feat(agents-api): Added mmr search and get history system tool + configurable doc search params in chat.py

.github/workflows/generate-changelog.yml

@@ -0,0 +1,81 @@
+name: Julep-Changelog-Generation
+run-name: ${{ github.actor }} is generating changelog for the last two weeks using Julep
+
+on:
+  workflow_dispatch:
+
+jobs:
+  changelog_generation:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+          ref: dev
+
+      - name: Setup GitHub CLI
+        run: |
+          echo "${{ secrets.GITHUB_TOKEN }}" | gh auth login --with-token
+
+      - name: Collect merged PRs from the last two weeks
+        id: collect_prs
+        run: |
+          # Set date threshold for fetching PRs
+          if [[ "$OSTYPE" == "darwin"* ]]; then
+            date_threshold=$(date -v-14d +"%Y-%m-%d")
+          else
+            date_threshold=$(date -d '-14 days' +"%Y-%m-%d")
+          fi
+
+          echo "Fetching merged PRs since $date_threshold..."
+
+          # Find merged PRs from the last two weeks
+          merged_prs=$(gh pr list --state merged --json number,title,body,author --search "merged:>=$date_threshold" --jq 'map({number, title, body, author: .author.login})')
+
+          if [ -z "$merged_prs" ] || [ "$merged_prs" = "null" ]; then
+            echo "No merged PRs found in the last two weeks."
+            echo "pr_data=[]" >> $GITHUB_ENV
+            echo '{"pr_data": []}' > pr_data.json
+            exit 0
+          fi
+
+          echo "pr_data=$merged_prs" >> $GITHUB_ENV
+          echo "pr_data=$merged_prs" >> "$GITHUB_OUTPUT"
+          echo "{\"pr_data\": $merged_prs}" > pr_data.json
+
+      - name: Setup Python v3.10.12
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.10.12"
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          python -m pip install PyYAML julep git+https://github.com/Jwink3101/parmapper
+
+      - name: Send PR data to Python script
+        if: steps.collect_prs.outputs.pr_data != '[]'
+        id: generate_changelog
+        run: |
+          if ! python scripts/generate_changelog.py; then
+            echo "Error: Failed to generate changelog"
+            exit 1
+          fi
+        env:
+          JULEP_API_KEY: ${{ secrets.JULEP_API_KEY }}
+          TASK_UUID: ${{ secrets.TASK_UUID }}
+          AGENT_UUID: ${{ secrets.AGENT_UUID }}
+
+      - name: Create Pull Request
+        if: success() && steps.collect_prs.outputs.pr_data != '[]'
+        uses: peter-evans/create-pull-request@v7
+        with:
+          token: ${{ secrets.GITHUB_TOKEN }}
+          commit-message: "chore(changelog): update CHANGELOG.md"
+          title: "Update CHANGELOG.md"
+          body: "This PR updates the changelog with PRs from the last two weeks."
+          branch: "update-changelog"
+          delete-branch: true
+          add-paths: |
+            CHANGELOG.md

agents-api/agents_api/activities/execute_system.py

@@ -6,7 +6,6 @@
 
 from beartype import beartype
 from box import Box, BoxList
-from fastapi import HTTPException
 from fastapi.background import BackgroundTasks
 from temporalio import activity
 
@@ -110,6 +109,7 @@ async def execute_system(
             await bg_runner()
             return res
 
+        # Handle create operations
         if system.operation == "create" and system.resource == "session":
             developer_id = arguments.pop("developer_id")
             session_id = arguments.pop("session_id", None)

agents-api/agents_api/activities/utils.py

@@ -303,6 +303,7 @@ def get_handler(system: SystemDef) -> Callable:
     from ..models.agent.update_agent import update_agent as update_agent_query
     from ..models.docs.delete_doc import delete_doc as delete_doc_query
     from ..models.docs.list_docs import list_docs as list_docs_query
+    from ..models.entry.get_history import get_history as get_history_query
     from ..models.session.create_session import create_session as create_session_query
     from ..models.session.delete_session import delete_session as delete_session_query
     from ..models.session.get_session import get_session as get_session_query
@@ -376,6 +377,8 @@ def get_handler(system: SystemDef) -> Callable:
             return delete_session_query
         case ("session", None, "chat"):
             return chat
+        case ("session", None, "history"):
+            return get_history_query
 
