Open Source Neural Search Software
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Neural Search Software
View 15 business solutionsBrowse free open source Neural Search software and projects below. Use the toggles on the left to filter open source Neural Search software by OS, license, language, programming language, and project status.
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Qdrant
Vector Database for the next generation of AI applications
Qdrant is a vector similarity engine & vector database. It deploys as an API service providing search for the nearest high-dimensional vectors. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more! Provides the OpenAPI v3 specification to generate a client library in almost any programming language. Alternatively, utilize ready-made client for Python or other programming languages with additional functionality. Implement a unique custom modification of the HNSW algorithm for the Approximate Nearest Neighbor Search. Search with a State-of-the-Art speed and apply search filters without compromising on results. Support additional payload associated with vectors. Not only stores payload but also allows filter results based on payload values. Unlike Elasticsearch post-filtering, Qdrant guarantees all relevant vectors are retrieved. -
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Weaviate
Weaviate is a cloud-native, modular, real-time vector search engine
Weaviate in a nutshell: Weaviate is a vector search engine and vector database. Weaviate uses machine learning to vectorize and store data, and to find answers to natural language queries. With Weaviate you can also bring your custom ML models to production scale. Weaviate in detail: Weaviate is a low-latency vector search engine with out-of-the-box support for different media types (text, images, etc.). It offers Semantic Search, Question-Answer-Extraction, Classification, Customizable Models (PyTorch/TensorFlow/Keras), and more. Built from scratch in Go, Weaviate stores both objects and vectors, allowing for combining vector search with structured filtering with the fault-tolerance of a cloud-native database, all accessible through GraphQL, REST, and various language clients. -
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Haystack
Haystack is an open source NLP framework to interact with your data
Apply the latest NLP technology to your own data with the use of Haystack's pipeline architecture. Implement production-ready semantic search, question answering, summarization and document ranking for a wide range of NLP applications. Evaluate components and fine-tune models. Ask questions in natural language and find granular answers in your documents using the latest QA models with the help of Haystack pipelines. Perform semantic search and retrieve ranked documents according to meaning, not just keywords! Make use of and compare the latest pre-trained transformer-based languages models like OpenAI’s GPT-3, BERT, RoBERTa, DPR, and more. Pick any Transformer model from Hugging Face's Model Hub, experiment, find the one that works. Use Haystack NLP components on top of Elasticsearch, OpenSearch, or plain SQL. Boost search performance with Pinecone, Milvus, FAISS, or Weaviate vector databases, and dense passage retrieval. -
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Cherche
Neural Search
Cherche allows the creation of efficient neural search pipelines using retrievers and pre-trained language models as rankers. Cherche's main strength is its ability to build diverse and end-to-end pipelines from lexical matching, semantic matching, and collaborative filtering-based models. Cherche provides modules dedicated to summarization and question answering. These modules are compatible with Hugging Face's pre-trained models and fully integrated into neural search pipelines. Search is fully compatible with the collaborative filtering library Implicit. It is advantageous if you have a history associated with users and you want to retrieve / re-rank documents based on user preferences. -
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Elastiknn
Elasticsearch plugin for nearest neighbor search
Elasticsearch plugin for nearest neighbor search. Store vectors and run similarity searches using exact and approximate algorithms. Methods like word2vec and convolutional neural nets can convert many data modalities (text, images, users, items, etc.) into numerical vectors, such that pairwise distance computations on the vectors correspond to semantic similarity of the original data. Elasticsearch is a ubiquitous search solution, but its support for vectors is limited. This plugin fills the gap by bringing efficient exact and approximate vector search to Elasticsearch. This enables users to combine traditional queries (e.g., “some product”) with vector search queries (e.g., an image (vector) of a product) for an enhanced search experience. -
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refinery
Open-source choice to scale, assess and maintain natural language data
