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Pinecone: rename retriever #170

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Feb 12, 2024
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Pinecone: rename retriever #170

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6 changes: 3 additions & 3 deletions integrations/pinecone-document-store.md
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Expand Up @@ -98,15 +98,15 @@ indexing.run({"converter": {"sources": ["filename.md"]}})

### Using Pinecone in a RAG Pipeline

Once you have documents in your `PineconeDocumentStore`, it's ready to be used in any Haystack pipeline. Then, you can use `PineconeDenseRetriever` to retrieve data from your PineconeDocumentStore. For example, below is a pipeline that makes use of a custom prompt that is designed to answer questions for the retrieved documents.
Once you have documents in your `PineconeDocumentStore`, it's ready to be used in any Haystack pipeline. Then, you can use `PineconeEmbeddingRetriever` to retrieve data from your PineconeDocumentStore. For example, below is a pipeline that makes use of a custom prompt that is designed to answer questions for the retrieved documents.

```python
from haystack.utils import Secret
from haystack.components.embedders import SentenceTransformersTextEmbedder
from haystack.components.builders import PromptBuilder
from haystack.components.generators import OpenAIGenerator
from haystack_integrations.document_stores.pinecone import PineconeDocumentStore
from haystack_integrations.components.retrievers.pinecone import PineconeDenseRetriever
from haystack_integrations.components.retrievers.pinecone import PineconeEmbeddingRetriever


document_store = PineconeDocumentStore(api_key='YOUR_API_KEY',
Expand All @@ -124,7 +124,7 @@ prompt_template = """Answer the following query based on the provided context. I

query_pipeline = Pipeline()
query_pipeline.add_component("text_embedder", SentenceTransformersTextEmbedder())
query_pipeline.add_component("retriever", PineconeDenseRetriever(document_store=document_store))
query_pipeline.add_component("retriever", PineconeEmbeddingRetriever(document_store=document_store))
query_pipeline.add_component("prompt_builder", PromptBuilder(template=prompt_template))
query_pipeline.add_component("generator", OpenAIGenerator(api_key=Secret.from_token("YOUR_OPENAI_API_KEY"), model="gpt-4"))
query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")
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