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<b>All-in-one solution for search, recommendations, and RAG</b>
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<a href="https://trieve.ai">
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## 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
### 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
```
### System Dependencies
#### Linux (Debian/Ubuntu)
```sh
sudo apt install curl \
gcc \
g++ \
make \
pkg-config \
python3 \
python3-pip \
libpq-dev \
libssl-dev \
openssl
```
#### Linux (Arch)
```sh
sudo pacman -S base-devel postgresql-libs
```
#### MacOS
```sh
# Install Xcode command line tools
xcode-select --install
# Install Homebrew if not already installed
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
# Install required packages
brew install pkg-config openssl
```
### 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.
### Override the following keys in your terminal for local dev
The PAGEFIND_CDN_BASE_URL and S3_SECRET_KEY_CSVJSONL can 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
```
### Start embedding servers
We offer 2 docker-compose files for embedding servers. One for GPU and one for CPU.
```sh
docker compose -f docker-compose-cpu-embeddings.yml up -d
```
or
```sh
docker compose -f docker-compose-gpu-embeddings.yml up -d
```
* Note on embedding servers. If you want to use a separate GPU enabled device for embedding servers you will need to update the following parameters
```
SPARSE_SERVER_QUERY_ORIGIN
SPARSE_SERVER_DOC_ORIGIN
EMBEDDING_SERVER_ORIGIN
SPARSE_SERVER_QUERY_ORIGIN
```
#### Using CPU embeddings on MacOS
```
cp docker-compose-cpu-embeddings.override.mac docker-compose-cpu-embeddings.override.yml
docker-compose -f docker-compose-cpu-embeddings.yml -f docker-compose-cpu-embeddings.override.yml docker up -d
```
### 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();`
## Running evals
The evals package loads an mcp client that then runs the index.ts file, so there is no need to rebuild between tests. You can load environment variables by prefixing the npx command. Full documentation can be found [here](https://www.mcpevals.io/docs).
```bash
OPENAI_API_KEY=your-key npx mcp-eval evals.ts clients/mcp-server/src/index.ts
```
## 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
<a href="https://github.com/devflowinc/trieve/graphs/contributors">
<img alt="contributors" src="https://contrib.rocks/image?repo=devflowinc/trieve"/>
</a>
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