- Move RAG from Langchain to crewai

config/specialists/RAG_SPECIALIST/1.0.0.yaml

@@ -0,0 +1,53 @@
+version: "1.0.0"
+name: "RAG Specialist"
+framework: "crewai"
+configuration:
+  name:
+    name: "name"
+    type: "str"
+    description: "The name the specialist is called upon."
+    required: true
+  company:
+    name: "company"
+    type: "str"
+    description: "The name of your company. If not provided, your tenant's name will be used."
+    required: false
+arguments:
+  language:
+    name: "Language"
+    type: "str"
+    description: "Language code to be used for receiving questions and giving answers"
+    required: true
+  query:
+    name: "query"
+    type: "str"
+    description: "Query or response to process"
+    required: true
+results:
+  rag_output:
+    answer:
+      name: "answer"
+      type: "str"
+      description: "Answer to the query"
+      required: true
+    citations:
+      name: "citations"
+      type: "List[str]"
+      description: "List of citations"
+      required: false
+    insufficient_info:
+      name: "insufficient_info"
+      type: "bool"
+      description: "Whether or not the query is insufficient info"
+      required: true
+agents:
+  - type: "RAG_AGENT"
+    version: "1.0"
+tasks:
+  - type: "RAG_TASK"
+    version: "1.0"
+metadata:
+  author: "Josako"
+  date_added: "2025-01-08"
+  changes: "Initial version"
+  description: "A Specialist that performs Q&A activities"

config/type_defs/specialist_types.py

@@ -4,6 +4,10 @@ SPECIALIST_TYPES = {
         "name": "Q&A RAG Specialist",
         "description": "Standard Q&A through RAG Specialist",
     },
+    "RAG_SPECIALIST": {
+        "name": "RAG Specialist",
+        "description": "Q&A through RAG Specialist",
+    },
     "SPIN_SPECIALIST": {
         "name": "Spin Sales Specialist",
         "description": "A specialist that allows to answer user queries, try to get SPIN-information and Identification",

eveai_chat_workers/retrievers/standard_rag.py

@@ -27,9 +27,11 @@ class StandardRAGRetriever(BaseRetriever):
         self.catalog_id = retriever.catalog_id
         self.tenant_id = tenant_id
         catalog = Catalog.query.get_or_404(self.catalog_id)
+        embedding_model = "mistral.mistral-embed"
+
         self.embedding_model, self.embedding_model_class = get_embedding_model_and_class(self.tenant_id,
                                                                                          self.catalog_id,
-                                                                                         catalog.embedding_model)
+                                                                                         embedding_model)
         self.similarity_threshold = retriever.configuration.get('es_similarity_threshold', 0.3)
         self.k = retriever.configuration.get('es_k', 8)
         self.tuning = retriever.tuning

