Bump version: 0.13.0-beta.0 → 0.13.0-beta.1

Co-authored-by: Akela Drissner-Schmid <32450038+akelad@users.noreply.github.com>

docs/mkdocs.yml

@@ -138,6 +138,7 @@ nav:
           - Pydantic: python/pydantic.md
           - Voxel51: integrations/voxel51.md
           - PromptTools: integrations/prompttools.md
+          - dlt: integrations/dlt.md
       - 🎯 Examples:
           - Overview: examples/index.md
           - 🐍 Python:
@@ -147,6 +148,8 @@ nav:
               - Rag: examples/python_examples/rag.md
               - Vector Search: examples/python_examples/vector_search.md
               - Chatbot: examples/python_examples/chatbot.md
+              - Evaluation: examples/python_examples/evaluations.md
+              - AI Agent: examples/python_examples/aiagent.md
               - Miscellaneous: 
                   - YouTube Transcript Search: notebooks/youtube_transcript_search.ipynb
                   - Documentation QA Bot using LangChain: notebooks/code_qa_bot.ipynb
@@ -226,6 +229,7 @@ nav:
       - Pydantic: python/pydantic.md
       - Voxel51: integrations/voxel51.md
       - PromptTools: integrations/prompttools.md
+      - dlt: integrations/dlt.md
   - Examples:
       - examples/index.md
       - 🐍 Python:
@@ -235,6 +239,8 @@ nav:
           - Rag: examples/python_examples/rag.md
           - Vector Search: examples/python_examples/vector_search.md
           - Chatbot: examples/python_examples/chatbot.md
+          - Evaluation: examples/python_examples/evaluations.md
+          - AI Agent: examples/python_examples/aiagent.md
           - Miscellaneous: 
               - YouTube Transcript Search: notebooks/youtube_transcript_search.ipynb
               - Documentation QA Bot using LangChain: notebooks/code_qa_bot.ipynb

docs/src/examples/python_examples/aiagent.md

@@ -0,0 +1,27 @@
+# AI Agents: Intelligent Collaboration🤖
+
+Think of a platform💻 where AI Agents🤖 can seamlessly exchange information, coordinate over tasks, and achieve shared targets with great efficiency📈🚀.
+
+## Vector-Based Coordination: The Technical Advantage
+Leveraging LanceDB's vector-based capabilities, our coordination application enables AI agents to communicate and collaborate through dense vector representations 🤖. AI agents can exchange information, coordinate on a task or work towards a common goal, just by giving queries📝.
+
+| **AI Agents** | **Description** | **Links** |
+|:--------------|:----------------|:----------|
+| **AI Agents: Reducing Hallucinationt📊** | 🤖💡 Reduce AI hallucinations using Critique-Based Contexting! Learn by Simplifying and Automating tedious workflows by going through fitness trainer agent example.💪 | [![Github](../../assets/github.svg)][hullucination_github] <br>[![Open In Collab](../../assets/colab.svg)][hullucination_colab] <br>[![Python](../../assets/python.svg)][hullucination_python] <br>[![Ghost](../../assets/ghost.svg)][hullucination_ghost] |                                                                                                                                                                                                                                                
+| **AI Trends Searcher: CrewAI🔍️** | 🔍️ Learn about CrewAI Agents ! Utilize the features of CrewAI - Role-based Agents, Task Management, and Inter-agent Delegation ! Make AI agents work together to do tricky stuff 😺| [![Github](../../assets/github.svg)][trend_github] <br>[![Open In Collab](../../assets/colab.svg)][trend_colab] <br>[![Ghost](../../assets/ghost.svg)][trend_ghost] |                                                                                                                                                                                                                                                
+| **SuperAgent Autogen🤖** | 💻 AI interactions with the Super Agent! Integrating Autogen, LanceDB, LangChain, LiteLLM, and Ollama to create AI agent that excels in understanding and processing complex queries.🤖 | [![Github](../../assets/github.svg)][superagent_github] <br>[![Open In Collab](../../assets/colab.svg)][superagent_colab] |
+
+
+[hullucination_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/reducing_hallucinations_ai_agents
+[hullucination_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/reducing_hallucinations_ai_agents/main.ipynb
+[hullucination_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/reducing_hallucinations_ai_agents/main.py
+[hullucination_ghost]: https://blog.lancedb.com/how-to-reduce-hallucinations-from-llm-powered-agents-using-long-term-memory-72f262c3cc1f/
+
+[trend_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/AI-Trends-with-CrewAI
+[trend_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/AI-Trends-with-CrewAI/CrewAI_AI_Trends.ipynb 
+[trend_ghost]: https://blog.lancedb.com/track-ai-trends-crewai-agents-rag/
+
+[superagent_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/SuperAgent_Autogen
+[superagent_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/SuperAgent_Autogen/main.ipynb
+
+

