fix search, add improvements

.github/workflows/test_common.yml

@@ -72,13 +72,13 @@ jobs:
       - name: Install dependencies
         run: poetry install --no-interaction
 
-      # - name: Build with Poetry
-      #   run: poetry build
-
-      # - name: Install Package
-      #   run: |
-      #     cd dist
-      #     pip install *.whl
+      #      - name: Build with Poetry
+      #        run: poetry build
+      #
+      #      - name: Install Package
+      #        run: |
+      #         cd dist
+      #         pip install *.whl
 
       # - name: Download NLTK Punkt Tokenizer Models
       #   run: |

cognee/api/v1/cognify/cognify.py

@@ -126,6 +126,8 @@ async def process_text(chunk_collection: str, chunk_id: str, input_text: str, fi
 
     print(f"Chunk ({chunk_id}) classified.")
 
+    print("document_id", document_id)
+
     content_summary = await get_content_summary(input_text)
     await add_summary_nodes(graph_client, document_id, content_summary)
 
@@ -171,16 +173,16 @@ async def process_text(chunk_collection: str, chunk_id: str, input_text: str, fi
 if __name__ == "__main__":
 
     async def test():
-
-        from cognee.api.v1.add import add
-
-        await add(["A large language model (LLM) is a language model notable for its ability to achieve general-purpose language generation and other natural language processing tasks such as classification"], "test")
-
-        graph = await cognify()
+        #
+        # from cognee.api.v1.add import add
+        #
+        # await add(["A large language model (LLM) is a language model notable for its ability to achieve general-purpose language generation and other natural language processing tasks such as classification"], "code")
+        #
+        # graph = await cognify()
 
         from cognee.utils import render_graph
 
-        await render_graph(graph, include_color=True, include_nodes=True, include_size=True)
+        await render_graph(graph, include_color=True, include_nodes=False, include_size=False)
 
     import asyncio
     asyncio.run(test())

cognee/api/v1/search/search.py

@@ -18,6 +18,9 @@ class SearchType(Enum):
     CATEGORIES = 'CATEGORIES'
     NEIGHBOR = 'NEIGHBOR'
     SUMMARY = 'SUMMARY'
+    SUMMARY_CLASSIFICATION = 'SUMMARY_CLASSIFICATION'
+    NODE_CLASSIFICATION = 'NODE_CLASSIFICATION'
+    DOCUMENT_CLASSIFICATION = 'DOCUMENT_CLASSIFICATION'
 
     @staticmethod
     def from_str(name: str):

cognee/infrastructure/databases/graph/networkx/adapter.py

@@ -41,6 +41,9 @@ class NetworkXAdapter(GraphDBInterface):
     ) -> None:
         self.graph.add_nodes_from(nodes)
         await self.save_graph_to_file(self.filename)
+
+    async def get_graph(self):
+        return self.graph
     
     async def add_edge(
         self,

cognee/infrastructure/llm/prompts/categorize_categories.txt

@@ -0,0 +1,2 @@
+Chose the summary that is the most relevant to the query`{{ query }}`
+Here are the categories:`{{ categories }}`

cognee/infrastructure/llm/prompts/categorize_summary.txt

@@ -0,0 +1,2 @@
+Chose the summary that is the most relevant to the query`{{ query }}`
+Here are the summaries:`{{ summaries }}`

cognee/modules/cognify/graph/add_summary_nodes.py

@@ -7,6 +7,7 @@ async def add_summary_nodes(graph_client, document_id, summary):
         summary_node_id,
         dict(
             name = "Summary",
+            document_id = document_id,
             summary = summary["summary"],
         ),
     )
@@ -20,6 +21,7 @@ async def add_summary_nodes(graph_client, document_id, summary):
         description_node_id,
         dict(
             name = "Description",
+            document_id= document_id,
             description = summary["description"],
         ),
     )

cognee/modules/search/graph/search_categories.py

@@ -1,11 +1,19 @@
-from typing import Union, Dict
+from typing import Union, Dict, re
+
+from cognee.modules.search.llm.extraction.categorize_relevant_category import categorize_relevant_category
 
 """ Search categories in the graph and return their summary attributes. """
 
-from cognee.shared.data_models import GraphDBType
+from cognee.shared.data_models import GraphDBType, DefaultContentPrediction
 import networkx as nx
 
-async def search_categories(graph: Union[nx.Graph, any], query_label: str, infrastructure_config: Dict):
+def strip_exact_regex(s, substring):
+    # Escaping substring to be used in a regex pattern
+    pattern = re.escape(substring)
+    # Regex to match the exact substring at the start and end
+    return re.sub(f"^{pattern}|{pattern}$", "", s)
+
+async def search_categories(query:str, graph: Union[nx.Graph, any], query_label: str, infrastructure_config: Dict):
     """
     Filter nodes in the graph that contain the specified label and return their summary attributes.
     This function supports both NetworkX graphs and Neo4j graph databases.
@@ -22,8 +30,25 @@ async def search_categories(graph: Union[nx.Graph, any], query_label: str, infra
     """
     # Determine which client is in use based on the configuration
     if infrastructure_config.get_config()["graph_engine"] == GraphDBType.NETWORKX:
-        # Logic for NetworkX
-        return {node: data.get('content_labels') for node, data in graph.nodes(data=True) if query_label in node and 'content_labels' in data}
+
+        categories_and_ids = [
+            {'document_id': strip_exact_regex(_, "DATA_SUMMARY__"), 'Summary': data['summary']}
+            for _, data in graph.nodes(data=True)
+            if 'summary' in data
+        ]
+        print("summaries_and_ids", categories_and_ids)
+        check_relevant_category = await categorize_relevant_category(query, categories_and_ids, response_model= infrastructure_config.get_config()["classification_model"])
+        print("check_relevant_summary", check_relevant_category)
+
+        connected_nodes = list(graph.neighbors(check_relevant_category['document_id']))
+        print("connected_nodes", connected_nodes)
+        descriptions = {node: graph.nodes[node].get('description', 'No desc available') for node in connected_nodes}
+        print("descs", descriptions)
+        return descriptions
+
+        #
+        # # Logic for NetworkX
+        # return {node: data.get('content_labels') for node, data in graph.nodes(data=True) if query_label in node and 'content_labels' in data}
 