The data scientist's open-source choice to scale, assess and maintain natural language data. Treat training data like a software artifact. You are one of the people we've built refinery for. refinery helps you to build better NLP models in a data-centric approach. Semi-automate your labeling, find low-quality subsets in your training data, and monitor your data in one place. refinery doesn't get rid of manual labeling, but it makes sure that your valuable time is spent well. Also, the makers of refinery currently work on integrations to other labeling tools, such that you can easily switch between different choices. refinery is a multi-repository project, you can find all integrated services in the architecture below. The app builds on top of Hugging Face and spaCy to leverage pre-built language models for your NLP tasks, as well as qdrant for neural search. -
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AnnLite
A fast embedded library for approximate nearest neighbor search
AnnLite is a lightweight and embeddable library for fast and filterable approximate nearest neighbor search (ANNS). It allows to search for nearest neighbors in a dataset of millions of points with a Pythonic API. A simple API is designed to be used with Python. It is easy to use and intuitive to set up to production. The library uses a highly optimized approximate nearest neighbor search algorithm (HNSW) to search for nearest neighbors. The library allows you to search for nearest neighbors within a subset of the dataset. Smooth integration with neural search ecosystem including Jina and DocArray, so that users can easily expose search API with gRPC and/or HTTP. The library is easy to install and use. It is designed to be used with Python. To support search with filters, the annlite must be created with colums parameter, which is a series of fields you want to filter by. -
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Aquila DB
An easy to use Neural Search Engine
Aquila DB is a Neural search engine. In other words, it is a database to index Latent Vectors generated by ML models along with JSON Metadata to perform k-NN retrieval. It is dead simple to set up, language-agnostic, and drop in addition to your Machine Learning Applications. Aquila DB, as of current features is a ready solution for Machine Learning engineers and Data scientists to build Neural Information Retrieval applications out of the box with minimal dependencies. -
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CLIP-as-service
Embed images and sentences into fixed-length vectors
CLIP-as-service is a low-latency high-scalability service for embedding images and text. It can be easily integrated as a microservice into neural search solutions. Serve CLIP models with TensorRT, ONNX runtime and PyTorch w/o JIT with 800QPS[*]. Non-blocking duplex streaming on requests and responses, designed for large data and long-running tasks. Horizontally scale up and down multiple CLIP models on single GPU, with automatic load balancing. Easy-to-use. No learning curve, minimalist design on client and server. Intuitive and consistent API for image and sentence embedding. Async client support. Easily switch between gRPC, HTTP, WebSocket protocols with TLS and compression. Smooth integration with neural search ecosystem including Jina and DocArray. Build cross-modal and multi-modal solutions in no time. -
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DocArray
The data structure for multimodal data
DocArray is a library for nested, unstructured, multimodal data in transit, including text, image, audio, video, 3D mesh, etc. It allows deep-learning engineers to efficiently process, embed, search, recommend, store, and transfer multimodal data with a Pythonic API. Door to multimodal world: super-expressive data structure for representing complicated/mixed/nested text, image, video, audio, 3D mesh data. The foundation data structure of Jina, CLIP-as-service, DALL·E Flow, DiscoArt etc. Data science powerhouse: greatly accelerate data scientists’ work on embedding, k-NN matching, querying, visualizing, evaluating via Torch/TensorFlow/ONNX/PaddlePaddle on CPU/GPU. Data in transit: optimized for network communication, ready-to-wire at anytime with fast and compressed serialization in Protobuf, bytes, base64, JSON, CSV, DataFrame. Perfect for streaming and out-of-memory data. One-stop k-NN: Unified and consistent API for mainstream vector databases. -
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Jina
Build cross-modal and multimodal applications on the cloud
Jina is a framework that empowers anyone to build cross-modal and multi-modal applications on the cloud. It uplifts a PoC into a production-ready service. Jina handles the infrastructure complexity, making advanced solution engineering and cloud-native technologies accessible to every developer. Build applications that deliver fresh insights from multiple data types such as text, image, audio, video, 3D mesh, PDF with Jina AI’s DocArray. Polyglot gateway that supports gRPC, Websockets, HTTP, GraphQL protocols with TLS. Intuitive design pattern for high-performance microservices. Seamless Docker container integration: sharing, exploring, sandboxing, versioning and dependency control via Jina Hub. Fast deployment to Kubernetes, Docker Compose and Jina Cloud. Improved engineering efficiency thanks to the Jina AI ecosystem, so you can focus on innovating with the data applications you build. -
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MTEB
MTEB: Massive Text Embedding Benchmark
