From ff927d1a5189280b61bb89f08178be33d5c77e17 Mon Sep 17 00:00:00 2001
From: "Embruch, Gerd" <gerd.embruch@uni-hamburg.de>
Date: Sat, 27 Apr 2024 15:09:23 +0200
Subject: [PATCH] switched to ollama model
---
.env.template | 4 ++++
evaluateRAG.py | 9 +++++----
using_llamaindex_with_huggingface_models.ipynb | 0
3 files changed, 9 insertions(+), 4 deletions(-)
create mode 100755 using_llamaindex_with_huggingface_models.ipynb
diff --git a/.env.template b/.env.template
index f91a75c..b42cfed 100644
--- a/.env.template
+++ b/.env.template
@@ -4,6 +4,10 @@ PHOENIX_PORT=6006
# API KEY TO OPENAI / CHATGPT API
OPENAI_API_KEY=sk-hj8dfs8f9dsfsdgibberish
+# PATH TO LOCALLY INSTALLED OLLAMA DAEMON
+# to use a LLM via OLLAMA it must be installed already
+OLLAMA_API_BASE=http://localhost:11434
+
#CHUNK SIZE OF DOMAIN FILES
DOCUMENTS_CHUNK_SIZE=512
diff --git a/evaluateRAG.py b/evaluateRAG.py
index a096e58..45886c9 100644
--- a/evaluateRAG.py
+++ b/evaluateRAG.py
@@ -21,7 +21,7 @@ nest_asyncio.apply()
from colorist import Color, BrightColor, bright_yellow, magenta, red, green
# phoenix is the framework & webservice from arize (https://docs.arize.com/phoenix)
import phoenix as px
-from phoenix.evals import OpenAIModel, llm_generate, HallucinationEvaluator, QAEvaluator, run_evals
+from phoenix.evals import OpenAIModel, LiteLLMModel, llm_generate, HallucinationEvaluator, QAEvaluator, run_evals
from phoenix.session.evaluation import get_retrieved_documents, get_qa_with_reference
from phoenix.evals import RelevanceEvaluator, run_evals
from phoenix.trace import DocumentEvaluations, SpanEvaluations
@@ -114,7 +114,7 @@ nodes = node_parser.get_nodes_from_documents(documents)
vector_index = VectorStoreIndex(nodes)
print(f'created {length_hint(nodes)} chunks')
countQuestions = length_hint(nodes) * int(os.environ['QUESTIONS_PER_CHUNK'])
-# # Build a QueryEngine and start querying.
+# Build a QueryEngine and start querying.
query_engine = vector_index.as_query_engine()
##########
@@ -153,11 +153,12 @@ def output_parser(response: str, index: int):
return {"__error__": str(e)}
# prompt template to LLM and store > questions_df
+# You can choose among multiple models supported by LiteLLM (https://docs.litellm.ai/docs/providers)
questions_df = llm_generate(
dataframe=document_chunks_df,
template=generate_questions_template,
- model=OpenAIModel(
- model="gpt-3.5-turbo",
+ model=LiteLLMModel(
+ model="ollama/llama2",
),
output_parser=output_parser,
concurrency=20,
diff --git a/using_llamaindex_with_huggingface_models.ipynb b/using_llamaindex_with_huggingface_models.ipynb
new file mode 100755
index 0000000..e69de29
--
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