updated code and implemented a new test: test_workflow

step3.py

@@ -121,6 +121,7 @@ class RAGWorkflow(Workflow):
     @step
     async def set_up(self, ctx: Context, ev: StartEvent) -> ParseFormEvent:
         self.llm = global_llm
+        self.storage_dir = storage_dir
         if not ev.resume_file:
             raise ValueError("No resume file provided")
 
@@ -226,13 +227,13 @@ async def main():
         application_form="data/fake_application_form.pdf"
     )
     print(result)
-    '''
+
     # Display of the workflow
-    workflow_file = Path(__file__).parent / "workflows" / "rag_workflow.html"
+    workflow_file = Path(__file__).parent / "workflows" / "form_parsing_workflow.html"
     draw_all_possible_flows(w, filename=str(workflow_file))
     html_content = extract_html_content(str(workflow_file))
     display(HTML(html_content), metadata=dict(isolated=True))
-    '''
+
 
 if __name__ == "__main__":
     asyncio.run(main())

step4.py

@@ -13,6 +13,7 @@ from llama_index.core import (
     load_index_from_storage
 )
 import nest_asyncio
+from llama_index.core.workflow import InputRequiredEvent, HumanResponseEvent
 from llama_index.core.workflow import (
     StartEvent,
     StopEvent,
@@ -40,28 +41,232 @@ global_llm = Groq(api_key=GROQ_API_KEY, model="llama3-70b-8192")
 global_embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
 Settings.embed_model = global_embed_model
 
-# Parse the application form
-parser = LlamaParse(
-    result_type="markdown",
-    content_guideline_instruction="This is a job application form. Create a list of all the fields "
-                                  "that need to be filled in.",
-    formatting_instruction="Return a bulleted list of the fields ONLY."
-)
 
