Refactor langgraph_agent_simple notebook execution counts and handle Langfuse client errors
- Set execution counts to null for initial cells in langgraph_agent_simple.ipynb - Update execution counts for subsequent cells to maintain order - Change output stream name from stdout to stderr for error handling - Capture and log detailed error messages for failed Langfuse client authentication Update uv.lock to manage accelerate dependency - Remove accelerate from main dependencies - Add accelerate to dev dependencies with version specification - Adjust requires-dist section to reflect changes in dependency management
This commit is contained in:
acano
parent
5b424f8409
commit
5a666079a4
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@ -6,15 +6,17 @@ services:
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container_name: brunix-assistance-engine
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ports:
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- "50052:50051"
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network_mode: "host"
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environment:
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ELASTICSEARCH_URL: ${ELASTICSEARCH_URL}
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DATABASE_URL: ${DATABASE_URL}
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OLLAMA_URL: ${OLLAMA_URL}
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ELASTICSEARCH_INDEX: ${ELASTICSEARCH_INDEX}
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POSTGRES_URL: ${POSTGRES_URL}
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LANGFUSE_HOST: ${LANGFUSE_HOST}
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LANGFUSE_PUBLIC_KEY: ${LANGFUSE_PUBLIC_KEY}
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LANGFUSE_SECRET_KEY: ${LANGFUSE_SECRET_KEY}
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ELASTICSEARCH_INDEX: ${ELASTICSEARCH_INDEX}
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OLLAMA_URL: ${OLLAMA_LOCAL_URL}
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OLLAMA_MODEL_NAME: ${OLLAMA_MODEL_NAME}
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OLLAMA_EMB_MODEL_NAME: ${OLLAMA_EMB_MODEL_NAME}
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extra_hosts:
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- "host.docker.internal:host-gateway"
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|
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@ -1,7 +1,5 @@
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# This file was autogenerated by uv via the following command:
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# uv export --format requirements-txt --no-hashes --no-dev -o Docker/requirements.txt
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accelerate==1.12.0
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# via assistance-engine
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aiohappyeyeballs==2.6.1
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# via aiohttp
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aiohttp==3.13.3
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@ -35,10 +33,6 @@ colorama==0.4.6 ; sys_platform == 'win32'
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# click
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# loguru
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# tqdm
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cuda-bindings==12.9.4 ; platform_machine == 'x86_64' and sys_platform == 'linux'
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# via torch
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cuda-pathfinder==1.3.5 ; platform_machine == 'x86_64' and sys_platform == 'linux'
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# via cuda-bindings
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dataclasses-json==0.6.7
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# via langchain-community
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elastic-transport==8.17.1
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@ -46,17 +40,13 @@ elastic-transport==8.17.1
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elasticsearch==8.19.3
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# via langchain-elasticsearch
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filelock==3.24.3
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# via
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# huggingface-hub
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# torch
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# via huggingface-hub
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frozenlist==1.8.0
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# via
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# aiohttp
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# aiosignal
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fsspec==2025.10.0
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# via
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# huggingface-hub
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# torch
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# via huggingface-hub
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greenlet==3.3.2 ; platform_machine == 'AMD64' or platform_machine == 'WIN32' or platform_machine == 'aarch64' or platform_machine == 'amd64' or platform_machine == 'ppc64le' or platform_machine == 'win32' or platform_machine == 'x86_64'
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# via sqlalchemy
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grpcio==1.78.1
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@ -83,7 +73,6 @@ httpx-sse==0.4.3
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# via langchain-community
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huggingface-hub==0.36.2
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# via
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# accelerate
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# langchain-huggingface
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# tokenizers
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idna==3.11
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@ -92,8 +81,6 @@ idna==3.11
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# httpx
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# requests
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# yarl
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jinja2==3.1.6
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# via torch
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jmespath==1.1.0
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# via
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# boto3
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@ -150,69 +137,23 @@ langsmith==0.7.6
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# langchain-core
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loguru==0.7.3
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# via assistance-engine
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markupsafe==3.0.3
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# via jinja2
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marshmallow==3.26.2
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# via dataclasses-json
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mpmath==1.3.0
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# via sympy
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multidict==6.7.1
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# via
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# aiohttp
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# yarl
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mypy-extensions==1.1.0
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# via typing-inspect
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networkx==3.6.1
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# via torch
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nltk==3.9.3
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# via assistance-engine
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numpy==2.4.2
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# via
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# accelerate
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# assistance-engine
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# elasticsearch
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# langchain-aws
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# langchain-community
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# pandas
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nvidia-cublas-cu12==12.8.4.1 ; platform_machine == 'x86_64' and sys_platform == 'linux'
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# via
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# nvidia-cudnn-cu12
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# nvidia-cusolver-cu12
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# torch
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nvidia-cuda-cupti-cu12==12.8.90 ; platform_machine == 'x86_64' and sys_platform == 'linux'
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# via torch
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nvidia-cuda-nvrtc-cu12==12.8.93 ; platform_machine == 'x86_64' and sys_platform == 'linux'
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# via torch
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nvidia-cuda-runtime-cu12==12.8.90 ; platform_machine == 'x86_64' and sys_platform == 'linux'
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# via torch
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nvidia-cudnn-cu12==9.10.2.21 ; platform_machine == 'x86_64' and sys_platform == 'linux'
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# via torch
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nvidia-cufft-cu12==11.3.3.83 ; platform_machine == 'x86_64' and sys_platform == 'linux'
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# via torch
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nvidia-cufile-cu12==1.13.1.3 ; platform_machine == 'x86_64' and sys_platform == 'linux'
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# via torch
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nvidia-curand-cu12==10.3.9.90 ; platform_machine == 'x86_64' and sys_platform == 'linux'
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# via torch
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nvidia-cusolver-cu12==11.7.3.90 ; platform_machine == 'x86_64' and sys_platform == 'linux'
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# via torch
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nvidia-cusparse-cu12==12.5.8.93 ; platform_machine == 'x86_64' and sys_platform == 'linux'
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# via
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# nvidia-cusolver-cu12
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# torch
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nvidia-cusparselt-cu12==0.7.1 ; platform_machine == 'x86_64' and sys_platform == 'linux'
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# via torch
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nvidia-nccl-cu12==2.27.5 ; platform_machine == 'x86_64' and sys_platform == 'linux'
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# via torch
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nvidia-nvjitlink-cu12==12.8.93 ; platform_machine == 'x86_64' and sys_platform == 'linux'
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# via
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# nvidia-cufft-cu12
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# nvidia-cusolver-cu12
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# nvidia-cusparse-cu12
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# torch
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nvidia-nvshmem-cu12==3.4.5 ; platform_machine == 'x86_64' and sys_platform == 'linux'
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# via torch
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nvidia-nvtx-cu12==12.8.90 ; platform_machine == 'x86_64' and sys_platform == 'linux'
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# via torch
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ollama==0.6.1
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# via langchain-ollama
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orjson==3.11.7
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@ -223,7 +164,6 @@ ormsgpack==1.12.2
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# via langgraph-checkpoint
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packaging==24.2
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# via
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# accelerate
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# huggingface-hub
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# langchain-core
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# langsmith
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@ -238,8 +178,6 @@ protobuf==6.33.5
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# via
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# grpcio-reflection
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# grpcio-tools
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psutil==7.2.2
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# via accelerate
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pydantic==2.12.5
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# via
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# langchain
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@ -265,7 +203,6 @@ python-dotenv==1.2.1
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# pydantic-settings
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pyyaml==6.0.3
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# via
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# accelerate
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# huggingface-hub
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# langchain-classic
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# langchain-community
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@ -285,12 +222,8 @@ requests-toolbelt==1.0.0
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# via langsmith
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s3transfer==0.16.0
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# via boto3
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safetensors==0.7.0
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# via accelerate
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setuptools==82.0.0
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||||
# via
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# grpcio-tools
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# torch
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# via grpcio-tools
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simsimd==6.5.13
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||||
# via elasticsearch
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||||
six==1.17.0
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@ -299,23 +232,17 @@ sqlalchemy==2.0.46
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# via
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# langchain-classic
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# langchain-community