docs/src/examples/python_examples/evaluations.md

@@ -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

docs/src/examples/python_examples/multimodal.md

@@ -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 💻.
 
 
 

docs/src/examples/python_examples/rag.md

@@ -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**                  |
 |----------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------|                                                                                                                                                     

docs/src/examples/python_examples/vector_search.md

@@ -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** |
 |:-----------------|:---------------|:---------|

docs/src/integrations/dlt.md

@@ -0,0 +1,142 @@
+# dlt  
+
+[dlt](https://dlthub.com/docs/intro) is an open-source library that you can add to your Python scripts to load data from various and often messy data sources into well-structured, live datasets. dlt's [integration with LanceDB](https://dlthub.com/docs/dlt-ecosystem/destinations/lancedb) lets you ingest data from any source (databases, APIs, CSVs, dataframes, JSONs, and more) into LanceDB with a few lines of simple python code. The integration enables automatic normalization of nested data, schema inference, incremental loading and embedding the data. dlt also has integrations with several other tools like dbt, airflow, dagster etc. that can be inserted into your LanceDB workflow.
+
+## How to ingest data into LanceDB  
+
+In this example, we will be fetching movie information from the [Open Movie Database (OMDb) API](https://www.omdbapi.com/) and loading it into a local LanceDB instance. To implement it, you will need an API key for the OMDb API (which can be created freely [here](https://www.omdbapi.com/apikey.aspx)).
+  
+1. **Install `dlt` with LanceDB extras:**  
+    ```sh 
+    pip install dlt[lancedb]
+    ```
+
+2. **Inside an empty directory, initialize a `dlt` project with:**  
+    ```sh
+    dlt init rest_api lancedb
+    ```
+    This will add all the files necessary to create a `dlt` pipeline that can ingest data from any REST API (ex: OMDb API) and load into LanceDB.
+    ```text
+    ├── .dlt
+    │   ├── config.toml
+    │   └── secrets.toml
+    ├── rest_api
+    ├── rest_api_pipeline.py
+    └── requirements.txt
+    ```
+  
+    dlt has a list of pre-built [sources](https://dlthub.com/docs/dlt-ecosystem/verified-sources/) like [SQL databases](https://dlthub.com/docs/dlt-ecosystem/verified-sources/sql_database), [REST APIs](https://dlthub.com/docs/dlt-ecosystem/verified-sources/rest_api), [Google Sheets](https://dlthub.com/docs/dlt-ecosystem/verified-sources/google_sheets), [Notion](https://dlthub.com/docs/dlt-ecosystem/verified-sources/notion) etc., that can be used out-of-the-box by running `dlt init <source_name> lancedb`. Since dlt is a python library, it is also very easy to modify these pre-built sources or to write your own custom source from scratch.
+
+
+3. **Specify necessary credentials and/or embedding model details:**  
+    
+    In order to fetch data from the OMDb API, you will need to pass a valid API key into your pipeline. Depending on whether you're using LanceDB OSS or LanceDB cloud, you also may need to provide the necessary credentials to connect to the LanceDB instance. These can be pasted inside `.dlt/sercrets.toml`. 
+
+    dlt's LanceDB integration also allows you to automatically embed the data during ingestion. Depending on the embedding model chosen, you may need to paste the necessary credentials inside `.dlt/sercrets.toml`:
+    ```toml
+    [sources.rest_api]
+    api_key = "api_key" # Enter the API key for the OMDb API
+
+    [destination.lancedb]
+    embedding_model_provider = "sentence-transformers"
+    embedding_model = "all-MiniLM-L6-v2"
+    [destination.lancedb.credentials]
+    uri = ".lancedb"
+    api_key = "api_key" # API key to connect to LanceDB Cloud. Leave out if you are using LanceDB OSS.
