mirror of
https://github.com/hwchase17/langchain
synced 2024-11-08 07:10:35 +00:00
d39b4b61b6
Ran the following bash script for all templates
```bash
#!/bin/bash
set -e
current_dir="$(pwd)"
for directory in */; do
if [ -d "$directory" ]; then
(cd "$directory" && poetry lock --no-update)
fi
done
cd "$current_dir"
```
Co-authored-by: Bagatur <baskaryan@gmail.com>
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| .. | ||
| llama2_functions | ||
| tests | ||
| llama2-functions.ipynb | ||
| poetry.lock | ||
| pyproject.toml | ||
| README.md | ||
Extraction with LLaMA2 Function Calling
This template shows how to do extraction of structured data from unstructured data, using LLaMA2 fine-tuned for grammars and jsonschema.
Query transformations are one great application area for open source, private LLMs:
- The tasks are often narrow and well-defined (e.g., generatae multiple questions from a user input)
- They also are tasks that users may want to run locally (e.g., in a RAG workflow)
Specify the scehma you want to extract in chain.py
LLM
This template will use a Replicate hosted version of LLaMA2 that has support for grammars and jsonschema.
Based on the Replicate example, the JSON schema is supplied directly in the prompt.
Be sure that REPLICATE_API_TOKEN is set in your environment.