-result = parser.load_data(application_file)[0]
-#print(result.text)
-
-# Convert the above output into a JSON object
-raw_json = global_llm.complete(
-    f"""
-    This is a parsed form.
-    Convert it into a JSON object containing only the list 
-    of fields to be filled in, in the form {{ fields: [...] }}. 
-    <form>{result.text}</form>. 
-    Return JSON ONLY, no markdown."""
-)
+class ParseFormEvent(Event):
+    application_form: str
+
+
+class QueryEvent(Event):
+    query: str
+    field: str
+
+
+class ResponseEvent(Event):
+    response: str
+
+
+# new!
+class FeedbackEvent(Event):
+    feedback: str
+
+
+class GenerateQuestionsEvent(Event):
+    pass
+
+
+class RAGWorkflow(Workflow):
+    storage_dir = "./storage"
+    llm: Groq
+    query_engine: VectorStoreIndex
+
+    @step
+    async def set_up(self, ctx: Context, ev: StartEvent) -> ParseFormEvent:
+        self.llm = global_llm
+        self.storage_dir = storage_dir
+        if not ev.resume_file:
+            raise ValueError("No resume file provided")
+
+        if not ev.application_form:
+            raise ValueError("No application form provided")
+
+        # ingest the data and set up the query engine
+        if os.path.exists(self.storage_dir):
+            # you've already ingested the resume document
+            storage_context = StorageContext.from_defaults(persist_dir=self.storage_dir)
+            index = load_index_from_storage(storage_context)
+        else:
+            # parse and load the resume document
+            documents = LlamaParse(
+                result_type="markdown",
+                content_guideline_instruction="This is a resume, gather related facts together and format it as "
+                                              "bullet points with headers"
+            ).load_data(ev.resume_file)
+            # embed and index the documents
+            index = VectorStoreIndex.from_documents(
+                documents,
+                embed_model=global_embed_model
+            )
+            index.storage_context.persist(persist_dir=self.storage_dir)
+
+        # create a query engine
+        self.query_engine = index.as_query_engine(llm=self.llm, similarity_top_k=5)
+
+        # you no longer need a query to be passed in,
+        # you'll be generating the queries instead
+        # let's pass the application form to a new step to parse it
+        return ParseFormEvent(application_form=ev.application_form)
+
+    # new - separated the form parsing from the question generation
+    @step
+    async def parse_form(self, ctx: Context, ev: ParseFormEvent) -> GenerateQuestionsEvent:
+        parser = LlamaParse(
+            result_type="markdown",
+            content_guideline_instruction="This is a job application form. Create a list of all the fields "
+                                          "that need to be filled in.",
+            formatting_instruction="Return a bulleted list of the fields ONLY."
+        )
+
+        # get the LLM to convert the parsed form into JSON
+        result = parser.load_data(ev.application_form)[0]
+        raw_json = self.llm.complete(
+            f"""
+            This is a parsed form. 
+            Convert it into a JSON object containing only the list 
+            of fields to be filled in, in the form {{ fields: [...] }}. 
+            <form>{result.text}</form>. 
+            Return JSON ONLY, no markdown.
+            """)
+        fields = json.loads(raw_json.text)["fields"]
+
+        await ctx.set("fields_to_fill", fields)
+        print("\n DEBUG: all fields written to Context >>>>>>>>>>>>>>>>>>>>>>>>>>\n")
+
+        return GenerateQuestionsEvent()
+
+    # new - this step can get triggered either by GenerateQuestionsEvent or a FeedbackEvent
+    @step
+    async def generate_questions(self, ctx: Context, ev: GenerateQuestionsEvent | FeedbackEvent) -> QueryEvent:
+
+        # get the list of fields to fill in
+        fields = await ctx.get("fields_to_fill")
+        print("\n DEBUG:all fields Read from Context >>>>>>>>>>>>>>>>>>>>>>>>>>\n")
+
+        # generate one query for each of the fields, and fire them off
+        for field in fields:
+            question = f"How would you answer this question about the candidate? <field>{field}</field>"
+            ctx.send_event(QueryEvent(
+                field=field,
+                query=question
+            ))
+
+        # store the number of fields so we know how many to wait for later
+        await ctx.set("total_fields", len(fields))
