Merge pull request #208 from valory-xyz/fix/tools

Fix/tools

packages/napthaai/customs/prediction_request_rag_claude/__init__.py

@@ -0,0 +1,20 @@
+# -*- coding: utf-8 -*-
+# ------------------------------------------------------------------------------
+#
+#   Copyright 2024 Valory AG
+#
+#   Licensed under the Apache License, Version 2.0 (the "License");
+#   you may not use this file except in compliance with the License.
+#   You may obtain a copy of the License at
+#
+#       http://www.apache.org/licenses/LICENSE-2.0
+#
+#   Unless required by applicable law or agreed to in writing, software
+#   distributed under the License is distributed on an "AS IS" BASIS,
+#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#   See the License for the specific language governing permissions and
+#   limitations under the License.
+#
+# ------------------------------------------------------------------------------
+
+"""This module contains the prediction request rag claude tool."""

packages/napthaai/customs/prediction_request_rag_claude/component.yaml

@@ -0,0 +1,39 @@
+name: prediction_request_rag_claude
+author: napthaai
+version: 0.1.0
+type: custom
+description: A tool for making binary predictions on markets using claude.
+license: Apache-2.0
+aea_version: '>=1.0.0, <2.0.0'
+fingerprint:
+  __init__.py: bafybeihd72xjbjzbqcpm3qfaimqk3ts3qx2l4w2lhca2e4ruso7jngmmce
+  prediction_request_rag_claude.py: bafybeibxeaoizixha4po7apf4k7gix2hi7x54hwqx4oyp23bwdgao6fv2u
+fingerprint_ignore_patterns: []
+entry_point: prediction_request_rag_claude.py
+callable: run
+dependencies:
+  google-api-python-client:
+    version: ==2.95.0
+  googlesearch-python:
+    version: ==1.2.3
+  requests: {}
+  markdownify:
+    version: ==0.11.6
+  readability-lxml:
+    version: ==0.8.1
+  anthropic:
+    version: ==0.21.3
+  tiktoken:
+    version: ==0.5.1
+  pydantic:
+    version: '>=1.9.0,<3'
+  faiss-cpu:
+    version: ==1.7.4
+  openai:
+    version: ==1.11.0
+  docstring-parser:
+    version: ==0.15
+  pypdf2:
+    version: ==3.0.1
+  numpy:
+    version: '>=1.19.0'

packages/napthaai/customs/prediction_request_reasoning/component.yaml

@@ -7,7 +7,7 @@ license: Apache-2.0
 aea_version: '>=1.0.0, <2.0.0'
 fingerprint:
   __init__.py: bafybeib36ew6vbztldut5xayk5553rylrq7yv4cpqyhwc5ktvd4cx67vwu
-  prediction_request_reasoning.py: bafybeifjm24tqil3nan37dbg4u7qm3xw3jxfu5eleg3cojmka67dwaclpa
+  prediction_request_reasoning.py: bafybeid3umzaz7qzxyf4pda6ffmayyhmr7fjx35bk4qzpsi33rtanddrem
 fingerprint_ignore_patterns: []
 entry_point: prediction_request_reasoning.py
 callable: run

packages/napthaai/customs/prediction_request_reasoning/prediction_request_reasoning.py

@@ -681,114 +681,117 @@ def extract_question(prompt: str) -> str:
 
 def run(**kwargs) -> Tuple[str, Optional[str], Optional[Dict[str, Any]], Any]:
     """Run the task"""
-    with OpenAIClientManager(kwargs["api_keys"]["openai"]):
-        tool = kwargs["tool"]
-        prompt = extract_question(kwargs["prompt"])
-        num_urls = kwargs.get("num_urls", DEFAULT_NUM_URLS[tool])
-        counter_callback = kwargs.get("counter_callback", None)
-        api_keys = kwargs.get("api_keys", {})
-        google_api_key = api_keys.get("google_api_key", None)
-        google_engine_id = api_keys.get("google_engine_id", None)
-        temperature = kwargs.get("temperature", DEFAULT_OPENAI_SETTINGS["temperature"])
-        max_tokens = kwargs.get("max_tokens", DEFAULT_OPENAI_SETTINGS["max_tokens"])
-        engine = kwargs.get("model", TOOL_TO_ENGINE[tool])
-        print(f"ENGINE: {engine}")
-        if tool not in ALLOWED_TOOLS:
-            raise ValueError(f"Tool {tool} is not supported.")
-
-        (
-            additional_information,
-            queries,
-            counter_callback,
-        ) = fetch_additional_information(
-            client=client,
-            prompt=prompt,
-            engine=engine,
-            google_api_key=google_api_key,
-            google_engine_id=google_engine_id,
-            counter_callback=counter_callback,
-            source_links=kwargs.get("source_links", None),
-            num_urls=num_urls,
-            temperature=temperature,
-            max_tokens=max_tokens,
-        )
+    try:
+        with OpenAIClientManager(kwargs["api_keys"]["openai"]):
+            tool = kwargs["tool"]
+            prompt = extract_question(kwargs["prompt"])
+            num_urls = kwargs.get("num_urls", DEFAULT_NUM_URLS[tool])
+            counter_callback = kwargs.get("counter_callback", None)
+            api_keys = kwargs.get("api_keys", {})
+            google_api_key = api_keys.get("google_api_key", None)
+            google_engine_id = api_keys.get("google_engine_id", None)
+            temperature = kwargs.get("temperature", DEFAULT_OPENAI_SETTINGS["temperature"])
+            max_tokens = kwargs.get("max_tokens", DEFAULT_OPENAI_SETTINGS["max_tokens"])
+            engine = kwargs.get("model", TOOL_TO_ENGINE[tool])
+            print(f"ENGINE: {engine}")
+            if tool not in ALLOWED_TOOLS:
+                raise ValueError(f"Tool {tool} is not supported.")
+
+            (
+                additional_information,
+                queries,
+                counter_callback,
+            ) = fetch_additional_information(
+                client=client,
+                prompt=prompt,
+                engine=engine,
+                google_api_key=google_api_key,
+                google_engine_id=google_engine_id,
+                counter_callback=counter_callback,
+                source_links=kwargs.get("source_links", None),
+                num_urls=num_urls,
+                temperature=temperature,
+                max_tokens=max_tokens,
+            )
 
