Merge pull request #17 from topoteretes/extend_search_and_tokenizers
Extend search and tokenizers
This commit is contained in:
Vasilije
GitHub
commit
89678869ce
9 changed files with 146 additions and 134 deletions
6
level_3/.dlt/secrets.toml.example
Normal file
6
level_3/.dlt/secrets.toml.example
Normal file
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@ -0,0 +1,6 @@
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[destination.weaviate.credentials]
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url = "https://your-weaviate-url"
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api_key = "your-weaviate-api-key"
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[destination.weaviate.credentials.additional_headers]
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X-OpenAI-Api-Key = "your-openai-api-key"
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15
level_3/database/database_crud.py
Normal file
15
level_3/database/database_crud.py
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@ -0,0 +1,15 @@
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@contextmanager
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def session_scope(session):
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"""Provide a transactional scope around a series of operations."""
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try:
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yield session
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session.commit()
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except Exception as e:
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session.rollback()
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logger.error(f"Session rollback due to: {str(e)}")
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raise
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finally:
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session.close()
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@ -12,8 +12,7 @@ from database.database import Base
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class User(Base):
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__tablename__ = 'users'
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id = Column(String, primary_key=True)
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name = Column(String, nullable=False, unique=True, index=True)
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id = Column(String, primary_key=True, index=True)
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session_id = Column(String, nullable=True, unique=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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@ -27,4 +26,4 @@ class User(Base):
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metadatas = relationship("MetaDatas", back_populates="user")
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def __repr__(self):
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return f"<User(id={self.id}, name={self.name}, created_at={self.created_at}, updated_at={self.updated_at})>"
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return f"<User(id={self.id}, created_at={self.created_at}, updated_at={self.updated_at})>"
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93
level_3/poetry.lock
generated
93
level_3/poetry.lock
generated
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@ -685,13 +685,13 @@ pdf = ["pypdf (>=3.3.0,<4.0.0)"]
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[[package]]
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name = "deepeval"
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version = "0.20.0"
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version = "0.20.1"
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description = "DeepEval provides evaluation and unit testing to accelerate development of LLMs and Agents."
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optional = false
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python-versions = "*"
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files = [
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{file = "deepeval-0.20.0-py3-none-any.whl", hash = "sha256:81b73d0742974b6ee516c26d8235f3aa62dca765893d41f0eddd870507f70373"},
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{file = "deepeval-0.20.0.tar.gz", hash = "sha256:0e7ec2bbe69b03f9b5f21e5b285559363c2a84a9df25b1f7a278091f31fe7049"},
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{file = "deepeval-0.20.1-py3-none-any.whl", hash = "sha256:f9880a1246a2a8ba77d88b1d2f977759d34741df6d584bb3c55fadc95c52bc89"},
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{file = "deepeval-0.20.1.tar.gz", hash = "sha256:e3e36745f5e77bc6055def0b98e7a3274c87564f498f50337b670a291fde32a5"},
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]
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[package.dependencies]
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@ -705,7 +705,7 @@ sentence-transformers = "*"
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tabulate = "*"
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tqdm = "*"
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transformers = "*"
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typer = "*"
