thomfoolery commited on
Commit
c1367c2
·
1 Parent(s): 0d77f31

evaluate with fine-tuned embeddings

Browse files
data/evaluation/finetuned/evaluation_data.json ADDED
The diff for this file is too large to render. See raw diff
 
data/evaluation/finetuned/evaluation_results.json ADDED
The diff for this file is too large to render. See raw diff
 
data/evaluation/finetuned/evaluation_results_average.json ADDED
@@ -0,0 +1,8 @@
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "context_recall":0.7104166667,
3
+ "faithfulness":0.8851135006,
4
+ "factual_correctness":0.4325,
5
+ "answer_relevancy":0.9614896608,
6
+ "context_entity_recall":0.3135290494,
7
+ "noise_sensitivity_relevant":0.2666666667
8
+ }
data/evaluation/finetuned/testset_data.json ADDED
The diff for this file is too large to render. See raw diff
 
evaluation.py CHANGED
@@ -20,8 +20,10 @@ from rag_graph import RagGraph
20
  # Load environment variables
21
  load_dotenv()
22
 
 
 
23
  # Create data folders
24
- eval_data_filepath = Path("data/evaluation")
25
  eval_data_filepath.mkdir(parents=True, exist_ok=True)
26
 
27
  # Necessary for dependencies for DirectoryLoader
@@ -59,7 +61,7 @@ def evaluate_rag():
59
  """Generate RAG responses for evaluation"""
60
  # Create Qdrant client and RAG graph
61
  qdrant_client = QdrantClient(path='data/vectors')
62
- rag_graph = RagGraph(qdrant_client)
63
 
64
  # Load data set from testset_data.json
65
  dataset_df = pd.read_json(eval_data_filepath / "testset_data.json")
@@ -92,7 +94,7 @@ def evaluate_rag():
92
  print("Writing evaluation results to file...")
93
  (eval_data_filepath / "evaluation_results.json").write_text(result.to_pandas().to_json(orient="records", indent=4))
94
 
95
- def print_average_evaluation_results():
96
  """Get the average evaluation results from the evaluation_results.json file"""
97
  evaluation_results = json.loads((eval_data_filepath / "evaluation_results.json").read_text())
98
  fields = ["context_recall", "faithfulness", "factual_correctness", "answer_relevancy", "context_entity_recall", "noise_sensitivity_relevant"]
@@ -107,8 +109,8 @@ def print_average_evaluation_results():
107
  def main():
108
  """Test the RAG graph."""
109
  # generate_test_data()
110
- # evaluate_rag()
111
- print_average_evaluation_results()
112
 
113
 
114
  if __name__ == "__main__":
 
20
  # Load environment variables
21
  load_dotenv()
22
 
23
+ USE_FINE_TUNED_EMBEDDINGS = True
24
+
25
  # Create data folders
26
+ eval_data_filepath = Path("data/evaluation") if not USE_FINE_TUNED_EMBEDDINGS else Path("data/evaluation/finetuned")
27
  eval_data_filepath.mkdir(parents=True, exist_ok=True)
28
 
29
  # Necessary for dependencies for DirectoryLoader
 
61
  """Generate RAG responses for evaluation"""
62
  # Create Qdrant client and RAG graph
63
  qdrant_client = QdrantClient(path='data/vectors')
64
+ rag_graph = RagGraph(qdrant_client, use_finetuned_embeddings=USE_FINE_TUNED_EMBEDDINGS)
65
 
66
  # Load data set from testset_data.json
67
  dataset_df = pd.read_json(eval_data_filepath / "testset_data.json")
 
94
  print("Writing evaluation results to file...")
95
  (eval_data_filepath / "evaluation_results.json").write_text(result.to_pandas().to_json(orient="records", indent=4))
96
 
97
+ def calculate_average_evaluation_results():
98
  """Get the average evaluation results from the evaluation_results.json file"""
99
  evaluation_results = json.loads((eval_data_filepath / "evaluation_results.json").read_text())
100
  fields = ["context_recall", "faithfulness", "factual_correctness", "answer_relevancy", "context_entity_recall", "noise_sensitivity_relevant"]
 
109
  def main():
110
  """Test the RAG graph."""
111
  # generate_test_data()
112
+ evaluate_rag()
113
+ calculate_average_evaluation_results()
114
 
115
 
116
  if __name__ == "__main__":
fine_tune_embeddings.py ADDED
File without changes
pyproject.toml CHANGED
@@ -23,4 +23,6 @@ dependencies = [
23
  "unstructured>=0.14.8",
24
  "chainlit>=2.2.1",
25
  "ragas>=0.2.13",
 
 
26
  ]
 
23
  "unstructured>=0.14.8",
24
  "chainlit>=2.2.1",
25
  "ragas>=0.2.13",
26
+ "langchain-huggingface>=0.1.2",
27
+ "langchain-text-splitters>=0.3.6",
28
  ]
rag_graph.py CHANGED
@@ -8,6 +8,7 @@ from langchain_core.documents import Document
8
  from langchain_core.prompts import ChatPromptTemplate
9
 
10
  from langchain_qdrant import QdrantVectorStore
 
11
 
12
  from langgraph.graph import START, StateGraph
13
  from langchain.prompts import ChatPromptTemplate
@@ -40,20 +41,25 @@ You must only use the provided context, and cannot use your own knowledge.
40
  """
41
 