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sympy==1.14.0
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# via torch
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tenacity==9.1.4
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# via
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# langchain-community
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# langchain-core
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tokenizers==0.22.2
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# via langchain-huggingface
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torch==2.10.0
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# via accelerate
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tqdm==4.67.3
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||||
# via
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# assistance-engine
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# huggingface-hub
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# nltk
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triton==3.6.0 ; platform_machine == 'x86_64' and sys_platform == 'linux'
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||||
# via torch
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typing-extensions==4.15.0
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||||
# via
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# aiosignal
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@ -327,7 +254,6 @@ typing-extensions==4.15.0
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# pydantic
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# pydantic-core
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# sqlalchemy
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# torch
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# typing-inspect
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# typing-inspection
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typing-inspect==0.9.0
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|
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@ -0,0 +1,57 @@
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# graph.py
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from langchain_core.documents import Document
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from langchain_core.messages import SystemMessage
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from langgraph.graph import StateGraph, END
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from langgraph.graph.state import CompiledStateGraph
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from prompts import REFORMULATE_PROMPT, GENERATE_PROMPT
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from state import AgentState
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def format_context(docs: list[Document]) -> str:
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chunks = []
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for i, doc in enumerate(docs, 1):
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source = (doc.metadata or {}).get("source", "Untitled")
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source_id = (doc.metadata or {}).get("id", f"chunk-{i}")
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text = doc.page_content or ""
|
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chunks.append(f"[{i}] id={source_id} source={source}\n{text}")
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return "\n\n".join(chunks)
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def build_graph(llm, vector_store) -> CompiledStateGraph:
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def reformulate(state: AgentState) -> AgentState:
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user_msg = state["messages"][-1]
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resp = llm.invoke([REFORMULATE_PROMPT, user_msg])
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reformulated = resp.content.strip()
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print(f"[reformulate] '{user_msg.content}' → '{reformulated}'")
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return {"reformulated_query": reformulated}
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def retrieve(state: AgentState) -> AgentState:
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query = state["reformulated_query"]
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docs = vector_store.as_retriever(
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search_type="similarity",
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search_kwargs={"k": 3},
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).invoke(query)
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context = format_context(docs)
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print(f"[retrieve] {len(docs)} docs fetched")
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print(context)
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return {"context": context}
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|
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def generate(state: AgentState) -> AgentState:
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prompt = SystemMessage(
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content=GENERATE_PROMPT.content.format(context=state["context"])
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)
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resp = llm.invoke([prompt] + state["messages"])
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return {"messages": [resp]}
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|
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graph_builder = StateGraph(AgentState)
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graph_builder.add_node("reformulate", reformulate)
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graph_builder.add_node("retrieve", retrieve)
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graph_builder.add_node("generate", generate)
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graph_builder.set_entry_point("reformulate")
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graph_builder.add_edge("reformulate", "retrieve")
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graph_builder.add_edge("retrieve", "generate")
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graph_builder.add_edge("generate", END)
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return graph_builder.compile()
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|
|
@ -0,0 +1,24 @@
|
|||
import os
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|
||||
from langchain_elasticsearch import ElasticsearchStore
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|
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from utils.llm_factory import create_chat_model
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from utils.emb_factory import create_embedding_model
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|
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llm = create_chat_model(
|
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provider="ollama",
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model=os.getenv("OLLAMA_MODEL_NAME"),
|
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temperature=0,
|
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validate_model_on_init=True,
|
||||
)
|
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embeddings = create_embedding_model(
|
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provider="ollama",
|
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model=os.getenv("OLLAMA_EMB_MODEL_NAME"),
|
||||
)
|
||||
vector_store = ElasticsearchStore(
|
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es_url=os.getenv("ELASTICSEARCH_URL"),
|
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index_name=os.getenv("ELASTICSEARCH_INDEX"),
|
||||
embedding=embeddings,
|
||||
query_field="text",
|
||||
vector_query_field="vector",
|
||||
)
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|
|
@ -0,0 +1,89 @@
|
|||
from langchain_core.messages import SystemMessage
|
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|
||||
REFORMULATE_PROMPT = SystemMessage(
|
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content=(
|
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"You are a deterministic lexical query rewriter used for vector retrieval.\n"
|
||||
"Your task is to rewrite user questions into optimized keyword search queries.\n\n"
|
||||
|
||||
"CRITICAL RULES (ABSOLUTE):\n"
|
||||
"1. NEVER answer the question.\n"
|
||||
"2. NEVER expand acronyms.\n"
|
||||
"3. NEVER introduce new terms not present in the original query.\n"
|
||||
"4. NEVER infer missing information.\n"
|
||||
"5. NEVER add explanations, definitions, or interpretations.\n"
|
||||
"6. Preserve all technical tokens exactly as written.\n"
|
||||
"7. Only remove filler words (e.g., what, does, is, explain, tell me, please).\n"
|
||||
"8. You may reorder terms for better retrieval.\n"
|
||||
"9. Output must be a single-line plain keyword query.\n"
|
||||
"10. If the query is already optimal, return it unchanged.\n\n"
|
||||
"11. If you receive something that looks like code, do NOT attempt to rewrite it. Return it verbatim.\n\n"
|
||||
|
||||
"ALLOWED OPERATIONS:\n"
|
||||
"- Remove interrogative phrasing.\n"
|
||||
"- Remove stopwords.\n"
|
||||
"- Reorder words.\n"
|
||||
"- Convert to noun phrase form.\n\n"
|
||||
|
||||
"FORBIDDEN OPERATIONS:\n"
|
||||
"- Expanding abbreviations.\n"
|
||||
"- Paraphrasing into unseen vocabulary.\n"
|
||||
"- Adding definitions.\n"
|
||||
"- Answering implicitly.\n\n"
|
||||
|
||||
"Examples:\n"
|
||||
"Input: What does AVAP stand for?\n"
|
||||
"Output: AVAP stand for\n"
|
||||
|
||||
"Input: Hey, I'm trying to understand how AVAP handels a ZeroDivisionError when doing divison or modulus operatoins. Can you explane what situatoins cause a ZeroDivisionError to be raised and how I can catch it in my AVAP scripts?\n"
|
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"Output: AVAP ZeroDivisionError division / modulus % catch try except\n"
|
||||
|
||||
"Input: What does AVAP stand for?\n"
|
||||
"Output: AVAP stand for\n"
|
||||
|
||||
"Input: Please explain how the import statement works in AVAP scripts.\n"
|
||||
"Output: AVAP import statement syntax behavior\n\n"
|
||||
|
||||
"Return only the rewritten query."
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||||
)
|
||||
)
|
||||
|
||||
GENERATE_PROMPT = SystemMessage(
|
||||
content=(
|
||||
"""You are a retrieval-grounded assistant.
|
||||
|
||||
CRITICAL SCOPE RULES
|
||||
- AVAP (Advanced Virtual API Programming) is a new/unknown programming language.
|
||||
- If the user message is AVAP-related (explicitly mentions "AVAP" case-insensitive OR the provided context is AVAP code/docs), you MUST answer using ONLY <CONTEXT>.
|
||||
- You MUST NOT use general programming knowledge to interpret AVAP.
|
||||
- You MUST NOT assume AVAP behaves like any other language even if syntax looks similar.
|
||||
- You MUST NOT infer missing details. Only state what is explicitly present in <CONTEXT>.
|
||||
|
||||
GROUNDING REQUIREMENTS (AVAP-RELATED)
|
||||
1) Every non-trivial factual claim MUST be directly supported by an EXACT QUOTE from <CONTEXT>.
|
||||
2) If a claim is not supported by a quote, DO NOT include it.
|
||||
3) If <CONTEXT> does not contain enough information to answer, reply with EXACTLY:
|
||||
"I don't have enough information in the provided context to answer that."
|
||||
|
||||
WORKFLOW (AVAP-RELATED) — FOLLOW IN ORDER
|
||||
A) Identify the specific question(s) being asked.
|
||||
B) Extract the minimum necessary quotes from <CONTEXT> that answer those question(s).
|
||||
C) Write the answer using ONLY those quotes (paraphrase is allowed, but every statement must be backed by at least one quote).
|
||||
D) Verify: for EACH sentence in your answer, confirm there is a supporting quote. If any sentence lacks a quote, delete it or refuse.
|
||||
|
||||
OUTPUT FORMAT (AVAP-RELATED ONLY)
|
||||
Answer:
|
||||
<short, direct answer; no extra speculation; no unrelated tips>
|
||||
|
||||
Evidence:
|
||||
- "<exact quote 1>"
|
||||
- "<exact quote 2>"
|
||||
(Include only quotes you actually used. Prefer the smallest quotes that fully support the statements.)
|
||||
|
||||
NON-AVAP QUESTIONS
|
||||
- If the question is clearly not AVAP-related, answer normally using general knowledge.
|
||||
|
||||
<CONTEXT>
|
||||
{context}
|
||||
</CONTEXT>"""
|
||||
)
|
||||
)
|
||||
|
|
@ -1,140 +0,0 @@
|
|||
import logging
|
||||
import os
|
||||
import sys
|
||||
from concurrent import futures
|
||||
from pathlib import Path
|
||||
from typing import Any
|
||||
|
||||
import brunix_pb2
|
||||
import brunix_pb2_grpc
|
||||
import grpc
|
||||
from grpc_reflection.v1alpha import reflection
|
||||
from langchain_core.prompts import ChatPromptTemplate
|
||||
from langchain_elasticsearch import ElasticsearchStore
|
||||
|
||||
PROJECT_ROOT = Path(__file__).resolve().parents[2]
|
||||
if str(PROJECT_ROOT) not in sys.path:
|
||||
sys.path.insert(0, str(PROJECT_ROOT))
|
||||
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger("brunix-engine")
|
||||
|
||||
|
||||
def _provider_kwargs(provider: str, base_url: str) -> dict[str, Any]:
|
||||
if provider == "ollama":
|
||||
return {"base_url": base_url}
|
||||
return {}
|
||||
|
||||
|
||||
class BrunixEngine(brunix_pb2_grpc.AssistanceEngineServicer):
|
||||
def __init__(self):
|
||||
from src.emb_factory import create_embedding_model
|
||||
from src.llm_factory import create_chat_model
|
||||
|
||||
self.base_url = os.getenv("OLLAMA_LOCAL_URL", "http://ollama-light-service:11434")
|
||||
self.chat_provider = os.getenv("CHAT_PROVIDER", "ollama")
|
||||
self.embedding_provider = os.getenv("EMBEDDING_PROVIDER", self.chat_provider)
|
||||
self.chat_model_name = os.getenv("OLLAMA_MODEL_NAME")
|
||||
self.embedding_model_name = os.getenv(
|
||||
"OLLAMA_EMB_MODEL_NAME", self.chat_model_name
|
||||
)
|
||||
|
||||
if not self.chat_model_name:
|
||||
raise ValueError("OLLAMA_MODEL_NAME is required")
|
||||
|
||||
logger.info("Starting server")
|
||||
|
||||
self.llm = create_chat_model(
|
||||
provider=self.chat_provider,
|
||||
model=self.chat_model_name,
|
||||
**_provider_kwargs(self.chat_provider, self.base_url),
|
||||
)
|
||||
|
||||
self.embeddings = create_embedding_model(
|
||||
provider=self.embedding_provider,
|
||||
model=self.embedding_model_name,
|
||||
**_provider_kwargs(self.embedding_provider, self.base_url),
|
||||
)
|
||||
|
||||
es_url = os.getenv("ELASTICSEARCH_URL", "http://elasticsearch:9200")
|
||||
logger.info("ElasticSearch on: %s", es_url)
|
||||
|
||||
self.vector_store = ElasticsearchStore(
|
||||
es_url=es_url,
|
||||
index_name=os.getenv("ELASTICSEARCH_INDEX"),
|
||||
embedding=self.embeddings,
|
||||
query_field="text",
|
||||
vector_query_field="embedding",
|
||||
)
|
||||
|
||||
def format_context(self, docs) -> str:
|
||||
parts = []
|
||||
for i, d in enumerate(docs, start=1):
|
||||
meta = d.metadata or {}
|
||||
source = meta.get("source", "unknown")
|
||||
doc_id = meta.get("doc_id", "unknown")
|
||||
chunk_id = meta.get("chunk_id", "unknown")
|
||||
|
||||
parts.append(
|
||||
f"[{i}] source={source} doc_id={doc_id} chunk_id={chunk_id}\n{d.page_content}"
|
||||
)
|
||||
return "\n\n---\n\n".join(parts)
|
||||
|
||||
def AskAgent(self, request, context):
|
||||
logger.info(f"request {request.session_id}): {request.query[:50]}.")