+        print(f"\n DEBUG: total fields from Context : {len(fields)}")
+
+        return
+
+    @step
+    async def ask_question(self, ctx: Context, ev: QueryEvent) -> ResponseEvent:
+        response = self.query_engine.query(
+            f"This is a question about the specific resume we have in our database: {ev.query}")
+        return ResponseEvent(field=ev.field, response=response.response)
+
+    # new - we now emit an InputRequiredEvent
+    @step
+    async def fill_in_application(self, ctx: Context, ev: ResponseEvent) -> InputRequiredEvent:
+        # get the total number of fields to wait for
+        total_fields = await ctx.get("total_fields")
+
+        responses = ctx.collect_events(ev, [ResponseEvent] * total_fields)
+        if responses is None:
+            return None  # do nothing if there's nothing to do yet
+
+        # we've got all the responses!
+        responseList = "\n".join("Field: " + r.field + "\n" + "Response: " + r.response for r in responses)
+        print("\n DEBUG: got all responses :\n")
+
+        result = self.llm.complete(f"""
+            You are given a list of fields in an application form and responses to
+            questions about those fields from a resume. Combine the two into a list of
+            fields and succinct, factual answers to fill in those fields.
+
+            <responses>
+            {responseList}
+            </responses>
+        """)
+
+        print("\n DEBUG: llm combined the fields and responses from resume")
+
+        # new! save the result for later
+        await ctx.set("filled_form", str(result))
+
+        print("\n DEBUG: Write all form fields to context. Now will emit InputRequiredEvent")
+
+        # new! Let's get a human in the loop
+        return InputRequiredEvent(
+            prefix="How does this look? Give me any feedback you have on any of the answers.",
+            result=result
+        )
+
+    # new! Accept the feedback.
+    @step
+    async def get_feedback(self, ctx: Context, ev: HumanResponseEvent) -> FeedbackEvent | StopEvent:
+
+        result = self.llm.complete(f"""
+            You have received some human feedback on the form-filling task you've done.
+            Does everything look good, or is there more work to be done?
+            <feedback>
+            {ev.response}
+            </feedback>
+            If everything is fine, respond with just the word 'OKAY'.
+            If there's any other feedback, respond with just the word 'FEEDBACK'.
+        """)
+
+        verdict = result.text.strip()
+
+        print(f"LLM says the verdict was {verdict}")
+        if (verdict == "OKAY"):
+            return StopEvent(result=await ctx.get("filled_form"))
+        else:
+            return FeedbackEvent(feedback=ev.response)
+
+
+async def main():
+    w = RAGWorkflow(timeout=600, verbose=True)
+    handler = w.run(
+        resume_file="data/fake_resume.pdf",
+        application_form="data/fake_application_form.pdf"
+    )
+
+    '''
+    print("DEBUG: Awaiting next event manually...")
+    event = await handler.next_event()
+    print(f"DEBUG: Received event - {event}")
+
+    # Handle the first event if it's InputRequiredEvent
+    if isinstance(event, InputRequiredEvent):
+        print("We've filled in your form! Here are the results:\n")
+        print(event.result)
+        response = input(event.prefix)
+        handler.ctx.send_event(HumanResponseEvent(response=response))
+    '''
+
+    print("DEBUG: Starting event stream...")
+    async for event in handler.stream_events():
+        print(f"DEBUG: Received event type {type(event).__name__}")
+        if isinstance(event, InputRequiredEvent):
+            print("We've filled in your form! Here are the results:\n")
+            print(event.result)
+            # now ask for input from the keyboard
+            response = input(event.prefix)
+            handler.ctx.send_event(
+                HumanResponseEvent(
+                    response=response
+                )
+            )
+        else:
+            print("\n handler received event ", event)
+
+    response = await handler
+    print("Agent complete! Here's your final result:")
+    print(str(response))
+
+    # Display of the workflow
+    workflow_file = Path(__file__).parent / "workflows" / "form_parsing_workflow.html"
+    draw_all_possible_flows(w, filename=str(workflow_file))
+    html_content = extract_html_content(str(workflow_file))
+    display(HTML(html_content), metadata=dict(isolated=True))
+
 
