Super simple "memory" and common functionality for LLM projects, semantic search, etc.
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.
# 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]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.
Currently we have supported (and have tested) the following models:
- Llama based models (llama 1 & 2, Mistral, etc.) - recommended
- Gptj (e.g. GPT4All)
NOTE: If the test collection does not initially exist, it'll be created.
> 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:
> 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:
{
"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.
> 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": <internal_id>, // reference to this particular segment text.
"document_id": <document UUID>, // The original document that this came from.
"segment": <document section>,
"content": <content block>,
"score": <relevancy score>
}, ...]
}> curl http://localhost:8181/api/action/ask \
-H "Content-Type: application/json" \
-X POST \
-d "{\"text\": \"<context if any>\", \"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..."
}
}
HOST: Defaults to127.0.0.1PORT: Defaults to8181DATABASE_CONNECTION: Connection URI for either an sqlite or postgres databaseVECTOR_CONNECTION: Eitherhnsw://<path>for a file-based vector store (but very limited) oropensearch+https://<uri>for OpenSearch support.
For any of these examples, make sure you have memex running in the background.
# 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
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