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import asyncio
from python.helpers import dotenv, memory, perplexity_search, duckduckgo_search
from python.helpers.tool import Tool, Response
from python.helpers.print_style import PrintStyle
from python.helpers.errors import handle_error
from python.helpers.searxng import search as searxng
from python.tools.memory_load import DEFAULT_THRESHOLD as DEFAULT_MEMORY_THRESHOLD
from python.helpers.document_query import DocumentQueryHelper
SEARCH_ENGINE_RESULTS = 10
class Knowledge(Tool):
async def execute(self, question="", **kwargs):
if not question:
question = kwargs.get("query", "")
if not question:
return Response(message="No question provided", break_loop=False)
# Create tasks for all search methods
tasks = [
self.searxng_search(question),
# self.perplexity_search(question),
# self.duckduckgo_search(question),
self.mem_search_enhanced(question),
]
# Run all tasks concurrently
results = await asyncio.gather(*tasks, return_exceptions=True)
# perplexity_result, duckduckgo_result, memory_result = results
searxng_result, memory_result = results
# enrich results with qa
searxng_result = await self.searxng_document_qa(searxng_result, question)
# Handle exceptions and format results
searxng_result = self.format_result_searxng(searxng_result, "Search Engine")
memory_result = self.format_result(memory_result, "Memory")
msg = self.agent.read_prompt(
"fw.knowledge_tool.response.md",
# online_sources = ((perplexity_result + "\n\n") if perplexity_result else "") + str(duckduckgo_result),
online_sources=((searxng_result + "\n\n") if searxng_result else ""),
memory=memory_result,
)
await self.agent.handle_intervention(
msg
) # wait for intervention and handle it, if paused
return Response(message=msg, break_loop=False)
async def perplexity_search(self, question):
if dotenv.get_dotenv_value("API_KEY_PERPLEXITY"):
return await asyncio.to_thread(
perplexity_search.perplexity_search, question
)
else:
PrintStyle.hint(
"No API key provided for Perplexity. Skipping Perplexity search."
)
self.agent.context.log.log(
type="hint",
content="No API key provided for Perplexity. Skipping Perplexity search.",
)
return None
async def duckduckgo_search(self, question):
return await asyncio.to_thread(duckduckgo_search.search, question)
async def searxng_search(self, question):
return await searxng(question)
async def searxng_document_qa(self, result, query):
if isinstance(result, Exception) or not query or not result or not result["results"]:
return result
result["results"] = result["results"][:SEARCH_ENGINE_RESULTS]
tasks = []
helper = DocumentQueryHelper(self.agent)
for index, item in enumerate(result["results"]):
tasks.append(helper.document_qa(item["url"], [query]))
task_results = list(await asyncio.gather(*tasks, return_exceptions=True))
for index, item in enumerate(result["results"]):
if isinstance(task_results[index], BaseException):
continue
found, qa = task_results[index] # type: ignore
if not found:
continue
result["results"][index]["qa"] = qa
return result
async def mem_search(self, question: str):
db = await memory.Memory.get(self.agent)
docs = await db.search_similarity_threshold(
query=question, limit=5, threshold=DEFAULT_MEMORY_THRESHOLD
)
text = memory.Memory.format_docs_plain(docs)
return "\n\n".join(text)
async def mem_search_enhanced(self, question: str):
"""
Enhanced memory search with knowledge source awareness.
Separates and prioritizes knowledge sources vs conversation memories.