eveai_chat_workers/specialists/RAG_SPECIALIST/1_0.py

@@ -0,0 +1,156 @@
+import json
+from os import wait
+from typing import Optional, List
+
+from crewai.flow.flow import start, listen, and_
+from flask import current_app
+from pydantic import BaseModel, Field
+
+from common.utils.business_event_context import current_event
+from eveai_chat_workers.retrievers.retriever_typing import RetrieverArguments
+from eveai_chat_workers.specialists.crewai_base_specialist import CrewAIBaseSpecialistExecutor
+from eveai_chat_workers.specialists.specialist_typing import SpecialistResult, SpecialistArguments
+from eveai_chat_workers.outputs.rag.rag_v1_0 import RAGOutput
+from eveai_chat_workers.specialists.crewai_base_classes import EveAICrewAICrew, EveAICrewAIFlow, EveAIFlowState
+
+
+class SpecialistExecutor(CrewAIBaseSpecialistExecutor):
+    """
+    type: RAG_SPECIALIST
+    type_version: 1.0
+    RAG Specialist Executor class
+    """
+
+    def __init__(self, tenant_id, specialist_id, session_id, task_id, **kwargs):
+        self.rag_crew = None
+
+        super().__init__(tenant_id, specialist_id, session_id, task_id)
+
+    @property
+    def type(self) -> str:
+        return "RAG_SPECIALIST"
+
+    @property
+    def type_version(self) -> str:
+        return "1.0"
+
+    def _config_task_agents(self):
+        self._add_task_agent("rag_task", "rag_agent")
+
+    def _config_pydantic_outputs(self):
+        self._add_pydantic_output("rag_task", RAGOutput, "rag_output")
+
+    def _instantiate_specialist(self):
+        verbose = self.tuning
+
+        rag_agents = [self.rag_agent]
+        rag_tasks = [self.rag_task]
+        self.rag_crew = EveAICrewAICrew(
+            self,
+            "Rag Crew",
+            agents=rag_agents,
+            tasks=rag_tasks,
+            verbose=verbose,
+        )
+
+        self.flow = RAGFlow(
+            self,
+            self.rag_crew,
+        )
+
+    def execute(self, arguments: SpecialistArguments) -> SpecialistResult:
+        formatted_context, citations = self.retrieve_context(arguments)
+
+        self.log_tuning("RAG Specialist execution started", {})
+
+        flow_inputs = {
+            "language": arguments.language,
+            "query": arguments.query,
+            "context": formatted_context,
+            "citations": citations,
+            "history": self.formatted_history,
+            "name": self.specialist.configuration.get('name', ''),
+            "company": self.specialist.configuration.get('company', ''),
+        }
+        # crew_results = self.rag_crew.kickoff(inputs=flow_inputs)
+        # current_app.logger.debug(f"Test Crew Output received: {crew_results}")
+        flow_results = self.flow.kickoff(inputs=flow_inputs)
+
+        flow_state = self.flow.state
+
+        results = RAGSpecialistResult.create_for_type(self.type, self.type_version)
+        update_data = {}
+        if flow_state.rag_output:  # Fallback
+            update_data["rag_output"] = flow_state.rag_output
+
+        results = results.model_copy(update=update_data)
+
+        self.log_tuning(f"RAG Specialist execution ended", {"Results": results.model_dump()})
+
+        return results
+
+
+class RAGSpecialistInput(BaseModel):
+    language: Optional[str] = Field(None, alias="language")
+    query: Optional[str] = Field(None, alias="query")
+    context: Optional[str] = Field(None, alias="context")
+    citations: Optional[List[int]] = Field(None, alias="citations")
+    history: Optional[str] = Field(None, alias="history")
+    name: Optional[str] = Field(None, alias="name")
+    company: Optional[str] = Field(None, alias="company")
+
+
+class RAGSpecialistResult(SpecialistResult):
+    rag_output: Optional[RAGOutput] = Field(None, alias="Rag Output")
+
+
+class RAGFlowState(EveAIFlowState):
+    """Flow state for RAG specialist that automatically updates from task outputs"""
+    input: Optional[RAGSpecialistInput] = None
+    rag_output: Optional[RAGOutput] = None
+
+
+class RAGFlow(EveAICrewAIFlow[RAGFlowState]):
+    def __init__(self,
+                 specialist_executor: CrewAIBaseSpecialistExecutor,
+                 rag_crew: EveAICrewAICrew,
+                 **kwargs):
+        super().__init__(specialist_executor, "RAG Specialist Flow", **kwargs)
+        self.specialist_executor = specialist_executor
+        self.rag_crew = rag_crew
+        self.exception_raised = False
+
+    @start()
+    def process_inputs(self):
+        return ""
+
+    @listen(process_inputs)
+    def execute_rag(self):
+        inputs = self.state.input.model_dump()
+        try:
+            crew_output = self.rag_crew.kickoff(inputs=inputs)
+            self.specialist_executor.log_tuning("RAG Crew Output", crew_output.model_dump())
+            output_pydantic = crew_output.pydantic
+            if not output_pydantic:
+                raw_json = json.loads(crew_output.raw)
+                output_pydantic = RAGOutput.model_validate(raw_json)
+            self.state.rag_output = output_pydantic
+            return crew_output
+        except Exception as e:
+            current_app.logger.error(f"CREW rag_crew Kickoff Error: {str(e)}")
+            self.exception_raised = True
+            raise e
+
+    def kickoff(self, inputs=None):
+        with current_event.create_span("RAG Specialist Execution"):
+            self.specialist_executor.log_tuning("Inputs retrieved", inputs)
+            self.state.input = RAGSpecialistInput.model_validate(inputs)
+            self.specialist.update_progress("EveAI Flow Start", {"name": "RAG"})
+            try:
+                result = super().kickoff()
+            except Exception as e:
+                current_app.logger.error(f"Error kicking of Flow: {str(e)}")
+
+            self.specialist.update_progress("EveAI Flow End", {"name": "RAG"})
+
+            return self.state