feat: add mixedbread reranker docs (#242)

integrations/mixedbread-ai.md

@@ -24,7 +24,7 @@ toc: true
 
 ## Overview
 
-[mixedbread ai](https://www.mixedbread.ai) is an ai start-up that provides open-source, as well as, in-house embedding models. You can choose from various foundation models to find the one best suited for your use case. More information can be found on the [documentation page](https://www.mixedbread.ai/api-reference/integrations#haystack).
+[mixedbread ai](https://www.mixedbread.ai) is an AI start-up that provides open-source, as well as, in-house embedding and reranking models. You can choose from various foundation models to find the one best suited for your use case. More information can be found on the [documentation page](https://www.mixedbread.ai/api-reference/integrations#haystack).
 
 ## Installation
 
@@ -36,48 +36,145 @@ pip install mixedbread-ai-haystack
 
 ## Usage
 
-This integration comes with 2 components:
-- [`MixedbreadAiTextEmbedder`](https://github.com/mixedbread-ai/mixedbread-ai-haystack/blob/main/mixedbread_ai_haystack/embedders/text_embedder.py)
-- [`MixedbreadAiDocumentEmbedder`](https://github.com/mixedbread-ai/mixedbread-ai-haystack/blob/main/mixedbread_ai_haystack/embedders/document_embedder.py).
+This integration comes with 3 components:
+- [`MixedbreadAITextEmbedder`](https://github.com/mixedbread-ai/mixedbread-ai-haystack/blob/main/mixedbread_ai_haystack/embedders/text_embedder.py)
+- [`MixedbreadAIDocumentEmbedder`](https://github.com/mixedbread-ai/mixedbread-ai-haystack/blob/main/mixedbread_ai_haystack/embedders/document_embedder.py).
+- [`MixedbreadAIReranker`](https://github.com/mixedbread-ai/mixedbread-ai-haystack/blob/main/mixedbread_ai_haystack/rerankers/reranker.py)
 
-For documents you can use `MixedbreadAiDocumentEmbedder` and for queries you can use `MixedbreadAiTextEmbedder`. Once you've selected the component for your specific use case, initialize the component with the `model` and the [`api_key`](https://www.mixedbread.ai/dashboard?next=api-keys). You can also set the environment variable `MIXEDBREAD_API_KEY` instead of passing the api key as an argument.
+For documents you can use `MixedbreadAIDocumentEmbedder` and for queries you can use `MixedbreadAITextEmbedder`. Once you've selected the component for your specific use case, initialize the component with the `model` and the [`api_key`](https://www.mixedbread.ai/dashboard?next=api-keys). You can also set the environment variable `MXBAI_API_KEY` instead of passing the api key as an argument.
 
