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Natural language filtering

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Yiorgis Gozadinos 2026-01-09 12:10:53 +02:00
parent 36f964eaa9
commit 8579ded7bb
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2 changed files with 73 additions and 18 deletions
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67
app/backend/agent.py
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@ -87,6 +87,17 @@ How to decide which tool to use:
- "ask" - Use for general questions about topics in the knowledge base when no specific document is named. It searches across all documents and returns answers with citations.
- "search" - Use when the user explicitly asks to search/find/explore documents. Call it ONCE. After calling search, just output the list of results returned by the tool verbatim. Do NOT summarize or add commentary.
IMPORTANT - When user mentions a document in search/ask:
- If user says "search in <doc>", "find in <doc>", "answer from <doc>", or "<topic> in <doc>":
- Extract the TOPIC as `query`/`question`
- Extract the DOCUMENT NAME as `document_name`
- Examples for search:
- "search for latrines in TB MED 593" → query="latrines", document_name="TB MED 593"
- "find waste disposal in the army manual" → query="waste disposal", document_name="army manual"
- Examples for ask:
- "what does TB MED 593 say about latrines?" → question="what are the guidelines for latrines?", document_name="TB MED 593"
- "answer from the army manual about sanitation" → question="what are the sanitation guidelines?", document_name="army manual"
Be friendly and conversational. When you use the "ask" tool, summarize the key findings for the user."""
@ -105,6 +116,7 @@ def create_chat_agent(config: AppConfig) -> Agent[ChatDeps, str]:
async def search(
ctx: RunContext[ChatDeps],
query: str,
document_name: str | None = None,
) -> str:
"""Search the knowledge base for relevant documents.
@ -112,21 +124,36 @@ def create_chat_agent(config: AppConfig) -> Agent[ChatDeps, str]:
Results are displayed to the user - just list the titles found.
Args:
query: The search query
query: The search query (what to search for)
document_name: Optional document name/title to search within (e.g., "tbmed593", "army manual")
"""
from search_agent import SearchAgent
if ctx.deps.agui_emitter:
ctx.deps.agui_emitter.log(f"Searching: {query}")
msg = f"Searching: {query}"
if document_name:
msg += f" (in {document_name})"
ctx.deps.agui_emitter.log(msg)
# Build context from conversation history
context = None
if ctx.deps.session_state and ctx.deps.session_state.qa_history:
context = format_conversation_context(ctx.deps.session_state.qa_history)
# Build filter from document_name
doc_filter = None
if document_name:
escaped = document_name.replace("'", "''")
# Also try without spaces for matching "TB MED 593" to "tbmed593"
no_spaces = escaped.replace(" ", "")
doc_filter = (
f"LOWER(uri) LIKE LOWER('%{escaped}%') OR LOWER(title) LIKE LOWER('%{escaped}%') "
f"OR LOWER(uri) LIKE LOWER('%{no_spaces}%') OR LOWER(title) LIKE LOWER('%{no_spaces}%')"
)
# Use search agent for query expansion and deduplication
search_agent = SearchAgent(ctx.deps.client, ctx.deps.config)
results = await search_agent.search(query, context=context)
results = await search_agent.search(query, context=context, filter=doc_filter)
# Store for potential citation resolution
ctx.deps.search_results = results
@ -178,7 +205,7 @@ def create_chat_agent(config: AppConfig) -> Agent[ChatDeps, str]:
async def ask(
ctx: RunContext[ChatDeps],
question: str,
document_filter: str | None = None,
document_name: str | None = None,
) -> str:
"""Answer a specific question using the knowledge base.
@ -186,10 +213,24 @@ def create_chat_agent(config: AppConfig) -> Agent[ChatDeps, str]:
Args:
question: The question to answer
document_filter: Optional SQL WHERE clause to filter documents (e.g. "id IN ('doc1', 'doc2')")
document_name: Optional document name/title to search within (e.g., "tbmed593", "army manual")
"""
if ctx.deps.agui_emitter:
ctx.deps.agui_emitter.log(f"Answering: {question}")
msg = f"Answering: {question}"
if document_name:
msg += f" (in {document_name})"
ctx.deps.agui_emitter.log(msg)
# Build filter from document_name
doc_filter = None
if document_name:
escaped = document_name.replace("'", "''")
# Also try without spaces for matching "TB MED 593" to "tbmed593"
no_spaces = escaped.replace(" ", "")
doc_filter = (
f"LOWER(uri) LIKE LOWER('%{escaped}%') OR LOWER(title) LIKE LOWER('%{escaped}%') "
f"OR LOWER(uri) LIKE LOWER('%{no_spaces}%') OR LOWER(title) LIKE LOWER('%{no_spaces}%')"
)
# Build context-aware system prompt if we have history
system_prompt = None
@ -205,7 +246,7 @@ def create_chat_agent(config: AppConfig) -> Agent[ChatDeps, str]:
)
answer, citations = await ctx.deps.client.ask(
question, system_prompt=system_prompt, filter=document_filter
question, system_prompt=system_prompt, filter=doc_filter
)
# Accumulate Q&A in session state
@ -283,19 +324,23 @@ def create_chat_agent(config: AppConfig) -> Agent[ChatDeps, str]:
doc = await ctx.deps.client.get_document_by_uri(query)
escaped_query = query.replace("'", "''")
# Also try without spaces for matching "TB MED 593" to "tbmed593"
no_spaces = escaped_query.replace(" ", "")
# If not found, try partial URI match
# If not found, try partial URI match (with and without spaces)
if doc is None:
docs = await ctx.deps.client.list_documents(
limit=1, filter=f"LOWER(uri) LIKE LOWER('%{escaped_query}%')"
limit=1,
filter=f"LOWER(uri) LIKE LOWER('%{escaped_query}%') OR LOWER(uri) LIKE LOWER('%{no_spaces}%')",
)
if docs:
doc = docs[0]
# If still not found, try partial title match
# If still not found, try partial title match (with and without spaces)
if doc is None:
docs = await ctx.deps.client.list_documents(
limit=1, filter=f"LOWER(title) LIKE LOWER('%{escaped_query}%')"
limit=1,
filter=f"LOWER(title) LIKE LOWER('%{escaped_query}%') OR LOWER(title) LIKE LOWER('%{no_spaces}%')",
)
if docs:
doc = docs[0]