Text embeddings are commonly evaluated on a small set of datasets from a single task not covering their possible applications to other tasks. It is unclear whether state-of-the-art embeddings on semantic textual similarity (STS) can be equally well applied to other tasks like clustering or reranking. This makes progress in the field difficult to track, as various models are constantly being proposed without proper evaluation. To solve this problem, we introduce the Massive Text Embedding Benchmark (MTEB). MTEB spans 8 embedding tasks covering a total of 58 datasets and 112 languages. Through the benchmarking of 33 models on MTEB, we establish the most comprehensive benchmark of text embeddings to date. We find that no particular text embedding method dominates across all tasks. This suggests that the field has yet to converge on a universal text embedding method and scale it up sufficiently to provide state-of-the-art results on all embedding tasks. -
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NOW
No-code tool for creating a neural search solution in minutes
One line to host them all. Bootstrap your multimodal search case in minutes. NOW gives the world access to multimodal neural search with just one command. NOW supports various formats for uploading your dataset to your search application. You may either choose a demo dataset hosted by NOW, or use your own custom dataset, to build an application. NOW can support your custom data in the form of a DocumentArray, as a path to a local folder, or S3 bucket. You can choose a demo dataset to get started quickly. The demo datasets are hosted by NOW which can be easily used to build a search application. There is a large variety of datasets, including images, text, and audio. Perhaps your data is stored in an S3 bucket, which is an option NOW also supports. In this case, NOW asks for the URI to the S3 bucket, as well as the credentials and region thereof. A final step in loading your data is to choose the fields of your data that you would like to use for search and filter respectively. -
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PaddleNLP
Easy-to-use and powerful NLP library with Awesome model zoo
PaddleNLP It is a natural language processing development library for flying paddles, with Easy-to-use text area API, Examples of applications for multiple scenarios, and High-performance distributed training Three major features, aimed at improving the modeling efficiency of the flying oar developer's text field, aiming to improve the developer's development efficiency in the text field, and provide rich examples of NLP applications. Provide rich industry-level pre-task capabilities Taskflow And process-wide text area API: Support for the loading of rich Chinese data sets Dataset API, can flexibly and efficiently complete data pretreatment Data API, Preset 60 + pre-training word vector Embedding API, Providing 100 + pre-training model Transformer API Wait, the efficiency of NLP task modeling can be greatly improved. -
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Prime QA
State-of-the-art Multilingual Question Answering research
PrimeQA is a public open source repository that enables researchers and developers to train state-of-the-art models for question answering (QA). By using PrimeQA, a researcher can replicate the experiments outlined in a paper published in the latest NLP conference while also enjoying the capability to download pre-trained models (from an online repository) and run them on their own custom data. PrimeQA is built on top of the Transformers toolkit and uses datasets and models that are directly downloadable. -
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Vector AI
A platform for building vector based applications
Vector AI is a framework designed to make the process of building production-grade vector-based applications as quick and easily as possible. Create, store, manipulate, search and analyze vectors alongside json documents to power applications such as neural search, semantic search, personalized recommendations etc. Image2Vec, Audio2Vec, etc (Any data can be turned into vectors through machine learning). Store your vectors alongside documents without having to do a db lookup for metadata about the vectors. Enable searching of vectors and rich multimedia with vector similarity search. The backbone of many popular A.I use cases like reverse image search, recommendations, personalization, etc. There are scenarios where vector search is not as effective as traditional search, e.g. searching for skus. Vector AI lets you combine vector search with all the features of traditional search such as filtering, fuzzy search, and keyword matching to create an even more powerful search. -
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VectorDB
A Python vector database you just need, no more, no less
vectordb is a Pythonic vector database offers a comprehensive suite of CRUD (Create, Read, Update, Delete) operations and robust scalability options, including sharding and replication. It's readily deployable in a variety of environments, from local to on-premise and cloud. vectordb delivers exactly what you need - no more, no less. It's a testament to effective Pythonic design without over-engineering, making it a lean yet powerful solution for all your needs. vectordb capitalizes on the powerful retrieval prowess of DocArray and the scalability, reliability, and serving capabilities of Jina. Here's the magic: DocArray serves as the engine driving vector search logic, while Jina guarantees efficient and scalable index serving. This synergy culminates in a robust, yet user-friendly vector database experience, that's vectordb for you. -
