query. Defaults to 5 trace_id (str, optional): Trace identif

(self, queries: List[Dict], k: int = 5, trace_id: str = None, topic: str = None, scene_number: int = None)

        vector_store.persist()

    def find_relevant_docs(self, queries: List[Dict], k: int = 5, trace_id: str = None, topic: str = None, scene_number: int = None) -> List[str]:
        """Finds relevant documentation based on the provided queries.

        Args:
            queries (List[Dict]): List of query dictionaries with 'type' and 'query' keys
            k (int, optional): Number of results to return per query. Defaults to 5
            trace_id (str, optional): Trace identifier for logging. Defaults to None
            topic (str, optional): Topic name for logging. Defaults to None
            scene_number (int, optional): Scene number for logging. Defaults to None

        Returns:
            List[str]: Formatted string containing relevant documentation snippets
        """
        manim_core_formatted_results = []
        manim_plugin_formatted_results = []
        
        # Create a Langfuse span if enabled
        if self.use_langfuse:
            langfuse = Langfuse()
            span = langfuse.span(
                trace_id=trace_id,  # Use the passed trace_id
                name=f"RAG search for {topic} - scene {scene_number}",
                metadata={
                    "topic": topic,
                    "scene_number": scene_number,
                    "session_id": self.session_id
                }
            )
        
        # Separate queries by type
        manim_core_queries = [query for query in queries if query["type"] == "manim-core"]
        manim_plugin_queries = [query for query in queries if query["type"] != "manim-core" and query["type"] in self.plugin_stores]
        
        if len([q for q in queries if q["type"] != "manim-core"]) != len(manim_plugin_queries):
            print("Warning: Some plugin queries were skipped because their types weren't found in available plugin stores")
        
        # Search in core manim docs
        for query in manim_core_queries:
            query_text = query["query"]
            self.core_vector_store._embedding_function.parent_observation_id = span.id
            manim_core_results = self.core_vector_store.similarity_search_with_relevance_scores(
                query=query_text,
                k=k,
                score_threshold=0.5
            )
            for result in manim_core_results:
                manim_core_formatted_results.append({
                    "query": query_text,
                    "source": result[0].metadata['source'],
                    "content": result[0].page_content,
                    "score": result[1]
                })
        
        # Search in relevant plugin docs
        for query in manim_plugin_queries:
            plugin_name = query["type"]
            query_text = query["query"]
            self.plugin_stores[plugin_name]._embedding_function.parent_observation_id = span.id