     elif infrastructure_config.get_config()["graph_engine"] == GraphDBType.NEO4J:
         # Logic for Neo4j

cognee/modules/search/graph/search_summary.py

@@ -3,9 +3,19 @@
 
 from typing import Union, Dict
 import networkx as nx
-from cognee.shared.data_models import GraphDBType
+from cognee.infrastructure import infrastructure_config
 
-async def search_summary(graph: Union[nx.Graph, any], query: str, infrastructure_config: Dict, other_param: str = None) -> Dict[str, str]:
+from cognee.modules.search.llm.extraction.categorize_relevant_summary import categorize_relevant_summary
+from cognee.shared.data_models import GraphDBType, ResponseSummaryModel
+
+import re
+
+def strip_exact_regex(s, substring):
+    # Escaping substring to be used in a regex pattern
+    pattern = re.escape(substring)
+    # Regex to match the exact substring at the start and end
+    return re.sub(f"^{pattern}|{pattern}$", "", s)
+async def search_summary( query: str,  graph: Union[nx.Graph, any]) -> Dict[str, str]:
     """
     Filter nodes based on a condition (such as containing 'SUMMARY' in their identifiers) and return their summary attributes.
     Supports both NetworkX graphs and Neo4j graph databases based on the configuration.
@@ -19,8 +29,24 @@ async def search_summary(graph: Union[nx.Graph, any], query: str, infrastructure
     Returns:
     - Dict[str, str]: A dictionary where keys are node identifiers containing the query string, and values are their 'summary' attributes.
     """
+
     if infrastructure_config.get_config()["graph_engine"] == GraphDBType.NETWORKX:
-        return {node: data.get('summary') for node, data in graph.nodes(data=True) if query in node and 'summary' in data}
+        print("graph", graph)
+        summaries_and_ids = [
+            {'document_id': strip_exact_regex(_, "DATA_SUMMARY__"), 'Summary': data['summary']}
+            for _, data in graph.nodes(data=True)
+            if 'summary' in data
+        ]
+        print("summaries_and_ids", summaries_and_ids)
+        check_relevant_summary = await categorize_relevant_summary(query, summaries_and_ids, response_model=ResponseSummaryModel)
+        print("check_relevant_summary", check_relevant_summary)
+
+        connected_nodes = list(graph.neighbors(check_relevant_summary['document_id']))
+        print("connected_nodes", connected_nodes)
+        descriptions = {node: graph.nodes[node].get('description', 'No desc available') for node in connected_nodes}
+        print("descs", descriptions)
+        return descriptions
+
 
     elif infrastructure_config.get_config()["graph_engine"] == GraphDBType.NEO4J:
         cypher_query = f"""

cognee/modules/search/llm/extraction/categorize_relevant_category.py

@@ -0,0 +1,17 @@
+from typing import Type
+from pydantic import BaseModel
+from cognee.infrastructure.llm.prompts import  render_prompt
+from cognee.infrastructure.llm.get_llm_client import get_llm_client
+
+async def categorize_relevant_category(query: str, summary, response_model: Type[BaseModel]):
+    llm_client = get_llm_client()
+
+    enriched_query= render_prompt("categorize_category.txt", {"query": query, "categories": summary})
+
+    print("enriched_query", enriched_query)
+
+    system_prompt = " Choose the relevant categories and return appropriate output based on the model"
+
+    llm_output = await llm_client.acreate_structured_output(enriched_query, system_prompt, response_model)
+
+    return llm_output.model_dump()

cognee/modules/search/llm/extraction/categorize_relevant_summary.py

@@ -0,0 +1,17 @@
+from typing import Type
+from pydantic import BaseModel
+from cognee.infrastructure.llm.prompts import  render_prompt
+from cognee.infrastructure.llm.get_llm_client import get_llm_client
+
+async def categorize_relevant_summary(query: str, summary, response_model: Type[BaseModel]):
+    llm_client = get_llm_client()
+
+    enriched_query= render_prompt("categorize_summary.txt", {"query": query, "summaries": summary})
+
+    print("enriched_query", enriched_query)
+
+    system_prompt = " Choose the relevant summary and return appropriate output based on the model"
+
+    llm_output = await llm_client.acreate_structured_output(enriched_query, system_prompt, response_model)
+
+    return llm_output.model_dump()

cognee/modules/search/llm/get_relevant_summary.py

@@ -0,0 +1,17 @@
+import logging
+from typing import List, Dict
+from cognee.infrastructure import infrastructure_config
+from.extraction.categorize_relevant_summary import categorize_relevant_summary
+
+logger = logging.getLogger(__name__)
+
+async def get_cognitive_layers(content: str, categories: List[Dict]):
+    try:
+        return (await categorize_relevant_summary(
+            content,
+            categories[0],
+            infrastructure_config.get_config()["categorize_summary_model"]
+        )).cognitive_layers
+    except Exception as error:
+        logger.error("Error extracting cognitive layers from content: %s", error, exc_info = True)
+        raise error

cognee/shared/data_models.py

@@ -244,3 +244,10 @@ class DefaultGraphModel(BaseModel):
     documents: List[Document] = []
     default_fields: Optional[Dict[str, Any]] = {}
     default_relationship: Relationship = Relationship(type = "has_properties")
+
+
+class ResponseSummaryModel(BaseModel):
+    """ Response summary model and existing document id """
+    document_id: str
+    response_summary: str
+

poetry.lock

@@ -215,6 +215,17 @@ doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphin
 test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
 trio = ["trio (>=0.23)"]
 