"""
try:
db = await memory.Memory.get(self.agent)
# Search for knowledge sources (knowledge_source=True)
knowledge_docs = await db.search_similarity_threshold(
query=question, limit=5, threshold=DEFAULT_MEMORY_THRESHOLD,
filter="knowledge_source == True"
)
# Search for conversation memories (field doesn't exist or is not True)
conversation_docs = await db.search_similarity_threshold(
query=question, limit=5, threshold=DEFAULT_MEMORY_THRESHOLD,
filter="not knowledge_source if 'knowledge_source' in locals() else True"
)
# Combine and fallback to lower threshold if needed
all_docs = knowledge_docs + conversation_docs
threshold_note = ""
# If no results with default threshold, try with lower threshold
if not all_docs:
lower_threshold = DEFAULT_MEMORY_THRESHOLD * 0.8
knowledge_docs = await db.search_similarity_threshold(
query=question, limit=5, threshold=lower_threshold,
filter="knowledge_source == True"
)
conversation_docs = await db.search_similarity_threshold(
query=question, limit=5, threshold=lower_threshold,
filter="not knowledge_source if 'knowledge_source' in locals() else True"
)
all_docs = knowledge_docs + conversation_docs
if all_docs:
threshold_note = f" (threshold: {lower_threshold})"
if not all_docs:
return await self._get_memory_diagnostics(db, question)
# Separate knowledge sources from conversation memories
knowledge_sources = knowledge_docs
conversation_memories = conversation_docs
result_parts = []
# Add search summary
result_parts.append(f"## 🔍 Search Results for: '{question}'")
result_parts.append(f"**Found:** {len(knowledge_sources)} knowledge sources, {len(conversation_memories)} conversation memories{threshold_note}")
# Show knowledge sources
if knowledge_sources:
result_parts.append("")
result_parts.append("## 📚 Knowledge Sources:")
for index, doc in enumerate(knowledge_sources):
source_file = doc.metadata.get('source_file', 'Unknown source')
file_type = doc.metadata.get('file_type', '').upper()
area = doc.metadata.get('area', 'main').upper()
result_parts.append(f"**Source:** {source_file} ({file_type}) [{area}]")
result_parts.append(f"**Content:** {doc.page_content}")
if index < len(knowledge_sources) - 1:
result_parts.append("-" * 80)
# Show conversation memories
if conversation_memories:
if knowledge_sources:
result_parts.append("")
result_parts.append("## 💭 Related Experience:")
for index, doc in enumerate(conversation_memories):
timestamp = doc.metadata.get('timestamp', 'Unknown time')
area = doc.metadata.get('area', 'main').upper()
consolidation_action = doc.metadata.get('consolidation_action', '')
metadata_info = f"{timestamp} [{area}]"
if consolidation_action:
metadata_info += f" (consolidated: {consolidation_action})"
result_parts.append(f"**Experience:** {metadata_info}")
result_parts.append(f"**Content:** {doc.page_content}")
if index < len(conversation_memories) - 1:
result_parts.append("-" * 80)
return "\n".join(result_parts)
except Exception as e:
handle_error(e)
return f"Memory search failed: {str(e)}"
async def _get_memory_diagnostics(self, db, query: str):
"""Provide memory diagnostics when no search results are found."""
try:
# Get sample of all documents to see what's in memory
sample_docs = await db.search_similarity_threshold(
query="test", limit=20, threshold=0.0
)
if not sample_docs:
return f"## 🔍 No Results for: '{query}'\n**Memory database appears to be empty.**"
# Analyze what's in memory
area_counts: dict[str, int] = {}
knowledge_count = 0
for doc in sample_docs:
area = doc.metadata.get('area', 'unknown')
area_counts[area] = area_counts.get(area, 0) + 1
if doc.metadata.get('knowledge_source', False):
knowledge_count += 1
result_parts = [
f"## 🔍 No Results for: '{query}'",
f"**Database contains:** {len(sample_docs)} total documents",
f"**Areas:** {', '.join([f'{area.upper()}: {count}' for area, count in area_counts.items()])}",
f"**Knowledge sources:** {knowledge_count} documents",
"",
"**Suggestions:**",
"- Try different or more general search terms",
"- Check if the information was recently memorized",
f"- Current search threshold: {DEFAULT_MEMORY_THRESHOLD}"
]
return "\n".join(result_parts)
except Exception as e:
return f"Memory diagnostics failed: {str(e)}"
def format_result(self, result, source):
if isinstance(result, Exception):
handle_error(result)
return f"{source} search failed: {str(result)}"
return result if result else ""
def format_result_searxng(self, result, source):
if isinstance(result, Exception):
handle_error(result)
return f"{source} search failed: {str(result)}"
if not result or "results" not in result:
return ""
outputs = []
for item in result["results"]:
if "qa" in item:
outputs.append(
f"## Next Result\n"
f"*Title*: {item['title'].strip()}\n"
f"*URL*: {item['url'].strip()}\n"
f"*Search Engine Summary*:\n{item['content'].strip()}\n"
f"*Query Result*:\n{item['qa'].strip()}"
)
else:
outputs.append(
f"## Next Result\n"
f"*Title*: {item['title'].strip()}\n"
f"*URL*: {item['url'].strip()}\n"
f"*Search Engine Summary*:\n{item['content'].strip()}"
)
return "\n\n".join(outputs[:SEARCH_ENGINE_RESULTS]).strip()
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