Added a few fixes and refactored the base app
This commit is contained in:
Vasilije
parent
f81fc276c4
commit
81b8fd923c
3 changed files with 55 additions and 39 deletions
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@ -13,9 +13,12 @@ class Operation(Base):
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id = Column(String, primary_key=True)
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user_id = Column(String, ForeignKey('users.id'), index=True) # Link to User
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operation_type = Column(String, nullable=True)
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operation_params = Column(String, nullable=True)
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test_set_id = Column(String, ForeignKey('test_sets.id'), index=True)
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created_at = Column(DateTime, default=datetime.utcnow)
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updated_at = Column(DateTime, onupdate=datetime.utcnow)
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memories = relationship("MemoryModel", back_populates="operation")
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# Relationships
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user = relationship("User", back_populates="operations")
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@ -282,7 +282,10 @@ def generate_letter_uuid(length=8):
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letters = string.ascii_uppercase # A-Z
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return ''.join(random.choice(letters) for _ in range(length))
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async def start_test(data, test_set=None, user_id=None, params=None, job_id=None, metadata=None, generate_test_set=False, only_llm_context=False):
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async def start_test(data, test_set=None, user_id=None, params=None, job_id=None, metadata=None, generate_test_set=False, retriever_type:str=None):
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"""retriever_type = "llm_context, single_document_context, multi_document_context, "cognitive_architecture"""""
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async with session_scope(session=AsyncSessionLocal()) as session:
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@ -294,9 +297,7 @@ async def start_test(data, test_set=None, user_id=None, params=None, job_id=None
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await memory.add_memory_instance("ExampleMemory")
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existing_user = await Memory.check_existing_user(user_id, session)
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if job_id is None:
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job_id = str(uuid.uuid4())
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await add_entity(session, Operation(id=job_id, user_id=user_id))
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if test_set_id is None:
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test_set_id = str(uuid.uuid4())
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@ -318,8 +319,13 @@ async def start_test(data, test_set=None, user_id=None, params=None, job_id=None
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"path": data
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}
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if job_id is None:
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job_id = str(uuid.uuid4())
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async def run_test(test, loader_settings, metadata, test_id=None,only_llm_context=False):
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await add_entity(session, Operation(id=job_id, user_id=user_id, operation_params =str(test_params), operation_type=retriever_type, test_set_id=test_set_id))
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async def run_test(test, loader_settings, metadata, test_id=None,retriever_type=False):
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if test_id is None:
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test_id = str(generate_letter_uuid()) + "_" +"SEMANTICMEMORY"
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@ -372,7 +378,7 @@ async def start_test(data, test_set=None, user_id=None, params=None, job_id=None
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test_eval_pipeline =[]
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if only_llm_context:
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if retriever_type == "llm_context":
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for test_qa in test_set:
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context=""
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test_result = await run_eval(test_qa, context)
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@ -399,13 +405,13 @@ async def start_test(data, test_set=None, user_id=None, params=None, job_id=None
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results = []
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if only_llm_context:
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if retriever_type:
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test_id, result = await run_test(test=None, loader_settings=loader_settings, metadata=metadata,
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only_llm_context=only_llm_context)
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retriever_type=retriever_type)
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results.append(result)
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for param in test_params:
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test_id, result = await run_test(param, loader_settings, metadata, only_llm_context=only_llm_context)
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test_id, result = await run_test(param, loader_settings, metadata, retriever_type=retriever_type)
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results.append(result)
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@ -458,7 +464,7 @@ async def main():
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]
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# "https://www.ibiblio.org/ebooks/London/Call%20of%20Wild.pdf"
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#http://public-library.uk/ebooks/59/83.pdf
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result = await start_test(".data/3ZCCCW.pdf", test_set=test_set, user_id="677", params=None, metadata=metadata)
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result = await start_test(".data/3ZCCCW.pdf", test_set=test_set, user_id="677", params=None, metadata=metadata, retriever_type='llm_context')
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#
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# parser = argparse.ArgumentParser(description="Run tests against a document.")
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# parser.add_argument("--url", required=True, help="URL of the document to test.")
