add: citations

CHANGELOG.md

@@ -5,6 +5,10 @@
     *Andrea Sponziello* 
 ### **Copyrigth**: *Tiledesk SRL*
 
+## [2024-08-31]
+### 0.2.12
+- add: citations
+
 ## [2024-07-31]
 ### 0.2.11
 - fix: log

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "tilellm"
-version = "0.2.11"
+version = "0.2.12"
 description = "tiledesk for RAG"
 authors = ["Gianluca Lorenzo <gianluca.lorenzo@gmail.com>"]
 repository = "https://github.com/Tiledesk/tiledesk-llm"
@@ -41,6 +41,8 @@ docx2txt= "0.8"
 wikipedia= "1.4.0"
 html2text= "2024.2.26"
 psutil= "6.0.0"
+httpx= "0.27.0"
+gql= "3.5.0"
 
 
 [tool.poetry.dependencies.uvicorn]

tilellm/__main__.py

@@ -22,7 +22,8 @@
                                        ScrapeStatusReq,
                                        ScrapeStatusResponse,
                                        PineconeIndexingResult, RetrievalResult, PineconeNamespaceResult,
-                                       PineconeDescNamespaceResult, PineconeItems, QuestionToLLM, SimpleAnswer)
+                                       PineconeDescNamespaceResult, PineconeItems, QuestionToLLM, SimpleAnswer,
+                                       QuestionToAgent)
 
 from tilellm.store.redis_repository import redis_xgroup_create
 from tilellm.controller.controller import (ask_with_memory,
@@ -36,7 +37,7 @@
                                            get_desc_namespace,
                                            get_list_namespace,
                                            get_sources_namespace,
-                                           ask_to_llm)
+                                           ask_to_llm, ask_to_agent)
 
 import logging
 
@@ -350,6 +351,23 @@ async def post_ask_with_memory_main(question_answer: QuestionAnswer):
     return JSONResponse(content=result.model_dump())
 
 
+@app.post("/api/agent", response_model=SimpleAnswer)
+async def post_ask_to_agent_main(question_to_agent: QuestionToAgent):
+    """
+    Query and Aswer with chat history
+    :param question_to_agent:
+    :return: SimpleAnswer
+    """
+    print(question_to_agent)
+    logger.debug(question_to_agent)
+
+    result = await ask_to_agent(question_to_agent)
+
+    logger.debug(result)
+    return JSONResponse(content=result.model_dump())
+
+
+
 @app.post("/api/ask", response_model=SimpleAnswer)
 async def post_ask_to_llm_main(question: QuestionToLLM):
     """

tilellm/agents/shopify_agent.py

@@ -0,0 +1,130 @@
+from langchain_core.prompts import PromptTemplate
+
+
+
+from langchain.agents import create_react_agent, AgentExecutor
+from langchain_core.tools import Tool
+from langchain_openai import ChatOpenAI
+
+from tilellm.tools.shopify_tool import get_graphql_answer
+from tilellm.shared.const import react_prompt_template
+from functools import partial
+
+
+def lookup(question_to_agent, chat_model, chat_history:str):
+    #You are an API agent.
+    template1 = """
+       Given the question {question} I want you to find the answer. the first step is to create a GraphQL query 
+       for Shopify Admin client that answer the question, then pass the query to tool in GraphQL format. 
+       (USE ONLY GraphQL format. No comment is needed! Use the parameter in the same language as the question).
+       Use this schema {schema} for GraphQL and not exceed 10 items. 
+       Examples of GraphQL query:  
+       - query {{ products(first: 100) {{ edges {{ node {{ id title price }} }} }} }}
+       - query {{ products(first: 10) {{ edges {{ node {{ id title }} }} }} }}
+       - query {{ products(first: 10, query: "price:<50") {{ edges {{ node {{ title variants(first: 1) {{ edges {{ node {{ price }} }} }} }} }} }} }}
+
+        
+       In Your Final answer, use the same language of the question, the response should interpret and summarize the key information from the query result 
+       in a clear and concise manner. If there isn't product that answer the question, simply say that there isn't products.
+       """
+    template="""
+    Follow these instructions exactly to answer the question: {question}
+
+    1. Create a GraphQL query for Shopify Admin client that answers the question.
+       - Use ONLY GraphQL format, no comments.
+       - Use parameters in the same language as the question.
+       - Use this schema: {schema}
+       - Limit results to a maximum of 10 items.
+    
+    2. Query format:
+       query {{
+         // Your code here
+       }}
+    
+    3. Examples of valid queries:
+       - query {{ products(first: 10) {{ edges {{ node {{ id title price }} }} }} }}
+       - query {{ products(first: 10, query: "price:<50") {{ edges {{ node {{ title variants(first: 1) {{ edges {{ node {{ price }} }} }} }} }} }} }}
+    
+    4. Present ONLY the GraphQL query, nothing else.
+    
+    5. After receiving the query results, provide the final answer:
+       - Use the same language as the original question.
+       - Interpret and summarize key information from the query results.
+       - Be clear and concise.
+       - If there are no products that answer the question, state this explicitly.
+    
+    Remember: Follow these instructions to the letter. Do not add explanations or comments that are not requested.
+    """
+    question = question_to_agent.question
+
+    for tool in question_to_agent.tools:
+        if 'shopify' in tool:
+            shopify_tool = tool['shopify']
+
+            # Safely access the root dictionary
+            api_key = shopify_tool.root.get('api_key')
+            url = shopify_tool.root.get('url')
+            break  # Exit the loop once 'shopify' is found
+
+    get_graphql_answer_with_key = partial(get_graphql_answer,  url=url, api_key=api_key)
+    tools_for_agent_shopify = [
+        Tool(
+            name="Retrieve content from Shopify given GraphQL query",
+            func=get_graphql_answer_with_key,
+            description="useful when you need get the useful information from shopify"
+
+        ),
+    ]
+
+    schema = """
+    products(first: 10, query: "") {
+    edges {
+      node {
+        title
+        variants(first: 1, last: 10) {
+          edges {
+            node {
+              price
+              availableForSale
+              barcode
+              displayName
+              id
+            }
+          }
+        }
+        bodyHtml
+        descriptionHtml
+        id
+        productType
+        tags
+        totalInventory
+      }
+    }
+  }
+    """
+    prompt_template = PromptTemplate(
+        input_variables=["question", "schema"], template=template
+    )
+
+    #react_prompt = hub.pull("hwchase17/react")
+    react_prompt = PromptTemplate.from_template(react_prompt_template)
+
+    agent = create_react_agent(
+        llm=chat_model, tools=tools_for_agent_shopify, prompt=react_prompt
+    )
+
+    agent_executor = AgentExecutor(
+        agent=agent,
+        tools=tools_for_agent_shopify,
+        verbose=True,
+        max_iterations=4,
+        early_stopping_method="force",
+        handle_parsing_errors=True
+    )
+
+    result = agent_executor.invoke(
+        input={"input": prompt_template.format_prompt(question=question, schema=schema),
+               "chat_history": chat_history}
+    )
+
+    return result