Merge branch 'dev' into add_docstrings

README.md

@@ -85,7 +85,7 @@ import os
 os.environ["LLM_API_KEY"] = "YOUR OPENAI_API_KEY"
 
 ```
-or 
+or
 ```
 import cognee
 cognee.config.set_llm_api_key("YOUR_OPENAI_API_KEY")
@@ -115,7 +115,7 @@ DB_PORT=5432
 DB_NAME=cognee_db
 DB_USERNAME=cognee
 DB_PASSWORD=cognee
-``` 
+```
 
 ### Simple example
 
@@ -140,14 +140,14 @@ async def main():
     Natural language processing (NLP) is an interdisciplinary
     subfield of computer science and information retrieval.
     """
-    
+
     print("Adding text to cognee:")
-    print(text.strip())  
+    print(text.strip())
     # Add the text, and make it available for cognify
     await cognee.add(text)
     print("Text added successfully.\n")
 
-    
+
     print("Running cognify to create knowledge graph...\n")
     print("Cognify process steps:")
     print("1. Classifying the document: Determining the type and category of the input text.")
@@ -156,19 +156,19 @@ async def main():
     print("4. Adding data points: Storing the extracted chunks for processing.")
     print("5. Generating knowledge graph: Extracting entities and relationships to form a knowledge graph.")
     print("6. Summarizing text: Creating concise summaries of the content for quick insights.\n")
-    
+
     # Use LLMs and cognee to create knowledge graph
     await cognee.cognify()
     print("Cognify process complete.\n")
 
-    
+
     query_text = 'Tell me about NLP'
     print(f"Searching cognee for insights with query: '{query_text}'")
     # Query cognee for insights on the added text
     search_results = await cognee.search(
         SearchType.INSIGHTS, query_text=query_text
     )
-    
+
     print("Search results:")
     # Display results
     for result_text in search_results:
@@ -212,7 +212,7 @@ Cognee supports a variety of tools and services for different operations:
 - **Language Models (LLMs)**: You can use either Anyscale or Ollama as your LLM provider.
 
 - **Graph Stores**: In addition to NetworkX, Neo4j is also supported for graph storage.
-  
+
 - **User management**: Create individual user graphs and manage permissions
 
 ## Demo
@@ -258,13 +258,13 @@ pip install cognee
 
 
 
-| Name     | Type               | Current state     | Known Issues |
-|----------|--------------------|-------------------|--------------|
-| Qdrant   | Vector             | Stable ✅   |              |
-| Weaviate | Vector             | Stable ✅   |              |
-| LanceDB  | Vector             | Stable ✅   |              |
-| Neo4j    | Graph              | Stable ✅   |              |
-| NetworkX | Graph              | Stable ✅   |              |
-| FalkorDB | Vector/Graph       | Unstable ❌ |              |
-| PGVector | Vector             | Stable ✅   |              |
-| Milvus   | Vector             | Stable ✅   |              |
+| Name     | Type               | Current state (Mac/Linux) | Known Issues | Current state (Windows) | Known Issues |
+|----------|--------------------|---------------------------|--------------|-------------------------|--------------|
+| Qdrant   | Vector             | Stable ✅           |              | Unstable ❌       |              |
+| Weaviate | Vector             | Stable ✅           |              | Unstable ❌       |              |
+| LanceDB  | Vector             | Stable ✅           |              | Stable ✅         |              |
+| Neo4j    | Graph              | Stable ✅           |              | Stable ✅         |              |
+| NetworkX | Graph              | Stable ✅           |              | Stable ✅         |              |
+| FalkorDB | Vector/Graph       | Stable ✅         |              | Unstable ❌       |              |
+| PGVector | Vector             | Stable ✅           |              | Unstable ❌       |              |
+| Milvus   | Vector             | Stable ✅           |              | Unstable ❌       |              |

cognee/infrastructure/databases/vector/lancedb/LanceDBAdapter.py

@@ -152,7 +152,9 @@ class LanceDBAdapter(VectorDBInterface):
         connection = await self.get_connection()
         collection = await connection.open_table(collection_name)
 
-        results = await collection.vector_search(query_vector).to_pandas()
+        collection_size = await collection.count_rows()
+
+        results = await collection.vector_search(query_vector).limit(collection_size).to_pandas()
 
         result_values = list(results.to_dict("index").values())
 