-# print ou the fields one by one
-fields = json.loads(raw_json.text)["fields"]
-for field in fields:
-    print(field)
+if __name__ == "__main__":
+    asyncio.run(main())

storage/default__vector_store.json

@@ -1 +1 @@
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-0.00901325885206461, -0.05899050086736679, -0.002732750028371811, 0.053825657814741135, -0.02401808835566044, -0.00637316657230258, 0.0200877096503973, 0.04043968766927719, -0.00733644375577569, 0.009193996898829937, -0.07564065605401993, -0.0051546101458370686, -0.007716289721429348, 0.05438130348920822, 0.03217924386262894, 0.04263687506318092, -0.06775613874197006, -0.03516385331749916, -0.09700813889503479, -0.04214322194457054, 0.019693685695528984, -0.030054202303290367, -0.04886225238442421, -0.006975262891501188, -0.015622291713953018, -0.004899874329566956, 0.06864847242832184, 0.018434016034007072, -0.033688925206661224, -0.05086328089237213, -0.089083731174469, -0.010642810724675655, 0.006491978187114, -0.005771412048488855, 0.006113583687692881, 0.03179429844021797]}, "text_id_to_ref_doc_id": {"2a25c832-3901-402b-9b80-9bc7367ab25a": "e059701e-89af-4b9c-b9e6-db21d67f7d18", "d44e301d-9b6d-4fa1-9f41-e8bd90383001": "14afd538-346d-4c78-b013-fc6e356531eb", "a7015cff-c541-44f1-92d1-41d0207b4507": "6cf10823-2aa7-4ba4-8feb-20578f5b8ea8"}, "metadata_dict": {"2a25c832-3901-402b-9b80-9bc7367ab25a": {"_node_type": "TextNode", "document_id": "e059701e-89af-4b9c-b9e6-db21d67f7d18", "doc_id": "e059701e-89af-4b9c-b9e6-db21d67f7d18", "ref_doc_id": "e059701e-89af-4b9c-b9e6-db21d67f7d18"}, "d44e301d-9b6d-4fa1-9f41-e8bd90383001": {"_node_type": "TextNode", "document_id": "14afd538-346d-4c78-b013-fc6e356531eb", "doc_id": "14afd538-346d-4c78-b013-fc6e356531eb", "ref_doc_id": "14afd538-346d-4c78-b013-fc6e356531eb"}, "a7015cff-c541-44f1-92d1-41d0207b4507": {"_node_type": "TextNode", "document_id": "6cf10823-2aa7-4ba4-8feb-20578f5b8ea8", "doc_id": "6cf10823-2aa7-4ba4-8feb-20578f5b8ea8", "ref_doc_id": "6cf10823-2aa7-4ba4-8feb-20578f5b8ea8"}}}