+[[package]]
+name = "appdirs"
+version = "1.4.4"
+description = "A small Python module for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
+optional = false
+python-versions = "*"
+files = [
+    {file = "appdirs-1.4.4-py2.py3-none-any.whl", hash = "sha256:a841dacd6b99318a741b166adb07e19ee71a274450e68237b4650ca1055ab128"},
+    {file = "appdirs-1.4.4.tar.gz", hash = "sha256:7d5d0167b2b1ba821647616af46a749d1c653740dd0d2415100fe26e27afdf41"},
+]
+
 [[package]]
 name = "appnope"
 version = "0.1.4"
@@ -508,33 +519,33 @@ lxml = ["lxml"]
 
 [[package]]
 name = "black"
-version = "24.4.1"
+version = "24.4.2"
 description = "The uncompromising code formatter."
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "black-24.4.2.tar.gz", hash = "sha256:c872b53057f000085da66a19c55d68f6f8ddcac2642392ad3a355878406fbd4d"},
 ]
 
 [package.dependencies]
@@ -572,17 +583,17 @@ css = ["tinycss2 (>=1.1.0,<1.3)"]
 
 [[package]]
 name = "boto3"
-version = "1.34.91"
+version = "1.34.93"
 description = "The AWS SDK for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "boto3-1.34.91-py3-none-any.whl", hash = "sha256:97fac686c47647db4b44e4789317e4aeecd38511d71e84f8d20abe33eb630ff1"},
-    {file = "boto3-1.34.91.tar.gz", hash = "sha256:5077917041adaaae15eeca340289547ef905ca7e11516e9bd22d394fb5057d2a"},
+    {file = "boto3-1.34.93-py3-none-any.whl", hash = "sha256:b59355bf4a1408563969526f314611dbeacc151cf90ecb22af295dcc4fe18def"},
+    {file = "boto3-1.34.93.tar.gz", hash = "sha256:e39516e4ca21612932599819662759c04485d53ca457996a913163da11f052a4"},
 ]
 
 [package.dependencies]
-botocore = ">=1.34.91,<1.35.0"
+botocore = ">=1.34.93,<1.35.0"
 jmespath = ">=0.7.1,<2.0.0"
 s3transfer = ">=0.10.0,<0.11.0"
 
@@ -591,13 +602,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
 
 [[package]]
 name = "botocore"
-version = "1.34.91"
+version = "1.34.93"
 description = "Low-level, data-driven core of boto 3."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "botocore-1.34.91-py3-none-any.whl", hash = "sha256:4d1b13f2b1c28ce1743b1e5895ae62bb7e67f892b51882164ea19c27a130852b"},
-    {file = "botocore-1.34.91.tar.gz", hash = "sha256:93ef7071292a1b2b9fc26537f8ae3a8227da1177969241939ea3fbdb1a1a1d0c"},
+    {file = "botocore-1.34.93-py3-none-any.whl", hash = "sha256:6fbd5a53a2adc9b3d4ebd90ae0ede83a91a41d96231f8a5984051f75495f246d"},
+    {file = "botocore-1.34.93.tar.gz", hash = "sha256:79d39b0b87e962991c6dd55e78ce15155099f6fb741be88b1b8a456a702cc150"},
 ]
 
 [package.dependencies]
@@ -624,13 +635,13 @@ files = [
 
 [[package]]
 name = "cairocffi"
-version = "1.6.1"
+version = "1.7.0"
 description = "cffi-based cairo bindings for Python"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "cairocffi-1.6.1-py3-none-any.whl", hash = "sha256:aa78ee52b9069d7475eeac457389b6275aa92111895d78fbaa2202a52dac112e"},
-    {file = "cairocffi-1.6.1.tar.gz", hash = "sha256:78e6bbe47357640c453d0be929fa49cd05cce2e1286f3d2a1ca9cbda7efdb8b7"},
+    {file = "cairocffi-1.7.0-py3-none-any.whl", hash = "sha256:1f29a8d41dbda4090c0aa33bcdea64f3b493e95f74a43ea107c4a8a7b7f632ef"},
+    {file = "cairocffi-1.7.0.tar.gz", hash = "sha256:7761863603894305f3160eca68452f373433ca8745ab7dd445bd2c6ce50dcab7"},
 ]
 
 [package.dependencies]
@@ -638,7 +649,7 @@ cffi = ">=1.1.0"
 
 [package.extras]
 doc = ["sphinx", "sphinx_rtd_theme"]
-test = ["flake8", "isort", "numpy", "pikepdf", "pytest"]
+test = ["numpy", "pikepdf", "pytest", "ruff"]
 xcb = ["xcffib (>=1.4.0)"]
 
 [[package]]
@@ -1138,6 +1149,21 @@ files = [
 docs = ["ipython", "matplotlib", "numpydoc", "sphinx"]
 tests = ["pytest", "pytest-cov", "pytest-xdist"]
 
+[[package]]
+name = "dataclasses-json"
+version = "0.6.4"
+description = "Easily serialize dataclasses to and from JSON."
+optional = false
+python-versions = ">=3.7,<4.0"
+files = [
+    {file = "dataclasses_json-0.6.4-py3-none-any.whl", hash = "sha256:f90578b8a3177f7552f4e1a6e535e84293cd5da421fcce0642d49c0d7bdf8df2"},
+    {file = "dataclasses_json-0.6.4.tar.gz", hash = "sha256:73696ebf24936560cca79a2430cbc4f3dd23ac7bf46ed17f38e5e5e7657a6377"},
+]
+
+[package.dependencies]
+marshmallow = ">=3.18.0,<4.0.0"
+typing-inspect = ">=0.4.0,<1"
+
 [[package]]
 name = "datasets"
 version = "2.14.7"
@@ -1223,6 +1249,41 @@ files = [
     {file = "decorator-5.1.1.tar.gz", hash = "sha256:637996211036b6385ef91435e4fae22989472f9d571faba8927ba8253acbc330"},
 ]
 