@@ -250,9 +252,16 @@ class LanceDBAdapter(VectorDBInterface):
         )
 
     async def prune(self):
-        # Clean up the database if it was set up as temporary
+        connection = await self.get_connection()
+        collection_names = await connection.table_names()
+
+        for collection_name in collection_names:
+            collection = await connection.open_table(collection_name)
+            await collection.delete("id IS NOT NULL")
+            await connection.drop_table(collection_name)
+
         if self.url.startswith("/"):
-            LocalStorage.remove_all(self.url)  # Remove the temporary directory and files inside
+            LocalStorage.remove_all(self.url)
 
     def get_data_point_schema(self, model_type):
         return copy_model(

entrypoint.sh

@@ -30,4 +30,4 @@ if [ "$ENVIRONMENT" = "dev" ]; then
 else
   gunicorn -w 3 -k uvicorn.workers.UvicornWorker -t 30000 --bind=0.0.0.0:8000 --log-level error cognee.api.client:app
   # python ./cognee/api/client.py
-fi
+fi

evals/eval_on_hotpot.py

@@ -9,7 +9,7 @@ from cognee.infrastructure.llm.get_llm_client import get_llm_client
 from cognee.infrastructure.llm.prompts import read_query_prompt, render_prompt
 from evals.qa_dataset_utils import load_qa_dataset
 from evals.qa_metrics_utils import get_metrics
-from evals.qa_context_provider_utils import qa_context_providers
+from evals.qa_context_provider_utils import qa_context_providers, create_cognee_context_getter
 
 logger = logging.getLogger(__name__)
 
@@ -94,14 +94,29 @@ async def eval_on_QA_dataset(
     return results
 
 
-if __name__ == "__main__":
+async def incremental_eval_on_QA_dataset(
+    dataset_name_or_filename: str, num_samples, metric_name_list
+):
+    pipeline_slice_names = ["base", "extract_chunks", "extract_graph", "summarize"]
+
+    incremental_results = {}
+    for pipeline_slice_name in pipeline_slice_names:
+        results = await eval_on_QA_dataset(
+            dataset_name_or_filename, pipeline_slice_name, num_samples, metric_name_list
+        )
+        incremental_results[pipeline_slice_name] = results
+
+    return incremental_results
+
+
+async def main():
     parser = argparse.ArgumentParser()
 
     parser.add_argument("--dataset", type=str, required=True, help="Which dataset to evaluate on")
     parser.add_argument(
         "--rag_option",
         type=str,
-        choices=qa_context_providers.keys(),
+        choices=list(qa_context_providers.keys()) + ["cognee_incremental"],
         required=True,
         help="RAG option to use for providing context",
     )
@@ -110,7 +125,18 @@ if __name__ == "__main__":
 
     args = parser.parse_args()
 
-    avg_scores = asyncio.run(
-        eval_on_QA_dataset(args.dataset, args.rag_option, args.num_samples, args.metrics)
-    )
+    if args.rag_option == "cognee_incremental":
+        avg_scores = await incremental_eval_on_QA_dataset(
+            args.dataset, args.num_samples, args.metrics
+        )
+
+    else:
+        avg_scores = await eval_on_QA_dataset(
+            args.dataset, args.rag_option, args.num_samples, args.metrics
+        )
+
     logger.info(f"{avg_scores}")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

evals/official_hotpot_metrics.py

@@ -4,11 +4,8 @@ These are the official evaluation metrics for HotpotQA taken from https://hotpot
 
 import re
 import string
-import sys
 from collections import Counter
 
-import ujson as json
-
 
 def normalize_answer(s):
     def remove_articles(text):

evals/qa_context_provider_utils.py

@@ -3,35 +3,49 @@ from cognee.api.v1.search import SearchType
 from cognee.infrastructure.databases.vector import get_vector_engine
 from cognee.modules.retrieval.brute_force_triplet_search import brute_force_triplet_search
 from cognee.tasks.completion.graph_query_completion import retrieved_edges_to_string
+from functools import partial
+from cognee.api.v1.cognify.cognify_v2 import get_default_tasks
 
 
 async def get_raw_context(instance: dict) -> str:
     return instance["context"]
 
 
-async def cognify_instance(instance: dict):
+async def cognify_instance(instance: dict, task_indices: list[int] = None):
     await cognee.prune.prune_data()
     await cognee.prune.prune_system(metadata=True)
-
     for title, sentences in instance["context"]:
         await cognee.add("\n".join(sentences), dataset_name="QA")
-    await cognee.cognify("QA")
+    all_cognify_tasks = await get_default_tasks()
+    if task_indices:
+        selected_tasks = [all_cognify_tasks[ind] for ind in task_indices]
+    else:
+        selected_tasks = all_cognify_tasks
+    await cognee.cognify("QA", tasks=selected_tasks)
 
