ollama + langchain

Example code to use langchain library to run queries against an ollama models.

See Ollama examples for reference code.

Setup Local models:

To use OpenAI (ollama mistral seems to produce better results)

• Register with OpenAI: https://platform.openai.com/
• Create an API key
• export the key: export OPENAI_API_KEY=<KEY>
• export the flag: export USE_OPEN_AI=true (to clear openai flag, run unset USE_OPEN_AI)

Once the above steps have been completed, move to the Setup Env steps

To use Ollama:

• See install instructions for ollama app: https://ollama.ai/download
  • Reference: https://python.langchain.com/docs/guides/local_llms#quickstart
• From commandline run:
  • ollama pull mistral (4GB on disk, ~8GB in RAM)
  • ollama pull llama2 (4GB on disk, ~8GB in RAM)
• First time you run this it will be slow as the models needs to be downloaded.
• Models are stored under ~/.ollama/models/blobs/
• Ollama runs a local API : you can browse to http://localhost:11434/
• To stop ollama - see top right of mac toolbar near the clock: ollama/ollama#690 (comment)
• Re-start ollama - ollama serve mistral or ollama serve llama2

Once the setup steps above have been run, you should see a server running on http://localhost:11434/

Setup a python venv:

• create new venv: python3 -m venv env
• enable venv: source env/bin/activate
• install requirements: pip install -r requirements.txt
• if you install anymore packages run: pip freeze > requirements.txt

Next, move to the Run Examples steps

Run Examples:

• Instruction processing:
  • Run the UI, from the commandline run: streamlit run src/instruct/ui.py
  • Or Run the same prompts via the command line: python src/instruct/cli.py
• Document Summaries:
  • Run the UI, from the commandline run: streamlit run src/summarise/ui.py
• Document Comparison:
  • Run the UI, from the commandline run: streamlit run src/comparison/ui.py