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finetuner
Task-oriented finetuning for better embeddings on neural search
Fine-tuning is an effective way to improve performance on neural search tasks. However, setting up and performing fine-tuning can be very time-consuming and resource-intensive. Jina AI’s Finetuner makes fine-tuning easier and faster by streamlining the workflow and handling all the complexity and infrastructure in the cloud. With Finetuner, you can easily enhance the performance of pre-trained models, making them production-ready without extensive labeling or expensive hardware. Create high-quality embeddings for semantic search, visual similarity search, cross-modal text image search, recommendation systems, clustering, duplication detection, anomaly detection, or other uses. Bring considerable improvements to model performance, making the most out of as little as a few hundred training samples, and finish fine-tuning in as little as an hour. -
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txtai
Build AI-powered semantic search applications
txtai executes machine-learning workflows to transform data and build AI-powered semantic search applications. Traditional search systems use keywords to find data. Semantic search applications have an understanding of natural language and identify results that have the same meaning, not necessarily the same keywords. Backed by state-of-the-art machine learning models, data is transformed into vector representations for search (also known as embeddings). Innovation is happening at a rapid pace, models can understand concepts in documents, audio, images and more. Machine-learning pipelines to run extractive question-answering, zero-shot labeling, transcription, translation, summarization and text extraction. Cloud-native architecture that scales out with container orchestration systems (e.g. Kubernetes). Applications range from similarity search to complex NLP-driven data extractions to generate structured databases. The following applications are powered by txtai.
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Guide to Open Source Neural Search Software
Open source neural search software is a type of software designed to facilitate the search for specific data within large datasets. It uses artificial intelligence algorithms and neural networks to process inputs into relevant results. Many organizations have adopted this type of technology as an efficient and cost-effective alternative to traditional searching methods.
Unlike traditional searching methods, open source neural search software does not rely on keywords or pre-defined queries to find information from a corpus. Instead, it uses natural language processing (NLP) techniques that mimic how the human brain would process relevant information from a dataset. This allows for more precise results than standard keyword searches, leading to faster and more accurate outcomes. Additionally, open source neural search software is intended to be easy to use – ideal for users with limited technical expertise.
The main features of open source neural search software include its ability to understand context and user intent in order to provide better results; its speed and scalability; its compatibility with various formats; and most importantly, its integrations with existing applications, allowing users to create powerful experiences without needing specialized coding skills or knowledge. Moreover, since such solutions are typically provided as open source technologies, developers can customize them according to their needs – something which may not be possible with proprietary technologies due to potential copyright issues.
Ultimately, open source neural search software offers an effective way for businesses and organizations of all sizes to quickly access large amounts of data without spending too much time manually navigating through complex systems or investing in specialized infrastructure beyond basic computing equipment such as servers and data storage hardware.
Features Offered by Open Source Neural Search Software
Open source neural search software can be used for many purposes, from natural language processing to facial recognition. Here are some of the features it provides:
- Natural Language Processing (NLP): Open source neural search software offers powerful NLP capabilities, such as keyword extraction and semantic analysis, to help identify concepts more accurately and quickly in text-based documents.
- Machine Learning (ML): ML algorithms can be used to identify patterns in large datasets in order to make predictions or recommendations. This allows machines to learn from prior experiences and draw conclusions without explicit programming.
- Deep Learning (DL): DL uses sophisticated layers of artificial neurons that enable the machine to better interpret complex data sources. This type of learning is a key component in computer vision and speech recognition.