         # TASKS
         case ("task", None, "list"):

agents-api/agents_api/autogen/Docs.py

@@ -14,11 +14,17 @@ class BaseDocSearchRequest(BaseModel):
         populate_by_name=True,
     )
     limit: Annotated[int, Field(ge=1, le=50)] = 10
+    """
+    The limit of documents to return
+    """
     lang: Literal["en-US"] = "en-US"
     """
     The language to be used for text-only search. Support for other languages coming soon.
     """
     metadata_filter: dict[str, Any] = {}
+    """
+    Metadata filter to apply to the search
+    """
     mmr_strength: Annotated[float, Field(ge=0.0, lt=1.0)] = 0
     """
     MMR Strength (mmr_strength = 1 - mmr_lambda)

agents-api/agents_api/autogen/Sessions.py

@@ -96,10 +96,41 @@ class RecallOptions(BaseModel):
         populate_by_name=True,
     )
     mode: Literal["hybrid", "vector", "text"] = "vector"
+    """
+    The mode to use for the search.
+    """
     num_search_messages: int = 4
+    """
+    The number of search messages to use for the search.
+    """
     max_query_length: int = 1000
-    hybrid_alpha: float = 0.7
-    confidence: float = 0.6
+    """
+    The maximum query length to use for the search.
+    """
+    alpha: Annotated[float, Field(ge=0.0, le=1.0)] = 0.7
+    """
+    The weight to apply to BM25 vs Vector search results. 0 => pure BM25; 1 => pure vector;
+    """
+    confidence: Annotated[float, Field(ge=0.0, le=1.0)] = 0.6
+    """
+    The confidence cutoff level
+    """
+    limit: Annotated[int, Field(ge=1, le=50)] = 10
+    """
+    The limit of documents to return
+    """
+    lang: Literal["en-US"] = "en-US"
+    """
+    The language to be used for text-only search. Support for other languages coming soon.
+    """
+    metadata_filter: dict[str, Any] = {}
+    """
+    Metadata filter to apply to the search
+    """
+    mmr_strength: Annotated[float, Field(ge=0.0, lt=1.0)] = 0
+    """
+    MMR Strength (mmr_strength = 1 - mmr_lambda)
+    """
 
 
 class RecallOptionsUpdate(RecallOptions):

agents-api/agents_api/models/chat/gather_messages.py

@@ -1,15 +1,18 @@
-from typing import TypeVar
+from typing import Any, Dict, List, Optional, Tuple, TypeVar, Union
 from uuid import UUID
 
+import numpy as np
 from beartype import beartype
 from fastapi import HTTPException
 from pycozo.client import QueryException
 from pydantic import ValidationError
 
 from ...autogen.openapi_model import ChatInput, DocReference, History
+from ...autogen.Sessions import RecallOptions
 from ...clients import litellm
 from ...common.protocol.developers import Developer
 from ...common.protocol.sessions import ChatContext
+from ...models.docs.mmr import maximal_marginal_relevance
 from ..docs.search_docs_by_embedding import search_docs_by_embedding
 from ..docs.search_docs_by_text import search_docs_by_text
 from ..docs.search_docs_hybrid import search_docs_hybrid
@@ -23,6 +26,52 @@
 T = TypeVar("T")
 
 
+def get_search_fn_and_params(
+    recall_options: RecallOptions,
+    query_text: str | None,
+    query_embedding: list[float] | None,
+) -> Tuple[
+    Any,
+    Optional[Dict[str, Union[float, int, str, Dict[str, float], List[float], None]]],
+]:
+    search_fn, params = None, None
+
+    match recall_options.mode:
+        case "text":
+            search_fn = search_docs_by_text
+            params = dict(
+                query=query_text,
+                k=recall_options.limit,
+                metadata_filter=recall_options.metadata_filter,
+            )
+
+        case "vector":
+            search_fn = search_docs_by_embedding
+            params = dict(
+                query_embedding=query_embedding,
+                k=recall_options.limit * 3
+                if recall_options.mmr_strength > 0
+                else recall_options.limit,
+                confidence=recall_options.confidence,
+                metadata_filter=recall_options.metadata_filter,
+            )
+
+        case "hybrid":
+            search_fn = search_docs_hybrid
+            params = dict(
+                query=query_text,
+                query_embedding=query_embedding,
+                k=recall_options.limit * 3
+                if recall_options.mmr_strength > 0
+                else recall_options.limit,
+                embed_search_options=dict(confidence=recall_options.confidence),
+                alpha=recall_options.alpha,
+                metadata_filter=recall_options.metadata_filter,
+            )
+
+    return search_fn, params
+
+
 @rewrap_exceptions(
     {
         QueryException: partialclass(HTTPException, status_code=400),
@@ -98,44 +147,62 @@ async def gather_messages(
         ]
     ).strip()
 
