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c1367c2
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Parent(s):
0d77f31
evaluate with fine-tuned embeddings
Browse files- data/evaluation/finetuned/evaluation_data.json +0 -0
- data/evaluation/finetuned/evaluation_results.json +0 -0
- data/evaluation/finetuned/evaluation_results_average.json +8 -0
- data/evaluation/finetuned/testset_data.json +0 -0
- evaluation.py +7 -5
- fine_tune_embeddings.py +0 -0
- pyproject.toml +2 -0
- rag_graph.py +10 -4
- uv.lock +464 -0
data/evaluation/finetuned/evaluation_data.json
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data/evaluation/finetuned/evaluation_results.json
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data/evaluation/finetuned/evaluation_results_average.json
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@@ -0,0 +1,8 @@
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{
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"context_recall":0.7104166667,
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"faithfulness":0.8851135006,
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"factual_correctness":0.4325,
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"answer_relevancy":0.9614896608,
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"context_entity_recall":0.3135290494,
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"noise_sensitivity_relevant":0.2666666667
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}
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data/evaluation/finetuned/testset_data.json
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evaluation.py
CHANGED
@@ -20,8 +20,10 @@ from rag_graph import RagGraph
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# Load environment variables
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load_dotenv()
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# Create data folders
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eval_data_filepath = Path("data/evaluation")
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eval_data_filepath.mkdir(parents=True, exist_ok=True)
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# Necessary for dependencies for DirectoryLoader
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@@ -59,7 +61,7 @@ def evaluate_rag():
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"""Generate RAG responses for evaluation"""
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# Create Qdrant client and RAG graph
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qdrant_client = QdrantClient(path='data/vectors')
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rag_graph = RagGraph(qdrant_client)
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# Load data set from testset_data.json
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dataset_df = pd.read_json(eval_data_filepath / "testset_data.json")
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@@ -92,7 +94,7 @@ def evaluate_rag():
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print("Writing evaluation results to file...")
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(eval_data_filepath / "evaluation_results.json").write_text(result.to_pandas().to_json(orient="records", indent=4))
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-
def
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"""Get the average evaluation results from the evaluation_results.json file"""
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evaluation_results = json.loads((eval_data_filepath / "evaluation_results.json").read_text())
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fields = ["context_recall", "faithfulness", "factual_correctness", "answer_relevancy", "context_entity_recall", "noise_sensitivity_relevant"]
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def main():
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"""Test the RAG graph."""
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# generate_test_data()
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-
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-
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if __name__ == "__main__":
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# Load environment variables
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load_dotenv()
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USE_FINE_TUNED_EMBEDDINGS = True
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# Create data folders
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eval_data_filepath = Path("data/evaluation") if not USE_FINE_TUNED_EMBEDDINGS else Path("data/evaluation/finetuned")
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eval_data_filepath.mkdir(parents=True, exist_ok=True)
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# Necessary for dependencies for DirectoryLoader
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"""Generate RAG responses for evaluation"""
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# Create Qdrant client and RAG graph
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qdrant_client = QdrantClient(path='data/vectors')
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rag_graph = RagGraph(qdrant_client, use_finetuned_embeddings=USE_FINE_TUNED_EMBEDDINGS)
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# Load data set from testset_data.json
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dataset_df = pd.read_json(eval_data_filepath / "testset_data.json")
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print("Writing evaluation results to file...")
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(eval_data_filepath / "evaluation_results.json").write_text(result.to_pandas().to_json(orient="records", indent=4))
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def calculate_average_evaluation_results():
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"""Get the average evaluation results from the evaluation_results.json file"""
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evaluation_results = json.loads((eval_data_filepath / "evaluation_results.json").read_text())
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fields = ["context_recall", "faithfulness", "factual_correctness", "answer_relevancy", "context_entity_recall", "noise_sensitivity_relevant"]
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def main():
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"""Test the RAG graph."""
