feat: implement RAG capabilities and Context Pane integration

- Add RAG capabilities using LlamaIndex and ChromaDB
- Implement RAGManager for PHB indexing and retrieval
- Integrate RAG pipeline into orchestrator to trigger queries based on extracted entities
- Update TUI to include a 3-column layout with a real-time Context Pane
- Define ContextUpdate data models in src/llm/models.py
- Update requirements.txt with new dependencies

src/rag/manager.py

@@ -0,0 +1,86 @@
+import os
+from typing import List, Optional
+
+import chromadb
+import pdfplumber
+from llama_index.core import Document, Settings, StorageContext, VectorStoreIndex
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+from llama_index.vector_stores.chroma import ChromaVectorStore
+
+from src.llm.models import ContextUpdate
+
+
+class RAGManager:
+    def __init__(self, persist_dir: str = "data/rag_index"):
+        self.persist_dir = persist_dir
+        self.db = chromadb.PersistentClient(path=self.persist_dir)
+        self.collection_name = "phb_collection"
+
+        # Initialize Chroma Vector Store
+        self.vector_store = ChromaVectorStore(
+            chroma_collection=self.db.get_or_create_collection(self.collection_name)
+        )
+
+        # Initialize Storage Context
+        self.storage_context = StorageContext.from_defaults(
+            vector_store=self.vector_store
+        )
+
+        # Use a local HuggingFace embedding model to avoid API key issues during verification
+        Settings.embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
+
+        # Load index if it exists, otherwise initialize
+        try:
+            self.index = VectorStoreIndex.from_vector_store(
+                self.vector_store, storage_context=self.storage_context
+            )
+        except Exception:
+            self.index = None
+
+    def ingest_pdf(self, pdf_path: str):
+        """
+        Parses a PDF, chunks it, and stores embeddings in ChromaDB.
+        """
+        documents = []
+        with pdfplumber.open(pdf_path) as pdf:
+            for i, page in enumerate(pdf.pages):
+                text = page.extract_text()
+                if text:
+                    # Create a document for each page
+                    # In a real scenario, we might use a recursive character splitter
+                    # but for PHB, page-level chunking is a good start.
+                    doc = Document(
+                        text=text, metadata={"source": f"PHB p. {i + 1}", "page": i + 1}
+                    )
+                    documents.append(doc)
+
+        if not documents:
+            print(f"No text extracted from {pdf_path}")
+            return
+
+        # Create index from documents
+        self.index = VectorStoreIndex.from_documents(
+            documents, storage_context=self.storage_context
+        )
+        print(f"Successfully ingested {pdf_path} into the vector store.")
+
+    def retrieve(self, query: str, top_k: int = 3) -> List[ContextUpdate]:
+        """
+        Retrieves the top-K most relevant snippets for a given query.
+        """
+        if not self.index:
+            print("Index not initialized. Please ingest documents first.")
+            return []
+
+        # Create a retriever
+        retriever = self.index.as_retriever(similarity_top_k=top_k)
+        nodes = retriever.retrieve(query)
+
+        results = []
+        for node in nodes:
+            # Extract metadata
+            source = node.metadata.get("source", "Unknown Source")
+
+            results.append(ContextUpdate(query=query, snippet=node.text, source=source))
+
+        return results