storage/docstore.json

@@ -1 +1 @@
-{"docstore/metadata": {"cc9d8436-8704-4296-96ff-18b2425417af": {"doc_hash": "137681331020ba45370f85060fa7e57df067388da716943a2b1814f82d1a7aaf"}, "b8a90649-5538-4fc0-826c-168a50587c4b": {"doc_hash": "462ec5c5c45da617a23c1ba05c2ea8fdfb12232e5b2277ad17b2aa54393e610a"}, "ec090e43-ef25-4902-b24b-a080d45f07b1": {"doc_hash": "ed022f5a93cadb684fc4406cd0f8c89e891cd27ab480fe74ddc16de7b928c698"}, "91c98c96-747f-4252-85ee-aeb0a25a8e94": {"doc_hash": "d1749b9fd0ac387f090f97b52a516f23271a74a9afb93d281d05876baab03fdb", "ref_doc_id": "cc9d8436-8704-4296-96ff-18b2425417af"}, "a14ffc58-aa00-481c-8261-b5120fa5b1d0": {"doc_hash": "a33cba242c9cfef420917121ecfbd3cebcdac0922422f6231f1d12d125e0429b", "ref_doc_id": "b8a90649-5538-4fc0-826c-168a50587c4b"}, "7146b5c5-5c20-4f5d-a008-49571aa58334": {"doc_hash": "3ccf436e9b52028f19deac56f16026e88aaac1182998185346b04b2e4354cd83", "ref_doc_id": "ec090e43-ef25-4902-b24b-a080d45f07b1"}}, "docstore/data": {"91c98c96-747f-4252-85ee-aeb0a25a8e94": {"__data__": {"id_": "91c98c96-747f-4252-85ee-aeb0a25a8e94", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "cc9d8436-8704-4296-96ff-18b2425417af", "node_type": "4", "metadata": {}, "hash": "137681331020ba45370f85060fa7e57df067388da716943a2b1814f82d1a7aaf", "class_name": "RelatedNodeInfo"}}, "metadata_template": "{key}: {value}", "metadata_separator": "\n", "text": "Sarah Chen\n\nEmail: sarah.chen@email.com\n\nLinkedIn: linkedin.com/in/sarahchen\n\nFull Stack Web Developer\n\nGitHub: github.com/sarahcodes\n\nPortfolio: sarahchen.dev\n\nLocation: San Francisco, CA\n\n# Professional Summary\n\nInnovative Full Stack Web Developer with 6+ years of experience crafting scalable web applications and microservices. Specialized in React, Node.js, and cloud architecture. Proven track record of leading technical teams and implementing CI/CD pipelines that reduced deployment time by 40%. Passionate about clean code, accessibility, and mentoring junior developers.\n\n# Professional Experience\n\n# Senior Full Stack Developer\n\nTechFlow Solutions | San Francisco, CA January 2022 - Present\n\n- Architected and implemented a microservices-based e-commerce platform serving 100K+ daily users\n- Led a team of 5 developers in rebuilding the company's flagship product using React and Node.js\n- Implemented GraphQL API gateway that reduced API response times by 60%\n- Established coding standards and review processes that improved code quality by 45%\n\n# Technical Skills\n\n# Frontend:\n\n- React.js, Redux, Next.js, TypeScript\n- Vue.js, Nuxt.js\n- HTML5, CSS3, SASS/SCSS\n- Jest, React Testing Library\n- WebPack, Babel\n\n# Backend:\n\n- Node.js, Express.js\n- Python, Django\n- GraphQL, REST APIs\n- PostgreSQL, MongoDB", "mimetype": "text/plain", "start_char_idx": 0, "end_char_idx": 1315, "metadata_seperator": "\n", "text_template": "{metadata_str}\n\n{content}", "class_name": "TextNode"}, "__type__": "1"}, "a14ffc58-aa00-481c-8261-b5120fa5b1d0": {"__data__": {"id_": "a14ffc58-aa00-481c-8261-b5120fa5b1d0", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "b8a90649-5538-4fc0-826c-168a50587c4b", "node_type": "4", "metadata": {}, "hash": "462ec5c5c45da617a23c1ba05c2ea8fdfb12232e5b2277ad17b2aa54393e610a", "class_name": "RelatedNodeInfo"}}, "metadata_template": "{key}: {value}", "metadata_separator": "\n", "text": "# Full Stack Developer\n\nInnovateSoft | Oakland, CA March 2019 - December 2021\n\n- Mentored 3 junior developers who were promoted to mid-level positions\n- Developed and maintained 10+ customer-facing applications using Vue.js and Django\n- Implemented automated testing suite that increased code coverage from 65% to 95%\n- Optimized database queries resulting in 30% faster page load times\n- Collaborated with UX team to implement accessibility features (WCAG 2.1 compliance)\n- Created documentation that reduced onboarding time for new developers by 50%\n\n# Tools & Others:\n\n- Docker, Kubernetes\n- AWS (EC2, S3, Lambda)\n- Git, GitHub Actions\n- Jenkins, CircleCI\n- Agile/Scrum methodology\n- Performance optimization\n\n# Junior Web Developer\n\nStartupHub | San Jose, CA June 2017 - February 2019\n\n- Built responsive web applications using React.js and Express.js\n- Implemented user authentication system using JWT and OAuth2.0\n- Contributed to migration of legacy PHP applications to modern JavaScript stack\n- Developed RESTful APIs consumed by mobile and web applications\n\n# Education\n\nBachelor