vector_store = SupabaseVectorStore( postgres_connection_string=connectionString, collection_name="llama_demo", ) storage_context = StorageContext.from_defaults(vector_store=vector_store) index = VectorStoreIndex.from_documents(documents, storage_context=storage_context) filters = MetadataFilters(filters=[ExactMatchFilter(key="workspaceId", value="25juldeplo482af4cd83")]) retriever = index.as_retriever(filters=filters) ans = retriever.retrieve("query?")
\nGiven the new context, refine the original answer to better answer the question. If the context isn\'t useful, return the original answer.", "stream": false, "model": "text-davinci-003", "temperature": 0.0, "max_tokens": 1740}' message='Post details'