Integration: Needle

Use Needle document store and retriever in Haystack.

Authors

Needle Team

GitHub Repo PyPI Package

Needle RAG tools for Haystack

This package provides NeedleDocumentStore and NeedleEmbeddingRetriever component for use in Haystack projects.

Usage ⚡️

Get started by installing the package via pip.

pip install needle-haystack-ai

API Keys

We will show you building a common RAG pipeline using Needle tools and OpenAI generator. For using these tools you must set your environment variables, NEEDLE_API_KEY and OPENAI_API_KEY respectively.

You can get your Needle API key from from Developer settings.

Example Pipeline 🧱

In Needle document stores are called collections. For detailed information, see our docs. You can create a reference to your Needle collection using NeedleDocumentStore and use NeedleEmbeddingRetriever to retrieve documents from it.

from needle_haystack import NeedleDocumentStore, NeedleEmbeddingRetriever

document_store = NeedleDocumentStore(collection_id="<your-collection-id>")
retriever = NeedleEmbeddingRetriever(document_store=document_store)

Use the retriever in a Haystack pipeline. Example:

from haystack import Pipeline
from haystack.components.generators import OpenAIGenerator
from haystack.components.builders import PromptBuilder

prompt_template = """
Given the following retrieved documents, generate a concise and informative answer to the query:

Query: {{query}}
Documents:
{% for doc in documents %}
    {{ doc.content }}
{% endfor %}

Answer:
"""

prompt_builder = PromptBuilder(template=prompt_template)
llm = OpenAIGenerator()

# Add components to pipeline
pipeline = Pipeline()
pipeline.add_component("retriever", retriever)
pipeline.add_component("prompt_builder", prompt_builder)
pipeline.add_component("llm", llm)

# Connect the components
pipeline.connect("retriever", "prompt_builder.documents")
pipeline.connect("prompt_builder", "llm")

Run your RAG pipeline:

prompt = "What is the topic of the news?"

result = basic_rag_pipeline.run({
    "retriever": {"text": prompt},
    "prompt_builder": {"query": prompt}
})

# Print final answer
print(result['llm']['replies'][0])

Support 📞

For detailed guides, take a look at our docs. If you have questions or requests you can contact us in our Discord channel.