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typer = "0.9.0"
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[package.extras]
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bias = ["Dbias", "tensorflow"]
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@ -731,13 +731,13 @@ dev = ["PyTest", "PyTest-Cov", "bump2version (<1)", "sphinx (<2)", "tox"]
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[[package]]
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name = "dlt"
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version = "0.3.14"
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version = "0.3.18"
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description = "DLT is an open-source python-native scalable data loading framework that does not require any devops efforts to run."
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optional = false
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python-versions = ">=3.8.1,<4.0"
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files = [
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{file = "dlt-0.3.14-py3-none-any.whl", hash = "sha256:b7672e153065796d0e7b0bc7eacfc48feff32a28e091eeca30f5a7180e42da2c"},
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{file = "dlt-0.3.14.tar.gz", hash = "sha256:b398ee07a1b87a6ac93130fc8e143d77e99a30d1bf957468d0252f23f563c01e"},
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{file = "dlt-0.3.18-py3-none-any.whl", hash = "sha256:4ce792fc36382ae5d8bbcbd9510c2cfd4a312149da6ad94e099dcd2c22eb052a"},
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{file = "dlt-0.3.18.tar.gz", hash = "sha256:dd7f64c918aebd02f68c4a7a642566821c239829ac35ba7c2a3d19729e4402d3"},
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]
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[package.dependencies]
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@ -750,7 +750,6 @@ gitpython = ">=3.1.29"
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giturlparse = ">=0.10.0"
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hexbytes = ">=0.2.2"
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humanize = ">=4.4.0"
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json-logging = "1.4.1rc0"
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jsonpath-ng = ">=1.5.3"
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makefun = ">=1.15.0"
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orjson = {version = ">=3.6.7", markers = "platform_python_implementation != \"PyPy\""}
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@ -771,20 +770,22 @@ typing-extensions = ">=4.0.0"
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tzdata = ">=2022.1"
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[package.extras]
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athena = ["boto3 (>=1.25)", "pyarrow (>=8.0.0)", "pyathena (>=2.9.6)", "s3fs (>=2022.4.0)"]
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athena = ["botocore (>=1.28)", "pyarrow (>=8.0.0)", "pyathena (>=2.9.6)", "s3fs (>=2022.4.0)"]
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az = ["adlfs (>=2022.4.0)"]
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bigquery = ["gcsfs (>=2022.4.0)", "google-cloud-bigquery (>=2.26.0)", "grpcio (>=1.50.0)", "pyarrow (>=8.0.0)"]
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cli = ["cron-descriptor (>=1.2.32)", "pipdeptree (>=2.9.0,<2.10)"]
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dbt = ["dbt-athena-community (>=1.2.0)", "dbt-bigquery (>=1.2.0)", "dbt-core (>=1.2.0)", "dbt-duckdb (>=1.2.0)", "dbt-redshift (>=1.2.0)", "dbt-snowflake (>=1.2.0)"]
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duckdb = ["duckdb (>=0.6.1,<0.9.0)"]
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filesystem = ["boto3 (>=1.25)", "s3fs (>=2022.4.0)"]
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filesystem = ["botocore (>=1.28)", "s3fs (>=2022.4.0)"]
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gcp = ["gcsfs (>=2022.4.0)", "google-cloud-bigquery (>=2.26.0)", "grpcio (>=1.50.0)"]
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gs = ["gcsfs (>=2022.4.0)"]
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motherduck = ["duckdb (>=0.6.1,<0.9.0)", "pyarrow (>=8.0.0)"]
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mssql = ["pyodbc (>=4.0.39,<5.0.0)"]
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parquet = ["pyarrow (>=8.0.0)"]
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postgres = ["psycopg2-binary (>=2.9.1)", "psycopg2cffi (>=2.9.0)"]
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pydantic = ["pydantic (>=1.10,<2.0)"]
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redshift = ["psycopg2-binary (>=2.9.1)", "psycopg2cffi (>=2.9.0)"]
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s3 = ["boto3 (>=1.25)", "s3fs (>=2022.4.0)"]
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s3 = ["botocore (>=1.28)", "s3fs (>=2022.4.0)"]
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snowflake = ["snowflake-connector-python[pandas] (>=2.9.0)"]
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weaviate = ["weaviate-client (>=3.22)"]
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@ -1419,17 +1420,6 @@ files = [
