feat: implements code retreiver

cognee/infrastructure/llm/prompts/patch_gen_kg_instructions.txt

@@ -1,3 +1,6 @@
-I need you to solve this issue by looking at the provided edges retrieved from a knowledge graph and 
+You are a senior software engineer. I need you to solve this issue by looking at the provided context and
-generate a single patch file that I can apply directly to this repository using git apply. 
+generate a single patch file that I can apply directly to this repository using git apply.
-Please respond with a single patch file in the following format.
+Additionally, please make sure that you provide code only with correct syntax and
+you apply the patch on the relevant files (together with their path that you can try to find out from the github issue). Don't change the names of existing
+functions or classes, as they may be referenced from other code.
+Please respond only with a single patch file in the following format without adding any additional context or string.

cognee/modules/retrieval/description_to_codepart_search.py

@@ -8,20 +8,27 @@ from cognee.modules.graph.cognee_graph.CogneeGraph import CogneeGraph
 from cognee.modules.users.methods import get_default_user
 from cognee.modules.users.models import User
 from cognee.shared.utils import send_telemetry
+from cognee.api.v1.search import SearchType
+from cognee.api.v1.search.search_v2 import search
+from cognee.infrastructure.llm.get_llm_client import get_llm_client
 
 
-async def code_description_to_code_part_search(query: str, user: User = None, top_k=5) -> list:
+async def code_description_to_code_part_search(
+    query: str, include_docs=False, user: User = None, top_k=5
+) -> list:
     if user is None:
         user = await get_default_user()
 
     if user is None:
         raise PermissionError("No user found in the system. Please create a user.")
 
-    retrieved_codeparts = await code_description_to_code_part(query, user, top_k)
+    retrieved_codeparts = await code_description_to_code_part(query, user, top_k, include_docs)
     return retrieved_codeparts
 
 
-async def code_description_to_code_part(query: str, user: User, top_k: int) -> List[str]:
+async def code_description_to_code_part(
+    query: str, user: User, top_k: int, include_docs: bool
+) -> List[str]:
     """
     Maps a code description query to relevant code parts using a CodeGraph pipeline.
 
@@ -29,6 +36,7 @@ async def code_description_to_code_part(query: str, user: User, top_k: int) -> L
         query (str): The search query describing the code parts.
         user (User): The user performing the search.
         top_k (int): Number of codegraph descriptions to match ( num of corresponding codeparts will be higher)
+        include_docs(bool): Boolean showing whether we have the docs in the graph or not
 
     Returns:
         Set[str]: A set of unique code parts matching the query.
@@ -37,6 +45,7 @@ async def code_description_to_code_part(query: str, user: User, top_k: int) -> L
         ValueError: If arguments are invalid.
         RuntimeError: If an unexpected error occurs during execution.
     """
+    print(include_docs)
     if not query or not isinstance(query, str):
         raise ValueError("The query must be a non-empty string.")
     if top_k <= 0 or not isinstance(top_k, int):
@@ -55,6 +64,26 @@ async def code_description_to_code_part(query: str, user: User, top_k: int) -> L
     )
 
     try:
+        if include_docs:
+            search_results = await search(SearchType.INSIGHTS, query_text=query)
+
+            concatenated_descriptions = " ".join(
+                obj["description"]
+                for tpl in search_results
+                for obj in tpl
+                if isinstance(obj, dict) and "description" in obj
+            )
+
+            llm_client = get_llm_client()
+            context_from_documents = await llm_client.acreate_structured_output(
+                text_input=f"The retrieved context from documents"
+                f" is {concatenated_descriptions}.",
+                system_prompt="You are a Senior Software Engineer, summarize the context from documents"
+                f" in a way that it is gonna be provided next to codeparts as context"
+                f" while trying to solve this github issue connected to the project: {query}]",
+                response_model=str,
+            )
+
         code_summaries = await vector_engine.search(
             "code_summary_text", query_text=query, limit=top_k
         )
@@ -102,6 +131,9 @@ async def code_description_to_code_part(query: str, user: User, top_k: int) -> L
         for code_piece in code_pieces_to_return:
             context = context + code_piece.get_attribute("source_code")
 
+        if include_docs:
+            context = context_from_documents + context
+
         return context
 
     except Exception as exec_error:

evals/eval_swe_bench.py

@@ -14,8 +14,6 @@ from cognee.infrastructure.llm.prompts import read_query_prompt
 from cognee.modules.retrieval.description_to_codepart_search import (
     code_description_to_code_part_search,
 )
-from cognee.shared.utils import render_graph
-from evals.eval_utils import download_github_repo, retrieved_edges_to_string
 
 
 def check_install_package(package_name):
@@ -36,14 +34,17 @@ def check_install_package(package_name):
 
 async def generate_patch_with_cognee(instance):
     """repo_path = download_github_repo(instance, "../RAW_GIT_REPOS")"""
+    include_docs = True
     problem_statement = instance["problem_statement"]
     instructions = read_query_prompt("patch_gen_kg_instructions.txt")
 
-    repo_path = "/Users/laszlohajdu/Documents/GitHub/test/"
+    repo_path = "/Users/laszlohajdu/Documents/GitHub/graph_rag/"
-    async for result in run_code_graph_pipeline(repo_path, include_docs=False):
+    async for result in run_code_graph_pipeline(repo_path, include_docs=include_docs):
         print(result)
 
-    retrieved_codeparts = await code_description_to_code_part_search(problem_statement)
+    retrieved_codeparts = await code_description_to_code_part_search(
+        problem_statement, include_docs=include_docs
+    )
 
     prompt = "\n".join(
         [
@@ -51,7 +52,7 @@ async def generate_patch_with_cognee(instance):
             "<patch>",
             PATCH_EXAMPLE,
             "</patch>",
-            "These are the retrieved edges:",
+            "This is the additional context to solve the problem (description from documentation together with codeparts):",
             retrieved_codeparts,
         ]
     )