42
  class RagGraph:
43
- def __init__(self, qdrant_client):
44
  self.llm = ChatOpenAI(model="gpt-4-turbo-preview", streaming=True)
45
- self.collection_name = "rag_collection"
46
- self.embeddings_model = OpenAIEmbeddings(model="text-embedding-3-small")
 
 
47
  self.text_splitter = RecursiveCharacterTextSplitter(chunk_size=CHUNK_SIZE, chunk_overlap=CHUNK_OVERLAP)
48
  self.rag_prompt = ChatPromptTemplate.from_template(RAG_PROMPT)
49
  self.qdrant_client = qdrant_client
50
 
51
  does_collection_exist = self.qdrant_client.collection_exists(collection_name=self.collection_name)
 
 
 
52
 
53
  if not does_collection_exist:
54
  qdrant_client.create_collection(
55
  collection_name=self.collection_name,
56
- vectors_config=VectorParams(size=1536, distance=Distance.COSINE),
57
  )
58
 
59
  self.vector_store = QdrantVectorStore(
 
8
  from langchain_core.prompts import ChatPromptTemplate
9
 
10
  from langchain_qdrant import QdrantVectorStore
11
+ from langchain_huggingface import HuggingFaceEmbeddings
12
 
13
  from langgraph.graph import START, StateGraph
14
  from langchain.prompts import ChatPromptTemplate
 
41
  """
42
 
43
  class RagGraph:
44
+ def __init__(self, qdrant_client, use_finetuned_embeddings=False):
45
  self.llm = ChatOpenAI(model="gpt-4-turbo-preview", streaming=True)
46
+ self.collection_name = "rag_collection" if not use_finetuned_embeddings else "rag_collection_finetuned"
47
+ self.embeddings_model = OpenAIEmbeddings(model="text-embedding-3-small") \
48
+ if not use_finetuned_embeddings else HuggingFaceEmbeddings(model_name="thomfoolery/AIE5-MidTerm-finetuned-embeddings")
49
+
50
  self.text_splitter = RecursiveCharacterTextSplitter(chunk_size=CHUNK_SIZE, chunk_overlap=CHUNK_OVERLAP)
51
  self.rag_prompt = ChatPromptTemplate.from_template(RAG_PROMPT)
52
  self.qdrant_client = qdrant_client
53
 
54
  does_collection_exist = self.qdrant_client.collection_exists(collection_name=self.collection_name)
55
+ dimension_size = 1536 if not use_finetuned_embeddings else 1024
56
+
57
+ print(f"Collection {self.collection_name} exists: {does_collection_exist}")
58
 
59
  if not does_collection_exist:
60
  qdrant_client.create_collection(
61
  collection_name=self.collection_name,
62
+ vectors_config=VectorParams(size=dimension_size, distance=Distance.COSINE),
63
  )
64
 
65
  self.vector_store = QdrantVectorStore(
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784
 
 
 
 
 
 
 
 
 
 
 
 
 
785
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786
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787
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918
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919
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920
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
921
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922
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923
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1087
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1090
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1091
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1092
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1100
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1102
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1103
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1104
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1203
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1204
 
 
 
 
 
 
 
 
 
 
1205
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1206
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1207
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1255
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1256
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1257
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1258
  [[package]]
1259
  name = "openai"
1260
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@@ -1488,6 +1690,44 @@ wheels = [
1488
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1490
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1491
  [[package]]
1492
  name = "portalocker"
1493
  version = "2.10.1"
@@ -1980,6 +2220,112 @@ wheels = [
1980
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1981
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1982
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1983
  [[package]]
1984
  name = "setuptools"
1985
  version = "75.8.0"
@@ -2003,8 +2349,10 @@ dependencies = [
2003
  { name = "langchain" },
2004
  { name = "langchain-community" },
2005
  { name = "langchain-core" },
 
2006
  { name = "langchain-openai" },
2007
  { name = "langchain-qdrant" },
 
2008
  { name = "langgraph" },
2009
  { name = "lxml" },
2010
  { name = "qdrant-client" },
@@ -2025,8 +2373,10 @@ requires-dist = [
2025
  { name = "langchain", specifier = ">=0.3.19" },
2026
  { name = "langchain-community", specifier = ">=0.3.18" },
2027
  { name = "langchain-core", specifier = ">=0.3.37" },
 
2028
  { name = "langchain-openai", specifier = ">=0.3.6" },
2029
  { name = "langchain-qdrant", specifier = ">=0.2.0" },
 
2030
  { name = "langgraph", specifier = ">=0.2.74" },
2031
  { name = "lxml", specifier = ">=5.1.0" },
2032
  { name = "qdrant-client", specifier = ">=1.13.2" },
@@ -2116,6 +2466,18 @@ wheels = [
2116
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2117
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2118
 
 
 
 
 
 
 
 
 
 
 
 
 
2119
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2120
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2121
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2140
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2142
 
 
 
 
 
 
 
 
 
 
2143
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2144
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2145
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2164
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2165
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2166
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2167
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2168
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2169
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2193
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2196
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2197
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2198
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2205
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2206
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2208
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2209
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2210
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782
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785
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797
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798
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930
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932
 
933
+ [[package]]
934
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935
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936
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937
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949
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950
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951
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1115
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1118
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2469
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2502
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2535
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2589
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2639
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