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fix issue #1174 #1177

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merged 1 commit into from
Nov 24, 2024
Merged

fix issue #1174 #1177

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13 changes: 10 additions & 3 deletions deepeval/synthesizer/chunking/context_generator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -37,12 +37,17 @@ def __init__(
):
from chromadb.api.models.Collection import Collection

self.model, self.using_native_model = initialize_model(model)
self.embedder = embedder
# Chunking parameters
self.chunk_size = chunk_size
self.chunk_overlap = chunk_overlap
self.document_paths: List[str] = document_paths
self.total_chunks = 0
self.document_paths: List[str] = document_paths

# Model parameters
self.model, self.using_native_model = initialize_model(model)
self.embedder = embedder

# Quality parameters
self.max_retries = max_retries
self.filter_threshold = filter_threshold
self.similarity_threshold = similarity_threshold
Expand Down Expand Up @@ -106,6 +111,7 @@ def generate_contexts(
)

# Generate contexts
self.total_chunks = 0
for path, collection in self.source_files_to_collections_map.items():
num_chunks = collection.count()
min_num_context = min(num_context_per_document, num_chunks)
Expand Down Expand Up @@ -181,6 +187,7 @@ async def a_chunk_and_store(key, chunker: DocumentChunker):
)

# Generate contexts
self.total_chunks = 0
tasks = [
self._a_process_document_async(
path,
Expand Down
18 changes: 18 additions & 0 deletions tests/test_context_generator.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,18 @@
from deepeval.synthesizer.chunking.context_generator import ContextGenerator
from deepeval.models.openai_embedding_model import OpenAIEmbeddingModel
from itertools import chain

context_generator = ContextGenerator(
document_paths=["./synthesizer_data/pdf_example.pdf"],
embedder=OpenAIEmbeddingModel()
)
context_generator._load_docs()
context_generator._load_docs()
context_generator._load_docs()

contexts, source_files, context_scores = context_generator.generate_contexts(num_context_per_document=10)
print(f"Utilizing {len(set(chain.from_iterable(contexts)))} out of {context_generator.total_chunks} chunks.")

context_generator._load_docs()
contexts, source_files, context_scores = context_generator.generate_contexts(num_context_per_document=10)
print(f"Utilizing {len(set(chain.from_iterable(contexts)))} out of {context_generator.total_chunks} chunks.")
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