Update generate answer node

pyproject.toml

@@ -13,6 +13,7 @@ authors = [
     { name = "Lorenzo Padoan", email = "lorenzo.padoan977@gmail.com" }
 ]
 dependencies = [
+    "langchain-community==0.2.9",
     "langchain>=0.2.10",
     "langchain-google-genai>=1.0.7",
     "langchain-google-vertexai",
@@ -92,4 +93,4 @@ dev-dependencies = [
 [tool.rye.scripts]
 pylint-local = "pylint scrapegraphai/**/*.py"
 pylint-ci = "pylint --disable=C0114,C0115,C0116 --exit-zero scrapegraphai/**/*.py"
-update-requirements = "python 'manual deployment/autorequirements.py'"
+update-requirements = "python 'manual deployment/autorequirements.py'"

requirements-dev.lock

@@ -12,6 +12,7 @@ aiofiles==24.1.0
     # via burr
 aiohttp==3.9.5
     # via langchain
+    # via langchain-community
     # via langchain-fireworks
     # via langchain-nvidia-ai-endpoints
 aiosignal==1.3.1
@@ -73,6 +74,8 @@ contourpy==1.2.1
     # via matplotlib
 cycler==0.12.1
     # via matplotlib
+dataclasses-json==0.6.7
+    # via langchain-community
 defusedxml==0.7.1
     # via langchain-anthropic
 dill==0.3.8
@@ -177,7 +180,6 @@ graphviz==0.20.3
     # via scrapegraphai
 greenlet==3.0.3
     # via playwright
-    # via sqlalchemy
 groq==0.9.0
     # via langchain-groq
 grpc-google-iam-v1==0.13.1
@@ -249,15 +251,19 @@ jsonschema-specifications==2023.12.1
 kiwisolver==1.4.5
     # via matplotlib
 langchain==0.2.10
+    # via langchain-community
     # via scrapegraphai
 langchain-anthropic==0.1.20
     # via scrapegraphai
 langchain-aws==0.1.12
     # via scrapegraphai
+langchain-community==0.2.9
+    # via scrapegraphai
 langchain-core==0.2.22
     # via langchain
     # via langchain-anthropic
     # via langchain-aws
+    # via langchain-community
     # via langchain-fireworks
     # via langchain-google-genai
     # via langchain-google-vertexai
@@ -281,6 +287,7 @@ langchain-text-splitters==0.2.2
     # via langchain
 langsmith==0.1.93
     # via langchain
+    # via langchain-community
     # via langchain-core
 loguru==0.7.2
     # via burr
@@ -290,6 +297,8 @@ markdown-it-py==3.0.0
     # via rich
 markupsafe==2.1.5
     # via jinja2
+marshmallow==3.21.3
+    # via dataclasses-json
 matplotlib==3.9.1
     # via burr
 mccabe==0.7.0
@@ -313,6 +322,7 @@ numpy==1.26.4
     # via faiss-cpu
     # via langchain
     # via langchain-aws
+    # via langchain-community
     # via matplotlib
     # via pandas
     # via pyarrow
@@ -333,6 +343,7 @@ packaging==24.1
     # via google-cloud-bigquery
     # via huggingface-hub
     # via langchain-core
+    # via marshmallow
     # via matplotlib
     # via pytest
     # via sphinx
@@ -423,6 +434,7 @@ pytz==2024.1
 pyyaml==6.0.1
     # via huggingface-hub
     # via langchain
+    # via langchain-community
     # via langchain-core
     # via uvicorn
 referencing==0.35.1
@@ -438,6 +450,7 @@ requests==2.32.3
     # via google-cloud-storage
     # via huggingface-hub
     # via langchain
+    # via langchain-community
     # via langchain-fireworks
     # via langsmith
     # via sphinx
@@ -495,12 +508,14 @@ sphinxcontrib-serializinghtml==1.1.10
     # via sphinx
 sqlalchemy==2.0.31
     # via langchain
+    # via langchain-community
 starlette==0.37.2
     # via fastapi
 streamlit==1.36.0
     # via burr
 tenacity==8.5.0
     # via langchain
+    # via langchain-community
     # via langchain-core
     # via streamlit
 tiktoken==0.7.0
@@ -551,6 +566,7 @@ typing-extensions==4.12.2
     # via typing-inspect
     # via uvicorn
 typing-inspect==0.9.0
+    # via dataclasses-json
     # via sf-hamilton
 tzdata==2024.1
     # via pandas