|
||||
|
||||
docs_and_scores = self.vector_store.similarity_search_with_score(
|
||||
request.query, k=4
|
||||
)
|
||||
|
||||
try:
|
||||
context_text = self.format_context([doc for doc, _ in docs_and_scores])
|
||||
# 4. Prompt Engineering
|
||||
prompt = ChatPromptTemplate.from_template("""
|
||||
You are a helpful assistant. Use the following retrieved documents to answer the question.
|
||||
If you don't know the answer, say you don't know.
|
||||
|
||||
CONTEXT:
|
||||
{context}
|
||||
|
||||
QUESTION:
|
||||
{question}
|
||||
""")
|
||||
|
||||
chain = prompt | self.llm
|
||||
|
||||
result = chain.invoke({"context": context_text, "question": request.query})
|
||||
result_text = getattr(result, "content", str(result))
|
||||
yield brunix_pb2.AgentResponse(
|
||||
text=str(result_text), avap_code="AVAP-2026", is_final=True
|
||||
)
|
||||
|
||||
yield brunix_pb2.AgentResponse(text="", avap_code="", is_final=True)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in AskAgent: {str(e)}")
|
||||
yield brunix_pb2.AgentResponse(
|
||||
text=f"[Error Motor]: {str(e)}", is_final=True
|
||||
)
|
||||
|
||||
|
||||
def serve():
|
||||
|
||||
server = grpc.server(futures.ThreadPoolExecutor(max_workers=10))
|
||||
|
||||
brunix_pb2_grpc.add_AssistanceEngineServicer_to_server(BrunixEngine(), server)
|
||||
|
||||
SERVICE_NAMES = (
|
||||
brunix_pb2.DESCRIPTOR.services_by_name["AssistanceEngine"].full_name,
|
||||
reflection.SERVICE_NAME,
|
||||
)
|
||||
reflection.enable_server_reflection(SERVICE_NAMES, server)
|
||||
|
||||
server.add_insecure_port("[::]:50051")
|
||||
logger.info("Brunix Engine on port 50051")
|
||||
server.start()
|
||||
server.wait_for_termination()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
serve()
|
||||
|
|
@ -12,6 +12,10 @@ from grpc_reflection.v1alpha import reflection
|
|||
from langchain_core.prompts import ChatPromptTemplate
|
||||
from langchain_elasticsearch import ElasticsearchStore
|
||||
|
||||
from utils.llm_factory import create_chat_model
|
||||
from utils.emb_factory import create_embedding_model
|
||||
from graph import build_graph
|
||||
|
||||
# PROJECT_ROOT = Path(__file__).resolve().parents[2]
|
||||
# if str(PROJECT_ROOT) not in sys.path:
|
||||
# sys.path.insert(0, str(PROJECT_ROOT))
|
||||
|
|
@ -28,93 +32,54 @@ def _provider_kwargs(provider: str, base_url: str) -> dict[str, Any]:
|
|||
|
||||
class BrunixEngine(brunix_pb2_grpc.AssistanceEngineServicer):
|
||||
def __init__(self):
|
||||
from emb_factory import create_embedding_model
|
||||
from llm_factory import create_chat_model
|
||||
|
||||
self.base_url = os.getenv("OLLAMA_LOCAL_URL", "http://ollama-light-service:11434")
|
||||
self.chat_provider = os.getenv("CHAT_PROVIDER", "ollama")
|
||||
self.embedding_provider = os.getenv("EMBEDDING_PROVIDER", self.chat_provider)
|
||||
self.chat_model_name = os.getenv("OLLAMA_MODEL_NAME")
|
||||
self.embedding_model_name = os.getenv(
|
||||
"OLLAMA_EMB_MODEL_NAME", self.chat_model_name
|
||||
)
|
||||
|
||||
if not self.chat_model_name:
|
||||
raise ValueError("OLLAMA_MODEL_NAME is required")
|
||||
|
||||
logger.info("Starting server")
|
||||
|
||||
self.llm = create_chat_model(
|
||||
provider=self.chat_provider,
|
||||
model=self.chat_model_name,
|
||||
**_provider_kwargs(self.chat_provider, self.base_url),
|
||||
provider="ollama",
|
||||
model=os.getenv("OLLAMA_MODEL_NAME"),
|
||||
temperature=0,
|
||||
validate_model_on_init=True,
|
||||
)
|
||||
|
||||
self.embeddings = create_embedding_model(
|
||||
provider=self.embedding_provider,
|
||||
model=self.embedding_model_name,
|
||||
**_provider_kwargs(self.embedding_provider, self.base_url),
|
||||
provider="ollama",
|
||||
model=os.getenv("OLLAMA_EMB_MODEL_NAME"),
|
||||
)
|
||||
|
||||
es_url = os.getenv("ELASTICSEARCH_URL", "http://elasticsearch:9200")
|
||||
logger.info("ElasticSearch on: %s", es_url)
|
||||
|
||||
self.vector_store = ElasticsearchStore(
|
||||
es_url=es_url,
|
||||
es_url=os.getenv("ELASTICSEARCH_URL"),
|
||||
index_name=os.getenv("ELASTICSEARCH_INDEX"),
|
||||
embedding=self.embeddings,
|
||||
query_field="text",
|
||||
vector_query_field="embedding",
|
||||
vector_query_field="vector",
|
||||
)
|
||||
self.graph = build_graph(
|
||||
llm=self.llm,
|
||||
vector_store=self.vector_store
|
||||
)
|
||||
logger.info("Brunix Engine initializing.")
|
||||
|
||||
def format_context(self, docs) -> str:
|
||||
parts = []
|
||||
for i, d in enumerate(docs, start=1):
|
||||
meta = d.metadata or {}
|
||||
source = meta.get("source", "unknown")
|
||||
doc_id = meta.get("doc_id", "unknown")
|
||||
chunk_id = meta.get("chunk_id", "unknown")
|
||||
|
||||
parts.append(
|
||||
f"[{i}] source={source} doc_id={doc_id} chunk_id={chunk_id}\n{d.page_content}"
|
||||
)
|
||||
return "\n\n---\n\n".join(parts)
|
||||
|
||||
def AskAgent(self, request, context):
|
||||
logger.info(f"request {request.session_id}): {request.query[:50]}.")
|
||||
|
||||
docs_and_scores = self.vector_store.similarity_search_with_score(
|
||||
request.query, k=4
|
||||
)
|
||||
|
||||
try:
|
||||
context_text = self.format_context([doc for doc, _ in docs_and_scores])
|
||||
# 4. Prompt Engineering
|
||||
prompt = ChatPromptTemplate.from_template("""
|
||||
You are a helpful assistant. Use the following retrieved documents to answer the question.
|
||||
If you don't know the answer, say you don't know.