-        # Adjust the additional_information to fit within the token budget
-        adjusted_info = adjust_additional_information(
-            prompt=PREDICTION_PROMPT,
-            additional_information=additional_information,
-            model=engine,
-        )
+            # Adjust the additional_information to fit within the token budget
+            adjusted_info = adjust_additional_information(
+                prompt=PREDICTION_PROMPT,
+                additional_information=additional_information,
+                model=engine,
+            )
 
-        # Reasoning prompt
-        reasoning_prompt = REASONING_PROMPT.format(
-            user_prompt=prompt, formatted_docs=adjusted_info
-        )
+            # Reasoning prompt
+            reasoning_prompt = REASONING_PROMPT.format(
+                user_prompt=prompt, formatted_docs=adjusted_info
+            )
 
-        # Do reasoning
-        messages = [
-            {"role": "system", "content": SYSTEM_PROMPT},
-            {
-                "role": "user",
-                "content": reasoning_prompt,
-            },
-        ]
+            # Do reasoning
+            messages = [
+                {"role": "system", "content": SYSTEM_PROMPT},
+                {
+                    "role": "user",
+                    "content": reasoning_prompt,
+                },
+            ]
 
-        # Reasoning
-        response_reasoning = client.chat.completions.create(
-            model=engine,
-            messages=messages,
-            temperature=temperature,
-            max_tokens=max_tokens,
-            n=1,
-            timeout=150,
-            stop=None,
-        )
+            # Reasoning
+            response_reasoning = client.chat.completions.create(
+                model=engine,
+                messages=messages,
+                temperature=temperature,
+                max_tokens=max_tokens,
+                n=1,
+                timeout=150,
+                stop=None,
+            )
 
-        # Extract the reasoning
-        reasoning = response_reasoning.choices[0].message.content
+            # Extract the reasoning
+            reasoning = response_reasoning.choices[0].message.content
 
-        # Prediction prompt
-        prediction_prompt = PREDICTION_PROMPT.format(
-            user_prompt=prompt, reasoning=reasoning
-        )
+            # Prediction prompt
+            prediction_prompt = PREDICTION_PROMPT.format(
+                user_prompt=prompt, reasoning=reasoning
+            )
 
-        # Make the prediction
-        messages = [
-            {"role": "system", "content": SYSTEM_PROMPT},
-            {
-                "role": "user",
-                "content": prediction_prompt,
-            },
-        ]
+            # Make the prediction
+            messages = [
+                {"role": "system", "content": SYSTEM_PROMPT},
+                {
+                    "role": "user",
+                    "content": prediction_prompt,
+                },
+            ]
 
