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change ResourceManager to fileManager
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,147 @@ | ||
| import os | ||
|
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| from llama_index import SimpleDirectoryReader | ||
|
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||
| from superagi.config.config import get_config | ||
| from superagi.helper.resource_helper import ResourceHelper | ||
| from llama_index.schema import Document | ||
|
|
||
| from superagi.lib.logger import logger | ||
| from superagi.types.vector_store_types import VectorStoreType | ||
| from llama_index.indices.response import ResponseMode | ||
|
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|
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| class ResourceManager: | ||
| @classmethod | ||
| async def create_llama_document(cls, file_path: str = None, file_object=None): | ||
| """ | ||
| Creates a document index from a given directory. | ||
| """ | ||
|
|
||
| if file_path is None and file_object is None: | ||
| raise Exception("Either file_path or file_object must be provided") | ||
|
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| if file_path is not None and file_object is not None: | ||
| raise Exception("Only one of file_path or file_object must be provided") | ||
|
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| save_directory = ResourceHelper.get_root_input_dir() + "/" | ||
|
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| if file_object is not None: | ||
| file_path = save_directory + file_object.filename | ||
| with open(file_path, "wb") as f: | ||
| contents = await file_object.read() | ||
| f.write(contents) | ||
| file_object.file.close() | ||
|
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| documents = SimpleDirectoryReader(input_files=[file_path]).load_data() | ||
|
|
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| if file_object is not None: | ||
| os.remove(file_path) | ||
|
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| return documents | ||
|
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||
| @classmethod | ||
| def generate_summary_of_document(cls, documents: list[Document], openai_api_key: str = None): | ||
| openai_api_key = openai_api_key or get_config("OPENAI_API_KEY") | ||
| from llama_index import LLMPredictor, ServiceContext, ResponseSynthesizer, DocumentSummaryIndex | ||
| from langchain.chat_models import ChatOpenAI | ||
| os.environ["OPENAI_API_KEY"] = openai_api_key | ||
| llm_predictor_chatgpt = LLMPredictor(llm=ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo", | ||
| openai_api_key=openai_api_key)) | ||
| service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor_chatgpt, chunk_size=1024) | ||
| response_synthesizer = ResponseSynthesizer.from_args(response_mode=ResponseMode.TREE_SUMMARIZE, use_async=True) | ||
| doc_summary_index = DocumentSummaryIndex.from_documents( | ||
| documents=documents, | ||
| service_context=service_context, | ||
| response_synthesizer=response_synthesizer | ||
| ) | ||
|
|
||
| return doc_summary_index.get_document_summary(documents[0].doc_id) | ||
|
|
||
| @classmethod | ||
| def generate_summary_of_texts(cls, texts: list[str], openai_api_key: str): | ||
| from llama_index import Document | ||
| documents = [Document(doc_id=f"doc_id_{i}", text=text) for i, text in enumerate(texts)] | ||
| return cls.generate_summary_of_document(documents, openai_api_key) | ||
|
|
||
| @classmethod | ||
| def llama_vector_store_factory(cls, vector_store_name: VectorStoreType, index_name, embedding_model): | ||
| """ | ||
| Creates a llama vector store. | ||
| """ | ||
| from superagi.vector_store.vector_factory import VectorFactory | ||
|
|
||
| vector_factory_support = [VectorStoreType.PINECONE, VectorStoreType.WEAVIATE] | ||
| if vector_store_name in vector_factory_support: | ||
| vector_store = VectorFactory.get_vector_storage(vector_store_name, index_name, | ||
| embedding_model) | ||
| if vector_store_name == VectorStoreType.PINECONE: | ||
| from llama_index.vector_stores import PineconeVectorStore | ||
| return PineconeVectorStore(vector_store.index) | ||
|
|
||
| if vector_store_name == VectorStoreType.WEAVIATE: | ||
| raise ValueError("Weaviate vector store is not supported yet.") | ||
|
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| if vector_store_name == VectorStoreType.REDIS: | ||
| redis_url = get_config("REDIS_VECTOR_STORE_URL") or "redis://super__redis:6379" | ||
| from llama_index.vector_stores import RedisVectorStore | ||
| return RedisVectorStore( | ||
| index_name=index_name, | ||
| redis_url=redis_url, | ||
| metadata_fields=["agent_id", "resource_id"] | ||
| ) | ||
|
|
||
| if vector_store_name == VectorStoreType.CHROMA: | ||
| from llama_index.vector_stores import ChromaVectorStore | ||
| import chromadb | ||
| from chromadb.config import Settings | ||
| chroma_host_name = get_config("CHROMA_HOST_NAME") or "localhost" | ||
| chroma_port = get_config("CHROMA_PORT") or 8000 | ||
| chroma_client = chromadb.Client( | ||
| Settings(chroma_api_impl="rest", chroma_server_host=chroma_host_name, | ||
| chroma_server_http_port=chroma_port)) | ||
| chroma_collection = chroma_client.get_or_create_collection(index_name) | ||
| return ChromaVectorStore(chroma_collection), chroma_collection | ||
|
|
||
| if vector_store_name == VectorStoreType.QDRANT: | ||
| from llama_index.vector_stores import QdrantVectorStore | ||
| qdrant_host_name = get_config("QDRANT_HOST_NAME") or "localhost" | ||
| qdrant_port = get_config("QDRANT_PORT") or 6333 | ||
| from qdrant_client import QdrantClient | ||
| qdrant_client = QdrantClient(host=qdrant_host_name, port=qdrant_port) | ||
| return QdrantVectorStore(client=qdrant_client, collection_name=index_name) | ||
|
|
||
| @classmethod | ||
| def save_document_to_vector_store(cls, documents: list, agent_id: str, resource_id: str): | ||
| from llama_index import VectorStoreIndex, StorageContext | ||
| import openai | ||
| from superagi.vector_store.embedding.openai import OpenAiEmbedding | ||
| model_api_key = get_config("OPENAI_API_KEY") | ||
| for docs in documents: | ||
| if docs.metadata is None: | ||
| docs.metadata = {"agent_id": agent_id, "resource_id": resource_id} | ||
| else: | ||
| docs.metadata["agent_id"] = agent_id | ||
| docs.metadata["resource_id"] = resource_id | ||
| os.environ["OPENAI_API_KEY"] = get_config("OPENAI_API_KEY") | ||
| vector_store = None | ||
| storage_context = None | ||
| vector_store_name = VectorStoreType.get_enum(get_config("RESOURCE_VECTOR_STORE") or "Redis") | ||
| vector_store_index_name = get_config("RESOURCE_VECTOR_STORE_INDEX_NAME") or "super-agent-index" | ||
| try: | ||
| print(vector_store_name, vector_store_index_name) | ||
| vector_store = cls.llama_vector_store_factory(vector_store_name, vector_store_index_name, | ||
| OpenAiEmbedding(model_api_key)) | ||
| if vector_store_name == VectorStoreType.CHROMA: | ||
| vector_store, chroma_collection = vector_store | ||
| storage_context = StorageContext.from_defaults(vector_store=vector_store) | ||
| except ValueError as e: | ||
| logger.error(f"Vector store not found{e}") | ||
| openai.api_key = get_config("OPENAI_API_KEY") | ||
| try: | ||
| index = VectorStoreIndex.from_documents(documents, storage_context=storage_context) | ||
| index.set_index_id(f'Agent {agent_id}') | ||
| except Exception as e: | ||
| print(e) | ||
| if vector_store_name == VectorStoreType.REDIS: | ||
| vector_store.persist(persist_path="") |
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