|
||||
final_state = self.graph.invoke({"messages": [{"role": "user",
|
||||
"content": request.query}]})
|
||||
|
||||
CONTEXT:
|
||||
{context}
|
||||
messages = final_state.get("messages", [])
|
||||
last_msg = messages[-1] if messages else None
|
||||
result_text = getattr(last_msg, "content", str(last_msg)) if last_msg else ""
|
||||
|
||||
QUESTION:
|
||||
{question}
|
||||
""")
|
||||
|
||||
chain = prompt | self.llm
|
||||
|
||||
result = chain.invoke({"context": context_text, "question": request.query})
|
||||
result_text = getattr(result, "content", str(result))
|
||||
yield brunix_pb2.AgentResponse(
|
||||
text=str(result_text), avap_code="AVAP-2026", is_final=True
|
||||
text=result_text,
|
||||
avap_code="AVAP-2026",
|
||||
is_final=True,
|
||||
)
|
||||
|
||||
yield brunix_pb2.AgentResponse(text="", avap_code="", is_final=True)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in AskAgent: {str(e)}")
|
||||
logger.error(f"Error in AskAgent: {str(e)}", exc_info=True)
|
||||
yield brunix_pb2.AgentResponse(
|
||||
text=f"[Error Motor]: {str(e)}", is_final=True
|
||||
text=f"[Error Motor]: {str(e)}",
|
||||
is_final=True,
|
||||
)
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,9 @@
|
|||
from typing import TypedDict, Annotated
|
||||
|
||||
from langgraph.graph.message import add_messages
|
||||
|
||||
|
||||
class AgentState(TypedDict):
|
||||
messages: Annotated[list, add_messages]
|
||||
reformulated_query: str
|
||||
context: str
|
||||
|
|
@ -360,7 +360,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 18,
|
||||
"id": "53b89690",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
|
|
|
|||
|
|
@ -5,7 +5,6 @@ description = "Add your description here"
|
|||
readme = "README.md"
|
||||
requires-python = ">=3.11"
|
||||
dependencies = [
|
||||
"accelerate>=1.12.0",
|
||||
"grpcio>=1.78.0",
|
||||
"grpcio-reflection>=1.78.0",
|
||||
"grpcio-tools>=1.78.0",
|
||||
|
|
@ -26,6 +25,7 @@ dependencies = [
|
|||
|
||||
[dependency-groups]
|
||||
dev = [
|
||||
"accelerate>=1.12.0",
|
||||
"beir>=2.2.0",
|
||||
"evidently>=0.7.20",
|
||||
"jupyter>=1.1.1",
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@
|
|||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 1,
|
||||
"id": "0a8abbfa",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -23,7 +23,6 @@
|
|||
"from langchain_ollama import OllamaEmbeddings\n",
|
||||
"from transformers import AutoTokenizer, AutoModel, AutoConfig\n",
|
||||
"from elasticsearch import Elasticsearch\n",
|
||||
"from langchain_elasticsearch import ElasticsearchStore\n",
|
||||
"import nltk\n",
|
||||
"from nltk.tokenize import sent_tokenize\n",
|
||||
"nltk.download(\"punkt\", quiet=True)\n",
|
||||
|
|
@ -693,10 +692,106 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 5,
|
||||
"id": "5ab78d28",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from chonkie import CodeChunker\n",
|
||||
"\n",
|
||||
"tokenizer = AutoTokenizer.from_pretrained(HF_EMB_MODEL_NAME, use_fast=True)\n",
|
||||
"chunker = CodeChunker(\n",
|
||||
" language=\"python\",\n",
|
||||
" tokenizer=tokenizer,\n",
|
||||
" chunk_size=128\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "37676348",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Chunk text: \n",
|
||||
"registerEndpoint(\"/v1/user\", \"POST\", [], \"Crear Usuario\", main, final_res)\n",
|
||||
"\n",
|
||||
"function main(){\n",
|
||||
" addParam(\"user\", u)\n",
|
||||
" addParam(\"pass\", p)\n",
|
||||
"\n",
|
||||
" if\n",
|
||||
"Token count: 38\n",
|
||||
"Chunk text: (u, None, \"==\")\n",
|
||||
" addVar(_status, 400)\n",
|
||||
" return(\"Usuario requerido\")\n",
|
||||
" end()\n",
|
||||
"\n",
|
||||
" encodeSHA256(p, pass_hash)\n",
|
||||
"\n",
|
||||
" go_async(\"audit\")\n",
|
||||
" ormDirect(\"INSERT INTO audit (event) VALUES ('User creation attempt')\", r)\n",
|
||||
" end()\n",
|
||||
"\n",
|
||||
" db = avapConnector(\"TOKEN_DB\")\n",
|
||||
" res_db = db.query(\"INSERT INTO users (name, pass) VALUES ('%s', '%s')\" % (u, pass_hash))\n",
|
||||
"\n",
|
||||
" addVar(_status, 201)\n",
|
||||
" addResult(res_db)\n",
|
||||
" return(res_db)\n",
|
||||
"\n",
|
||||
"Token count: 128\n",
|
||||
"Chunk text: }\n",
|
||||
"\n",
|
||||
"Token count: 1\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"code = \"\"\"\n",
|
||||
"registerEndpoint(\"/v1/user\", \"POST\", [], \"Crear Usuario\", main, final_res)\n",
|
||||
"\n",
|
||||
"function main(){\n",
|
||||
" addParam(\"user\", u)\n",
|
||||
" addParam(\"pass\", p)\n",
|
||||
" \n",
|
||||
" if(u, None, \"==\")\n",
|
||||
" addVar(_status, 400)\n",
|
||||
" return(\"Usuario requerido\")\n",
|
||||
" end()\n",
|
||||
" \n",
|
||||
" encodeSHA256(p, pass_hash)\n",
|
||||
" \n",
|
||||
" go_async(\"audit\")\n",
|
||||
" ormDirect(\"INSERT INTO audit (event) VALUES ('User creation attempt')\", r)\n",
|
||||
" end()\n",
|
||||
" \n",
|
||||
" db = avapConnector(\"TOKEN_DB\")\n",
|
||||
" res_db = db.query(\"INSERT INTO users (name, pass) VALUES ('%s', '%s')\" % (u, pass_hash))\n",
|
||||
" \n",
|
||||
" addVar(_status, 201)\n",
|
||||
" addResult(res_db)\n",
|
||||
" return(res_db)\n",
|
||||
"}\n",
|
||||
"\"\"\"\n",
|
||||
"\n",
|
||||
"chunks = chunker.chunk(code)\n",
|
||||
"\n",
|
||||
"for chunk in chunks:\n",
|
||||
" print(f\"Chunk text: {chunk.text}\")\n",
|
||||
" print(f\"Token count: {chunk.token_count}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "66cfd3c7",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@
|
|||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"execution_count": 6,
|
||||
"id": "8fed4518",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
|
|
@ -10,23 +10,23 @@
|
|||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/tmp/ipykernel_716860/1516785970.py:18: DeprecationWarning: Importing faithfulness from 'ragas.metrics' is deprecated and will be removed in v1.0. Please use 'ragas.metrics.collections' instead. Example: from ragas.metrics.collections import faithfulness\n",
|
||||
"/tmp/ipykernel_782131/4243561678.py:18: DeprecationWarning: Importing faithfulness from 'ragas.metrics' is deprecated and will be removed in v1.0. Please use 'ragas.metrics.collections' instead. Example: from ragas.metrics.collections import faithfulness\n",
|
||||
" from ragas.metrics import (\n",
|
||||
"/tmp/ipykernel_716860/1516785970.py:18: DeprecationWarning: Importing answer_relevancy from 'ragas.metrics' is deprecated and will be removed in v1.0. Please use 'ragas.metrics.collections' instead. Example: from ragas.metrics.collections import answer_relevancy\n",
|
||||
"/tmp/ipykernel_782131/4243561678.py:18: DeprecationWarning: Importing answer_relevancy from 'ragas.metrics' is deprecated and will be removed in v1.0. Please use 'ragas.metrics.collections' instead. Example: from ragas.metrics.collections import answer_relevancy\n",
|
||||
" from ragas.metrics import (\n",
|
||||
"/tmp/ipykernel_716860/1516785970.py:18: DeprecationWarning: Importing context_recall from 'ragas.metrics' is deprecated and will be removed in v1.0. Please use 'ragas.metrics.collections' instead. Example: from ragas.metrics.collections import context_recall\n",
|
||||
"/tmp/ipykernel_782131/4243561678.py:18: DeprecationWarning: Importing context_recall from 'ragas.metrics' is deprecated and will be removed in v1.0. Please use 'ragas.metrics.collections' instead. Example: from ragas.metrics.collections import context_recall\n",
|
||||
" from ragas.metrics import (\n",
|
||||
"/tmp/ipykernel_716860/1516785970.py:18: DeprecationWarning: Importing context_precision from 'ragas.metrics' is deprecated and will be removed in v1.0. Please use 'ragas.metrics.collections' instead. Example: from ragas.metrics.collections import context_precision\n",
|
||||
"/tmp/ipykernel_782131/4243561678.py:18: DeprecationWarning: Importing context_precision from 'ragas.metrics' is deprecated and will be removed in v1.0. Please use 'ragas.metrics.collections' instead. Example: from ragas.metrics.collections import context_precision\n",
|
||||
" from ragas.metrics import (\n",
|
||||
"/tmp/ipykernel_716860/1516785970.py:18: DeprecationWarning: Importing context_entity_recall from 'ragas.metrics' is deprecated and will be removed in v1.0. Please use 'ragas.metrics.collections' instead. Example: from ragas.metrics.collections import context_entity_recall\n",
|
||||
"/tmp/ipykernel_782131/4243561678.py:18: DeprecationWarning: Importing context_entity_recall from 'ragas.metrics' is deprecated and will be removed in v1.0. Please use 'ragas.metrics.collections' instead. Example: from ragas.metrics.collections import context_entity_recall\n",
|
||||
" from ragas.metrics import (\n",
|
||||
"/tmp/ipykernel_716860/1516785970.py:18: DeprecationWarning: Importing answer_similarity from 'ragas.metrics' is deprecated and will be removed in v1.0. Please use 'ragas.metrics.collections' instead. Example: from ragas.metrics.collections import answer_similarity\n",
|
||||
"/tmp/ipykernel_782131/4243561678.py:18: DeprecationWarning: Importing answer_similarity from 'ragas.metrics' is deprecated and will be removed in v1.0. Please use 'ragas.metrics.collections' instead. Example: from ragas.metrics.collections import answer_similarity\n",
|
||||
" from ragas.metrics import (\n",
|
||||
"/tmp/ipykernel_716860/1516785970.py:18: DeprecationWarning: Importing answer_correctness from 'ragas.metrics' is deprecated and will be removed in v1.0. Please use 'ragas.metrics.collections' instead. Example: from ragas.metrics.collections import answer_correctness\n",
|
||||
"/tmp/ipykernel_782131/4243561678.py:18: DeprecationWarning: Importing answer_correctness from 'ragas.metrics' is deprecated and will be removed in v1.0. Please use 'ragas.metrics.collections' instead. Example: from ragas.metrics.collections import answer_correctness\n",
|
||||
" from ragas.metrics import (\n",
|
||||