- Advanced Search: Neural search software can be used for a variety of advanced tasks ranging from image recognition and language translation to recommendation engines and semantic analysis. These technologies help users find relevant information more easily by narrowing down their searches.
- Speech Recognition: Open source neural search software is equipped with speech-recognition capabilities that allow it to accurately process spoken words or commands into text or other data formats for further processing.
- Facial Recognition: This technology allows machines to detect and identify a person's face from a digital image or video. It is commonly used in biometric security systems as well as applications such as photo tagging and access control.
- Expert Systems: Neural search software can be used to emulate the decision-making processes of experts. Expert systems use artificial intelligence algorithms combined with knowledge acquired from previous experiences in order to make decisions quickly and accurately.
What Are the Different Types of Open Source Neural Search Software?
- Artificial Neural Network (ANN): Artificial Neural Networks are software systems that emulate the behavior of biological neurons and replicate the way they process information. They use input, output, weights, and connections to simulate the human brain's decision-making process. This type of neural search software is commonly used for data analysis and pattern recognition.
- Natural Language Processing (NLP): NLP is a form of artificial intelligence that allows computers to understand human language and interpret it for their own purposes. It works by analyzing text or voice input from humans to extract meaning and provide more accurate searches based on natural language processing algorithms.
- Distributed Representation: Distributed representation is an approach that uses distributed patterns instead of exact matches when searching for information in large datasets. This type of neural search software uses a shared encoding scheme across different nodes in a network which allows information from multiple sources to be combined into one cohesive set of results.
- Knowledge Graphs: A knowledge graph is an interconnected web of facts about entities which can be used in neural search software applications such as natural language processing or recommendation engines. The main benefit of this type of artificial intelligence technology is its ability to learn from large data sets more quickly than traditional methods.
- Reinforcement Learning: Reinforcement learning is an approach where agents interact with their environment by exploring different options and then receiving rewards for success or punishment for failure until they reach optimal outcomes in complex situations. This type of technology can be applied to neural search software to help machines find better ways to respond to user queries based on rewards and punishments given by the system itself.
Benefits Provided by Open Source Neural Search Software
- Cost Savings: Open source neural search software can save companies time and money since the cost of development is generally cheaper than proprietary software, and the cost of ownership is often zero. Additionally, open source software can help businesses avoid vendor lock-in that may be associated with proprietary solutions.
- Flexibility & Control: Companies have full access to the source code and can modify or customize the software as needed. This allows for greater control over their search engine capabilities and usage scenarios without having to wait on a vendor's release schedule or feature requests to be implemented.
- Reliability & Security: Since open source neural search engines are typically tested by large communities of users before being released, they tend to be more reliable and secure than proprietary solutions. Additionally, since developers have full access to the codebase, any security flaws can be identified quickly and patched quickly as well.
- Improved Performance & Scalability: Open source neural search engines are often more robust than proprietary ones, resulting in improved performance and scalability when compared with a closed-source solution. As more developers contribute to optimizing the system’s codebase over time, businesses have assurance that their search engine will remain up-to-date with cutting edge technology while also benefitting from enhanced performance gains.
- Support: Since open source communities are usually large and vibrant, businesses benefit greatly from an active user base which provides free support through forums, chat tools or mailing lists where people can share knowledge about how best to use each product or come together in order solve issues quickly. For example, if a business has a complex query that requires help understanding specific parts of the codebase or architecture design then they can often find answers online by searching for community threads related to their issue(s).
Who Uses Open Source Neural Search Software?
- Developers: Developers use open source neural search software to design, develop, and customize search implementations. They often have a good understanding of coding and are eager to explore new technologies.
- Researchers: Researchers use open source neural search software to analyze large sets of data and develop machine learning algorithms. They often work with teams of other researchers in order to identify patterns within the data set.
- Businesses: Businesses use open source neural search software to improve website usability through improved search results and better customer insight. Additionally, businesses may use open source neural search software to create custom solutions for specific business needs.
