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All-in-one solution for search, recommendations, and RAG

## Quick Links - [API Reference + Docs](https://docs.trieve.ai/api-reference) - [OpenAPI specification](https://api.trieve.ai/redoc) - [Typescript SDK](https://ts-sdk.trieve.ai/) - [Python SDK](https://pypi.org/project/trieve-py-client/) ## Features - **🔒 Self-Hosting in your VPC or on-prem**: We have full self-hosting guides for AWS, GCP, Kubernetes generally, and docker compose available on our [documentation page here](https://docs.trieve.ai/self-hosting/docker-compose). - **🧠 Semantic Dense Vector Search**: Integrates with OpenAI or Jina embedding models and [Qdrant](https://qdrant.tech) to provide semantic vector search. - **🔍 Typo Tolerant Full-Text/Neural Search**: Every uploaded chunk is vector'ized with [naver/efficient-splade-VI-BT-large-query](https://huggingface.co/naver/efficient-splade-VI-BT-large-query) for typo tolerant, quality neural sparse-vector search. - **🖊️ Sub-Sentence Highlighting**: Highlight the matching words or sentences within a chunk and bold them on search to enhance UX for your users. Shout out to the [simsearch](https://github.com/smartdatalake/simsearch) crate! - **🌟 Recommendations**: Find similar chunks (or files if using grouping) with the recommendation API. Very helpful if you have a platform where users' favorite, bookmark, or upvote content. - **🤖 Convenient RAG API Routes**: We integrate with OpenRouter to provide you with access to any LLM you would like for RAG. Try our routes for [fully-managed RAG with topic-based memory management](https://api.trieve.ai/redoc#tag/message/operation/create_message_completion_handler) or [select your own context RAG](https://api.trieve.ai/redoc#tag/chunk/operation/generate_off_chunks). - **💼 Bring Your Own Models**: If you'd like, you can bring your own text-embedding, SPLADE, cross-encoder re-ranking, and/or large-language model (LLM) and plug it into our infrastructure. - **🔄 Hybrid Search with cross-encoder re-ranking**: For the best results, use hybrid search with [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) re-rank optimization. - **📆 Recency Biasing**: Easily bias search results for what was most recent to prevent staleness - **🛠️ Tunable Merchandizing**: Adjust relevance using signals like clicks, add-to-carts, or citations - **🕳️ Filtering**: Date-range, substring match, tag, numeric, and other filter types are supported. - **👥 Grouping**: Mark multiple chunks as being part of the same file and search on the file-level such that the same top-level result never appears twice **Are we missing a feature that your use case would need?** - call us at [628-222-4090](mailto:+16282224090), make a [Github issue](https://github.com/devflowinc/trieve/issues), or join the [Matrix community](https://matrix.to/#/#trieve-general:trieve.ai) and tell us! We are a small company who is still very hands-on and eager to build what you need; professional services are available. ## Local development with Linux ### Installing via Smithery To install Trieve for Claude Desktop automatically via [Smithery](https://smithery.ai/server/trieve-mcp-server): ```bash npx -y @smithery/cli install trieve-mcp-server --client claude ``` ### Debian/Ubuntu Packages needed packages ```sh sudo apt install curl \ gcc \ g++ \ make \ pkg-config \ python3 \ python3-pip \ libpq-dev \ libssl-dev \ openssl ``` ### Arch Packages needed ```sh sudo pacman -S base-devel postgresql-libs ``` ### Install NodeJS and Yarn You can install [NVM](https://github.com/nvm-sh/nvm) using its install script. ``` curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.39.5/install.sh | bash ``` You should restart the terminal to update bash profile with NVM. Then, you can install NodeJS LTS release and Yarn. ``` nvm install --lts npm install -g yarn ``` ### Make server tmp dir ``` mkdir server/tmp ``` ### Install rust ``` curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh ``` ### Install cargo-watch ``` cargo install cargo-watch ``` ### Setup env's You might need to create the `analytics` directory in ./frontends ``` cp .env.analytics ./frontends/analytics/.env cp .env.chat ./frontends/chat/.env cp .env.search ./frontends/search/.env cp .env.example ./server/.env cp .env.dashboard ./frontends/dashboard/.env ``` ### Add your `LLM_API_KEY` to `./server/.env` [Here is a guide for acquiring that](https://blog.streamlit.io/beginners-guide-to-openai-api/#get-your-own-openai-api-key). #### Steps once you have the key 1. Open the `./server/.env` file 2. Replace the value for `LLM_API_KEY` to be your own OpenAI API key. 3. Replace the value for `OPENAI_API_KEY` to be your own OpenAI API key. ### Export the following keys in your terminal for local dev The PAGEFIND_CDN_BASE_URL and S3_SECRET_KEY_CSVJSONL could be set to a random list of strings. ``` export OPENAI_API_KEY="your_OpenAI_api_key" \ LLM_API_KEY="your_OpenAI_api_key" \ PAGEFIND_CDN_BASE_URL="lZP8X4h0Q5Sj2ZmV,aAmu1W92T6DbFUkJ,DZ5pMvz8P1kKNH0r,QAqwvKh8rI5sPmuW,YMwgsBz7jLfN0oX8" \ S3_SECRET_KEY_CSVJSONL="Gq6wzS3mjC5kL7i4KwexnL3gP8Z1a5Xv,V2c4ZnL0uHqBzFvR2NcN8Pb1g6CjmX9J,TfA1h8LgI5zYkH9A9p7NvWlL0sZzF9p8N,pKr81pLq5n6MkNzT1X09R7Qb0Vn5cFr0d,DzYwz82FQiW6T3u9A4z9h7HLOlJb7L2V1" \ GROQ_API_KEY="GROQ_API_KEY_if_applicable" ``` ### Start docker container services needed for local dev ``` cat .env.chat .env.search .env.server .env.docker-compose > .env ./convenience.sh -l ``` ### Install front-end packages for local dev ``` cd frontends yarn ``` `cd ..` ``` cd clients/ts-sdk yarn build ``` `cd ../..` ### Start services for local dev It is recommend to manage services through [tmuxp, see the guide here](https://gist.github.com/skeptrunedev/101c7a13bb9b9242999830655470efac) or terminal tabs. ``` cd frontends yarn yarn dev ``` ``` cd server cargo watch -x run ``` ``` cd server cargo run --bin ingestion-worker ``` ``` cd server cargo run --bin file-worker ``` ``` cd server cargo run --bin delete-worker ``` ``` cd search yarn yarn dev ``` ### Verify Working Setup After the cargo build has finished (after the `tmuxp load trieve`): - check that you can see redoc with the OpenAPI reference at [localhost:8090/redoc](http://localhost:8090/redoc) - make an account create a dataset with test data at [localhost:5173](http://localhost:5173) - search that dataset with test data at [localhost:5174](http://localhost:5174) ### Additional Instructions for testing cross encoder reranking models To test the Cross Encoder rerankers in local dev, - click on the dataset, go to the Dataset Settings -> Dataset Options -> Additional Options and uncheck the `Fulltext Enabled` option. - in the Embedding Settings, select your reranker model and enter the respective key in the adjacent textbox, and hit save. - in the search playground, set Type -> Semantic and select Rerank By -> Cross Encoder - if AIMon Reranker is selected in the Embedding Settings, you can enter an optional Task Definition in the search playground to specify the domain of context documents to the AIMon reranker. ### Debugging issues with local dev Reach out to us on [discord](https://discord.gg/E9sPRZqpDT) for assistance. We are available and more than happy to assist. ## Debug diesel by getting the exact generated SQL `diesel::debug_query(&query).to_string();` ## Local Setup for Testing Stripe Features Install Stripe CLI. 1. `stripe login` 2. `stripe listen --forward-to localhost:8090/api/stripe/webhook` 3. set the `STRIPE_WEBHOOK_SECRET` in the `server/.env` to the resulting webhook signing secret 4. `stripe products create --name trieve --default-price-data.unit-amount 1200 --default-price-data.currency usd` 5. `stripe plans create --amount=1200 --currency=usd --interval=month --product={id from response of step 3}` ## Contributors

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