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{file = "joblib-1.3.2.tar.gz", hash = "sha256:92f865e621e17784e7955080b6d042489e3b8e294949cc44c6eac304f59772b1"},
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]
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[[package]]
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name = "json-logging"
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version = "1.4.1rc0"
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description = "JSON Python Logging"
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optional = false
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python-versions = "*"
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files = [
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{file = "json-logging-1.4.1rc0.tar.gz", hash = "sha256:381e00495bbd619d09c8c3d1fdd72c843f7045797ab63b42cfec5f7961e5b3f6"},
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{file = "json_logging-1.4.1rc0-py2.py3-none-any.whl", hash = "sha256:2b787c28f31fb4d8aabac16ac3816326031d92dd054bdabc9bbe68eb10864f77"},
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]
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[[package]]
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name = "jsonpatch"
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version = "1.33"
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@ -1474,13 +1464,13 @@ files = [
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[[package]]
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name = "langchain"
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version = "0.0.303"
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version = "0.0.308"
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description = "Building applications with LLMs through composability"
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optional = false
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python-versions = ">=3.8.1,<4.0"
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files = [
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{file = "langchain-0.0.303-py3-none-any.whl", hash = "sha256:1745961f66b60bc3b513820a34c560dd37c4ba4b7499ba82545dc4816d0133bd"},
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{file = "langchain-0.0.303.tar.gz", hash = "sha256:84d2727eb8b3b27a9d0aa0da9f05408c2564a4a923c7d5b154a16e488430e725"},
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{file = "langchain-0.0.308-py3-none-any.whl", hash = "sha256:807de0a8f4177e42e435682cfd33e600518d04e1688149afda8542b9d31a407f"},
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{file = "langchain-0.0.308.tar.gz", hash = "sha256:496ddef6c0aa8e73b3c28bad8c4cb02cdb7330e8ba80b238f1b3e0d663756b1b"},
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]
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[package.dependencies]
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@ -1489,8 +1479,7 @@ anyio = "<4.0"
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async-timeout = {version = ">=4.0.0,<5.0.0", markers = "python_version < \"3.11\""}
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dataclasses-json = ">=0.5.7,<0.7"
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jsonpatch = ">=1.33,<2.0"
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langsmith = ">=0.0.38,<0.1.0"
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numexpr = ">=2.8.4,<3.0.0"
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langsmith = ">=0.0.40,<0.1.0"
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numpy = ">=1,<2"
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pydantic = ">=1,<3"
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PyYAML = ">=5.3"
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@ -1499,16 +1488,16 @@ SQLAlchemy = ">=1.4,<3"
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tenacity = ">=8.1.0,<9.0.0"
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[package.extras]
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all = ["O365 (>=2.0.26,<3.0.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "amadeus (>=8.1.0)", "arxiv (>=1.4,<2.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "awadb (>=0.3.9,<0.4.0)", "azure-ai-formrecognizer (>=3.2.1,<4.0.0)", "azure-ai-vision (>=0.11.1b1,<0.12.0)", "azure-cognitiveservices-speech (>=1.28.0,<2.0.0)", "azure-cosmos (>=4.4.0b1,<5.0.0)", "azure-identity (>=1.12.0,<2.0.0)", "beautifulsoup4 (>=4,<5)", "clarifai (>=9.1.0)", "clickhouse-connect (>=0.5.14,<0.6.0)", "cohere (>=4,<5)", "deeplake (>=3.6.8,<4.0.0)", "docarray[hnswlib] (>=0.32.0,<0.33.0)", "duckduckgo-search (>=3.8.3,<4.0.0)", "elasticsearch (>=8,<9)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "google-api-python-client (==2.70.0)", "google-auth (>=2.18.1,<3.0.0)", "google-search-results (>=2,<3)", "gptcache (>=0.1.7)", "html2text (>=2020.1.16,<2021.0.0)", "huggingface_hub (>=0,<1)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "lancedb (>=0.1,<0.2)", "langkit (>=0.0.6,<0.1.0)", "lark (>=1.1.5,<2.0.0)", "libdeeplake (>=0.0.60,<0.0.61)", "librosa (>=0.10.0.post2,<0.11.0)", "lxml (>=4.9.2,<5.0.0)", "manifest-ml (>=0.0.1,<0.0.2)", "marqo (>=1.2.4,<2.0.0)", "momento (>=1.5.0,<2.0.0)", "nebula3-python (>=3.4.0,<4.0.0)", "neo4j (>=5.8.1,<6.0.0)", "networkx (>=2.6.3,<3.0.0)", "nlpcloud (>=1,<2)", "nltk (>=3,<4)", "nomic (>=1.0.43,<2.0.0)", "openai (>=0,<1)", "openlm (>=0.0.5,<0.0.6)", "opensearch-py (>=2.0.0,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pexpect (>=4.8.0,<5.0.0)", "pgvector (>=0.1.6,<0.2.0)", "pinecone-client (>=2,<3)", "pinecone-text (>=0.4.2,<0.5.0)", "psycopg2-binary (>=2.9.5,<3.0.0)", "pymongo (>=4.3.3,<5.0.0)", "pyowm (>=3.3.0,<4.