From 7d758fe71330c0e8b77c528f1fb8651051450d4f Mon Sep 17 00:00:00 2001 From: Onur Eken Date: Thu, 5 Sep 2024 15:43:23 +0200 Subject: [PATCH] Add Needle integration (#262) * Add Needle integration * Fix broken link --- integrations/needle.md | 101 +++++++++++++++++++++++++++++++++++++++++ logos/needle.png | Bin 0 -> 51891 bytes 2 files changed, 101 insertions(+) create mode 100644 integrations/needle.md create mode 100644 logos/needle.png diff --git a/integrations/needle.md b/integrations/needle.md new file mode 100644 index 00000000..b832379a --- /dev/null +++ b/integrations/needle.md @@ -0,0 +1,101 @@ +--- +layout: integration +name: Needle +description: Use Needle document store and retriever in Haystack. +authors: + - name: Needle Team + socials: + twitter: needlexAI + linkedin: needlexai +pypi: https://pypi.org/project/needle-haystack-ai +repo: https://github.com/JANHMS/needle-haystack +type: Document Store +report_issue: https://github.com/JANHMS/needle-haystack/issues +logo: /logos/needle.png +version: Haystack 2.x +--- + +# Needle RAG tools for Haystack + +[![PyPI - Version](https://img.shields.io/pypi/v/needle-haystack-ai.svg)](https://pypi.org/project/needle-haystack-ai) +[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/needle-haystack-ai.svg)](https://pypi.org/project/needle-haystack-ai) + +This package provides `NeedleDocumentStore` and `NeedleEmbeddingRetriever` component for use in Haystack projects. + +## Usage ⚡️ + +Get started by installing the package via `pip`. + +```bash +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](https://needle-ai.com/dashboard/settings). + +### Example Pipeline 🧱 + +In Needle document stores are called collections. For detailed information, see our [docs](https://docs.needle-ai.com). +You can create a reference to your Needle collection using `NeedleDocumentStore` and use `NeedleEmbeddingRetriever` to retrieve documents from it. + +```python +from needle_haystack import NeedleDocumentStore, NeedleEmbeddingRetriever + +document_store = NeedleDocumentStore(collection_id="") +retriever = NeedleEmbeddingRetriever(document_store=document_store) +``` + +Use the retriever in a Haystack pipeline. Example: + +```python +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: + +```python +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](https://docs.needle-ai.com). 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