"/tmp/ipykernel_716860/1516785970.py:18: DeprecationWarning: Importing NonLLMContextRecall from 'ragas.metrics' is deprecated and will be removed in v1.0. Please use 'ragas.metrics.collections' instead. Example: from ragas.metrics.collections import NonLLMContextRecall\n",
|
||||
"/tmp/ipykernel_782131/4243561678.py:18: DeprecationWarning: Importing NonLLMContextRecall from 'ragas.metrics' is deprecated and will be removed in v1.0. Please use 'ragas.metrics.collections' instead. Example: from ragas.metrics.collections import NonLLMContextRecall\n",
|
||||
" from ragas.metrics import (\n",
|
||||
"/tmp/ipykernel_716860/1516785970.py:18: DeprecationWarning: Importing NonLLMContextPrecisionWithReference from 'ragas.metrics' is deprecated and will be removed in v1.0. Please use 'ragas.metrics.collections' instead. Example: from ragas.metrics.collections import NonLLMContextPrecisionWithReference\n",
|
||||
"/tmp/ipykernel_782131/4243561678.py:18: DeprecationWarning: Importing NonLLMContextPrecisionWithReference from 'ragas.metrics' is deprecated and will be removed in v1.0. Please use 'ragas.metrics.collections' instead. Example: from ragas.metrics.collections import NonLLMContextPrecisionWithReference\n",
|
||||
" from ragas.metrics import (\n"
|
||||
]
|
||||
}
|
||||
|
|
@ -68,13 +68,14 @@
|
|||
" ELASTICSEARCH_INDEX,\n",
|
||||
" OLLAMA_MODEL_NAME,\n",
|
||||
" OLLAMA_EMB_MODEL_NAME,\n",
|
||||
" RAW_DIR\n",
|
||||
" RAW_DIR,\n",
|
||||
" INTERIM_DIR\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"execution_count": 3,
|
||||
"id": "4426d6c0",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -105,7 +106,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"execution_count": 4,
|
||||
"id": "fe524d14",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
|
|
@ -128,410 +129,10 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"id": "06103178",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
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|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>user_input</th>\n",
|
||||
" <th>reference_contexts</th>\n",
|
||||
" <th>reference</th>\n",
|
||||
" <th>persona_name</th>\n",
|
||||
" <th>query_style</th>\n",
|
||||
" <th>query_length</th>\n",
|
||||
" <th>synthesizer_name</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>How does AVAP handel a ZeroDivisionError when ...</td>\n",
|
||||
" <td>[Execution Model in AVAP\\n4.1. Structure of a ...</td>\n",
|
||||
" <td>In AVAP, when a division by zero occurs—whethe...</td>\n",
|
||||
" <td>Carlos Menendez</td>\n",
|
||||
" <td>MISSPELLED</td>\n",
|
||||
" <td>LONG</td>\n",
|
||||
" <td>single_hop_specific_query_synthesizer</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>As a backend developer who is learning AVAP an...</td>\n",
|
||||
" <td>[Execution Model in AVAP\\n4.1. Structure of a ...</td>\n",
|
||||
" <td>In AVAP, control flow structures include condi...</td>\n",
|
||||
" <td>Carlos Menendez</td>\n",
|
||||
" <td>PERFECT_GRAMMAR</td>\n",
|
||||
" <td>LONG</td>\n",
|
||||
" <td>single_hop_specific_query_synthesizer</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>hey so in AVAP when i do division by zero what...</td>\n",
|
||||
" <td>[Execution Model in AVAP\\n4.1. Structure of a ...</td>\n",
|
||||
" <td>In AVAP, when you perform a division by zero, ...</td>\n",
|
||||
" <td>Carlos Medina</td>\n",
|
||||
" <td>POOR_GRAMMAR</td>\n",
|
||||
" <td>MEDIUM</td>\n",
|
||||
" <td>single_hop_specific_query_synthesizer</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>what happen if file not found when i do import...</td>\n",
|
||||
" <td>[Execution Model in AVAP\\n4.1. Structure of a ...</td>\n",
|
||||
" <td>When an import statement is executed in AVAP, ...</td>\n",
|
||||
" <td>Carlos Medina</td>\n",
|
||||
" <td>POOR_GRAMMAR</td>\n",
|
||||
" <td>SHORT</td>\n",
|
||||
" <td>single_hop_specific_query_synthesizer</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>In AVAP, under what circumstances is a TypeErr...</td>\n",
|
||||
" <td>[Execution Model in AVAP\\n4.1. Structure of a ...</td>\n",
|
||||
" <td>In AVAP, a TypeError exception is raised in tw...</td>\n",
|
||||
" <td>Carlos Menendez</td>\n",
|
||||
" <td>PERFECT_GRAMMAR</td>\n",
|
||||
" <td>MEDIUM</td>\n",
|
||||
" <td>single_hop_specific_query_synthesizer</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>...</th>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>95</th>\n",
|
||||
" <td>How does the data model in AVAP™ compare to Py...</td>\n",
|
||||
" <td>[Introduction\\nThe data model in AVAP™ defines...</td>\n",
|
||||
" <td>The data model in AVAP™ is very similar to Pyt...</td>\n",
|
||||
" <td>Carlos Menendez</td>\n",
|
||||
" <td>PERFECT_GRAMMAR</td>\n",
|
||||
" <td>MEDIUM</td>\n",
|
||||
" <td>single_hop_specific_query_synthesizer</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>96</th>\n",
|
||||
" <td>What data types are available in AVAP™?</td>\n",
|
||||
" <td>[Introduction\\nThe data model in AVAP™ defines...</td>\n",
|
||||
" <td>In AVAP™, the most common data types include i...</td>\n",
|
||||
" <td>Carlos Medina</td>\n",
|
||||
" <td>PERFECT_GRAMMAR</td>\n",
|
||||
" <td>SHORT</td>\n",
|
||||
" <td>single_hop_specific_query_synthesizer</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>97</th>\n",
|
||||
" <td>AVAP strings Unicode</td>\n",
|
||||
" <td>[Introduction\\nThe data model in AVAP™ defines...</td>\n",
|
||||
" <td>In AVAP™, strings (str) represent sequences of...</td>\n",
|
||||
" <td>Carlos Medina</td>\n",
|
||||
" <td>WEB_SEARCH_LIKE</td>\n",
|
||||
" <td>SHORT</td>\n",
|
||||
" <td>single_hop_specific_query_synthesizer</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>98</th>\n",
|
||||
" <td>AVAP data model comparison with Python data ty...</td>\n",
|
||||
" <td>[Introduction\\nThe data model in AVAP™ defines...</td>\n",
|
||||
" <td>The data model in AVAP is similar to Python in...</td>\n",
|
||||
" <td>Carlos Mendieta</td>\n",
|
||||
" <td>WEB_SEARCH_LIKE</td>\n",
|
||||
" <td>MEDIUM</td>\n",
|
||||
" <td>single_hop_specific_query_synthesizer</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>99</th>\n",
|
||||
" <td>AVAP™ data types and data structures overview</td>\n",
|
||||
" <td>[Introduction\\nThe data model in AVAP™ defines...</td>\n",
|
||||
" <td>AVAP™ uses a flexible and dynamic data model s...</td>\n",
|
||||
" <td>Carlos Mendieta</td>\n",
|
||||
" <td>WEB_SEARCH_LIKE</td>\n",
|
||||
" <td>SHORT</td>\n",
|
||||
" <td>single_hop_specific_query_synthesizer</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"<p>100 rows × 7 columns</p>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" user_input \\\n",
|
||||
"0 How does AVAP handel a ZeroDivisionError when ... \n",
|
||||
"1 As a backend developer who is learning AVAP an... \n",
|
||||
"2 hey so in AVAP when i do division by zero what... \n",
|
||||
"3 what happen if file not found when i do import... \n",
|
||||
"4 In AVAP, under what circumstances is a TypeErr... \n",
|
||||
".. ... \n",
|
||||
"95 How does the data model in AVAP™ compare to Py... \n",
|
||||
"96 What data types are available in AVAP™? \n",
|
||||
"97 AVAP strings Unicode \n",
|
||||
"98 AVAP data model comparison with Python data ty... \n",
|
||||
"99 AVAP™ data types and data structures overview \n",
|
||||
"\n",
|
||||
" reference_contexts \\\n",
|
||||
"0 [Execution Model in AVAP\\n4.1. Structure of a ... \n",
|
||||
"1 [Execution Model in AVAP\\n4.1. Structure of a ... \n",
|
||||
"2 [Execution Model in AVAP\\n4.1. Structure of a ... \n",
|
||||
"3 [Execution Model in AVAP\\n4.1. Structure of a ... \n",
|
||||
"4 [Execution Model in AVAP\\n4.1. Structure of a ... \n",
|
||||
".. ... \n",
|
||||
"95 [Introduction\\nThe data model in AVAP™ defines... \n",
|
||||
"96 [Introduction\\nThe data model in AVAP™ defines... \n",
|
||||
"97 [Introduction\\nThe data model in AVAP™ defines... \n",
|
||||
"98 [Introduction\\nThe data model in AVAP™ defines... \n",
|
||||
"99 [Introduction\\nThe data model in AVAP™ defines... \n",
|
||||
"\n",
|
||||
" reference persona_name \\\n",
|
||||
"0 In AVAP, when a division by zero occurs—whethe... Carlos Menendez \n",
|
||||
"1 In AVAP, control flow structures include condi... Carlos Menendez \n",
|
||||
"2 In AVAP, when you perform a division by zero, ... Carlos Medina \n",
|
||||
"3 When an import statement is executed in AVAP, ... Carlos Medina \n",
|
||||
"4 In AVAP, a TypeError exception is raised in tw... Carlos Menendez \n",
|
||||
".. ... ... \n",
|
||||
"95 The data model in AVAP™ is very similar to Pyt... Carlos Menendez \n",
|
||||
"96 In AVAP™, the most common data types include i... Carlos Medina \n",
|
||||
"97 In AVAP™, strings (str) represent sequences of... Carlos Medina \n",
|
||||
"98 The data model in AVAP is similar to Python in... Carlos Mendieta \n",
|
||||
"99 AVAP™ uses a flexible and dynamic data model s... Carlos Mendieta \n",
|
||||
"\n",
|
||||
" query_style query_length synthesizer_name \n",
|
||||
"0 MISSPELLED LONG single_hop_specific_query_synthesizer \n",
|
||||
"1 PERFECT_GRAMMAR LONG single_hop_specific_query_synthesizer \n",
|
||||
"2 POOR_GRAMMAR MEDIUM single_hop_specific_query_synthesizer \n",
|
||||
"3 POOR_GRAMMAR SHORT single_hop_specific_query_synthesizer \n",
|
||||
"4 PERFECT_GRAMMAR MEDIUM single_hop_specific_query_synthesizer \n",
|
||||
".. ... ... ... \n",
|
||||
"95 PERFECT_GRAMMAR MEDIUM single_hop_specific_query_synthesizer \n",
|
||||
"96 PERFECT_GRAMMAR SHORT single_hop_specific_query_synthesizer \n",
|
||||
"97 WEB_SEARCH_LIKE SHORT single_hop_specific_query_synthesizer \n",
|
||||
"98 WEB_SEARCH_LIKE MEDIUM single_hop_specific_query_synthesizer \n",
|
||||
"99 WEB_SEARCH_LIKE SHORT single_hop_specific_query_synthesizer \n",
|
||||
"\n",
|
||||
"[100 rows x 7 columns]"
|
||||
]
|
||||
},
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"synthetic_dataset"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
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|
