rust/lancedb
1
0
Fork 0
mirror of https://github.com/lancedb/lancedb.git synced 2026-01-06 03:42:57 +00:00
Code Issues Packages Projects Releases Wiki Activity

docs: add evaluation example (#1552)

Browse Source 
before:
![Screenshot 2024-08-21
194228](https://github.com/user-attachments/assets/68d96658-7579-4934-85af-e8c898b64660)

After:
![Screenshot 2024-08-21
195258](https://github.com/user-attachments/assets/81ddb9cd-cb93-47fc-a121-ff82701fd11f)

---------

Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
This commit is contained in:
Rithik Kumar
2024-08-21 20:37:04 +05:30
committed by GitHub
parent 7d65dd97cf
commit 0cbc9cd551
5 changed files with 32 additions and 8 deletions
Download Patch File Download Diff File Expand all files Collapse all files

23
docs/src/examples/python_examples/evaluations.md Normal file
View File

@@ -0,0 +1,23 @@
**Evaluation: Assessing Text Performance with Precision 📊💡**
====================================================================
**Evaluation Fundamentals 📊**
Evaluation is a comprehensive tool designed to measure the performance of text-based inputs, enabling data-driven optimization and improvement 📈.
**Text Evaluation 101 📚**
By leveraging cutting-edge technologies, this provides a robust framework for evaluating reference and candidate texts across various metrics 📊, ensuring high-quality text outputs that meet specific requirements and standards 📝.
| **Evaluation** | **Description** | **Links** |
| -------------- | --------------- | --------- |
| **Evaluating Prompts with Prompttools 🤖** | Compare, visualize & evaluate embedding functions (incl. OpenAI) across metrics like latency & custom evaluation 📈📊 | [![Github](../../assets/github.svg)][prompttools_github] <br>[![Open In Collab](../../assets/colab.svg)][prompttools_colab] |
| **Evaluating RAG with RAGAs and GPT-4o 📊** | Evaluate RAG pipelines with cutting-edge metrics and tools, integrate with CI/CD for continuous performance checks, and generate responses with GPT-4o 🤖📈 | [![Github](../../assets/github.svg)][RAGAs_github] <br>[![Open In Collab](../../assets/colab.svg)][RAGAs_colab] |
[prompttools_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/prompttools-eval-prompts
[prompttools_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/prompttools-eval-prompts/main.ipynb
[RAGAs_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/Evaluating_RAG_with_RAGAs
[RAGAs_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Evaluating_RAG_with_RAGAs/Evaluating_RAG_with_RAGAs.ipynb

4
docs/src/examples/python_examples/multimodal.md
View File

@@ -1,10 +1,10 @@
# **Multimodal Search with LanceDB 🤹‍♂️🔍**
Experience the future of search with LanceDB's multimodal capabilities. Combine text and image queries to find the most relevant results in your corpus and unlock new possibilities! 🔓💡
Experience the future of search with LanceDB's multimodal capabilities. Combine text and image queries to find the most relevant results in your corpus ! 🔓💡
**Explore the Future of Search 🚀**
Unlock the power of multimodal search with LanceDB, enabling efficient vector-based retrieval of text and image data! 📊💻
LanceDB supports multimodal search by indexing and querying vector representations of text and image data 🤖. This enables efficient retrieval of relevant documents and images using vector-based similarity search 📊. The platform facilitates cross-modal search, allowing for text-image and image-text retrieval, and supports scalable indexing of high-dimensional vector spaces 💻.

5
docs/src/examples/python_examples/rag.md
View File

@@ -2,12 +2,11 @@
**RAG: Revolutionize Information Retrieval with LanceDB 🔓🧐**
====================================================================
Unlock the full potential of Retrieval-Augmented Generation (RAG) with LanceDB, the ultimate solution for efficient vector-based information retrieval 📊. Input text queries and retrieve relevant documents with lightning-fast speed ⚡️ and accuracy ✅. Generate comprehensive answers by combining retrieved information, uncovering new insights 🔍 and connections.
Unlock the full potential of Retrieval-Augmented Generation (RAG) with LanceDB, a solution for efficient vector-based information retrieval 📊.
**Experience the Future of Search 🔄**
Experience the future of search with RAG, transforming information retrieval and answer generation. Apply RAG to various industries, streamlining processes 📈, saving time ⏰, and resources 💰. Stay ahead of the curve with innovative technology 🔝, powered by LanceDB. Discover the power of RAG with LanceDB and transform your industry with innovative solutions 💡.
RAG integrates large language models (LLMs) with scalable knowledge bases, enabling efficient information retrieval and answer generation 🤖. By applying RAG to industry-specific use cases, developers can optimize query processing 📊, reduce response latency ⏱️, and improve resource utilization 💻. LanceDB provides a robust framework for integrating LLMs with external knowledge sources, facilitating accurate and informative responses 📝.
| **RAG** | **Description** | **Links** |
|----------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------|

6
docs/src/examples/python_examples/vector_search.md
View File

@@ -1,11 +1,11 @@
**Vector Search: Unlock Efficient Document Retrieval 🔓👀**
====================================================================
Unlock the power of vector search with LanceDB, a cutting-edge solution for efficient vector-based document retrieval 📊. Input text queries to find the most relevant documents from your corpus, and discover a new world of possibilities with our inbuilt hybrid search features 🌐.
Unlock the power of vector search with LanceDB, a cutting-edge solution for efficient vector-based document retrieval 📊.
**Unlock the Future of Search🔝**
**Vector Search Capabilities in LanceDB🔝**
Experience the transformative power of vector search with LanceDB. Discover, analyze, and retrieve documents with unprecedented efficiency and accuracy. 💡
LanceDB implements vector search algorithms for efficient document retrieval and analysis 📊. This enables fast and accurate discovery of relevant documents, leveraging dense vector representations 🤖. The platform supports scalable indexing and querying of high-dimensional vector spaces, facilitating precise document matching and retrieval 📈.
| **Vector Search** | **Description** | **Links** |
|:-----------------|:---------------|:---------|