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# generate_test_data()
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evaluate_rag()
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calculate_average_evaluation_results()
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if __name__ == "__main__":
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fine_tune_embeddings.py
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File without changes
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pyproject.toml
CHANGED
@@ -23,4 +23,6 @@ dependencies = [
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"unstructured>=0.14.8",
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"chainlit>=2.2.1",
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"ragas>=0.2.13",
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]
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"unstructured>=0.14.8",
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"chainlit>=2.2.1",
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"ragas>=0.2.13",
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"langchain-huggingface>=0.1.2",
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"langchain-text-splitters>=0.3.6",
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]
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rag_graph.py
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@@ -8,6 +8,7 @@ from langchain_core.documents import Document
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from langchain_core.prompts import ChatPromptTemplate
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from langchain_qdrant import QdrantVectorStore
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from langgraph.graph import START, StateGraph
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from langchain.prompts import ChatPromptTemplate
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"""
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class RagGraph:
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def __init__(self, qdrant_client):
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self.llm = ChatOpenAI(model="gpt-4-turbo-preview", streaming=True)
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self.collection_name = "rag_collection"
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self.embeddings_model = OpenAIEmbeddings(model="text-embedding-3-small")
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self.text_splitter = RecursiveCharacterTextSplitter(chunk_size=CHUNK_SIZE, chunk_overlap=CHUNK_OVERLAP)
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self.rag_prompt = ChatPromptTemplate.from_template(RAG_PROMPT)
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self.qdrant_client = qdrant_client
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does_collection_exist = self.qdrant_client.collection_exists(collection_name=self.collection_name)
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if not does_collection_exist:
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qdrant_client.create_collection(
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collection_name=self.collection_name,
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vectors_config=VectorParams(size=
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)
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self.vector_store = QdrantVectorStore(
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from langchain_core.prompts import ChatPromptTemplate
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from langchain_qdrant import QdrantVectorStore
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from langchain_huggingface import HuggingFaceEmbeddings
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from langgraph.graph import START, StateGraph
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from langchain.prompts import ChatPromptTemplate
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"""
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class RagGraph:
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def __init__(self, qdrant_client, use_finetuned_embeddings=False):
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self.llm = ChatOpenAI(model="gpt-4-turbo-preview", streaming=True)
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self.collection_name = "rag_collection" if not use_finetuned_embeddings else "rag_collection_finetuned"
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self.embeddings_model = OpenAIEmbeddings(model="text-embedding-3-small") \
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if not use_finetuned_embeddings else HuggingFaceEmbeddings(model_name="thomfoolery/AIE5-MidTerm-finetuned-embeddings")
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self.text_splitter = RecursiveCharacterTextSplitter(chunk_size=CHUNK_SIZE, chunk_overlap=CHUNK_OVERLAP)
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self.rag_prompt = ChatPromptTemplate.from_template(RAG_PROMPT)
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self.qdrant_client = qdrant_client
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does_collection_exist = self.qdrant_client.collection_exists(collection_name=self.collection_name)
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dimension_size = 1536 if not use_finetuned_embeddings else 1024
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print(f"Collection {self.collection_name} exists: {does_collection_exist}")
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if not does_collection_exist:
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qdrant_client.create_collection(
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collection_name=self.collection_name,
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vectors_config=VectorParams(size=dimension_size, distance=Distance.COSINE),
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)
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self.vector_store = QdrantVectorStore(
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uv.lock