+[[package]]
+name = "deepeval"
+version = "0.21.36"
+description = "The open-source evaluation framework for LLMs."
+optional = false
+python-versions = "*"
+files = [
+    {file = "deepeval-0.21.36-py3-none-any.whl", hash = "sha256:e1c9fa0a37c74e79eb1c21a98437d04d4be964e9db0cc7ce46d2ff7c190772e6"},
+    {file = "deepeval-0.21.36.tar.gz", hash = "sha256:5447512c6e60ec9840c1c11b48f697470b26c718e729e7d48b97887a5db095a3"},
+]
+
+[package.dependencies]
+docx2txt = ">=0.8,<1.0"
+importlib-metadata = ">=6.0.2"
+langchain = "*"
+langchain-core = "*"
+langchain-openai = "*"
+portalocker = "*"
+protobuf = "4.25.1"
+pydantic = "*"
+pytest = "*"
+pytest-repeat = "*"
+pytest-xdist = "*"
+ragas = "*"
+requests = "*"
+rich = "*"
+sentry-sdk = "*"
+tabulate = "*"
+tenacity = ">=8.2.3,<8.3.0"
+tqdm = "*"
+typer = "*"
+
+[package.extras]
+dev = ["black"]
+
 [[package]]
 name = "defusedxml"
 version = "0.7.1"
@@ -1356,6 +1417,16 @@ files = [
     {file = "docstring_parser-0.16.tar.gz", hash = "sha256:538beabd0af1e2db0146b6bd3caa526c35a34d61af9fd2887f3a8a27a739aa6e"},
 ]
 
+[[package]]
+name = "docx2txt"
+version = "0.8"
+description = "A pure python-based utility to extract text and images from docx files."
+optional = false
+python-versions = "*"
+files = [
+    {file = "docx2txt-0.8.tar.gz", hash = "sha256:2c06d98d7cfe2d3947e5760a57d924e3ff07745b379c8737723922e7009236e5"},
+]
+
 [[package]]
 name = "dspy-ai"
 version = "2.4.3"
@@ -1478,6 +1549,20 @@ files = [
 [package.extras]
 test = ["pytest (>=6)"]
 
+[[package]]
+name = "execnet"
+version = "2.1.1"
+description = "execnet: rapid multi-Python deployment"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "execnet-2.1.1-py3-none-any.whl", hash = "sha256:26dee51f1b80cebd6d0ca8e74dd8745419761d3bef34163928cbebbdc4749fdc"},
+    {file = "execnet-2.1.1.tar.gz", hash = "sha256:5189b52c6121c24feae288166ab41b32549c7e2348652736540b9e6e7d4e72e3"},
+]
+
+[package.extras]
+testing = ["hatch", "pre-commit", "pytest", "tox"]
+
 [[package]]
 name = "executing"
 version = "2.0.1"
@@ -2346,22 +2431,22 @@ files = [
 
 [[package]]
 name = "importlib-metadata"
-version = "7.1.0"
+version = "6.1.0"
 description = "Read metadata from Python packages"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.7"
 files = [
-    {file = "importlib_metadata-7.1.0-py3-none-any.whl", hash = "sha256:30962b96c0c223483ed6cc7280e7f0199feb01a0e40cfae4d4450fc6fab1f570"},
-    {file = "importlib_metadata-7.1.0.tar.gz", hash = "sha256:b78938b926ee8d5f020fc4772d487045805a55ddbad2ecf21c6d60938dc7fcd2"},
+    {file = "importlib_metadata-6.1.0-py3-none-any.whl", hash = "sha256:ff80f3b5394912eb1b108fcfd444dc78b7f1f3e16b16188054bd01cb9cb86f09"},
+    {file = "importlib_metadata-6.1.0.tar.gz", hash = "sha256:43ce9281e097583d758c2c708c4376371261a02c34682491a8e98352365aad20"},
 ]
 
 [package.dependencies]
 zipp = ">=0.5"
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
 perf = ["ipython"]
-testing = ["flufl.flake8", "importlib-resources (>=1.3)", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy", "pytest-perf (>=0.9.2)", "pytest-ruff (>=0.2.1)"]
+testing = ["flake8 (<5)", "flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)"]
 
 [[package]]
 name = "importlib-resources"
@@ -2596,6 +2681,20 @@ files = [
     {file = "json5-0.9.25.tar.gz", hash = "sha256:548e41b9be043f9426776f05df8635a00fe06104ea51ed24b67f908856e151ae"},
 ]
 
+[[package]]
+name = "jsonpatch"
+version = "1.33"
+description = "Apply JSON-Patches (RFC 6902)"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*"
+files = [
+    {file = "jsonpatch-1.33-py2.py3-none-any.whl", hash = "sha256:0ae28c0cd062bbd8b8ecc26d7d164fbbea9652a1a3693f3b956c1eae5145dade"},
+    {file = "jsonpatch-1.33.tar.gz", hash = "sha256:9fcd4009c41e6d12348b4a0ff2563ba56a2923a7dfee731d004e212e1ee5030c"},
+]
+
+[package.dependencies]
+jsonpointer = ">=1.9"
+
 [[package]]
 name = "jsonpath-ng"
 version = "1.6.1"
@@ -2804,13 +2903,13 @@ test = ["jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-jupyter[server] (>
 