of Science in Computer Science\n\nUniversity of California, Berkeley 2013 - 2017\n\n- GPA: 3.8/4.0\n- Minor in User Experience Design\n- President of Women in Tech Society", "mimetype": "text/plain", "start_char_idx": 0, "end_char_idx": 1254, "metadata_seperator": "\n", "text_template": "{metadata_str}\n\n{content}", "class_name": "TextNode"}, "__type__": "1"}, "7146b5c5-5c20-4f5d-a008-49571aa58334": {"__data__": {"id_": "7146b5c5-5c20-4f5d-a008-49571aa58334", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "ec090e43-ef25-4902-b24b-a080d45f07b1", "node_type": "4", "metadata": {}, "hash": "ed022f5a93cadb684fc4406cd0f8c89e891cd27ab480fe74ddc16de7b928c698", "class_name": "RelatedNodeInfo"}}, "metadata_template": "{key}: {value}", "metadata_separator": "\n", "text": "# Projects\n\n# EcoTrack | GitHub\n\n- Built full-stack application for tracking carbon footprint using React, Node.js, and MongoDB\n- Implemented machine learning algorithm for providing personalized sustainability recommendations\n- Featured in TechCrunch's \"Top 10 Environmental Impact Apps of 2023\"\n\n# ChatFlow | Demo\n\n- Developed real-time chat application using WebSocket protocol and React\n- Implemented end-to-end encryption and message persistence\n- Serves 5000+ monthly active users\n\n# Certifications\n\n- AWS Certified Solutions Architect (2023)\n- Google Cloud Professional Developer (2022)\n- MongoDB Certified Developer (2021)\n\n# Languages\n\n- English (Native)\n- Mandarin Chinese (Fluent)\n- Spanish (Intermediate)\n\n# Interests\n\n- Open source contribution\n- Tech blogging (15K+ Medium followers)\n- Hackathon mentoring\n- Rock climbing", "mimetype": "text/plain", "start_char_idx": 0, "end_char_idx": 835, "metadata_seperator": "\n", "text_template": "{metadata_str}\n\n{content}", "class_name": "TextNode"}, "__type__": "1"}}, "docstore/ref_doc_info": {"cc9d8436-8704-4296-96ff-18b2425417af": {"node_ids": ["91c98c96-747f-4252-85ee-aeb0a25a8e94"], "metadata": {}}, "b8a90649-5538-4fc0-826c-168a50587c4b": {"node_ids": ["a14ffc58-aa00-481c-8261-b5120fa5b1d0"], "metadata": {}}, "ec090e43-ef25-4902-b24b-a080d45f07b1": {"node_ids": ["7146b5c5-5c20-4f5d-a008-49571aa58334"], "metadata": {}}}}
+{"docstore/metadata": {"e059701e-89af-4b9c-b9e6-db21d67f7d18": {"doc_hash": "137681331020ba45370f85060fa7e57df067388da716943a2b1814f82d1a7aaf"}, "14afd538-346d-4c78-b013-fc6e356531eb": {"doc_hash": "462ec5c5c45da617a23c1ba05c2ea8fdfb12232e5b2277ad17b2aa54393e610a"}, "6cf10823-2aa7-4ba4-8feb-20578f5b8ea8": {"doc_hash": "ed022f5a93cadb684fc4406cd0f8c89e891cd27ab480fe74ddc16de7b928c698"}, "2a25c832-3901-402b-9b80-9bc7367ab25a": {"doc_hash": "d1749b9fd0ac387f090f97b52a516f23271a74a9afb93d281d05876baab03fdb", "ref_doc_id": "e059701e-89af-4b9c-b9e6-db21d67f7d18"}, "d44e301d-9b6d-4fa1-9f41-e8bd90383001": {"doc_hash": "a33cba242c9cfef420917121ecfbd3cebcdac0922422f6231f1d12d125e0429b", "ref_doc_id": "14afd538-346d-4c78-b013-fc6e356531eb"}, "a7015cff-c541-44f1-92d1-41d0207b4507": {"doc_hash": "3ccf436e9b52028f19deac56f16026e88aaac1182998185346b04b2e4354cd83", "ref_doc_id": "6cf10823-2aa7-4ba4-8feb-20578f5b8ea8"}}, "docstore/data": {"2a25c832-3901-402b-9b80-9bc7367ab25a": {"__data__": {"id_": "2a25c832-3901-402b-9b80-9bc7367ab25a", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "e059701e-89af-4b9c-b9e6-db21d67f7d18", "node_type": "4", "metadata": {}, "hash": "137681331020ba45370f85060fa7e57df067388da716943a2b1814f82d1a7aaf", "class_name": "RelatedNodeInfo"}}, "metadata_template": "{key}: {value}", "metadata_separator": "\n", "text": "Sarah Chen\n\nEmail: sarah.chen@email.com\n\nLinkedIn: linkedin.com/in/sarahchen\n\nFull Stack Web Developer\n\nGitHub: github.com/sarahcodes\n\nPortfolio: sarahchen.dev\n\nLocation: San Francisco, CA\n\n# Professional Summary\n\nInnovative Full Stack Web Developer with 6+ years of experience crafting scalable web applications and microservices. Specialized in React, Node.js, and cloud architecture. Proven track record of leading technical teams and implementing CI/CD pipelines that reduced deployment time by 40%. Passionate about clean code, accessibility, and mentoring junior developers.