CHANGED
@@ -782,6 +782,18 @@ wheels = [
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{ url = "https://files.pythonhosted.org/packages/a0/d9/a1e041c5e7caa9a05c925f4bdbdfb7f006d1f74996af53467bc394c97be7/importlib_metadata-8.5.0-py3-none-any.whl", hash = "sha256:45e54197d28b7a7f1559e60b95e7c567032b602131fbd588f1497f47880aa68b", size = 26514 },
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[[package]]
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name = "jiter"
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version = "0.8.2"
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{ url = "https://files.pythonhosted.org/packages/6a/f5/9ce2a94bc49b64c0bf53b17524d5fc5c926070e911b11d489979d47d5491/langchain_core-0.3.37-py3-none-any.whl", hash = "sha256:8202fd6506ce139a3a1b1c4c3006216b1c7fffa40bdd1779f7d2c67f75eb5f79", size = 413717 },
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[[package]]
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name = "langchain-openai"
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version = "0.3.6"
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{ url = "https://files.pythonhosted.org/packages/80/83/8c54533b3576f4391eebea88454738978669a6cad0d8e23266224007939d/lxml-5.3.1-cp313-cp313-win_amd64.whl", hash = "sha256:91fb6a43d72b4f8863d21f347a9163eecbf36e76e2f51068d59cd004c506f332", size = 3814484 },
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[[package]]
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name = "marshmallow"
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version = "3.26.1"
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{ url = "https://files.pythonhosted.org/packages/34/75/51952c7b2d3873b44a0028b1bd26a25078c18f92f256608e8d1dc61b39fd/marshmallow-3.26.1-py3-none-any.whl", hash = "sha256:3350409f20a70a7e4e11a27661187b77cdcaeb20abca41c1454fe33636bea09c", size = 50878 },
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[[package]]
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name = "msgpack"
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version = "1.1.0"
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{ url = "https://files.pythonhosted.org/packages/a0/c4/c2971a3ba4c6103a3d10c4b0f24f461ddc027f0f09763220cf35ca1401b3/nest_asyncio-1.6.0-py3-none-any.whl", hash = "sha256:87af6efd6b5e897c81050477ef65c62e2b2f35d51703cae01aff2905b1852e1c", size = 5195 },
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]
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[[package]]
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name = "nltk"
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version = "3.9.1"
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{ url = "https://files.pythonhosted.org/packages/97/9b/484f7d04b537d0a1202a5ba81c6f53f1846ae6c63c2127f8df869ed31342/numpy-2.2.3-cp313-cp313t-win_amd64.whl", hash = "sha256:aee2512827ceb6d7f517c8b85aa5d3923afe8fc7a57d028cffcd522f1c6fd082", size = 12706784 },
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[[package]]
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name = "openai"
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version = "1.63.2"
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{ url = "https://files.pythonhosted.org/packages/ab/5f/b38085618b950b79d2d9164a711c52b10aefc0ae6833b96f626b7021b2ed/pandas-2.2.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:ad5b65698ab28ed8d7f18790a0dc58005c7629f227be9ecc1072aa74c0c1d43a", size = 13098436 },
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]
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[[package]]
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name = "portalocker"
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version = "2.10.1"
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@@ -1980,6 +2220,112 @@ wheels = [
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{ url = "https://files.pythonhosted.org/packages/3f/51/d4db610ef29373b879047326cbf6fa98b6c1969d6f6dc423279de2b1be2c/requests_toolbelt-1.0.0-py2.py3-none-any.whl", hash = "sha256:cccfdd665f0a24fcf4726e690f65639d272bb0637b9b92dfd91a5568ccf6bd06", size = 54481 },
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[[package]]
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name = "setuptools"
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version = "75.8.0"
|
@@ -2003,8 +2349,10 @@ dependencies = [
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2003 |
{ name = "langchain" },
|
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{ name = "langchain-community" },
|
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{ name = "langchain-core" },
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{ name = "langchain-openai" },
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{ name = "langchain-qdrant" },
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{ name = "langgraph" },
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{ name = "lxml" },
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{ name = "qdrant-client" },
|
@@ -2025,8 +2373,10 @@ requires-dist = [
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{ name = "langchain", specifier = ">=0.3.19" },
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{ name = "langchain-community", specifier = ">=0.3.18" },
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{ name = "langchain-core", specifier = ">=0.3.37" },
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{ name = "langchain-openai", specifier = ">=0.3.6" },
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{ name = "langchain-qdrant", specifier = ">=0.2.0" },
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{ name = "langgraph", specifier = ">=0.2.74" },
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{ name = "lxml", specifier = ">=5.1.0" },
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{ name = "qdrant-client", specifier = ">=1.13.2" },
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@@ -2116,6 +2466,18 @@ wheels = [
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