diff --git a/.gitignore b/.gitignore index 7be08cd..112401c 100644 --- a/.gitignore +++ b/.gitignore @@ -4,3 +4,5 @@ data/* .cache/* .nv/* .DS_Store +.venv/* +indices/ \ No newline at end of file diff --git a/Pipfile b/Pipfile index f3dc2bb..8071b47 100644 --- a/Pipfile +++ b/Pipfile @@ -21,6 +21,8 @@ torch = { version = "*", sys_platform = "!= 'darwin'" } colorama = { version = "*", sys_platform = "== 'win32'" } ebooklib = "*" llama-index-core = "*" +typing_extensions = "*" +fsspec = "*" [dev-packages] diff --git a/Pipfile.lock b/Pipfile.lock index 3689e6b..f8aac94 100644 --- a/Pipfile.lock +++ b/Pipfile.lock @@ -1,7 +1,7 @@ { "_meta": { "hash": { - "sha256": "f426c78bc66b0e2eb5ef13d01f5719cd218e830a4419ad3ee95da8e6007d02de" + "sha256": "dcd4ba71fb8f54a8713015b8ec1a109108aa1964660eda6f2a17e499d381deaa" }, "pipfile-spec": 6, "requires": { @@ -15,3527 +15,6 @@ } ] }, - "default": { - "aiohttp": { - "hashes": [ - "sha256:0605cc2c0088fcaae79f01c913a38611ad09ba68ff482402d3410bf59039bfb8", - 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import streamlit as st - -import utils.helpers as func -import utils.ollama as ollama -import utils.llama_index as llama_index -import utils.logs as logs import utils.rag_pipeline as rag supported_files = ( @@ -42,9 +34,22 @@ def local_files(): disabled=True, ) - if len(uploaded_files) > 0: + st.text_input( + "Persisted Index ID", + key="persisted_index_id", + placeholder="local-rag-index", + help="The unique identifier for the persisted index.", + ) + st.caption( + "Persisted indices are stored on disk and can be reloaded for future use. This is useful for retaining the index state across sessions." + ) + + if len(uploaded_files) > 0 or st.session_state["persisted_index_id"]: st.session_state["file_list"] = uploaded_files + if len(uploaded_files) == 0: + uploaded_files = None + with st.spinner("Processing..."): # Initiate the RAG pipeline, providing documents to be saved on disk if necessary error = rag.rag_pipeline(uploaded_files) diff --git a/utils/llama_index.py b/utils/llama_index.py index bee7e10..69b8b38 100644 --- a/utils/llama_index.py +++ b/utils/llama_index.py @@ -5,6 +5,10 @@ import utils.logs as logs from llama_index.embeddings.huggingface import HuggingFaceEmbedding +from llama_index.core import StorageContext, load_index_from_storage + +from typing import Sequence +from llama_index.core.schema import Document # This is not used but required by llama-index and must be set FIRST os.environ["OPENAI_API_KEY"] = "sk-abc123" @@ -142,6 +146,58 @@ def create_index(_documents): raise Exception(f"Index creation failed: {err}") +def get_persisted_index( + index_id: str, persist_dir: str, _documents: Sequence[Document] +): + + storage_context = StorageContext.from_defaults(persist_dir=persist_dir) + try: + # Rebuild storage context + print(storage_context.index_store.index_structs()) + # Load index + index = load_index_from_storage(storage_context, index_id=index_id) + logs.log.info("Index loaded from persisted storage successfully") + + st.caption("✔️ Loaded Persisted Index") + + # Check if all upload documents are present in the index + if _documents is not None: + document_hashes = index.docstore.get_all_document_hashes() + + for doc in _documents: + if doc.hash not in document_hashes: + logs.log.info( + "Document {} not found in index. Adding it...".format( + doc.get_doc_id() + ) + ) + index.insert(doc) + + except Exception as e: + logs.log.error(f"Error loading persisted index: {e}") + + if _documents is not None: + try: + index = VectorStoreIndex.from_documents( + documents=_documents, + show_progress=True, + storage_context=storage_context, + ) + + logs.log.info("Index created from loaded documents successfully") + + index.set_index_id(index_id) + index.storage_context.persist(persist_dir=persist_dir) + st.caption("✔️ Created Persisted Index") + except Exception as err: + logs.log.error(f"Index creation failed: {err}") + raise Exception(f"Index creation failed: {err}") + else: + raise Exception(f"Cannot create persisted index without documents.") + + return index + + ################################### # # Create Query Engine @@ -169,7 +225,15 @@ def create_query_engine(_documents): This function uses the `create_index` function to create an index from the provided documents and service context, and then creates a query engine from the resulting index. The `query_engine` parameter is used to specify the parameters of the query engine, including the number of top-ranked items to return (`similarity_top_k`) and the response mode (`response_mode`). """ try: - index = create_index(_documents) + if st.session_state["persisted_index_id"]: + index = get_persisted_index( + index_id=st.session_state["persisted_index_id"], + persist_dir=os.getcwd() + "/indices", + _documents=_documents, + ) + else: + index = create_index(_documents) + st.caption("✔️ Created File Index") query_engine = index.as_query_engine( similarity_top_k=st.session_state["top_k"], diff --git a/utils/rag_pipeline.py b/utils/rag_pipeline.py index c38bb0d..9826fae 100644 --- a/utils/rag_pipeline.py +++ b/utils/rag_pipeline.py @@ -110,9 +110,12 @@ def rag_pipeline(uploaded_files: list = None): if ( st.session_state["documents"] is not None and len(st.session_state["documents"]) > 0 + and not st.session_state["persisted_index_id"] ): logs.log.info("Documents are already available; skipping document loading") st.caption("✔️ Processed File Data") + elif st.session_state["persisted_index_id"] and uploaded_files is None: + logs.log.info("No new documents to load; using persisted index") else: try: save_dir = os.getcwd() + "/data" @@ -133,7 +136,6 @@ def rag_pipeline(uploaded_files: list = None): llama_index.create_query_engine( st.session_state["documents"], ) - st.caption("✔️ Created File Index") except Exception as err: logs.log.error(f"Index Creation Error: {str(err)}") error = err