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pytesseract (>=0.3.10,<0.4.0)", "python-arango (>=7.5.9,<8.0.0)", "pyvespa (>=0.33.0,<0.34.0)", "qdrant-client (>=1.3.1,<2.0.0)", "rdflib (>=6.3.2,<7.0.0)", "redis (>=4,<5)", "requests-toolbelt (>=1.0.0,<2.0.0)", "sentence-transformers (>=2,<3)", "singlestoredb (>=0.7.1,<0.8.0)", "tensorflow-text (>=2.11.0,<3.0.0)", "tigrisdb (>=1.0.0b6,<2.0.0)", "tiktoken (>=0.3.2,<0.4.0)", "torch (>=1,<3)", "transformers (>=4,<5)", "weaviate-client (>=3,<4)", "wikipedia (>=1,<2)", "wolframalpha (==5.0.0)"]
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all = ["O365 (>=2.0.26,<3.0.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "amadeus (>=8.1.0)", "arxiv (>=1.4,<2.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "awadb (>=0.3.9,<0.4.0)", "azure-ai-formrecognizer (>=3.2.1,<4.0.0)", "azure-ai-vision (>=0.11.1b1,<0.12.0)", "azure-cognitiveservices-speech (>=1.28.0,<2.0.0)", "azure-cosmos (>=4.4.0b1,<5.0.0)", "azure-identity (>=1.12.0,<2.0.0)", "beautifulsoup4 (>=4,<5)", "clarifai (>=9.1.0)", "clickhouse-connect (>=0.5.14,<0.6.0)", "cohere (>=4,<5)", "deeplake (>=3.6.8,<4.0.0)", "docarray[hnswlib] (>=0.32.0,<0.33.0)", "duckduckgo-search (>=3.8.3,<4.0.0)", "elasticsearch (>=8,<9)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "google-api-python-client (==2.70.0)", "google-auth (>=2.18.1,<3.0.0)", "google-search-results (>=2,<3)", "gptcache (>=0.1.7)", "html2text (>=2020.1.16,<2021.0.0)", "huggingface_hub (>=0,<1)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "lancedb (>=0.1,<0.2)", "langkit (>=0.0.6,<0.1.0)", "lark (>=1.1.5,<2.0.0)", "libdeeplake (>=0.0.60,<0.0.61)", "librosa (>=0.10.0.post2,<0.11.0)", "lxml (>=4.9.2,<5.0.0)", "manifest-ml (>=0.0.1,<0.0.2)", "marqo (>=1.2.4,<2.0.0)", "momento (>=1.5.0,<2.0.0)", "nebula3-python (>=3.4.0,<4.0.0)", "neo4j (>=5.8.1,<6.0.0)", "networkx (>=2.6.3,<4)", "nlpcloud (>=1,<2)", "nltk (>=3,<4)", "nomic (>=1.0.43,<2.0.0)", "openai (>=0,<1)", "openlm (>=0.0.5,<0.0.6)", "opensearch-py (>=2.0.0,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pexpect (>=4.8.0,<5.0.0)", "pgvector (>=0.1.6,<0.2.0)", "pinecone-client (>=2,<3)", "pinecone-text (>=0.4.2,<0.5.0)", "psycopg2-binary (>=2.9.5,<3.0.0)", "pymongo (>=4.3.3,<5.0.0)", "pyowm (>=3.3.0,<4.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pytesseract (>=0.3.10,<0.4.0)", "python-arango (>=7.5.9,<8.0.0)", "pyvespa (>=0.33.0,<0.34.0)", "qdrant-client (>=1.3.1,<2.0.0)", "rdflib (>=6.3.2,<7.0.0)", "redis (>=4,<5)", "requests-toolbelt (>=1.0.0,<2.0.0)", "sentence-transformers (>=2,<3)", "singlestoredb (>=0.7.1,<0.8.0)", "tensorflow-text (>=2.11.0,<3.0.0)", "tigrisdb (>=1.0.0b6,<2.0.0)", "tiktoken (>=0.3.2,<0.6.0)", "torch (>=1,<3)", "transformers (>=4,<5)", "weaviate-client (>=3,<4)", "wikipedia (>=1,<2)", "wolframalpha (==5.0.0)"]
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azure = ["azure-ai-formrecognizer (>=3.2.1,<4.0.0)", "azure-ai-vision (>=0.11.1b1,<0.12.0)", "azure-cognitiveservices-speech (>=1.28.0,<2.0.0)", "azure-core (>=1.26.4,<2.0.0)", "azure-cosmos (>=4.4.0b1,<5.0.0)", "azure-identity (>=1.12.0,<2.0.0)", "azure-search-documents (==11.4.0b8)", "openai (>=0,<1)"]
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clarifai = ["clarifai (>=9.1.0)"]
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cohere = ["cohere (>=4,<5)"]
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docarray = ["docarray[hnswlib] (>=0.32.0,<0.33.0)"]
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embeddings = ["sentence-transformers (>=2,<3)"]
|
||||
extended-testing = ["amazon-textract-caller (<2)", "assemblyai (>=0.17.0,<0.18.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.1.0,<0.2.0)", "chardet (>=5.1.0,<6.0.0)", "dashvector (>=1.0.1,<2.0.0)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "feedparser (>=6.0.10,<7.0.0)", "geopandas (>=0.13.1,<0.14.0)", "gitpython (>=3.1.32,<4.0.0)", "gql (>=3.4.1,<4.0.0)", "html2text (>=2020.1.16,<2021.0.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "lxml (>=4.9.2,<5.0.0)", "markdownify (>=0.11.6,<0.12.0)", "mwparserfromhell (>=0.6.4,<0.7.0)", "mwxml (>=0.3.3,<0.4.0)", "newspaper3k (>=0.2.8,<0.3.0)", "openai (>=0,<1)", "openapi-schema-pydantic (>=1.2,<2.0)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "rank-bm25 (>=0.2.2,<0.3.0)", "rapidfuzz (>=3.1.1,<4.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "sqlite-vss (>=0.1.2,<0.2.0)", "streamlit (>=1.18.0,<2.0.0)", "sympy (>=1.12,<2.0)", "telethon (>=1.28.5,<2.0.0)", "timescale-vector (>=0.0.1,<0.0.2)", "tqdm (>=4.48.0)", "xata (>=1.0.0a7,<2.0.0)", "xmltodict (>=0.13.0,<0.14.0)"]
|
||||