-
-
-### In a Pipeline
+### Embedders In a Pipeline
 
 ```python
 from haystack import Document, Pipeline
-from haystack.document_stores.in_memory import InMemoryDocumentStore, InMemoryDocumentStore
+from haystack.document_stores.in_memory import InMemoryDocumentStore
 from haystack.components.writers import DocumentWriter
 from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
-from mixedbread_ai_haystack.embedders import MixedbreadAiDocumentEmbedder, MixedbreadAiTextEmbedder
+from mixedbread_ai_haystack.embedders import MixedbreadAIDocumentEmbedder, MixedbreadAITextEmbedder
 
-# -------------------------------------
-# Indexing Pipeline
-# -------------------------------------
+# Set-up the Document Store and Documents
 document_store = InMemoryDocumentStore(embedding_similarity_function="cosine")
-documents = [Document(content="china is the most populous country in the world."), Document(content="india is the second most populous country in the world."), Document(content="united states is the third most populous country in the world.")]
+documents = [
+    Document(content="china is the most populous country in the world."), 
+    Document(content="india is the second most populous country in the world."), 
+    Document(content="united states is the third most populous country in the world.")
+]
 
+# Indexing Pipeline
 indexing_pipeline = Pipeline()
-indexing_pipeline.add_component("doc_embedder", MixedbreadAiDocumentEmbedder(api_key="MIXEDBREAD_API_KEY", model="UAE-Large-V1"))
+indexing_pipeline.add_component("doc_embedder", MixedbreadAIDocumentEmbedder(model="mixedbread-ai/mxbai-embed-large-v1"))
 indexing_pipeline.add_component("writer", DocumentWriter(document_store=document_store))
 indexing_pipeline.connect("doc_embedder", "writer")
 
 indexing_pipeline.run({"doc_embedder": {"documents": documents}})
 
-# -------------------------------------
-# Query Pipeline
-# -------------------------------------
-text_embedder = MixedbreadAiTextEmbedder(model="UAE-Large-V1", api_key="MIXEDBREAD_API_KEY")
-
 # Query Pipeline
 query_pipeline = Pipeline()
-query_pipeline.add_component("text_embedder", text_embedder)
+query_pipeline.add_component("text_embedder",  MixedbreadAITextEmbedder(model="mixedbread-ai/mxbai-embed-large-v1"))
 query_pipeline.add_component("retriever", InMemoryEmbeddingRetriever(document_store=document_store))
 query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")
 
 results = query_pipeline.run({"text_embedder": {"text": "Which country has the biggest population?"}})
 top_document = results["retriever"]["documents"][0].content
 print(top_document)
 ```
+
+### Reranker In a Pipeline
+```python
+from haystack import Document, Pipeline
+from haystack.document_stores.in_memory import InMemoryDocumentStore
+from haystack.components.retrievers.in_memory import InMemoryBM25Retriever
+from mixedbread_ai_haystack.rerankers import MixedbreadAIReranker
+
+# Set-up the Document Store and Documents
+documents = [
+    Document(content="china is the most populous country in the world."),
+    Document(content="india is the second most populous country in the world."),
+    Document(content="united states is the third most populous country in the world.")
+]
+document_store = InMemoryDocumentStore()
+document_store.write_documents(documents)
+
+# Define the Retriever and Reranker
+retriever = InMemoryBM25Retriever(document_store=document_store)
+reranker = MixedbreadAIReranker(model="mixedbread-ai/mxbai-rerank-large-v1", top_k=3)
+
+# Rerank Pipeline
+rerank_pipeline = Pipeline()
+rerank_pipeline.add_component("retriever", retriever)
+rerank_pipeline.add_component("reranker", reranker)
+rerank_pipeline.connect("retriever.documents", "reranker.documents")
+
+# Query and Rerank
+query = "Which country has the second largest population"
+results = rerank_pipeline.run({"retriever": {"query": query}, "reranker": {"query": query, "top_k": 3}})
+print(results)
+```
+
+### Full Example With Metadata
+```python
+import os
+from datasets import load_dataset
+from haystack import Pipeline, Document
+from haystack.document_stores.in_memory import InMemoryDocumentStore
+from haystack.components.writers import DocumentWriter
+from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
+from mixedbread_ai_haystack import MixedbreadAIDocumentEmbedder, MixedbreadAITextEmbedder, MixedbreadAIReranker
+
+# Set API Key
+os.environ["MXBAI_API_KEY"] = "YOUR_API_KEY"
+
+# Load the Dataset and Prepare Documents
+ds = load_dataset("rajuptvs/ecommerce_products_clip")
+documents = [
+    Document(
+        id=str(i),
+        content=data["Description"], meta={
+        "name": data["Product_name"],
+        "price": data["Price"],
+        "colors": data["colors"],
+        "pattern": data["Pattern"],
+        "extra": data["Other Details"]
+    }) for i, data in enumerate(ds["train"])
+]
+meta_fields = documents[0].meta.keys()
+
+# Define the Components
+document_store = InMemoryDocumentStore(embedding_similarity_function="cosine")
+document_writer = DocumentWriter(document_store=document_store)
+embedding_retriever = InMemoryEmbeddingRetriever(document_store=document_store, top_k=20)
+
+embed_model = "mixedbread-ai/mxbai-embed-large-v1"
+reranking_model = "mixedbread-ai/mxbai-rerank-large-v1" 
+
+text_embedder = MixedbreadAITextEmbedder(model=embed_model)
+document_embedder = MixedbreadAIDocumentEmbedder(model=embed_model, max_concurrency=3, meta_fields_to_embed=meta_fields, show_progress_bar=True)
+reranker = MixedbreadAIReranker(model=reranking_model, meta_fields_to_rank=meta_fields, top_k=5)
+
+# Indexing Pipeline
+indexing_pipeline = Pipeline()
+indexing_pipeline.add_component(instance=document_embedder, name="document_embedder")
+indexing_pipeline.add_component(instance=document_writer, name="document_writer")
+indexing_pipeline.connect("document_embedder", "document_writer")
+
+# Query Pipeline
+query_pipeline = Pipeline()
+query_pipeline.add_component(instance=text_embedder, name="text_embedder")
+query_pipeline.add_component(instance=embedding_retriever, name="embedding_retriever")
+query_pipeline.add_component(instance=reranker, name="reranker")
+query_pipeline.connect("text_embedder", "embedding_retriever")
+query_pipeline.connect("embedding_retriever.documents", "reranker.documents")
+
+# Index the dataset
+indexing_pipeline.run({"document_embedder": {"documents": documents}})
+
+# Query to get results
+query = "I am looking for a regular fit t-shirt in blue color. Ideally without any prints. What are my options?"
+results = query_pipeline.run(
+    {
+        "text_embedder": {"text": query},
+        "reranker": {"query": query}
+    }
+)
+print(results["reranker"]["documents"])
+```