Integration: Pinecone
Use a Pinecone database with Haystack
Table of Contents
Overview
Pinecone is a fast and scalable vector database that you can use in Haystack pipelines with the PineconeDocumentStore.
For a detailed overview of all the available methods and settings for the PineconeDocumentStore, visit the Haystack API Reference.
Installation
pip install pinecone-haystack Usage
To use Pinecone as your data storage for your Haystack LLM pipelines, you must have an account with Pinecone and an API Key. Once you have those, you can initialize a PineconeDocumentStore for Haystack:
from haystack_integrations.document_stores.pinecone import PineconeDocumentStore # Make sure you have the PINECONE_API_KEY environment variable set document_store = PineconeDocumentStore( index="YOUR_INDEX_NAME", metric="cosine", dimension=768, spec={"serverless": {"region": "us-east-1", "cloud": "aws"}}, ) Writing Documents to PineconeDocumentStore
To write documents to your PineconeDocumentStore, create an indexing pipeline, or use the write_documents() function. For this step, you may make use of the available Converters and PreProcessors, as well as other Integrations that might help you fetch data from other resources. Below is an example indexing pipeline that indexes your Markdown files into a Pinecone database.
Indexing Pipeline
from haystack import Pipeline from haystack.components.converters import MarkdownToDocument from haystack.components.writers import DocumentWriter from haystack.components.embedders import SentenceTransformersDocumentEmbedder from haystack.components.preprocessors import DocumentSplitter from haystack_integrations.document_stores.pinecone import PineconeDocumentStore # Make sure you have the PINECONE_API_KEY environment variable set document_store = PineconeDocumentStore( index="YOUR_INDEX_NAME", metric="cosine", dimension=768, spec={"serverless": {"region": "us-east-1", "cloud": "aws"}}, ) indexing = Pipeline() indexing.add_component("converter", MarkdownToDocument()) indexing.add_component("splitter", DocumentSplitter(split_by="sentence", split_length=2)) indexing.add_component("embedder", SentenceTransformersDocumentEmbedder()) indexing.add_component("writer", DocumentWriter(document_store)) indexing.connect("converter", "splitter") indexing.connect("splitter", "embedder") indexing.connect("embedder", "writer") indexing.run({"converter": {"sources": ["filename.md"]}}) Using Pinecone in a RAG Pipeline
Once you have documents in your PineconeDocumentStore, they can be used in any Haystack pipeline. Then, you can use PineconeEmbeddingRetriever to retrieve data from your PineconeDocumentStore. For example, below is a pipeline that uses a custom prompt designed to answer questions for the retrieved documents.
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 PineconeEmbeddingRetriever # Make sure you have the PINECONE_API_KEY environment variable set document_store = PineconeDocumentStore( index="YOUR_INDEX_NAME", metric="cosine", dimension=768, spec={"serverless": {"region": "us-east-1", "cloud": "aws"}}, ) prompt_template = """Answer the following query based on the provided context. If the context does not include an answer, reply with 'I don't know'.\n Query: {{query}} Documents: {% for doc in documents %} {{ doc.content }} {% endfor %} Answer: """ query_pipeline = Pipeline() query_pipeline.add_component("text_embedder", SentenceTransformersTextEmbedder()) 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") query_pipeline.connect("retriever.documents", "prompt_builder.documents") query_pipeline.connect("prompt_builder", "generator") query = "What is Pinecone?" results = query_pipeline.run( { "text_embedder": {"text": query}, "prompt_builder": {"query": query}, } )