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@ -2,13 +2,14 @@
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# Make sure to install the following packages: dlt, langchain, duckdb, python-dotenv, openai, weaviate-client
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import logging
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from marshmallow import Schema, fields
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from loaders.loaders import _document_loader
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# Add the parent directory to sys.path
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logging.basicConfig(level=logging.INFO)
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from langchain.retrievers import WeaviateHybridSearchRetriever
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from langchain.retrievers import WeaviateHybridSearchRetriever, ParentDocumentRetriever
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from weaviate.gql.get import HybridFusion
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import tracemalloc
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tracemalloc.start()
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@ -56,9 +57,8 @@ class WeaviateVectorDB(VectorDB):
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super().__init__(*args, **kwargs)
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self.init_weaviate(embeddings= self.embeddings, namespace = self.namespace)
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def init_weaviate(self, embeddings =OpenAIEmbeddings() , namespace: str=None):
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def init_weaviate(self, embeddings=OpenAIEmbeddings(), namespace=None,retriever_type="",):
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# Weaviate initialization logic
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# embeddings = OpenAIEmbeddings()
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auth_config = weaviate.auth.AuthApiKey(
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api_key=os.environ.get("WEAVIATE_API_KEY")
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)
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@ -67,28 +67,36 @@ class WeaviateVectorDB(VectorDB):
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auth_client_secret=auth_config,
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additional_headers={"X-OpenAI-Api-Key": os.environ.get("OPENAI_API_KEY")},
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)
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retriever = WeaviateHybridSearchRetriever(
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client=client,
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index_name=namespace,
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text_key="text",
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attributes=[],
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embedding=embeddings,
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create_schema_if_missing=True,
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)
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return retriever
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def init_weaviate_client(self, namespace: str):
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# Weaviate client initialization logic
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auth_config = weaviate.auth.AuthApiKey(
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api_key=os.environ.get("WEAVIATE_API_KEY")
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)
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client = weaviate.Client(
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url=os.environ.get("WEAVIATE_URL"),
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auth_client_secret=auth_config,
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additional_headers={"X-OpenAI-Api-Key": os.environ.get("OPENAI_API_KEY")},
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)
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return client
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if retriever_type == "single_document_context":
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retriever = WeaviateHybridSearchRetriever(
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client=client,
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index_name=namespace,
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text_key="text",
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attributes=[],
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embedding=embeddings,
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create_schema_if_missing=True,
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)
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return retriever
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elif retriever_type == "multi_document_context":
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retriever = WeaviateHybridSearchRetriever(
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client=client,
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index_name=namespace,
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text_key="text",
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attributes=[],
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embedding=embeddings,
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create_schema_if_missing=True,
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)
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return retriever
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else :
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return client
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# child_splitter = RecursiveCharacterTextSplitter(chunk_size=400)
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# store = InMemoryStore()
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# retriever = ParentDocumentRetriever(
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# vectorstore=vectorstore,
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# docstore=store,
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# child_splitter=child_splitter,
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# )
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from marshmallow import Schema, fields
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def create_document_structure(observation, params, metadata_schema_class=None):
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@ -140,7 +148,7 @@ class WeaviateVectorDB(VectorDB):
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# Update Weaviate memories here
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if namespace is None:
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namespace = self.namespace
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retriever = self.init_weaviate(embeddings=embeddings,namespace = namespace)
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retriever = self.init_weaviate(embeddings=embeddings,namespace = namespace, retriever_type="single_document_context")
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if loader_settings:
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# Assuming _document_loader returns a list of documents
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documents = await _document_loader(observation, loader_settings)
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@ -174,7 +182,7 @@ class WeaviateVectorDB(VectorDB):
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Example:
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fetch_memories(query="some query", search_type='text', additional_param='value')
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"""
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client = self.init_weaviate_client(self.namespace)
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client = self.init_weaviate(namespace =self.namespace)
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if search_type is None:
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search_type = 'hybrid'
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@ -258,7 +266,7 @@ class WeaviateVectorDB(VectorDB):
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async def delete_memories(self, namespace:str, params: dict = None):
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if namespace is None:
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namespace = self.namespace
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client = self.init_weaviate_client(self.namespace)
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client = self.init_weaviate(namespace = self.namespace)
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if params:
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where_filter = {
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"path": ["id"],
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@ -283,13 +291,12 @@ class WeaviateVectorDB(VectorDB):
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)
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def update_memories(self, observation, namespace: str, params: dict = None):
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client = self.init_weaviate_client(self.namespace)
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client = self.init_weaviate(namespace = self.namespace)
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client.data_object.update(
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data_object={
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# "text": observation,
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"user_id": str(self.user_id),
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"buffer_id": str(self.buffer_id),
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"version": params.get("version", None) or "",
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"agreement_id": params.get("agreement_id", None) or "",
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"privacy_policy": params.get("privacy_policy", None) or "",
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