Added preamble and minimap as a default tool

src/backend/chat/custom/langchain.py

@@ -59,6 +59,8 @@ def chat(self, chat_request: LangchainChatRequest, **kwargs: Any) -> Any:
             verbose=True,
         )
 
+        raise NotImplementedError("Langchain is not yet implemented")
+
         return self.agent_executor.stream(
             {
                 "input": chat_request.message,

src/backend/config/deployments.py

@@ -34,20 +34,20 @@ class ModelDeploymentName(StrEnum):
         is_available=CohereDeployment.is_available(),
         env_vars=COHERE_ENV_VARS,
     ),
-    ModelDeploymentName.SageMaker: Deployment(
-        name=ModelDeploymentName.SageMaker,
-        deployment_class=SageMakerDeployment,
-        models=SageMakerDeployment.list_models(),
-        is_available=SageMakerDeployment.is_available(),
-        env_vars=SAGE_MAKER_ENV_VARS,
-    ),
-    ModelDeploymentName.Azure: Deployment(
-        name=ModelDeploymentName.Azure,
-        deployment_class=AzureDeployment,
-        models=AzureDeployment.list_models(),
-        is_available=AzureDeployment.is_available(),
-        env_vars=AZURE_ENV_VARS,
-    ),
+    # ModelDeploymentName.SageMaker: Deployment(
+    #     name=ModelDeploymentName.SageMaker,
+    #     deployment_class=SageMakerDeployment,
+    #     models=SageMakerDeployment.list_models(),
+    #     is_available=SageMakerDeployment.is_available(),
+    #     env_vars=SAGE_MAKER_ENV_VARS,
+    # ),
+    # ModelDeploymentName.Azure: Deployment(
+    #     name=ModelDeploymentName.Azure,
+    #     deployment_class=AzureDeployment,
+    #     models=AzureDeployment.list_models(),
+    #     is_available=AzureDeployment.is_available(),
+    #     env_vars=AZURE_ENV_VARS,
+    # ),
     ModelDeploymentName.Bedrock: Deployment(
         name=ModelDeploymentName.Bedrock,
         deployment_class=BedrockDeployment,

src/backend/config/tools.py

@@ -59,7 +59,7 @@ class ToolName(StrEnum):
         implementation=LangChainMinimapRetriever,
         parameter_definitions={
             "query": {
-                "description": "Search API that takes a query or phrase. ",
+                "description": "Search API that takes a query or phrase. Results should be presented as an executive summary, grouped and summarized for the user with section headings and bullet points.",
                 "type": "str",
                 "required": True,
             }
@@ -68,7 +68,7 @@ class ToolName(StrEnum):
         is_available=LangChainMinimapRetriever.is_available(),
         error_message="Minimap API not available.",
         category=Category.DataLoader,
-        description="Fetches the most relevant news and content from Minimap.ai. Results should be presented as an executive summary, grouped and summarized for the user with section headings and bullet points.",
+        description="Fetches the most relevant news and content from Minimap.ai.",
     ),
     # ToolName.Search_File: ManagedTool(
     #     name=ToolName.Search_File,

src/backend/model_deployments/cohere_platform.py

@@ -1,6 +1,7 @@
 import json
 import logging
 import os
+import time
 from typing import Any, Dict, Generator, List
 
 import cohere
@@ -12,11 +13,57 @@
 from backend.model_deployments.base import BaseDeployment
 from backend.model_deployments.utils import get_model_config_var
 from backend.schemas.cohere_chat import CohereChatRequest
+from backend.tools.minimap import LangChainMinimapRetriever
+
+from cohere.types.tool import Tool
+
+MINIMAP_TOOL = Tool(
+    name="Minimap",
+    description="Fetches the most relevant news and content from Minimap.ai.",
+    parameter_definitions={
+        "query": {
+            "description": "Search API that takes a query or phrase. Results should be presented as an executive summary, grouped and summarized for the user with section headings and bullet points.",
+            "type": "str",
+            "required": True,
+        }
+    },
+)
 
 COHERE_API_KEY_ENV_VAR = "COHERE_API_KEY"
 COHERE_ENV_VARS = [COHERE_API_KEY_ENV_VAR]
 
 
+MODELS =  [
+        {
+            'name': 'command-r',
+            'endpoints': ['generate', 'chat', 'summarize'],
+            'finetuned': False,
+            'context_length': 128000,
+            'tokenizer_url': 'https://storage.googleapis.com/cohere-public/tokenizers/command-r.json',
+            'default_endpoints': []
+        },
+        {
+            'name': 'command-r-plus',
+            'endpoints': ['generate', 'chat', 'summarize'],
+            'finetuned': False,
+            'context_length': 128000,
+            'tokenizer_url': 'https://storage.googleapis.com/cohere-public/tokenizers/command-r-plus.json',
+            'default_endpoints': ['chat']
+        },
+]
+
+preamble = f"""
+You are a news summarization assistant. You're equipped with Minimap.ai's news search tool.
+
+You will be provided with large number of news articles. You're task is to provide users with salient summaries of major trends in the news. Summaries should be presented as a mini news brief, with topic headings and contain bullet points with key information.
+
+You can elaborate on or use a more precise query than what the user provided to get more specific results.
+
+Always ask the user if there's a specific point or topic they want to drill down on.
+
+Today's date is {time.strftime("%Y-%m-%d")}.
+"""
+
 class CohereDeployment(BaseDeployment):
     """Cohere Platform Deployment."""
 
