apunkt/dograh
1
0
Fork 0
mirror of https://github.com/dograh-hq/dograh.git synced 2026-06-07 07:55:16 +02:00
Code Issues Projects Releases Packages Wiki Activity

feat: knowledge base functionality for the voice agent (#120)

Browse source 
* feat: upload file and store embedding

* feat: add documents in nodes

* feat: add openai embedding service
This commit is contained in:
Abhishek 2026-01-17 14:37:03 +05:30 • committed by GitHub
parent e2fa4bbb98
commit ef5b9e40a9
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
52 changed files with 4551 additions and 114 deletions
Download patch file Download diff file Expand all files Collapse all files

61
api/services/workflow/pipecat_engine.py
View file

@ -41,6 +41,10 @@ from api.services.workflow.pipecat_engine_variable_extractor import (
VariableExtractionManager,
)
from api.services.workflow.tools.calculator import get_calculator_tools, safe_calculator
from api.services.workflow.tools.knowledge_base import (
get_knowledge_base_tool,
retrieve_from_knowledge_base,
)
from api.services.workflow.tools.timezone import (
convert_time,
get_current_time,
@ -290,6 +294,48 @@ class PipecatEngine:
self.llm.register_function("get_current_time", get_current_time_func)
self.llm.register_function("convert_time", convert_time_func)
async def _register_knowledge_base_function(
self, document_uuids: list[str]
) -> None:
"""Register knowledge base retrieval function with the LLM.
Args:
document_uuids: List of document UUIDs to filter the search by
"""
logger.debug(
f"Registering knowledge base retrieval function with {len(document_uuids)} document(s)"
)
async def retrieve_kb_func(function_call_params: FunctionCallParams) -> None:
logger.info("LLM Function Call EXECUTED: retrieve_from_knowledge_base")
logger.info(f"Arguments: {function_call_params.arguments}")
try:
query = function_call_params.arguments.get("query", "")
organization_id = await self._get_organization_id()
if not organization_id:
raise ValueError(
"Organization ID not available for knowledge base retrieval"
)
result = await retrieve_from_knowledge_base(
query=query,
organization_id=organization_id,
document_uuids=document_uuids,
limit=3, # Return top 3 most relevant chunks
)
await function_call_params.result_callback(result)
except Exception as e:
logger.error(f"Knowledge base retrieval failed: {e}")
await function_call_params.result_callback(
{"error": str(e), "chunks": [], "query": query, "total_results": 0}
)
# Register the function with the LLM
self.llm.register_function("retrieve_from_knowledge_base", retrieve_kb_func)
async def _perform_variable_extraction_if_needed(
self, previous_node: Optional[Node]
) -> None:
@ -346,6 +392,10 @@ class PipecatEngine:
if node.tool_uuids and self._custom_tool_manager:
await self._custom_tool_manager.register_handlers(node.tool_uuids)
# Register knowledge base retrieval handler if node has documents
if node.document_uuids:
await self._register_knowledge_base_function(node.document_uuids)
# Set up system message and functions
(
system_message,
@ -575,6 +625,17 @@ class PipecatEngine:
# Add built-in function schemas (calculator and timezone tools)
functions.extend(self.builtin_function_schemas)
# Add knowledge base retrieval tool if node has documents
if node.document_uuids:
kb_tool_def = get_knowledge_base_tool(node.document_uuids)
kb_schema = get_function_schema(
kb_tool_def["function"]["name"],
kb_tool_def["function"]["description"],
properties=kb_tool_def["function"]["parameters"].get("properties", {}),
required=kb_tool_def["function"]["parameters"].get("required", []),
)
functions.append(kb_schema)
# Add custom tools from node.tool_uuids
if node.tool_uuids and self._custom_tool_manager:
custom_tool_schemas = await self._custom_tool_manager.get_tool_schemas(