- Data Scientists: Data scientists use open source neural search software as an aid when collecting data from various sources and manipulating it in order to find answers or solve problems. Their expertise helps them interpret the results correctly so they can make informed decisions about future strategies.
- End Users: End users benefit from using open source neural search software by having access to more user friendly interfaces that allow for easier navigation between webpages and improved accuracy when searching for information online. These users will generally have limited technical knowledge but can still get the most out of their experience with the help of advanced features found in open source solutions.
- Educators: Educators use open source neural search software to teach students about machine learning and its various applications. This gives the students an opportunity to gain hands on experience with a technology that is becoming increasingly important in today’s economy.
- Hobbyists: Hobbyists use open source neural search software for fun and experimentation, allowing them to explore the possibilities of this technology without having to invest too heavily. They often work with small datasets and experiment with different algorithms in order to see what works best for their needs.
How Much Does Open Source Neural Search Software Cost?
Open source neural search software is typically offered for free due to the spirit of open source development. This means that anyone can use and modify it without restriction or cost, allowing developers to collaborate on improving the software. Companies may choose to pay for technical support or customizations, but these services are not necessary in order to use and benefit from the software. As technology continues to evolve, so too do open source solutions; this allows developers to stay ahead of trends while remaining flexible and cost-efficient. Open source neural search software is an invaluable resource for anyone seeking to build and improve upon existing technologies, making it an attractive option for businesses of all sizes.
What Software Can Integrate With Open Source Neural Search Software?
Open source neural search software can integrate with a variety of different types of software. These include natural language processing software, recommendation algorithms, search engines, analytics and data visualization platforms, as well as machine learning and deep learning frameworks. Natural language processing software can be used to better understand user queries in order to provide more accurate search results. Recommendation algorithms help to improve the accuracy of searches by taking into account additional factors such as user history or contextual data. Search engines use neural networks to better understand how documents are related and can then quickly return accurate results for queries. Analytics and data visualization platforms allow users to gain insights from their data that would not otherwise be possible. Machine learning and deep learning frameworks are essential for training neural networks so they can learn which searches should return which specific results. All of these types of software can be used together with open source neural search software to provide a powerful and efficient search experience.
Recent Trends Related to Open Source Neural Search Software
- Open source neural search software is becoming more popular as a tool to provide intelligent search capabilities to web applications.
- This type of software uses artificial intelligence algorithms to learn from large datasets and generate accurate results.
- It has the potential to reduce development costs and time-to-market for organizations looking to add search functions to their websites or products.
- Many open source projects are in active development, providing developers with access to the latest technologies and features.
- These projects often also allow developers to customize the algorithms and parameters used for searches, allowing them greater control over the accuracy of their results.
- There is an increasing focus on using neural search techniques for natural language processing tasks such as text summarization, question answering, document classification, sentiment analysis and image captioning.
- As more companies realize the value of these tools, there is likely to be an increase in investment in open source projects related to this technology.
How To Get Started With Open Source Neural Search Software
Getting started with open source neural search software is a relatively straightforward process. The first step is to make sure your computer meets the minimum system requirements for the software. This will usually include having enough memory, disc space, and computing power for the program. To ensure you have all of the necessary components, you can refer to the system requirements listed on the product's website or in any related documentation.
Next, download and install the software onto your computer. Many open source neural search tools come in the form of an installation package that can be run directly from your desktop. Once installed, you should be able to launch it and begin using it right away.
The third step is to set up your data sources within the program. Depending on which software you use, this could involve creating indexes that contain all of your documents or setting up a connection between your existing document database and the software itself. During this process, it may also be helpful to familiarize yourself with how to query the software once everything has been set up properly so that you can start retrieving results as soon as possible.
Finally, depending on what type of search results you are looking for, there may be some additional configuration settings that need to be enabled before using them in production environments (e.g., disabling certain types of searches or filtering options). Generally speaking though, after completing these steps and familiarizing yourself with how to create queries within the software, users should be able to get up-and-running with open source neural search tools pretty quickly.
