diff --git a/docs.json b/docs.json index bb804d6..0ab1f09 100644 --- a/docs.json +++ b/docs.json @@ -225,7 +225,8 @@ "v3/integrations/openai-sdk-typescript", "v3/integrations/langchain", "v3/integrations/pydantic-ai", - "v3/integrations/aisuite" + "v3/integrations/aisuite", + "v3/integrations/haystack" ] }, { diff --git a/integration-logo/haystack-icondoc.svg b/integration-logo/haystack-icondoc.svg new file mode 100644 index 0000000..500bb59 --- /dev/null +++ b/integration-logo/haystack-icondoc.svg @@ -0,0 +1 @@ + diff --git a/v3/integrations/haystack.mdx b/v3/integrations/haystack.mdx new file mode 100644 index 0000000..b4994e9 --- /dev/null +++ b/v3/integrations/haystack.mdx @@ -0,0 +1,170 @@ +--- +title: "Haystack" +icon: "/integration-logo/haystack-icondoc.svg" +description: "Use Eden AI with Haystack (deepset) to build LLM and RAG pipelines on 500+ AI models through one EU-hosted API key." +--- + +import { TechArticleSchema } from "/snippets/TechArticleSchema.mdx"; + + + +Use Eden AI with Haystack (deepset) to build LLM and RAG pipelines on 500+ AI models through one EU-hosted API key. + +## Overview + +[Haystack](https://haystack.deepset.ai/) is deepset's open-source Python framework for building production LLM applications, RAG pipelines and agents. **Eden AI ships as an official Haystack integration** (the `edenai-haystack` package), so your pipelines reach models from OpenAI, Anthropic, Google, Mistral and 500+ more behind one key, with EU-based, GDPR-aligned inference. + +The integration provides three components: + +- `EdenAIChatGenerator` for chat completion +- `EdenAITextEmbedder` to embed a query string +- `EdenAIDocumentEmbedder` to embed documents for indexing + +Models use Eden AI's `provider/model` naming convention, for example `anthropic/claude-sonnet-4-5` or `mistral/mistral-large-latest`. + +## Installation + + +```bash pip +pip install edenai-haystack +``` + +```bash uv +uv add edenai-haystack +``` + + +## Quick Start + +Set your key, then generate a chat response with the `EdenAIChatGenerator`: + + +```bash .env +EDENAI_API_KEY=your_api_key_here +``` + +{/* skip-test */} +```python Python +from haystack.dataclasses import ChatMessage +from haystack_integrations.components.generators.edenai import EdenAIChatGenerator + +generator = EdenAIChatGenerator(model="mistral/mistral-large-latest") + +response = generator.run([ChatMessage.from_user("What is Eden AI?")]) +print(response["replies"][0].text) +``` + + +The generator reads your key from the `EDENAI_API_KEY` environment variable by default. + +## Switching models + +Change providers by changing the `model` string. Every Eden AI model is reachable the same way: + + +{/* skip-test */} +```python Python +from haystack.dataclasses import ChatMessage +from haystack_integrations.components.generators.edenai import EdenAIChatGenerator + +messages = [ChatMessage.from_user("Write a haiku about the sea.")] + +for model in [ + "openai/gpt-4o-mini", + "anthropic/claude-sonnet-4-5", + "mistral/mistral-large-latest", +]: + generator = EdenAIChatGenerator(model=model) + print(model, "->", generator.run(messages)["replies"][0].text) +``` + + +## Streaming + +Pass a `streaming_callback` to stream tokens as they are generated: + + +{/* skip-test */} +```python Python +from haystack.components.generators.utils import print_streaming_chunk +from haystack.dataclasses import ChatMessage +from haystack_integrations.components.generators.edenai import EdenAIChatGenerator + +generator = EdenAIChatGenerator( + model="mistral/mistral-large-latest", + streaming_callback=print_streaming_chunk, +) +generator.run([ChatMessage.from_user("Count from one to five.")]) +``` + + +## Embeddings and RAG + +Use `EdenAITextEmbedder` to embed a query and `EdenAIDocumentEmbedder` to embed documents, so you can build a fully sovereign RAG stack (retrieval and generation) on EU-hosted models: + + +{/* skip-test */} +```python Python +from haystack import Document +from haystack.document_stores.in_memory import InMemoryDocumentStore +from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever +from haystack_integrations.components.embedders.edenai import ( + EdenAIDocumentEmbedder, + EdenAITextEmbedder, +) + +document_store = InMemoryDocumentStore(embedding_similarity_function="cosine") + +# Index documents with their embeddings +documents = [Document(content="Eden AI is a unified API for 500+ AI models.")] +doc_embedder = EdenAIDocumentEmbedder(model="openai/text-embedding-3-small") +document_store.write_documents(doc_embedder.run(documents)["documents"]) + +# Embed the query and retrieve +text_embedder = EdenAITextEmbedder(model="openai/text-embedding-3-small") +retriever = InMemoryEmbeddingRetriever(document_store=document_store) +query_embedding = text_embedder.run("What is Eden AI?")["embedding"] +print(retriever.run(query_embedding=query_embedding)["documents"]) +``` + + +## Available Models + +Use the `provider/model` format for any Eden AI model: + +**Chat** +- `openai/gpt-4o-mini` +- `anthropic/claude-sonnet-4-5` +- `mistral/mistral-large-latest` +- `google/gemini-2.5-flash` + +**Embeddings** +- `openai/text-embedding-3-small` +- `mistral/mistral-embed` + +Browse the full list in the [Eden AI models catalog](https://www.edenai.co/models). + +## Environment Variables + + +```bash .env +EDENAI_API_KEY=your_api_key_here +``` + + +## Next Steps + +- [Chat Completions](/v3/llms/chat-completions) - Core LLM endpoint +- [List LLM Models](/v3/llms/listing-models) - Browse available providers and models +- [aisuite](/v3/integrations/aisuite) - Another unified LLM interface +- [LangChain](/v3/integrations/langchain) - Build LLM apps with LangChain