\n\n# Professional Experience\n\n# Senior Full Stack Developer\n\nTechFlow Solutions | San Francisco, CA January 2022 - Present\n\n- Architected and implemented a microservices-based e-commerce platform serving 100K+ daily users\n- Led a team of 5 developers in rebuilding the company's flagship product using React and Node.js\n- Implemented GraphQL API gateway that reduced API response times by 60%\n- Established coding standards and review processes that improved code quality by 45%\n\n# Technical Skills\n\n# Frontend:\n\n- React.js, Redux, Next.js, TypeScript\n- Vue.js, Nuxt.js\n- HTML5, CSS3, SASS/SCSS\n- Jest, React Testing Library\n- WebPack, Babel\n\n# Backend:\n\n- Node.js, Express.js\n- Python, Django\n- GraphQL, REST APIs\n- PostgreSQL, MongoDB", "mimetype": "text/plain", "start_char_idx": 0, "end_char_idx": 1315, "metadata_seperator": "\n", "text_template": "{metadata_str}\n\n{content}", "class_name": "TextNode"}, "__type__": "1"}, "d44e301d-9b6d-4fa1-9f41-e8bd90383001": {"__data__": {"id_": "d44e301d-9b6d-4fa1-9f41-e8bd90383001", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "14afd538-346d-4c78-b013-fc6e356531eb", "node_type": "4", "metadata": {}, "hash": "462ec5c5c45da617a23c1ba05c2ea8fdfb12232e5b2277ad17b2aa54393e610a", "class_name": "RelatedNodeInfo"}}, "metadata_template": "{key}: {value}", "metadata_separator": "\n", "text": "# Full Stack Developer\n\nInnovateSoft | Oakland, CA March 2019 - December 2021\n\n- Mentored 3 junior developers who were promoted to mid-level positions\n- Developed and maintained 10+ customer-facing applications using Vue.js and Django\n- Implemented automated testing suite that increased code coverage from 65% to 95%\n- Optimized database queries resulting in 30% faster page load times\n- Collaborated with UX team to implement accessibility features (WCAG 2.1 compliance)\n- Created documentation that reduced onboarding time for new developers by 50%\n\n# Tools & Others:\n\n- Docker, Kubernetes\n- AWS (EC2, S3, Lambda)\n- Git, GitHub Actions\n- Jenkins, CircleCI\n- Agile/Scrum methodology\n- Performance optimization\n\n# Junior Web Developer\n\nStartupHub | San Jose, CA June 2017 - February 2019\n\n- Built responsive web applications using React.js and Express.js\n- Implemented user authentication system using JWT and OAuth2.0\n- Contributed to migration of legacy PHP applications to modern JavaScript stack\n- Developed RESTful APIs consumed by mobile and web applications\n\n# Education\n\nBachelor of Science in Computer Science\n\nUniversity of California, Berkeley 2013 - 2017\n\n- GPA: 3.8/4.0\n- Minor in User Experience Design\n- President of Women in Tech Society", "mimetype": "text/plain", "start_char_idx": 0, "end_char_idx": 1254, "metadata_seperator": "\n", "text_template": "{metadata_str}\n\n{content}", "class_name": "TextNode"}, "__type__": "1"}, "a7015cff-c541-44f1-92d1-41d0207b4507": {"__data__": {"id_": "a7015cff-c541-44f1-92d1-41d0207b4507", "embedding": null, "metadata": {}, "excluded_embed_metadata_keys": [], "excluded_llm_metadata_keys": [], "relationships": {"1": {"node_id": "6cf10823-2aa7-4ba4-8feb-20578f5b8ea8", "node_type": "4", "metadata": {}, "hash": "ed022f5a93cadb684fc4406cd0f8c89e891cd27ab480fe74ddc16de7b928c698", "class_name": "RelatedNodeInfo"}}, "metadata_template": "{key}: {value}", "metadata_separator": "\n", "text": "# Projects\n\n# EcoTrack | GitHub\n\n- Built full-stack application for tracking carbon footprint using React, Node.js, and MongoDB\n- Implemented machine learning algorithm for providing personalized sustainability recommendations\n- Featured in TechCrunch's \"Top 10 Environmental Impact Apps of 2023\"\n\n# ChatFlow | Demo\n\n- Developed real-time chat application using WebSocket protocol and React\n- Implemented end-to-end encryption and message persistence\n- Serves 5000+ monthly active users\n\n# Certifications\n\n- AWS Certified Solutions Architect (2023)\n- Google Cloud Professional Developer (2022)\n- MongoDB Certified Developer (2021)\n\n# Languages\n\n- English (Native)\n- Mandarin Chinese (Fluent)\n- Spanish (Intermediate)\n\n# Interests\n\n- Open source contribution\n- Tech blogging (15K+ Medium followers)\n- Hackathon mentoring\n- Rock climbing", "mimetype": "text/plain", "start_char_idx": 0, "end_char_idx": 835, "metadata_seperator": "\n", "text_template": "{metadata_str}\n\n{content}", "class_name": "TextNode"}, "__type__": "1"}}, "docstore/ref_doc_info": {"e059701e-89af-4b9c-b9e6-db21d67f7d18": {"node_ids": ["2a25c832-3901-402b-9b80-9bc7367ab25a"], "metadata": {}}, "14afd538-346d-4c78-b013-fc6e356531eb": {"node_ids": ["d44e301d-9b6d-4fa1-9f41-e8bd90383001"], "metadata": {}}, "6cf10823-2aa7-4ba4-8feb-20578f5b8ea8": {"node_ids": ["a7015cff-c541-44f1-92d1-41d0207b4507"], "metadata": {}}}}