-    [query_embedding, *_] = await litellm.aembedding(
-        # Truncate on the left to keep the last `search_query_chars` characters
-        inputs=embed_text[-(recall_options.max_query_length) :],
-        # TODO: Make this configurable once it's added to the ChatInput model
-        embed_instruction="Represent the query for retrieving supporting documents: ",
-    )
+    # Set the query text and embedding
+    query_text, query_embedding = None, None
+
+    # Embed the query
+    if recall_options.mode != "text":
+        [query_embedding, *_] = await litellm.aembedding(
+            # Truncate on the left to keep the last `search_query_chars` characters
+            inputs=embed_text[-(recall_options.max_query_length) :],
+            # TODO: Make this configurable once it's added to the ChatInput model
+            embed_instruction="Represent the query for retrieving supporting documents: ",
+        )
 
     # Truncate on the right to take only the first `search_query_chars` characters
-    query_text = search_messages[-1]["content"].strip()[
-        : recall_options.max_query_length
-    ]
+    if recall_options.mode == "text" or recall_options.mode == "hybrid":
+        query_text = search_messages[-1]["content"].strip()[
+            : recall_options.max_query_length
+        ]
 
     # List all the applicable owners to search docs from
     active_agent_id = chat_context.get_active_agent().id
     user_ids = [user.id for user in chat_context.users]
     owners = [("user", user_id) for user_id in user_ids] + [("agent", active_agent_id)]
 
+    # Get the search function and parameters
+    search_fn, params = get_search_fn_and_params(
+        recall_options=recall_options,
+        query_text=query_text,
+        query_embedding=query_embedding,
+    )
+
     # Search for doc references
-    doc_references: list[DocReference] = []
-    match recall_options.mode:
-        case "vector":
-            doc_references: list[DocReference] = search_docs_by_embedding(
-                developer_id=developer.id,
-                owners=owners,
-                query_embedding=query_embedding,
-            )
-        case "hybrid":
-            doc_references: list[DocReference] = search_docs_hybrid(
-                developer_id=developer.id,
-                owners=owners,
-                query=query_text,
-                query_embedding=query_embedding,
-            )
-        case "text":
-            doc_references: list[DocReference] = search_docs_by_text(
-                developer_id=developer.id,
-                owners=owners,
-                query=query_text,
-            )
+    doc_references: list[DocReference] = search_fn(
+        developer_id=developer.id,
+        owners=owners,
+        **params,
+    )
+
+    # Apply MMR if enabled
+    if (
+        # MMR is enabled
+        recall_options.mmr_strength > 0
+        # The number of doc references is greater than the limit
+        and len(doc_references) > recall_options.limit
+        # MMR is not applied to text search
+        and recall_options.mode != "text"
+    ):
+        # Apply MMR
+        indices = maximal_marginal_relevance(
+            np.asarray(query_embedding),
+            [doc.snippet.embedding for doc in doc_references],
+            k=recall_options.limit,
+        )
+        # Apply MMR
+        doc_references = [
+            doc for i, doc in enumerate(doc_references) if i in set(indices)
+        ]
 
+    # Return the past messages and doc references
     return past_messages, doc_references

agents-api/agents_api/workflows/task_execution/transition.py

@@ -14,7 +14,6 @@
     from ...common.retry_policies import DEFAULT_RETRY_POLICY
     from ...env import (
         debug,
-        temporal_activity_after_retry_timeout,
         temporal_heartbeat_timeout,
         temporal_schedule_to_close_timeout,
         testing,

agents-api/tests/test_chat_routes.py

@@ -87,9 +87,13 @@ async def _(
             agent=agent.id,
             situation="test session about",
             recall_options={
-                "mode": "text",
-                "num_search_messages": 10,
-                "max_query_length": 1001,
+                "mode": "hybrid",
+                "num_search_messages": 6,
+                "max_query_length": 800,
+                "confidence": 0.6,
+                "alpha": 0.7,
+                "limit": 10,
+                "mmr_strength": 0.5,
             },
         ),
         client=client,
@@ -135,9 +139,12 @@ async def _(
             agent=agent.id,
             situation="test session about",
             recall_options={
-                "mode": "vector",
-                "num_search_messages": 5,
+                "mode": "text",
+                "num_search_messages": 10,
                 "max_query_length": 1001,
+                "confidence": 0.6,
+                "limit": 5,
+                "mmr_strength": 0.5,
             },
         ),
         client=client,

agents-api/tests/test_execution_workflow.py

@@ -16,7 +16,6 @@
 from agents_api.models.task.create_task import create_task
 from agents_api.routers.tasks.create_task_execution import start_execution
 from tests.fixtures import (
-    async_cozo_client,
     cozo_client,
     cozo_clients_with_migrations,
     test_agent,