||||
"execution_count": null,
|
||||
"id": "ab1932b7",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/tmp/ipykernel_716860/244266171.py:1: DeprecationWarning: LangchainLLMWrapper is deprecated and will be removed in a future version. Use llm_factory instead: from openai import OpenAI; from ragas.llms import llm_factory; llm = llm_factory('gpt-4o-mini', client=OpenAI(api_key='...'))\n",
|
||||
" synth = SingleHopSpecificQuerySynthesizer(llm=LangchainLLMWrapper(llm))\n",
|
||||
"/tmp/ipykernel_716860/244266171.py:3: DeprecationWarning: LangchainLLMWrapper is deprecated and will be removed in a future version. Use llm_factory instead: from openai import OpenAI; from ragas.llms import llm_factory; llm = llm_factory('gpt-4o-mini', client=OpenAI(api_key='...'))\n",
|
||||
" generator = TestsetGenerator(llm=LangchainLLMWrapper(llm), embedding_model=LangchainEmbeddingsWrapper(embeddings))\n",
|
||||
"/tmp/ipykernel_716860/244266171.py:3: DeprecationWarning: LangchainEmbeddingsWrapper is deprecated and will be removed in a future version. Use the modern embedding providers instead: embedding_factory('openai', model='text-embedding-3-small', client=openai_client) or from ragas.embeddings import OpenAIEmbeddings, GoogleEmbeddings, HuggingFaceEmbeddings\n",
|
||||
" generator = TestsetGenerator(llm=LangchainLLMWrapper(llm), embedding_model=LangchainEmbeddingsWrapper(embeddings))\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
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||||
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||||
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||||
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|
||||
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|
||||
"synth = SingleHopSpecificQuerySynthesizer(llm=LangchainLLMWrapper(llm))\n",
|
||||
"\n",
|
||||
|
|
@ -544,6 +145,16 @@
|
|||
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|
||||
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|
||||
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@ -976,13 +587,297 @@
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||||
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||||
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|
||||
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|
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|
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|
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
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|
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
".. ... ... ... \n",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"\n",
|
||||
"[100 rows x 7 columns]"
|
||||
]
|
||||
},
|
||||
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|
||||
"metadata": {},
|
||||
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|
||||
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|
||||
],
|
||||
"source": [
|
||||
"synthetic_dataset"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"id": "71743384",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"\n",
|
||||
"from evidently import Dataset\n",
|
||||
"from evidently import DataDefinition\n",
|
||||
"from evidently.descriptors import *\n",
|
||||
"\n",
|
||||
"from evidently import Report\n",
|
||||
"from evidently.presets import TextEvals\n",
|
||||
"from evidently.metrics import *\n",
|
||||
"from evidently.tests import *"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"id": "e1ac1a41",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "ValidationError",
|
||||
"evalue": "1 validation error for OllamaOptions\napi_url\n field required (type=value_error.missing)",
|
||||
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|
||||
"traceback": [
|
||||
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
|
||||
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|
||||
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[24]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m context_based_evals = \u001b[43mDataset\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfrom_pandas\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 2\u001b[39m \u001b[43m \u001b[49m\u001b[43msynthetic_dataset\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 3\u001b[39m \u001b[43m \u001b[49m\u001b[43mdata_definition\u001b[49m\u001b[43m=\u001b[49m\u001b[43mDataDefinition\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtext_columns\u001b[49m\u001b[43m=\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43muser_input\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mreference_contexts\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mreference\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 4\u001b[39m \u001b[43m \u001b[49m\u001b[43mdescriptors\u001b[49m\u001b[43m=\u001b[49m\u001b[43m[\u001b[49m\u001b[43mContextQualityLLMEval\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mreference_contexts\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mquestion\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43muser_input\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprovider\u001b[49m\u001b[43m=\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mollama\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m=\u001b[49m\u001b[43mOLLAMA_MODEL_NAME\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\n\u001b[32m 5\u001b[39m \u001b[43m)\u001b[49m\n\u001b[32m 6\u001b[39m context_based_evals.as_dataframe()\n",
|
||||
"\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/evidently/core/datasets.py:1271\u001b[39m, in \u001b[36mDataset.from_pandas\u001b[39m\u001b[34m(cls, data, data_definition, descriptors, options, metadata, tags)\u001b[39m\n\u001b[32m 1269\u001b[39m dataset = PandasDataset(data, data_definition, metadata=metadata, tags=tags)\n\u001b[32m 1270\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m descriptors \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1271\u001b[39m \u001b[43mdataset\u001b[49m\u001b[43m.\u001b[49m\u001b[43madd_descriptors\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdescriptors\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1272\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m dataset\n",
|
||||
"\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/evidently/core/datasets.py:1382\u001b[39m, in \u001b[36mDataset.add_descriptors\u001b[39m\u001b[34m(self, descriptors, options)\u001b[39m\n\u001b[32m 1375\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Add multiple descriptors to the dataset.\u001b[39;00m\n\u001b[32m 1376\u001b[39m \n\u001b[32m 1377\u001b[39m \u001b[33;03mArgs:\u001b[39;00m\n\u001b[32m 1378\u001b[39m \u001b[33;03m* `descriptors`: List of `Descriptor` objects to compute\u001b[39;00m\n\u001b[32m 1379\u001b[39m \u001b[33;03m* `options`: Optional options for descriptor computation\u001b[39;00m\n\u001b[32m 1380\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 1381\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m descriptor \u001b[38;5;129;01min\u001b[39;00m descriptors:\n\u001b[32m-> \u001b[39m\u001b[32m1382\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43madd_descriptor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdescriptor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/evidently/core/datasets.py:1688\u001b[39m, in \u001b[36mPandasDataset.add_descriptor\u001b[39m\u001b[34m(self, descriptor, options)\u001b[39m\n\u001b[32m 1686\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34madd_descriptor\u001b[39m(\u001b[38;5;28mself\u001b[39m, descriptor: Descriptor, options: AnyOptions = \u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[32m 1687\u001b[39m descriptor.validate_input(\u001b[38;5;28mself\u001b[39m._data_definition)\n\u001b[32m-> \u001b[39m\u001b[32m1688\u001b[39m new_columns = \u001b[43mdescriptor\u001b[49m\u001b[43m.\u001b[49m\u001b[43mgenerate_data\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mOptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfrom_any_options\u001b[49m\u001b[43m(\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1689\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(new_columns, DatasetColumn):\n\u001b[32m 1690\u001b[39m new_columns = {descriptor.alias: new_columns}\n",
|
||||
"\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/evidently/core/datasets.py:1099\u001b[39m, in \u001b[36mFeatureDescriptor.generate_data\u001b[39m\u001b[34m(self, dataset, options)\u001b[39m\n\u001b[32m 1096\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mgenerate_data\u001b[39m(\n\u001b[32m 1097\u001b[39m \u001b[38;5;28mself\u001b[39m, dataset: \u001b[33m\"\u001b[39m\u001b[33mDataset\u001b[39m\u001b[33m\"\u001b[39m, options: Options\n\u001b[32m 1098\u001b[39m ) -> Union[DatasetColumn, Dict[DisplayName, DatasetColumn]]:\n\u001b[32m-> \u001b[39m\u001b[32m1099\u001b[39m feature = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mfeature\u001b[49m\u001b[43m.\u001b[49m\u001b[43mgenerate_features_renamed\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1100\u001b[39m \u001b[43m \u001b[49m\u001b[43mdataset\u001b[49m\u001b[43m.\u001b[49m\u001b[43mas_dataframe\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1101\u001b[39m \u001b[43m \u001b[49m\u001b[43mcreate_data_definition\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdataset\u001b[49m\u001b[43m.\u001b[49m\u001b[43mas_dataframe\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mColumnMapping\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1102\u001b[39m \u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1103\u001b[39m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1104\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m {\n\u001b[32m 1105\u001b[39m col.display_name: \u001b[38;5;28mself\u001b[39m.get_dataset_column(col.name, feature[col.name])\n\u001b[32m 1106\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m col \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.feature.list_columns()\n\u001b[32m 1107\u001b[39m }\n",