 [[package]]
 name = "jupyterlab"
-version = "4.1.6"
+version = "4.1.8"
 description = "JupyterLab computational environment"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyterlab-4.1.6-py3-none-any.whl", hash = "sha256:cf3e862bc10dbf4331e4eb37438634f813c238cfc62c71c640b3b3b2caa089a8"},
-    {file = "jupyterlab-4.1.6.tar.gz", hash = "sha256:7935f36ba26eb615183a4f5c2bbca5791b5108ce2a00b5505f8cfd100d53648e"},
+    {file = "jupyterlab-4.1.8-py3-none-any.whl", hash = "sha256:c3baf3a2f91f89d110ed5786cd18672b9a357129d4e389d2a0dead15e11a4d2c"},
+    {file = "jupyterlab-4.1.8.tar.gz", hash = "sha256:3384aded8680e7ce504fd63b8bb89a39df21c9c7694d9e7dc4a68742cdb30f9b"},
 ]
 
 [package.dependencies]
@@ -2822,7 +2921,7 @@ jinja2 = ">=3.0.3"
 jupyter-core = "*"
 jupyter-lsp = ">=2.0.0"
 jupyter-server = ">=2.4.0,<3"
-jupyterlab-server = ">=2.19.0,<3"
+jupyterlab-server = ">=2.27.1,<3"
 notebook-shim = ">=0.2"
 packaging = "*"
 tomli = {version = ">=1.2.2", markers = "python_version < \"3.11\""}
@@ -3017,17 +3116,17 @@ files = [
 
 [[package]]
 name = "lancedb"
-version = "0.6.10"
+version = "0.6.11"
 description = "lancedb"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "lancedb-0.6.10-cp38-abi3-macosx_10_15_x86_64.whl", hash = "sha256:c7e10eb5f5fdb22452d7678e9bf3df14e2463331868607067aba4ba19a0f4022"},
-    {file = "lancedb-0.6.10-cp38-abi3-macosx_11_0_arm64.whl", hash = "sha256:3a1548bf3820ff606e4efce840bd0e979206eb1815915fff9f738c1d8e337293"},
-    {file = "lancedb-0.6.10-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3874f8aee55c5b4f41b953f4eac28d69c8a9bc10ad992fc7fd2b699a1f4b88ed"},
-    {file = "lancedb-0.6.10-cp38-abi3-manylinux_2_24_aarch64.whl", hash = "sha256:73c0909ac7ae95d8128d25936fc0ff670cf6d9ae1b44e06ec48f3a60081d61ca"},
-    {file = "lancedb-0.6.10-cp38-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:750fb23a6eaa7daea241a124be3970447d45ace1c4f894df197e9869d677b6ba"},
-    {file = "lancedb-0.6.10-cp38-abi3-win_amd64.whl", hash = "sha256:06a299d91e1d9d2663fea7086efc784bf8643f51ff1532617c4910ec63cd2004"},
+    {file = "lancedb-0.6.11-cp38-abi3-macosx_10_15_x86_64.whl", hash = "sha256:79dbc2a79dac7b843e328a3b7eecb1b42f38ad524742111a38efbeaa1f58ddfe"},
+    {file = "lancedb-0.6.11-cp38-abi3-macosx_11_0_arm64.whl", hash = "sha256:bfc6661e1fe5dd75346b4a1980243b4ccecd2b0ad991f8becc6e506a608c2d8a"},
+    {file = "lancedb-0.6.11-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0998c2dae676c24b95d492358f80ef6e5100b86335b96bf402af3f28f7ec4f3b"},
+    {file = "lancedb-0.6.11-cp38-abi3-manylinux_2_24_aarch64.whl", hash = "sha256:b6b289599c4c6a61a70da89d4c3201abec7483d0e03573506014af2dcddfe0c4"},
+    {file = "lancedb-0.6.11-cp38-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:d789d5a181c4bc42650567cb63ce2772ed902046b08e8e401970df284a4df1c1"},
+    {file = "lancedb-0.6.11-cp38-abi3-win_amd64.whl", hash = "sha256:ea12fc94545afb03933d080cce593491e593ab95cbfddf05ab7183bce2bc8d62"},
 ]
 
 [package.dependencies]
@@ -3051,6 +3150,143 @@ docs = ["mkdocs", "mkdocs-jupyter", "mkdocs-material", "mkdocstrings[python]"]
 embeddings = ["awscli (>=1.29.57)", "boto3 (>=1.28.57)", "botocore (>=1.31.57)", "cohere", "google-generativeai", "huggingface-hub", "instructorembedding", "open-clip-torch", "openai (>=1.6.1)", "pillow", "sentence-transformers", "torch"]
 tests = ["aiohttp", "boto3", "duckdb", "pandas (>=1.4)", "polars (>=0.19)", "pytest", "pytest-asyncio", "pytest-mock", "pytz", "tantivy"]
 
