attending PR comments

deepset-ai · Mar 4, 2024 · 564e341 · 564e341
1 parent 56f302e
commit 564e341
Show file tree

Hide file tree

Showing 2 changed files with 17 additions and 13 deletions.
diff --git a/...ent/src/haystack_integrations/components/embedders/gradient/gradient_document_embedder.py b/...ent/src/haystack_integrations/components/embedders/gradient/gradient_document_embedder.py
@@ -28,24 +28,25 @@ class GradientDocumentEmbedder:
 
     Usage example:
     ```python
-    from haystack_integrations.components.embedders.gradient import GradientDocumentEmbedder
-    from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
     from haystack import Pipeline
+    from haystack.document_stores.in_memory import InMemoryDocumentStore
+    from haystack.components.writers import DocumentWriter
+    from haystack import Document
 
-    embedder = GradientDocumentEmbedder(model="bge_large")
+    from haystack_integrations.components.embedders.gradient import GradientDocumentEmbedder
 
     documents = [
         Document(content="My name is Jean and I live in Paris."),
         Document(content="My name is Mark and I live in Berlin."),
         Document(content="My name is Giorgio and I live in Rome."),
     ]
 
-    p = Pipeline()
-    p.add_component(embedder, name="document_embedder")
-    p.add_component(instance=GradientDocumentEmbedder(), name="document_embedder")
-    p.add_component(instance=DocumentWriter(document_store=InMemoryDocumentStore()), name="document_writer")
-    p.connect("document_embedder", "document_writer")
-    p.run(data={"document_embedder": {"documents": documents}})
+    indexing_pipeline = Pipeline()
+    indexing_pipeline.add_component(instance=GradientDocumentEmbedder(), name="document_embedder")
+    indexing_pipeline.add_component(instance=DocumentWriter(document_store=InMemoryDocumentStore()), name="document_writer")
+    indexing_pipeline.connect("document_embedder", "document_writer")
+    indexing_pipeline.run({"document_embedder": {"documents": documents}})
+    >>> {'document_writer': {'documents_written': 3}}
     ```
     """
 

diff --git a/...radient/src/haystack_integrations/components/embedders/gradient/gradient_text_embedder.py b/...radient/src/haystack_integrations/components/embedders/gradient/gradient_text_embedder.py
@@ -14,14 +14,17 @@ class GradientTextEmbedder:
     ```python
     from haystack_integrations.components.embedders.gradient import GradientTextEmbedder
     from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
+    from haystack.document_stores.in_memory import InMemoryDocumentStore
     from haystack import Pipeline
 
-    embedder = GradientTextEmbedder(model="bge_large")
+    embedder =
     p = Pipeline()
-    p.add_component(instance=embedder, name="text_embedder")
-    p.add_component(instance=InMemoryEmbeddingRetriever(document_store=InMemoryDocumentStore()), name="retriever")
+    p.add_component("text_embedder", GradientTextEmbedder(model="bge-large"))
+    p.add_component("retriever", InMemoryEmbeddingRetriever(document_store=InMemoryDocumentStore()))
     p.connect("text_embedder", "retriever")
-    p.run("embed me!!!")
+    p.run(data={"text_embedder": {"text":"You can embed me put I'll return no matching documents"}})
+    >>> No Documents found with embeddings. Returning empty list. To generate embeddings, use a DocumentEmbedder.
+    >>> {'retriever': {'documents': []}}
     ```
     """