@@ -36,19 +83,20 @@ def list_models(cls) -> List[str]:
         if not CohereDeployment.is_available():
             return []
 
-        url = "https://api.cohere.ai/v1/models"
-        headers = {
-            "accept": "application/json",
-            "authorization": f"Bearer {cls.api_key}",
-        }
+        # url = "https://api.cohere.ai/v1/models"
+        # headers = {
+        #     "accept": "application/json",
+        #     "authorization": f"Bearer {cls.api_key}",
+        # }
 
-        response = requests.get(url, headers=headers)
+        # response = requests.get(url, headers=headers)
+        # logging.info(response.json())
+        # if not response.ok:
+        #     logging.warning("Couldn't get models from Cohere API.")
+        #     return []
 
-        if not response.ok:
-            logging.warning("Couldn't get models from Cohere API.")
-            return []
+        models = MODELS
 
-        models = response.json()["models"]
         return [
             model["name"]
             for model in models
@@ -60,6 +108,7 @@ def is_available(cls) -> bool:
         return all([os.environ.get(var) is not None for var in COHERE_ENV_VARS])
 
     def invoke_chat(self, chat_request: CohereChatRequest, **kwargs: Any) -> Any:
+        logging.info(f"Invoking chat with chat_request")
         response = self.client.chat(
             **chat_request.model_dump(exclude={"stream"}),
             **kwargs,
@@ -69,10 +118,22 @@ def invoke_chat(self, chat_request: CohereChatRequest, **kwargs: Any) -> Any:
     def invoke_chat_stream(
         self, chat_request: CohereChatRequest, **kwargs: Any
     ) -> Generator[StreamedChatResponse, None, None]:
-        stream = self.client.chat_stream(
+
+        chat_params = {
             **chat_request.model_dump(exclude={"stream", "file_ids"}),
-            **kwargs,
+            **kwargs
+        }
+
+        # Append "Minimap" to the tools list
+        chat_params["tools"] = chat_params.get("tools", []) + [MINIMAP_TOOL]
+
+        # Set the preamble
+        chat_params["preamble"] = preamble
+
+        stream = self.client.chat_stream(
+            **chat_params,
         )
+
         for event in stream:
             yield to_dict(event)
 
@@ -106,4 +167,5 @@ def invoke_tools(
         chat_request: CohereChatRequest,
         **kwargs: Any,
     ) -> Generator[StreamedChatResponse, None, None]:
+
         yield from self.invoke_chat_stream(chat_request, **kwargs)

src/backend/tools/minimap.py

@@ -1,4 +1,5 @@
 import os
+import pandas as pd
 from typing import Any, Dict, List
 
 # from langchain.text_splitter import CharacterTextSplitter
@@ -35,6 +36,16 @@
 
 logging = logging.getLogger(__name__)
 
+
+def hash_string(s: str) -> str:
+    """
+    Hash a string using the djb2 algorithm.
+    """
+    hash = 5381
+    for x in s:
+        hash = ((hash << 5) + hash) + ord(x)
+    return str(hash)
+
 class MinimapAPIWrapper(BaseModel):
     """
     Wrapper around Minimap.ai API.
@@ -64,7 +75,7 @@ class MinimapAPIWrapper(BaseModel):
     max_retry: int = 5
 
     # Default values for the parameters
-    top_k_results: int = 100
+    top_k_results: int = 50
     MAX_QUERY_LENGTH: int = 300
     doc_content_chars_max: int = 2000
 
@@ -90,16 +101,19 @@ def run(self, query: str) -> str:
 
             results = response_json.get("results", [])
 
+
+            df = pd.DataFrame(results)
+
+            df['title_hash'] = df['title'].apply(hash_string)
+
+            # drop duplicates
+            df = df.drop_duplicates(subset=['title_hash'])
+
+            results = df.to_dict(orient='records')
+
             # limit the number of results to top_k_results
             results = results[:self.top_k_results]
 
-            # Rename id to doc_id
-            for result in results:
-                try:
-                    result['doc_id'] = result.get('id')
-                except Exception as ex:
-                    ...
-
             return results
 
         except Exception as ex:

src/community/config/deployments.py

@@ -13,13 +13,13 @@ class ModelDeploymentName(StrEnum):
 
 
 AVAILABLE_MODEL_DEPLOYMENTS = {
-    ModelDeploymentName.HuggingFace: Deployment(
-        name=ModelDeploymentName.HuggingFace,
-        deployment_class=HuggingFaceDeployment,
-        models=HuggingFaceDeployment.list_models(),
-        is_available=HuggingFaceDeployment.is_available(),
-        env_vars=[],
-    ),
+    # ModelDeploymentName.HuggingFace: Deployment(
+    #     name=ModelDeploymentName.HuggingFace,
+    #     deployment_class=HuggingFaceDeployment,
+    #     models=HuggingFaceDeployment.list_models(),
+    #     is_available=HuggingFaceDeployment.is_available(),
+    #     env_vars=[],
+    # ),
     # Add the below for local model deployments
     # ModelDeploymentName.LocalModel: Deployment(
     #     name=ModelDeploymentName.LocalModel,

src/interfaces/coral_web/src/components/Configuration/Tools.tsx

@@ -72,12 +72,12 @@ const ToolSection = () => {
     updateEnabledTools(updatedTools);
   };
 
-  // useEffect to enable all tools by default
-  React.useEffect(() => {
-    if (tools.length > 0 && enabledTools.length === 0) {
-      updateEnabledTools(tools);
-    }
-  }, []);
+  // // useEffect to enable all tools by default
+  // React.useEffect(() => {
+  //   if (tools.length > 0 && enabledTools.length === 0) {
+  //     updateEnabledTools(tools);
+  //   }
+  // }, []);
 
   return (
     <section className="relative flex flex-col gap-y-5 px-5">