+    embedding_model_provider_api_key = "embedding_model_provider_api_key" # Not needed for providers that don't need authentication (ollama, sentence-transformers).
+    ```
+    See [here](https://dlthub.com/docs/dlt-ecosystem/destinations/lancedb#configure-the-destination) for more information and for a list of available models and model providers.  
+
+
+4. **Write the pipeline code inside `rest_api_pipeline.py`:**  
+
+    The following code shows how you can configure dlt's REST API source to connect to the [OMDb API](https://www.omdbapi.com/), fetch all movies with the word "godzilla" in the title, and load it into a LanceDB table. The REST API source allows you to pull data from any API with minimal code, to learn more read the [dlt docs](https://dlthub.com/docs/dlt-ecosystem/verified-sources/rest_api).
+
+    ```python   
+
+    # Import necessary modules
+    import dlt
+    from rest_api import rest_api_source
+
+    # Configure the REST API source
+    movies_source = rest_api_source(
+        {
+            "client": {
+                "base_url": "https://www.omdbapi.com/",
+                "auth": { # authentication strategy for the OMDb API
+                    "type": "api_key",
+                    "name": "apikey",
+                    "api_key": dlt.secrets["sources.rest_api.api_token"], # read API credentials directly from secrets.toml
+                    "location": "query"
+                },
+                "paginator": { # pagination strategy for the OMDb API 
+                    "type": "page_number",
+                    "base_page": 1,
+                    "total_path": "totalResults",
+                    "maximum_page": 5
+                }
+            },
+            "resources": [ # list of API endpoints to request
+                {
+                    "name": "movie_search",
+                    "endpoint": {
+                        "path": "/",
+                        "params": {
+                            "s": "godzilla",
+                            "type": "movie"
+                        }
+                    }
+                }
+            ]
+        })
+
+
+    if __name__ == "__main__":
+        # Create a pipeline object
+        pipeline = dlt.pipeline(
+            pipeline_name='movies_pipeline',
+            destination='lancedb', # this tells dlt to load the data into LanceDB
+            dataset_name='movies_data_pipeline',
+        )
+
+        # Run the pipeline
+        load_info = pipeline.run(movies_source)
+
+        # pretty print the information on data that was loaded
+        print(load_info)
+    ```
+
+    The script above will ingest the data into LanceDB as it is, i.e. without creating any embeddings. If we want to embed one of the fields (for example, `"Title"` that contains the movie titles), then we will use dlt's `lancedb_adapter` and modify the script as follows:  
+    
+    - Add the following import statement:
+        ```python
+        from dlt.destinations.adapters import lancedb_adapter
+        ```
+    - Modify the pipeline run like this:
+        ```python
+        load_info = pipeline.run(
+            lancedb_adapter(
+                movies_source,
+                embed="Title",
+            )
+        )
+        ```
+    This will use the embedding model specified inside `.dlt/secrets.toml` to embed the field `"Title"`.
+
+5. **Install necessary dependencies:**  
+    ```sh
+    pip install -r requirements.txt
+    ```  
+
+    Note: You may need to install the dependencies for your embedding models separately.
+    ```sh
+    pip install sentence-transformers
+    ```
+
+6. **Run the pipeline:**
+    Finally, running the following command will ingest the data into your LanceDB instance.
+    ```sh
+    python custom_source.py
+    ```
+
+For more information and advanced usage of dlt's LanceDB integration, read [the dlt documentation](https://dlthub.com/docs/dlt-ecosystem/destinations/lancedb).