 
-async def get_context_with_cognee(instance: dict) -> str:
-    await cognify_instance(instance)
+async def get_context_with_cognee(
+    instance: dict,
+    task_indices: list[int] = None,
+    search_types: list[SearchType] = [SearchType.SUMMARIES, SearchType.CHUNKS],
+) -> str:
+    await cognify_instance(instance, task_indices)
 
-    # TODO: Fix insights
-    # insights = await cognee.search(SearchType.INSIGHTS, query_text=instance["question"])
-    summaries = await cognee.search(SearchType.SUMMARIES, query_text=instance["question"])
-    # search_results = insights + summaries
-    search_results = summaries
+    search_results = []
+    for search_type in search_types:
+        search_results += await cognee.search(search_type, query_text=instance["question"])
 
     search_results_str = "\n".join([context_item["text"] for context_item in search_results])
 
     return search_results_str
 
 
+def create_cognee_context_getter(
+    task_indices=None, search_types=[SearchType.SUMMARIES, SearchType.CHUNKS]
+):
+    return partial(get_context_with_cognee, task_indices=task_indices, search_types=search_types)
+
+
 async def get_context_with_simple_rag(instance: dict) -> str:
     await cognee.prune.prune_data()
     await cognee.prune.prune_system(metadata=True)
@@ -57,9 +71,19 @@ async def get_context_with_brute_force_triplet_search(instance: dict) -> str:
     return search_results_str
 
 
+valid_pipeline_slices = {
+    "base": [0, 1, 5],
+    "extract_chunks": [0, 1, 2, 5],
+    "extract_graph": [0, 1, 2, 3, 5],
+    "summarize": [0, 1, 2, 3, 4, 5],
+}
+
 qa_context_providers = {
     "no_rag": get_raw_context,
     "cognee": get_context_with_cognee,
     "simple_rag": get_context_with_simple_rag,
     "brute_force": get_context_with_brute_force_triplet_search,
+} | {
+    name: create_cognee_context_getter(task_indices=slice)
+    for name, slice in valid_pipeline_slices.items()
 }

evals/qa_eval_parameters.json

@@ -3,8 +3,9 @@
         "hotpotqa"
     ],
     "rag_option": [
-        "no_rag",
+        "cognee_incremental",
         "cognee",
+        "no_rag",
         "simple_rag",
         "brute_force"
     ],

evals/qa_eval_utils.py

@@ -44,7 +44,7 @@ def save_results_as_image(results, out_path):
         for num_samples, table_data in num_samples_data.items():
             df = pd.DataFrame.from_dict(table_data, orient="index")
             df.index.name = f"Dataset: {dataset}, Num Samples: {num_samples}"
-            image_path = Path(out_path) / Path(f"table_{dataset}_{num_samples}.png")
+            image_path = out_path / Path(f"table_{dataset}_{num_samples}.png")
             save_table_as_image(df, image_path)
 
 

examples/python/dynamic_steps_example.py

@@ -204,4 +204,9 @@ if __name__ == "__main__":
         "retriever": retrieve,
     }
 
-    asyncio.run(main(steps_to_enable))
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+    try:
+        loop.run_until_complete(main(steps_to_enable))
+    finally:
+        loop.run_until_complete(loop.shutdown_asyncgens())

examples/python/graphiti_example.py

@@ -69,4 +69,9 @@ async def main():
 
 if __name__ == "__main__":
     setup_logging(logging.ERROR)
-    asyncio.run(main())
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+    try:
+        loop.run_until_complete(main())
+    finally:
+        loop.run_until_complete(loop.shutdown_asyncgens())

examples/python/multimedia_example.py

@@ -1,9 +1,11 @@
 import os
 import asyncio
 import pathlib
+import logging
 
 import cognee
 from cognee.api.v1.search import SearchType
+from cognee.shared.utils import setup_logging
 
 # Prerequisites:
 # 1. Copy `.env.template` and rename it to `.env`.
@@ -45,4 +47,10 @@ async def main():
 
 
 if __name__ == "__main__":
-    asyncio.run(main())
+    setup_logging(logging.ERROR)
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+    try:
+        loop.run_until_complete(main())
+    finally:
+        loop.run_until_complete(loop.shutdown_asyncgens())

examples/python/simple_example.py

@@ -1,6 +1,8 @@
 import asyncio
 import cognee
+import logging
 from cognee.api.v1.search import SearchType
+from cognee.shared.utils import setup_logging
 
 # Prerequisites:
 # 1. Copy `.env.template` and rename it to `.env`.
@@ -66,4 +68,10 @@ async def main():
 
 
 if __name__ == "__main__":
-    asyncio.run(main())
+    setup_logging(logging.ERROR)
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+    try:
+        loop.run_until_complete(main())
+    finally:
+        loop.run_until_complete(loop.shutdown_asyncgens())