extended-testing = ["amazon-textract-caller (<2)", "anthropic (>=0.3.11,<0.4.0)", "arxiv (>=1.4,<2.0)", "assemblyai (>=0.17.0,<0.18.0)", "atlassian-python-api (>=3.36.0,<4.0.0)", "beautifulsoup4 (>=4,<5)", "bibtexparser (>=1.4.0,<2.0.0)", "cassio (>=0.1.0,<0.2.0)", "chardet (>=5.1.0,<6.0.0)", "dashvector (>=1.0.1,<2.0.0)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "feedparser (>=6.0.10,<7.0.0)", "geopandas (>=0.13.1,<0.14.0)", "gitpython (>=3.1.32,<4.0.0)", "gql (>=3.4.1,<4.0.0)", "html2text (>=2020.1.16,<2021.0.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "lxml (>=4.9.2,<5.0.0)", "markdownify (>=0.11.6,<0.12.0)", "motor (>=3.3.1,<4.0.0)", "mwparserfromhell (>=0.6.4,<0.7.0)", "mwxml (>=0.3.3,<0.4.0)", "newspaper3k (>=0.2.8,<0.3.0)", "numexpr (>=2.8.6,<3.0.0)", "openai (>=0,<1)", "openapi-schema-pydantic (>=1.2,<2.0)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "psychicapi (>=0.8.0,<0.9.0)", "py-trello (>=0.19.0,<0.20.0)", "pymupdf (>=1.22.3,<2.0.0)", "pypdf (>=3.4.0,<4.0.0)", "pypdfium2 (>=4.10.0,<5.0.0)", "pyspark (>=3.4.0,<4.0.0)", "rank-bm25 (>=0.2.2,<0.3.0)", "rapidfuzz (>=3.1.1,<4.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "scikit-learn (>=1.2.2,<2.0.0)", "sqlite-vss (>=0.1.2,<0.2.0)", "streamlit (>=1.18.0,<2.0.0)", "sympy (>=1.12,<2.0)", "telethon (>=1.28.5,<2.0.0)", "timescale-vector (>=0.0.1,<0.0.2)", "tqdm (>=4.48.0)", "xata (>=1.0.0a7,<2.0.0)", "xmltodict (>=0.13.0,<0.14.0)"]
|
||||
javascript = ["esprima (>=4.0.1,<5.0.0)"]
|
||||
llms = ["clarifai (>=9.1.0)", "cohere (>=4,<5)", "huggingface_hub (>=0,<1)", "manifest-ml (>=0.0.1,<0.0.2)", "nlpcloud (>=1,<2)", "openai (>=0,<1)", "openlm (>=0.0.5,<0.0.6)", "torch (>=1,<3)", "transformers (>=4,<5)"]
|
||||
openai = ["openai (>=0,<1)", "tiktoken (>=0.3.2,<0.4.0)"]
|
||||
openai = ["openai (>=0,<1)", "tiktoken (>=0.3.2,<0.6.0)"]
|
||||
qdrant = ["qdrant-client (>=1.3.1,<2.0.0)"]
|
||||
text-helpers = ["chardet (>=5.1.0,<6.0.0)"]
|
||||
|
||||
|
|
@ -1982,48 +1971,6 @@ plot = ["matplotlib"]
|
|||
tgrep = ["pyparsing"]
|
||||
twitter = ["twython"]
|
||||
|
||||
[[package]]
|
||||
name = "numexpr"
|
||||
version = "2.8.5"
|
||||
description = "Fast numerical expression evaluator for NumPy"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "numexpr-2.8.5-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:51f3ab160c3847ebcca93cd88f935a7802b54a01ab63fe93152994a64d7a6cf2"},
|
||||
{file = "numexpr-2.8.5-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:de29c77f674e4eb8f0846525a475cab64008c227c8bc4ba5153ab3f72441cc63"},
|
||||
{file = "numexpr-2.8.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bf85ba1327eb87ec82ae7936f13c8850fb969a0ca34f3ba9fa3897c09d5c80d7"},
|
||||
{file = "numexpr-2.8.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3c00be69f747f44a631830215cab482f0f77f75af2925695adff57c1cc0f9a68"},
|
||||
{file = "numexpr-2.8.5-cp310-cp310-win32.whl", hash = "sha256:c46350dcdb93e32f033eea5a21269514ffcaf501d9abd6036992d37e48a308b0"},
|
||||
{file = "numexpr-2.8.5-cp310-cp310-win_amd64.whl", hash = "sha256:894b027438b8ec88dea32a19193716c79f4ff8ddb92302dcc9731b51ba3565a8"},
|
||||
{file = "numexpr-2.8.5-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:6df184d40d4cf9f21c71f429962f39332f7398147762588c9f3a5c77065d0c06"},
|
||||
{file = "numexpr-2.8.5-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:178b85ad373c6903e55d75787d61b92380439b70d94b001cb055a501b0821335"},
|
||||
{file = "numexpr-2.8.5-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:578fe4008e4d5d6ff01bbeb2d7b7ba1ec658a5cda9c720cd26a9a8325f8ef438"},
|
||||
{file = "numexpr-2.8.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ef621b4ee366a5c6a484f6678c9259f5b826569f8bfa0b89ba2306d5055468bb"},
|
||||
{file = "numexpr-2.8.5-cp311-cp311-win32.whl", hash = "sha256:dd57ab1a3d3aaa9274aff1cefbf93b8ddacc7973afef5b125905f6bf18fabab0"},
|
||||
{file = "numexpr-2.8.5-cp311-cp311-win_amd64.whl", hash = "sha256:783324ba40eb804ecfc9ebae86120a1e339ab112d0ab8a1f0d48a26354d5bf9b"},
|
||||
{file = "numexpr-2.8.5-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:183d5430db76826e54465c69db93a3c6ecbf03cda5aa1bb96eaad0147e9b68dc"},
|
||||
{file = "numexpr-2.8.5-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:39ce106f92ccea5b07b1d6f2f3c4370f05edf27691dc720a63903484a2137e48"},
|
||||
{file = "numexpr-2.8.5-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b594dc9e2d6291a0bc5c065e6d9caf3eee743b5663897832e9b17753c002947a"},
|
||||
{file = "numexpr-2.8.5-cp37-cp37m-win32.whl", hash = "sha256:62b4faf8e0627673b0210a837792bddd23050ecebc98069ab23eb0633ff1ef5f"},
|
||||
{file = "numexpr-2.8.5-cp37-cp37m-win_amd64.whl", hash = "sha256:db5c65417d69414f1ab31302ea01d3548303ef31209c38b4849d145be4e1d1ba"},
|
||||
{file = "numexpr-2.8.5-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:eb36ffcfa1606e41aa08d559b4277bcad0e16b83941d1a4fee8d2bd5a34f8e0e"},
|
||||
{file = "numexpr-2.8.5-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:34af2a0e857d02a4bc5758bc037a777d50dacb13bcd57c7905268a3e44994ed6"},
|
||||
{file = "numexpr-2.8.5-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5a8dad2bfaad5a5c34a2e8bbf62b9df1dfab266d345fda1feb20ff4e264b347a"},
|
||||
{file = "numexpr-2.8.5-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b93f5a866cd13a808bc3d3a9c487d94cd02eec408b275ff0aa150f2e8e5191f8"},
|
||||
{file = "numexpr-2.8.5-cp38-cp38-win32.whl", hash = "sha256:558390fea6370003ac749ed9d0f38d708aa096f5dcb707ddb6e0ca5a0dd37da1"},
|
||||
{file = "numexpr-2.8.5-cp38-cp38-win_amd64.whl", hash = "sha256:55983806815035eb63c5039520688c49536bb7f3cc3fc1d7d64c6a00cf3f353e"},
|
||||