-        response = client.chat.completions.create(
-            model=engine,
-            messages=messages,
-            temperature=temperature,
-            max_tokens=max_tokens,
-            n=1,
-            timeout=150,
-            stop=None,
-            functions=[Results.openai_schema],
-            function_call={'name':'Results'}
-        )
-        results = str(Results.from_response(response))
-
-        pairs = str(results).split()
-        result_dict = {}
-        for pair in pairs:
-            key, value = pair.split("=")
-            result_dict[key] = float(value)  # Convert value to float
-        results = result_dict
-        results = json.dumps(results)
-        if counter_callback is not None:
-            counter_callback(
-                input_tokens=response_reasoning.usage.prompt_tokens
-                + response.usage.prompt_tokens,
-                output_tokens=response_reasoning.usage.completion_tokens
-                + response.usage.completion_tokens,
+            response = client.chat.completions.create(
                 model=engine,
-                token_counter=count_tokens,
+                messages=messages,
+                temperature=temperature,
+                max_tokens=max_tokens,
+                n=1,
+                timeout=150,
+                stop=None,
+                functions=[Results.openai_schema],
+                function_call={'name':'Results'}
             )
-        return results, reasoning_prompt + "////" + prediction_prompt, None, counter_callback
+            results = str(Results.from_response(response))
+
+            pairs = str(results).split()
+            result_dict = {}
+            for pair in pairs:
+                key, value = pair.split("=")
+                result_dict[key] = float(value)  # Convert value to float
+            results = result_dict
+            results = json.dumps(results)
+            if counter_callback is not None:
+                counter_callback(
+                    input_tokens=response_reasoning.usage.prompt_tokens
+                    + response.usage.prompt_tokens,
+                    output_tokens=response_reasoning.usage.completion_tokens
+                    + response.usage.completion_tokens,
+                    model=engine,
+                    token_counter=count_tokens,
+                )
+            return results, reasoning_prompt + "////" + prediction_prompt, None, counter_callback
+    except Exception as e:
+        return f"Invalid response. The following issue was encountered: {str(e)}", "", None, None

packages/napthaai/customs/prediction_request_reasoning_claude/component.yaml

@@ -0,0 +1,39 @@
+name: prediction_request_reasoning_claude
+author: napthaai
+version: 0.1.0
+type: custom
+description: A tool for making binary predictions on markets using claude.
+license: Apache-2.0
+aea_version: '>=1.0.0, <2.0.0'
+fingerprint:
+  __init__.py: bafybeib36ew6vbztldut5xayk5553rylrq7yv4cpqyhwc5ktvd4cx67vwu
+  prediction_request_reasoning_claude.py: bafybeian5oyo5v4qvnocxhpgtxcvitlzwsa7hag4lwydmyyth2r7u6dine
+fingerprint_ignore_patterns: []
+entry_point: prediction_request_reasoning_claude.py
+callable: run
+dependencies:
+  google-api-python-client:
+    version: ==2.95.0
+  googlesearch-python:
+    version: ==1.2.3
+  requests: {}
+  markdownify:
+    version: ==0.11.6
+  readability-lxml:
+    version: ==0.8.1
+  openai:
+    version: ==1.11.0
+  tiktoken:
+    version: ==0.5.1
+  pypdf2:
+    version: ==3.0.1
+  numpy:
+    version: '>=1.19.0'
+  pydantic:
+    version: '>=1.9.0,<3'
+  faiss-cpu:
+    version: ==1.7.4
+  docstring-parser:
+    version: ==0.15
+  anthropic:
+    version: ==0.21.3

packages/napthaai/customs/prediction_url_cot/__init__.py

@@ -0,0 +1,20 @@
+# -*- coding: utf-8 -*-
+# ------------------------------------------------------------------------------
+#
+#   Copyright 2024 Valory AG
+#
+#   Licensed under the Apache License, Version 2.0 (the "License");
+#   you may not use this file except in compliance with the License.
+#   You may obtain a copy of the License at
+#
+#       http://www.apache.org/licenses/LICENSE-2.0
+#
+#   Unless required by applicable law or agreed to in writing, software
+#   distributed under the License is distributed on an "AS IS" BASIS,
+#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#   See the License for the specific language governing permissions and
+#   limitations under the License.
+#
+# ------------------------------------------------------------------------------
+
+"""This module contains the prediction url cot tool."""

packages/napthaai/customs/prediction_url_cot/component.yaml

@@ -0,0 +1,37 @@
+name: prediction_url_cot
+author: napthaai
+version: 0.1.0
+type: custom
+description: A tool for making binary predictions on markets.
+license: Apache-2.0
+aea_version: '>=1.0.0, <2.0.0'
+fingerprint:
+  __init__.py: bafybeiflni5dkn5fqe7fnu4lgbqxzfrgochhqfbgzwz3vlf5grijp3nkpm
+  prediction_url_cot.py: bafybeiczvut645b7xma2x6ivadhdbbqrvwusjoczidv2bwfbhidazshlfu
+fingerprint_ignore_patterns: []
+entry_point: prediction_url_cot.py
+callable: run
+dependencies:
+  google-api-python-client:
+    version: ==2.95.0
+  googlesearch-python:
+    version: ==1.2.3
+  requests: {}
+  markdownify:
+    version: ==0.11.6
+  readability-lxml:
+    version: ==0.8.1
+  anthropic:
+    version: ==0.21.3
+  tiktoken:
+    version: ==0.5.1
+  pypdf2:
+    version: ==3.0.1
+  numpy:
+    version: '>=1.19.0'
+  pydantic:
+    version: '>=1.9.0,<3'
+  openai:
+    version: ==1.11.0
+  docstring-parser:
+    version: ==0.15