requirements.lock

@@ -10,6 +10,7 @@
 -e file:.
 aiohttp==3.9.5
     # via langchain
+    # via langchain-community
     # via langchain-fireworks
     # via langchain-nvidia-ai-endpoints
 aiosignal==1.3.1
@@ -44,6 +45,8 @@ certifi==2024.7.4
     # via requests
 charset-normalizer==3.3.2
     # via requests
+dataclasses-json==0.6.7
+    # via langchain-community
 defusedxml==0.7.1
     # via langchain-anthropic
 dill==0.3.8
@@ -125,7 +128,6 @@ graphviz==0.20.3
     # via scrapegraphai
 greenlet==3.0.3
     # via playwright
-    # via sqlalchemy
 groq==0.9.0
     # via langchain-groq
 grpc-google-iam-v1==0.13.1
@@ -170,15 +172,19 @@ jsonpatch==1.33
 jsonpointer==3.0.0
     # via jsonpatch
 langchain==0.2.10
+    # via langchain-community
     # via scrapegraphai
 langchain-anthropic==0.1.20
     # via scrapegraphai
 langchain-aws==0.1.12
     # via scrapegraphai
+langchain-community==0.2.9
+    # via scrapegraphai
 langchain-core==0.2.22
     # via langchain
     # via langchain-anthropic
     # via langchain-aws
+    # via langchain-community
     # via langchain-fireworks
     # via langchain-google-genai
     # via langchain-google-vertexai
@@ -202,9 +208,12 @@ langchain-text-splitters==0.2.2
     # via langchain
 langsmith==0.1.93
     # via langchain
+    # via langchain-community
     # via langchain-core
 lxml==5.2.2
     # via free-proxy
+marshmallow==3.21.3
+    # via dataclasses-json
 minify-html==0.15.0
     # via scrapegraphai
 mpire==2.10.2
@@ -214,10 +223,13 @@ multidict==6.0.5
     # via yarl
 multiprocess==0.70.16
     # via mpire
+mypy-extensions==1.0.0
+    # via typing-inspect
 numpy==1.26.4
     # via faiss-cpu
     # via langchain
     # via langchain-aws
+    # via langchain-community
     # via pandas
     # via shapely
 openai==1.37.0
@@ -231,6 +243,7 @@ packaging==24.1
     # via google-cloud-bigquery
     # via huggingface-hub
     # via langchain-core
+    # via marshmallow
 pandas==2.2.2
     # via scrapegraphai
 pillow==10.4.0
@@ -288,6 +301,7 @@ pytz==2024.1
 pyyaml==6.0.1
     # via huggingface-hub
     # via langchain
+    # via langchain-community
     # via langchain-core
 regex==2024.5.15
     # via tiktoken
@@ -298,6 +312,7 @@ requests==2.32.3
     # via google-cloud-storage
     # via huggingface-hub
     # via langchain
+    # via langchain-community
     # via langchain-fireworks
     # via langsmith
     # via tiktoken
@@ -321,8 +336,10 @@ soupsieve==2.5
     # via beautifulsoup4
 sqlalchemy==2.0.31
     # via langchain
+    # via langchain-community
 tenacity==8.5.0
     # via langchain
+    # via langchain-community
     # via langchain-core
 tiktoken==0.7.0
     # via langchain-openai
@@ -347,6 +364,9 @@ typing-extensions==4.12.2
     # via pydantic-core
     # via pyee
     # via sqlalchemy
+    # via typing-inspect
+typing-inspect==0.9.0
+    # via dataclasses-json
 tzdata==2024.1
     # via pandas
 undetected-playwright==0.3.0

scrapegraphai/nodes/generate_answer_csv_node.py

@@ -116,24 +116,24 @@ def execute(self, state):
 
         chains_dict = {}
 
-        # Use tqdm to add progress bar
+        if len(doc) == 1:
+            prompt = PromptTemplate(
+                template=template_no_chunks_csv_prompt,
+                input_variables=["question"],
+                partial_variables={
+                    "context": doc,
+                    "format_instructions": format_instructions,
+                },
+            )
+
+            chain =  prompt | self.llm_model | output_parser
+            answer = chain.invoke({"question": user_prompt})
+            state.update({self.output[0]: answer})
+            return state
+
         for i, chunk in enumerate(
             tqdm(doc, desc="Processing chunks", disable=not self.verbose)
         ):
-            if len(doc) == 1:
-                prompt = PromptTemplate(
-                    template=template_no_chunks_csv_prompt,
-                    input_variables=["question"],
-                    partial_variables={
-                        "context": chunk,
-                        "format_instructions": format_instructions,
-                    },
-                )
-
-                chain =  prompt | self.llm_model | output_parser
-                answer = chain.invoke({"question": user_prompt})
-                break
-
             prompt = PromptTemplate(
                     template=template_chunks_csv_prompt,
                     input_variables=["question"],
@@ -144,24 +144,21 @@ def execute(self, state):
                     },
                 )
 
-            # Dynamically name the chains based on their index
             chain_name = f"chunk{i+1}"
             chains_dict[chain_name] = prompt | self.llm_model | output_parser
 
-        if len(chains_dict) > 1:
-            # Use dictionary unpacking to pass the dynamically named chains to RunnableParallel
-            map_chain = RunnableParallel(**chains_dict)
-            # Chain
-            answer = map_chain.invoke({"question": user_prompt})
-            # Merge the answers from the chunks
-            merge_prompt = PromptTemplate(
-                template=template_merge_csv_prompt,
+        async_runner = RunnableParallel(**chains_dict)
+
+        batch_results =  async_runner.invoke({"question": user_prompt})
+
+        merge_prompt = PromptTemplate(
+                template = template_merge_csv_prompt,
                 input_variables=["context", "question"],
                 partial_variables={"format_instructions": format_instructions},
             )
-            merge_chain = merge_prompt | self.llm_model | output_parser
-            answer = merge_chain.invoke({"context": answer, "question": user_prompt})
 
-        # Update the state with the generated answer
+        merge_chain = merge_prompt | self.llm_model | output_parser
+        answer = merge_chain.invoke({"context": batch_results, "question": user_prompt})
+
         state.update({self.output[0]: answer})
-        return state
+        return state