+[[package]]
+name = "langchain"
+version = "0.1.10"
+description = "Building applications with LLMs through composability"
+optional = false
+python-versions = ">=3.8.1,<4.0"
+files = [
+    {file = "langchain-0.1.10-py3-none-any.whl", hash = "sha256:dcc1c0968b8d946a812155584ecbbeda690c930c3ee27bb5ecc113d954f6cf1a"},
+    {file = "langchain-0.1.10.tar.gz", hash = "sha256:17951bcd6d74adc74aa081f260ef5514c449488815314420b7e0f8349f15d932"},
+]
+
+[package.dependencies]
+aiohttp = ">=3.8.3,<4.0.0"
+async-timeout = {version = ">=4.0.0,<5.0.0", markers = "python_version < \"3.11\""}
+dataclasses-json = ">=0.5.7,<0.7"
+jsonpatch = ">=1.33,<2.0"
+langchain-community = ">=0.0.25,<0.1"
+langchain-core = ">=0.1.28,<0.2"
+langchain-text-splitters = ">=0.0.1,<0.1"
+langsmith = ">=0.1.0,<0.2.0"
+numpy = ">=1,<2"
+pydantic = ">=1,<3"
+PyYAML = ">=5.3"
+requests = ">=2,<3"
+SQLAlchemy = ">=1.4,<3"
+tenacity = ">=8.1.0,<9.0.0"
+
+[package.extras]
+azure = ["azure-ai-formrecognizer (>=3.2.1,<4.0.0)", "azure-ai-textanalytics (>=5.3.0,<6.0.0)", "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 (<2)"]
+clarifai = ["clarifai (>=9.1.0)"]
+cli = ["typer (>=0.9.0,<0.10.0)"]
+cohere = ["cohere (>=4,<5)"]
+docarray = ["docarray[hnswlib] (>=0.32.0,<0.33.0)"]
+embeddings = ["sentence-transformers (>=2,<3)"]
+extended-testing = ["aiosqlite (>=0.19.0,<0.20.0)", "aleph-alpha-client (>=2.15.0,<3.0.0)", "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)", "cohere (>=4,<5)", "couchbase (>=4.1.9,<5.0.0)", "dashvector (>=1.0.1,<2.0.0)", "databricks-vectorsearch (>=0.21,<0.22)", "datasets (>=2.15.0,<3.0.0)", "dgml-utils (>=0.3.0,<0.4.0)", "esprima (>=4.0.1,<5.0.0)", "faiss-cpu (>=1,<2)", "feedparser (>=6.0.10,<7.0.0)", "fireworks-ai (>=0.9.0,<0.10.0)", "geopandas (>=0.13.1,<0.14.0)", "gitpython (>=3.1.32,<4.0.0)", "google-cloud-documentai (>=2.20.1,<3.0.0)", "gql (>=3.4.1,<4.0.0)", "hologres-vector (>=0.0.6,<0.0.7)", "html2text (>=2020.1.16,<2021.0.0)", "javelin-sdk (>=0.1.8,<0.2.0)", "jinja2 (>=3,<4)", "jq (>=1.4.1,<2.0.0)", "jsonschema (>1)", "langchain-openai (>=0.0.2,<0.1)", "lxml (>=4.9.2,<5.0.0)", "markdownify (>=0.11.6,<0.12.0)", "motor (>=3.3.1,<4.0.0)", "msal (>=1.25.0,<2.0.0)", "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 (<2)", "openapi-pydantic (>=0.3.2,<0.4.0)", "pandas (>=2.0.1,<3.0.0)", "pdfminer-six (>=20221105,<20221106)", "pgvector (>=0.1.6,<0.2.0)", "praw (>=7.7.1,<8.0.0)", "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)", "rapidocr-onnxruntime (>=1.3.2,<2.0.0)", "rdflib (==7.0.0)", "requests-toolbelt (>=1.0.0,<2.0.0)", "rspace_client (>=2.5.0,<3.0.0)", "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)", "upstash-redis (>=0.15.0,<0.16.0)", "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 (<2)", "openlm (>=0.0.5,<0.0.6)", "torch (>=1,<3)", "transformers (>=4,<5)"]
+openai = ["openai (<2)", "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)"]
+
+[[package]]
+name = "langchain-community"
+version = "0.0.34"
+description = "Community contributed LangChain integrations."
+optional = false
+python-versions = "<4.0,>=3.8.1"
+files = [
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+[[package]]
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+description = "Building applications with LLMs through composability"
+optional = false
+python-versions = "<4.0,>=3.8.1"
+files = [
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+[package.extras]
+extended-testing = ["jinja2 (>=3,<4)"]
+
+[[package]]
+name = "langchain-openai"
+version = "0.1.4"
+description = "An integration package connecting OpenAI and LangChain"
+optional = false
+python-versions = "<4.0,>=3.8.1"
+files = [
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+
+[[package]]
+name = "langchain-text-splitters"
+version = "0.0.1"
+description = "LangChain text splitting utilities"
+optional = false
+python-versions = ">=3.8.1,<4.0"
+files = [
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+
+[[package]]
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+optional = false
+python-versions = ">=3.8.1,<4.0"
+files = [
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+[package.dependencies]
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@@ -3210,6 +3446,25 @@ files = [
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+name = "marshmallow"
+version = "3.21.1"
+description = "A lightweight library for converting complex datatypes to and from native Python datatypes."
+optional = false
+python-versions = ">=3.8"
+files = [
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+tests = ["pytest", "pytz", "simplejson"]
+
 [[package]]
 name = "matplotlib"
 version = "3.8.4"
@@ -3832,12 +4087,12 @@ test = ["pep440", "pre-commit", "pytest", "testpath"]
 
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 name = "neo4j"
-version = "5.19.0"
+version = "5.20.0"
 description = "Neo4j Bolt driver for Python"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -4198,13 +4453,13 @@ files = [
 
 [[package]]
 name = "packaging"
-version = "24.0"
+version = "23.2"
 description = "Core utilities for Python packages"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [[package]]
@@ -4665,22 +4920,22 @@ wcwidth = "*"
 