|
||||
"\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/evidently/legacy/features/generated_features.py:56\u001b[39m, in \u001b[36mGeneratedFeatures.generate_features_renamed\u001b[39m\u001b[34m(self, data, data_definition, options)\u001b[39m\n\u001b[32m 53\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mgenerate_features_renamed\u001b[39m(\n\u001b[32m 54\u001b[39m \u001b[38;5;28mself\u001b[39m, data: pd.DataFrame, data_definition: DataDefinition, options: Options\n\u001b[32m 55\u001b[39m ) -> pd.DataFrame:\n\u001b[32m---> \u001b[39m\u001b[32m56\u001b[39m features = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mgenerate_features\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata_definition\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 57\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m features.rename(columns={col: \u001b[38;5;28mself\u001b[39m._create_column_name(col) \u001b[38;5;28;01mfor\u001b[39;00m col \u001b[38;5;129;01min\u001b[39;00m features.columns}).set_index(\n\u001b[32m 58\u001b[39m data.index\n\u001b[32m 59\u001b[39m )\n",
|
||||
"\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/evidently/legacy/features/llm_judge.py:54\u001b[39m, in \u001b[36mLLMJudge.generate_features\u001b[39m\u001b[34m(self, data, data_definition, options)\u001b[39m\n\u001b[32m 53\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mgenerate_features\u001b[39m(\u001b[38;5;28mself\u001b[39m, data: pd.DataFrame, data_definition: DataDefinition, options: Options) -> pd.DataFrame:\n\u001b[32m---> \u001b[39m\u001b[32m54\u001b[39m result: Union[List, Dict] = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mget_llm_wrapper\u001b[49m\u001b[43m(\u001b[49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m.run_batch_sync(\n\u001b[32m 55\u001b[39m requests=\u001b[38;5;28mself\u001b[39m.template.iterate_messages(data, \u001b[38;5;28mself\u001b[39m.get_input_columns())\n\u001b[32m 56\u001b[39m )\n\u001b[32m 57\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(result, \u001b[38;5;28mlist\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(o, \u001b[38;5;28mdict\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m o \u001b[38;5;129;01min\u001b[39;00m result):\n\u001b[32m 58\u001b[39m result = {\u001b[38;5;28mself\u001b[39m.display_name \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m.template.get_main_output_column(): result}\n",
|
||||
"\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/evidently/legacy/features/llm_judge.py:43\u001b[39m, in \u001b[36mLLMJudge.get_llm_wrapper\u001b[39m\u001b[34m(self, options)\u001b[39m\n\u001b[32m 41\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mget_llm_wrapper\u001b[39m(\u001b[38;5;28mself\u001b[39m, options: Options) -> LLMWrapper:\n\u001b[32m 42\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._llm_wrapper \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m---> \u001b[39m\u001b[32m43\u001b[39m \u001b[38;5;28mself\u001b[39m._llm_wrapper = \u001b[43mget_llm_wrapper\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mprovider\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 44\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._llm_wrapper\n",
|
||||
"\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/evidently/llm/utils/wrapper.py:437\u001b[39m, in \u001b[36mget_llm_wrapper\u001b[39m\u001b[34m(provider, model, options)\u001b[39m\n\u001b[32m 435\u001b[39m key = (provider, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[32m 436\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m _wrappers:\n\u001b[32m--> \u001b[39m\u001b[32m437\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_wrappers\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptions\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 438\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m find_spec(\u001b[33m\"\u001b[39m\u001b[33mlitellm\u001b[39m\u001b[33m\"\u001b[39m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m 439\u001b[39m litellm_wrapper = get_litellm_wrapper(provider, model, options)\n",
|
||||
"\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/evidently/llm/utils/wrapper.py:583\u001b[39m, in \u001b[36mLiteLLMWrapper.__init__\u001b[39m\u001b[34m(self, model, options)\u001b[39m\n\u001b[32m 581\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, model: \u001b[38;5;28mstr\u001b[39m, options: Options):\n\u001b[32m 582\u001b[39m \u001b[38;5;28mself\u001b[39m.model = model\n\u001b[32m--> \u001b[39m\u001b[32m583\u001b[39m \u001b[38;5;28mself\u001b[39m.options: LLMOptions = \u001b[43moptions\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m__llm_options_type__\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/evidently/legacy/options/base.py:51\u001b[39m, in \u001b[36mOptions.get\u001b[39m\u001b[34m(self, option_type)\u001b[39m\n\u001b[32m 49\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28missubclass\u001b[39m(possible_subclass, option_type):\n\u001b[32m 50\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m.custom[possible_subclass] \u001b[38;5;66;03m# type: ignore[return-value]\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m51\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43moption_type\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
|
||||
"\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/evidently/llm/utils/wrapper.py:472\u001b[39m, in \u001b[36mLLMOptions.__init__\u001b[39m\u001b[34m(self, api_key, rpm_limit, **data)\u001b[39m\n\u001b[32m 465\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Initialize LLM options.\u001b[39;00m\n\u001b[32m 466\u001b[39m \n\u001b[32m 467\u001b[39m \u001b[33;03mArgs:\u001b[39;00m\n\u001b[32m 468\u001b[39m \u001b[33;03m* `api_key`: Optional API key for the provider.\u001b[39;00m\n\u001b[32m 469\u001b[39m \u001b[33;03m* `rpm_limit`: Optional requests per minute limit (backward compatibility).\u001b[39;00m\n\u001b[32m 470\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 471\u001b[39m \u001b[38;5;28mself\u001b[39m.api_key = SecretStr(api_key) \u001b[38;5;28;01mif\u001b[39;00m api_key \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m472\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[34;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 473\u001b[39m \u001b[38;5;66;03m# backward comp\u001b[39;00m\n\u001b[32m 474\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m rpm_limit \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
|
||||
"\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/evidently/pydantic_utils.py:89\u001b[39m, in \u001b[36mFrozenBaseModel.__init__\u001b[39m\u001b[34m(self, **data)\u001b[39m\n\u001b[32m 88\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, **data: Any):\n\u001b[32m---> \u001b[39m\u001b[32m89\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m.\u001b[49m\u001b[34;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m__init_values__\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 90\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m private_attr \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.__private_attributes__:\n\u001b[32m 91\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m private_attr \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.__init_values__:\n",
|
||||
"\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/pydantic/v1/main.py:347\u001b[39m, in \u001b[36mBaseModel.__init__\u001b[39m\u001b[34m(__pydantic_self__, **data)\u001b[39m\n\u001b[32m 345\u001b[39m values, fields_set, validation_error = validate_model(__pydantic_self__.\u001b[34m__class__\u001b[39m, data)\n\u001b[32m 346\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m validation_error:\n\u001b[32m--> \u001b[39m\u001b[32m347\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m validation_error\n\u001b[32m 348\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 349\u001b[39m object_setattr(__pydantic_self__, \u001b[33m'\u001b[39m\u001b[33m__dict__\u001b[39m\u001b[33m'\u001b[39m, values)\n",
|
||||
"\u001b[31mValidationError\u001b[39m: 1 validation error for OllamaOptions\napi_url\n field required (type=value_error.missing)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"context_based_evals = Dataset.from_pandas(\n",
|
||||
" synthetic_dataset,\n",
|
||||
" data_definition=DataDefinition(text_columns=[\"user_input\", \"reference_contexts\", \"reference\"]),\n",
|
||||
" descriptors=[ContextQualityLLMEval(\"reference_contexts\", question=\"user_input\", provider=\"ollama\", model=OLLAMA_MODEL_NAME)]\n",
|
||||
")\n",
|
||||
"context_based_evals.as_dataframe()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "c2d127ad",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
|
|
|
|||
|
|
@ -50,7 +50,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"execution_count": null,
|
||||
"id": "30edcecc",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -67,7 +67,6 @@
|
|||
" provider=\"ollama\",\n",
|
||||
" model=OLLAMA_EMB_MODEL_NAME,\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"vector_store = ElasticsearchStore(\n",
|
||||
" es_url=ELASTICSEARCH_LOCAL_URL,\n",
|
||||
" index_name=ELASTICSEARCH_INDEX,\n",
|
||||
|
|
@ -83,15 +82,30 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"execution_count": null,
|
||||
"id": "ad98841b",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Langfuse client is authenticated and ready!\n"
|
||||