24
app/backend/search_agent.py
View file

@ -14,16 +14,22 @@ class SearchDeps:
client: HaikuRAG
config: AppConfig
filter: str | None = None
search_results: list[SearchResult] = field(default_factory=list)
SEARCH_SYSTEM_PROMPT = """You are a search query optimizer. Given a user's search request:
SEARCH_SYSTEM_PROMPT = """You are a search query optimizer for a document knowledge base.
1. Generate 2-4 diverse search queries that cover different aspects/phrasings of the request
2. For each query, call the run_search tool
3. After all searches complete, respond with "Search complete"
Given a user's search request:
1. ALWAYS run the original query first as-is
2. Then generate 1-2 alternative queries using different keywords or phrasings
3. Keep queries SHORT (2-5 words) - use keywords, not full sentences
4. After all searches, respond with "Search complete"
Be thorough but focused. Generate queries that will find relevant results without being redundant."""
Example: User asks "latrines" → queries: "latrines", "latrine sanitation", "field toilet"
Example: User asks "waste disposal" → queries: "waste disposal", "garbage management", "refuse handling"
Do NOT generate long verbose queries like "environmental impact of waste disposal methods" - keep it simple."""
class SearchAgent:
@ -52,7 +58,9 @@ class SearchAgent:
query: The search query
"""
limit = ctx.deps.config.search.limit
results = await ctx.deps.client.search(query, limit=limit)
results = await ctx.deps.client.search(
query, limit=limit, filter=ctx.deps.filter
)
results = await ctx.deps.client.expand_context(results)
ctx.deps.search_results.extend(results)
@ -64,12 +72,14 @@ class SearchAgent:
self,
query: str,
context: str | None = None,
filter: str | None = None,
) -> list[SearchResult]:
"""Execute search with query expansion and deduplication.
Args:
query: The user's search request
context: Optional conversation context
filter: Optional SQL WHERE clause to filter documents
Returns:
Deduplicated list of SearchResult sorted by score
@ -78,7 +88,7 @@ class SearchAgent:
if context:
prompt = f"Context: {context}\n\nSearch request: {query}"
deps = SearchDeps(client=self._client, config=self._config)
deps = SearchDeps(client=self._client, config=self._config, filter=filter)
await self._agent.run(prompt, deps=deps)
# Deduplicate by chunk_id, keeping highest score