feat: add ZeroShotPrompt

langflow/backend/customs.py

@@ -0,0 +1,40 @@
+from langchain.agents.mrkl import prompt
+
+
+def get_custom_prompts():
+    return {
+        "ZeroShotPrompt": {
+            "template": {
+                "_type": "zero_shot",
+                "prefix": {
+                    "type": "str",
+                    "required": False,
+                    "placeholder": "",
+                    "list": False,
+                    "show": True,
+                    "multiline": True,
+                    "value": prompt.PREFIX,
+                },
+                "suffix": {
+                    "type": "str",
+                    "required": True,
+                    "placeholder": "",
+                    "list": False,
+                    "show": True,
+                    "multiline": True,
+                    "value": prompt.SUFFIX,
+                },
+                "format_instructions": {
+                    "type": "str",
+                    "required": False,
+                    "placeholder": "",
+                    "list": False,
+                    "show": True,
+                    "multiline": True,
+                    "value": prompt.FORMAT_INSTRUCTIONS,
+                },
+            },
+            "description": "Prompt template for Zero Shot Agent.",
+            "base_classes": ["BasePromptTemplate"],
+        }
+    }

langflow/backend/endpoints.py

@@ -9,6 +9,7 @@ from langchain.llms.loading import load_llm_from_config
 from langchain.prompts.loading import load_prompt_from_config
 from typing import Any
 
+
 # build router
 router = APIRouter()
 
@@ -26,6 +27,10 @@ def get_type_list():
 
 @router.get("/all")
 def get_all():
+    # library_prompts = {
+    #     prompt: signature.get_prompt(prompt) for prompt in list_endpoints.list_prompts()
+    # }
+    # custom_prompts = customs.get_custom_prompts()
     return {
         "chains": {
             chain: signature.get_chain(chain) for chain in list_endpoints.list_chains()
@@ -33,6 +38,7 @@ def get_all():
         "agents": {
             agent: signature.get_agent(agent) for agent in list_endpoints.list_agents()
         },
+        # "prompts": {**library_prompts, **custom_prompts},
         "prompts": {
             prompt: signature.get_prompt(prompt)
             for prompt in list_endpoints.list_prompts()
@@ -67,8 +73,22 @@ def get_all():
 
 @router.post("/predict")
 def get_load(data: dict[str, Any]):
+    # Get type list
     type_list = get_type_list()
 
+    # Substitute ZeroShotPromt with PromptTemplate
+    for node in data['nodes']:
+        if node["data"]["type"] == "ZeroShotPrompt":
+            # Build Prompt Template
+            tools = [
+                tool
+                for tool in data['nodes']
+                if tool["type"] != "chatOutputNode"
+                and "Tool" in tool["data"]["node"]["base_classes"]
+            ]
+            node["data"] = build_prompt_template(prompt=node["data"], tools=tools)
+            break
+
     # Add input variables
     data = payload.extract_input_variables(data)
 
@@ -96,12 +116,75 @@ def get_load(data: dict[str, Any]):
     else:
         return {"result": "Error: Type should be either agent, chain or llm"}
 
-    # elif extracted_json["_type"] in type_list["prompts"]:
-    #     loaded = load_prompt_from_config(extracted_json)
-    #     print(loaded.format(product=''))
 
-    #     return {'result': loaded.format(product=message)}
+def build_prompt_template(prompt, tools):
+    prefix = prompt["node"]["template"]["prefix"]["value"]
+    suffix = prompt["node"]["template"]["suffix"]["value"]
+    format_instructions = prompt["node"]["template"]["format_instructions"]["value"]
 
-    # if type in a["prompts"]:
+    tool_strings = "\n".join(
+        [
+            f"{tool['data']['node']['name']}: {tool['data']['node']['description']}"
+            for tool in tools
+        ]
+    )
+    tool_names = ", ".join([tool["data"]["node"]["name"] for tool in tools])
+    format_instructions = format_instructions.format(tool_names=tool_names)
+    value = "\n\n".join([prefix, tool_strings, format_instructions, suffix])
 
-    # return a
+    prompt["type"] = "PromptTemplate"
+    # prompt["value"] = value
+
+    prompt["node"] = {
+        "template": {
+            "_type": "prompt",
+            "input_variables": {
+                "type": "str",
+                "required": True,
+                "placeholder": "",
+                "list": True,
+                "show": False,
+                "multiline": False,
+            },
+            "output_parser": {
+                "type": "BaseOutputParser",
+                "required": False,
+                "placeholder": "",
+                "list": False,
+                "show": False,
+                "multline": False,
+                "value": None,
+            },
+            "template": {
+                "type": "str",
+                "required": True,
+                "placeholder": "",
+                "list": False,
+                "show": True,
+                "multiline": True,
+                "value": value,
+            },
+            "template_format": {
+                "type": "str",
+                "required": False,
+                "placeholder": "",
+                "list": False,
+                "show": False,
+                "multline": False,
+                "value": "f-string",
+            },
+            "validate_template": {
+                "type": "bool",
+                "required": False,
+                "placeholder": "",
+                "list": False,
+                "show": False,
+                "multline": False,
+                "value": True,
+            },
+        },
+        "description": "Schema to represent a prompt for an LLM.",
+        "base_classes": ["BasePromptTemplate"],
+    }
+
+    return prompt

langflow/backend/list_endpoints.py

@@ -7,6 +7,7 @@ from langchain import llms
 from langchain.chains.conversation import memory as memories
 from langchain.agents.load_tools import get_all_tool_names
 from langflow.backend import util
+from langflow.backend import customs
 
 
 # build router
@@ -52,10 +53,12 @@ def list_agents():
 @router.get("/prompts")
 def list_prompts():
     """List all prompt types"""
-    return [
+    custom_prompts = customs.get_custom_prompts()
+    library_prompts = [
         prompt.__annotations__["return"].__name__
         for prompt in prompts.loading.type_to_loader_dict.values()
     ]
+    return library_prompts + list(custom_prompts.keys())
 
 
 @router.get("/llms")

langflow/backend/payload.py

@@ -59,12 +59,16 @@ def build_json(root, nodes, edges):
         # if module_type == "Tool":
         #     pass
         if module_type in ["str", "bool", "int", "float", "Any"]:
+            # print(key)
+            # try:
             value = value["value"]
+            # except:
+            #     pass
         elif "dict" in module_type:
             value = {}
         else:
             # if value['list']:
-            print(key)
+            # print(key)
             children = []
             for c in local_nodes:
                 module_types = [c["data"]["type"]]

langflow/backend/signature.py

@@ -10,6 +10,7 @@ from langchain.agents.load_tools import (
 from langchain.chains.conversation import memory as memories
 
 from langflow.backend import util
+from langflow.backend import customs
 
 # build router
 router = APIRouter(
@@ -42,6 +43,8 @@ def get_agent(name: str):
 def get_prompt(name: str):
     """Get the signature of a prompt."""
     try:
+        if name in customs.get_custom_prompts().keys():
+            return customs.get_custom_prompts()[name]
         return util.build_template_from_function(
             name, prompts.loading.type_to_loader_dict
         )