 [[package]]
 name = "protobuf"
-version = "4.25.3"
+version = "4.25.1"
 description = ""
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "protobuf-4.25.1-cp310-abi3-win32.whl", hash = "sha256:193f50a6ab78a970c9b4f148e7c750cfde64f59815e86f686c22e26b4fe01ce7"},
+    {file = "protobuf-4.25.1-cp310-abi3-win_amd64.whl", hash = "sha256:3497c1af9f2526962f09329fd61a36566305e6c72da2590ae0d7d1322818843b"},
+    {file = "protobuf-4.25.1-cp37-abi3-macosx_10_9_universal2.whl", hash = "sha256:0bf384e75b92c42830c0a679b0cd4d6e2b36ae0cf3dbb1e1dfdda48a244f4bcd"},
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+    {file = "protobuf-4.25.1-cp37-abi3-manylinux2014_x86_64.whl", hash = "sha256:ca37bf6a6d0046272c152eea90d2e4ef34593aaa32e8873fc14c16440f22d4b7"},
+    {file = "protobuf-4.25.1-cp38-cp38-win32.whl", hash = "sha256:abc0525ae2689a8000837729eef7883b9391cd6aa7950249dcf5a4ede230d5dd"},
+    {file = "protobuf-4.25.1-cp38-cp38-win_amd64.whl", hash = "sha256:1484f9e692091450e7edf418c939e15bfc8fc68856e36ce399aed6889dae8bb0"},
+    {file = "protobuf-4.25.1-cp39-cp39-win32.whl", hash = "sha256:8bdbeaddaac52d15c6dce38c71b03038ef7772b977847eb6d374fc86636fa510"},
+    {file = "protobuf-4.25.1-cp39-cp39-win_amd64.whl", hash = "sha256:becc576b7e6b553d22cbdf418686ee4daa443d7217999125c045ad56322dda10"},
+    {file = "protobuf-4.25.1-py3-none-any.whl", hash = "sha256:a19731d5e83ae4737bb2a089605e636077ac001d18781b3cf489b9546c7c80d6"},
+    {file = "protobuf-4.25.1.tar.gz", hash = "sha256:57d65074b4f5baa4ab5da1605c02be90ac20c8b40fb137d6a8df9f416b0d0ce2"},
 ]
 
 [[package]]
@@ -4999,13 +5254,13 @@ testutils = ["gitpython (>3)"]
 
 [[package]]
 name = "pymdown-extensions"
-version = "10.8"
+version = "10.8.1"
 description = "Extension pack for Python Markdown."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "pymdown_extensions-10.8-py3-none-any.whl", hash = "sha256:3539003ff0d5e219ba979d2dc961d18fcad5ac259e66c764482e8347b4c0503c"},
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+    {file = "pymdown_extensions-10.8.1-py3-none-any.whl", hash = "sha256:f938326115884f48c6059c67377c46cf631c733ef3629b6eed1349989d1b30cb"},
+    {file = "pymdown_extensions-10.8.1.tar.gz", hash = "sha256:3ab1db5c9e21728dabf75192d71471f8e50f216627e9a1fa9535ecb0231b9940"},
 ]
 
 [package.dependencies]
@@ -5061,6 +5316,16 @@ files = [
     {file = "pyreadline3-3.4.1.tar.gz", hash = "sha256:6f3d1f7b8a31ba32b73917cefc1f28cc660562f39aea8646d30bd6eff21f7bae"},
 ]
 
+[[package]]
+name = "pysbd"
+version = "0.3.4"
+description = "pysbd (Python Sentence Boundary Disambiguation) is a rule-based sentence boundary detection that works out-of-the-box across many languages."
+optional = false
+python-versions = ">=3"
+files = [
+    {file = "pysbd-0.3.4-py3-none-any.whl", hash = "sha256:cd838939b7b0b185fcf86b0baf6636667dfb6e474743beeff878e9f42e022953"},
+]
+
 [[package]]
 name = "pytest"
 version = "7.4.4"
@@ -5117,6 +5382,40 @@ black = ">=23"
 pytest = ">=7"
 ruff = ">=0.0.258"
 
+[[package]]
+name = "pytest-repeat"
+version = "0.9.3"
+description = "pytest plugin for repeating tests"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "pytest_repeat-0.9.3-py3-none-any.whl", hash = "sha256:26ab2df18226af9d5ce441c858f273121e92ff55f5bb311d25755b8d7abdd8ed"},
+    {file = "pytest_repeat-0.9.3.tar.gz", hash = "sha256:ffd3836dfcd67bb270bec648b330e20be37d2966448c4148c4092d1e8aba8185"},
+]
+
+[package.dependencies]
+pytest = "*"
+
+[[package]]
+name = "pytest-xdist"
+version = "3.5.0"
+description = "pytest xdist plugin for distributed testing, most importantly across multiple CPUs"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "pytest-xdist-3.5.0.tar.gz", hash = "sha256:cbb36f3d67e0c478baa57fa4edc8843887e0f6cfc42d677530a36d7472b32d8a"},
+    {file = "pytest_xdist-3.5.0-py3-none-any.whl", hash = "sha256:d075629c7e00b611df89f490a5063944bee7a4362a5ff11c7cc7824a03dfce24"},
+]
+
+[package.dependencies]
+execnet = ">=1.1"
+pytest = ">=6.2.0"
+
+[package.extras]
+psutil = ["psutil (>=3.0)"]
+setproctitle = ["setproctitle"]
+testing = ["filelock"]
+
 [[package]]
 name = "python-dateutil"
 version = "2.9.0.post0"
@@ -5401,6 +5700,33 @@ urllib3 = ">=1.26.14,<3"
 [package.extras]
 fastembed = ["fastembed (==0.2.6)"]
 
+[[package]]
+name = "ragas"
+version = "0.1.7"
+description = ""
+optional = false
+python-versions = "*"
+files = [
+    {file = "ragas-0.1.7-py3-none-any.whl", hash = "sha256:abe02b40a8d11842c42e222226901287858beb70203f1227a403a9261d0bb684"},
+    {file = "ragas-0.1.7.tar.gz", hash = "sha256:db857262dda63fc01a7eef837cbba166202084b5d535b2e8ad408c63a66f9319"},
+]
+
+[package.dependencies]
+appdirs = "*"
+datasets = "*"
+langchain = "*"
+langchain-community = "*"
+langchain-core = "*"
+langchain-openai = "*"
+nest-asyncio = "*"
+numpy = "*"
+openai = ">1"
+pysbd = ">=0.3.4"
+tiktoken = "*"
+
+[package.extras]
+all = ["sentence-transformers"]
+
 [[package]]
 name = "ratelimiter"
 version = "1.2.0.post0"
@@ -5905,6 +6231,53 @@ nativelib = ["pyobjc-framework-Cocoa", "pywin32"]
 objc = ["pyobjc-framework-Cocoa"]
 win32 = ["pywin32"]
 