"Failed to export span batch code: 404, reason: <!DOCTYPE html><html lang=\"en\"><head><meta charSet=\"utf-8\"/><meta name=\"viewport\" content=\"width=device-width\"/><meta name=\"next-head-count\" content=\"2\"/><link rel=\"preload\" href=\"/_next/static/css/1d1bf8ebf039f876.css\" as=\"style\"/><link rel=\"stylesheet\" href=\"/_next/static/css/1d1bf8ebf039f876.css\" data-n-g=\"\"/><noscript data-n-css=\"\"></noscript><script defer=\"\" nomodule=\"\" src=\"/_next/static/chunks/polyfills-42372ed130431b0a.js\"></script><script src=\"/_next/static/chunks/webpack-fafe7122277f5772.js\" defer=\"\"></script><script src=\"/_next/static/chunks/framework-d2dcb18632b956b2.js\" defer=\"\"></script><script src=\"/_next/static/chunks/main-19694d19b6f6c228.js\" defer=\"\"></script><script src=\"/_next/static/chunks/pages/_app-9e6c56ce79ebaf0a.js\" defer=\"\"></script><script src=\"/_next/static/chunks/pages/_error-881d11b9e6da73d1.js\" defer=\"\"></script><script src=\"/_next/static/Eu5QEyLpa3Avvo-CHkMDG/_buildManifest.js\" defer=\"\"></script><script src=\"/_next/static/Eu5QEyLpa3Avvo-CHkMDG/_ssgManifest.js\" defer=\"\"></script></head><body><div id=\"__next\"><script>!function(){try{var d=document.documentElement,c=d.classList;c.remove('light','dark');var e=localStorage.getItem('theme');if('system'===e||(!e&&true)){var t='(prefers-color-scheme: dark)',m=window.matchMedia(t);if(m.media!==t||m.matches){d.style.colorScheme = 'dark';c.add('dark')}else{d.style.colorScheme = 'light';c.add('light')}}else if(e){c.add(e|| '')}if(e==='light'||e==='dark')d.style.colorScheme=e}catch(e){}}()</script><div class=\"flex min-h-full flex-1 flex-col justify-center py-12 sm:px-6 lg:px-8\" data-sentry-component=\"Spinner\" data-sentry-source-file=\"spinner.tsx\"><div class=\"sm:mx-auto sm:w-full sm:max-w-md\"><img src=\"/icon.svg\" width=\"42\" height=\"42\" alt=\"Langfuse Icon\" class=\"mx-auto motion-safe:animate-spin\" data-sentry-component=\"LangfuseIcon\" data-sentry-source-file=\"LangfuseLogo.tsx\"/><h2 class=\"mt-5 text-center text-2xl font-bold leading-9 tracking-tight text-primary\">Loading<!-- --> ...</h2></div></div></div><script id=\"__NEXT_DATA__\" type=\"application/json\">{\"props\":{\"pageProps\":{\"statusCode\":404}},\"page\":\"/_error\",\"query\":{},\"buildId\":\"Eu5QEyLpa3Avvo-CHkMDG\",\"nextExport\":true,\"isFallback\":false,\"gip\":true,\"locale\":\"en\",\"locales\":[\"en\"],\"defaultLocale\":\"en\",\"scriptLoader\":[]}</script></body></html>\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"ename": "ValidationError",
|
||||
"evalue": "1 validation error for ParsingModel[Projects]\n__root__ -> data -> 0 -> metadata\n field required (type=value_error.missing)",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
|
||||
"\u001b[31mValidationError\u001b[39m Traceback (most recent call last)",
|
||||
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mlangfuse\u001b[49m\u001b[43m.\u001b[49m\u001b[43mauth_check\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[32m 2\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33mLangfuse client is authenticated and ready!\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m 3\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n",
|
||||
"\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/langfuse/_client/client.py:1702\u001b[39m, in \u001b[36mLangfuse.auth_check\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 1693\u001b[39m \u001b[38;5;250m\u001b[39m\u001b[33;03m\"\"\"Check if the provided credentials (public and secret key) are valid.\u001b[39;00m\n\u001b[32m 1694\u001b[39m \n\u001b[32m 1695\u001b[39m \u001b[33;03mRaises:\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 1699\u001b[39m \u001b[33;03m This method is blocking. It is discouraged to use it in production code.\u001b[39;00m\n\u001b[32m 1700\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m 1701\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1702\u001b[39m projects = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mapi\u001b[49m\u001b[43m.\u001b[49m\u001b[43mprojects\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1703\u001b[39m langfuse_logger.debug(\n\u001b[32m 1704\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mAuth check successful, found \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(projects.data)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m projects\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 1705\u001b[39m )\n\u001b[32m 1706\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(projects.data) == \u001b[32m0\u001b[39m:\n",
|
||||
"\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/langfuse/api/resources/projects/client.py:65\u001b[39m, in \u001b[36mProjectsClient.get\u001b[39m\u001b[34m(self, request_options)\u001b[39m\n\u001b[32m 63\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 64\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[32m200\u001b[39m <= _response.status_code < \u001b[32m300\u001b[39m:\n\u001b[32m---> \u001b[39m\u001b[32m65\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpydantic_v1\u001b[49m\u001b[43m.\u001b[49m\u001b[43mparse_obj_as\u001b[49m\u001b[43m(\u001b[49m\u001b[43mProjects\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_response\u001b[49m\u001b[43m.\u001b[49m\u001b[43mjson\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n\u001b[32m 66\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m _response.status_code == \u001b[32m400\u001b[39m:\n\u001b[32m 67\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m Error(pydantic_v1.parse_obj_as(typing.Any, _response.json())) \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n",
|
||||
"\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/pydantic/v1/tools.py:38\u001b[39m, in \u001b[36mparse_obj_as\u001b[39m\u001b[34m(type_, obj, type_name)\u001b[39m\n\u001b[32m 36\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mparse_obj_as\u001b[39m(type_: Type[T], obj: Any, *, type_name: Optional[NameFactory] = \u001b[38;5;28;01mNone\u001b[39;00m) -> T:\n\u001b[32m 37\u001b[39m model_type = _get_parsing_type(type_, type_name=type_name) \u001b[38;5;66;03m# type: ignore[arg-type]\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m38\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmodel_type\u001b[49m\u001b[43m(\u001b[49m\u001b[43m__root__\u001b[49m\u001b[43m=\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m.__root__\n",
|
||||
"\u001b[36mFile \u001b[39m\u001b[32m~/PycharmProjects/assistance-engine/.venv/lib/python3.11/site-packages/pydantic/v1/main.py:347\u001b[39m, in \u001b[36mBaseModel.__init__\u001b[39m\u001b[34m(__pydantic_self__, **data)\u001b[39m\n\u001b[32m 345\u001b[39m values, fields_set, validation_error = validate_model(__pydantic_self__.\u001b[34m__class__\u001b[39m, data)\n\u001b[32m 346\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m validation_error:\n\u001b[32m--> \u001b[39m\u001b[32m347\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m validation_error\n\u001b[32m 348\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 349\u001b[39m object_setattr(__pydantic_self__, \u001b[33m'\u001b[39m\u001b[33m__dict__\u001b[39m\u001b[33m'\u001b[39m, values)\n",
|
||||
"\u001b[31mValidationError\u001b[39m: 1 validation error for ParsingModel[Projects]\n__root__ -> data -> 0 -> metadata\n field required (type=value_error.missing)"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
|
@ -112,7 +126,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"execution_count": null,
|
||||
"id": "5f8c88cf",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -125,7 +139,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"execution_count": 6,
|
||||
"id": "fd8ed542",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -144,7 +158,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"execution_count": 7,
|
||||
"id": "f0a21230",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -154,7 +168,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"execution_count": 8,
|
||||
"id": "f9359747",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -193,7 +207,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"execution_count": 9,
|
||||
"id": "66ae23f0",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -237,7 +251,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"execution_count": 10,
|
||||
"id": "36d0f54e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -275,7 +289,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"execution_count": 11,
|
||||
"id": "f073edc9",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -295,7 +309,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"execution_count": 12,
|
||||
"id": "fae46a58",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -318,7 +332,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"execution_count": 13,
|
||||
"id": "7f57b543",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
|
|
@ -342,7 +356,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"execution_count": 14,
|
||||
"id": "f7a0993f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
|
|
@ -368,7 +382,7 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"execution_count": 15,
|
||||
"id": "2fec3fdb",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
|
|
|
|||
4
uv.lock
4
uv.lock
|
|
@ -268,7 +268,6 @@ name = "assistance-engine"
|
|||
version = "0.1.0"
|
||||
source = { virtual = "." }
|
||||
dependencies = [
|
||||
{ name = "accelerate" },
|
||||
{ name = "grpcio" },
|
||||
{ name = "grpcio-reflection" },
|
||||
{ name = "grpcio-tools" },
|
||||
|
|
@ -289,6 +288,7 @@ dependencies = [
|
|||
|
||||
[package.dev-dependencies]
|
||||
dev = [
|
||||
{ name = "accelerate" },
|
||||
{ name = "beir" },
|
||||
{ name = "evidently" },
|
||||
{ name = "jupyter" },
|
||||
|
|
@ -305,7 +305,6 @@ dev = [
|
|||
|
||||
[package.metadata]
|
||||
requires-dist = [
|
||||
{ name = "accelerate", specifier = ">=1.12.0" },
|
||||
{ name = "grpcio", specifier = ">=1.78.0" },
|
||||
{ name = "grpcio-reflection", specifier = ">=1.78.0" },
|
||||
{ name = "grpcio-tools", specifier = ">=1.78.0" },
|
||||
|
|
@ -326,6 +325,7 @@ requires-dist = [
|
|||
|
||||
[package.metadata.requires-dev]
|
||||
dev = [
|
||||
{ name = "accelerate", specifier = ">=1.12.0" },
|
||||
{ name = "beir", specifier = ">=2.2.0" },
|
||||
{ name = "evidently", specifier = ">=0.7.20" },
|
||||
{ name = "jupyter", specifier = ">=1.1.1" },
|
||||
|
|
|
|||
Loading…
Reference in New Issue