https://github.com/deepset-ai/haystack-cookbook
👩🏻🍳 A collection of example notebooks
https://github.com/deepset-ai/haystack-cookbook
Last synced: about 2 months ago
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👩🏻🍳 A collection of example notebooks
- Host: GitHub
- URL: https://github.com/deepset-ai/haystack-cookbook
- Owner: deepset-ai
- Created: 2024-01-02T12:11:38.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-04-22T10:22:05.000Z (about 1 year ago)
- Last Synced: 2024-04-22T11:34:58.313Z (about 1 year ago)
- Language: Jupyter Notebook
- Size: 1.63 MB
- Stars: 122
- Watchers: 3
- Forks: 24
- Open Issues: 11
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome - deepset-ai/haystack-cookbook - 👩🏻🍳 A collection of example notebooks using Haystack (Jupyter Notebook)
- dmg-data-science-awesome - Haystack Cookbook - Farklı araçlar kullanılarak yapılmış çeşitli GenAI uygulama örnekleri bulabileceğiniz bir repo. (🤖Generative AI / 🔗 Useful Links)
- dmg-data-science-awesome - Haystack Cookbook - Farklı araçlar kullanılarak yapılmış çeşitli GenAI uygulama örnekleri bulabileceğiniz bir repo. (🤖Generative AI / 🔗 Useful Links)
README
# 👩🏻🍳 Haystack Cookbook
# 🧑🍳🍳Discover The Haystack Cookbook [here](https://haystack.deepset.ai/cookbook)
A collection of example notebooks using [Haystack](https://github.com/deepset-ai/haystack) 👇
You can use these examples as guidelines on how to make use of different model providers, vector databases, retrieval techniques and more with Haystack. Most of them showcase a specific, small demo.
> 🧑🍳 [Guidelines on How to Contribute a Cookbook](#how-to-contribute-to-this-repository)
To learn more about _how_ to use Haystack, please visit our [Docs](https://docs.haystack.deepset.ai/docs) and official [Tutorials](https://haystack.deepset.ai/tutorials).
For more examples, you may also find our [Blog](https://haystack.deepset.ai/blog) useful.
| Name | Colab|
|----------------------------------------------------------------------------------------------------| ---- |
| Extracting Metadata with an LLM | 
|
| Improving Retrieval with Auto-Merging | |
| Speaker Diarization with AssemblyAI | |
| Advance Prompt Customization for Anthropic | |
| Advanced RAG: Query Decomposition and Reasoning | |
| Advanced RAG: Automated Structured Metadata Enrichment | |
| Techcrunch News Digest with Local LLMs using TitanML Takeoff | |
| Use Gemini Models with Vertex AI | |
| Gradient AI Embedders and Generators for RAG | |
| Mixtral 8x7B with Hugging Face TGI for Web QA | |
| Amazon Bedrock and OpenSearch for PDF QA | |
| Use Zephyr 7B Beta with Hugging Face for RAG | |
| Hacker News RAG with Custom Component | |
| Use Chroma for RAG and Indexing | |
| Using the Jina-embeddings-v2-base-en model in a Haystack RAG pipeline for legal document analsysis | |
| Multilingual RAG from a podcast with Whisper, Qdrant and Mistral | |
| Improve retrieval by embedding meaningful metadata | |
| Advanced RAG: Query Expansion | |
| Information extraction via LLMs (Gorilla OpenFunctions) | |
| Information extraction via LLMs (NexusRaven) | |
| Using AstraDB as a data store in your Haystack pipelines | |
| Streaming model explorer: compare how different models handle the same prompt. | |
| Function Calling with OpenAIChatGenerator | |
| Use the vLLM inference engine with Haystack | |
| Build with Google Gemma: chat and RAG | |
| Optimizing Retrieval with HyDE | |
| RAG pipeline using FastEmbed for embeddings generation | |
| Sparse Embedding Retrieval with Qdrant and FastEmbed | |
| Hybrid Retrieval: BM42 + Dense Retrieval (with Qdrant and FastEmbed) | |
| Air-Gapped RAG pipelines with NVIDIA NIMs | |
| Evaluate a RAG pipeline using Haystack-UpTrain integration | |
| RAG on the Oscars using Llama 3.1 models | |
| Chatting with SQL Databases | |
| Evaluate a RAG pipeline using DeepEval integration | |
| Evaluate a RAG pipeline using Ragas integration | |
| Extract Metadata Filters from a Query | |
| Run tasks concurrently within a custom component | |
| Prompt Optimization with DSPy | |
| RAG Evaluation with Prometheus 2 | |
| Build quizzes and adventures with Character Codex and llamafile | |
| Invoking APIs with `OpenAPITool` | |
| RAG Web Search and Analysis with Apify and Haystack | |
| Extract and use website content for RAG with Apify | |
| Analyze Your Instagram Comments’ Vibe with Apify and Haystack | |
| Conversational RAG using Memory | |
| Define & Run Tools | |
| Agentic RAG with Llama 3.2 3B | |
| Create a Swarm of Agents | |
| Build a GitHub Issue Resolver Agent | |
## How to Contribute a Cookbook
If you have an example that uses Haystack, you can add it to this repository by creating a PR. You can also create a PR from Colab by creating a Fork of this repository and selecting "Save a Copy to GitHub". Once you add your example to your fork, you can create a PR onto this repository.
1. Add your Notebook
2. Give a descriptive name to your file that includes the names of (if applicable) the model providers, databases the technologies you use in your example and/or the task you are completing in the example.
3. Make sure you add it to `index.toml` including its title and topics.
4. Make sure to add a row in the table above 🎉