+[[package]]
+name = "sentry-sdk"
+version = "2.0.1"
+description = "Python client for Sentry (https://sentry.io)"
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "sentry_sdk-2.0.1-py2.py3-none-any.whl", hash = "sha256:b54c54a2160f509cf2757260d0cf3885b608c6192c2555a3857e3a4d0f84bdb3"},
+    {file = "sentry_sdk-2.0.1.tar.gz", hash = "sha256:c278e0f523f6f0ee69dc43ad26dcdb1202dffe5ac326ae31472e012d941bee21"},
+]
+
+[package.dependencies]
+certifi = "*"
+urllib3 = ">=1.26.11"
+
+[package.extras]
+aiohttp = ["aiohttp (>=3.5)"]
+arq = ["arq (>=0.23)"]
+asyncpg = ["asyncpg (>=0.23)"]
+beam = ["apache-beam (>=2.12)"]
+bottle = ["bottle (>=0.12.13)"]
+celery = ["celery (>=3)"]
+celery-redbeat = ["celery-redbeat (>=2)"]
+chalice = ["chalice (>=1.16.0)"]
+clickhouse-driver = ["clickhouse-driver (>=0.2.0)"]
+django = ["django (>=1.8)"]
+falcon = ["falcon (>=1.4)"]
+fastapi = ["fastapi (>=0.79.0)"]
+flask = ["blinker (>=1.1)", "flask (>=0.11)", "markupsafe"]
+grpcio = ["grpcio (>=1.21.1)"]
+httpx = ["httpx (>=0.16.0)"]
+huey = ["huey (>=2)"]
+loguru = ["loguru (>=0.5)"]
+openai = ["openai (>=1.0.0)", "tiktoken (>=0.3.0)"]
+opentelemetry = ["opentelemetry-distro (>=0.35b0)"]
+opentelemetry-experimental = ["opentelemetry-distro (>=0.40b0,<1.0)", "opentelemetry-instrumentation-aiohttp-client (>=0.40b0,<1.0)", "opentelemetry-instrumentation-django (>=0.40b0,<1.0)", "opentelemetry-instrumentation-fastapi (>=0.40b0,<1.0)", "opentelemetry-instrumentation-flask (>=0.40b0,<1.0)", "opentelemetry-instrumentation-requests (>=0.40b0,<1.0)", "opentelemetry-instrumentation-sqlite3 (>=0.40b0,<1.0)", "opentelemetry-instrumentation-urllib (>=0.40b0,<1.0)"]
+pure-eval = ["asttokens", "executing", "pure-eval"]
+pymongo = ["pymongo (>=3.1)"]
+pyspark = ["pyspark (>=2.4.4)"]
+quart = ["blinker (>=1.1)", "quart (>=0.16.1)"]
+rq = ["rq (>=0.6)"]
+sanic = ["sanic (>=0.8)"]
+sqlalchemy = ["sqlalchemy (>=1.2)"]
+starlette = ["starlette (>=0.19.1)"]
+starlite = ["starlite (>=1.48)"]
+tornado = ["tornado (>=5)"]
+
 [[package]]
 name = "setuptools"
 version = "69.5.1"
@@ -6644,6 +7017,21 @@ files = [
     {file = "typing_extensions-4.11.0.tar.gz", hash = "sha256:83f085bd5ca59c80295fc2a82ab5dac679cbe02b9f33f7d83af68e241bea51b0"},
 ]
 
+[[package]]
+name = "typing-inspect"
+version = "0.9.0"
+description = "Runtime inspection utilities for typing module."
+optional = false
+python-versions = "*"
+files = [
+    {file = "typing_inspect-0.9.0-py3-none-any.whl", hash = "sha256:9ee6fc59062311ef8547596ab6b955e1b8aa46242d854bfc78f4f6b0eff35f9f"},
+    {file = "typing_inspect-0.9.0.tar.gz", hash = "sha256:b23fc42ff6f6ef6954e4852c1fb512cdd18dbea03134f91f856a95ccc9461f78"},
+]
+
+[package.dependencies]
+mypy-extensions = ">=0.3.0"
+typing-extensions = ">=3.7.4"
+
 [[package]]
 name = "tzdata"
 version = "2024.1"
@@ -7227,7 +7615,7 @@ duckdb = ["duckdb"]
 filesystem = []
 gcp = []
 gs = []
-lancedb = []
+lancedb = ["lancedb"]
 motherduck = ["duckdb", "pyarrow"]
 mssql = []
 neo4j = ["neo4j"]
@@ -7244,4 +7632,4 @@ weaviate = ["weaviate-client"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9.0,<3.12"
-content-hash = "9609dfc41209efd1fd5e53fd1b77353cb5bce3244ffdb65f81eee686a68b6fc5"
+content-hash = "0f66c0ad86b74b152f430a3ed9376bf63154eac83b12b7c3dd96f86af4399079"

pyproject.toml

@@ -63,6 +63,9 @@ tiktoken = "^0.6.0"
 dspy-ai = "2.4.3"
 posthog = "^3.5.0"
 lancedb = "^0.6.10"
+importlib-metadata = "6.1.0"
+deepeval = "^0.21.36"
+
 
 
 [tool.poetry.extras]