storage/index_store.json

@@ -1 +1 @@
-{"index_store/data": {"38555257-a541-4ca0-bb9f-74a51eab93ae": {"__type__": "vector_store", "__data__": "{\"index_id\": \"38555257-a541-4ca0-bb9f-74a51eab93ae\", \"summary\": null, \"nodes_dict\": {\"91c98c96-747f-4252-85ee-aeb0a25a8e94\": \"91c98c96-747f-4252-85ee-aeb0a25a8e94\", \"a14ffc58-aa00-481c-8261-b5120fa5b1d0\": \"a14ffc58-aa00-481c-8261-b5120fa5b1d0\", \"7146b5c5-5c20-4f5d-a008-49571aa58334\": \"7146b5c5-5c20-4f5d-a008-49571aa58334\"}, \"doc_id_dict\": {}, \"embeddings_dict\": {}}"}}}
+{"index_store/data": {"e64b09b8-b99a-4d40-85b7-4070dc10988c": {"__type__": "vector_store", "__data__": "{\"index_id\": \"e64b09b8-b99a-4d40-85b7-4070dc10988c\", \"summary\": null, \"nodes_dict\": {\"2a25c832-3901-402b-9b80-9bc7367ab25a\": \"2a25c832-3901-402b-9b80-9bc7367ab25a\", \"d44e301d-9b6d-4fa1-9f41-e8bd90383001\": \"d44e301d-9b6d-4fa1-9f41-e8bd90383001\", \"a7015cff-c541-44f1-92d1-41d0207b4507\": \"a7015cff-c541-44f1-92d1-41d0207b4507\"}, \"doc_id_dict\": {}, \"embeddings_dict\": {}}"}}}

test_workflow.py

@@ -0,0 +1,83 @@
+from llama_index.core.workflow import (
+    StartEvent,
+    StopEvent,
+    Workflow,
+    step,
+    Event,
+    Context,
+)
+import asyncio
+import nest_asyncio
+from llama_index.llms.groq import Groq
+from llama_index.utils.workflow import draw_all_possible_flows
+from IPython.display import display, HTML
+from dotenv import load_dotenv
+from helper import extract_html_content
+from pathlib import Path
+import os
+
+nest_asyncio.apply()
+
+load_dotenv()
+GROQ_API_KEY = os.getenv("GROQ_API_KEY")
+global_llm = Groq(api_key=GROQ_API_KEY, model="llama3-70b-8192")
+
+
+class FirstEvent(Event):
+    first_output: str
+
+
+class SecondEvent(Event):
+    second_output: str
+    response: str
+
+
+class ProgressEvent(Event):
+    msg: str
+
+
+class MyWorkflow(Workflow):
+    @step
+    async def step_one(self, ctx: Context, ev: StartEvent) -> FirstEvent:
+        ctx.write_event_to_stream(ProgressEvent(msg="Step one is happening"))
+        return FirstEvent(first_output="First step complete.")
+
+    @step
+    async def step_two(self, ctx: Context, ev: FirstEvent) -> SecondEvent:
+        llm = global_llm
+        generator = await llm.astream_complete(
+            "Please give me the first 3 paragraphs of Moby Dick, a book in the public domain."
+        )
+        async for response in generator:
+            # Allow the workflow to stream this piece of response
+            ctx.write_event_to_stream(ProgressEvent(msg=response.delta))
+        return SecondEvent(
+            second_output="Second step complete, full response attached",
+            response=str(response),
+        )
+
+    @step
+    async def step_three(self, ctx: Context, ev: SecondEvent) -> StopEvent:
+        ctx.write_event_to_stream(ProgressEvent(msg="Step three is happening"))
+        return StopEvent(result="Workflow complete.")
+
+
+async def main():
+    w = MyWorkflow(timeout=30, verbose=True)
+    handler = w.run(first_input="Start the workflow.")
+
+    async for ev in handler.stream_events():
+        if isinstance(ev, ProgressEvent):
+            print(ev.msg)
+
+    final_result = await handler
+    print("Final result", final_result)
+
+    workflow_file = Path(__file__).parent / "workflows" / "streaming_workflow.html"
+    draw_all_possible_flows(w, filename=str(workflow_file))
+    html_content = extract_html_content(str(workflow_file))
+    display(HTML(html_content), metadata=dict(isolated=True))
+
+
+if __name__ == "__main__":
+    asyncio.run(main())