{file = "numexpr-2.8.5-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:1510da20e6f5f45333610b1ded44c566e2690c6c437c84f2a212ca09627c7e01"},
|
||||
{file = "numexpr-2.8.5-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:9e8b5bf7bcb4e8dcd66522d8fc96e1db7278f901cb4fd2e155efbe62a41dde08"},
|
||||
{file = "numexpr-2.8.5-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4ed0e1c1ef5f34381448539f1fe9015906d21c9cfa2797c06194d4207dadb465"},
|
||||
{file = "numexpr-2.8.5-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:aea6ab45c87c0a7041183c08a798f0ad4d7c5eccbce20cfe79ce6f1a45ef3702"},
|
||||
{file = "numexpr-2.8.5-cp39-cp39-win32.whl", hash = "sha256:cbfd833ee5fdb0efb862e152aee7e6ccea9c596d5c11d22604c2e6307bff7cad"},
|
||||
{file = "numexpr-2.8.5-cp39-cp39-win_amd64.whl", hash = "sha256:283ce8609a7ccbadf91a68f3484558b3e36d27c93c98a41ec205efb0ab43c872"},
|
||||
{file = "numexpr-2.8.5.tar.gz", hash = "sha256:45ed41e55a0abcecf3d711481e12a5fb7a904fe99d42bc282a17cc5f8ea510be"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
numpy = ">=1.13.3"
|
||||
|
||||
[[package]]
|
||||
name = "numpy"
|
||||
version = "1.25.2"
|
||||
|
|
@ -4831,4 +4778,4 @@ multidict = ">=4.0"
|
|||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = "^3.10"
|
||||
content-hash = "90d5e6dc901e0ee6324452b378fd92b03872c4d63968379743247224c217a12c"
|
||||
content-hash = "cf14b96576c57633fea1b0cc1f7266a5fb265d4ff4ce2d3479a036483082b3b4"
|
||||
|
|
|
|||
|
|
@ -8,7 +8,7 @@ readme = "README.md"
|
|||
[tool.poetry.dependencies]
|
||||
python = "^3.10"
|
||||
#langchain = {git = "https://github.com/topoteretes/langchain.git" , tag = "v0.0.209"}
|
||||
langchain = "v0.0.303"
|
||||
langchain = "v0.0.308"
|
||||
|
||||
nltk = "3.8.1"
|
||||
openai = "0.27.8"
|
||||
|
|
|
|||
|
|
@ -145,7 +145,8 @@ def generate_param_variants(base_params=None, increments=None, ranges=None, incl
|
|||
'chunk_size': 500,
|
||||
'chunk_overlap': 20,
|
||||
'similarity_score': 0.5,
|
||||
'metadata_variation': 0
|
||||
'metadata_variation': 0,
|
||||
'search_type': 'hybrid'
|
||||
}
|
||||
|
||||
# Update defaults with provided base parameters
|
||||
|
|
@ -178,8 +179,10 @@ def generate_param_variants(base_params=None, increments=None, ranges=None, incl
|
|||
for key in ['chunk_size', 'chunk_overlap', 'similarity_score', 'metadata_variation']
|
||||
}
|
||||
|
||||
|
||||
param_ranges['cognitive_architecture'] = ["simple_index", "cognitive_architecture"]
|
||||
param_ranges['search_strategy'] = ["similarity_score", "fusion_score"]
|
||||
# Add search_type with possible values
|
||||
param_ranges['search_type'] = ['text', 'hybrid', 'bm25', 'generate', 'generate_grouped']
|
||||
|
||||
# Filter param_ranges based on included_params
|
||||
if included_params is not None:
|
||||
|
|
@ -279,8 +282,8 @@ def fetch_test_set_id(session, user_id, id):
|
|||
return (
|
||||
session.query(TestSet.id)
|
||||
.filter_by(user_id=user_id, id=id)
|
||||
.order_by(TestSet.created_at.desc())
|
||||
.first()
|
||||
.order_by(TestSet.created_at)
|
||||
.desc().first()
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error(f"An error occurred while retrieving the job: {str(e)}")
|
||||
|
|
@ -292,6 +295,9 @@ async def start_test(data, test_set=None, user_id=None, params=None, job_id=None
|
|||
Session = sessionmaker(bind=engine)
|
||||
session = Session()
|
||||
|
||||
|
||||
memory = Memory.create_memory(user_id, session, namespace="SEMANTICMEMORY")
|
||||
|
||||
job_id = fetch_job_id(session, user_id = user_id,job_id =job_id)
|
||||
test_set_id = fetch_test_set_id(session, user_id=user_id, id=job_id)
|
||||
if job_id is None:
|
||||
|
|
@ -321,13 +327,6 @@ async def start_test(data, test_set=None, user_id=None, params=None, job_id=None
|
|||
for test in test_params:
|
||||
test_id = str(generate_letter_uuid()) + "_" + "SEMANTICEMEMORY"
|
||||
|
||||
|
||||
#handle test data here
|
||||
|
||||
Session = sessionmaker(bind=engine)
|
||||
session = Session()
|
||||
memory = Memory.create_memory(user_id, session, namespace=test_id)
|
||||
|
||||
# Adding a memory instance
|
||||
memory.add_memory_instance("ExampleMemory")
|
||||
|
||||
|
|
@ -379,10 +378,8 @@ async def start_test(data, test_set=None, user_id=None, params=None, job_id=None
|
|||
memory.add_method_to_class(dynamic_memory_class, 'delete_memories')
|
||||
else:
|
||||
print(f"No attribute named {test_class.lower()} in memory.")
|
||||
load_action = await memory.dynamic_method_call(dynamic_memory_class, 'delete_memories',
|
||||
namespace ='some_observation', params=metadata,
|
||||
loader_settings=loader_settings)
|
||||
memory.delete_memories(namespace=test_id)
|
||||
delete_mems = await memory.dynamic_method_call(dynamic_memory_class, 'delete_memories',
|
||||
namespace =test_id)
|
||||
|
||||
print(test_result_collection)
|
||||
|
||||
|
|
|
|||
|
|
@ -56,6 +56,7 @@ class VectorDBFactory:
|
|||
buffer_id: str = BUFFER_ID_DEFAULT,
|
||||
db_type: str = "pinecone",
|
||||
namespace: str = None,
|
||||
embeddings = None,
|
||||
):
|
||||
db_map = {"pinecone": PineconeVectorDB, "weaviate": WeaviateVectorDB}
|
||||
|
||||
|
|
@ -68,6 +69,7 @@ class VectorDBFactory:
|
|||
st_memory_id,
|
||||
buffer_id,
|
||||
namespace,
|
||||
embeddings
|
||||
)
|
||||
|
||||
raise ValueError(f"Unsupported database type: {db_type}")
|
||||
|
|