python/.bumpversion.toml

@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.13.0-beta.0"
+current_version = "0.13.0-beta.1"
 parse = """(?x)
     (?P<major>0|[1-9]\\d*)\\.
     (?P<minor>0|[1-9]\\d*)\\.

python/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "lancedb-python"
-version = "0.13.0-beta.0"
+version = "0.13.0-beta.1"
 edition.workspace = true
 description = "Python bindings for LanceDB"
 license.workspace = true

python/python/lancedb/query.py

@@ -457,6 +457,22 @@ class LanceQueryBuilder(ABC):
             },
         ).explain_plan(verbose)
 
+    @abstractmethod
+    def rerank(self, reranker: Reranker) -> LanceQueryBuilder:
+        """Rerank the results using the specified reranker.
+
+        Parameters
+        ----------
+        reranker: Reranker
+            The reranker to use.
+
+        Returns
+        -------
+
+        The LanceQueryBuilder object.
+        """
+        raise NotImplementedError
+
 
 class LanceVectorQueryBuilder(LanceQueryBuilder):
     """
@@ -841,6 +857,21 @@ class LanceEmptyQueryBuilder(LanceQueryBuilder):
             limit=self._limit,
         )
 
+    def rerank(self, reranker: Reranker) -> LanceEmptyQueryBuilder:
+        """Rerank the results using the specified reranker.
+
+        Parameters
+        ----------
+        reranker: Reranker
+            The reranker to use.
+
+        Returns
+        -------
+        LanceEmptyQueryBuilder
+            The LanceQueryBuilder object.
+        """
+        raise NotImplementedError("Reranking is not yet supported.")
+
 
 class LanceHybridQueryBuilder(LanceQueryBuilder):
     """

python/python/lancedb/rerankers/__init__.py

@@ -1,16 +1,3 @@
-#  Copyright (c) 2023. LanceDB Developers
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-
 from .base import Reranker
 from .cohere import CohereReranker
 from .colbert import ColbertReranker

python/python/lancedb/rerankers/base.py

@@ -1,16 +1,3 @@
-#  Copyright (c) 2023. LanceDB Developers
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-
 from abc import ABC, abstractmethod
 from packaging.version import Version
 from typing import Union, List, TYPE_CHECKING

python/python/lancedb/rerankers/cohere.py

@@ -1,16 +1,3 @@
-#  Copyright (c) 2023. LanceDB Developers
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-
 import os
 from packaging.version import Version
 from functools import cached_property

python/python/lancedb/rerankers/colbert.py

@@ -1,16 +1,3 @@
-#  Copyright (c) 2023. LanceDB Developers
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-
 import pyarrow as pa
 
 from ..util import attempt_import_or_raise

python/python/lancedb/rerankers/cross_encoder.py

@@ -1,16 +1,3 @@
-#  Copyright (c) 2023. LanceDB Developers
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-
 from functools import cached_property
 from typing import Union
 

python/python/lancedb/rerankers/jinaai.py

@@ -1,16 +1,3 @@
-#  Copyright (c) 2023. LanceDB Developers
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-
 import os
 import requests
 from functools import cached_property

python/python/lancedb/rerankers/linear_combination.py

@@ -1,16 +1,3 @@
-#  Copyright (c) 2023. LanceDB Developers
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-
 import pyarrow as pa
 
 from .base import Reranker

python/python/lancedb/rerankers/openai.py

@@ -1,16 +1,3 @@
-#  Copyright (c) 2023. LanceDB Developers
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-
 import json
 import os
 from functools import cached_property

python/python/lancedb/rerankers/rrf.py

@@ -1,16 +1,3 @@
-#  Copyright (c) 2023. LanceDB Developers
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-
 from typing import Union, List, TYPE_CHECKING
 import pyarrow as pa