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https://github.com/spyglass-search/memex

Super-simple, fully Rust powered "memory" (doc store + semantic search) for LLM projects, semantic search, etc.
https://github.com/spyglass-search/memex

llm rust rust-lang semantic-search

Last synced: 18 days ago
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Super-simple, fully Rust powered "memory" (doc store + semantic search) for LLM projects, semantic search, etc.

Host: GitHub
URL: https://github.com/spyglass-search/memex
Owner: spyglass-search
Created: 2023-06-09T04:49:39.000Z (about 1 year ago)
Default Branch: main
Last Pushed: 2023-10-09T20:56:08.000Z (9 months ago)
Last Synced: 2024-02-25T00:32:45.472Z (4 months ago)
Topics: llm, rust, rust-lang, semantic-search
Language: Rust
Homepage:
Size: 4.26 MB
Stars: 38
Watchers: 3
Forks: 3
Open Issues: 0
Metadata Files:
- Readme: README.md

Lists

awesome-rust-llm - memex - Super-simple, fully Rust powered "memory" (doc store + semantic search) for LLM projects, semantic search, etc. (LLM Memory)
awesome-stars - spyglass-search/memex - Super-simple, fully Rust powered "memory" (doc store + semantic search) for LLM projects, semantic search, etc. (rust)

README

# memex

Super simple "memory" and common functionality for LLM projects, semantic search, etc.

## Running the service

Note that if you're running on Apple silicon (M1/M2/etc.), it's best to run natively (and faster)
since Linux ARM builds are very finicky.

``` bash
# Build and run the docker image. This will build & run memex as well as an opensearch
# node for document storage + search.
> docker-compose up
# OR run natively in you have the rust toolchain installed. This uses the default file
# based vector store instead of opensearch which may yield worse results.
> cp .env.template .env
> cargo run --release -p memex serve
# If everything is running correctly, you should see something like:
2023-06-13T05:04:21.518732Z INFO memex: starting server with roles: [Api, Worker]
```

## Using a LLM
You can use either OpenAI or a local LLM for LLM based functionality (such as the
summarization or extraction APIs).

Set `OPENAI_API_KEY` to your API key in the `.env` file or set `LOCAL_LLM_CONFIG` to
a LLM configuration file. See `resources/config.llama2.toml` for an example. By
default, a base memex will use the llama-2 configuration file.

### Supported local models

Currently we have supported (and have tested) the following models:
- Llama based models (llama 1 & 2, Mistral, etc.) - *recommended*
- Gptj (e.g. GPT4All)

## Adding a document

NOTE: If the `test` collection does not initially exist, it'll be created.

``` bash
> curl http://localhost:8181/api/collections/test \
-H "Content-Type: application/json" \
--data @example_docs/state_of_the_union_2023.json
{
"time": 0.123,
"status": "ok",
"result": {
"taskId": 1,
"collection": "test",
"status": "Queued",
...
}
}
```

Feel free to add as many documents as you want. Each one will be enqueued and processed
as they are added.

Wait a couple seconds per document to be processed. You can check the status
using the `task_id` above like so:

## Check task status

``` bash
> curl http://localhost:8181/api/tasks/1
{
"time": 0.123,
"status": "ok",
"result": {
"taskId": 1,
"status": "Processing"
}
}
```

Or if it's finished, something like so:
```bash
{
"time": 0.123,
"status": "ok",
"result": {
"taskId": 1,
"collection": "test"
"status": "Completed",
"createdAt": "2023-09-19T00:00:00Z"
}
}
```

One the task is shown as "Completed", you can now run a query against the doc(s)
you've just added.

## Run a search query

``` bash
> curl http://localhost:8181/api/collections/test/search \
-H "Content-Type: application/json" \
-X GET \
-d "{\"query\": \"what does Biden say about taxes?\", \"limit\": 3}"
{
"time": 1.234,
"status": "ok",
"result": [{
"_id": , // reference to this particular segment text.
"document_id": , // The original document that this came from.
"segment": ,
"content": ,
"score":
}, ...]
}
```

## Ask a question
```bash
> curl http://localhost:8181/api/action/ask \
-H "Content-Type: application/json" \
-X POST \
-d "{\"text\": \"\", \"query\": \"What is the airspeed velocity of an unladen swallow?\", "json_schema": { .. }}"
{
"time": 1.234,
"status": "ok",
"result": {
"answer": "The airspeed velocity of an unladen swallow is..."
}
}

```

## Env variables

- `HOST`: Defaults to `127.0.0.1`
- `PORT`: Defaults to `8181`
- `DATABASE_CONNECTION`: Connection URI for either an sqlite or postgres database
- `VECTOR_CONNECTION`: Either `hnsw://` for a file-based vector store (but _very_ limited) or `opensearch+https://` for OpenSearch support.

## Examples

For any of these examples, make sure you have `memex` running in the background.

### Clippy

#### Ask questions about a single or many document(s)

``` bash
# In a different terminal, run memex
> cargo run --release -p memex serve
# Make sure the LLM model is downloaded
> make setup-examples
# In your main terminal
> cd examples/clippy
# NOTE: there is no duplicate detection so running this twice will add the file twice.
> cargo run -- load-file example_docs/state_of_the_union.txt
# To ask clippy about your files, use "ask"
> cargo run -- ask "what does biden say about taxes?"
# To ask clippy without referring to it's memex and _ONLY_ relying on the knowledge
# inside it's LLM, use "qq" / "quick-question"
> cargo run -- qq "wget command to save a file to a directory"
# To clear clippy's memory
> cargo run -- forget
```