@ -80,11 +82,13 @@ class BaseMemory:
|
|||
index_name: Optional[str],
|
||||
db_type: str,
|
||||
namespace: str,
|
||||
embeddings: Optional[None],
|
||||
):
|
||||
self.user_id = user_id
|
||||
self.memory_id = memory_id
|
||||
self.index_name = index_name
|
||||
self.namespace = namespace
|
||||
self.embeddings = embeddings
|
||||
self.db_type = db_type
|
||||
factory = VectorDBFactory()
|
||||
self.vector_db = factory.create_vector_db(
|
||||
|
|
@ -93,11 +97,12 @@ class BaseMemory:
|
|||
self.memory_id,
|
||||
db_type=self.db_type,
|
||||
namespace=self.namespace,
|
||||
embeddings=self.embeddings
|
||||
)
|
||||
|
||||
def init_client(self, namespace: str):
|
||||
def init_client(self, embeddings, namespace: str):
|
||||
|
||||
return self.vector_db.init_weaviate_client(namespace)
|
||||
return self.vector_db.init_weaviate_client(embeddings, namespace)
|
||||
|
||||
def create_field(self, field_type, **kwargs):
|
||||
field_mapping = {
|
||||
|
|
@ -174,6 +179,7 @@ class BaseMemory:
|
|||
params: Optional[dict] = None,
|
||||
namespace: Optional[str] = None,
|
||||
custom_fields: Optional[str] = None,
|
||||
embeddings: Optional[str] = None,
|
||||
|
||||
):
|
||||
from ast import literal_eval
|
||||
|
|
@ -209,24 +215,25 @@ class BaseMemory:
|
|||
|
||||
return await self.vector_db.add_memories(
|
||||
observation=observation, loader_settings=loader_settings,
|
||||
params=loaded_params, namespace=namespace, metadata_schema_class = schema_instance
|
||||
params=loaded_params, namespace=namespace, metadata_schema_class = schema_instance, embeddings=embeddings
|
||||
)
|
||||
# Add other db_type conditions if necessary
|
||||
|
||||
async def fetch_memories(
|
||||
self,
|
||||
observation: str,
|
||||
search_type: Optional[str] = None,
|
||||
params: Optional[str] = None,
|
||||
namespace: Optional[str] = None,
|
||||
n_of_observations: Optional[int] = 2,
|
||||
):
|
||||
|
||||
return await self.vector_db.fetch_memories(
|
||||
observation=observation, params=params,
|
||||
observation=observation, search_type= search_type, params=params,
|
||||
namespace=namespace,
|
||||
n_of_observations=n_of_observations
|
||||
)
|
||||
|
||||
async def delete_memories(self, params: Optional[str] = None):
|
||||
return await self.vector_db.delete_memories(params)
|
||||
async def delete_memories(self, namespace:str, params: Optional[str] = None):
|
||||
return await self.vector_db.delete_memories(namespace,params)
|
||||
|
||||
|
|
|
|||
|
|
@ -36,6 +36,7 @@ class VectorDB:
|
|||
st_memory_id: str = ST_MEMORY_ID_DEFAULT,
|
||||
buffer_id: str = BUFFER_ID_DEFAULT,
|
||||
namespace: str = None,
|
||||
embeddings = None,
|
||||
):
|
||||
self.user_id = user_id
|
||||
self.index_name = index_name
|
||||
|
|
@ -44,6 +45,7 @@ class VectorDB:
|
|||
self.ltm_memory_id = ltm_memory_id
|
||||
self.st_memory_id = st_memory_id
|
||||
self.buffer_id = buffer_id
|
||||
self.embeddings = embeddings
|
||||
|
||||
class PineconeVectorDB(VectorDB):
|
||||
def __init__(self, *args, **kwargs):
|
||||
|
|
@ -54,15 +56,15 @@ class PineconeVectorDB(VectorDB):
|
|||
# Pinecone initialization logic
|
||||
pass
|
||||
|
||||
|
||||
import langchain.embeddings
|
||||
class WeaviateVectorDB(VectorDB):
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
self.init_weaviate(self.namespace)
|
||||
self.init_weaviate(embeddings= self.embeddings, namespace = self.namespace)
|
||||
|
||||
def init_weaviate(self, namespace: str):
|
||||
def init_weaviate(self, embeddings =OpenAIEmbeddings() , namespace: str=None):
|
||||
# Weaviate initialization logic
|
||||
embeddings = OpenAIEmbeddings()
|
||||
# embeddings = OpenAIEmbeddings()
|
||||
auth_config = weaviate.auth.AuthApiKey(
|
||||
api_key=os.environ.get("WEAVIATE_API_KEY")
|
||||
)
|
||||
|
|
@ -112,11 +114,11 @@ class WeaviateVectorDB(VectorDB):
|
|||
CurrentDocumentSchema = get_document_schema()
|
||||
loaded_document = CurrentDocumentSchema().load(document_data)
|
||||
return [loaded_document]
|
||||
async def add_memories(self, observation, loader_settings=None, params=None, namespace=None, metadata_schema_class=None):
|
||||
async def add_memories(self, observation, loader_settings=None, params=None, namespace=None, metadata_schema_class=None, embeddings = 'hybrid'):
|
||||
# Update Weaviate memories here
|
||||
if namespace is None:
|
||||
namespace = self.namespace
|
||||
retriever = self.init_weaviate(namespace)
|
||||
retriever = self.init_weaviate(embeddings=embeddings,namespace = namespace)
|
||||
if loader_settings:
|
||||
# Assuming _document_loader returns a list of documents
|
||||
documents = _document_loader(observation, loader_settings)
|
||||
|
|
@ -133,25 +135,26 @@ class WeaviateVectorDB(VectorDB):
|
|||
retriever.add_documents([
|
||||
Document(metadata=document_to_load[0]['metadata'], page_content=document_to_load[0]['page_content'])])
|
||||
|
||||
async def fetch_memories(
|
||||
self, observation: str, namespace: str, params: dict = None, n_of_observations: int = 2
|
||||
):
|
||||
async def fetch_memories(self, observation: str, namespace: str = None, search_type: str = 'hybrid', **kwargs):
|
||||
"""
|
||||
Fetch documents from weaviate.
|
||||
|
||||
Parameters:
|
||||
- observation (str): User query.
|
||||
- namespace (str): Type of memory accessed.
|
||||
- params (dict, optional): Filtering parameters.
