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54 lines
1.6 KiB
54 lines
1.6 KiB
2 years ago
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# Data query example
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This example makes use of [langchain and chroma](https://blog.langchain.dev/langchain-chroma/) to enable question answering on a set of documents.
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## Setup
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Download the models and start the API:
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```bash
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# Clone LocalAI
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git clone https://github.com/go-skynet/LocalAI
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cd LocalAI/examples/query_data
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wget https://huggingface.co/skeskinen/ggml/resolve/main/all-MiniLM-L6-v2/ggml-model-q4_0.bin -O models/bert
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wget https://gpt4all.io/models/ggml-gpt4all-j.bin -O models/ggml-gpt4all-j
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# start with docker-compose
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docker-compose up -d --build
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```
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### Python requirements
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```
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pip install -r requirements.txt
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```
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### Create a storage
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In this step we will create a local vector database from our document set, so later we can ask questions on it with the LLM.
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```bash
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export OPENAI_API_BASE=http://localhost:8080/v1
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export OPENAI_API_KEY=sk-
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wget https://raw.githubusercontent.com/hwchase17/chat-your-data/master/state_of_the_union.txt
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python store.py
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```
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After it finishes, a directory "storage" will be created with the vector index database.
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## Query
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We can now query the dataset.
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```bash
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export OPENAI_API_BASE=http://localhost:8080/v1
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export OPENAI_API_KEY=sk-
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python query.py
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# President Trump recently stated during a press conference regarding tax reform legislation that "we're getting rid of all these loopholes." He also mentioned that he wants to simplify the system further through changes such as increasing the standard deduction amount and making other adjustments aimed at reducing taxpayers' overall burden.
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```
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Keep in mind now things are hit or miss!
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