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🔧 fix: Catch deleteVectors Errors & Update RAG API docs (#2299)

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* fix(deleteVectors): handle errors gracefully

* chore: update docs based on new alternate env vars prefixed with RAG to avoid conflicts with LibreChat keys
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Danny Avila 2024-04-03 14:24:46 -04:00 • committed by GitHub
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docs/features/rag_api.md
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@ -60,19 +60,14 @@ This contrasts Docker, where is already set in the default `docker-compose.yml`
## Configuration
The RAG API provides several configuration options that can be set using environment variables from an `.env` file accessible to the API. Most of them are optional, asides from the credentials/paths necessary for the provider you configured. In the default setup, only OPENAI_API_KEY is required.
The RAG API provides several configuration options that can be set using environment variables from an `.env` file accessible to the API. Most of them are optional, asides from the credentials/paths necessary for the provider you configured. In the default setup, only `RAG_OPENAI_API_KEY` is required.
> !!! **Important:** When using the default docker setup, the .env file is shared between LibreChat and the RAG API.
> You will need to utilize the [Docker Compose Override File](../install/configuration/docker_override.md) to set a unique OPENAI_API_KEY value for RAG API, that is different from the one in your `.env` file.
> This may be necessary if you wish to use OpenAI for vector embeddings, but have set `OPENAI_API_KEY=user_provided`
> There is an example for this in `docker-compose.override.yml.example`
> !!! **Important:** When using the default docker setup, the .env file is shared between LibreChat and the RAG API. For this reason, it's important to define the needed variables shown in the [RAG API readme.md](https://github.com/danny-avila/rag_api/blob/main/README.md)
Here are some notable configurations:
- `OPENAI_API_KEY`: The API key for OpenAI API Embeddings (if using default settings).
- `RAG_OPENAI_API_KEY`: The API key for OpenAI API Embeddings (if using default settings).
- Note: `OPENAI_API_KEY` will work but `RAG_OPENAI_API_KEY` will override it in order to not conflict with the LibreChat credential.
- `RAG_PORT`: The port number where the API server will run. Defaults to port 8000.
- `RAG_HOST`: The hostname or IP address where the API server will run. Defaults to "0.0.0.0"
- `COLLECTION_NAME`: The name of the collection in the vector store. Default is "testcollection".
@ -136,7 +131,7 @@ RAG consists of two main phases: retrieval and content generation.
### Challenges and Ongoing Research
While RAG is currently the best-known tool for grounding LLMs on the latest, verifiable information and lowering the costs of constant retraining and updating, it is not perfect. Some challenges include:
While RAG is currently one of the best-known tools for grounding LLMs on the latest, verifiable information and lowering the costs of constant retraining and updating, it's not perfect. Some challenges include:
1. **Recognizing unanswerable questions**: LLMs need to be explicitly trained to recognize questions they can't answer based on the available information. This may require fine-tuning on thousands of examples of answerable and unanswerable questions.
2. **Improving retrieval and generation**: Ongoing research focuses on innovating at both ends of the RAG process: improving the retrieval of the most relevant information possible to feed the LLM, and optimizing the structure of that information to obtain the richest responses from the LLM.