|
||||
- n_of_observations (int, optional): For weaviate, equals to autocut. Defaults to 2. Ranges from 1 to 3.
|
||||
- namespace (str, optional): Type of memory accessed.
|
||||
- search_type (str, optional): Type of search ('text', 'hybrid', 'bm25', 'generate', 'generate_grouped'). Defaults to 'hybrid'.
|
||||
- **kwargs: Additional parameters for flexibility.
|
||||
|
||||
Returns:
|
||||
List of documents matching the query.
|
||||
List of documents matching the query or an empty list in case of error.
|
||||
|
||||
Example:
|
||||
fetch_memories(query="some query", path=['year'], operator='Equal', valueText='2017*')
|
||||
fetch_memories(query="some query", search_type='text', additional_param='value')
|
||||
"""
|
||||
client = self.init_weaviate_client(self.namespace)
|
||||
if search_type is None:
|
||||
search_type = 'hybrid'
|
||||
|
||||
|
||||
if not namespace:
|
||||
namespace = self.namespace
|
||||
|
|
@ -176,23 +179,51 @@ class WeaviateVectorDB(VectorDB):
|
|||
["id", "creationTimeUnix", "lastUpdateTimeUnix", "score", 'distance']
|
||||
).with_where(params_user_id).with_limit(10)
|
||||
|
||||
if params:
|
||||
query_output = (
|
||||
base_query
|
||||
.with_where(params)
|
||||
.with_near_text({"concepts": [observation]})
|
||||
.do()
|
||||
)
|
||||
else:
|
||||
query_output = (
|
||||
base_query
|
||||
.with_hybrid(
|
||||
query=observation,
|
||||
fusion_type=HybridFusion.RELATIVE_SCORE
|
||||
try:
|
||||
if search_type == 'text':
|
||||
query_output = (
|
||||
base_query
|
||||
.with_near_text({"concepts": [observation]})
|
||||
.do()
|
||||
)
|
||||
.with_autocut(n_of_observations)
|
||||
.do()
|
||||
)
|
||||
elif search_type == 'hybrid':
|
||||
n_of_observations = kwargs.get('n_of_observations', 2)
|
||||
|
||||
|
||||
query_output = (
|
||||
base_query
|
||||
.with_hybrid(query=observation, fusion_type=HybridFusion.RELATIVE_SCORE)
|
||||
.with_autocut(n_of_observations)
|
||||
.do()
|
||||
)
|
||||
elif search_type == 'bm25':
|
||||
query_output = (
|
||||
base_query
|
||||
.with_bm25(query=observation)
|
||||
.do()
|
||||
)
|
||||
elif search_type == 'generate':
|
||||
generate_prompt = kwargs.get('generate_prompt', "")
|
||||
query_output = (
|
||||
base_query
|
||||
.with_generate(single_prompt=generate_prompt)
|
||||
.with_near_text({"concepts": [observation]})
|
||||
.do()
|
||||
)
|
||||
elif search_type == 'generate_grouped':
|
||||
generate_prompt = kwargs.get('generate_prompt', "")
|
||||
query_output = (
|
||||
base_query
|
||||
.with_generate(grouped_task=generate_prompt)
|
||||
.with_near_text({"concepts": [observation]})
|
||||
.do()
|
||||
)
|
||||
else:
|
||||
logging.error(f"Invalid search_type: {search_type}")
|
||||
return []
|
||||
except Exception as e:
|
||||
logging.error(f"Error executing query: {str(e)}")
|
||||
return []
|
||||
|
||||
return query_output
|
||||
|
||||
|
|
|
|||
|
|
@ -30,14 +30,24 @@ from vectordb.basevectordb import BaseMemory
|
|||
|
||||
|
||||
class DynamicBaseMemory(BaseMemory):
|
||||
def __init__(self, name, user_id, memory_id, index_name, db_type, namespace):
|
||||
super().__init__(user_id, memory_id, index_name, db_type, namespace)
|
||||
def __init__(
|
||||
self,
|
||||
name: str,
|
||||
user_id: str,
|
||||
memory_id: str,
|
||||
index_name: str,
|
||||
db_type: str,
|
||||
namespace: str,
|
||||
embeddings=None
|
||||
):
|
||||
super().__init__(user_id, memory_id, index_name, db_type, namespace, embeddings)
|
||||
self.name = name
|
||||
self.attributes = set()
|
||||
self.methods = set()
|
||||
self.inheritance = None
|
||||
self.associations = []
|
||||
|
||||
|
||||
def add_method(self, method_name):
|
||||
"""
|
||||
Add a method to the memory class.
|
||||
|
|
@ -178,7 +188,7 @@ class Memory:
|
|||
def handle_new_user(user_id: str, session):
|
||||
"""Handle new user creation in the DB and return the new memory ID."""
|
||||
memory_id = str(uuid.uuid4())
|
||||
new_user = User(id=user_id, name="john doe")
|
||||
new_user = User(id=user_id)
|
||||
session.add(new_user)
|
||||
session.commit()
|
||||
|
||||
|
|
@ -310,9 +320,9 @@ async def main():
|
|||
sss = await memory.dynamic_method_call(memory.semanticmemory_class, 'add_memories',
|
||||
observation='some_observation', params=params)
|
||||
|
||||
# susu = await memory.dynamic_method_call(memory.semanticmemory_class, 'fetch_memories',
|
||||
# observation='some_observation')
|
||||
# print(susu)
|
||||
susu = await memory.dynamic_method_call(memory.semanticmemory_class, 'fetch_memories',
|
||||
observation='some_observation')
|
||||
print(susu)
|
||||
|
||||
# Adding a dynamic memory class
|
||||
# dynamic_memory = memory.add_dynamic_memory_class("DynamicMemory", "ExampleNamespace")
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue