Adress inmemory import path changes after beta5 (#138)

integrations/assemblyai.md

@@ -64,7 +64,7 @@ from haystack.components.writers import DocumentWriter
 from haystack.components.preprocessors import DocumentSplitter
 from haystack.components.embedders import SentenceTransformersDocumentEmbedder
 from haystack.pipeline import Pipeline
-from haystack.document_stores import InMemoryDocumentStore
+from haystack.document_stores.in_memory import InMemoryDocumentStore
 from assemblyai_haystack.transcriber import AssemblyAITranscriber
 
 document_store = InMemoryDocumentStore()
@@ -117,7 +117,7 @@ The example below illustrates a generative QA pipeline that seamlessly integrate
 
 ```python
 from haystack import Pipeline
-from haystack.components.retrievers import InMemoryEmbeddingRetriever
+from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
 from haystack.components.builders.prompt_builder import PromptBuilder
 from haystack.components.generators import OpenAIGenerator
 from assemblyai_haystack.transcriber import AssemblyAITranscriber
@@ -152,7 +152,7 @@ Explore the example below to see how to index speaker diarization information an
 ```python 
 from haystack.document_stores.in_memory import InMemoryDocumentStore
 from haystack import Pipeline
-from haystack.components.retrievers import InMemoryBM25Retriever
+from haystack.components.retrievers.in_memory import InMemoryBM25Retriever
 from haystack.components.builders.prompt_builder import PromptBuilder
 from haystack.components.generators import OpenAIGenerator
 from assemblyai_haystack.transcriber import AssemblyAITranscriber

integrations/cohere.md

@@ -50,7 +50,7 @@ Below is the example indexing pipeline with `InMemoryDocumentStore`, `CohereDocu
 
 ```python
 from haystack import Document, Pipeline
-from haystack.document_stores import InMemoryDocumentStore
+from haystack.document_stores.in_memory import InMemoryDocumentStore
 from haystack.components.writers import DocumentWriter
 from cohere_haystack.embedders.document_embedder import CohereDocumentEmbedder
 
@@ -77,7 +77,7 @@ Below is the example of generative questions answering pipeline using RAG with `
 
 ```python
 from haystack import Pipeline
-from haystack.components.retrievers import InMemoryEmbeddingRetriever
+from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
 from haystack.components.builders.prompt_builder import PromptBuilder
 from cohere_haystack.embedders.text_embedder import CohereTextEmbedder
 from cohere_haystack.generator import CohereGenerator

integrations/gradient.md

@@ -42,7 +42,7 @@ You can use embedding models with `GradientDocumentEmbedder`` to create embeddin
 import os
 
 from haystack import Pipeline
-from haystack.document_stores import InMemoryDocumentStore
+from haystack.document_stores.in_memory import InMemoryDocumentStore
 from haystack.components.writers import DocumentWriter
 
 from haystack_integrations.components.embedders.gradient import GradientDocumentEmbedder
@@ -69,7 +69,7 @@ You can use embedding models with `GradientTextEmbedder` and generative models w
 ```python
 from haystack.components.builders.answer_builder import AnswerBuilder
 from haystack.components.builders.prompt_builder import PromptBuilder
-from haystack.components.retrievers import InMemoryEmbeddingRetriever
+from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
 
 from haystack_integrations.components.embedders.gradient import GradientTextEmbedder
 from haystack_integrations.components.generators.gradient import GradientGenerator

integrations/huggingface.md

@@ -51,7 +51,7 @@ Below is the example indexing pipeline with `InMemoryDocumentStore`, `DocumentWr
 ```python
 from haystack import Document
 from haystack import Pipeline
-from haystack.document_stores import InMemoryDocumentStore
+from haystack.document_stores.in_memory import InMemoryDocumentStore
 from haystack.components.embedders import SentenceTransformersDocumentEmbedder
 from haystack.components.writers import DocumentWriter
 
@@ -80,7 +80,7 @@ Below is the example query pipeline that uses `mistralai/Mistral-7B-v0.1` hosted
 
 ```python
 from haystack import Pipeline
-from haystack.components.retrievers import InMemoryBM25Retriever
+from haystack.components.retrievers.in_memory import InMemoryBM25Retriever
 from haystack.components.builders.prompt_builder import PromptBuilder
 from haystack.components.generators import HuggingFaceTGIGenerator
 
@@ -117,8 +117,8 @@ Below is the example of document retrieval pipeline with `InMemoryBM25Retriever`
 
 ```python
 from haystack import Document, Pipeline
-from haystack.document_stores import InMemoryDocumentStore
-from haystack.components.retrievers import InMemoryBM25Retriever
+from haystack.document_stores.in_memory import InMemoryDocumentStore
+from haystack.components.retrievers.in_memory import InMemoryBM25Retriever
 from haystack.components.rankers import TransformersSimilarityRanker
 
 docs = [Document(content="Paris is in France"), 
@@ -148,8 +148,8 @@ Below is the example of extractive question answering pipeline with `InMemoryBM2
 
 ```python
 from haystack import Document, Pipeline
-from haystack.document_stores import InMemoryDocumentStore
-from haystack.components.retrievers import InMemoryBM25Retriever
+from haystack.document_stores.in_memory import InMemoryDocumentStore
+from haystack.components.retrievers.in_memory import InMemoryBM25Retriever
 from haystack.components.readers import ExtractiveReader
 
 docs = [Document(content="Paris is the capital of France."),

integrations/instructor-embedder.md

@@ -98,7 +98,7 @@ print(f"Embedding Dimension: {len(result['embedding'])}")
 
 ```python
 from instructor_embedders.instructor_document_embedder import InstructorDocumentEmbedder
-from haystack.preview.dataclasses import Document
+from haystack.dataclasses import Document
 
 
 doc_embedding_instruction = "Represent the Medical Document for retrieval:"
@@ -147,11 +147,11 @@ print(f"Embedding Dimension: {len(result['documents'][0].embedding)}")
 
 ```python
 # Import necessary modules and classes
-from haystack.preview.document_stores import InMemoryDocumentStore
-from haystack.preview.dataclasses import Document
-from haystack.preview import Pipeline
-from haystack.preview.components.writers import DocumentWriter
-from haystack.preview.components.retrievers import InMemoryEmbeddingRetriever
+from haystack.document_stores.in_memory import InMemoryDocumentStore
+from haystack.dataclasses import Document
+from haystack import Pipeline
+from haystack.components.writers import DocumentWriter
+from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
 from datasets import load_dataset
 
 # Import custom INSTRUCTOR Embedders

integrations/jina.md

@@ -54,7 +54,7 @@ Below is the example indexing pipeline with `InMemoryDocumentStore`, `JinaDocume
 
 ```python
 from haystack import Document, Pipeline
-from haystack.document_stores import InMemoryDocumentStore
+from haystack.document_stores.in_memory import InMemoryDocumentStore
 from haystack.components.writers import DocumentWriter
 from jina_haystack import JinaDocumentEmbedder
 

integrations/lmformatenforcer.md

@@ -100,7 +100,7 @@ To activate the the enforcer with Haystack V2, a `LMFormatEnforcerLocalGenerator
 
 Here is a simple example:
 ```python
-from haystack.preview.components.generators.hugging_face.hugging_face_local import HuggingFaceLocalGenerator
+from haystack.components.generators.hugging_face.hugging_face_local import HuggingFaceLocalGenerator
 from lmformatenforcer.integrations.haystackv2 import LMFormatEnforcerLocalGenerator
 
 

integrations/notion-extractor.md

@@ -43,7 +43,7 @@ exported_pages = exporter.run(file_paths=["<list-of-page-ids>"])
 The following example shows how to use the `NotionExporter` inside an indexing pipeline:
 ```python
 from notion_haystack import NotionExporter
-from haystack.document_stores import InMemoryDocumentStore
+from haystack.document_stores.in_memory import InMemoryDocumentStore
 from haystack import Pipeline
 
 document_store = InMemoryDocumentStore()

integrations/ollama.md

@@ -71,7 +71,7 @@ Below is the example of generative questions answering pipeline using RAG with `
 ```python
 from haystack import Document, Pipeline
 from haystack.components.builders.prompt_builder import PromptBuilder
-from haystack.components.retrievers import InMemoryBM25Retriever
+from haystack.components.retrievers.in_memory import InMemoryBM25Retriever
 from haystack.document_stores.in_memory import InMemoryDocumentStore
 
 from haystack_integrations.components.generators.ollama import OllamaGenerator

integrations/openai.md

@@ -50,7 +50,7 @@ Below is the example indexing pipeline with `InMemoryDocumentStore`, `OpenAIDocu
 
 ```python
 from haystack import Document, Pipeline
-from haystack.document_stores import InMemoryDocumentStore
+from haystack.document_stores.in_memory import InMemoryDocumentStore
 from haystack.components.embedders import OpenAIDocumentEmbedder
 from haystack.components.writers import DocumentWriter
 
@@ -78,7 +78,7 @@ Below is the example of generative questions answering pipeline using RAG with `
 
 ```python
 from haystack import Pipeline
-from haystack.components.retrievers import InMemoryBM25Retriever
+from haystack.components.retrievers.in_memory import InMemoryBM25Retriever
 from haystack.components.builders.prompt_builder import PromptBuilder
 from haystack.components.generators import GPTGenerator
 
@@ -124,7 +124,7 @@ from haystack import Pipeline
 from haystack.components.audio import LocalWhisperTranscriber
 from haystack.components.preprocessors import DocumentSplitter, DocumentCleaner
 from haystack.components.writers import DocumentWriter
-from haystack.document_stores import InMemoryDocumentStore
+from haystack.document_stores.in_memory import InMemoryDocumentStore
 
 document_store = InMemoryDocumentStore()
 pipeline = Pipeline()
@@ -159,7 +159,7 @@ Below is the example indexing pipeline with `PreProcessor`, `InMemoryDocumentSto
 
 ```python
 from haystack.nodes import EmbeddingRetriever
-from haystack.document_stores import InMemoryDocumentStore
+from haystack.document_stores.in_memory import InMemoryDocumentStore
 from haystack.pipelines import Pipeline
 from haystack.schema import Document
 

integrations/unstructured-file-converter.md

@@ -51,9 +51,9 @@ documents = converter.run(paths = ["a/file/path.pdf", "a/directory/path"])["docu
 ### In a Haystack Pipeline
 ```python
 import os
-from haystack.preview import Pipeline
-from haystack.preview.components.writers import DocumentWriter
-from haystack.preview.document_stores import InMemoryDocumentStore
+from haystack import Pipeline
+from haystack.components.writers import DocumentWriter
+from haystack.document_stores.in_memory import InMemoryDocumentStore
 from haystack_integrations.components.converters.unstructured import UnstructuredFileConverter
 
 document_store = InMemoryDocumentStore()

integrations/voyage.md

@@ -58,10 +58,10 @@ Load the dataset:
 # Install HuggingFace Datasets using "pip install datasets"
 from datasets import load_dataset
 from haystack import Pipeline
-from haystack.components.retrievers import InMemoryEmbeddingRetriever
+from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
 from haystack.components.writers import DocumentWriter
 from haystack.dataclasses import Document
-from haystack.document_stores import InMemoryDocumentStore
+from haystack.document_stores.in_memory import InMemoryDocumentStore
 
 # Import Voyage Embedders
 from voyage_embedders.voyage_document_embedder import VoyageDocumentEmbedder